Transparent, lightweight application execution replay on commodity multiprocessor operating systems

We present Scribe, the first system to provide transparent, low-overhead application record-replay and the ability to go live from replayed execution. Scribe introduces new lightweight operating system mechanisms, rendezvous and sync points, to efficiently record nondeterministic interactions such as related system calls, signals, and shared memory accesses. Rendezvous points make a partial ordering of execution based on system call dependencies sufficient for replay, avoiding the recording overhead of maintaining an exact execution ordering. Sync points convert asynchronous interactions that can occur at arbitrary times into synchronous events that are much easier to record and replay.
 We have implemented Scribe without changing, relinking, or recompiling applications, libraries, or operating system kernels, and without any specialized hardware support such as hardware performance counters. It works on commodity Linux operating systems, and commodity multi-core and multiprocessor hardware. Our results show for the first time that an operating system mechanism can correctly and transparently record and replay multi-process and multi-threaded applications on commodity multiprocessors. Scribe recording overhead is less than 2.5% for server applications including Apache and MySQL, and less than 15% for desktop applications including Firefox, Acrobat, OpenOffice, parallel kernel compilation, and movie playback.


The Jalview Java alignment editor

Multiple sequence alignment remains a crucial method for understanding the function of groups of related nucleic acid and protein sequences. However, it is known that automatic multiple sequence alignments can often be improved by manual editing. Therefore, tools are needed to view and edit multiple sequence alignments. Due to growth in the sequence databases, multiple sequence alignments can often be large and difficult to view efficiently. The Jalview Java alignment editor is presented here, which enables fast viewing and editing of large multiple sequence alignments.


Improvement of pixel electrode circuit for active-matrix OLED by application of reversed-biased voltage

In this paper, an improved ac pixel electrode circuit for active-matrix organic light-emitting display (AMOLED) has been proposed by adding a thin-film transistor. This circuit can provide an ac driving mode for AMOLED and makes the OLED in a reversed-biased voltage during the reverse cycle. And a circuit design for understanding ac driving mode was presented. The circuit simulation results indicate that this circuit is feasible. The circuit structure is practical for the AMOLED pixel driver; it can improve the performance of OLED.


The theory of multidimensional persistence

Persistent homology captures the topology of a filtration - a one-parameter family of increasing spaces - in terms of a complete discrete invariant. This invariant is a multiset of intervals that denote the lifetimes of the topological entities within the filtration. In many applications of topology, we need to study a multifiltration: a family of spaces parameterized along multiple geometric dimensions. In this paper, we show that no similar complete discrete invariant exists for multidimensional persistence. Instead, we propose the rank invariant, a discrete invariant for the robust estimation of Betti numbers in a multifiltration, and prove its completeness in one dimension.


Analysis of a Quadrotor with a Two-Degree-of-Freedom Robotic Arm

The classical kinematic approach used in analyzing systems of a small number of interconnected bodies is unsuitable for researching complex kinematics. Moreover, solving nonlinear algebraic equations that involve trigonometric functions is difficult. However, the computational kinematic approach involves using a set of algebraic kinematic equations that describe the joint connectivity between the bodies of a system as well as the specified motion trajectories. This paper presents an analytical approach for a quadrotor with a two-degree-of-freedom (DOF) robotic arm. The approach, which is based on the computational kinematic approach, was adopted to model algebraic equations to obtain the values of position, velocity, acceleration, and the trajectories of motion of the joint and end effector. MATLAB was employed to simulate and solve the kinematic equations. The results showed that the analysis approach is feasible for the quadrotor with a two-DOF robotic arm.


Emerging tobacco hazards in China: 1. Retrospective proportional mortality study of one million deaths.

OBJECTIVE
To assess the hazards at an early phase of the growing epidemic of deaths from tobacco in China.

DESIGN
Smoking habits before 1980 (obtained from family or other informants) of 0.7 million adults who had died of neoplastic, respiratory, or vascular causes were compared with those of a reference group of 0.2 million who had died of other causes.

SETTING
24 urban and 74 rural areas of China.

SUBJECTS
One million people who had died during 1986-8 and whose families could be interviewed.

MAIN OUTCOME MEASURES
Tobacco attributable mortality in middle or old age from neoplastic, respiratory, or vascular disease.

RESULTS
Among male smokers aged 35-69 there was a 51% (SE 2) excess of neoplastic deaths, a 31% (2) excess of respiratory deaths, and a 15% (2) excess of vascular deaths. All three excesses were significant (P<0.0001). Among male smokers aged >/70 there was a 39% (3) excess of neoplastic deaths, a 54% (2) excess of respiratory deaths, and a 6% (2) excess of vascular deaths. Fewer women smoked, but those who did had tobacco attributable risks of lung cancer and respiratory disease about the same as men. For both sexes, the lung cancer rates at ages 35-69 were about three times as great in smokers as in non-smokers, but because the rates among non-smokers in different parts of China varied widely the absolute excesses of lung cancer in smokers also varied. Of all deaths attributed to tobacco, 45% were due to chronic obstructive pulmonary disease and 15% to lung cancer; oesophageal cancer, stomach cancer, liver cancer, tuberculosis, stroke, and ischaemic heart disease each caused 5-8%. Tobacco caused about 0.6 million Chinese deaths in 1990 (0.5 million men). This will rise to 0.8 million in 2000 (0.4 million at ages 35-69) or to more if the tobacco attributed fractions increase.

CONCLUSIONS
At current age specific death rates in smokers and non-smokers one in four smokers would be killed by tobacco, but as the epidemic grows this proportion will roughly double. If current smoking uptake rates persist in China (where about two thirds of men but few women become smokers) tobacco will kill about 100 million of the 0.3 billion males now aged 0-29, with half these deaths in middle age and half in old age.


Chapter 3 Eye Tracking and Eye-Based Human – Computer Interaction

Eye tracking has a long history in medical and psychological research as a tool for recording and studying human visual behavior. Real-time gaze-based text entry can also be a powerful means of communication and control for people with physical disabilities. Following recent technological advances and the advent of affordable eye trackers, there is a growing interest in pervasive attention-aware systems and interfaces that have the potential to revolutionize mainstream humantechnology interaction. In this chapter, we provide an introduction to the state-of-the art in eye tracking technology and gaze estimation. We discuss challenges involved in using a perceptual organ, the eye, as an input modality. Examples of real life applications are reviewed, together with design solutions derived from research results. We also discuss how to match the user requirements and key features of different eye tracking systems to find the best system for each task and application.


Emerging ethical issues in neuroscience

There is growing public awareness of the ethical issues raised by progress in many areas of neuroscience. This commentary reviews the issues, which are triaged in terms of their novelty and their imminence, with an exploration of the relevant ethical principles in each case.


Age, executive function, and social decision making: a dorsolateral prefrontal theory of cognitive aging.

Current neuropsychological models propose that some age-related cognitive changes are due to frontal-lobe deterioration. However, these models have not considered the possible subdivision of the frontal lobes into the dorsolateral and ventromedial regions. This study assessed the age effects on 3 tasks of executive function and working memory, tasks dependent on dorsolateral prefrontal dysfunction; and 3 tasks of emotion and social decision making, tasks dependent on ventromedial prefrontal dysfunction. Age-related differences in performance were found on all tasks dependent on dorsolateral prefrontal dysfunction. In contrast, age-related differences were not found on the majority of the tasks dependent on ventromedial prefrontal dysfunction. The results support a specific dorsolateral prefrontal theory of cognitive changes with age, rather than a global decline in frontal-lobe function.


Efficient multiple-click models in web search

Many tasks that leverage web search users' implicit feedback rely on a proper and unbiased interpretation of user clicks. Previous eye-tracking experiments and studies on explaining position-bias of user clicks provide a spectrum of hypotheses and models on how an average user examines and possibly clicks web documents returned by a search engine with respect to the submitted query. In this paper, we attempt to close the gap between previous work, which studied how to model a single click, and the reality that multiple clicks on web documents in a single result page are not uncommon. Specifically, we present two multiple-click models: the independent click model (ICM) which is reformulated from previous work, and the dependent click model (DCM) which takes into consideration dependencies between multiple clicks. Both models can be efficiently learned with linear time and space complexities. More importantly, they can be incrementally updated as new click logs flow in. These are well-demanded properties in reality.
 We systematically evaluate the two models on click logs obtained in July 2008 from a major commercial search engine. The data set, after preprocessing, contains over 110 thousand distinct queries and 8.8 million query sessions. Extensive experimental studies demonstrate the gain of modeling multiple clicks and their dependencies. Finally, we note that since our experimental setup does not rely on tweaking search result rankings, it can be easily adopted by future studies.


A Systematic Review of Virtual Reality in Education.

Virtual reality has existed in the realm of education for over half a century. However, its widespread adoption is still yet to occur. This is a result of a myriad of limitations to both the technologies themselves, and the costs and logistics required to deploy them. In order to gain a better understanding of what these issues are, and what it is that educators hope to gain by using these technologies in the first place, we have performed both a systematic review of the use of virtual reality in education, as well as two distinct thematic analyses. The first analysis investigated the applications and reported motivations provided by educators in academic literature for developing virtual reality educational systems, while the second investigated the reported problems associated with doing so. These analyses indicate that the majority of researchers use virtual reality to increase the intrinsic motivation of students, and refer to a narrow range of factors such as constructivist pedagogy, collaboration, and gamification in the design of their experiences. Similarly, a small number of educational areas account for the vast majority of educational virtual reality implementations identified in our analyses. Next, we introduced and compared a multitude of recent virtual reality technologies, discussing their potential to overcome several of the problems identified in our analyses, including cost, user experience and interactivity. However, these technologies are not without their own issues, thus we conclude this paper by providing several novel techniques to potentially address them, as well as potential directions for future researchers wishing to apply these emerging technologies to education.


Clustering-based selection for evolutionary multi-objective optimization

In this study, a novel clustering-based selection strategy of nondominated individuals for evolutionary multi-objective optimization is proposed. The new strategy partitions the nondominated individuals in current Pareto front adaptively into desired clusters. Then one representative individual will be selected in each cluster for pruning nondominated individuals. In order to evaluate the validity of the new strategy, we apply it into one state of the art multi-objective evolutionary algorithm. The experimental results based on thirteen benchmark problems show that the new strategy improves the performance obviously in terms of breadth and uniformity of nondominated solutions.


Characterizing Email Search using Large-scale Behavioral Logs and Surveys

As the number of email users and messages continues to grow, search is becoming more important for finding information in personal archives. In spite of its importance, email search is much less studied than web search, particularly using large-scale behavioral log analysis. In this paper we report the results of a large-scale log analysis of email search and complement this with a survey to better understand email search intent and success. We characterize email search behaviors and highlight differences from web search. When searching for email, people know many attributes about what they are looking for; they often look for specific known items; their queries are shorter and they click on fewer items than in web search. Although repeat queries are common in both email and web search, repeat visits to the same search result are much less common in email search suggesting that the same query is used for different search intents over time. We consider search intent from multiple angles. In email search logs, we find that people use email search not just to find information but also to perform tasks such as cleanup or organization, and that the distribution of actions they perform depends on the type of query. In our survey, people reported that they looked for specific information in both email search and web search, but they were much less likely to search for general information on a topic in email. The differences in overall behavior, re-finding patterns and search intents we observed between email and web search have important implications for the design of email search algorithms and interfaces.


Accurate Approximations for Posterior Moments and Marginal Densities Author ( s ) :

Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.


Review of battery cell balancing techniques

A highly reliable and efficient battery management system (BMS) is crucial for applications that are powered by electrochemical power. Cell balancing is one of the most important features of a BMS. Cell balancing techniques help to distribute energy evenly among battery cells. Without cell balancing, a portion of the capacity or energy in the battery bank will be wasted, especially for long battery string which operates in frequent recycling condition. In this paper, some popular cell balancing techniques are described and categorized according to the way of processing redundant energy in battery cells.


Transductive Face Sketch-Photo Synthesis

Face sketch-photo synthesis plays a critical role in many applications, such as law enforcement and digital entertainment. Recently, many face sketch-photo synthesis methods have been proposed under the framework of inductive learning, and these have obtained promising performance. However, these inductive learning-based face sketch-photo synthesis methods may result in high losses for test samples, because inductive learning minimizes the empirical loss for training samples. This paper presents a novel transductive face sketch-photo synthesis method that incorporates the given test samples into the learning process and optimizes the performance on these test samples. In particular, it defines a probabilistic model to optimize both the reconstruction fidelity of the input photo (sketch) and the synthesis fidelity of the target output sketch (photo), and efficiently optimizes this probabilistic model by alternating optimization. The proposed transductive method significantly reduces the expected high loss and improves the synthesis performance for test samples. Experimental results on the Chinese University of Hong Kong face sketch data set demonstrate the effectiveness of the proposed method by comparing it with representative inductive learning-based face sketch-photo synthesis methods.


Integrating constraints and metric learning in semi-supervised clustering

Semi-supervised clustering employs a small amount of labeled data to aid unsupervised learning. Previous work in the area has utilized supervised data in one of two approaches: 1) constraint-based methods that guide the clustering algorithm towards a better grouping of the data, and 2) distance-function learning methods that adapt the underlying similarity metric used by the clustering algorithm. This paper provides new methods for the two approaches as well as presents a new semi-supervised clustering algorithm that integrates both of these techniques in a uniform, principled framework. Experimental results demonstrate that the unified approach produces better clusters than both individual approaches as well as previously proposed semi-supervised clustering algorithms.


Semi-supervised Clustering by Seeding


Introduction: attachment theory and psychotherapy.

In this introduction to the JCLP: In Session 69(11) issue on attachment theory and psychotherapy, the key points of attachment theory (Bowlby, , , 1981) and its relevance to psychotherapy are briefly described. The aim of this issue is to provide case illustrations of how an attachment theory perspective and principles can expand our understanding of psychotherapy practice.


Neural Networks - A Systematic Introduction

In what case do you like reading so much? What about the type of the neural networks a systematic introduction book? The needs to read? Well, everybody has their own reason why should read some books. Mostly, it will relate to their necessity to get knowledge from the book and want to read just to get entertainment. Novels, story book, and other entertaining books become so popular this day. Besides, the scientific books will also be the best reason to choose, especially for the students, teachers, doctors, businessman, and other professions who are fond of reading.


Constrained Deep Metric Learning for Person Re-identification

Person re-identification aims to re-identify the probe image from a given set of images under different camera views. It is challenging due to large variations of pose, illumination, occlusion and camera view. Since the convolutional neural networks (CNN) have excellent capability of feature extraction, certain deep learning methods have been recently applied in person re-identification. However, in person re-identification, the deep networks often suffer from the over-fitting problem. In this paper, we propose a novel CNN-based method to learn a discriminative metric with good robustness to the over-fitting problem in person re-identification. Firstly, a novel deep architecture is built where the Mahalanobis metric is learned with a weight constraint. This weight constraint is used to regularize the learning, so that the learned metric has a better generalization ability. Secondly, we find that the selection of intraclass sample pairs is crucial for learning but has received little attention. To cope with the large intra-class variations in pedestrian images, we propose a novel training strategy named moderate positive mining to prevent the training process from over-fitting to the extreme samples in intraclass pairs. Experiments show that our approach significantly outperforms state-of-the-art methods on several benchmarks of person re-identification.


SQL-to-NoSQL Schema Denormalization and Migration: A Study on Content Management Systems

Content management systems (CMSs) are able to let people, who have no technical skill to manage websites, rapidly create, edit, organize and publish online contents. For example, CMSs are often used to establish e-commerce online shops, community portals, personal blogs, or organization websites. Thus, CMSs play the role of collecting data and then can be easily extended to become intelligent Internet systems. Most popular CMSs, e.g., Word Press and Joomla, rely on relational databases or SQL databases, e.g., MySQL, PostgreSQL, etc. However, due to the explosive growth of huge data, the well-structured characteristic of SQL databases may limit the scalability to handle horizontal scaling. Therefore, regarding the flexibility and the feasibility of parallel processing, cloud computing takes Not Only SQL (NoSQL) databases into consideration. How to migrate the original CMS software from SQL databases to NoSQL databases becomes one emerging and critical research issue. This paper is motivated to propose an autonomous SQL-to-NoSQL schema denormalization and migration. At the same time, this paper also evaluates the proposed mechanism on the realistic CMS software. Based on our experimental results, our mechanism not only helps the current CMS software migrate into the cloud platform without re-designing their database schemas, but also improves at least 45% access performance after schema migration.


Testing Unconstrained Optimization Software

Much of the testing of optimization software is inadequate because the number of test functmns is small or the starting points are close to the solution. In addition, there has been too much emphasm on measurmg the efficmncy of the software and not enough on testing reliability and robustness. To address this need, we have produced a relatwely large but easy-to-use collection of test functions and designed gmdelines for testing the reliability and robustness of unconstrained optimization software.


Short Text Similarity with Word Embeddings

Determining semantic similarity between texts is important in many tasks in information retrieval such as search, query suggestion, automatic summarization and image finding. Many approaches have been suggested, based on lexical matching, handcrafted patterns, syntactic parse trees, external sources of structured semantic knowledge and distributional semantics. However, lexical features, like string matching, do not capture semantic similarity beyond a trivial level. Furthermore, handcrafted patterns and external sources of structured semantic knowledge cannot be assumed to be available in all circumstances and for all domains. Lastly, approaches depending on parse trees are restricted to syntactically well-formed texts, typically of one sentence in length.
 We investigate whether determining short text similarity is possible using only semantic features---where by semantic we mean, pertaining to a representation of meaning---rather than relying on similarity in lexical or syntactic representations. We use word embeddings, vector representations of terms, computed from unlabelled data, that represent terms in a semantic space in which proximity of vectors can be interpreted as semantic similarity.
 We propose to go from word-level to text-level semantics by combining insights from methods based on external sources of semantic knowledge with word embeddings. A novel feature of our approach is that an arbitrary number of word embedding sets can be incorporated. We derive multiple types of meta-features from the comparison of the word vectors for short text pairs, and from the vector means of their respective word embeddings. The features representing labelled short text pairs are used to train a supervised learning algorithm. We use the trained model at testing time to predict the semantic similarity of new, unlabelled pairs of short texts
 We show on a publicly available evaluation set commonly used for the task of semantic similarity that our method outperforms baseline methods that work under the same conditions.


Securing vulnerable home IoT devices with an in-hub security manager

The proliferation of consumer Internet of Things (IoT) devices offers as many convenient benefits as it poses significant vulnerabilities. Patching or otherwise mitigating these vulnerabilities will be difficult for the existing home security ecosystem. This paper proposes a central security manager that is built on top of the smarthome's hub or gateway router and positioned to intercept all traffic to and from devices. Aware of the status of all devices in the home and of reported vulnerabilities, the security manager could intervene as needed to deter or alleviate many types of security risks. Modules built atop this manager could offer convenient installation of software updates, filter traffic that might otherwise exploit devices, and strengthen authentication for both legacy and future devices. We believe that this design offers the potential to increase security for smarthome IoT devices, and we encourage other researchers and regulators to explore and extend our ideas.


Nutritional Ecology and Human Health.

In contrast to the spectacular advances in the first half of the twentieth century with micronutrient-related diseases, human nutrition science has failed to stem the more recent rise of obesity and associated cardiometabolic disease (OACD). This failure has triggered debate on the problems and limitations of the field and what change is needed to address these. We briefly review the two broad historical phases of human nutrition science and then provide an overview of the main problems that have been implicated in the poor progress of the field with solving OACD. We next introduce the field of nutritional ecology and show how its ecological-evolutionary foundations can enrich human nutrition science by providing the theory to help address its limitations. We end by introducing a modeling approach from nutritional ecology, termed nutritional geometry, and demonstrate how it can help to implement ecological and evolutionary theory in human nutrition to provide new direction and to better understand and manage OACD.


Innovative Cost Effective Approach for Cell Phone Based Remote Controlled Embedded System for Irrigation

This work describes the development of innovative low cost cell phone based remote control application for induction motor-pump based irrigation in agriculture. Rural areas in many states of India are plagued by frequent power cuts and abnormal voltage conditions. The developed system ensures that water is distributed to field whenever normal conditions exist based on task specified. A novel concept of miscall for specified duration has been used to reduce the operational cost of the system and for the convenience of farmers facing difficulty in typing messages. Information is exchanged in form of miscalls / message between the system and the user cell phones. The system is based on AVR ATMega32 micro-controller and includes protection against single phasing, over-current, dry running and other desirable features. DS1307 and DS18S20 are used for time and temperature measurement respectively. It is expected that system will relieve hardships of farmers relating water distribution to a great extent.


Predicting Domain-Specific Risk Taking With the HEXACO Personality Structure

Although risk taking traditionally has been viewed as a unitary, stable individual difference variable, emerging evidence in behavioral decision-making research suggests that risk taking is a domain-specific construct. Utilizing a psychological risk-return framework that regresses risk taking on the perceived benefits and perceived riskiness of an activity (Weber & Milliman, 1997), this study examined the relations between risk attitude and broad personality dimensions using the new HEXACO personality framework (Lee &Ashton, 2004) across four risk domains. This personality framework, which has been replicated in lexical studies in over 12 natural languages, assess personality over six broad personality dimensions, as opposed to the traditional Five-Factor Model, or ‘‘Big Five.’’ Through path analysis, we regressed risk taking in four separate domains on risk perceptions, perceived benefits, and the six HEXACO dimensions. Across all risk domains, we found that the emotionality dimension was associated with heightened risk perceptions and high conscientiousness was associated with less perceived benefits. We also report several unique patterns of domain-specific relations between the HEXACO dimensions and risk attitude. Specifically, openness was associated with risk taking and perceived benefits for social and recreational risks, whereas lower honesty/humility was associated with greater health/safety and ethical risk taking. These findings extend our understanding of how individuals approach risk across a variety of contexts, and further highlight the utility of honesty/humility, a dimension not recovered in Big Five models, in individual differences research. Copyright # 2010 John Wiley & Sons, Ltd. key words risk taking; risk perception; risk-return framework; personality; HEXACO; honesty/humility Risk taking has traditionally been viewed as an enduring, stable, and domain-invariant construct in both behavioral decision making and personality research (e.g., Eysenck & Eysenck, 1977; Kahneman & Tversky, 1979; Paunonen & Jackson, 1996; Tellegen, 1985). However, recent advances suggest that risk taking is content, or domain, specific (Blais &Weber, 2006; Hanoch, Johnson, &Wilke, 2006; Soane & Chmiel, 2005; Weber, Blais, & Betz, 2002). In light of this knowledge, psychologists would benefit from a deeper ler, Decision Research, Eugene, OR 97401, USA. E-mail: jweller@decisionresearch.org


Robust Interactive Image Segmentation Using Convex Active Contours

The state-of-the-art interactive image segmentation algorithms are sensitive to the user inputs and often unable to produce an accurate boundary with a small amount of user interaction. They frequently rely on laborious user editing to refine the segmentation boundary. In this paper, we propose a robust and accurate interactive method based on the recently developed continuous-domain convex active contour model. The proposed method exhibits many desirable properties of an effective interactive image segmentation algorithm, including robustness to user inputs and different initializations, the ability to produce a smooth and accurate boundary contour, and the ability to handle topology changes. Experimental results on a benchmark data set show that the proposed tool is highly effective and outperforms the state-of-the-art interactive image segmentation algorithms.


Model Reference Adaptive Control-Based Speed Control of Brushless DC Motors With Low-Resolution Hall-Effect Sensors

A control system with a novel speed estimation approach based on model reference adaptive control (MRAC) is presented for low cost brushless dc motor drives with low-resolution hall sensors. The back EMF is usually used to estimate speed. But the estimation result is not accurate enough at low speeds because of the divided voltage of stator resistors and too small back EMF. Moreover, the stator resistor is always varying with the motor's temperature. A speed estimation algorithm based on MRAC was proposed to correct the speed error estimated by using back EMF. The proposed algorithm's most innovative feature is its adaptability to the entire speed range including low speeds and high speeds and temperature and different motors do not affect the accuracy of the estimation result. The effectiveness of the algorithm was verified through simulations and experiments.


A Survey on Learning to Hash

Nearest neighbor search is a problem of finding the data points from the database such that the distances from them to the query point are the smallest. Learning to hash is one of the major solutions to this problem and has been widely studied recently. In this paper, we present a comprehensive survey of the learning to hash algorithms, categorize them according to the manners of preserving the similarities into: pairwise similarity preserving, multiwise similarity preserving, implicit similarity preserving, as well as quantization, and discuss their relations. We separate quantization from pairwise similarity preserving as the objective function is very different though quantization, as we show, can be derived from preserving the pairwise similarities. In addition, we present the evaluation protocols, and the general performance analysis, and point out that the quantization algorithms perform superiorly in terms of search accuracy, search time cost, and space cost. Finally, we introduce a few emerging topics.


KBQA: An Online Template Based Question Answering System over Freebase

Question answering (QA) has become a popular way for humans to access billion-scale knowledge bases. QA systems over knowledge bases produce accurate and concise answers. The key of QA over knowledge bases is to map the question to a certain substructure in the knowledge base. To do this, KBQA (Question Answering over Knowledge Bases) uses a new kind of question representation: templates, learned from a million scale QA corpora. For example, for questions about a city’s population, KBQA learns templates such as What’s the population of $city?, How many people are there in $city?. It learns overall 1171303 templates for 4690 relations. Based on these templates, KBQA effectively and efficiently supports binary factoid questions or complex questions.


Effects of gratitude meditation on neural network functional connectivity and brain-heart coupling

A sense of gratitude is a powerful and positive experience that can promote a happier life, whereas resentment is associated with life dissatisfaction. To explore the effects of gratitude and resentment on mental well-being, we acquired functional magnetic resonance imaging and heart rate (HR) data before, during, and after the gratitude and resentment interventions. Functional connectivity (FC) analysis was conducted to identify the modulatory effects of gratitude on the default mode, emotion, and reward-motivation networks. The average HR was significantly lower during the gratitude intervention than during the resentment intervention. Temporostriatal FC showed a positive correlation with HR during the gratitude intervention, but not during the resentment intervention. Temporostriatal resting-state FC was significantly decreased after the gratitude intervention compared to the resentment intervention. After the gratitude intervention, resting-state FC of the amygdala with the right dorsomedial prefrontal cortex and left dorsal anterior cingulate cortex were positively correlated with anxiety scale and depression scale, respectively. Taken together, our findings shed light on the effect of gratitude meditation on an individual’s mental well-being, and indicate that it may be a means of improving both emotion regulation and self-motivation by modulating resting-state FC in emotion and motivation-related brain regions.


Effective or Efficient ? Effects of Beijing ’ s Vehicle Lottery System on Fleet Composition and Environment *

To control vehicle growth and air pollution, Beijing’s municipal government imposed a vehicle lottery system in 2011, which randomly allocated a quota of licenses to potential buyers. This paper investigates the effect of this policy on fleet composition, fuel consumption, air pollution, and social welfare. Using car registration data, we estimate a random coefficient discrete choice model and conduct counterfactual analysis based on the estimated parameters. We find that the lottery reduced new passenger vehicle *We gratefully acknowledge the kind help and constructive comments of Professors Andrew Cassey, Ana Espinola-Arredondo, Benjamin Cowan, Gregmar Galinato, Dong Lu, Jill McCluskey, Mark Gibson, Shanjun Li, Jia Yan, Dan Yang and seminar participants at Washington State University. Financial support from the China National Science Fund (grant #71620107005) is acknowledged. †Corresponding author. Email: yziying1988@gmail.com (Z. Yang), fmunoz@wsu.edu (F. Muñoz-García), tangmp.sicau@gmail.com (M. Tang). sales by 50.15%, fuel consumption by 48.69%, and pollutant emissions by 48.69% in 2012. Also, such lottery shifted new auto purchases towards high-end but less fuel efficient vehicles. In our counterfactual analysis, we show that a progressive tax scheme works better than the lottery system at decreasing fuel consumption and air pollution, and leads to a higher fleet fuel efficiency and less welfare loss.


Smoothed Sarsa: Reinforcement learning for robot delivery tasks

Our goal in this work is to make high level decisions for mobile robots. In particular, given a queue of prioritized object delivery tasks, we wish to find a sequence of actions in real time to accomplish these tasks efficiently. We introduce a novel reinforcement learning algorithm called Smoothed Sarsa that learns a good policy for these delivery tasks by delaying the backup reinforcement step until the uncertainty in the state estimate improves. The state space is modeled by a Dynamic Bayesian Network and updated using a Region-based Particle Filter. We take advantage of the fact that only discrete (topological) representations of entity locations are needed for decision-making, to make the tracking and decision making more efficient. Our experiments show that policy search leads to faster task completion times as well as higher total reward compared to a manually crafted policy. Smoothed Sarsa learns a policy orders of magnitude faster than previous policy search algorithms. We demonstrate our results on the Player/Stage simulator and on the Pioneer robot.


A Wideband Dual-Polarized Dielectric Magnetoelectric Dipole Antenna

A new wideband ±45° dual-polarized metal loop dielectric resonator magnetoelectric antenna is proposed in this communication. The antenna consists of two orthogonal dielectric bars that support two orthogonal electric dipoles and two cross interlaced metal semi-loops that are equivalent to two orthogonal magnetic dipoles above the ground. With a legitimate combination of the electric and magnetic dipoles, a unidirectional radiation with low backward radiation and equal E-plane and H-plane radiation patterns can be achieved. The antenna can be made of a monolithic dielectric block with simple installation. To validate the new antenna configuration, a prototype is designed, manufactured, and measured. The prototype antenna using ceramic with the electrical size of <inline-formula> <tex-math notation="LaTeX">$0.33\lambda _{o} \times 0.33\lambda _{o} \times 0.21\lambda _{o}$ </tex-math></inline-formula> demonstrates that an impedance bandwidth of 11.4% (<inline-formula> <tex-math notation="LaTeX">$\vert S_{11}\vert < -15$ </tex-math></inline-formula> dB) in the 3.5 GHz frequency band can be achieved with the measured forward/backward ratio of better than 22 dB. The proposed antenna is suitable as antenna element for massive-MIMO array antennas, where a large number of antenna elements need to be installed in a compact space.


Neural Monkey: The Current State and Beyond

Neural Monkey is an open-source toolkit for sequence-to-sequence learning. The focus of this paper is to present the current state of the toolkit to the intended audience, which includes students and researchers, both active in the deep learning community and newcomers. For each of these target groups, we describe the most relevant features of the toolkit, including the simple configuration scheme, methods of model inspection that promote useful intuitions, or a modular design for easy prototyping. We summarize relevant contributions to the research community which were made using this toolkit and discuss the characteristics of our toolkit with respect to other existing systems. We conclude with a set of proposals for future development.


Introduction to the talking points project

A number of systems, technological and otherwise, exist for helping blind and visually impaired users navigate from place to place. [5, 3] However, in focusing only on the destination, these systems often neglect the journey. We propose to rectify this, with a device to improve a user’s peripheral awareness of their surroundings. This is the motivation and the primary goal of this project. The Talking Points project aims to create a system for attaching information to places and objects, in a format that can be easily converted to speech. It allows blind and visually impaired users to get information about the places and objects they are walking by, and can also be used to provide digital information to other passersby. We have created a proof-of-concept of this system. The system reads passive RFID tags from a mobile reader. It looks up a tag’s ID number to find associated text, then uses text-to-speech software to read that speech to the user.


CAST: Collaborative Agents for Simulating Teamwork

Psychological studies on teamwork have shown that an effective team often can anticipate information needs of teammates based on a shared mental model. Existing multi-agent models for teamwork are limited in their ability to support proactive information exchange among teammates. To address this issue, we have developed and implemented a multi-agent architecture called CAST that simulates teamwork and supports proactive information exchange in a dynamic environment. We present a formal model for proactive information exchange. Knowledge regarding the structure and process of a team is described in a language called MALLET. Beliefs about shared team processes and their states are represented using Petri Nets. Based on this model, CAST agents offer information proactively to those who might need it using an algorithm called DIARG. Empirical evaluations using a multi-agent synthetic testbed application indicate that CAST enhances the effectiveness of teamwork among agents without sacrificing a high cost for communications.


Community Detection in Networks using Node Attributes and Modularity

Community detection in network is of vital importance to find cohesive subgroups. Node attributes can improve the accuracy of community detection when combined with link information in a graph. Community detection using node attributes has not been investigated in detail. To explore the aforementioned idea, we have adopted an approach by modifying the Louvain algorithm. We have proposed Louvain-ANDAttribute (LAA) and Louvain-OR-Attribute (LOA) methods to analyze the effect of using node attributes with modularity. We compared this approach with existing community detection approaches using different datasets. We found the performance of both algorithms better than Newman’s Eigenvector method in achieving modularity and relatively good results of gain in modularity in LAA than LOA. We used density, internal and external edge density for the evaluation of quality of detected communities. LOA provided highly dense partitions in the network as compared to Louvain and Eigenvector algorithms and close values to Clauset. Moreover, LOA achieved few numbers of edges between communities. Keywords—Community Detection; Louvain algorithm; Node attributes; and Modularity


Chinese Word Segmentation and Named Entity Recognition Based on Conditional Random Fields

Chinese word segmentation (CWS), named entity recognition (NER) and part-ofspeech tagging is the lexical processing in Chinese language. This paper describes the work on these tasks done by France Telecom Team (Beijing) at the fourth International Chinese Language Processing Bakeoff. In particular, we employ Conditional Random Fields with different features for these tasks. In order to improve NER relatively low recall; we exploit non-local features and alleviate class imbalanced distribution on NER dataset to enhance the recall and keep its relatively high precision. Some other post-processing measures such as consistency checking and transformation-based error-driven learning are used to improve word segmentation performance. Our systems participated in most CWS and POS tagging evaluations and all the NER tracks. As a result, our NER system achieves the first ranks on MSRA open track and MSRA/CityU closed track. Our CWS system achieves the first rank on CityU open track, which means that our systems achieve state-of-the-art performance on Chinese lexical processing.


A survey of named entity recognition and classification

The term “Named Entity” , now widely used in Natural Language Processing, was coined for the Sixth Message Understanding Conference (MUC-6) (R. Grishman & Sundheim 1996). At that time, MUC was focusing on Information Extraction (IE) tasks where structured information of company activities and defense related activities is extracted from unstructured text, such as newspaper articles. In defining the task, people noticed that it is essential to recognize information units like names, including person, organization and location names, and numeric expressions including time, date, money and percent expressions. Identifying references to these entities in text was recognized as one of the important sub-tasks of IE and was called “Named Entity Recognition and Classification (NERC)” . We present here a survey of fifteen years of research in the NERC field, from 1991 to 2006. While early systems were making use of handcrafted rule-based algorithms, modern systems most often resort to machine learning techniques. We survey these techniques as well as other critical aspects of NERC such as features and evaluation methods. It was indeed concluded in a recent conference that the choice of features is at least as important as the choice of technique for obtaining a good NERC system (E. Tjong Kim Sang & De Meulder 2003). Moreover, the way NERC systems are evaluated and compared is essential to progress in the field. To the best of our knowledge, NERC features, techniques, and evaluation methods have not been surveyed extensively yet. The first section of this survey presents some observations on published work from the point of view of activity per year, supported languages, preferred textual genre and domain, and supported entity types. It was collected from the review of a hundred English language papers sampled from the major conferences and journals. We do not claim this review to be exhaustive or representative of all the research in all languages, but we believe it gives a good feel for the breadth and depth of previous work. Section 2 covers the algorithmic techniques that were proposed for addressing the NERC task. Most techniques are borrowed from the Machine Learning (ML) field. Instead of elaborating on techniques themselves, the third section lists and classifies the proposed features, i.e., descriptions and characteristic of words for algorithmic consumption. Section 4 presents some of the evaluation paradigms that were proposed throughout the major forums. Finally, we present our conclusions.


Chinese Word Segmentation and Named Entity Recognition: A Pragmatic Approach

This article presents a pragmatic approach to Chinese word segmentation. It differs from most previous approaches mainly in three respects. First, while theoretical linguists have defined Chinese words using various linguistic criteria, Chinese words in this study are defined pragmatically as segmentation units whose definition depends on how they are used and processed in realistic computer applications. Second, we propose a pragmatic mathematical framework in which segmenting known words and detecting unknown words of different types (i.e., morphologically derived words, factoids, named entities, and other unlisted words) can be performed simultaneously in a unified way. These tasks are usually conducted separately in other systems. Finally, we do not assume the existence of a universal word segmentation standard that is application-independent. Instead, we argue for the necessity of multiple segmentation standards due to the pragmatic fact that different natural language processing applications might require different granularities of Chinese words. These pragmatic approaches have been implemented in an adaptive Chinese word segmenter, called MSRSeg, which will be described in detail. It consists of two components: (1) a generic segmenter that is based on the framework of linear mixture models and provides a unified approach to the five fundamental features of word-level Chinese language processing: lexicon word processing, morphological analysis, factoid detection, named entity recognition, and new word identification; and (2) a set of output adaptors for adapting the output of (1) to different application-specific standards. Evaluation on five test sets with different standards shows that the adaptive system achieves state-of-the-art performance on all the test sets.


Generating narrative variation in interactive fiction

GENERATING NARRATIVE VARIATION IN INTERACTIVE FICTION Nick Montfort Mitchell P. Marcus and Gerald Prince A general method for the generation of natural language narrative is described. It allows the expression, or narrative discourse, to vary independently of the underlying events and existents that are the narrative’s content. Specifically, this variation is accomplished in an interactive fiction (IF) system which replies to typed input by narrating what has happened in a simulated world. IF works have existed for about 30 years as forms of text-based computer simulation, instances of dialog systems, and examples of literary art. Theorists of narrative have carefully distinguished between the level of underlying content (corresponding to the simulated world in interactive fiction) and that of expression (corresponding to the textual exchange between computer arnd user) since the mid-1960s, when the field of narratology began to develop, but IF systems have not yet made use of this distinction. The current project contributes new techniques for automatic narration by building on work done in computational linguistics, specifically natural language generation, and in narratology. First, types of narrative variation that are possible in IF are identified and formalized in a way that is suitable for a natural language generation system. An architecture for an IF system is then described and implemented; the result allows multiple works of interactive fiction to be realized and, using a general plan for narrating, allows them to be narrated in different ways during interaction. The system’s ability to generate text is considered in a pilot evaluation. Plans for future work are also discussed. They include publicly released systems for IF development and narratology education, adding a planning capability that uses actors’ individual perspectives, and adapting automatic narration to different sorts of interactive systems.


Recognising Complex Mental States from Naturalistic Human-Computer Interactions


Semantic Similarity Strategies for Job Title Classification

Automatic and accurate classification of items enables numerous downstream applications in many domains. These applications can range from faceted browsing of items to product recommendations and big data analytics. In the online recruitment domain, we refer to classifying job ads to pre-defined or custom occupation categories as job title classification. A large-scale job title classification system can power various downstream applications such as semantic search, job recommendations and labor market analytics. In this paper, we discuss experiments conducted to improve our in-house job title classification system. The classification component of the system is composed of a two-stage coarse and fine level classifier cascade that classifies input text such as job title and/or job ads to one of the thousands of job titles in our taxonomy. To improve classification accuracy and effectiveness, we experiment with various semantic representation strategies such as average W2V vectors and document similarity measures such as Word Movers Distance (WMD). Our initial results show an overall improvement in accuracy of Carotene[1].


Blepharophimosis mental retardation syndrome Say-Barber/Biesecker/Young-Simpson type - new findings with neuroimaging.

We report on a female patient with blepharophimosis mental retardation syndrome of Say/Barber/Biesecker/Young-Simpson (SBBYS) type. Main findings in her were marked developmental delay, blepharophimosis, ptosis, cleft palate, external auditory canal stenosis, small and malformed teeth, hypothyroidism, hearing impairment, and joint limitations. We performed diffusion tensor magnetic resonance imaging (MRI) and tractography of the brain which showed inappropriate myelination and disturbed white matter integrity. Cytogenetic analysis, subtelomeric fluorescence in situ hybridization and comparative genomic hybridization failed to identify an abnormality. It remains uncertain whether the MRI findings are specific to the present patient or form part of the SBBYS syndrome.


Classification of Moral Foundations in Microblog Political Discourse

Previous works in computer science, as well as political and social science, have shown correlation in text between political ideologies and the moral foundations expressed within that text. Additional work has shown that policy frames, which are used by politicians to bias the public towards their stance on an issue, are also correlated with political ideology. Based on these associations, this work takes a first step towards modeling both the language and how politicians frame issues on Twitter, in order to predict the moral foundations that are used by politicians to express their stances on issues. The contributions of this work includes a dataset annotated for the moral foundations, annotation guidelines, and probabilistic graphical models which show the usefulness of jointly modeling abstract political slogans, as opposed to the unigrams of previous works, with policy frames for the prediction of the morality underlying political tweets.


A 0.1-1.8 GHz, 100 W GaN HEMT Power Amplifier Module

We have demonstrated a 0.1-1.8 GHz, 100 W GaN HEMT power amplifier module with 11 dB gain, 94-142 W CW output power and 40.6-74.0 % drain efficiency over the band. The power amplifier module uses broadband low loss coaxial baluns to combine four 30 W broadband lossy matched GaN HEMT PAs on a 2 x 2 inch compact PCB. The individual PAs are fully matched to 50 Ohms and obtains 23.6-30.9 W with 44.5-63.7 % drain efficiency over the band. The packaged amplifiers contain a GaN on SiC device operating at 50 V drain voltage with a GaAs integrated passive matching circuitry. These amplifiers are targeted for use in multi-band multi-standard communication systems and for instrumentation applications.


Improving performance of recurrent neural network with relu nonlinearity

In recent years significant progress has been made in successfully training recurrent neural networks (RNNs) on sequence learning problems involving long range temporal dependencies. The progress has been made on three fronts: (a) Algorithmic improvements involving sophisticated optimization techniques, (b) network design involving complex hidden layer nodes and specialized recurrent layer connections and (c) weight initialization methods. In this paper, we focus on recently proposed weight initialization with identity matrix for the recurrent weights in a RNN. This initialization is specifically proposed for hidden nodes with Rectified Linear Unit (ReLU) non linearity. We offer a simple dynamical systems perspective on weight initialization process, which allows us to propose a modified weight initialization strategy. We show that this initialization technique leads to successfully training RNNs composed of ReLUs. We demonstrate that our proposal produces comparable or better solution for three toy problems involving long range temporal structure: the addition problem, the multiplication problem and the MNIST classification problem using sequence of pixels. In addition, we present results for a benchmark action recognition problem.


Interactive play objects and the effects of open-ended play on social interaction and fun

This paper describes a study that examines the influence of openended play in interactive play objects on social interaction and fun experience of children. We developed a prototype to examine whether children enjoy playing with simple intelligent objects. Children between 7 and 11 years old were asked to play with the objects in a free-play and pre-set game session. The study shows that children create a wide variety of games and practice many social skills when negotiating the rules of various games. Overall, children felt playing with the objects in the free-play sessions was more fun than in the pre-set sessions. The insights will be used to design the next version of our play concept.


Conductive polymers: towards a smart biomaterial for tissue engineering.

Developing stimulus-responsive biomaterials with easy-to-tailor properties is a highly desired goal of the tissue engineering community. A novel type of electroactive biomaterial, the conductive polymer, promises to become one such material. Conductive polymers are already used in fuel cells, computer displays and microsurgical tools, and are now finding applications in the field of biomaterials. These versatile polymers can be synthesised alone, as hydrogels, combined into composites or electrospun into microfibres. They can be created to be biocompatible and biodegradable. Their physical properties can easily be optimized for a specific application through binding biologically important molecules into the polymer using one of the many available methods for their functionalization. Their conductive nature allows cells or tissue cultured upon them to be stimulated, the polymers' own physical properties to be influenced post-synthesis and the drugs bound in them released, through the application of an electrical signal. It is thus little wonder that these polymers are becoming very important materials for biosensors, neural implants, drug delivery devices and tissue engineering scaffolds. Focusing mainly on polypyrrole, polyaniline and poly(3,4-ethylenedioxythiophene), we review conductive polymers from the perspective of tissue engineering. The basic properties of conductive polymers, their chemical and electrochemical synthesis, the phenomena underlying their conductivity and the ways to tailor their properties (functionalization, composites, etc.) are discussed.


Marker-based tracking with unmanned aerial vehicles

With the availability of low-cost micro aerial vehicles (MAVs), unmanned aerial vehicles (UAVs) quickly gain popularity and application potential. This requires techniques that can be understood by non-experts and flexibly applied for rapid prototyping. Visual tracking is an essential task with many applications, such as autonomous navigation and scene acquisition. While marker-less methods emerge, marker-based methods still have major advantages including simplicity, robustness, accuracy and performance. In practice, however, multi-marker setups introduce complexity and calibration efforts that can void the advantages. This work proposes a solution for practical, robust and easy-to-use marker-based tracking with independent compound targets. We introduce two novel target designs and describe pose estimation, noise removal and geometric transformations. The concepts are implemented in a tracking library for the Parrot AR. Drone 2.0. We explain its video access and camera calibration, and provide a first set of intrinsic parameters, jointly estimated from 14 units with high accuracy and low variance. The library is applied in a one-day contest on automatic visual navigation of UAVs, where students without technical background and programming skills achieved learning by experience and rapid development. This shows the effectiveness of combining capability with simplicity, and provides a case study on robotics in interdisciplinary education.


Program Synthesis Using Natural Language

Interacting with computers is a ubiquitous activity for millions of people. Repetitive or specialized tasks often require creation of small, often one-off, programs. End-users struggle with learning and using the myriad of domain-specific languages (DSLs) to effectively accomplish these tasks.
 We present a general framework for constructing program synthesizers that take natural language (NL) inputs and produce expressions in a target DSL. The framework takes as input a DSL definition and training data consisting of NL/DSL pairs. From these it constructs a synthesizer by learning optimal weights and classifiers (using NLP features) that rank the outputs of a keyword-programming based translation. We applied our framework to three domains: repetitive text editing, an intelligent tutoring system, and flight information queries. On 1200+ English descriptions, the respective synthesizers rank the desired program as the top-1 and top-3 for 80% and 90% descriptions respectively.


Reading and Interpreting Ethnography

Although ethnographic methods are still regarded, to an extent, as new aspects of HCI research practice, they have been part of HCI research almost since its inception, and certainly since the early 1980s, about the same time as the CHI conference was founded. What, then, accounts for this sense of novelty and the mystery that goes along with it? One reason is that ethnographic methods have generally been associated with what we might call non-traditional settings in relation to HCI’s cognitive science roots, emerging at first particular in organizational studies of collaborative work (in the domain of CSCW), being applied later in studies of alternative interaction patterns in ubiquitous computing, and being associated with domains such as domestic life, experience design, and cultural analysis that have been more recent arrivals on the scene. Another is that ethnographic methods are often associated with forms of analysis and theorizing of human action – ethnomethodology stands out as an example here – that are themselves alien to HCI’s intellectual traditions and which have not always been clearly explained. Indeed, debates within the field have often founded on these sorts of confusions, so that in the internecine battles amongst social theorists, ethnographic methods suffer collateral damage (e.g. Crabtree et al. 2009). Finally, in a discipline that has often proceeded with something of a mix-and-match approach, liberally and creatively borrowing ideas and elements from different places, ethnography has often been seen instrumentally as a way of understanding important aspects of technological practice while its own epistemological commitments have remained somewhat murky.


AZR-LEACH: An Energy Efficient Routing Protocol for Wireless Sensor Networks

Reducing the energy consumption of available resources is still a problem to be solved in Wireless Sensor Networks (WSNs). Many types of existing routing protocols are developed to save power consumption. In these protocols, cluster-based routing protocols are found to be more energy efficient. A cluster head is selected to aggregate the data received from root nodes and forwards these data to the base station in cluster-based routing. The selection of cluster heads should be efficient to save energy. In our proposed protocol, we use static clustering for the efficient selection of cluster heads. The proposed routing protocol works efficiently in large as well as small areas. For an optimal number of cluster head selection we divide a large sensor field into rectangular clusters. Then these rectangular clusters are further grouped into zones for efficient communication between cluster heads and a base station. We perform MATLAB simulations to observe the network stability, throughput, energy consumption, network lifetime and the number of cluster heads. Our proposed routing protocol outperforms in large areas in comparison with the LEACH, MH-LEACH, and SEP routing protocols.


A CMOS voltage reference based on weighted difference of gate-source voltages between PMOS and NMOS transistors for low dropout regulators

A CMOS voltage reference, which takes advantage of weighted difference of the gate-source voltages between a PMOS and an NMOS transistor operating in saturation region, is presented in this paper. The reference has been implemented in a standard 0.6-µm CMOS process (Vthn≈ |Vthp| ≈ 0.9 V@0° C) and gives a temperature coefficient of not greater than 62 ppm/°C from 0 to 100° C without trimming, while consuming a maximum of 9.7 µA with a minimum supply of 1.4 V. The worst-case line regulation is ±0.17 %/V. The occupied chip area is 0.055 mm2. The proposed reference has been applied to a 10-mA CMOS low dropout regulator, and a temperature coefficient of 94 ppm/°C is achieved when the regulator delivers maximum load current.


Deep Sentence-Level Authorship Attribution

We examine the problem of authorship attribution in collaborative documents. We seek to develop new deep learning models tailored to this task. We have curated a novel dataset by parsing Wikipedia’s edit history, which we use to demonstrate the feasiblity of deep models to multi-author attribution at the sentence-level. Though we attempt to formulate models which learn stylometric features based on both grammatical structure and vocabulary, our error analysis suggests that our models mostly learn to recognize vocabulary-based cues, making them non-competitive with baselines tailored to vocabulary-based features. We explore why this may be, and suggest directions for future models to mitigate this shortcoming.


MEMS Capacitive Pressure Sensors : A Review on Recent Development and Prospective

Recently MEMS Capacitive Pressure Sensor gains more advantage over micromachined piezoresistive pressure sensor due to high sensitivity, low power consumption, free from temperature effects, IC compatibility, etc,. The spectrum of capacitive pressure sensor application is increasing, hence it is essential to review the path of technological development and further prospective of micromachined capacitive pressure sensor. This paper focuses on the review of various types of capacitive pressure sensor principle, MEMS materials used in fabrication, procedures adopted in microfabrication for silicon and polymer material diaphragm, bonding and packaging techniques used. Selected result on capacitive sensitivity, effect of temperature on capacitive sensitivity was also presented. Finally, the development of smart sensor was discussed. MEMS Capacitive pressure sensor, Review on pressure sensor, CDPS, MEMS Fabrication, MEMS Material, pressure sensor, MEMS (Micro Electro Mechanical System)


The Interplay of Institutional Logics in it Public?Private Partnerships

Public-private partnerships (PPPs) offer a popular means by which the public sector can obtain infor-mation technology (IT) innovations and management know-how from private firms. However, these IT PPPs are extremely difficult to realize, especially considering the divergent interests of public- and private-side stake-holders. Our case study of an IT PPP reveals public- and private-side differences that initially impeded the establishment of a partnership; using institutional logics theory as meta-theoretical lens, we propose a model that explains how public and private parties managed to negotiate their mode of collaboration by balancing their competing institutional norms and practices which ultimately resulted in the convergence of the two divergent logics. Our paper contributes to theory and practice by (1) elucidating the theoretical foundations and role of institutional logics for IT project management that we found dominated by public and private norms and practices, (2) explaining why collaboration in IT PPPs is so diffi-cult, and (3) how eventually an IT PPP can be estab-lished. We discuss theoretical and practical implica-tions in the paper.


Grid-based DBSCAN for clustering extended objects in radar data

The online observation using high-resolution radar of a scene containing extended objects imposes new requirements on a robust and fast clustering algorithm. This paper presents an algorithm based on the most cited and common clustering algorithm: DBSCAN [1]. The algorithm is modified to deal with the non-equidistant sampling density and clutter of radar data while maintaining all its prior advantages. Furthermore, it uses varying sampling resolution to perform an optimized separation of objects at the same time it is robust against clutter. The algorithm is independent of difficult to estimate input parameters such as the number or shape of available objects. The algorithm outperforms DBSCAN in terms of speed by using the knowledge of the sampling density of the sensor (increase of app. 40-70%). The algorithm obtains an even better result than DBSCAN by including the Doppler and amplitude information (unitless distance criteria).


Generalized Mongue-Elkan Method for Approximate Text String Comparison

The Mongue-Elkan method is a general text string comparison method based on an internal character-based similarity measure (e.g. edit distance) combined with a token level (i.e. word level) similarity measure. We propose a generalization of this method based on the notion of the generalized arithmetic mean instead of the simple average used in the expression to calculate the Monge-Elkan method. The experiments carried out with 12 well-known name-matching data sets show that the proposed approach outperforms the original Monge-Elkan method when character-based measures are used to compare tokens.


Good Features to Correlate for Visual Tracking

During the recent years, correlation filters have shown dominant and spectacular results for visual object tracking. The types of the features that are employed in this family of trackers significantly affect the performance of visual tracking. The ultimate goal is to utilize the robust features invariant to any kind of appearance change of the object, while predicting the object location as properly as in the case of no appearance change. As the deep learning based methods have emerged, the study of learning features for specific tasks has accelerated. For instance, discriminative visual tracking methods based on deep architectures have been studied with promising performance. Nevertheless, correlation filter based (CFB) trackers confine themselves to use the pre-trained networks, which are trained for object classification problem. To this end, in this manuscript the problem of learning deep fully convolutional features for the CFB visual tracking is formulated. In order to learn the proposed model, a novel and efficient backpropagation algorithm is presented based on the loss function of the network. The proposed learning framework enables the network model to be flexible for a custom design. Moreover, it alleviates the dependency on the network trained for classification. Extensive performance analysis shows the efficacy of the proposed custom design in the CFB tracking framework. By fine-tuning the convolutional parts of a state-of-the-art network and integrating this model to a CFB tracker, which is the top performing one of VOT2016, 18% increase is achieved in terms of expected average overlap, and tracking failures are decreased by 25%, while maintaining the superiority over the state-of-the-art methods in OTB-2013 and OTB-2015 tracking datasets.


Internet of intelligent things and robot as a service

Article history: Received 21 January 2012 Received in revised form 16 March 2012 Accepted 17 March 2012 Available online 17 April 2012


Integration of face recognition and sound localization for a smart door phone system

This paper proposes a smart system using both face recognition and sound localization techniques to identify the faces of visitors from a door phone in much efficient and accurate ways. This system is effectively used to recognize the faces when their locations are out of the boundaries of the camera scope of the door phone. The smart door phone system proposed in this paper uses a visitor's voice source to rotate the camera to his face and then to recognize the face accurately. The integrated system has been designed with one FPGA(Field Programmable Gate Array) chip and tested for actual use in door phone environments.


Balance of power: dynamic thermal management for Internet data centers

Internet-based applications and their resulting multitier distributed architectures have changed the focus of design for large-scale Internet computing. Internet server applications execute in a horizontally scalable topology across hundreds or thousands of commodity servers in Internet data centers. Increasing scale and power density significantly impacts the data center's thermal properties. Effective thermal management is essential to the robustness of mission-critical applications. Internet service architectures can address multisystem resource management as well as thermal management within data centers.


DTAM: Dense tracking and mapping in real-time

DTAM is a system for real-time camera tracking and reconstruction which relies not on feature extraction but dense, every pixel methods. As a single hand-held RGB camera flies over a static scene, we estimate detailed textured depth maps at selected keyframes to produce a surface patchwork with millions of vertices. We use the hundreds of images available in a video stream to improve the quality of a simple photometric data term, and minimise a global spatially regularised energy functional in a novel non-convex optimisation framework. Interleaved, we track the camera's 6DOF motion precisely by frame-rate whole image alignment against the entire dense model. Our algorithms are highly parallelisable throughout and DTAM achieves real-time performance using current commodity GPU hardware. We demonstrate that a dense model permits superior tracking performance under rapid motion compared to a state of the art method using features; and also show the additional usefulness of the dense model for real-time scene interaction in a physics-enhanced augmented reality application.


A Theory of Shape by Space Carving

In this paper we consider the problem of computing the 3D shape of an unknown, arbitrarily-shaped scene from multiple photographs taken at known but arbitrarily-distributed viewpoints. By studying the equivalence class of all 3D shapes that reproduce the input photographs, we prove the existence of a special member of this class, the photo hull, that (1) can be computed directly from photographs of the scene, and (2) subsumes all other members of this class. We then give a provably-correct algorithm, called Space Carving, for computing this shape and present experimental results on complex real-world scenes. The approach is designed to (1) capture photorealistic shapes that accurately model scene appearance from a wide range of viewpoints, and (2) account for the complex interactions between occlusion, parallax, shading, and their view-dependent effects on scene-appearance.


New types of acetate-oxidizing, sulfate-reducing Desulfobacter species, D. hydrogenophilus sp. nov., D. latus sp. nov., and D. curvatus sp. nov.

Sulfate-reducing bacteria with oval to rod-shaped cells (strains AcRS1, AcRS2) and vibrio-shaped cells (strains AcRM3, AcRM4, AcRM5) differing by size were isolated from anaerobic marine sediment with acetate as the only electron donor. A vibrio-shaped type (strain AcKo) was also isolated from freshwater sediment. Two strains (AcRS1, AcRM3) used ethanol and pyruvate in addition to acetate, and one strain (AcRS1) grew autotrophically with H2, sulfate and CO2. Higher fatty acids or lactate were never utilized. All isolates were able to grow in ammonia-free medium in the presence of N2. Nitrogenase activity under such conditions was demonstrated by the acetylene reduction test. The facultatively lithoautotrophic strain (AcRS1), a strain (AcRS2) with unusually large cells (2×5 μm), and a vibrio-shaped strain (AcRM3) are described as new Desulfobacter species, D. hydrogenophilus, D. latus, and D. curvatus, respectively.


Offline and Online calibration of Mobile Robot and SLAM Device for Navigation

Robot navigation technology is required to accomplish difficult tasks in various environments. In navigation, it is necessary to know the information of the external environments and the state of the robot under the environment. On the other hand, various studies have been done on SLAM technology, which is also used for navigation, but also applied to devices for Mixed Reality and the like. In this paper, we propose a robot-device calibration method for navigation with a device using SLAM technology on a robot. The calibration is performed by using the position and orientation information given by the robot and the device. In the calibration, the most efficient way of movement is clarified according to the restriction of the robot movement. Furthermore, we also show a method to dynamically correct the position and orientation of the robot so that the information of the external environment and the shape information of the robot maintain consistency in order to reduce the dynamic error occurring during navigation. Our method can be easily used for various kinds of robots and localization with sufficient precision for navigation is possible with offline calibration and online position correction. In the experiments, we confirm the parameters obtained by two types of offline calibration according to the degree of freedom of robot movement and validate the effectiveness of online correction method by plotting localized position error during robot’s intense movement. Finally, we show the demonstration of navigation using SLAM device.


Text Detection and Recognition in Imagery: A Survey

This paper analyzes, compares, and contrasts technical challenges, methods, and the performance of text detection and recognition research in color imagery. It summarizes the fundamental problems and enumerates factors that should be considered when addressing these problems. Existing techniques are categorized as either stepwise or integrated and sub-problems are highlighted including text localization, verification, segmentation and recognition. Special issues associated with the enhancement of degraded text and the processing of video text, multi-oriented, perspectively distorted and multilingual text are also addressed. The categories and sub-categories of text are illustrated, benchmark datasets are enumerated, and the performance of the most representative approaches is compared. This review provides a fundamental comparison and analysis of the remaining problems in the field.


A Neural Network Framework for Fair Classifier

Machine learning models are extensively being used in decision making, especially for prediction tasks. These models could be biased or unfair towards a specific sensitive group either of a specific race, gender or age. Researchers have put efforts into characterizing a particular definition of fairness and enforcing them into the models. In this work, mainly we are concerned with the following three definitions, Disparate Impact, Demographic Parity and Equalized Odds. Researchers have shown that Equalized Odds cannot be satisfied in calibrated classifiers unless the classifier is perfect. Hence the primary challenge is to ensure a degree of fairness while guaranteeing as much accuracy as possible. Fairness constraints are complex and need not be convex. Incorporating them into a machine learning algorithm is a significant challenge. Hence, many researchers have tried to come up with a surrogate loss which is convex in order to build fair classifiers. Besides, certain papers try to build fair representations by preprocessing the data, irrespective of the classifier used. Such methods, not only require a lot of unrealistic assumptions but also require human engineered analytical solutions to build a machine learning model. We instead propose an automated solution which is generalizable over any fairness constraint. We use a neural network which is trained on batches and directly enforces the fairness constraint as the loss function without modifying it further. We have also experimented with other complex performance measures such as H-mean loss, Q-mean-loss, F-measure; without the need for any surrogate loss functions. Our experiments prove that the network achieves similar performance as state of the art. Thus, one can just plug-in appropriate loss function as per required fairness constraint and performance measure of the classifier and train a neural network to achieve that.


On Modeling and Predicting Individual Paper Citation Count over Time

Evaluating a scientist’s past and future potential impact is key in decision making concerning with recruitment and funding, and is increasingly linked to publication citation count. Meanwhile, timely identifying those valuable work with great potential before they receive wide recognition and become highly cited papers is both useful for readers and authors in many regards. We propose a method for predicting the citation counts of individual publications, over an arbitrary time period. Our approach explores paper-specific covariates, and a point process model to account for the aging effect and triggering role of recent citations, through which papers lose and gain their popularity, respectively. Empirical results on the Microsoft Academic Graph data suggests that our model can be useful for both prediction and interpretability.


Adaptive Knee Joint Exoskeleton Based on Biological Geometries

This paper presents a relatively complete analytical model of a knee joint interacting with a two-link exoskeleton for investigating the effects of different exoskeleton designs on the internal joint forces/torque in the knee. The closed kinematic chain formed by the leg and exoskeleton has a significant effect on the joint forces/torque in the knee. A bio-joint model is used to capture this effect by relaxing a commonly made assumption that approximates a knee joint as a perfect engineering pin-joint in designing an exoskeleton. Based on the knowledge of a knee-joint kinematics, an adaptive knee-joint exoskeleton has been designed to eliminate negative effects associated with the closed leg-exoskeleton kinematic chain on a human knee. For experimental validation, the flexion motion of an artificial human knee is investigated comparing the performances of five exoskeleton designs against the case with no exoskeleton. Analytical results that estimate internal forces/torque using the kinematic and dynamic models (based on the properties of a knee joint) agree well with data obtained experimentally. This investigation illustrates the applications of the analytical model for designing an adaptive exoskeleton that minimizes internal joint forces due to a knee-exoskeleton interaction.


Constrained Two-dimensional Cutting Stock Problems a Best-first Branch-and-bound Algorithm Constrained Two-dimensional Cutting Stock Problems a Best-first Branch-and-bound Algorithm

In this work, we develop a new version of the algorithm proposed in 17] for solving exactly some variants of (un)weighted constrained two-dimensional cutting stock problems. We introduce one-dimensional bounded knapsacks in order to obtain an improved initial lower bound for limiting initially the size of the search space, an improved upper bound at each internal node and a symmetry detection for neglecting some useless duplicate patterns. A new eecient data structure of the set representing the best subprob-lems is used to implement the algorithms over the BOB library. The performance of our algorithms is evaluated on some problem instances of the literature and on other randomly generated hard problem instances (a total of 27 problem instances). Les Probl emes de D ecoupe 2-Dimensions Contraints Un Algorithme de Branch-and-Bound meilleur d'abord R esum e : Dans ce travail, nous concevons une nouvelle version de l'algorithme pro-pos e dans 17] pour r esoudre de mani ere exacte des probl emes de d ecoupe en 2-dimensions contraints avec ou sans pond eration. Nous introduisons des sacs a dos unidimension-nels contraints aan d'obtenir une meilleure borne inf erieure initiale pour limiter la taille de l'espace de recherche, une meilleure borne sup erieure pour chaque nnud interne et une d etection de sym etries pour supprimer des conngurations identiques. Une nouvelle structure de donn ees eecace pour g erer les meilleurs sous-probl emes a et e employ ee pour l'impl ementation des algorithmes sur la biblioth eque BOB. La performance de nos algo-rithmes est evalu ee sur 27 instances de probl emes extraites pour certaines de la litt erature et d'autres diiciles, engendr ees al eatoirement.


Developing an intelligent data discriminating system of anti-money laundering based on SVM

Statistical learning theory (SLT) is introduced to improve the embarrassments of anti-money laundering (AML) intelligence collection. A set of unusual behavior detection algorithm is presented in this paper based on support vector machine (SVM) in order to take the place of traditional predefined-rule suspicious transaction data filtering system. It could efficiently surmount the worst forms of suspicious data analyzing and reporting mechanism among bank branches including enormous data volume, dimensionality disorder with massive variances and feature overload.


Specific language impairment: a convenient label for whom?

BACKGROUND
The term 'specific language impairment' (SLI), in use since the 1980s, describes children with language impairment whose cognitive skills are within normal limits where there is no identifiable reason for the language impairment. SLI is determined by applying exclusionary criteria, so that it is defined by what it is not rather than by what it is. The recent decision to not include SLI in DSM-5 provoked much debate and concern from researchers and clinicians.

AIMS
To explore how the term 'specific language impairment' emerged, to consider how disorders, including SLI, are generally defined and to explore how societal changes might impact on use the term.

METHODS & PROCEDURES
We reviewed the literature to explore the origins of the term 'specific language impairment' and present published evidence, as well as new analyses of population data, to explore the validity of continuing to use the term.

OUTCOMES & RESULTS AND CONCLUSIONS & IMPLICATIONS
We support the decision to exclude the term 'specific language impairment' from DSM-5 and conclude that the term has been a convenient label for researchers, but that the current classification is unacceptably arbitrary. Furthermore, we argue there is no empirical evidence to support the continued use of the term SLI and limited evidence that it has provided any real benefits for children and their families. In fact, the term may be disadvantageous to some due to the use of exclusionary criteria to determine eligibility for and access to speech pathology services. We propose the following recommendations. First, that the word 'specific' be removed and the label 'language impairment' be used. Second, that the exclusionary criteria be relaxed and in their place inclusionary criteria be adopted that take into account the fluid nature of language development particularly in the preschool period. Building on the goodwill and collaborations between the clinical and research communities we propose the establishment of an international consensus panel to develop an agreed definition and set of criteria for language impairment. Given the rich data now available in population studies it is possible to test the validity of these definitions and criteria. Consultation with service users and policy-makers should be incorporated into the decision-making process.


The Optimality of Naive Bayes

Naive Bayes is one of the most efficient and effective inductive learning algorithms for machine learning and data mining. Its competitive performance in classification is surprising, because the conditional independence assumption on which it is based, is rarely true in realworld applications. An open question is: what is the true reason for the surprisingly good performance of naive Bayes in classification? In this paper, we propose a novel explanation on the superb classification performance of naive Bayes. We show that, essentially, the dependence distribution; i.e., how the local dependence of a node distributes in each class, evenly or unevenly, and how the local dependencies of all nodes work together, consistently (supporting a certain classification) or inconsistently (canceling each other out), plays a crucial role. Therefore, no matter how strong the dependences among attributes are, naive Bayes can still be optimal if the dependences distribute evenly in classes, or if the dependences cancel each other out. We propose and prove a sufficient and necessary conditions for the optimality of naive Bayes. Further, we investigate the optimality of naive Bayes under the Gaussian distribution. We present and prove a sufficient condition for the optimality of naive Bayes, in which the dependence between attributes do exist. This provides evidence that dependence among attributes may cancel out each other. In addition, we explore when naive Bayes works well. Naive Bayes and Augmented Naive Bayes Classification is a fundamental issue in machine learning and data mining. In classification, the goal of a learning algorithm is to construct a classifier given a set of training examples with class labels. Typically, an example E is represented by a tuple of attribute values (x1, x2, , · · · , xn), where xi is the value of attribute Xi. Let C represent the classification variable, and let c be the value of C. In this paper, we assume that there are only two classes: + (the positive class) or − (the negative class). A classifier is a function that assigns a class label to an example. From the probability perspective, according to Bayes Copyright c © 2004, American Association for Artificial Intelligence (www.aaai.org). All rights reserved. Rule, the probability of an example E = (x1, x2, · · · , xn) being class c is p(c|E) = p(E|c)p(c) p(E) . E is classified as the class C = + if and only if fb(E) = p(C = +|E) p(C = −|E) ≥ 1, (1) where fb(E) is called a Bayesian classifier. Assume that all attributes are independent given the value of the class variable; that is, p(E|c) = p(x1, x2, · · · , xn|c) = n ∏


Using Topic Models in Content-Based News Recommender Systems

We study content-based recommendation of Finnish news in a system with a very small group of users. We compare three standard methods, Naïve Bayes (NB), K-Nearest Neighbor (kNN) Regression and Regulairized Linear Regression in a novel online simulation setting and in a coldstart simulation. We also apply Latent Dirichlet Allocation (LDA) on the large corpus of news and compare the learned features to those found by Singular Value Decomposition (SVD). Our results indicate that Naïve Bayes is the worst of the three models. K-Nearest Neighbor performs consistently well across input features. Regularized Linear Regression performs generally worse than kNN, but reaches similar performance as kNN with some features. Regularized Linear Regression gains statistically significant improvements over the word-features with LDA both on the full data set and in the cold-start simulation. In the cold-start simulation we find that LDA gives statistically significant improvements for all the methods.


SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-Rays

Chest X-ray (CXR) is one of the most commonly prescribed medical imaging procedures, often with over 2– 10x more scans than other imaging modalities such as MRI, CT scan, and PET scans. These voluminous CXR scans place significant workloads on radiologists and medical practitioners. Organ segmentation is a crucial step to obtain effective computer-aided detection on CXR. In this work, we propose Structure Correcting Adversarial Network (SCAN) to segment lung fields and the heart in CXR images. SCAN incorporates a critic network to impose on the convolutional segmentation network the structural regularities emerging from human physiology. During training, the critic network learns to discriminate between the ground truth organ annotations from the masks synthesized by the segmentation network. Through this adversarial process the critic network learns the higher order structures and guides the segmentation model to achieve realistic segmentation outcomes. Extensive experiments show that our method produces highly accurate and natural segmentation. Using only very limited training data available, our model reaches human-level performance without relying on any existing trained model or dataset. Our method also generalizes well to CXR images from a different patient population and disease profiles, surpassing the current state-of-the-art.


Quantizing Convolutional Neural Networks for Low-Power High-Throughput Inference Engines

Deep learning as a means to inferencing has proliferated thanks to its versatility and ability to approach or exceed human-level accuracy. These computational models have seemingly insatiable appetites for computational resources not only while training, but also when deployed at scales ranging from data centers all the way down to embedded devices. As such, increasing consideration is being made to maximize the computational efficiency given limited hardware and energy resources and, as a result, inferencing with reduced precision has emerged as a viable alternative to the IEEE 754 Standard for Floating-Point Arithmetic. We propose a quantization scheme that allows inferencing to be carried out using arithmetic that is fundamentally more efficient when compared to even half-precision floating-point. Our quantization procedure is significant in that we determine our quantization scheme parameters by calibrating against its reference floating-point model using a single inference batch rather than (re)training and achieve end-to-end post quantization accuracies comparable to the reference model.


SCARE: Side-Channel Analysis Based Reverse Engineering for Post-Silicon Validation

Reverse Engineering (RE) has been historically considered as a powerful approach to understand electronic hardware in order to gain competitive intelligence or accomplish piracy. In recent years, it has also been looked at as a way to authenticate hardware intellectual properties in the court of law. In this paper, we propose a beneficial role of RE in post-silicon validation of integrated circuits (IC) with respect to IC functionality, reliability and integrity. Unlike traditional destructive RE approaches, we propose a fast non-destructive side-channel analysis approach that can hierarchically extract structural information from an IC through its transient current signature. Such a top-down side-channel analysis approach is capable of reliably identifying pipeline stages and functional blocks. It is also suitable to distinguish sequential elements from combinational gates. For extraction of random logic structures (e.g. control blocks and finite state machines) we combine side-channel analysis with logic testing based Boolean function extraction. The proposed approach is amenable to automation, scalable, and can be applied as part of post-silicon validation process to verify that each IC implements exclusively the functionality described in the specification and is free from malicious modification or Trojan attacks. Simulation results on a pipelined DLX processor demonstrate the effectiveness of the proposed approach.


Process models in the practice of distributed software development: A systematic review of the literature

0950-5849/$ see front matter 2010 Elsevier B.V. A doi:10.1016/j.infsof.2010.03.009 * Corresponding author. E-mail addresses: rafaelp@pucrs.br (R. Prikladnicki) Context: Distributed Software Development (DSD) has recently become an active research area. Although considerable research effort has been made in this area, as yet, no agreement has been reached as to an appropriate process model for DSD. Purpose: This paper is intended to identify and synthesize papers that describe process models for distributed software development in the context of overseas outsourcing, i.e. ‘‘offshoring”. Method: We used a systematic review methodology to search seven digital libraries and one topic-specific conference. Results: We found 27 primary studies describing stage-related DSD process models. Only five of such studies looked into outsourcing to a subsidiary company (i.e. ‘‘internal offshoring”). Nineteen primary studies addressed the need for DSD process models. Eight primary studies and three literature surveys described stage-based DSD process models, but only three of such models were empirically evaluated. Conclusion: We need more research aimed at internal offshoring. Furthermore, proposed models need to be empirically validated. 2010 Elsevier B.V. All rights reserved.