Query: Question Passage Question & Passage Encoding Question-Passage Matching Passage Self-Matching Word Character Answer Prediction

Relevant passages:
Glove: Global Vectors for Word Representation <=SEP=> Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75% on a recent word analogy task. It also outperforms related models on similarity tasks and named entity recognition.

Structural Embedding of Syntactic Trees for Machine Comprehension <=SEP=> Deep neural networks for machine comprehension typically utilizes only word or character embeddings without explicitly taking advantage of structured linguistic information such as constituency trees and dependency trees. In this paper, we propose structural embedding of syntactic trees (SEST), an algorithm framework to utilize structured information and encode them into vector representations that can boost the performance of algorithms for the machine comprehension. We evaluate our approach using a state-of-the-art neural attention model on the SQuAD dataset. Experimental results demonstrate that our model can accurately identify the syntactic boundaries of the sentences and extract answers that are syntactically coherent over the baseline methods.

Modeling Biological Processes for Reading Comprehension <=SEP=> Machine reading calls for programs that read and understand text, but most current work only attempts to extract facts from redundant web-scale corpora. In this paper, we focus on a new reading comprehension task that requires complex reasoning over a single document. The input is a paragraph describing a biological process, and the goal is to answer questions that require an understanding of the relations between entities and events in the process. To answer the questions, we first predict a rich structure representing the process in the paragraph. Then, we map the question to a formal query, which is executed against the predicted structure. We demonstrate that answering questions via predicted structures substantially improves accuracy over baselines that use shallower representations.

SQuAD: 100, 000+ Questions for Machine Comprehension of Text <=SEP=> We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage. We analyze the dataset to understand the types of reasoning required to answer the questions, leaning heavily on dependency and constituency trees. We build a strong logistic regression model, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%). However, human performance (86.8%) is much higher, indicating that the dataset presents a good challenge problem for future research. The dataset is freely available at https://stanford-qa.com.

Attention-over-Attention Neural Networks for Reading Comprehension <=SEP=> Cloze-style queries are representative problems in reading comprehension. Over the past few months, we have seen much progress that utilizing neural network approach to solve Cloze-style questions. In this paper, we present a novel model called attention-over-attention reader for the Cloze-style reading comprehension task. Our model aims to place another attention mechanism over the document-level attention, and induces “attended attention” for final predictions. Unlike the previous works, our neural network model requires less pre-defined hyper-parameters and uses an elegant architecture for modeling. Experimental results show that the proposed attentionover-attention model significantly outperforms various state-of-the-art systems by a large margin in public datasets, such as CNN and Children’s Book Test datasets.

The transition to high school as a developmental process among multiethnic urban youth. <=SEP=> The high school transition was examined in an ethnically diverse, urban sample of 1,979 adolescents, followed from 7th to 10th grade (M(age) = 14.6, SD = .37 in 7th grade). Twice annually, data were gathered on adolescents' perceptions of school climate, psychological functioning, and academic behaviors. Piecewise growth modeling results indicate that adolescents were doing well before the transition but experienced transition disruptions in psychological functioning and grades, and many continued to struggle across high school. The immediate experience of the transition appeared to be particularly challenging for African American and Latino students when the numerical representation of their ethnic groups declined significantly from middle to high school. Findings highlight the value of examining the transition in a larger developmental context and the importance of implementing transition support.

Smart Kitchen : A User Centric Cooking Support System <=SEP=> Several cooking support systems have been studied to give users instructions based on the recipes stepby-step, using multi-media contents. These systems usually disturb users’ cooking process forcing them to provide information to the system in order to give them beneficial information. In this sense, these systems are considered to be ”system centric”. We propose a system called ”Smart Kitchen” considered to be in ”user centric”, in which a user can cook normally without being concerned about the system. Smart Kitchen can understand cooking processes, in other words, what the user is doing. In this paper, we discuss the design of the Smart Kitchen system and explain three essential modules of tracking food, recognizing food material, and recognizing cooking action.

Ally Friendly Jamming: How to Jam Your Enemy and Maintain Your Own Wireless Connectivity at the Same Time <=SEP=> This paper presents a novel mechanism, called Ally Friendly Jamming, which aims at providing an intelligent jamming capability that can disable unauthorized (enemy) wireless communication but at the same time still allow authorized wireless devices to communicate, even if all these devices operate at the same frequency. The basic idea is to jam the wireless channel continuously but properly control the jamming signals with secret keys, so that the jamming signals are unpredictable interference to unauthorized devices, but are recoverable by authorized ones equipped with the secret keys. To achieve the ally friendly jamming capability, we develop new techniques to generate ally jamming signals, to identify and synchronize with multiple ally jammers. This paper also reports the analysis, implementation, and experimental evaluation of ally friendly jamming on a software defined radio platform. Both the analytical and experimental results indicate that the proposed techniques can effectively disable enemy wireless communication and at the same time maintain wireless communication between authorized devices.

From GPS traces to a routable road map <=SEP=> This paper presents a method for automatically converting raw GPS traces from everyday vehicles into a routable road network. The method begins by smoothing raw GPS traces using a novel aggregation technique. This technique pulls together traces that belong on the same road in response to simulated potential energy wells created around each trace. After the traces are moved in response to the potential fields, they tend to coalesce into smooth paths. To help adjust the parameters of the constituent potential fields, we present a theoretical analysis of the behavior of our algorithm on a few different road configurations. With the resulting smooth traces, we apply a custom clustering algorithm to create a graph of nodes and edges representing the road network. We show how this network can be used to plan reasonable driving routes, much like consumer-oriented mapping Web sites. We demonstrate our algorithms using real GPS data collected on public roads, and we evaluate the effectiveness of our approach on public roads, and we evaluate the effectiveness of our approach by comparing the route planning results suggested by our generated graph to a commercial route planner.

Abstract Semantic Differencing for Numerical Programs <=SEP=> Semantic Differencing for Numerical Programs Nimrod Partush Eran Yahav

Vision: towards real time epidemic vigilance through online social networks: introducing SNEFT -- social network enabled flu trends <=SEP=> Our vision is to achieve faster and near real time detection and prediction of the emergence and spread of an influenza epidemic, through sophisticated data collection and analysis of Online Social Networks (OSNs) such as Facebook, MySpace, and Twitter. In particular, we present the design of a system called SNEFT (Social Network Enabled Flu Trends), which will be developed in a 12-month SBIR (Small Business Innovation Research) project funded by the National Institutes of Health (NIH). We describe the innovative technologies that will be developed in this project for collecting and aggregating OSN data, extracting information from it, and integrating it with mathematical models of influenza. One of the monitoring tools used by the Centers for Disease Control and Prevention (CDC) is reports of Influenza-Like Illness (ILI) cases; these reports are authoritative but typically have a delay of one to two weeks due to the largely manual process. We describe the SNEFT prototype in the context of predicting ILI cases well in advance of the CDC reports. We observe that OSN data is individually noisy but collectively revealing, and speculate on other applications that can potentially be enabled by OSN data collection and analysis.

Radar Cross-Section Analysis of Backscattering RFID Tags <=SEP=> In this letter, we present a graphical method based on Green's analysis of scattering antennas to optimize the load impedances for achieving maximum modulated radar cross-section (RCS) of backscattering radio-frequency identification (RFID) tags. A planar tag antenna specifically designed for working in the ultra-high-frequency band is used to experimentally validate the method. The measured RCS of the loaded tag antenna shows acceptable agreement with the theory and simulated results. This method is especially valuable to the design of semipassive RFID tags when the communication range is solely determined by the backscattering modulation efficiency of tags.

Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders <=SEP=> The Cancer Genome Atlas (TCGA) has profiled over 10,000 tumors across 33 different cancer-types for many genomic features, including gene expression levels. Gene expression measurements capture substantial information about the state of each tumor. Certain classes of deep neural network models are capable of learning a meaningful latent space. Such a latent space could be used to explore and generate hypothetical gene expression profiles under various types of molecular and genetic perturbation. For example, one might wish to use such a model to predict a tumor's response to specific therapies or to characterize complex gene expression activations existing in differential proportions in different tumors. Variational autoencoders (VAEs) are a deep neural network approach capable of generating meaningful latent spaces for image and text data. In this work, we sought to determine the extent to which a VAE can be trained to model cancer gene expression, and whether or not such a VAE would capture biologically-relevant features. In the following report, we introduce a VAE trained on TCGA pan-cancer RNA-seq data, identify specific patterns in the VAE encoded features, and discuss potential merits of the approach. We name our method "Tybalt" after an instigative, cat-like character who sets a cascading chain of events in motion in Shakespeare's "Romeo and Juliet". From a systems biology perspective, Tybalt could one day aid in cancer stratification or predict specific activated expression patterns that would result from genetic changes or treatment effects.

EEG-based BCI and video games: a progress report <=SEP=> This paper presents a systematic review of electroencephalography (EEG)-based brain–computer interfaces (BCIs) used in the video games, a vibrant field of research that touches upon all relevant questions concerning the future directions of BCI. The paper examines the progress of BCI research with regard to games and shows that gaming applications offer numerous advantages by orienting BCI to concerns and expectations of a gaming application. Different BCI paradigms are investigated, and future directions are discussed.

A wall climbing robot for tank inspection. An autonomous prototype <=SEP=> This paper describes a solution to a mobile climbing robot on magnetic wheels, designed for inspecting exterior oil tank surfaces made of metal sheets. A mechanical design has been developed which presents a practical solution without an umbilical cord. The inspection system has been developed based on client/server architecture. The robot runs a client application and a remote PC executes the server functions. They will both allow any necessary inspections to be performed simultaneously by more than one robot. A sensorial system and a data fusion strategy to estimate the absolute robot position is proposed to allow the robot to navigate autonomously. The graphical monitoring of the robot position in the remote PC (server application) provides the operator with the possibility of controlling the robot, even in situations in which the operator visibility of an area tank is very low or inexistent. Previous experiments have demonstrated the mechanical system's robustness. These experiments consist of robot trajectory measurements and the comparison to a motion kinematic model.

The impact of emotional intelligence on work engagement of registered nurses: the mediating role of organisational justice. <=SEP=> AIMS AND OBJECTIVES
To explore the impact of emotional intelligence and organisational justice on work engagement in Chinese nurses and to examine the mediating role of organisational justice to provide implications for promoting clinical nurses' work engagement.


BACKGROUND
The importance of work engagement on nurses' well-being and quality of care has been well documented. Work engagement is significantly predicted by job resources. However, little research has concentrated simultaneously on the influence of both personal and organisational resources on nurses' work engagement.


DESIGN
A descriptive, cross-sectional design was employed.


METHODS
A total of 511 nurses from four public hospitals were enrolled by multistage sampling. Data collection was undertaken using the Wong and Law Emotional Intelligence Scale, the Organizational Justice questionnaire and the Utrecht Work Engagement Scale-9. We analysed the data using structural equation modelling.


RESULTS
Emotional intelligence and organisational justice were significant predictors and they accounted for 44% of the variance in nurses' work engagement. Bootstrap estimation confirmed an indirect effect of emotional intelligence on work engagement via organisational justice.


CONCLUSIONS
Emotional intelligence and organisational justice positively predict work engagement and organisational justice partially mediates the relationship between emotional intelligence and work engagement.


RELEVANCE TO CLINICAL PRACTICE
Our study supports the idea that enhancing organisational justice can increase the impact of emotional intelligence. Managers should take into account the importance of emotional intelligence and perceptions of organisational justice in human resources management and apply targeted interventions to foster work engagement.

Investigating the usability of iPad mobile tablet in formative assessment of a mathematics course <=SEP=> Formative assessment allows students to judge their learning progress against a given standard or assessment criteria during the course of study. There are challenges related to the generation and delivery of feedback for formative purposes, for instance the students' engagement in their learning, the pedagogy adopted and the usability of the technology tools used in this regard. This paper presents a study of iPad mobile tablet technology-supported peer-to-peer assessment (P2PASS), which is a modern form of formative assessment. The experimental work was undertaken at Kigali Institute of Science and Technology (KIST) in Rwanda. The implementation of P2PASS in this study shows the students' engagement in learning mathematics, and allowed the investigation on the possibilities of improving their mastery of the subject. This paper further discusses the students' attitudes towards the P2PASS process as well as the technological tools used. The usability problems of the iPad applications are discussed, together with the implications of using mobile tablet technology in providing the feedback on the mathematical work.

Design and Analysis of Low-Power 11- Transistor Full Adder <=SEP=> Full adders are exigent components in applications such as digital signal processors (DSP) architectures and microprocessors. In this paper, we propose a technique to build a new 11-transistor FA .We have done HSPICE simulation runs the new design 11-T full adders .In CMOS integrated circuit design there is a tradeoff between static power consumption and technology scaling. Static power dissipation is a challenge for the circuit designer. So we reduce the static power dissipation. In order to achieve lower static power consumption, one has to scarifies design area and circuit performance. In this paper we propose a new circuit of 11-Transistor full adder in CMOS VLSI circuit.

Phishing counter measures and their effectiveness – literature review <=SEP=> Purpose – Phishing is essentially a social engineering crime on the Web, whose rampant occurrences and technique advancements are posing big challenges for researchers in both academia and the industry. The purpose of this study is to examine the available phishing literatures and phishing countermeasures, to determine how research has evolved and advanced in terms of quantity, content and publication outlets. In addition to that, this paper aims to identify the important trends in phishing and its countermeasures and provides a view of the research gap that is still prevailing in this field of study. Design/methodology/approach – This paper is a comprehensive literature review prepared after analysing 16 doctoral theses and 358 papers in this field of research. The papers were analyzed based on their research focus, empirical basis on phishing and proposed countermeasures. Findings – The findings reveal that the current anti-phishing approaches that have seen significant deployments over the internet can be classified into eight categories. Also, the different approaches proposed so far are all preventive in nature. A Phisher will mainly target the innocent consumers who happen to be the weakest link in the security chain and it was found through various usability studies that neither server-side security indicators nor client-side toolbars and warnings are successful in preventing vulnerable users from being deceived. Originality/value – Educating the internet users about phishing, as well as the implementation and proper application of anti-phishing measures, are critical steps in protecting the identities of online consumers against phishing attacks. Further research is required to evaluate the effectiveness of the available countermeasures against fresh phishing attacks. Also there is the need to find out the factors which influence internet user’s ability to correctly identify phishing websites.

Metasurfaces for general transformations of electromagnetic fields. <=SEP=> In this review paper I discuss electrically thin composite layers, designed to perform desired operations on applied electromagnetic fields. Starting from a historical overview and based on a general classification of metasurfaces, I give an overview of possible functionalities of the most general linear metasurfaces. The review is concluded with a short research outlook discussion.

Automotive big data: Applications, workloads and infrastructures <=SEP=> Data is increasingly affecting the automotive industry, from vehicle development, to manufacturing and service processes, to online services centered around the connected vehicle. Connected, mobile and Internet of Things devices and machines generate immense amounts of sensor data. The ability to process and analyze this data to extract insights and knowledge that enable intelligent services, new ways to understand business problems, improvements of processes and decisions, is a critical capability. Hadoop is a scalable platform for compute and storage and emerged as de-facto standard for Big Data processing at Internet companies and in the scientific community. However, there is a lack of understanding of how and for what use cases these new Hadoop capabilities can be efficiently used to augment automotive applications and systems. This paper surveys use cases and applications for deploying Hadoop in the automotive industry. Over the years a rich ecosystem emerged around Hadoop comprising tools for parallel, in-memory and stream processing (most notable MapReduce and Spark), SQL and NOSQL engines (Hive, HBase), and machine learning (Mahout, MLlib). It is critical to develop an understanding of automotive applications and their characteristics and requirements for data discovery, integration, exploration and analytics. We then map these requirements to a confined technical architecture consisting of core Hadoop services and libraries for data ingest, processing and analytics. The objective of this paper is to address questions, such as: What applications and datasets are suitable for Hadoop? How can a diverse set of frameworks and tools be managed on multi-tenant Hadoop cluster? How do these tools integrate with existing relational data management systems? How can enterprise security requirements be addressed? What are the performance characteristics of these tools for real-world automotive applications? To address the last question, we utilize a standard benchmark (TPCx-HS), and two application benchmarks (SQL and machine learning) that operate on a dataset of multiple Terabytes and billions of rows.

Smart home garden irrigation system using Raspberry Pi <=SEP=> Irrigation system is a method of allowing water to drip slowly to the roots of plants, either onto the soil surface or directly onto the root zone, through solenoid valve. However, it is found that the market price of the system is expensive for small area coverage. Thus, this paper proposes a design for smart home garden irrigation system that implements ready-to-use, energy-efficient, and cost effective devices. Raspberry Pi, which is implemented in this system is integrated with multi-sensors such as soil moisture sensors, ultrasonic sensors, and light sensors. This proposed system managed to reduce cost, minimize waste water, and reduce physical human interface. In this paper, the relay is utilized to control the switching of solenoid valve. The system also managed to measure moisture of the soil and control the solenoid valve according to human's requirements. It is conducted with Graphical User Interface (GUI) using Android application to activate watering activity. Email notification is also sent to the home user for alert purposes either for normal or critical operations. An experimental setup has been tested and it is proven that the system can intelligently control and monitor the soil moisture levels in the experiment field.

Online payment system using steganography and visual cryptography <=SEP=> A rapid growth in E-Commerce market is seen in recent time throughout the world. With ever increasing popularity of online shopping, Debit or Credit card fraud and personal information security are major concerns for customers, merchants and banks specifically in the case of CNP (Card Not Present). This paper presents a new approach for providing limited information only that is necessary for fund transfer during online shopping thereby safeguarding customer data and increasing customer confidence and preventing identity theft. The method uses combined application of steganography and visual cryptography for this purpose.

Algorithmic motion planning in robotics <=SEP=> A survey is presented of an approach to motion planning that emphasizes object-oriented, exact, and discrete (or combinatorial) algorithmic techniques in which worst-case asymptotically efficient solutions are being sought. Following a statement of the problem, motion planning in static and known environments is treated. The discussion covers general solutions, lower bounds, the projection method, the retraction method, the expanded obstacles, the single-component approach, and a mobile convex object moving in a 2D polygonal space. Variants of the motion-planning problem are then considered, namely, optimal motion planning, adaptive and exploratory motion planning, motion planning in the presence of moving obstacles, constrained motion planning, motion planning with uncertainty, and general task planning.<<ETX>>

Elliptic operator for shape analysis <=SEP=> Many shape analysis methods treat the geometry of an object as a metric space that can be captured by the Laplace-Beltrami operator. In this paper, we propose to adapt a classical operator from quantum mechanics to the field of shape analysis where we suggest to integrate a scalar function through a unified elliptical Hamiltonian operator. We study the addition of a potential function to the Laplacian as a generator for dual spaces in which shape processing is performed. Then, we evaluate the resulting spectral basis for different applications such as mesh compression and shape matching. The suggested operator is shown to produce better functional spaces to operate with, as demonstrated by the proposed framework that outperforms existing spectral methods, for example, when applied to shape matching benchmarks.

R: A language and environment for statistical computing. <=SEP=> Copyright (©) 1999–2012 R Foundation for Statistical Computing. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this manual into another language, under the above conditions for modified versions, except that this permission notice may be stated in a translation approved by the R Core Team.

A New Distance Measure for Face Recognition System <=SEP=> This paper proposes a new powerful distance measure called Normalized Unmatched Points (NUP). This measure can be used in a face recognition system to discriminate facial images. It works by counting the number of unmatched pixels between query and database images. A face recognition system has been proposed which makes use of this proposed distance measure for taking the decision on matching. This system has been tested on four publicly available databases, viz. ORL, YALE, BERN and CALTECH databases. Experimental results show that the proposed measure achieves recognition rates more than 98.66% for the first five likely matched faces. It is observed that the NUP distance measure performs better than other existing similar variants on these databases.

Penile length, digit length, and anogenital distance according to birth weight in newborn male infants <=SEP=> PURPOSE
Anogential distance (AGD) and the 2:4 digit length ratio appear to provide a reliable guide to fetal androgen exposure. We intended to investigate the current status of penile size and the relationship between penile length and AGD or digit length according to birth weight in Korean newborn infants.


MATERIALS AND METHODS
Between May 2013 and February 2014, among a total of 78 newborn male infants, 55 infants were prospectively included in this study. Newborn male infants with a gestational age of 38 to 42 weeks and birth weight>2.5 kg were assigned to the NW group (n=24) and those with a gestational age<38 weeks and birth weight<2.5 kg were assigned to the LW group (n=31). Penile size and other variables were compared between the two groups.


RESULTS
Stretched penile length of the NW group was 3.3 ± 0.2 cm, which did not differ significantly from that reported in 1987. All parameters including height, weight, penile length, testicular size, AGD, and digit length were significantly lower in the LW group than in the NW group. However, there were no significant differences in AGD ratio or 2:4 digit length ratio between the two groups.


CONCLUSIONS
The penile length of newborn infants has not changed over the last quarter century in Korea. With normal penile appearance, the AGD ratio and 2:4 digit length ratio are consistent irrespective of birth weight, whereas AGD, digit length, and penile length are significantly smaller in newborns with low birth weight.

A Neural Network Approch to Efficient Valuation of Large Portfolios of Variable Annuities <=SEP=> Managing and hedging the risks associated with Variable Annuity (VA) products requires intraday valuation of key risk metrics for these products. The complex structure of VA products and computational complexity of their accurate evaluation has compelled insurance companies to adopt Monte Carlo (MC) simulations to value their large portfolios of VA products. Because the MC simulations are computationally demanding, especially for intraday valuations, insurance companies need more efficient valuation techniques. Recently, a framework based on traditional spatial interpolation techniques has been proposed that can significantly decrease the computational complexity of MC simulation (Gan and Lin, 2015). However, traditional interpolation techniques require the definition of a distance function that can significantly impact their accuracy. Moreover, none of the traditional spatial interpolation techniques provide all of the key properties of accuracy, efficiency, and granularity (Hejazi et al., 2015). In this paper, we present a neural network approach for the spatial interpolation framework that affords an efficient way to find an effective distance function. The proposed approach is accurate, efficient, and provides an accurate granular view of the input portfolio. Our numerical experiments illustrate the superiority of the performance of the proposed neural network approach compared to the traditional spatial interpolation schemes.

Hippocampal Neurogenesis Regulates Forgetting During Adulthood and Infancy <=SEP=> Throughout life, new neurons are continuously added to the dentate gyrus. As this continuous addition remodels hippocampal circuits, computational models predict that neurogenesis leads to degradation or forgetting of established memories. Consistent with this, increasing neurogenesis after the formation of a memory was sufficient to induce forgetting in adult mice. By contrast, during infancy, when hippocampal neurogenesis levels are high and freshly generated memories tend to be rapidly forgotten (infantile amnesia), decreasing neurogenesis after memory formation mitigated forgetting. In precocial species, including guinea pigs and degus, most granule cells are generated prenatally. Consistent with reduced levels of postnatal hippocampal neurogenesis, infant guinea pigs and degus did not exhibit forgetting. However, increasing neurogenesis after memory formation induced infantile amnesia in these species.