title: Deep learning based human action recognition: A survey

abstract: Human action recognition has attracted much attentions because of its great potential applications. With the rapid development of computer performance and Internet, the methods of human action recognition based on deep learning become mainstream and develop at a breathless pace. This paper will give a novel reasonable taxonomy and a review of deep learning human action recognition methods based on color videos, skeleton sequences and depth maps. In addition, some datasets and effective tricks in action recognition deep learning methods will be introduced, and also the development trend is discussed.


title: The role of the Industry 4.0 asset administration shell and the digital twin during the life cycle of a plant

abstract: Industry 4.0 has come up with an impressive number of additional terms and definitions e.g. Asset Administration Shell or Digital Twin. Those terms stand for Industry 4.0 coreparadigms, but their meaning is not harmonized even among experts. This is a source of misunderstanding and confusion. In this paper, the mentioned terms are discussed along the life cycle of a plant. A plant's life cycle comprises the whole process from its idea to its destruction. During the discussion of the terms not only the concepts, but also the visions of Industry 4.0 are clarified. Goal of this paper is not to define additional terms, but to explain and substantiate existing ones and to solve apparent contradictions. Additional outcome of the discussion are architectural recommendations for an upcoming Industry 4.0 architecture. Moreover, suggestions for device manufacturers, system integrators, plant owners and Industry 4.0 architects are given.


title: Detecting Newsworthy Topics in Twitter

abstract: The task of the SNOW 2014 Data Challenge is to mine Twitter streams to provide journalists a set of headlines and complementary information that summarize the most newsworthy topics for a number of given time intervals. We propose a 4-step approach to solve this. First, a classier


title: Optimal Path Planning Based on a Multi-Tree T-RRT* Approach for Robotic Task Planning in Continuous Cost Spaces

abstract: This paper presents an integrated approach to robotic task planning in continuous cost spaces. This consists of a low-level path planner and a high-level Planning Domain Definition Language (PDDL)-based task planner. The path planner is based on a multi-tree implementation of the optimal Transitionbased Rapidly-exploring Random Tree (T-RRT*) that searches the environment for paths between all pairs of configuration waypoints. A method for shortcutting paths based on cost function is also presented. The resulting minimized path costs are then passed to a PDDL planner to solve the high-level task planning problem while optimizing the overall cost of the solution plan. This approach is demonstrated on two scenarios consisting of different cost functions: obstacle clearance in a cluttered environment and elevation in a mountain environment. Preliminary results suggest that significant improvements to path quality can be achieved without significant increase to computation time when compared with a T-RRT-based implementation.


title: Bayesian Exploratory Factor Analysis.

abstract: This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confirms the validity of the approach. The method is used to produce interpretable low dimensional aggregates from a high dimensional set of psychological measurements.


Game Artificial Intelligence: Challenges for the Scientific Community


title: Orchestrating a mixed reality performance

abstract: A study of a professional touring mixed reality performance called Desert Rain yields insights into how performers orchestrate players' engagement in an interactive experience. Six players at a time journey through an extended physical and virtual set. Each sees a virtual world projected onto a screen made from a fine water spray. This acts as a traversable interface, supporting the illusion that performers physically pass between real and virtual worlds. Live and video-based observations of Desert Rain, coupled with interviews with players and the production team, have revealed how the performers create conditions for the willing suspension of disbelief, and how they monitor and intervene in the players experience without breaking their engagement. This involves carefully timed performances and “off-face” and “virtual” interventions. In turn, these are supported by the ability to monitor players' physical and virtual activity through asymmetric interfaces.


title: High-Power 20-100-MHz Linear and Efficient Power-Amplifier Design

abstract: This paper presents modeling and analysis of the realization of high-power broadband RF power amplifiers (PAs) and power combiners. In the scope of the study, a model is proposed for a transmission line with a ferrite core, which is the basic building block for the transmission line transformers (TLTs) used in broadband high-power amplifiers and power combiner/divider networks as a building block. Simulated performances of the designed networks using the proposed model possess high conformity with the empirical performance of the corresponding realized networks. Therefore, broadband TLT networks with low insertion loss, high power-handling capability, and low return loss are realized. RF power combiner/divider networks with wide frequency bandwidth and high isolation are also implemented. By using class-B biased pushpull network topology, a linear and wideband RF PA is realized with high drain efficiency at high output power levels. In order to broaden the operating frequency range of the indicated amplifier, a novel technique for TLT utilization that matches the output impedance of the RF power transistor in wideband is proposed and realized. Measurement results satisfy the design specifications.


title: SymNet: Scalable symbolic execution for modern networks

abstract: We present SymNet, a network static analysis tool based on symbolic execution. SymNet injects symbolic packets and tracks their evolution through the network. Our key novelty is SEFL, a language we designed for expressing data plane processing in a symbolic-execution friendly manner. SymNet statically analyzes an abstract data plane model that consists of the SEFL code for every node and the links between nodes. SymNet can check networks containing routers with hundreds of thousands of prefixes and NATs in seconds, while verifying packet header memory-safety and covering network functionality such as dynamic tunneling, stateful processing and encryption. We used SymNet to debug mid- dlebox interactions from the literature, to check properties of our department’s network and the Stanford backbone. Modeling network functionality is not easy. To aid users we have developed parsers that automatically generate SEFL models from router and switch tables, firewall configura- tions and arbitrary Click modular router configurations. The parsers rely on prebuilt models that are exact and fast to an- alyze. Finally, we have built an automated testing tool that combines symbolic execution and testing to check whether the model is an accurate representation of the real code.


title: LTE-Advanced Pro RF Front-End Implementations to Meet Emerging Carrier Aggregation and DL MIMO Requirements

abstract: RF front-end (RFFE) architectures and implementations are developing new ways to optimize LTE-Advanced PRO (Rel 13) multi-component carrier aggregation, advanced features to increase spectral efficiency such as higher order modulation and higher order MIMO, and the concurrent operation of all of these features together. In this article, we describe best practices for meeting the challenging coexistence, harmonic management, linearity, and efficiency performance related to the functional partitioning, optimized integration, and technology selection of the RFFE. Recent trends to improve radio performance are driving specific blocks (e.g., the low noise amplifier) into the RFFE, with associated architecture changes in both primary and diversity paths. Carrier aggregation features are supported in a number of different methods with different insertion loss, isolation, and noise figure trade-offs, and here we examine benefits of a new category of highly integrated diversity receive modules to enhance receiver sensitivity across all use cases. Movement toward higher order MIMO in the DL is compounding additional RF Rx path support and requirements, and cost-effective solutions for optimum performance trade-offs require a holistic and complete RF system view of both Tx and Rx in order to address these emerging requirements.


title: Natural Language Aggregate Query over RDF Data

abstract: Abstract Natural language question/answering over RDF (Resource Description Framework) data has received widespread attention. Although several studies can address a small number of aggregate queries, these studies have many restrictions (e.g., interactive information, controlled questions or query templates). Thus far, there has been no natural language querying mechanism that can process general aggregate queries over RDF data. Therefore, we propose a framework called NLAQ (Natural Language Aggregate Query). First, we propose a novel algorithm to automatically understand a user's query intention, which primarily contains semantic relations and aggregations. Second, to build a better bridge between the query intention and RDF data, we propose an extended paraphrase dictionary ED to obtain more candidate mappings for semantic relations, and we introduce a predicate-type adjacent set PT to filter out inappropriate candidate mapping combinations in semantic relations and basic graph patterns. Third, we design a suitable translation plan for each aggregate category and effectively distinguish whether an aggregate item is numeric, which will greatly affect the aggregate result. Finally, we conduct extensive experiments over real datasets (QALD benchmark and DBpedia). The experimental results demonstrate that our solution is effective.


title: Fisheye Lenses Calibration Using Straight-Line Spherical Perspective Projection Constraint

abstract: Fisheye lenses are often used to enlarge the field of view (FOV) of a conventional camera. But the images taken with fisheye lenses have severe distortions. This paper proposes a novel calibration method for fisheye lenses using images of space lines in a single fisheye image. Since some fish-eye cameras' FOV are around 180 degrees, the spherical perspective projection model is employed. It is well known that under spherical perspective projection, straight lines in space have to be projected into great circles in the spherical perspective image. That is called straight-line spherical perspective projection constraint (SLSPPC). In this paper, we use SLSPPC to determine the mapping between a fisheye image and its correspending spherical perspective image. Once the mapping is obtained, the fisheye lenses is calibrated. The parameters to be calibrated include principal point, aspect ratio, skew factor, and distortion parameters. Experimental results for synthetic data and real images are presented to demonstrate the performances of our calibration algorithm.


title: Running 8-bit dynamic fixed-point convolutional neural network on low-cost ARM platforms

abstract: In recent years, Convolutional Neural Network (CNN) has been extensively applied in the field of computer vision, which has also made remarkable achievements. However, the CNN models are computation-intensive and memory-consuming, which hinders the deployment of CNN-based methods on resource-limited embedded platforms. Therefore, this paper gives insight into low numerical precision Convolutional Neural Networks. At first, an image classification CNN model is quantized into 8-bit dynamic fixed-point with no more than 1% accuracy drop and then the method of conducting inference on low-cost ARM processor has been proposed. Experimental results verified the effectiveness of this method. Besides, our proof-of-concept prototype implementation can obtain a frame rate of 4.54fps when running on single Cortex-A72 core under 1.8GHz working frequency and 6.48 watts of gross power consumption.


MADA + TOKAN : A Toolkit for Arabic Tokenization , Diacritization , Morphological Disambiguation , POS Tagging , Stemming and Lemmatization


title: Implementation of Anomaly Detection Technique Using Machine Learning Algorithms

abstract: Data mining techniques make it possible to search large amounts of data for characteristic rules and patterns. If applied to network monitoring data recorded on a host or in a network, they can be used to detect intrusions, attacks and/or anomalies. In this paper, we present "machine learning" a method to cascade K-means clustering and the Id3 decision tree learning methods to classifying anomalous and normal activities in a computer network. The K-means clustering method first partitions the training instances into two clusters using Euclidean distance similarity. On each cluster, representing a density region of normal or anomaly instances, we build an ID3 decision tree. The decision tree on each cluster refines the decision boundaries by learning the subgroups within the cluster. Our work studies the best algorithm by using classifying anomalous and normal activities in a computer networks with supervised & unsupervised algorithms that have not been used before. We analyses the algorithm that have the best efficiency or the best learning and describes the proposed system of K-means&ID3 Decision Tree.


6-D Localization of a Magnetic Capsule Endoscope Using a Stationary Rotating Magnetic Dipole Field


title: The comfort assessment of wearable computers

abstract: This paper presents a tool to measure the comfort of wearable computers. The comfort rating scales (CRS) measure wearable comfort across 6 dimensions. These dimensions are Emotion, Attachment, Harm, Perceived change, Movement and Anxiety. This paper also presents two studies in which the CRS have been used to assess the comfort of two types of wearable technology currently being developed at the University of Birmingham, these are the SensVest and the /spl chi//sup 3/. The results of the studies show that the CRS can be used to aid designers and manufacturers focus on what modifications are needed to wearable computer design to make them more comfortable. They also show that assessments of wearable computer comfort must be made in situations and environments to which the computer will ultimately be introduced.


title: Data acquisition and control using Arduino-Android platform: Smart plug

abstract: In this work, we propose to develop a smart plug, an energy monitoring system that provides real time update of the energy consumption at the device level. The proposed device is using an arduino microcontroller board, an ENC28J60 ethernet module, and a current transformer sensor. The method of current sensing employed is non-invasive type. The User Interface of the device is to be developed in android and the data will be uploaded onto a server using the ethernet connection. The final output will be a smart plug that can monitor a remote device using arduino-android platform.


RiTUAL-UH at SemEval-2017 Task 5: Sentiment Analysis on Financial Data Using Neural Networks


title: Invariant Features from Interest Point Groups

abstract: This paper approaches the problem of ¯nding correspondences between images in which there are large changes in viewpoint, scale and illumi- nation. Recent work has shown that scale-space `interest points' may be found with good repeatability in spite of such changes. Further- more, the high entropy of the surrounding image regions means that local descriptors are highly discriminative for matching. For descrip- tors at interest points to be robustly matched between images, they must be as far as possible invariant to the imaging process. In this work we introduce a family of features which use groups of interest points to form geometrically invariant descriptors of image regions. Feature descriptors are formed by resampling the image rel- ative to canonical frames de¯ned by the points. In addition to robust matching, a key advantage of this approach is that each match implies a hypothesis of the local 2D (projective) transformation. This allows us to immediately reject most of the false matches using a Hough trans- form. We reject remaining outliers using RANSAC and the epipolar constraint. Results show that dense feature matching can be achieved in a few seconds of computation on 1GHz Pentium III machines.


title: Supplier evaluation and management system for strategic sourcing based on a new multicriteria sorting procedure

abstract: Abstract Supplier selection and evaluation is increasingly seen as a strategic issue for companies. Unlike the past, an emerging trend is to select suppliers where a long-term relationship is desired and supplier involvement in product development is required and to sort suppliers into categories based on performances. This paper describes a supplier evaluation and management methodology for strategic sourcing, in which suppliers are assessed considering supplier's co-design capabilities and categorized based on overall performances, potential reasons for differences in performance of supplier groups are identified, and performances of the suppliers are improved by applying supplier development programs. A new multicriteria sorting method based on the PROMETHEE methodology is also introduced. By means of a strategic supplier selection example, we demonstrate that our methodology is a flexible and responsive decision-making tool for assessing strategic suppliers.


title: Removal of Image Artifacts Due to Sensor Dust

abstract: Image artifacts that result from sensor dust are a common but annoying problem for many photographers. To reduce the appearance of dust in an image, we first formulate a model of artifact formation due to sensor dust. With this artifact formation model, we make use of contextual information in the image and a color consistency constraint on dust to remove these artifacts. When multiple images are available from the same camera, even under different camera settings, this approach can also be used to reliably detect dust regions on the sensor. In contrast to image inpainting or other hole-filling methods, the proposed technique utilizes image information within a dust region to guide the use of contextual data. Joint use of these multiple cues leads to image recovery results that are not only visually pleasing, but also faithful to the actual scene. The effectiveness of this method is demonstrated in experiments with various cameras.


title: Single-Image Superresolution of Natural Stochastic Textures Based on Fractional Brownian Motion

abstract: Texture enhancement presents an ongoing challenge, in spite of the considerable progress made in recent years. Whereas most of the effort has been devoted so far to enhancement of regular textures, stochastic textures that are encountered in most natural images, still pose an outstanding problem. The purpose of enhancement of stochastic textures is to recover details, which were lost during the acquisition of the image. In this paper, a texture model, based on fractional Brownian motion (fBm), is proposed. The model is global and does not entail using image patches. The fBm is a self-similar stochastic process. Self-similarity is known to characterize a large class of natural textures. The fBm-based model is evaluated and a single-image regularized superresolution algorithm is derived. The proposed algorithm is useful for enhancement of a wide range of textures. Its performance is compared with single-image superresolution methods and its advantages are highlighted.


title: Imaging Lesions of the Lateral Hip

abstract: The lateral aspect of the hip is composed of a complex array of osseous and soft tissue structures. Both common and uncommon clinical entities are encountered in the lateral hip. This article briefly introduces fundamental imaging anatomy and the functional roles of different osseous and soft tissue structures in the lateral aspect of the hip, followed by a discussion of relevant imaging findings of lateral hip pathology. Greater trochanteric pain syndrome is frequently encountered in patients with lateral hip pain and encompasses a spectrum of soft tissue abnormalities including trochanteric and subgluteal bursitis, and tendinopathy or tears of the gluteal tendons. In addition, different types of injuries to the gluteal myotendinous unit and injuries to the indirect head of the rectus femoris, proximal iliotibial band, and the lateral joint capsular ligaments can present with lateral hip pain. Some of the less common soft tissue abnormalities of the lateral hip include Morel-Lavallée lesion and meralgia paresthetica.


title: What affects organizational performance ? The linking of learning and knowledge management

abstract: Purpose – The purpose of this paper is to propose a conceptual structural equation model to investigate the relationships among self‐directed learning (SDL), organizational learning (OL), knowledge management capability (KMC) and organizational performance (OP) and to demonstrate the direct and indirect effect of SDL on OP from the perspectives of KMC and OL.Design/methodology/approach – An empirical study is conducted in 21 technological companies (N=236) in Taiwan and the collected survey data are used to test the relationships among the four dimensions expressed in the proposed structural equation model.Findings – The results show that SDL has a direct and significant impact on OL and KMC. SDL influences OP indirectly through OL and KMC. In addition, OL and KMC have direct and significant influences on OP.Research limitations/implications – The source of data collected is Taiwan, hence, the results may not be easily generalized to other areas or countries. However, the findings are valuable for manager...


title: A study of existing Ontologies in the IoT-domain

abstract: Several domains have adopted the increasing use of IoT-based devices to collect sensor data for generating abstractions and perceptions of the real world. This sensor data is multi-modal and heterogeneous in nature. This heterogeneity induces interoperability issues while developing cross-domain applications, thereby restricting the possibility of reusing sensor data to develop new applications. As a solution to this, semantic approaches have been proposed in the literature to tackle problems related to interoperability of sensor data. Several ontologies have been proposed to handle different aspects of IoT-based sensor data collection, ranging from discovering the IoT sensors for data collection to applying reasoning on the collected sensor data for drawing inferences. In this paper, we survey these existing semantic ontologies to provide an overview of the recent developments in this field. We highlight the fundamental ontological concepts (e.g., sensor-capabilities and context-awareness) required for an IoT-based application, and survey the existing ontologies which include these concepts. Based on our study, we also identify the shortcomings of currently available ontologies, which serves as a stepping stone to state the need for a common unified ontology for the IoT domain.


title: Jimenez-Delgadoet al.Reply:

abstract: We reply to the Comment of Brodsky and Gardner on our paper "New limits on intrinsic charm in the nucleon from global analysis of parton distributions" [Phys. Rev. Lett. 114, 082002 (2015)]. We address a number of incorrect claims made about our fitting methodology, and elaborate how global QCD analysis of all available high-energy data provides no evidence for a large intrinsic charm component of the nucleon.


title: A compositional context sensitive multi-document summarizer: exploring the factors that influence summarization

abstract: The usual approach for automatic summarization is sentence extraction, where key sentences from the input documents are selected based on a suite of features. While word frequency often is used as a feature in summarization, its impact on system performance has not been isolated. In this paper, we study the contribution to summarization of three factors related to frequency: content word frequency, composition functions for estimating sentence importance from word frequency, and adjustment of frequency weights based on context. We carry out our analysis using datasets from the Document Understanding Conferences, studying not only the impact of these features on automatic summarizers, but also their role in human summarization. Our research shows that a frequency based summarizer can achieve performance comparable to that of state-of-the-art systems, but only with a good composition function; context sensitivity improves performance and significantly reduces repetition.


title: A Probabilistic Interpretation of SVMs with an Application to Unbalanced Classification

abstract: In this paper, we show that the hinge loss can be interpreted as the neg-log-likelihood of a semi-parametric model of posterior probabilities. From this point of view, SVMs represent the parametric component of a semi-parametric model fitted by a maximum a posteriori estimation procedure. This connection enables to derive a mapping from SVM scores to estimated posterior probabilities. Unlike previous proposals, the suggested mapping is interval-valued, providing a set of posterior probabilities compatible with each SVM score. This framework offers a new way to adapt the SVM optimization problem to unbalanced classification, when decisions result in unequal (asymmetric) losses. Experiments show improvements over state-of-the-art procedures.


title: TRIZ 40 inventive principles application in project management

abstract: TRIZ — “teoriya resheniya izobretatelskikh zadach” (Russian abbreviation) — the theory of inventive problem solving — is a powerful creativity technique, which was originally applied as an engineering tool, but now is being extended to other areas as management and business. This paper shows how to apply the TRIZ tool — 40 Inventive Principles — to project management.


title: The square-root unscented Kalman filter for state and parameter-estimation

abstract: Over the last 20-30 years, the extended Kalman filter (EKF) has become the algorithm of choice in numerous nonlinear estimation and machine learning applications. These include estimating the state of a nonlinear dynamic system as well estimating parameters for nonlinear system identification (eg, learning the weights of a neural network). The EKF applies the standard linear Kalman filter methodology to a linearization of the true nonlinear system. This approach is sub-optimal, and can easily lead to divergence. Julier et al. (1997), proposed the unscented Kalman filter (UKF) as a derivative-free alternative to the extended Kalman filter in the framework of state estimation. This was extended to parameter estimation by Wan and Van der Merwe et al., (2000). The UKF consistently outperforms the EKF in terms of prediction and estimation error, at an equal computational complexity of (OL/sup 3/)/sup l/ for general state-space problems. When the EKF is applied to parameter estimation, the special form of the state-space equations allows for an O(L/sup 2/) implementation. This paper introduces the square-root unscented Kalman filter (SR-UKF) which is also O(L/sup 3/) for general state estimation and O(L/sup 2/) for parameter estimation (note the original formulation of the UKF for parameter-estimation was O(L/sup 3/)). In addition, the square-root forms have the added benefit of numerical stability and guaranteed positive semi-definiteness of the state covariances.


title: Graying the black box: Understanding DQNs

abstract: In recent years there is a growing interest in using deep representations for reinforcement learning. In this paper, we present a methodology and tools to analyze Deep Q-networks (DQNs) in a non-blind matter. Using our tools we reveal that the features learned by DQNs aggregate the state space in a hierarchical fashion, explaining its success. Moreover we are able to understand and describe the policies learned by DQNs for three different Atari2600 games and suggest ways to interpret, debug and optimize deep neural networks in reinforcement learning.


title: CTA-aware Prefetching for GPGPU

abstract: In this paper, we propose a thread group-aware stride prefetching scheme for GPUs. GPU kernels group threads into cooperative thread arrays (CTAs). Each thread typically uses its thread index and its associated CTA index to identify the data that it operates on. The starting base address accessed by the first warp in a CTA is difficult to predict, since that starting address is a complex function of thread index and CTA index and also depends on how the programmer distributes input data across CTAs. But threads within each CTA exhibit stride accesses. Hence, if the base address of each CTA can be computed early, it is possible to accurately predict prefetch addresses for threads within a CTA. To compute the base address of each CTA, a leading warp is used from each CTA. The leading warp is executed early by pairing it with warps from currently executing leading CTA. The warps in the leading CTA are used to compute the stride value. The stride value is then combined with base addresses computed from the leading warp of each CTA to prefetch the data for all the trailing warps in the trailing CTAs. Through simple enhancements to the existing two-level scheduler, prefetches can be issued sufficiently ahead of time before the demand requests. CTA-aware prefetch predicts addresses with over 99.27% accuracy and is able to improve GPU performance by 10%.


title: Antipyretic activity of alpha- and beta-santonin.

abstract: The antipyretic activity of two sesquiterpene lactones, beta-santonin and arsubin, isolated from the lipid fraction of Artemisia coerulescens subsp. gallica was determined in rats along with the activity of alpha-santonin. In its alpha- and beta-forms, santonin caused a decrease in the body temperature of rats made febrile by the subcutaneous injection of beer yeast. This decrease, more pronounced in the case of beta-santonin, was dose-dependent and antagonized by pretreatment with haloperidol, an agent that also opposes the antipyretic activity of dopamine. These findings seem to show that the alpha- and beta-forms of santonin act on rectal temperature in a way similar to dopamine.


title: Systematic reviews of psychosocial interventions for autism: an umbrella review

abstract: AIM ::: A wide range of psychosocial interventions for the treatment of individuals with autism-spectrum disorders (ASDs) have been evaluated in systematic reviews. We conducted an umbrella review of systematic reviews of the effectiveness of psychosocial interventions for ASD. ::: ::: ::: METHOD ::: Comprehensive searches were conducted in 25 bibliographic databases, relevant journals and reference lists up to May 2007. Studies included were systematic reviews on any psychosocial intervention for individuals with ASDs. Two reviewers independently assessed study relevance and quality. ::: ::: ::: RESULTS ::: Thirty systematic reviews were included. The majority of reviews evaluated interventions based on behavioural theory (n=9) or communication-focused (n=7) therapies. Positive intervention outcomes were reported in the majority of the reviews. Methodological quality of the reviews was generally poor. ::: ::: ::: INTERPRETATION ::: The reviews reported positive outcomes for many of the interventions, suggesting that some form of treatment is favourable over no treatment. However, there is little evidence for the relative effectiveness of these treatment options. Many of the systematic reviews had methodological weaknesses that make them vulnerable to bias. There is a need for further systematic reviews that adhere to strict scientific methods and for primary studies that make direct comparisons between different treatment options.


title: Development and Preliminary Validation of the Salzburg Emotional Eating Scale

abstract: Existing self-report questionnaires for the assessment of emotional eating do not differentiate between specific types of emotions and between increased or decreased food intake in response to these emotions. Therefore, we developed a new measure of emotional eating—the Salzburg Emotional Eating Scale (SEES)—for which higher scores indicate eating more than usual in response to emotions and lower scores indicate eating less than usual in response to emotions. In study 1, a pool of items describing 40 emotional states was used. Factor analysis yielded four factors, which represented both positive (happiness subscale) and negative emotions (sadness, anger, and anxiety subscales). Subsequently, the scale was reduced to 20 items (5 items for each subscale) and its four-factor structure was replicated in studies 2 and 3. In all three studies, internal consistencies of each subscale were  > .70 and mean subscale scores significantly differed from each other such that individuals reported the strongest tendency to eat more than usual when being sad and the strongest tendency to eat less than usual when being anxious (sadness > happiness > anger > anxiety). Higher scores on the happiness subscale related to lower scores on the negative emotions subscales, lower body mass index (BMI), and lower eating pathology. In contrast, higher scores on the negative emotions subscales related to lower scores on the happiness subscale, higher BMI, and higher eating pathology. The SEES represents a useful measure for the investigation of emotional eating by increasing both specificity (differentiation between specific emotional states) and breadth (differentiation between increase and decrease of food intake) in the assessment of the emotion–eating relationship.


title: Domestic Resource Mobilisation for Development in Pakistan

abstract: This paper examines the determinants of private, domestic, and household savings in Pakistan. The analysis shows that private savings can be expected to grow gradually as a result of rising per capita income, falling dependency burden, improved financial deepening, and macro stability. Bivariate causality tests between GNP and savings show that GNP causes both domestic and public savings. However, the causality test is inconclusive in the case of causation between GNP and private savings. This finding has important policy implication in the sense that once a virtual cycle succeeds in accelerating growth, saving would catch up with a lag. In this sense, financing of investment is not a major constraint. The paper underlines the following policy options: (i) a strong effort spread over tax policy (tax reforms as well as tax administration), expenditure restraint, effective expenditure management, and public sector corporate reforms should aim at raising public savings to about 6 percent of the GDP; (ii) the incentives for private savings in Pakistan need to be revamped.


title: Area Preserving Brain Mapping

abstract: Brain mapping transforms the brain cortical surface to canonical planar domains, which plays a fundamental role in morphological study. Most existing brain mapping methods are based on angle preserving maps, which may introduce large area distortions. This work proposes an area preserving brain mapping method based on Monge-Brenier theory. The brain mapping is intrinsic to the Riemannian metric, unique, and diffeomorphic. The computation is equivalent to convex energy minimization and power Voronoi diagram construction. Comparing to the existing approaches based on Monge-Kantorovich theory, the proposed one greatly reduces the complexity (from n2 unknowns to n ), and improves the simplicity and efficiency. Experimental results on caudate nucleus surface mapping and cortical surface mapping demonstrate the efficacy and efficiency of the proposed method. Conventional methods for caudate nucleus surface mapping may suffer from numerical instability, in contrast, current method produces diffeomorpic mappings stably. In the study of cortical surface classification for recognition of Alzheimer's Disease, the proposed method outperforms some other morphometry features.


title: Tangible products: redressing the balance between appearance and action

abstract: Over the past decade, our group has approached interaction design from an industrial design point of view. In doing so, we focus on a branch of design called “formgiving”Whilst formgiving is somewhat of a neologism in English, many other European languages do have a separate word for form-related design, including German (Gestaltung), Danish (formgivnin), Swedish (formgivning) and Dutch (vormgeving).Traditionally, formgiving has been concerned with such aspects of objects as form, colour, texture and material. In the context of interaction design, we have come to see formgiving as the way in which objects appeal to our senses and motor skills. In this paper, we first describe our approach to interaction design of electronic products. We start with how we have been first inspired and then disappointed by the Gibsonian perception movement [1], how we have come to see both appearance and actions as carriers of meaning, and how we see usability and aesthetics as inextricably linked. We then show a number of interaction concepts for consumer electronics with both our initial thinking and what we learnt from them. Finally, we discuss the relevance of all this for tangible interaction. We argue that, in addition to a data-centred view, it is also possible to take a perceptual-motor-centred view on tangible interaction. In this view, it is the rich opportunities for differentiation in appearance and action possibilities that make physical objects open up new avenues to meaning and aesthetics in interaction design.


title: Fully automatic wrapper generation for search engines

abstract: When a query is submitted to a search engine, the search engine returns a dynamically generated result page containing the result records, each of which usually consists of a link to and/or snippet of a retrieved Web page. In addition, such a result page often also contains information irrelevant to the query, such as information related to the hosting site of the search engine and advertisements. In this paper, we present a technique for automatically producing wrappers that can be used to extract search result records from dynamically generated result pages returned by search engines. Automatic search result record extraction is very important for many applications that need to interact with search engines such as automatic construction and maintenance of metasearch engines and deep Web crawling. The novel aspect of the proposed technique is that it utilizes both the visual content features on the result page as displayed on a browser and the HTML tag structures of the HTML source file of the result page. Experimental results indicate that this technique can achieve very high extraction accuracy.


title: A collocated spatially adaptive approach to smoke simulation in bifrost

abstract: Simulations of smoke are pervasive in the production of visual effects for commercials, movies and games: from cigarette smoke and subtle dust to large-scale clouds of soot and vapor emanating from fires and explosions. In this talk we present a new Eulerian method that targets the simulation of such phenomena on a structured spatially adaptive voxel grid --- thereby achieving an improvement in memory usage and computational performance over regular dense and sparse grids at uniform resolution. Contrary to e.g. Setaluri et al. [2014], we use velocities collocated at voxel corners which allows sharper interpolation for spatially adaptive simulations, is faster for sampling, and promotes ease-of-use in an open procedural environment where technical artists often construct small computational graphs that apply forces, dissipation etc. to the velocities. The collocated method requires special treatment when projecting out the divergent velocity modes to prevent non-physical high frequency oscillations (not addressed by Ferstl et al. [2014]). To this end we explored discretization and filtering methods from computational physics, combining them with a matrix-free adaptive multigrid scheme based on MLAT and FAS [Trottenberg and Schuller 2001]. Finally we contribute a new volumetric quadrature approach to temporally smooth emission which outperforms e.g. Gaussian quadrature at large time steps. We have implemented our method in the cross-platform Autodesk Bifrost procedural environment which facilitates customization by the individual technical artist, and our implementation is in production use at several major studios. We refer the reader to the accompanying video for examples that illustrate our novel workflows for spatially adaptive simulations and the benefits of our approach. We note that several methods for adaptive fluid simulation have been proposed in recent years, e.g. [Ferstl et al. 2014; Setaluri et al. 2014], and we have drawn a lot of inspiration from these. However, to the best of our knowledge we are the first in computer graphics to propose a collocated velocity, spatially adaptive and matrix-free smoke simulation method that explicitly mitigates non-physical divergent modes.


title: A Methodology for Architecting Collaborative Product Service System of Systems

abstract: Product service systems (PSS) can be understood as an innovation / business strategy that includes a set of products and services that are realized by an actor network. More recently, PSS that comprise System of Systems (SoS) have been of increasing interest, notably in the transportation (autonomous vehicle infrastructures, multi-modal transportation) and energy sector (smart grids). Architecting such PSS-SoS goes beyond classic SoS engineering, as they are often driven by new technology, without an a priori client and actor network, and thus, a much larger number of potential architectures. However, it seems that neither the existing PSS nor SoS literature provides solutions to how to architect such PSS. This paper presents a methodology for architecting PSS-SoS that are driven by technological innovation. The objective is to design PSS-SoS architectures together with their value proposition and business model from an initial technology impact assessment. For this purpose, we adapt approaches from the strategic management, business modeling, PSS and SoS architecting literature. We illustrate the methodology by applying it to the case of an automobile PSS.


title: The role of Facebook in romantic relationship development

abstract: This study examines the implications of social networking web sites (SNSs) within romantic relationships. Specifically, Knapp’s (1978) stage model of relationships is examined through a new lens wherein the role of SNSs, specifically Facebook, is explored in the escalation stages of romantic relationships (i.e., initiating, experimenting, intensifying, integrating, and bonding). Furthermore, this study sought to discern the interpersonal and social implications of publicly declaring oneself as “In a Relationship” with another person on Facebook (i.e., going “Facebook official” or “FBO”). Ten mixed-sex focus groups were conducted. Analysis revealed that Facebook is one of the primary means of uncertainty reduction in the initial stages of relationship formation. College students consider FBO to be indicative of an increased level of commitment in relationships. Typically, relationship exclusivity precedes a discussion on becoming FBO, which occurs when the relationship is considered stable. Going FBO has i...


title: Left Atrial Electromechanical Remodeling Following 2 Years of High-Intensity Exercise Training in Sedentary Middle-Aged Adults

abstract: Background: Moderate intensity exercise is associated with a decreased incidence of atrial fibrillation. However, extensive training in competitive athletes is associated with an increased atrial f...


title: Dendrites, deep learning, and sequences in the hippocampus.

abstract: The hippocampus places us both in time and space. It does so over remarkably large spans: milliseconds to years, and centimeters to kilometers. This works for sensory representations, for memory, and for behavioral context. How does it fit in such wide ranges of time and space scales, and keep order among the many dimensions of stimulus context? A key organizing principle for a wide sweep of scales and stimulus dimensions is that of order in time, or sequences. Sequences of neuronal activity are ubiquitous in sensory processing, in motor control, in planning actions, and in memory. Against this strong evidence for the phenomenon, there are currently more models than definite experiments about how the brain generates ordered activity. The flip side of sequence generation is discrimination. Discrimination of sequences has been extensively studied at the behavioral, systems, and modeling level, but again physiological mechanisms are fewer. It is against this backdrop that I discuss two recent developments in neural sequence computation, that at face value share little beyond the label ‘neural'. These are dendritic sequence discrimination, and deep learning. One derives from channel physiology and molecular signaling, the other from applied neural network theory - apparently extreme ends of the spectrum of neural circuit detail. I suggest that each of these topics has deep lessons about the possible mechanisms, scales, and capabilities of hippocampal sequence computation. This article is protected by copyright. All rights reserved.


title: Feature-based correspondence: an eigenvector approach

abstract: Abstract We propose a novel method for performing point-feature correspondence based on a modal shape description. Introducing shape information into a low-level matching process allows our algorithm to cope easily with rotations and translations in the image plane, yet still give a dense correspondence. We also show positive results for scale changes and small skews, and demonstrate how reflectional symmetries can be detected.


title: Evolving Players for an Ancient Game: Hnefatafl

abstract: Hnefatafl is an ancient Norse game - an ancestor of chess. In this paper, we report on the development of computer players for this game. In the spirit of Blondie24, we evolve neural networks as board evaluation functions for different versions of the game. An unusual aspect of this game is that there is no general agreement on the rules: it is no longer much played, and game historians attempt to infer the rules from scraps of historical texts, with ambiguities often resolved on gut feeling as to what the rules must have been in order to achieve a balanced game. We offer the evolutionary method as a means by which to judge the merits of alternative rule sets


title: Controlling Bad Behavior in Online Communities: An Examination of Moderation Work

abstract: A wide range of behavior may be seen as destructive to online communities. Yet behavior that is ‘bad’ in one community may be celebrated in another. The work of community maintenance is therefore strongly contextual, involving complex choices due to differing norms, community cross-membership, and the need to invoke fairness. The experienced, “lived in” work of moderators; how they enact norms and make choices about social maintenance, remains poorly understood. Our study addresses this gap, using a negotiated order lens. We employed netnographic techniques, analyzing online interviews with moderators of sub-communities in Reddit, and records of critical incidents. We find that moderators are intuitive prosecutors who draw on a variety of logics to accomplish their work. Controlling bad behavior, articulating and enforcing norms is, therefore, a collective accomplishment through which moderators make choices, create a jurisprudence record, and reconcile nested community norms in the maintenance of social order.


title: Securing Embedded User Interfaces: Android and Beyond

abstract: Web and smartphone applications commonly embed third-party user interfaces like advertisements and social media widgets. However, this capability comes with security implications, both for the embedded interfaces and the host page or application. While browsers have evolved over time to address many of these issues, mobile systems like Android--which do not yet support true cross-application interface embedding--present an opportunity to redesign support for secure embedded user interfaces from scratch. In this paper, we explore the requirements for a system to support secure embedded user interfaces by systematically analyzing existing systems like browsers, smartphones, and research systems. We describe our experience modifying Android to support secure interface embedding and evaluate our implementation using case studies that rely on embedded interfaces, such as advertisement libraries, Facebook social plugins (e.g., the "Like" button), and access control gadgets. We provide concrete techniques and reflect on lessons learned for secure embedded user interfaces.


title: Frequency Response Analysis and Bandwidth Extension of the Doherty Amplifier

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


title: On Improving the Security of Logic Locking

abstract: Due to globalization of integrated circuit (IC) design flow, rogue elements in the supply chain can pirate ICs, overbuild ICs, and insert hardware Trojans. EPIC locks the design by randomly inserting additional gates; only a correct key makes the design to produce correct outputs. We demonstrate that an attacker can decipher the locked netlist, in a time linear to the number of keys, by sensitizing the key-bits to the output. We then develop techniques to fix this vulnerability and make an attacker’s effort truly exponential in the number of inserted keys. We introduce a new security metric and a method to deliver strong logic locking.


title: Illuminating Generalization in Deep Reinforcement Learning through Procedural Level Generation

abstract: Deep reinforcement learning (RL) has shown impressive results in a variety of domains, learning directly from high-dimensional sensory streams. However, when neural networks are trained in a fixed environment, such as a single level in a video game, they will usually overfit and fail to generalize to new levels. When RL models overfit, even slight modifications to the environment can result in poor agent performance. This paper explores how procedurally generated levels during training can increase generality. We show that for some games procedural level generation enables generalization to new levels within the same distribution. Additionally, it is possible to achieve better performance with less data by manipulating the difficulty of the levels in response to the performance of the agent. The generality of the learned behaviors is also evaluated on a set of human-designed levels. The results suggest that the ability to generalize to human-designed levels highly depends on the design of the level generators. We apply dimensionality reduction and clustering techniques to visualize the generators' distributions of levels and analyze to what degree they can produce levels similar to those designed by a human.


title: Surface Quality Enhancement of Fused Deposition Modeling (FDM) Printed Samples Based on the Selection of Critical Printing Parameters

abstract: The present paper shows an experimental study on additive manufacturing for obtaining samples of polylactic acid (PLA). The process used for manufacturing these samples was fused deposition modeling (FDM). Little attention to the surface quality obtained in additive manufacturing processes has been paid by the research community. So, this paper aims at filling this gap. The goal of the study is the recognition of critical factors in FDM processes for reducing surface roughness. Two different types of experiments were carried out to analyze five printing parameters. The results were analyzed by means of Analysis of Variance, graphical methods, and non-parametric tests using Spearman’s ρ and Kendall’s τ correlation coefficients. The results showed how layer height and wall thickness are the most important factors for controlling surface roughness, while printing path, printing speed, and temperature showed no clear influence on surface roughness.


title: Issues and Challenges of Cyber Security for Social Networking Sites (Facebook)

abstract: Social networking websites such as Facebook, Twitter, Myspace, Google+, and LinkedIn are the popular social sites. Facebook is most popular social networking site. Social sites are most common platform to communicate with their other friends, family and share thoughts, photos, videos and lots of information. Social networking websites have become platforms for cybercriminals for cybercrime; cybercriminals exploit sensitive and personal information through social engineering and reverse social engineering. It is usual for the users of social websites to share information; however they lose privacy, while sharing information with strangers, they can fall in honey trap made by them. Privacy has become an important concern in online social networking sites. Users are unaware of the privacy risks involved when they share their sensitive information on the social network sites. The default settings share everything, users have to change their default privacy setting options to make their accounts and personal information more secure. Security attacks continue to be a major concern of all users. How to keep social networking sites more secure and more private are the challenges that have been concern for every user. It is difficult for social networking sites and users to make and adjust privacy setting to protect privacy without practical and effective way to identify measure and evaluate privacy. Maximum numbers of users are not aware of the security risk associated whenever they shared sensitive data on the social sites, so that privacy concern will be raised among those online communications if their personal data has been shared to other users. The users should be aware of their privacy quotient and should know where they stand in the privacy measuring scale. Unfortunately many users are not aware of this and become victim of privacy and identity breach. So we conducted a survey to find users view regarding security and privacy of social networking sites and regarding default privacy setting improvement particularly Facebook.


title: A sense of space in postrhinal cortex

abstract: INTRODUCTION Navigation and spatial learning depend upon a topographic representation of local space, which is defined with respect to the world (allocentric) and as opposed to the observer (egocentric). Spatial processing in the rodent brain has been primarily explored through recordings of neurons thought to encode elements of an allocentric spatial map, such as place cells in the hippocampus and grid cells in the medial entorhinal cortex (MEC). Such a map, however, must be constructed from sensory information that is initially coded egocentrically. Theoretical models have suggested mechanisms by which egocentric information can be transformed into an allocentric spatial reference frame, though the precise neural mechanisms underlying this integration are not well understood. RATIONALE A potential locus for the integration of egocentric and allocentric spatial information in the rat brain is the postrhinal cortex (POR). The POR is the rodent homolog of the human parahippocampal cortex, which is thought to be involved in topographic spatial learning and processing of local spatial cues. The POR is also extensively connected with brain areas thought to be involved in both egocentric and allocentric spatial processing. It has been suggested that cells encoding the egocentric bearing and distance of the geometric center of the local environment, as well as the animal’s allocentric head direction, could provide a readout of allocentric self-position. Such a signal could drive navigational behavior and support the high-level spatial maps exemplified by entorhinal grid cells and hippocampal place cells. RESULTS We recorded from 338 single neurons in POR (11 rats) as animals freely foraged for sugar pellets in a 1.2-m square arena. Thirty-nine percent of the cells encoded the egocentric bearing of the center of the arena (“center-bearing”). The tuning preferences of center-bearing cells were consistent across space and time. Seventeen percent of POR cells showed tuning to the animal’s distance from the center of the arena (“center-distance”), with both positive and negative linear responses to center-distance observed. Thirty-eight percent of POR cells were tuned to the rat’s head direction in an allocentric reference frame. Many POR cells were conjunctively tuned, with 51% of the cells encoding one of the three spatial variables showing conjunctive tuning to at least one other variable. The egocentric neuron types identified in POR were largely absent in neighboring MEC and parasubiculum, areas implicated in allocentric spatial processing. Unlike neurons in these areas, POR neurons rarely showed modulation of their spike trains by theta rhythm (5 to 11 Hz). Spiking properties of POR cells were sufficient to decode an animal’s allocentric position. All three spatial cell types continued processing elements of space in the presence of objects and in the dark. Decreasing the size of the arena did not affect the tuning slopes of center-distance cells. Rotation of the arena led to a corresponding shift in the preferred firing directions of head direction cells without altering the tuning of egocentric center-tuned cells. CONCLUSION Our results reveal a population code for allocentric space in POR that is more strongly tuned to the spatial layout of a local scene than to the contents of that scene. The cells supporting this code signal the instantaneous egocentric bearing and distance of the geometric center of the local environment, as well as allocentric head direction. Center-distance cells respond linearly to absolute distance from the environment center, whereas each center-bearing cell shows preferential firing to a single egocentric bearing of the apparatus center that remains constant across environmental manipulations. Responses of these cells are tied to local cues and maintain their tuning properties in darkness. Egocentric encoding of the local environment’s geometric center, as opposed to the encoding of its boundaries or to discrete physical cues within that environment, could allow for the use of a common spatial reference frame across environments with disparate geometries. POR projects strongly to MEC, where grid cells are found. The egocentric and allocentric correlates identified in POR might help to create or support that spatial metric. POR also projects to lateral entorhinal cortex, where egocentric correlates and object signals have been reported. The functional cell types investigated in POR may provide a foundation for spatial processing in both entorhinal subdivisions. Representations from each area can then be routed to the hippocampus and efficiently integrated for high-level spatial processing and encoding of episodic memory.


Latent Dirichlet Allocation for Text, Images, and Music


title: Naive Bayes Classifiers: A Probabilistic Detection Model for Breast Cancer

abstract: Naive Bayes is one of the most effective statistical and probabilistic classification algorithms. As health care environment is “information loaded” but “knowledge deprived”. So to extract knowledge, effective analysis tools are constructed to discover hidden relationships in data. The aim of this work is to design a Graphical User Interface to enter the patient screening record and detect the probability of having Breast cancer disease in women in her future using Naive Bayes Classifiers, a Probabilistic Classifier. As breast cancer is considered to be second leading cause of cancer deaths in women today so early detection can improve the survival rate of women. The prediction is performed from mining the patient’s historical data or data repository. Further from the experimental results it has been found that Naive Bayes Classifiers is providing improved accuracy with low computational effort and very high speed. The system has been implemented using java platform and trained using benchmark data from UCI machine learning repository. The system is expandable for the new dataset.


title: Off-line recognition of realistic Chinese handwriting using segmentation-free strategy

abstract: Great challenges are faced in the off-line recognition of realistic Chinese handwriting. This paper presents a segmentation-free strategy based on Hidden Markov Model (HMM) to handle this problem, where character segmentation stage is avoided prior to recognition. Handwritten textlines are first converted to observation sequence by sliding windows. Then embedded Baum-Welch algorithm is adopted to train character HMMs. Finally, best character string maximizing the a posteriori is located through Viterbi algorithm. Experiments are conducted on the HIT-MW database written by more than 780 writers. The results show the feasibility of such systems and reveal apparent complementary capacities between the segmentation-free systems and the segmentation-based ones.


title: Ensemble of feature-based and deep learning-based classifiers for detection of abnormal heart sounds

abstract: The goal of the 2016 PhysioNet/CinC Challenge is the development of an algorithm to classify normal/abnormal heart sounds. A total of 124 time-frequency features were extracted from the phonocardiogram (PCG) and input to a variant of the AdaBoost classifier. A second classifier using convolutional neural network (CNN) was trained using PCGs cardiac cycles decomposed into four frequency bands. The final decision rule to classify normal/abnormal heart sounds was based on an ensemble of classifiers combining the outputs of AdaBoost and the CNN. The algorithm was trained on a training dataset (normal= 2575, abnormal= 665) and evaluated on a blind test dataset. Our classifier ensemble approach obtained the highest score of the competition with a sensitivity, specificity, and overall score of 0.9424, 0.7781, and 0.8602, respectively.


title: Detection and quantification of cell-free fetal DNA in ovine maternal plasma; use it to predict fetal sex.

abstract: Prediction of fetal sex in the ovine species could be useful in the management decisions, such as sex selection in breeding programs, culling decisions, and lowering the progeny test cost. In the present study detection of fetal SRY gene in maternal blood plasma using the polymerase chain reaction technique was used to predict fetal sex at different times of gestation in the ewe. The quantitative changes of fetal DNA during pregnancy were also investigated using quantitative real-time polymerase chain reaction. Fetal DNA was isolated from blood plasma of 46 pregnant ewes during the second to fifth month of gestation. The 286-base pair DNA fragment was detected in all samples with male pregnancies, but no female pregnancies. The sensitivity and specificity of tests were 100% with no false negative or false positive results. It was also determined that fetal DNA levels are significantly increased during pregnancy, up to approximately 1.65-fold in the last 2 months of pregnancy. This is the first report of fetal sex determination and circulating fetal DNA quantification by a molecular method in the ovine species.


title: The External Nasal Valve.

abstract: The external nasal valve is a complex entity comprised of multiple structures and tissue types. As such, there is no single operation that can address all problems of the external valve. This article reviews the relevant anatomy, pathologic conditions, and treatments for external nasal valve dysfunction, including a detailed review of the nasal muscles and their contribution to external nasal valve patency. Surgical and nonsurgical options for treatment and the evidence supporting the importance of proper external nasal valve function on quality-of-life measures are discussed.


The “Italian model” of full inclusion: origins and current directions


title: Optimization of Control Parameters for Genetic Algorithms

abstract: The task of optimizing a complex system presents at least two levels of problems for the system designer. First, a class of optimization algorithms must be chosen that is suitable for application to the system. Second, various parameters of the optimization algorithm need to be tuned for efficiency. A class of adaptive search procedures called genetic algorithms (GA) has been used to optimize a wide variety of complex systems. GA's are applied to the second level task of identifying efficient GA's for a set of numerical optimization problems. The results are validated on an image registration problem. GA's are shown to be effective for both levels of the systems optimization problem.


title: Reflection, Refraction, and Self-Jamming

abstract: The ambition of achieving road traffic without serious injuries, since 1997 referred to as “Vision Zero,” has influenced the development of new car generations during the last decades. Since the early beginnings, radar sensors have taken responsibility for receiving this goal and played a major role for achieving the subsequent vision of accident-free driving. An increasing list of applications already covers comfort and safety functions for various traffic conditions. Besides already established applications like adaptive cruise control (ACC) as illustrated in Figure 1, other examples are the lane change assistant (LCA), watching for traffic when starting an overtaking maneuver (see Figure 2) and cross traffic alert (CTA), warning of approaching vehicles at a junction. However, those examples are just a few of the constantly increasing list of functions [1][2].


title: Simulation and analysis of a single phase AC-DC boost PFC converter with a passive snubber for power quality improvement

abstract: In this paper an ac-dc boost Power Factor Correction (PFC) converter with a passive snubber is introduced. The proposed passive snubber decreases the conduction losses but increases the range of soft switching achieved. The switch is given Zero Current Switching (ZCS) turn ON and Zero Voltage switching (ZVS) turn OFF. There are no additional stresses on all the semiconductor devices used. Due to the soft switching features, the PFC converter attains a high power factor of 0.98. The proposed converter with passive snubber is simulated in MATLAB and analyzed mode wise with detailed corresponding waveforms.


A Computational Analysis of Mahabharata


title: Language Modeling with Gated Convolutional Networks

abstract: The pre-dominant approach to language modeling to date is based on recurrent neural networks. Their success on this task is often linked to their ability to capture unbounded context. In this paper we develop a finite context approach through stacked convolutions, which can be more efficient since they allow parallelization over sequential tokens. We propose a novel simplified gating mechanism that outperforms Oord et al (2016) and investigate the impact of key architectural decisions. The proposed approach achieves state-of-the-art on the WikiText-103 benchmark, even though it features long-term dependencies, as well as competitive results on the Google Billion Words benchmark. Our model reduces the latency to score a sentence by an order of magnitude compared to a recurrent baseline. To our knowledge, this is the first time a non-recurrent approach is competitive with strong recurrent models on these large scale language tasks.


title: Learning to detect malicious executables in the wild

abstract: In this paper, we describe the development of a fielded application for detecting malicious executables in the wild. We gathered 1971 benign and 1651 malicious executables and encoded each as a training example using n-grams of byte codes as features. Such processing resulted in more than 255 million distinct n-grams. After selecting the most relevant n-grams for prediction, we evaluated a variety of inductive methods, including naive Bayes, decision trees, support vector machines, and boosting. Ultimately, boosted decision trees outperformed other methods with an area under the roc curve of 0.996. Results also suggest that our methodology will scale to larger collections of executables. To the best of our knowledge, ours is the only fielded application for this task developed using techniques from machine learning and data mining.


title: Action Branching Architectures for Deep Reinforcement Learning

abstract: Discrete-action algorithms have been central to numerous recent successes of deep reinforcement learning. However, applying these algorithms to high-dimensional action tasks requires tackling the combinatorial increase of the number of possible actions with the number of action dimensions. This problem is further exacerbated for continuous-action tasks that require fine control of actions via discretization. In this paper, we propose a novel neural architecture featuring a shared decision module followed by several network branches, one for each action dimension. This approach achieves a linear increase of the number of network outputs with the number of degrees of freedom by allowing a level of independence for each individual action dimension. To illustrate the approach, we present a novel agent, called Branching Dueling Q-Network (BDQ), as a branching variant of the Dueling Double Deep Q-Network (Dueling DDQN). We evaluate the performance of our agent on a set of challenging continuous control tasks. The empirical results show that the proposed agent scales gracefully to environments with increasing action dimensionality and indicate the significance of the shared decision module in coordination of the distributed action branches. Furthermore, we show that the proposed agent performs competitively against a state-of-the-art continuous control algorithm, Deep Deterministic Policy Gradient (DDPG).


title: Image Enhancement for Fingerprint Minutiae-Based Algorithms Using CLAHE, Standard Deviation Analysis and Sliding Neighborhood

abstract: the purpose of this paper is to investigate the performance of a three-step procedure for the fingerprint identification and enhancement, using CLAHE (contrast limited adaptive histogram equalization) together with 'Clip Limit', standard deviation and sliding neighborhood as stages during processing of the fingerprint image. Firstly, CLAHE with clip limit is applied to enhance the contrast of the small tiles existing in the fingerprint image and to combine the neighboring tiles using a bilinear interpolation in order to eliminate the artificially induced boundaries. In a second step, the image is decomposed into an array of distinct blocks and the discrimination of the blocks is obtained by computing the standard deviation of the matrix elements to remove the image background and obtain the boundaries for the region of interest. Finally, by using a slide neighborhood processing, an enhancement of the image is obtained by clarifying the Minutiae (endpoints and bifurcations) in each specific pixel, process known as thinning. The paper presents the motivation for developing this method, its phases, and its possible advantages through a simulated investigation.


title: Purification and characterization of novel organic-solvent-tolerant β-amylase and serine protease from a newly isolated Salimicrobium halophilum strain LY20.

abstract: A halophilic isolate Salimicrobium halophilum strain LY20 producing extracellular amylase and protease was isolated from Yuncheng, China. Production of both enzymes was synchronized with bacterial growth and reached a maximum level during the early-stationary phase. The amylase and protease were purified to homogeneity with molecular weights of 81 and 30 kDa, respectively. Optimal amylase activity was observed at 70 °C, pH 10.0% and 10% NaCl. Complete inhibition by EDTA, diethyl pyrocarbonate (DEPC), and phenylarsine oxide (PAO) indicated that the amylase was a metalloenzyme with histidine and cysteine residues essential for its catalysis. Maltose was the main product of starch hydrolysis, indicating an β-amylase activity. The purified protease from LY20 showed highest activity at 80 °C, pH 10.0% and 12.5% NaCl. Complete inhibition was shown by phenylmethylsulfonyl fluoride, DEPC, and PAO, indicating that the enzyme probably belonged to the subclass of the serine proteases with histidine and cysteine residues essential for catalysis. Furthermore, both enzymes were highly stable over broad temperature (30-80 °C), pH (6.0-12.0) and NaCl concentration (2.5-20%) ranges, showing excellent thermostable, alkalistable, and halotolerant nature. The surfactants (SDS, Tween 80, and Triton X-100) did not affect their activities. In addition, both enzymes from LY20 displayed remarkable stability in the presence of water-soluble organic solvents with log P(ow) ( ) ≤ -0.24.


title: A new baseline for image annotation

abstract: Automatically assigning keywords to images is of great interest as it allows one to index, retrieve, and understand large collections of image data. Many techniques have been proposed for image annotation in the last decade that give reasonable performance on standard datasets. However, most of these works fail to compare their methods with simple baseline techniques to justify the need for complex models and subsequent training. In this work, we introduce a new baseline technique for image annotation that treats annotation as a retrieval problem. The proposed technique utilizes low-level image features and a simple combination of basic distances to find nearest neighbors of a given image. The keywords are then assigned using a greedy label transfer mechanism. The proposed baseline outperforms the current state-of-the-art methods on two standard and one large Web dataset. We believe that such a baseline measure will provide a strong platform to compare and better understand future annotation techniques.


title: Fisher information, stochastic complexity, and universal modeling

abstract: The main objective in universal modeling is to construct a process for a class of model processes which for long strings, generated by any of the models in the class, behaves like the data generating one. Hence, such a universal process may be taken as a representation of the entire model class to be used for statistical inference. If f(x/sup n/) denotes the probability or density it assigns to the data string x/sup n/=x/sub 1/,..,x/sub n/, then the negative logarithm - log f(x/sup n/), which may be viewed as the shortest ideal code length for the data obtainable with the model class, is called the stochastic complexity of the string, relative to the considered model class. Unlike in related universal modeling, where the mean code length is sufficient, we also need an explicit asymptotic formula for the stochastic complexity. This is because it permits a comparison of different model classes by their stochastic complexity in accordance with the MDL (minimum description length) principle.


title: Humidity Sensors Principle, Mechanism, and Fabrication Technologies: A Comprehensive Review

abstract: Humidity measurement is one of the most significant issues in various areas of applications such as instrumentation, automated systems, agriculture, climatology and GIS. Numerous sorts of humidity sensors fabricated and developed for industrial and laboratory applications are reviewed and presented in this article. The survey frequently concentrates on the RH sensors based upon their organic and inorganic functional materials, e.g., porous ceramics (semiconductors), polymers, ceramic/polymer and electrolytes, as well as conduction mechanism and fabrication technologies. A significant aim of this review is to provide a distinct categorization pursuant to state of the art humidity sensor types, principles of work, sensing substances, transduction mechanisms, and production technologies. Furthermore, performance characteristics of the different humidity sensors such as electrical and statistical data will be detailed and gives an added value to the report. By comparison of overall prospects of the sensors it was revealed that there are still drawbacks as to efficiency of sensing elements and conduction values. The flexibility offered by thick film and thin film processes either in the preparation of materials or in the choice of shape and size of the sensor structure provides advantages over other technologies. These ceramic sensors show faster response than other types.


title: Infants learn phonotactic regularities from brief auditory experience

abstract: Abstract Two experiments investigated whether novel phonotactic regularities, not present in English, could be acquired by 16.5-month-old infants from brief auditory experience. Subjects listened to consonant–vowel–consonant syllables in which particular consonants were artificially restricted to either initial or final position (e.g. /baep/ not /paeb/). In a later head-turn preference test, infants listened longer to new syllables that violated the experimental phonotactic constraints than to new syllables that honored them. Thus, infants rapidly learned phonotactic regularities from brief auditory experience and extended them to unstudied syllables, documenting the sensitivity of the infant's language processing system to abstractions over linguistic experience.


title: Honeypots: catching the insider threat

abstract: In the past several years there has been extensive research into honeypot technologies, primarily for detection and information gathering against external threats. However, little research has been done for one of the most dangerous threats, the advance insider, the trusted individual who knows our internal organization. These individuals are not after our systems, they are after our information. We discuss how honeypot technologies can be used to detect, identify, and gather information on these specific threats.


title: Data mining methods for knowledge discovery in multi-objective optimization: Part B - New developments and applications

abstract: Abstract The first part of this paper served as a comprehensive survey of data mining methods that have been used to extract knowledge from solutions generated during multi-objective optimization. The current paper addresses three major shortcomings of existing methods, namely, lack of interactiveness in the objective space, inability to handle discrete variables and inability to generate explicit knowledge. Four data mining methods are developed that can discover knowledge in the decision space and visualize it in the objective space. These methods are (i) sequential pattern mining, (ii) clustering-based classification trees, (iii) hybrid learning, and (iv) flexible pattern mining. Each method uses a unique learning strategy to generate explicit knowledge in the form of patterns, decision rules and unsupervised rules. The methods are also capable of taking the decision maker’s preferences into account to generate knowledge unique to preferred regions of the objective space. Three realistic production systems involving different types of discrete variables are chosen as application studies. A multi-objective optimization problem is formulated for each system and solved using NSGA-II to generate the optimization datasets. Next, all four methods are applied to each dataset. In each application, the methods discover similar knowledge for specified regions of the objective space. Overall, the unsupervised rules generated by flexible pattern mining are found to be the most consistent, whereas the supervised rules from classification trees are the most sensitive to user-preferences.


title: Social Media Image Recognition for Food Trend Analysis

abstract: An increasing number of people share their thoughts and the images of their lives on social media platforms. People are exposed to food in their everyday lives and share on-line what they are eating by means of photos taken to their dishes. The hashtag #foodporn is constantly among the popular hashtags in Twitter and food photos are the second most popular subject in Instagram after selfies. The system that we propose, WorldFoodMap, captures the stream of food photos from social media and, thanks to a CNN food image classifier, identifies the categories of food that people are sharing. By collecting food images from the Twitter stream and associating food category and location to them, WorldFoodMap permits to investigate and interactively visualize the popularity and trends of the shared food all over the world.


title: Multiview Differential Geometry of Curves

abstract: The field of multiple view geometry has seen tremendous progress in reconstruction and calibration due to methods for extracting reliable point features and key developments in projective geometry. Point features, however, are not available in certain applications and result in unstructured point cloud reconstructions. General image curves provide a complementary feature when keypoints are scarce, and result in 3D curve geometry, but face challenges not addressed by the usual projective geometry of points and algebraic curves. We address these challenges by laying the theoretical foundations of a framework based on the differential geometry of general curves, including stationary curves, occluding contours, and non-rigid curves, aiming at stereo correspondence, camera estimation (including calibration, pose, and multiview epipolar geometry), and 3D reconstruction given measured image curves. By gathering previous results into a cohesive theory, novel results were made possible, yielding three contributions. First, we derive the differential geometry of an image curve (tangent, curvature, curvature derivative) from that of the underlying space curve (tangent, curvature, curvature derivative, torsion). Second, we derive the differential geometry of a space curve from that of two corresponding image curves. Third, the differential motion of an image curve is derived from camera motion and the differential geometry and motion of the space curve. The availability of such a theory enables novel curve-based multiview reconstruction and camera estimation systems to augment existing point-based approaches. This theory has been used to reconstruct a "3D curve sketch", to determine camera pose from local curve geometry, and tracking; other developments are underway.


title: Real-Time Process Adaptation: A Context-Aware Replanning Approach

abstract: Complexity and dynamism of day-to-day activities are inextricably linked, thus the need for constant adaptation to address emerging demands has grown. Process adaptation is the action of customizing a process instance to make it applicable to a particular situation. However, unplanned conditions may occur at any time during process execution. So, the design of a complete process model has given place to a flexible design based on reuse and adaptation. This paper addresses the problem of dynamic adaptation within a process-aware information system (IS). On top of a theory for context-aware ISs, we argue that an unexpected situation can be characterized by a number of known contextual elements and could be used to automate the decision of replanning the process flow in a specific instance, in order to preserve the process strategy. The solution was evaluated in real setting observational studies in the domains of oil and gas and air traffic control.


title: TEMPERATURE MODIFICATION OF MALE SEX PHEROMONE RESPONSE AND FACTORS AFFECTING FEMALE CALLING IN HOLOMELINA IMMACULATA (LEPIDOPTERA: ARCTIIDAE)

abstract: In Holomelina immaculata (Reakirt) periodicity of male attraction to synthetic 2-methylheptadecane, the female-produced sex pheromone, is modified by temperature cues. In the field this response interval occurs from approximately sunset to about 4 h after sunset on a warm day and night (30° to 17 °C) and for the 2 h prior to sunset on a cool day and night (23° to 16 °C). In laboratory studies at 24 °C female H . immaculata placed in continual scotophase have an endogenous calling rhythm, but they are apparently inhibited from calling by constant photophase. In 16:8 or 12:12 light–dark cycles at 24 °C calling occurs from the 2nd to the 6th hour of scotophase, whereas at 15 °C calling takes place from the initiation to the 5th hour of scotophase. The critical cues governing initiation of calling behaviour are lights-off or a temperature decrease cue, and a temperature decrease signal overrides the apparent inhibitory effect of continual photophase.


CRYPTOCURRENCY PRICE PREDICTION USING NEWS AND SOCIAL MEDIA SENTIMENT


Ezrin phosphorylation in T567 is mediated by atypical PKC in intestinal epithelial cells


The Ascending Reticular Activating System in a Patient With Severe Injury of the Cerebral Cortex


title: Designing Autonomous Vehicles: Evaluating the Role of Human Emotions and Social Norms

abstract: Humans are going to delegate the rights of driving to the autonomous vehicles in near future. However, to fulfill this complicated task, there is a need for a mechanism, which enforces the autonomous vehicles to obey the road and social rules that have been practiced by well-behaved drivers. This task can be achieved by introducing social norms compliance mechanism in the autonomous vehicles. This research paper is proposing an artificial society of autonomous vehicles as an analogy of human social society. Each AV has been assigned a social personality having different social influence. Social norms have been introduced which help the AVs in making the decisions, influenced by emotions, regarding road collision avoidance. Furthermore, social norms compliance mechanism, by artificial social AVs, has been proposed using prospect based emotion i.e. fear, which is conceived from OCC model. Fuzzy logic has been employed to compute the emotions quantitatively. Then, using SimConnect approach, fuzzy values of fear has been provided to the Netlogo simulation environment to simulate artificial society of AVs. Extensive testing has been performed using the behavior space tool to find out the performance of the proposed approach in terms of the number of collisions. For comparison, the random-walk model based artificial society of AVs has been proposed as well. A comparative study with a random walk, prove that proposed approach provides a better option to tailor the autopilots of future AVS, Which will be more socially acceptable and trustworthy by their riders in terms of safe road travel.


title: Reinforcement learning for automatic test case prioritization and selection in continuous integration

abstract: Testing in Continuous Integration (CI) involves test case prioritization, selection, and execution at each cycle. Selecting the most promising test cases to detect bugs is hard if there are uncertainties on the impact of committed code changes or, if traceability links between code and tests are not available. This paper introduces Retecs, a new method for automatically learning test case selection and prioritization in CI with the goal to minimize the round-trip time between code commits and developer feedback on failed test cases. The Retecs method uses reinforcement learning to select and prioritize test cases according to their duration, previous last execution and failure history. In a constantly changing environment, where new test cases are created and obsolete test cases are deleted, the Retecs method learns to prioritize error-prone test cases higher under guidance of a reward function and by observing previous CI cycles. By applying Retecs on data extracted from three industrial case studies, we show for the first time that reinforcement learning enables fruitful automatic adaptive test case selection and prioritization in CI and regression testing.


title: Accurate Scale Estimation for Robust Visual Tracking.

abstract: Robust scale estimation is a challenging problem in visual object tracking. Most existing methods fail to handle large scale variations in complex image sequences. This paper presents a novel approach for robust scale estimation in a tracking-by-detection framework. The proposed approach works by learning discriminative correlation filters based on a scale pyramid representation. We learn separate filters for translation and scale estimation, and show that this improves the performance compared to an exhaustive scale search. Our scale estimation approach is generic as it can be incorporated into any tracking method with no inherent scale estimation.Experiments are performed on 28 benchmark sequences with significant scale variations. Our results show that the proposed approach significantly improves the performance by 18.8 % in median distance precision compared to our baseline. Finally, we provide both quantitative and qualitative comparison of our approach with state-of-the-art trackers in literature. The proposed method is shown to outperform the best existing tracker by 16.6 % in median distance precision, while operating at real-time.


title: Quality attributes and quality models for ambient assisted living software systems: A systematic mapping

abstract: Abstract Context: Ambient Assisted Living (AAL) has become an essential, multidisciplinary research topic, aiming at providing software systems and services that assist people in their everyday life activities. Considering the critical nature of AAL systems, several initiatives have already contributed to the improvement of their quality, by mainly focusing on their non-functional requirements. Despite the importance of quality assurance in AAL systems, there is a lack of a comprehensive analysis on how quality assurance is performed in such systems. This fact might in turn lead to an absence of standardization with regard to the quality assurance process of these systems. Objective: We provide a broad, detailed panorama about the state of the art on quality models (QMs) and quality attributes (QAs) that are important for the AAL domain. Method: We performed a Systematic Mapping (SM). We used six publication databases to cover all published material pertinent for our SM. We initially obtained 287 studies that were filtered based on a set of well-defined inclusion/exclusion criteria, resulting into a set of 27 studies that were used for exploring QAs for AAL systems. Results: The most common QAs used in the development of AAL systems were identified and defined. We also characterized important critical attributes for software systems in the AAL domain. Additionally, QAs for some AAL sub-domains were defined. Furthermore, we investigated how QMQ (ii) considers variability of QAs among AAL sub-domains; (iii) analyses dependences among QAs; (iv) offers indicators or metrics to measure QAs; and (v) offers means to assess and predict quality of AAL systems.


title: Determinants of E-Commerce Customer Satisfaction, Trust, and Loyalty in Saudi Arabia

abstract: ABSTRACT Managing customer trust, satisfaction, and loyalty attitudes of e-commerce services is very important for the long-term growth of many businesses. Previous research has shown that e-retailers experience difficulty maintaining customer loyalty despite the recent rapid growth in Business to Customer (B2C) e-commerce applications. Numerous studies have empirically examined B2C e-commerce customer trust, satisfaction, and loyalty attitudes in various countries. Nevertheless, empirical research on these key constructs of e-commerce in developing Arab countries is generally limited. Thus, the main objective of this paper is to identify the factors that influence the extent to which Saudi consumers trust, are satisfied with, and are loyal towards B2C e-commerce. This study draws on previous research to build a conceptual framework which hypothesizes relationships between these three e-commerce constructs and their antecedents. A survey was conducted among B2C e-commerce customers in the eastern province of Saudi Arabia using a structured self-administered questionnaire. The findings of this study show that B2C e-commerce customer loyalty in Saudi Arabia is strongly influenced by customer satisfaction but weakly influenced by customer trust. The study limitations, implications, along with directions for further research are discussed. Keywords: B2C e-commerce, e-retailer, customer trust, customer satisfaction, customer loyalty 1. Introduction The Internet and Web development have been the most exciting developments in the field of information and communications technology in recent years. With increasing Internet access, the use of e-commerce services by the population is booming globally and locally in Saudi Arabia. The most recent estimate of e-commerce spending in Saudi Arabia (taken in 2010) is SR12 billion [The Arab Advisors Group 2009], the largest in the Arab world. According to [Wikipedia 2010], the currently estimated population of Saudi Arabia is around 28 million. It has been reported by the Arab Advisors Group [2009] that Saudi Arabia's IT sector is growing at a rate of 9.3%. Furthermore, the report showed that 3.5 million internet users or 14.26% of the Saudi population were engaged in e-commerce transactions. The results of the report indicate the country's emergence as an ideal market for e-commerce activities among the developing countries in the Middle East region. At a global level, customer loyalty (e.g. customer retention) is generally very strongly related to the profitability and long-term growth of a firm [Reichheld 1995]. Small increases in customer retention rates can dramatically increase profit [Huffmire 2001]. Loyal customers visit their favorite websites twice as often as non-loyal customers, and loyal customers spend more money [Dialscore.com 2000]. According to analysts, 35 to 40% of e-commerce website sales revenue comes from repeat visitors [Rosen 2001]. As such, it is not surprising that customer loyalty has been found to be a critical asset for e-retailers. Reichheld and Schefter [2000] found that the high cost of acquiring new online customers could lead to unprofitable customer relationships for up to three years. Consequently, it is very useful to determine the key antecedents or factors that influence customer loyalty. Previous research found that e-retailers experience difficulty maintaining customer loyalty despite the recent rapid growth in B2C e-commerce [Hoffman and Novak 2000]. Although there are certainly challenges shared by both traditional retailers and e-retailers, e-retailers face greater competition due to the fact that on the Internet a competitor is only a click away [Mithas, Ramasubbu, Krishnan, and Fornell 2007]. Therefore, attracting and retaining the attention of online shoppers who skim virtual shelves is not an easy task, and without a strong incentive for online shoppers to visit a website and without an anchor to keep customers on it, e-retailers may struggle retaining them in an increasingly competitive e-market. …


title: Modulation of memory storage

abstract: Abstract For several decades, the concept of modulation of memory storage has significantly influenced research investigating neurobiological memory mechanisms. New evidence provides additional support for the view that stress hormones released during emotionally arousing situations modulate memory processes. Recent experiments have investigated the role of sympathetic adrenomedullary hormones in emotional memory in humans, as well as the role of adrenocortical hormones, primarily in animal studies. Further, it is becoming increasingly clear that the sympathetic adrenomedullary and the pituitary adrenocortical systems interact to modulate memory storage. Other new evidence emphasizes the role of peripheral influences to the brain on emotional memory, as well as the critical contribution of the amygdaloid complex in modulation of memory by emotional arousal.