title: Social engineering attack detection model: SEADM

abstract: Social engineering is a real threat to industries in this day and age even though the severity of it is extremely downplayed. The difficulty with social engineering attacks is mostly the ability to identify them. Social engineers target call centre employees, as they are normally underpaid, under skilled workers whom have limited knowledge about the information technology infrastructure. These workers are thus easy targets for the social engineer. This paper proposes a model which can be used by these workers to detect social engineering attacks in a call centre environment. The model is a quick and effective way to determine if the requester is trying to manipulate an individual into disclosing information to which the requester does not have authorization for.


title: Overnight verbal memory retention correlates with the number of sleep spindles

abstract: Despite strong evidence supporting a role for sleep in the consolidation of newly acquired declarative memories, the contribution of specific sleep stages remains controversial. Based on electrophysiological studies in animals, synchronous sleep oscillations have been long proposed as possible origins of sleep-related memory improvement. Nevertheless, no studies to date have directly investigated the impact of sleep oscillations on overnight memory retention in humans. In the present study we provide evidence that overnight verbal memory retention is highly correlated with the number of sleep spindles detected by an automatic algorithm over left frontocentral areas. At the same time, overnight retention of newly learned faces was found to be independent of spindle activity but correlated with non-rapid-eye-movement sleep time. The data strongly support theories suggesting a link between sleep spindle activity and verbal memory consolidation.


title: A Computational Model for Situated Task Learning with Interactive Instruction

abstract: Learning novel tasks is a complex cognitive activity requiring the learner to acquire diverse declarative and procedural knowledge. Prior ACT-R models of acquiring task knowledge from instruction focused on learning procedural knowledge from declarative instructions encoded in semantic memory. In this paper, we identify the requirements for designing compu- tational models that learn task knowledge from situated task- oriented interactions with an expert and then describe and evaluate a model of learning from situated interactive instruc- tion that is implemented in the Soar cognitive architecture.


title: Container and Microservice Driven Design for Cloud Infrastructure DevOps

abstract: Emerging container technologies, such as Docker, offer unprecedented agility in developing and running applications in cloud environment especially when combined with a microservice-style architecture. However, it is often difficult to use containers to manage the cloud infrastructure, without sacrificing many benefits container offers. This paper identifies the key challenges that impede realizing the full promise of containerizing infrastructure services. Using OpenStack as a case study, we explore solutions to these challenges. Specifically, we redesign OpenStack deployment architecture to enable dynamic service registration and discovery, explore different ways to manage service state in containers, and enable containers to access the host kernel and devices. We quantify the efficiency of the container-based microservice-style DevOps compared to the VM-based approach, and study the scalability of the stateless and stateful containerized components. We also discuss limitations in our current design, and highlight open research problems that, if solved, can lead to wider adoption of containers in cloud infrastructure management.


title: Mining large-scale smartphone data for personality studies

abstract: In this paper, we investigate the relationship between automatically extracted behavioral characteristics derived from rich smartphone data and self-reported Big-Five personality traits (extraversion, agreeableness, conscientiousness, emotional stability and openness to experience). Our data stem from smartphones of 117 Nokia N95 smartphone users, collected over a continuous period of 17 months in Switzerland. From the analysis, we show that several aggregated features obtained from smartphone usage data can be indicators of the Big-Five traits. Next, we describe a machine learning method to detect the personality trait of a user based on smartphone usage. Finally, we study the benefits of using gender-specific models for this task. Apart from a psychological viewpoint, this study facilitates further research on the automated classification and usage of personality traits for personalizing services on smartphones.


title: Content-based book recommending using learning for text categorization

abstract: Recommender systems improve access to relevant products and information by making personalized suggestions based on previous examples of a user's likes and dislikes. Most existing recommender systems use collaborative filtering methods that base recommendations on other users' preferences. By contrast,content-based methods use information about an item itself to make suggestions.This approach has the advantage of being able to recommend previously unrated items to users with unique interests and to provide explanations for its recommendations. We describe a content-based book recommending system that utilizes information extraction and a machine-learning algorithm for text categorization. Initial experimental results demonstrate that this approach can produce accurate recommendations.


M-Payment Solutions and M-Commerce Fraud Management


title: Blood vessel extraction and optic disc removal using curvelet transform and kernel fuzzy c-means

abstract: This paper proposes an automatic blood vessel extraction method on retinal images using matched filtering in an integrated system design platform that involves curvelet transform and kernel based fuzzy c-means. Since curvelet transform represents the lines, the edges and the curvatures very well and in compact form (by less number of coefficients) compared to other multi-resolution techniques, this paper uses curvelet transform for enhancement of the retinal vasculature. Matched filtering is then used to intensify the blood vessels' response which is further employed by kernel based fuzzy c-means algorithm that extracts the vessel silhouette from the background through non-linear mapping. For pathological images, in addition to matched filtering, Laplacian of Gaussian filter is also employed to distinguish the step and the ramp like signal from that of vessel structure. To test the efficacy of the proposed method, the algorithm has also been applied to images in presence of additive white Gaussian noise where the curvelet transform has been used for image denoising. Performance is evaluated on publicly available DRIVE, STARE and DIARETDB1 databases and is compared with the large number of existing blood vessel extraction methodologies. Simulation results demonstrate that the proposed method is very much efficient in detecting the long and the thick as well as the short and the thin vessels with an average accuracy of 96.16% for the DRIVE and 97.35% for the STARE database wherein the existing methods fail to extract the tiny and the thin vessels. HighlightsAn integrated framework for retinal blood vessel extraction.Matched filtering, Laplacian of Gaussian, KFCM and morphological operation.Accurate vessel extraction from DRIVE, STARE and DIARETDB1 databases.Vessel extraction accuracy-96.16% for DRIVE and 97.35% for STARE databases.


title: Fast and Large-scale Unsupervised Relation Extraction

abstract: A common approach to unsupervised relation extraction builds clusters of patterns expressing the same relation. In order to obtain clusters of relational patterns of good quality, we have two major challenges: the semantic representation of relational patterns and the scalability to large data. In this paper, we explore various methods for modeling the meaning of a pattern and for computing the similarity of patterns mined from huge data. In order to achieve this goal, we apply algorithms for approximate frequency counting and efficient dimension reduction to unsupervised relation extraction. The experimental results show that approximate frequency counting and dimension reduction not only speeds up similarity computation but also improves the quality of pattern vectors.


title: From Local Kernel to Nonlocal Multiple-Model Image Denoising

abstract: We review the evolution of the nonparametric regression modeling in imaging from the local Nadaraya-Watson kernel estimate to the nonlocal means and further to transform-domain filtering based on nonlocal block-matching. The considered methods are classified mainly according to two main features: local/nonlocal and pointwise/multipoint. Here nonlocal is an alternative to local, and multipoint is an alternative to pointwise. These alternatives, though obvious simplifications, allow to impose a fruitful and transparent classification of the basic ideas in the advanced techniques. Within this framework, we introduce a novel single- and multiple-model transform domain nonlocal approach. The Block Matching and 3-D Filtering (BM3D) algorithm, which is currently one of the best performing denoising algorithms, is treated as a special case of the latter approach.


title: Evaluation of anomaly‐based IDS for mobile devices using machine learning classifiers

abstract: Mobile devices have evolved and experienced an immense popularity over the last few years. This growth however has exposed mobile devices to an increasing number of security threats. Despite the variety of peripheral protection mechanisms described in the literature, authentication and access control cannot provide integral protection against intrusions. Thus, a need for more intelligent and sophisticated security controls such as intrusion detection systems (IDSs) is necessary. Whilst much work has been devoted to mobile device IDSs, research on anomaly-based or behaviour-based IDS for such devices has been limited leaving several problems unsolved. Motivated by this fact, in this paper, we focus on anomaly-based IDS for modern mobile devices. A dataset consisting of iPhone users data logs has been created, and various classification and validation methods have been evaluated to assess their effectiveness in detecting misuses. Specifically, the experimental procedure includes and cross-evaluates four machine learning algorithms (i.e. Bayesian networks, radial basis function, K-nearest neighbours and random Forest), which classify the behaviour of the end-user in terms of telephone calls, SMS and Web browsing history. In order to detect illegitimate use of service by a potential malware or a thief, the experimental procedure examines the aforementioned services independently as well as in combination in a multimodal fashion. The results are very promising showing the ability of at least one classifier to detect intrusions with a high true positive rate of 99.8%. Copyright © 2011 John Wiley & Sons, Ltd.


title: Exploiting knowledge base to generate responses for natural language dialog listening agents

abstract: We developed a natural language dialog listening agent that uses a knowledge base (KB) to generate rich and relevant responses. Our system extracts an important named entity from a user utterance, then scans the KB to extract contents related to this entity. The system can generate diverse and relevant responses by assembling the related KB contents into appropriate sentences. Fifteen students tested our system; they gave it higher approval scores than they gave other systems. These results demonstrate that our system generated various responses and encouraged users to continue talking.


title: Generic edgelet-based tracking of 3D objects in real-time

abstract: This paper addresses the challenging issue of real-time camera localization relative to any object that have texture or not, sharp edges or occluding contours. 3D contour points, dynamically extracted from a CAD model by Analysis-by-Synthesis on the graphics hardware, are combined with a keyframe-based SLAM algorithm to estimate camera poses. Our tracking solution is accurate, robust to sudden motions and to occlusions, as demonstrated on synthetic and real data. This solution is also easy to deploy since it only uses an RGB camera and a CAD model of the object of interest, requires no manual intervention on this model and runs on a consumer tablet at a frequency of 40Hz on a HD video-stream. Videos are available as supplemental material.


title: How to Situate Cognition: Letting Nature Take its Course

abstract: 1. The Situation in Cognition The situated cognition movement in the cognitive sciences, like those sciences themselves, is a loose-knit family of approaches to understanding the mind and cognition. While it has both philosophical and psychological antecedents in thought stretching back over the last century (see Gallagher, this volume, Clancey, this volume,), it has developed primarily since the late 1970s as an alternative to, or a modification of, the then predominant paradigms for exploring the mind within the cognitive sciences. For this reason it has been common to characterize situated cognition in terms of what it is not, a cluster of "anti-isms". Situated cognition has thus been described as opposed to Platonism, Cartesianism, individualism, representationalism, and even


title: HTTP/2-Based Methods to Improve the Live Experience of Adaptive Streaming

abstract: HTTP Adaptive Streaming (HAS) is today the number one video technology for over-the-top video distribution. In HAS, video content is temporally divided into multiple segments and encoded at different quality levels. A client selects and retrieves per segment the most suited quality version to create a seamless playout. Despite the ability of HAS to deal with changing network conditions, HAS-based live streaming often suffers from freezes in the playout due to buffer under-run, low average quality, large camera-to-display delay, and large initial/channel-change delay. Recently, IETF has standardized HTTP/2, a new version of the HTTP protocol that provides new features for reducing the page load time in Web browsing. In this paper, we present ten novel HTTP/2-based methods to improve the quality of experience of HAS. Our main contribution is the design and evaluation of a push-based approach for live streaming in which super-short segments are pushed from server to client as soon as they become available. We show that with an RTT of 300 ms, this approach can reduce the average server-to-display delay by 90.1% and the average start-up delay by 40.1%.


title: A systematic literature review of the effect of different prosthetic components on human functioning with a lower-limb prosthesis

abstract: A correct prosthetic prescription can be derived from adapting the functional benefits of a prosthesis to the functional needs of the prosthetic user. For adequate matching, the functional abilities of the amputees are of value, as well as the technical and functional aspects of the various prosthetic components. No clear clinical consensus seems to be given on the precise prescription criteria. To obtain information about different prosthetic components and daily functioning of amputees with a prosthesis, we performed a systematic literature search. The quality of the studies was assessed with the use of predetermined methodological criteria. Out of 356 potentially relevant studies, 40 studies eventually qualified for final methodological analysis and review. Four satisfied all the criteria and were classified as A-level studies, 26 as B-level, and 10 studies as C-level studies. Despite a huge amount of literature, our formal clinical knowledge had considerable gaps concerning the effects of different prosthetic components and their mechanical characteristics on human functioning with a lower-limb prosthesis. Therefore, with regard to prosthetic guideline development, we must still largely rely on clinical consensus among experts. The integration of knowledge from research with the expert opinion of clinical professionals and the opinions and wishes of consumers can form a solid base for a procedure on guideline development for prosthetic prescription.


title: Exploiting hybrid contexts for Tweet segmentation

abstract: Twitter has attracted hundred millions of users to share and disseminate most up-to-date information. However, the noisy and short nature of tweets makes many applications in information retrieval (IR) and natural language processing (NLP) challenging. Recently, segment-based tweet representation has demonstrated effectiveness in named entity recognition (NER) and event detection from tweet streams. To split tweets into meaningful phrases or segments, the previous work is purely based on external knowledge bases, which ignores the rich local context information embedded in the tweets. In this paper, we propose a novel framework for tweet segmentation in a batch mode, called HybridSeg. HybridSeg incorporates local context knowledge with global knowledge bases for better tweet segmentation. HybridSeg consists of two steps: learning from off-the-shelf weak NERs and learning from pseudo feedback. In the first step, the existing NER tools are applied to a batch of tweets. The named entities recognized by these NERs are then employed to guide the tweet segmentation process. In the second step, HybridSeg adjusts the tweet segmentation results iteratively by exploiting all segments in the batch of tweets in a collective manner. Experiments on two tweet datasets show that HybridSeg significantly improves tweet segmentation quality compared with the state-of-the-art algorithm. We also conduct a case study by using tweet segments for the task of named entity recognition from tweets. The experimental results demonstrate that HybridSeg significantly benefits the downstream applications.


title: The finite domain constraint solver of SWI-Prolog

abstract: We present a new constraint solver over finite domains, freely available as library(clpfd) in SWI-Prolog. Our solver has several unique features, which we describe in this paper: Reasoning over arbitrarily large integers, always terminating propagation, and a domain-specific language that concisely expresses the full semantics of constraint reification. The library is entirely written in Prolog and can be easily ported to other Prolog systems that support attributed variables. The constraint solver is fast enough for teaching and research purposes and is already being used in courses at several universities in France, Germany, Italy, Austria and other countries.


Memory Bound vs . Compute Bound : A Quantitative Study of Cache and Memory Bandwidth in High Performance Applications


title: An axiomatic basis for computer programming

abstract: In this paper an attempt is made to explore the logical foundations of computer programming by use of techniques which were first applied in the study of geometry and have later been extended to other branches of mathematics. This involves the elucidation of sets of axioms and rules of inference which can be used in proofs of the properties of computer programs. Examples are given of such axioms and rules, and a formal proof of a simple theorem is displayed. Finally, it is argued that important advantage, both theoretical and practical, may follow from a pursuance of these topics.


title: A miniature jumping robot with self-recovery capabilities

abstract: In nature, many animals are able to jump, upright themselves after landing and jump again. This allows them to move in unstructured and rough terrain. As a further development of our previously presented 7 g jumping robot, we consider various mechanisms enabling it to recover and upright after landing and jump again. After a weighted evaluation of these different solutions, we present a spherical system with a mass of 9.8 g and a diameter of 12 cm that is able to jump, upright itself after landing and jump again. In order to do so autonomously, it has a control unit and sensors to detect its orientation and spring charging state. With its current configuration it can overcome obstacles of 76 cm at a take-off angle of 75°.


Eventually Returning to Strong Consistency


title: Converting Latin Treebank Data into an SQL Database for Query Purposes

abstract: This paper describes how to turn a Latin dependency treebank into queryable information so that it can be browsed online using a tree query engine and its web interface. The annotation layers of the treebank are first introduced, then the query system architecture is detailed, and finally the way the treebank is converted into a relational database architecture is described.


title: LTR-Retrotransposon Control by tRNA-Derived Small RNAs

abstract: Summary Transposon reactivation is an inherent danger in cells that lose epigenetic silencing during developmental reprogramming. In the mouse, long terminal repeat (LTR)-retrotransposons, or endogenous retroviruses (ERV), account for most novel insertions and are expressed in the absence of histone H3 lysine 9 trimethylation in preimplantation stem cells. We found abundant 18 nt tRNA-derived small RNA (tRF) in these cells and ubiquitously expressed 22 nt tRFs that include the 3′ terminal CCA of mature tRNAs and target the tRNA primer binding site (PBS) essential for ERV reverse transcription. We show that the two most active ERV families, IAP and MusD/ETn, are major targets and are strongly inhibited by tRFs in retrotransposition assays. 22 nt tRFs post-transcriptionally silence coding-competent ERVs, while 18 nt tRFs specifically interfere with reverse transcription and retrotransposon mobility. The PBS offers a unique target to specifically inhibit LTR-retrotransposons, and tRF-targeting is a potentially highly conserved mechanism of small RNA–mediated transposon control.


title: Combining content with user preferences for non-fiction multimedia recommendation: a study on TED lectures

abstract: This paper introduces a new dataset and compares several methods for the recommendation of non-fiction audio visual material, namely lectures from the TED website. The TED dataset contains 1,149 talks and 69,023 profiles of users, who have made more than 100,000 ratings and 200,000 comments. The corresponding metadata, which we make available, can be used for training and testing generic or personalized recommender systems. We define content-based, collaborative, and combined recommendation methods for TED lectures and use cross-validation to select the best parameters of keyword-based (TFIDF) and semantic vector space-based methods (LSI, LDA, RP, and ESA). We compare these methods on a personalized recommendation task in two settings, a cold-start and a non-cold-start one. In the cold-start setting, semantic vector spaces perform better than keywords. In the non-cold-start setting, where collaborative information can be exploited, content-based methods are outperformed by collaborative filtering ones, but the proposed combined method shows acceptable performances, and can be used in both settings. For the generic recommendation task, LSI and RP again outperform TF-IDF.


title: Question-Guided Hybrid Convolution for Visual Question Answering

abstract: In this paper, we propose a novel Question-Guided Hybrid Convolution (QGHC) network for Visual Question Answering (VQA). Most state-of-the-art VQA methods fuse the high-level textual and visual features from the neural network and abandon the visual spatial information when learning multi-modal features. To address these problems, question-guided kernels generated from the input question are designed to convolute with visual features for capturing the textual and visual relationship in the early stage. The question-guided convolution can tightly couple the textual and visual information but also introduce more parameters when learning kernels. We apply the group convolution, which consists of question-independent kernels and question-dependent kernels, to reduce the parameter size and alleviate over-fitting. The hybrid convolution can generate discriminative multi-modal features with fewer parameters. The proposed approach is also complementary to existing bilinear pooling fusion and attention based VQA methods. By integrating with them, our method could further boost the performance. Experiments on VQA datasets validate the effectiveness of QGHC.


title: The Relationship between Language Learning Anxiety and Language Learning Motivation among Iranian Intermediate EFL Learners

abstract: The present study was an attempt to investigate the degree of anxiety among Iranian intermediate EFL learners and its relation to their motivation. To this end, a total number of 80 EFL learners (35 males and 45 females) were selected through cluster random sampling from two language classes at Islamic Azad University, Sardasht Branch, Hormozgan Province, Iran as the participants in this study. The instruments used to collect the data from the participants were the Foreign Language Learning Anxiety Scale (FLCAS) and Gardner’s (1985) Attitude/Motivation Test Battery (AMTB). The results indicated that the majority of the participants experienced a mid to high level of language learning anxiety. Besides, it was found that the participants with lower levels of the language learning anxiety were more motivated to learn English while those with higher levels of the language learning anxiety were less motivated to learn English. However, there was no significant difference between the anxiety level of male and female participants. Similarly, there was no significant difference between the motivation level of male and female participants in this study. Finally, the findings indicated that there was no significant difference between the male and female participants concerning their language learning anxiety and motivation to learn English, indicating that gender did not play a determining role in these two variables.


title: Building a Bank of Semantically Encoded Narratives

abstract: We propose a methodology for a novel type of discourse annotation whose model is tuned to the analysis of a text as narrative. This is intended to be the basis of a “story bank” resource that would facilitate the automatic analysis of narrative structure and content. The methodology calls for annotators to construct propositions that approximate a reference text, by selecting predicates and arguments from among controlled vocabularies drawn from resources such as WordNet and VerbNet. Annotators then integrate the propositions into a conceptual graph that maps out the entire discourse; the edges represent temporal, causal and other relationships at the level of story content. Because annotators must identify the recurring objects and themes that appear in the text, they also perform coreference resolution and word sense disambiguation as they encode propositions. We describe a collection experiment and a method for determining inter-annotator agreement when multiple annotators encode the same short story. Finally, we describe ongoing work toward extending the method to integrate the annotator’s interpretations of character agency (the goals, plans and beliefs that are relevant, yet not explictly stated in the text).


title: A Developed Collaborative Filtering Similarity Method to Improve the Accuracy of Recommendations under Data Sparsity

abstract: This paper presented a new similarity method to improve the accuracy of traditional Collaborative Filtering (CF) method under sparse data issue. CF provides the user with items, that what they need, based on analyses the preferences of users who have a strong correlation to him/her preference. However, the accuracy is influencing by the method that use to find neighbors. Pearson correlation coefficient and Cosine measures, as the most widely used methods, depending on the rating of only co-rated items to find the correlations between users. Consequently, these methods have lack of ability in addressing the sparsity. This paper presented a new proposed similarity method based on the global user preference to address the sparsity issue and improve the accuracy of recommendation. Thus, the novelty of this method is the ability to solve the similarity issue with a capability of finding the relationship among non-correlated users. Furthermore, to determine the right neighbors during the process of computing the similarity between a pair of users, the developed method considered two main factors (fairness and proportion of co-rated). The MovieLens 100K benchmark dataset is used to evaluate the developed method accuracy. The experiments’ result showed that the accuracy of the developed method is improved compared to the traditional CF similarity methods using a specific common CF evaluation metrics.


title: DuLa-Net: A Dual-Projection Network for Estimating Room Layouts From a Single RGB Panorama

abstract: We present a deep learning framework, called DuLa-Net, to predict Manhattan-world 3D room layouts from a single RGB panorama. To achieve better prediction accuracy, our method leverages two projections of the panorama at once, namely the equirectangular panorama-view and the perspective ceiling-view, that each contains different clues about the room layouts. Our network architecture consists of two encoder-decoder branches for analyzing each of the two views. In addition, a novel feature fusion structure is proposed to connect the two branches, which are then jointly trained to predict the 2D floor plans and layout heights. To learn more complex room layouts, we introduce the Realtor360 dataset that contains panoramas of Manhattan-world room layouts with different numbers of corners. Experimental results show that our work outperforms recent state-of-the-art in prediction accuracy and performance, especially in the rooms with non-cuboid layouts.


title: A Theory of Focus Interpretation

abstract: According to the alternative semantics for focus, the semantic reflec of intonational focus is a second semantic value, which in the case of a sentence is a set of propositions. We examine a range of semantic and pragmatic applications of the theory, and extract a unitary principle specifying how the focus semantic value interacts with semantic and pragmatic processes. A strong version of the theory has the effect of making lexical or construction-specific stipulation of a focus-related effect in association-with-focus constructions impossible. Furthermore, while focus has a uniform import, the sources of meaning differences in association with focus are various.


title: An ultra-low noise current source for magnetoresistive biosensors biasing

abstract: This paper presents an ultra-low noise current source for magnetoresistive biosensors biasing. In biological applications signals can have very low amplitudes, therefore the noise of the biosensor biasing circuit can have a high impact on the measurement resolution. We present an ultra-low noise current source architecture that biases a magnetoresistive biosensor array with negligible noise penalization without using external noise filtering capacitors, allowing the integration of magnetoresistive sensors on CMOS without decreasing the measurement resolution. The circuit was designed and fabricated using AMS 0.35 μm 3.3 V technology.


title: An effective hop-by-hop Interest shaping mechanism for CCN communications

abstract: We introduce a rate-based congestion control mechanism for Content-Centric Networking (CCN). It builds on the fact that one Interest retrieves at most one Data packet. Congestion can occur when aggregate conversations arrive in excess and fill up the transmission queue of a CCN router. We compute the available capacity of each CCN router in a distributed way in order to shape their conversations Interest rate and therefore, adjust dynamically their Data rate and transmission buffer occupancy. We demonstrate the convergence properties of this Hop-by-hop Interest Shaping mechanism (HoBHIS) and provide a performance analysis based on various scenarios using our ns 2 simulation environment.


title: Decomposing adult age differences in working memory.

abstract: Two studies, involving a total of 460 adults between 18 and 87 years of age, were conducted to determine which of several hypothesized processing components was most responsible for age-related declines in working memory functioning. Significant negative correlations between age and measures of working memory (i.e., from -.39 to -.52) were found in both studies, and these relations were substantially attenuated by partialing measures hypothesized to reflect storage capacity, processing efficiency, coordination effectiveness, and simple comparison speed. Because the greatest attenuation of the age relations occurred with measures of simple processing speed, it was suggested that many of the age differences in working memory may be mediated by age-related reductions in the speed of executing elementary operations. Working memory is generally defined as the preservation of information while simultaneously processing the same or other information. It is distinguished from other forms of memory because the assumption that it reflects both processing and storage implies that it plays an important role in many cognitive tasks (e.g., Baddeley, 1986; Carpenter & Just, 1989; Salthouse, 1990). An illustration of the hypothesized functioning of working memory in one cognitive task, mental arithmetic, is presented in Figure 1. The left column in this figure indicates the operations to be performed, and the right column represents the intermediate products that must be temporarily stored while carrying out those operations. This figure is useful because it graphically illustrates both the importance and the complexity of working memory. That is, it is clear from this example that effective storage of information is essential in order for the successful performance of certain cognitive tasks. Figure 1 also suggests that it may be fruitful to think of working memory not as a single discrete structure, but rather as a dynamic interchange among three conceptually distinct aspects or components-processing efficiency, storage capacity, and coordination effectiveness. Processing is represented by the series of operations in the left column, storage is represented by the entries in the right column, and coordination can be assumed to correspond both to the sequencing of operations and to the arrows portraying the exchange of information between processing and storage. A primary purpose of this article was to investigate the contribution of these three hypothesized components to age-related differences in measures of working memory. Each of the components has been hypothesized to be an important source of adult age differences by one or more researchers, but few definitive conclusions have been possible because the currently available evidence is both weak and inconsistent. To illustrate,


title: Path-based image sequence interpolation guided by feature points

abstract: We present a method of image sequence interpolation, which can generate a sequence of continuous intermediate frames between two input images. This method is based on a path framework that describes the motion information in the images. A path which starts from one input image, and ends at another input image is constructed for each pixel in the images. The main contribution of this paper is that we take the feature points into consideration. By calculating the position deviation out of the feature points, information and guidance can be given to the process of path optimization, making the interpolation result more plausible and natural. We also increase the conditions and restrictions in the optimization procedure, hence the time and memory cost can be effectively decreased.


title: A Framework for Figurative Language Detection Based on Sense Differentiation

abstract: Various text mining algorithms require the process of feature selection. High-level semantically rich features, such as figurative language uses, speech errors etc., are very promising for such problems as e.g. writing style detection, but automatic extraction of such features is a big challenge. In this paper, we propose a framework for figurative language use detection. This framework is based on the idea of sense differentiation. We describe two algorithms illustrating the mentioned idea. We show then how these algorithms work by applying them to Russian language data.


title: A Deep Learning based Joint Segmentation and Classification Framework for Glaucoma Assesment in Retinal Color Fundus Images

abstract: Automated Computer Aided diagnostic tools can be used for the early detection of glaucoma to prevent irreversible vision loss. In this work, we present a Multi-task Convolutional Neural Network (CNN) that jointly segments the Optic Disc (OD), Optic Cup (OC) and predicts the presence of glaucoma in color fundus images. The CNN utilizes a combination of image appearance features and structural features obtained from the OD-OC segmentation to obtain a robust prediction. The use of fewer network parameters and the sharing of the CNN features for multiple related tasks ensures the good generalizability of the architecture, allowing it to be trained on small training sets. The cross-testing performance of the proposed method on an independent validation set acquired using a different camera and image resolution was found to be good with an average dice score of 0.92 for OD, 0.84 for OC and AUC of 0.95 on the task of glaucoma classification illustrating its potential as a mass screening tool for the early detection of glaucoma.


title: That's sick dude!: Automatic identification of word sense change across different timescales

abstract: In this paper, we propose an unsupervised method to identify noun sense changes based on rigorous analysis of time-varying text data available in the form of millions of digitized books. We construct distributional thesauri based networks from data at different time points and cluster each of them separately to obtain word-centric sense clusters corresponding to the different time points. Subsequently, we compare these sense clusters of two different time points to find if (i) there is birth of a new sense or (ii) if an older sense has got split into more than one sense or (iii) if a newer sense has been formed from the joining of older senses or (iv) if a particular sense has died. We conduct a thorough evaluation of the proposed methodology both manually as well as through comparison with WordNet. Manual evaluation indicates that the algorithm could correctly identify 60.4% birth cases from a set of 48 randomly picked samples and 57% split/join cases from a set of 21 randomly picked samples. Remarkably, in 44% cases the birth of a novel sense is attested by WordNet, while in 46% cases and 43% cases split and join are respectively confirmed by WordNet. Our approach can be applied for lexicography, as well as for applications like word sense disambiguation or semantic search.


Design and Calibration of an Underwater Stereo-video System for the Monitoring of Marine Fauna Populations


title: Multiple-event direct to histogram TDC in 65nm FPGA technology

abstract: A novel multiple-event Time to Digital Converter (TDC) with direct to histogram output is implemented in a 65nm Xilinx Virtex 5 FPGA. The delay-line based architecture achieves 16.3 ps temporal accuracy over a 2.86ns dynamic range. The measured maximum conversion rate of 6.17 Gsamples/s and the sampling rate of 61.7 Gsamples/s are the highest published in the literature. The system achieves a linearity of -0.9/+3 LSB DNL and -1.5/+5 LSB INL. The TDC is demonstrated in a direct time of flight optical ranging application with 12mm error over a 350mm range.


title: Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search

abstract: Bayesian model-based reinforcement learning is a formally elegant approach to learning optimal behaviour under model uncertainty, trading off exploration and exploitation in an ideal way. Unfortunately, finding the resulting Bayes-optimal policies is notoriously taxing, since the search space becomes enormous. In this paper we introduce a tractable, sample-based method for approximate Bayes-optimal planning which exploits Monte-Carlo tree search. Our approach outperformed prior Bayesian model-based RL algorithms by a significant margin on several well-known benchmark problems -- because it avoids expensive applications of Bayes rule within the search tree by lazily sampling models from the current beliefs. We illustrate the advantages of our approach by showing it working in an infinite state space domain which is qualitatively out of reach of almost all previous work in Bayesian exploration.


title: Statistical Mapping between Articulatory Movements and Acoustic Spectrum Using a Gaussian Mixture Model

abstract: In this paper, we describe a statistical approach to both an articulatory-to-acoustic mapping and an acoustic-to-articulatory inversion mapping without using phonetic information. The joint probability density of an articulatory parameter and an acoustic parameter is modeled using a Gaussian mixture model (GMM) based on a parallel acoustic-articulatory speech database. We apply the GMM-based mapping using the minimum mean-square error (MMSE) criterion, which has been proposed for voice conversion, to the two mappings. Moreover, to improve the mapping performance, we apply maximum likelihood estimation (MLE) to the GMM-based mapping method. The determination of a target parameter trajectory having appropriate static and dynamic properties is obtained by imposing an explicit relationship between static and dynamic features in the MLE-based mapping. Experimental results demonstrate that the MLE-based mapping with dynamic features can significantly improve the mapping performance compared with the MMSE-based mapping in both the articulatory-to-acoustic mapping and the inversion mapping.


title: Multi-label Ranking from Positive and Unlabeled Data

abstract: In this paper, we specifically examine the training of a multi-label classifier from data with incompletely assigned labels. This problem is fundamentally important in many multi-label applications because it is almost impossible for human annotators to assign a complete set of labels, although their judgments are reliable. In other words, a multilabel dataset usually has properties by which (1) assigned labels are definitely positive and (2) some labels are absent but are still considered positive. Such a setting has been studied as a positive and unlabeled (PU) classification problem in a binary setting. We treat incomplete label assignment problems as a multi-label PU ranking, which is an extension of classical binary PU problems to the wellstudied rank-based multi-label classification. We derive the conditions that should be satisfied to cancel the negative effects of label incompleteness. Our experimentally obtained results demonstrate the effectiveness of these conditions.


title: Anomaly detection on data streams for machine condition monitoring

abstract: Machine Condition Monitoring (MCM) is an important topic for the reliability of industrial machines in increasingly interconnected production facilities. The analysis of a huge amount of data to get information about the machine's condition is a difficult challenge. Current solutions for these analyses are often very specific, need a lot of manual configuration or are difficult to apply. In this paper, we present a system that uses anomaly detection in data streams to find hints for faulty machines in the data. The basis of this system is a Data stream management system (DSMS), which can handle huge amounts of streaming data and simplifies the definition of analyses. Due to the anomaly detection algorithms, the approach can be applied to a variety of data and scenarios. The outcome is a system that allows live analysis of machine data for MCM.


title: Applying Interval Type-2 Fuzzy Rule Based Classifiers Through a Cluster-Based Class Representation

abstract: Fuzzy Rule-Based Classification Systems (FRBCSs) have the potential to provide so-called interpretable classifiers, i.e. classifiers which can be introspective, understood, validated and augmented by human experts by relying on fuzzy-set based rules. This paper builds on prior work for interval type-2 fuzzy set based FRBCs where the fuzzy sets and rules of the classifier are generated using an initial clustering stage. By introducing Subtractive Clustering in order to identify multiple cluster prototypes, the proposed approach has the potential to deliver improved classification performance while maintaining good interpretability, i.e. without resulting in an excessive number of rules. The paper provides a detailed overview of the proposed FRBC framework, followed by a series of exploratory experiments on both linearly and non-linearly separable datasets, comparing results to existing rule-based and SVM approaches. Overall, initial results indicate that the approach enables comparable classification performance to non rule-based classifiers such as SVM, while often achieving this with a very small number of rules.


title: Distributed Document and Phrase Co-embeddings for Descriptive Clustering

abstract: © 2017 Association for Computational Linguistics. Descriptive document clustering aims to automatically discover groups of semantically related documents and to assign a meaningful label to characterise the content of each cluster. In this paper, we present a descriptive clustering approach that employs a distributed representation model, namely the paragraph vector model, to capture semantic similarities between documents and phrases. The proposed method uses a joint representation of phrases and documents (i.e., a coembedding) to automatically select a descriptive phrase that best represents each document cluster. We evaluate our method by comparing its performance to an existing state-of-the-art descriptive clustering method that also uses co-embedding but relies on a bag-of-words representation. Results obtained on benchmark datasets demonstrate that the paragraph vector-based method obtains superior performance over the existing approach in both identifying clusters and assigning appropriate descriptive labels to them.


title: Initial attachment of Candida albicans cells to buccal epithelial cells. Demonstration of ultrastructure with the rapid-freezing technique.

abstract: The rapid-freezing technique was applied in association with scanning and transmission electron microscopy to observe the initial attachment (or contact) of Candida albicans cells to exfoliated human buccal epithelial cells. Low temperature scanning electron microscopy provided detailed three-dimensional morphological features of the yeast-epithelial cell association; adhesion of C. albicans cells to host cells was primarily owing to an interaction between fibrillar layer of the yeast cell wall and the membrane interdigitations of the epithelial cells. Such a particular interconnection between the two cells was confirmed by the freeze-substitution fixation for transmission electron microscopy. These results clearly demonstrate the outermost fibrillar cell wall layer of C. albicans responsible for adhesion to host cells.


title: Joint Camera Pose Estimation and 3D Human Pose Estimation in a Multi-camera Setup

abstract: In this paper we propose an approach to jointly perform camera pose estimation and human pose estimation from videos recorded by a set of cameras separated by wide baselines. Multi-camera pose estimation is very challenging in case of wide baselines or in general when patch-based feature correspondences are difficult to establish across images.


title: Esthetic removable partial dentures.

abstract: This article provides information regarding the many ways that removable partial dentures (RPDs) may be used to solve restorative problems in the esthetic zone without displaying metal components or conspicuous acrylic resin flanges. The esthetic zone is defined and described, as are methods for recording it. Six dental categories are presented that assist the dentist in choosing a variety of RPD design concepts that may be used to avoid metal display while still satisfying basic principles of RPDs. New materials that may be utilized for optimal esthetics are presented and techniques for contouring acrylic resin bases and tinting denture bases are described.


Violent Media Content and Aggressiveness in Adolescents A Downward Spiral Model


title: On Evaluating and Comparing Conversational Agents

abstract: Conversational agents are exploding in popularity. However, much work remains in the area of non goal-oriented conversations, despite significant growth in research interest over recent years. To advance the state of the art in conversational AI, Amazon launched the Alexa Prize, a 2.5-million dollar university competition where sixteen selected university teams built conversational agents to deliver the best social conversational experience. Alexa Prize provided the academic community with the unique opportunity to perform research with a live system used by millions of users. The subjectivity associated with evaluating conversations is key element underlying the challenge of building non-goal oriented dialogue systems. In this paper, we propose a comprehensive evaluation strategy with multiple metrics designed to reduce subjectivity by selecting metrics which correlate well with human judgement. The proposed metrics provide granular analysis of the conversational agents, which is not captured in human ratings. We show that these metrics can be used as a reasonable proxy for human judgment. We provide a mechanism to unify the metrics for selecting the top performing agents, which has also been applied throughout the Alexa Prize competition. To our knowledge, to date it is the largest setting for evaluating agents with millions of conversations and hundreds of thousands of ratings from users. We believe that this work is a step towards an automatic evaluation process for conversational AIs.


title: Ten Simple Rules for Taking Advantage of Git and GitHub

abstract: This study was supported by Wellcome Trust [grant number WT101477MA] (http://www.wellcome.ac.uk/), BBSRC [grant numbers BB/K01997X/1, BB/I00095X/1, BB/L024225/1 and BB/L002817/1] (http://www.bbsrc.ac.uk/), BMBF grant de.NBI - German Network for Bioinformatics Infrastructure (FKZ031 A 534A) (https://www.denbi.de/), NIH grant numbers R01-GM-094231 and R01-EB-017205 (http://www.nih.gov/), EPSRC [reference EP/M022641/1] (https://www.epsrc.ac.uk), NSF grant number 1252893 (http://www.nsf.gov/), and Novo Nordisk Foundation (http://www.novonordiskfonden.dk/en).


title: Cost-sensitive prediction of airline delays using machine learning

abstract: This study provides a framework combining the sampling method called costing and supervised machine teaming algorithms to predict individual flight delays. The costing method converts cost-insensitive classifiers to cost-sensitive ones by subsampling examples from the original training dataset according to their misclassification costs. A weighted error function has been newly defined to evaluate the model's performance considering misclassification costs. And the function is measured by the various cost ratio between false positive error and false negative error. The cost ratio shows the relative importance of delays class to on-time class. The weighted error rate varies with the cost ratio and the model can have lower weighted error rate when the cost ratio is 10.


title: A review of transplant immunology.

abstract: Transplantation has been an accepted treatment for end-stage organ disease for more than 30 years. Advances in transplant immunology are the cornerstone to the success of the body’s ability to accept the allografted organ while continuing to perform immune functions such as tumor surveillance and fighting pathogenic organisms. This article describes immunosuppressant regimens and attendant nursing considerations in the care of transplant patients.


title: Rapid, Robust, and Reliable Blind Deconvolution via Nonconvex Optimization

abstract: Abstract We study the question of reconstructing two signals f and g from their convolution y = f ⁎ g . This problem, known as blind deconvolution, pervades many areas of science and technology, including astronomy, medical imaging, optics, and wireless communications. A key challenge of this intricate non-convex optimization problem is that it might exhibit many local minima. We present an efficient numerical algorithm that is guaranteed to recover the exact solution, when the number of measurements is (up to log-factors) slightly larger than the information-theoretical minimum, and under reasonable conditions on f and g. The proposed regularized gradient descent algorithm converges at a geometric rate and is provably robust in the presence of noise. To the best of our knowledge, our algorithm is the first blind deconvolution algorithm that is numerically efficient, robust against noise, and comes with rigorous recovery guarantees under certain subspace conditions. Moreover, numerical experiments do not only provide empirical verification of our theory, but they also demonstrate that our method yields excellent performance even in situations beyond our theoretical framework.


title: Measuring and averting underuse of necessary cardiac procedures: a summary of results and future directions.

abstract: BACKGROUND ::: Attempting to explain the marked variation in utilization of medical procedures has vexed health policy analysts for nearly three decades. Most health services research to date has been directed at identifying and reducing excessive utilization. Little attention has been given to underuse of care. THE LOS ANGELES CARDIAC UNDERUSE PROJECT OVERVIEW: A research group at the University of California, Los Angeles (UCLA), performed two separate, published studies seeking to measure underuse of coronary angiography and coronary artery revascu-larization (bypass surgery and angioplasty), two critical links in the chain of care leading from initial diagnosis of coronary artery disease to definitive treatment. In each study, the necessity criteria developed by the panel were used to identify patients needing an invasive procedure. ::: ::: ::: RESULTS ::: Within this population of patients (sampled predominantly from public hospitals), substantial underuse of clinically necessary coronary angiography (41% without refusers) and revascularization (23% without refusers) was detected. In this select population of patients, receiving a necessary revascularization procedure appeared to both reduce the risk of death and improve quality of life. ::: ::: ::: DISCUSSION ::: Despite limitations of the method, detection of underuse is feasible, valid, and affordable in the context of overall health care expenditures. Moreover, the case for implementing "underuse prevention" systems is increasingly compelling. Measuring and disseminating data on underuse of expensive but highly beneficial procedures would provide health care consumers (patients and employers) with useful information and enable health care providers to develop quality improvement strategies aimed at rational use of health care resources.


title: Geometric and quasi-static thermal error compensation for a laser digitizer equipped coordinate measuring machine

abstract: Abstract An emerging trend in dimensional inspection of manufactured parts is the use of coordinate measuring machines (CMMs) equipped with non-contact laser digitizers instead of traditional contact probes. Error mapping is generally proprietary to the CMM controller, and the accuracy of such a combined system is limited by the lack of integrated CMM/laser digitizer error compensation. This paper reports progress towards achieving such a system. Geometric error compensation measurements are made on a test machine at 20, 25 and 30 °C. Performance of the system was tested by measuring a special optically coated sphere ballbar artifact. The scan coordinates, the CMM axis scale positions and temperature were recorded simultaneously in real time. The CMM error compensation data was then used to post process the laser digitizer coordinates to obtain improved global part coordinates. Inclusion of the error compensation terms is shown to reduce the dispersion of results by 65 to 80%.


title: Match performance of high-standard soccer players with special reference to development of fatigue

abstract: The aim of this study was to assess physical fitness, match performance and development of fatigue during competitive matches at two high standards of professional soccer. Computerized time-motion analyses were performed 2-7 times during the competitive season on 18 top-class and 24 moderate professional soccer players. In addition, the players performed the Yo-Yo intermittent recovery test. The top-class players performed 28 and 58% more (P < 0.05) high-intensity running and sprinting, respectively, than the moderate players (2.43 ± 0.14 vs 1.90 ± 0.12 km and 0.65 ± 0.06 vs 0.41 ± 0.03 km, respectively). The top-class players were better (11%; P < 0.05) on the Yo-Yo intermittent recovery test than the moderate players (2.26 ± 0.08 vs 2.04 ± 0.06 km, respectively). The amount of high-intensity running, independent of competitive standard and playing position, was lower (35-45%; P < 0.05) in the last than in the first 15 min of the game. After the 5-min period during which the amount of high-intensity runn...


Summarizing Source Code with Transferred API Knowledge


title: Rule Extraction using Artificial Neural Networks

abstract: Abstract Artificial neural networks have been successfully applied to a variety of business application problems involving classification and regression. Although backpropagation neural networks generally predict better than decision trees do for pattern classification problems, they are often regarded as black boxes, i.e., their predictions are not as interpretable as those of decision trees. In many applications, it is desirable to extract knowledge from trained neural networks so that the users can gain a better understanding of the solution. This paper presents an efficient algorithm to extract rules from artificial neural networks. We use two-phase training algorithm for backpropagation learning. In the first phase, the number of hidden nodes of the network is determined automatically in a constructive fashion by adding nodes one after another based on the performance of the network on training data. In the second phase, the number of relevant input units of the network is determined using pruning algorithm. The pruning process attempts to eliminate as many connections as possible from the network. Relevant and irrelevant attributes of the data are distinguished during the training process. Those that are relevant will be kept and others will be automatically discarded. From the simplified networks having small number of connections and nodes we may easily able to extract symbolic rules using the proposed algorithm. Extensive experimental results on several benchmarks problems in neural networks demonstrate the effectiveness of the proposed approach with good generalization ability.


title: MtiBase: a database for decoding microRNA target sites located within CDS and 5′UTR regions from CLIP-Seq and expression profile datasets

abstract: MicroRNAs (miRNAs) play an important role in the regulation of gene expression. Previous studies on miRNA functions mainly focused on their target sites in the 3 0 untranslated regions (UTRs) of mRNAs. However, increasing evidence has revealed that miRNAs can also induce mRNA degradation and mediate translational repression via complementary interactions with the coding sequence (CDS) and 5 0 UTR of mRNAs. In this study, we developed a novel database, MtiBase, to facilitate the comprehensive exploration of CDS- and 5 0 UTR-located miRNA target sites identified from cross-linking immunoprecipitation sequencing (CLIP-Seq) datasets and to uncover their regulatory effects on mRNA stability and translation from expression profile datasets. By integrating 61 Argonaute protein-binding CLIP-Seq datasets and miRNA target sites predicted by five commonly used programs, we identified approximately 4 400 000 CDS-located and 470 000 5 0 UTR-located miRNA target sites. Moreover, we evaluated the regulatory effects of miRNAs on mRNA stability and translation using the data from 222 gene expression profiles, and 28 ribosome-protected fragment sequencing, and six pulsed stable isotope labeling with amino acids in culture. Finally, the effects of SNPs on the functions of


title: BEING RESPONSIVE TO YOUR CUSTOMER : DEVELOPING CUSTOMER AGILITY THROUGH INFORMATION MANAGEMENT

abstract: As e-commerce offers new channels for companies to reach consumers, it also brings about significant challenges to the ability to response to customer changing needs, which is named “customer agility”. Both practitioners and researchers consider information management as a source of customer agility. Drawing on the information management literature, this research program attempts to propose an integrative information management framework to study the achievement of customer agility. Subsequently, a process model of how information management helps firms achieve customer agility will be developed. This is achieved by conducting a case study of a Chinese B2C company. This research in progress article presents the preliminary findings from the first visit to the company. It shows that customer agility is achieved through establishing information management structure, developing information management capability and instilling information behaviors and values. More interviews will be conducted in the next phase and the findings will be expanded.


title: Forest mapping and monitoring with interferometric synthetic aperture radar (InSAR)

abstract: A synthetic aperture radar (SAR) is an active sensor transmitting pulses of polarized electromagnetic waves and receiving the backscattered radiation. SAR sensors at different wavelengths and with different polarimetric capabilities are being used in remote sensing of the earth. The value of an analysis of backscattered energy alone is limited due to ambiguities in the possible ecological factor configurations causing the signal. From two SAR images taken from similar viewing positions with a short time-lag, interference between the two waves can be observed. By subtracting the two phases of the signals, it is feasible to eliminate the random contribution of the scatterers to the phase. The interferometric correlation and the interferometric phase contain additional information on the three-dimensional structure of the scattering elements in the imaged area. A brief review of SAR sensors is given, followed by an outline of the physical foundations of SAR interferometry and the practical data-processing st...


title: Detecting Emotion Stimuli in Emotion-Bearing Sentences

abstract: Emotion, a pervasive aspect of human experience, has long been of interest to social and behavioural sciences. It is now the subject of multi-disciplinary research also in computational linguistics. Emotion recognition, studied in the area of sentiment analysis, has focused on detecting the expressed emotion. A related challenging question, why the experiencer feels that emotion, has, to date, received very little attention. The task is difficult and there are no annotated English resources. FrameNet refers to the person, event or state of affairs which evokes the emotional response in the experiencer as emotion stimulus. We automatically build a dataset annotated with both the emotion and the stimulus using FrameNet’s emotions-directed frame. We address the problem as information extraction: we build a CRF learner, a sequential learning model to detect the emotion stimulus spans in emotion-bearing sentences. We show that our model significantly outperforms all the baselines.


title: Effective use of a new quality and safety checklist for the steady and safe supply of fluorine-18 fluorodeoxyglucose for positron emission tomography/computed tomography.

abstract: Fluorine-18-fluorodeoxyglucose ((18)F-FDG ) positron emission tomography/computed tomography (PET/CT) with the in-hospital synthesis of (18)F-FDG was initiated in our hospital on April 1, 2010. We aim to perform stable supply of (18)F-FDG for patients and to avoid unnecessary radiation exposure due to mis-preparation of (18)F-FDG. Pharmacists perform quality control tests to determine whether (18)F-FDG meets official regulations. After the quality control test, we give (18)F-FDG that conforms to these standards to patients to conduct (18)F-FDG PET/CT. After a quality control test is initiated, various problems can occur including leakage and staff radiation exposure. We recorded daily radiation exposure in the hot lab and calculated the average daily radiation exposure on a monthly basis for a period of one year. We developed a checklist to safely and quickly synthesize(18)F-FDG for patients. The total radiation exposure of the three pharmacists was 394, 180, and 214μSv/y and overall lower than the occupational maximum values (≤50mSv/year and ≤100mSv/5years for males). In conclusion, using the new checklist, pharmacists and the operator of the Sumitomo Heavy Industries Accelerator service Co., Ltd. were able to practice their daily work effectively during the synthesis and quality control testing of (18)F-FDG. Notably the usual radiation exposure reported in the present study was quite lower than the allowable maximum.


title: Design and realization of planar bow-tie dipole array antenna with dual-polarization at 2.4 GHz frequency for Wi-Fi access point application

abstract: A research about design and realization of dual planar bow-tie dipole array antenna at 2.4 GHz will be presented for Wifi Outdoor BTS applications. The proposed antenna should have wide bandwidth, high gain, and ± 45° dual polarization. From the simulation and measurement results of realized antenna we can concluded that our proposed antenna has met the desired specifications; antenna reflection coefficient value < −10 dB, mutual coupling < −20 dB, antenna bandwidth exceeds 1.2 GHz, antenna gain reaches 16.62 dBi. Moreover, beside for Wifi Outdoor BTS applications, this implemented antenna actually can be used for another wireless systems simultaneously for GSM 1800 MHz, 3G 2100 Mz, and LTE/Wimax 2300/2500 MHz


PICS: Parameter-free Identification of Cohesive Subgroups in Large Attributed Graphs


title: Emotional Expression Online: the Impact of Task, Relationship and Personality Perception on Emoticon Usage in Instant Messenger

abstract: Emoticons have been widely used in Computer mediated communication (CMC) such as Instant Messenger (IM). This study investigates the effect of communication task, interpersonal perception, and relationship intimacy on the use of emoticons in IM. The results show that task formality and two aspects of interpersonal perception affect the emoticon usage. Detailed discussions and implications are addressed.


title: A Fuzzy AHP Approach for Selection of Measuring Instrument for Engineering College Selection

abstract: The Analytic Hierarchy Process (AHP) is a mathematical technique for multi-criteria decision-making. Complex problems or issues involving value or subjective judgments are suitable applications of the AHP approach. ‘Factors considered for the selection of Undergraduate Engineering Institution by the students’, is the research topic of interest. It involves a number of qualitative judgments based on multi-criteria at multi-levels and can be addressed using AHP. Students’ opinions are collected in AHP using pair wise comparisons. As the number of alternatives increases, the number of comparisons increases enormously. Hence the selection of a proper measuring instrument that will reduce the data collection


title: Ultrasound synthetic aperture imaging: monostatic approach

abstract: An ultrasound synthetic aperture imaging method based on a monostatic approach was studied experimentally. The proposed synthetic aperture method offers good dynamical resolution along with fast numerical reconstruction. In this study complex object data were recorded coherently in a two-dimensional hologram using a 3.5 MHz single transducer with a fairly wide-angle beam. Image reconstruction which applies the wavefront backward propagation method and the near-field curvature compensation was performed numerically in a microcomputer using the spatial frequency domain. This approach allows an efficient use of the FFT-algorithms. Because of the simple and fast scanning scheme and the efficient reconstruction algorithms the method can be made real-time. The image quality of the proposed method was studied by evaluating the spatial and dynamical resolution in a waterbath and in a typical tissue-mimicking phantom. The lateral as well as the range resolution (-6 dB) were approximately 1 mm in the depth range of 30-100 mm. The dynamical resolution could be improved considerably when the beam width was made narrower. Although it resulted in a slightly reduced spatial resolution this compromise has to be done for better resolution of low-contrast targets such as cysts. The study showed that cysts as small as 2 mm by diameter could be resolved. >


title: The virtue gap in humor: Exploring benevolent and corrective humor.

abstract: The recent emergence of positive psychology gave rise to the idea to conceptualize humor from a “good character” perspective. Present constructs, however, show a “virtue gap,” and the two concepts of benevolent and corrective humor were developed to fill this gap. The former describes a humorous outlook on life that entails the realistic observations and understanding of human weaknesses (and the imperfection of the world) but also their benevolent humorous treatment. By contrast, corrective humor involves moral-based ridicule—that is, the use of mockery to fight badness and mediocrity. Corrective humor, akin to satire, uses wit to ridicule vices, follies, abuses, and shortcomings with the intent of shaming individuals and groups into improvement. Participants (N = 340) filled in statements assessing the two virtue-related humor concepts, general sense of humor (subsample of n = 144), mockery, and 24 character strengths. As expected, benevolent humor showed positive correlations with most of the 24 character strengths and uniquely related to the strengths of several virtues (justice, temperance, and transcendence) beyond general sense of humor. Corrective humor related most strongly to strengths of the virtues wisdom, courage, and justice, especially once mockery was controlled for. Thus, both constructs capture important virtue-related humor aspects over and above the sense of humor and mockery and are thus suitable for—at least partially—filling the “virtue gap” in humor research. They have the potential to pave the way for developing and investigating further humor constructs that meaningfully relate to strengths and virtues. (PsycINFO Database Record (c) 2016 APA, all rights reserved)


title: Predictive information processing in music cognition. A critical review

abstract: Abstract Expectation and prediction constitute central mechanisms in the perception and cognition of music, which have been explored in theoretical and empirical accounts. We review the scope and limits of theoretical accounts of musical prediction with respect to feature-based and temporal prediction. While the concept of prediction is unproblematic for basic single-stream features such as melody, it is not straight-forward for polyphonic structures or higher-order features such as formal predictions. Behavioural results based on explicit and implicit (priming) paradigms provide evidence of priming in various domains that may reflect predictive behaviour. Computational learning models, including symbolic (fragment-based), probabilistic/graphical, or connectionist approaches, provide well-specified predictive models of specific features and feature combinations. While models match some experimental results, full-fledged music prediction cannot yet be modelled. Neuroscientific results regarding the early right-anterior negativity (ERAN) and mismatch negativity (MMN) reflect expectancy violations on different levels of processing complexity, and provide some neural evidence for different predictive mechanisms. At present, the combinations of neural and computational modelling methodologies are at early stages and require further research.


Addressing Complexities of Machine Learning in Big Data: Principles, Trends and Challenges from Systematical Perspectives


title: Security Viewpoint in a Reference Architecture Model for Cyber-Physical Production Systems

abstract: Cyber-physical Production Systems (CPPS) are one of the technical driving forces behind the transformation of industrial production towards "digital factory of the future" in the context of Industry 4.0. Security is a major concern for such systems as they become more intelligent, interconnected, and coupled with physical devices. For various security activities from security analysis to designing security controls and architecture, a systematic and structured view and presentation of security-related information is required. Based on the draft standard of Reference Architecture Model for Industry 4.0 (RAMI 4.0), we propose a practical approach to establish a security viewpoint in the CPPS reference architecture model. We investigate the feasibility of using an architecture modeling tool to implement the concept and leverage existing work on models of layered architecture. We demonstrate the applicability for security analysis in two example case studies.


title: ECG Monitoring Garment Using Conductive Carbon Paste for Reduced Motion Artifacts

abstract: The heart is a fundamental organ of the human circulatory system and the continuous measurement of electrocardiogram (ECG) signals is of great importance for pre-detection of heart diseases. Dry electrodes that do not require electrolyte gel have been developed for wearable ECG monitoring applications. However, this kind of electrode often introduces motion artifacts because of the high contact impedance between the electrode and skin. We propose a wearable ECG monitoring garment that employs electrodes made of conductive carbon-based paste. This paste is directly applied to the skin and after drying for 5 min, it forms a patch electrode that is detachable and flexible. The contact impedance between the patch electrode and the skin is very low because the paste covers the skin in a conformal manner. The experimental results show that the contact area of the carbon-based paste on the skin replica is almost 100%. At frequencies under 10 Hz, the contact impedance of the patch electrode is of 70.0 kΩ, much lower than the typical 118.7 kΩ impedance of a Ag/AgCl electrode. We also demonstrate that the ECG signals measured using the custom-designed garment and the patch electrodes are very stable even during actions such as walking and running.


Content analysis of online discussion in an applied educational psychology course


title: Understanding the gap between software process practices and actual practice in very small companies

abstract: This paper reports on a grounded theory to study into software developers' use of software development processes in actual practice in the specific context of very small companies. This study was conducted in three very small software product companies located in Ecuador. The data collection was based on semi-structured qualitative interviews with software project managers, focus group with software developers and was supplemented by the literature and document studies. We interviewed two types of participants (managers and developers), so as to ensure that we elicited a holistic perspective of how they approached the software development process in actual practice. The goal was to study what practices are actually used and their opinion and attitude toward the potential adopting of an international standard (ISO/IEC 29110) specifically designed for very small companies. With the collected data, we performed an analysis utilizing grounded theory coding techniques, as this methodology promotes the focus on uncovering the real concerns of the participants. This study highlighted three areas of concern: customer, software product and development tasks coordination and tracking. The findings in this study give an insight toward the work products as they relate to software development process practices in very small companies and the important factors that must be considered to assist project success.


title: Disintegration and Bayesian Inversion, Both Abstractly and Concretely

abstract: The notions of disintegration and Bayesian inversion are fundamental in conditional probability theory. They produce channels, as conditional probabilities, from a joint state, or from an already given channel (in opposite direction). These notions exist in the literature, in concrete situations, but are presented here in abstract graphical formulations. The resulting abstract descriptions are used for proving basic results in conditional probability theory. The existence of disintegration and Bayesian inversion is discussed for discrete probability, and also for measure-theoretic probability --- via standard Borel spaces and via likelihoods. Finally, the usefulness of disintegration and Bayesian inversion is illustrated in several non-trivial examples.


title: Physical Layer Security for Next Generation Wireless Networks: Theories, Technologies, and Challenges

abstract: Physical layer security (PHY-security) takes the advantages of channel randomness nature of transmission media to achieve communication confidentiality and authentication. Wiretap coding and signal processing technologies are expected to play vital roles in this new security mechanism. PHY-security has attracted a lot of attention due to its unique features and the fact that our daily life relies heavily on wireless communications for sensitive and private information transmissions. Compared to conventional cryptography that works to ensure all involved entities to load proper and authenticated cryptographic information, PHY-security technologies perform security functions without considering about how those security protocols are executed. In other words, it does not require to implement any extra security schemes or algorithms on other layers above the physical layer. This survey introduces the fundamental theories of PHY-security, covering confidentiality and authentication, and provides an overview on the state-of-the-art works on PHY-security technologies that can provide secure communications in wireless systems, along with the discussions on challenges and their proposed solutions. Furthermore, at the end of this paper, the open issues are identified as our future research directions.


title: Including Multi-feature Interactions and Redundancy for Feature Ranking in Mixed Datasets

abstract: Feature ranking is beneficial to gain knowledge and to identify the relevant features from a high-dimensional dataset. However, in several datasets, few features by itself might have small correlation with the target classes, but by combining these features with some other features, they can be strongly correlated with the target. This means that multiple features exhibit interactions among themselves. It is necessary to rank the features based on these interactions for better analysis and classifier performance. However, evaluating these interactions on large datasets is computationally challenging. Furthermore, datasets often have features with redundant information. Using such redundant features hinders both efficiency and generalization capability of the classifier. The major challenge is to efficiently rank the features based on relevance and redundance on mixed datasets. In this work, we propose a filter-based framework based on Relevance and Redundancy (RaR), RaR computes a single score that quantifies the feature relevance by considering interactions between features and redundancy. The top ranked features of RaR are characterized by maximum relevance and non-redundance. The evaluation on synthetic and real world datasets demonstrates that our approach outperforms several state-of-the-art feature selection techniques. Code and data related to this chapter are available at: https://doi.org/10.6084/m9.figshare.5418706.


Artistic Rendering of Digital Images Implementation of Texture Transfer Algorithm


title: Reactive Power Control of Three-Phase Grid-Connected PV System During Grid Faults Using Takagi–Sugeno–Kang Probabilistic Fuzzy Neural Network Control

abstract: An intelligent controller based on the Takagi–Sugeno–Kang-type probabilistic fuzzy neural network with an asymmetric membership function (TSKPFNN-AMF) is developed in this paper for the reactive and active power control of a three-phase grid-connected photovoltaic (PV) system during grid faults. The inverter of the three-phase grid-connected PV system should provide a proper ratio of reactive power to meet the low-voltage ride through (LVRT) regulations and control the output current without exceeding the maximum current limit simultaneously during grid faults. Therefore, the proposed intelligent controller regulates the value of reactive power to a new reference value, which complies with the regulations of LVRT under grid faults. Moreover, a dual-mode operation control method of the converter and inverter of the three-phase grid-connected PV system is designed to eliminate the fluctuation of dc-link bus voltage under grid faults. Furthermore, the network structure, the online learning algorithm, and the convergence analysis of the TSKPFNN-AMF are described in detail. Finally, some experimental results are illustrated to show the effectiveness of the proposed control for the three-phase grid-connected PV system.


title: Evaluation Methodology and Control Strategies for Improving Reliability of HEV Power Electronic System

abstract: The reliability prediction of hybrid electric vehicles (HEVs) is of paramount importance for planning, design, control, and operation management of vehicles, since it can provide an objective criterion for comparative evaluation of various configurations and topologies and can be used as an effective tool to improve the design and control of the overall system. This paper presents a mission-profile-dependent simulation model based on MATLAB for quantitatively assessing the reliability of the electric drivetrain of HEVs. This model takes into consideration the variable driving scenarios, dormant mode, electrical stresses, and thermal stresses. Therefore, more reliable and accurate prediction of system reliability has been achieved. The methodology is explained in detail, and the results of reliability assessment based on a series HEV are presented. Based on reliability analysis, two control strategies are proposed to increase the mean time to failure of HEV powertrains: 1) variable dc-link voltage control and 2) hybrid discontinuous pulsewidth modulation scheme. These novel control schemes reduce the power losses and thermal stresses of power converters, and consequently, enhance system reliability. Numerical simulation results verify the benefits of two proposed control strategies in terms of power losses and reliability.


title: Improving Content-Invariance in Gated Autoencoders for 2D and 3D Object Rotation

abstract: Content-invariance in mapping codes learned by GAEs is a useful feature for various relation learning tasks. In this paper we show that the content-invariance of mapping codes for images of 2D and 3D rotated objects can be substantially improved by extending the standard GAE loss (symmetric reconstruction error) with a regularization term that penalizes the symmetric cross-reconstruction error. This error term involves reconstruction of pairs with mapping codes obtained from other pairs exhibiting similar transformations. Although this would principally require knowledge of the transformations exhibited by training pairs, our experiments show that a bootstrapping approach can sidestep this issue, and that the regularization term can effectively be used in an unsupervised setting.


title: JM: An R Package for the Joint Modelling of Longitudinal and Time-to-Event Data

abstract: In longitudinal studies measurements are often collected on different types of outcomes for each subject. These may include several longitudinally measured responses (such as blood values relevant to the medical condition under study) and the time at which an event of particular interest occurs (e.g., death, development of a disease or dropout from the study). These outcomes are often separately analyzed; however, in many instances, a joint modeling approach is either required or may produce a better insight into the mechanisms that underlie the phenomenon under study. In this paper we present the R package JM that fits joint models for longitudinal and time-to-event data.


title: Comparative Study of Linear and Nonlinear Features Used in Imagined Vowels Classification Using a Backpropagation Neural Network Classifier

abstract: Imagined speech recognition is an alternative means to aid individuals with motor disabilities, illnesses and speech disorders, such as the mute and those under the state of coma, to communicate properly through silent communication. Imagined speech or unspoken speech is speech that is only uttered within the mind without the use of any muscle movement. In this study, the proponents compared the effects of linear and nonlinear features in an imagined speech classification model. This model was designed to classify the five vowels namely /a/ /e/ /i/ /o/ and /u/. Electroencephalogram (EEG) signals were measured in each study volunteers while they were imagining the different vowels. Linear and nonlinear features were extracted from the processed EEG signals, and these features were used as inputs to an Artificial Neural Network (ANN) classifier.


title: Comparison of linear, nonlinear, and feature selection methods for EEG signal classification

abstract: The reliable operation of brain-computer interfaces (BCIs) based on spontaneous electroencephalogram (EEG) signals requires accurate classification of multichannel EEG. The design of EEG representations and classifiers for BCI are open research questions whose difficulty stems from the need to extract complex spatial and temporal patterns from noisy multidimensional time series obtained from EEG measurements. The high-dimensional and noisy nature of EEG may limit the advantage of nonlinear classification methods over linear ones. This paper reports the results of a linear (linear discriminant analysis) and two nonlinear classifiers (neural networks and support vector machines) applied to the classification of spontaneous EEG during five mental tasks, showing that nonlinear classifiers produce only slightly better classification results. An approach to feature selection based on genetic algorithms is also presented with preliminary results of application to EEG during finger movement.


title: Cache contention and application performance prediction for multi-core systems

abstract: The ongoing move to chip multiprocessors (CMPs) permits greater sharing of last-level cache by processor cores but this sharing aggravates the cache contention problem, potentially undermining performance improvements. Accurately modeling the impact of inter-process cache contention on performance and power consumption is required for optimized process assignment. However, techniques based on exhaustive consideration of process-to-processor mappings and cycle-accurate simulation are inefficient or intractable for CMPs, which often permit a large number of potential assignments. This paper proposes CAMP, a fast and accurate shared cache aware performance model for multi-core processors. CAMP estimates the performance degradation due to cache contention of processes running on CMPs. It uses reuse distance histograms, cache access frequencies, and the relationship between the throughput and cache miss rate of each process to predict its effective cache size when running concurrently and sharing cache with other processes, allowing instruction throughput estimation.We also provide an automated way to obtain process-dependent characteristics, such as reuse distance histograms, without offline simulation, operating system (OS) modification, or additional hardware. We tested the accuracy of CAMP using 55 different combinations of 10 SPEC CPU2000 benchmarks on a dual-core CMP machine. The average throughput prediction error was 1.57%.


title: Congenital cleft of anterior arch and partial aplasia of the posterior arch of the c1.

abstract: Congenital anomalies in arches of the atlas are rare, and are usually discovered incidentally. However, a very rare subgroup of patients with unique radiographic features is predisposed to transient quadriparesis after minor cervical or head trauma. A 46-year-old male presented with a 2-month history of tremor and hyperesthesia of the lower extremities after experiencing a minor head trauma. He said that he had been quadriplegic for about 2 weeks after that trauma. Radiographs of his cervical spine revealed bilateral bony defects of the lateral aspects of the posterior arch of C1 and a midline cleft within the anterior arch of the atlas. A magnetic resonance imaging revealed an increased cord signal at the C2 level on the T2-weighted sagittal image. A posterior, suboccipital midline approach for excision of the remnant posterior tubercle was performed. The patient showed significant improvement of his motor and sensory functions. Since major neurologic deficits can be produced by a minor trauma, it is crucial to recognize this anomaly.


title: Indoor Human Navigation Systems: A Survey

abstract: Whereas outdoor navigation systems typically rely upon GPS, indoor systems have to rely upon dierent techniques for localizing the user, as GPS signals cannot be received indoors. Over the past decade various indoor navigation systems have been developed. This paper provides a comprehensive overview of existing indoor navigation systems and analyzes the dierent techniques used for: (1) locating the user; (2) planning a path; (3) representing the environment; and (4) interacting with the user. Our survey identies a number of research issues that could facilitate large scale deployment of indoor navigation systems.


title: System Infrastructure for Mobile-Cloud Convergence

abstract: The convergence of mobile computing and cloud computing enables new mobile applications that are both resource-intensive and interactive. For these applications, end-to-end network bandwidth and latency matter greatly when cloud resources are used to augment the computational power and battery life of a mobile device. This dissertation designs and implements a new architectural element called a cloudlet, that arises from the convergence of mobile computing and cloud computing. Cloudlets represent the middle tier of a 3-tier hierarchy, mobile device — cloudlet—cloud, to achieve the right balance between cloud consolidation and network responsiveness. We first present quantitative evidence that shows cloud location can affect the performance of mobile applications and cloud consolidation. We then describe an architectural solution using cloudlets that are a seamless extension of todays cloud computing infrastructure. Finally, we define minimal functionalities that cloudlets must offer above/beyond standard cloud computing, and address corresponding technical challenges.


title: Accurate Deep Representation Quantization with Gradient Snapping Layer for Similarity Search

abstract: Recent advance of large scale similarity search involves using deeply learned representations to improve the search accuracy and use vector quantization methods to increase the search speed. However, how to learn deep representations that strongly preserve similarities between data pairs and can be accurately quantized via vector quantization remains a challenging task. Existing methods simply leverage quantization loss and similarity loss, which result in unexpectedly biased back-propagating gradients and affect the search performances. To this end, we propose a novel gradient snapping layer (GSL) to directly regularize the back-propagating gradient towards a neighboring codeword, the generated gradients are un-biased for reducing similarity loss and also propel the learned representations to be accurately quantized. Joint deep representation and vector quantization learning can be easily performed by alternatively optimize the quantization codebook and the deep neural network. The proposed framework is compatible with various existing vector quantization approaches. Experimental results demonstrate that the proposed framework is effective, flexible and outperforms the state-of-the-art large scale similarity search methods.


title: Multidisciplinary Design Process of a 6-Slot 2-Pole High-Speed Permanent-Magnet Synchronous Machine

abstract: High-speed permanent-magnet synchronous machines (HS PMSMs) are a popular topology among modern electrical machines. Suitable applications for such machines are low-power vacuum pumps, compressors, and chillers. This paper describes a systematic design methodology for an HS PMSM using two case studies. The design process for such high-speed (HS) machines is multidisciplinary and highly iterative due to the complex interaction of the many design variables involved. Consequently, no single optimum solution exists, and multiple possible solutions can meet the customer requirements. Practical solutions should be within acceptable thermal limits, should be energy-efficient, and should be rigid enough to withstand the forces exerted during operation. The proposed design flow is divided into steps that are presented in this paper in the form of a flowchart with emphasis on mechanical aspects. Each step represents a task for a thermal, mechanical, or electrical engineer. The features of each step and the prerequisites for moving to the next step are discussed. The described methodology was implemented in the design of two HS PMSMs. The output performance results of the design flow are compared with measured results of the prototypes. The design process described in this paper provides a straightforward procedure for the multidisciplinary design of HS permanent magnet electrical machines.


title: Overnight therapy? The role of sleep in emotional brain processing. Psychol Bull

abstract: Cognitive neuroscience continues to build meaningful connections between affective behavior and human brain function. Within the biological sciences, a similar renaissance has taken place, focusing on the role of sleep in various neurocognitive processes and, most recently, on the interaction between sleep and emotional regulation. This review surveys an array of diverse findings across basic and clinical research domains, resulting in a convergent view of sleep-dependent emotional brain processing. On the basis of the unique neurobiology of sleep, the authors outline a model describing the overnight modulation of affective neural systems and the (re)processing of recent emotional experiences, both of which appear to redress the appropriate next-day reactivity of limbic and associated autonomic networks. Furthermore, a rapid eye movement (REM) sleep hypothesis of emotional-memory processing is proposed, the implications of which may provide brain-based insights into the association between sleep abnormalities and the initiation and maintenance of mood disturbances.


title: A SiC-based T-type three-phase three-level gridtied inverter

abstract: Due to the high thermal conductivity and the wide band gap, SiC power devices have many advantages against Si power devices such as higher operation temperature, higher breakdown electric field, higher switching speed and less switching loss. In this paper, a SiC-based T-type three-level topology is investigated. Considering the body diode characteristics of SiC power MOSFETs, three kinds of bidirectional switching circuits are presented and compared. One of them, with the lowest conduction loss, is used as the neutral point clamping branch of the T-type three-level inverter. Operation modes and modulation strategy of this inverter are analyzed. A 6 kW prototype using SiC MOSFET is built. The efficiency is tested and compared with a Si-based prototype. Experimental results show that using SiC power devices can improve the conversion efficiency obviously.


title: Cortical networks for working memory and executive functions sustain the conscious resting state in man

abstract: The cortical anatomy of the conscious resting state (REST) was investigated using a meta-analysis of nine positron emission tomography (PET) activation protocols that dealt with different cognitive tasks but shared REST as a common control state. During REST, subjects were in darkness and silence, and were instructed to relax, refrain from moving, and avoid systematic thoughts. Each protocol contrasted REST to a different cognitive task consisting either of language, mental imagery, mental calculation, reasoning, finger movement, or spatial working memory, using either auditory, visual or no stimulus delivery, and requiring either vocal, motor or no output. A total of 63 subjects and 370 spatially normalized PET scans were entered in the meta-analysis. Conjunction analysis revealed a network of brain areas jointly activated during conscious REST as compared to the nine cognitive tasks, including the bilateral angular gyrus, the left anterior precuneus and posterior cingulate cortex, the left medial frontal and anterior cingulate cortex, the left superior and medial frontal sulcus, and the left inferior frontal cortex. These results suggest that brain activity during conscious REST is sustained by a large scale network of heteromodal associative parietal and frontal cortical areas, that can be further hierarchically organized in an episodic working memory parieto-frontal network, driven in part by emotions, working under the supervision of an executive left prefrontal network.