title: Credit Risk Management (CRM) Practices in Commercial Banks of Bangladesh: “A Study on Basic Bank Ltd.”

abstract: This Paper is not only a way for getting acknowledged about the efficiency in managing credit risk of Bangladeshi Banks, but also a conclusive reference for studying how CRM practices helps to increase profitability and long term sustainability of commercial banks. Credit risk management encompasses identification, measurement, matching mitigations, monitoring and control of the credit risk exposures. For conducting this research, I have to collect secondary data relating to the financial status of Basic Bank Ltd.In my analysis I have divulged a comprehensive overview about CRM in different phase of my report. First, I have described about the CRM practice and performance of BBL. Then, I analyze the impact of CRM on financial performance of bank. I have used Ms Excel as well as SPSS software to compare relationship between CRM and banks profitability. After analysis and discussion I have identified some conclusive findings of my research paper.


title: Computational intelligence tools and databases in bioinformatics

abstract: Bioinformatics is overgrowing and has found many new applications which its progress caused the interaction with other fields. Thus presently, the Computational Intelligence based on Internet of Things in bioinformatics is used to archive, search, display, analysis and interpret biological data. Development of bioinformatics and growing need for actuarial science, computer, and programming result in the growth of new Computational Intelligence methods, tools, computer algorithms, and programming solutions. This study confers an extensive survey of the Computational Intelligence (CI) methods based on Big Data and IoT in bioinformatics and reviews some tools and databases of Computational Intelligence for bioinformatics issues such as DNA Sequence Analysis Tools, Analyze & Model 3D Structure, Phylogenetic Analysis, Protein Function Assignment, and Protein Sequence & Structure.


title: Predicting the impacts of climate change on genetic diversity in an endangered lizard species

abstract: Many endangered species persist as a series of isolated populations, with some populations more genetically diverse than others. If climate change disproportionately threatens the most diverse populations, the species’ ability to adapt (and hence its long-term viability) may be affected more severely than would be apparent by its numerical reduction. In the present study, we combine genetic data with modelling of species distributions under climate change to document this situation in an endangered lizard (Eulamprus leuraensis) from montane southeastern Australia. The species is known from only about 40 isolated swamps. Genetic diversity of lizard populations is greater in some sites than others, presumably reflecting consistently high habitat suitability over evolutionary time. Species distribution modelling suggests that the most genetically diverse populations are the ones most at risk from climate change, so that global warming will erode the species’ genetic variability faster than it curtails the species’ geographic distribution.


title: Annotating Discourse Connectives And Their Arguments

abstract: This paper describes a new, large scale discourse-level annotation project – the Penn Discourse TreeBank (PDTB). We present an approach to annotating a level of discourse structure that is based on identifying discourse connectives and their arguments. The PDTB is being built directly on top of the Penn TreeBank and Propbank, thus supporting the extraction of useful syntactic and semantic features and providing a richer substrate for the development and evaluation of practical algorithms. We provide a detailed preliminary analysis of inter-annotator agreement – both the level of agreement and the types of inter-annotator variation.


title: The language network

abstract: Language processing is supported by different regions located in separate parts of the brain. A crucial condition for these regions to function as a network is the information transfer between them. This is guaranteed by dorsal and ventral pathways connecting prefrontal and temporal language-relevant regions. Based on functional brain imaging studies, these pathways’ language functions can be assigned indirectly. Dorsally, one pathway connecting the temporal cortex (TC) and premotor cortex supports speech repetition, another one connecting the TC and posterior Broca's area supports complex syntactic processes. Ventrally, the uncinate fascile and the inferior fronto-occipital fascile subserve semantic and basic syntactic processes. Thus, the available evidence points towards a neural language network with at least two dorsal and two ventral pathways.


title: REVIEW SOCIAL SCIENCE Genesis of Suicide Terrorism

abstract: Contemporary suicide terrorists from the Middle East are publicly deemed crazed cowards bent on senseless destruction who thrive in poverty and ignorance. Recent research indicates they have no appreciable psychopathology and are as educated and economically well-off as surrounding populations. A first line of defense is to get the communities from which suicide attackers stem to stop the attacks by learning how to minimize the receptivity of mostly ordinary people to recruiting organizations.


title: 79 GHz UWB automotive short range radar – Spectrum allocation and technology trends

abstract: Abstract. Automotive UWB (Ultra-Wideband) short range radar (SSR) is on the market as a key technology for novel comfort and safety systems. SiGe based 79 GHz UWB SRR will be a definite candidate for the long term substitution of the 24 GHz UWB SRR. This paper will give an overview of the finished BMBF joint project KOKON and the recently started successing project RoCC, which concentrate on the development of this technology and sensor demonstrators. In both projects, the responsibilities of Daimler AG deal with application based sensor specification, test and evaluation of realized sensor demonstrators. Recent UWB SRR frequency regulation approaches and activitites will be introduced. Furthermore, some first results of Daimler activities within RoCC will be presented, dealing with the packaging and operation of these sensors within the complex car environment.


title: On Development of 100-Gram-Class Spacecraft for Swarm Applications

abstract: A novel space system architecture is proposed, which would enable 100-g-class spacecraft to be flown as swarms (100 s–1000 s) in low Earth orbit. Swarms of Silicon Wafer Integrated Femtosatellites (SWIFT) present a paradigm-shifting approach to distributed spacecraft development, missions, and applications. Potential applications of SWIFT swarms include sparse aperture arrays and distributed sensor networks. New swarm array configurations are introduced and shown to achieve the effective sparse aperture driven from optical performance metrics. A system cost analysis based on this comparison justifies deploying a large number of femtosatellites for sparse aperture applications. Moreover, this paper discusses promising guidance, control, and navigation methods for swarms of femtosatellites equipped with modest sensing and control capabilities.


title: Data Modeling and Data Analytics: A Survey from a Big Data Perspective

abstract: These last years we have been witnessing a tremendous growth in the volume and availability of data. This fact results primarily from the emergence of a multitude of sources (e.g. computers, mobile devices, sensors or social networks) that are continuously producing either structured, semi-structured or unstructured data. Database Management Systems and Data Warehouses are no longer the only technologies used to store and analyze datasets, namely due to the volume and complex structure of nowadays data that degrade their performance and scalability. Big Data is one of the recent challenges, since it implies new requirements in terms of data storage, processing and visualization. Despite that, analyzing properly Big Data can constitute great advantages because it allows discovering patterns and correlations in datasets. Users can use this processed information to gain deeper insights and to get business advantages. Thus, data modeling and data analytics are evolved in a way that we are able to process huge amounts of data without compromising performance and availability, but instead by “relaxing” the usual ACID properties. This paper provides a broad view and discussion of the current state of this subject with a particular focus on data modeling and data analytics, describing and clarifying the main differences between the three main approaches in what concerns these aspects, namely: operational databases, decision support databases and Big Data technologies.


title: Machine printed text and handwriting identification in noisy document images

abstract: In this paper, we address the problem of the identification of text in noisy document images. We are especially focused on segmenting and identifying between handwriting and machine printed text because: 1) Handwriting in a document often indicates corrections, additions, or other supplemental information that should be treated differently from the main content and 2) the segmentation and recognition techniques requested for machine printed and handwritten text are significantly different. A novel aspect of our approach is that we treat noise as a separate class and model noise based on selected features. Trained Fisher classifiers are used to identify machine printed text and handwriting from noise and we further exploit context to refine the classification. A Markov Random Field-based (MRF) approach is used to model the geometrical structure of the printed text, handwriting, and noise to rectify misclassifications. Experimental results show that our approach is robust and can significantly improve page segmentation in noisy document collections.


title: Effects of prediction and contextual support on lexical processing: Prediction takes precedence

abstract: Readers may use contextual information to anticipate and pre-activate specific lexical items during reading. However, prior studies have not clearly dissociated the effects of accurate lexical prediction from other forms of contextual facilitation such as plausibility or semantic priming. In this study, we measured electrophysiological responses to predicted and unpredicted target words in passages providing varying levels of contextual support. This method was used to isolate the neural effects of prediction from other potential contextual influences on lexical processing. While both prediction and discourse context influenced ERP amplitudes within the time range of the N400, the effects of prediction occurred much more rapidly, preceding contextual facilitation by approximately 100 ms. In addition, a frontal, post-N400 positivity (PNP) was modulated by both prediction accuracy and the overall plausibility of the preceding passage. These results suggest a unique temporal primacy for prediction in facilitating lexical access. They also suggest that the frontal PNP may index the costs of revising discourse representations following an incorrect lexical prediction.


title: Confidence-based policy learning from demonstration using Gaussian mixture models

abstract: We contribute an approach for interactive policy learning through expert demonstration that allows an agent to actively request and effectively represent demonstration examples. In order to address the inherent uncertainty of human demonstration, we represent the policy as a set of Gaussian mixture models (GMMs), where each model, with multiple Gaussian components, corresponds to a single action. Incrementally received demonstration examples are used as training data for the GMM set. We then introduce our confident execution approach, which focuses learning on relevant parts of the domain by enabling the agent to identify the need for and request demonstrations for specific parts of the state space. The agent selects between demonstration and autonomous execution based on statistical analysis of the uncertainty of the learned Gaussian mixture set. As it achieves proficiency at its task and gains confidence in its actions, the agent operates with increasing autonomy, eliminating the need for unnecessary demonstrations of already acquired behavior, and reducing both the training time and the demonstration workload of the expert. We validate our approach with experiments in simulated and real robot domains.


title: Suicidal Behavior and Psychotropic Medications for Adolescents: An Uneasy Association, as Yet Not Clarified

abstract: Despite considerable recent attention and wide-scale interventions by regulatory authorities that have changed drug usage patterns, the possible relationship between psychotropic pharmacotherapy and suicidal behavior among children and adolescents remains unclear. Confounding by diagnosis adds to confusion in the interpretation of the relationship between antidepressant use and suicidal behavior among young people. Recent research suggests that antidepressants may be protective against early readmission after hospitalization for suicide attempts or ideation, but that psychotropic polypharmacy (although common) may be associated with increased risk of rehospitalization. There remains an urgent need for high-quality, ongoing research into these clinical dilemmas.


title: The Influence of Pathological Narcissism On Emotional and Motivational Responses to Negative Events: The Roles of Visibility and Concern about Humiliation

abstract: The present study examined the associations between pathological forms of narcissism and responses to scenarios describing private or public negative events. This was accomplished using a randomized twowave experimental design with 600 community participants. The grandiose form of pathological narcissism was associated with increased negative affect and less forgiveness for public offenses, whereas the vulnerable form of pathological narcissism was associated with increased negative affect following private negative events. Concerns about humiliation mediated the association of pathological narcissism with increased negative affect but not the association between grandiose narcissism and lack of forgiveness for public offenses. These findings suggest that pathological narcissism may promote maladaptive responses to negative events that occur in private (vulnerable narcissism) or public (grandiose narcissism).


title: Extracting Semantic Indoor Maps from Occupancy Grids

abstract: Abstract The primary challenge for any autonomous system operating in realistic, rather unconstrained scenarios is to manage the complexity and uncertainty of the real world. While it is unclear how exactly humans and other higher animals master these problems, it seems evident, that abstraction plays an important role. The use of abstract concepts allows us to define the system behavior on higher levels. In this paper we focus on the semantic mapping of indoor environments. We propose a method to extract an abstracted floor plan from typical grid maps using Bayesian reasoning. The result of this procedure is a probabilistic generative model of the environment defined over abstract concepts. It is well suited for higher-level reasoning and communication purposes. We demonstrate the effectiveness of the approach using real-world data.


title: Prototype CNC machine design

abstract: The Computer Numeric Control (CNC) is a technology which aims to generate, parse and execute sequential actions that describes the behavior of the end effector. This article reports the development small sized prototype CNC machine, based on a modular system with the capability of communication through USB. It is also reported the development of the basic electronic devices to run a CNC machine and proposes, as well, a software developed in LabVIEW to establish the communication between the machine and the computer. The main objective of this work is the development of a machine which allows future researches on the process performance, on educational purposes, the demonstration the parts involved on the CNC machine.


title: DAvinCi: A cloud computing framework for service robots

abstract: We propose DAvinCi, a software framework that provides the scalability and parallelism advantages of cloud computing for service robots in large environments. We have implemented such a system around the Hadoop cluster with ROS (Robotic Operating system) as the messaging framework for our robotic ecosystem. We explore the possibilities of parallelizing some of the robotics algorithms as Map/Reduce tasks in Hadoop. We implemented the FastSLAM algorithm in Map/Reduce and show how significant performance gains in execution times to build a map of a large area can be achieved with even a very small eight-node Hadoop cluster. The global map can later be shared with other robots introduced in the environment via a Software as a Service (SaaS) Model. This reduces the burden of exploration and map building for the new robot and minimizes it's need for additional sensors. Our primary goal is to develop a cloud computing environment which provides a compute cluster built with commodity hardware exposing a suite of robotic algorithms as a SaaS and share data co-operatively across the robotic ecosystem.


From Concepts to Curricula : Content Exploration , Generation , and Evaluation


title: Crossing Minimization in Weighted Bipartite Graphs ⋆

abstract: Given a bipartite graph G = ( L 0 , L 1 , E ) and a fixed ordering of the nodes in L 0 , the problem of finding an ordering of the nodes in L 1 that minimizes the number of crossings has received much attention in literature. The problem is NP-complete in general and several practically efficient heuristics and polynomial-time algorithms with a constant approximation ratio have been suggested. We generalize the problem and consider the version where the edges have nonnegative weights. Although this problem is more general and finds specific applications in automatic graph layout problems similar to those of the unweighted case, it has not received as much attention. We provide a new technique that efficiently approximates a solution to this more general problem within a constant approximation ratio of 3. In addition we provide appropriate generalizations of some common heuristics usually employed for the unweighted case and compare their performances.


title: Video Paragraph Captioning Using Hierarchical Recurrent Neural Networks

abstract: We present an approach that exploits hierarchical Recurrent Neural Networks (RNNs) to tackle the video captioning problem, i.e., generating one or multiple sentences to describe a realistic video. Our hierarchical framework contains a sentence generator and a paragraph generator. The sentence generator produces one simple short sentence that describes a specific short video interval. It exploits both temporal- and spatial-attention mechanisms to selectively focus on visual elements during generation. The paragraph generator captures the inter-sentence dependency by taking as input the sentential embedding produced by the sentence generator, combining it with the paragraph history, and outputting the new initial state for the sentence generator. We evaluate our approach on two large-scale benchmark datasets: YouTubeClips and TACoS-MultiLevel. The experiments demonstrate that our approach significantly outperforms the current state-of-the-art methods with BLEU@4 scores 0.499 and 0.305 respectively.


title: Planes Detection for Robust Localization and Mapping in RGB-D SLAM Systems

abstract: This paper describes an extension of the popular simultaneous localisation and mapping system (RGB-D SLAM) introduced by Endres et al. In [2]. RGB-D SLAM uses a moving RGB-D sensor (i.e. A Kinect) to incrementally produce a graph-based camera pose trajectory along with a global 3D map of the environment composed of potentially millions of 3D points. The goal here is to produce a global 3D map which is not only composed of millions of 3D points but also 3D planes as indoor scenes are mostly composed of planar features such as floor, walls, desks and other furnitures. These planes are directly detected within the depth images provided by the sensor and therefore tend to "regularize" the noise and missing data in depth images during the whole RGB-D SLAM process. This process represents a first step towards a more semantic approach of RGB-D SLAM.


title: Adaptive Control of Servo Motor by MRAC Method

abstract: This paper describes the design of a multi input multi output (MIMO) non-linear Model Reference Adaptive controller (MRAC) system for the digital servo motor. The complete design is developed determining the rate of gradient adaptation to MRAC in this paper. The MIT rule is used in the original MRAC. The main goal is to follow the desired position set as square wave. The important role of computer aided- design tools is illustrated using MATLAB and associated toolboxes. The control algorithm is implemented using a Pentium computer running the lab-tech notebook control program. The adaptive controlled system satisfactorily controls set point changes in velocity and position.


title: Search Result Diversification Based on Hierarchical Intents

abstract: A large percentage of queries issued to search engines are broad or ambiguous. Search result diversification aims to solve this problem, by returning diverse results that can fulfill as many different information needs as possible. Most existing intent-aware search result diversification algorithms formulate user intents for a query as a flat list of subtopics. In this paper, we introduce a new hierarchical structure to represent user intents and propose two general hierarchical diversification models to leverage hierarchical intents. Experimental results show that our hierarchical diversification models outperform state-of-the-art diversification methods that use traditional flat subtopics.


Replication and Data Placement in Distributed Key-Value Stores


title: Towards an IT Service Lifecycle Management (ITSLM) Concept

abstract: Information Technology (IT) usage in enterprises has evolved over the last years. This led to today's complex, heterogeneous, and dynamic IT system landscapes that support business processes in enterprises. To manage these landscapes, the IT Service Management (ITSM) concept is gaining more importance in today's business and research. Studies demonstrate that introducing ITSM standards lead to positive effects, such as improved customer-orientation as well as efficiency and transparency of IT support, which justify the costs of implementation. However, companies still face difficulties in deciding which processes to be implement (first), and to which extent. Questions like: "How can the currently applied ITSM be adapted or extended when new business-related or technological challenges appear?" arise. Goods producing companies started early relying on Product Lifecycle Management (PLM). PLM delivers a solid means to define, discuss, analyze, and better standardize value creation processes. With PLM in mind, we propose a concept to adopt and further develop it towards IT Service Lifecycle Management (ITSLM) suitable for the IT services provider environment. After introducing ITSLM, analyzing its processes, and its correlation to PLM, we design ITSLM as a model-driven process support. The selection of appropriate models with different complexity can be used to implement and adapt standard supporting tasks with minimum effort. Two use cases are detailed: fault-tolerance design optimization as well as automation of IT service provisioning. In these areas, suitable model complexity levels, computer-aided task support as well as the knowledge transfer among these models are discussed.


title: Murder of the Newborn: A Psychiatric Review of Neonaticide

abstract: The author believes that the killing of a newborn baby is a separate entity from other filicides, differing in regard to the diagnosis, motives, and legal disposition of the murderer. Whereas most filicides are committed for "altruistic" reasons, most neonaticides are carried out simply because the child is not wanted. The author notes the different psychological characterisctics of mothers who commit these two crimes. Legal considerations and the present status of neonaticide are also discussed.


title: Max-Margin Deep Generative Models for (Semi-)Supervised Learning

abstract: Deep generative models (DGMs) can effectively capture the underlying distributions of complex data by learning multilayered representations and performing inference. However, it is relatively insufficient to boost the discriminative ability of DGMs. This paper presents max-margin deep generative models (mmDGMs) and a class-conditional variant (mmDCGMs), which explore the strongly discriminative principle of max-margin learning to improve the predictive performance of DGMs in both supervised and semi-supervised learning, while retaining the generative capability. In semi-supervised learning, we use the predictions of a max-margin classifier as the missing labels instead of performing full posterior inference for efficiency; we also introduce additional max-margin and label-balance regularization terms of unlabeled data for effectiveness. We develop an efficient doubly stochastic subgradient algorithm for the piecewise linear objectives in different settings. Empirical results on various datasets demonstrate that: (1) max-margin learning can significantly improve the prediction performance of DGMs and meanwhile retain the generative ability; (2) in supervised learning, mmDGMs are competitive to the best fully discriminative networks when employing convolutional neural networks as the generative and recognition models; and (3) in semi-supervised learning, mmDCGMs can perform efficient inference and achieve state-of-the-art classification results on several benchmarks.


title: Investigative study of planar array ambiguities based on "hyperhelical" parameterization

abstract: In this paper, a novel approach is proposed for the calculation of ambiguous generator sets of directions for planar arrays. This approach is based on the concept of hyperhelical curves and their equivalent linear arrays, which provide a vehicle for investigating planar array ambiguities by adopting and extending techniques proposed for linear arrays. Thus, ambiguous generator sets of directions of constant azimuth and different elevation are initially calculated, and the concept of ambiguous generator lines is introduced. Then, by using cone-angle parametrization, ambiguities associated with directions of different azimuth and different elevation are investigated.


title: Secret sharing made short

abstract: A well-known fact in the theory of secret sharing schemes is that shares must be of length at least as the secret itself. However, the proof of this lower bound uses the notion of information theoretic secrecy. A natural (and very practical) question is whether one can do better for secret sharing if the notion of secrecy is computational, namely, against resource bounded adversaries. In this note we observe that, indeed, one can do much better in the computational model (which is the one used in most applications).We present an m-threshold scheme, where m shares recover the secret but m - 1 shares give no (computational) information on the secret, in which shares corresponding to a secret S are of size |S|/m plus a short piece of information whose length does not depend on the secret size but just in the security parameter. (The bound of |S|/m is clearly optimal if the secret is to be recovered from m shares). Therefore, for moderately large secrets (a confidential file, a long message, a large data base) the savings in space and communication over traditional schemes is remarkable.The scheme is very simple and combines in a natural way traditional (perfect) secret sharing schemes, encryption, and information dispersal. It is provable secure given a secure (e.g., private key) encryption function.


title: A review on sample entropy applications for the non-invasive analysis of atrial fibrillation electrocardiograms

abstract: Abstract The application of non-linear metrics to physiological signals is a valuable tool because “hidden information” related to underlying mechanisms can be obtained. In this respect, approximate entropy (ApEn) is the most popular non-linear regularity index that has been applied to physiological time series. However, ApEn presents some shortcomings, such as bias, relative inconsistency and dependence on the sample length. A modification of ApEn, named sample entropy (SampEn), was introduced to overcome these deficiencies. Recently, in the context of electrocardiography, SampEn has been applied to study non-invasively atrial fibrillation (AF), which is the most common arrhythmia encountered in clinical practice with unknown mechanisms provoking its onset and termination. Useful clinical information, that could help for a better understanding of AF mechanisms, has been obtained through the application of SampEn to electrocardiographic (ECG) recordings. This work reviews its application in the context of non-invasive analysis of AF. During this arrhythmia, atrial and ventricular components can be regarded as unsynchronized activities, whereby, the application of SampEn to the analysis of each component will be described separately. In first place, clinical challenges in which SampEn has been successfully applied to estimate AF organization from the atrial activity pattern are presented. The AF organization study can provide information on the number of active reentries, which can help to improve AF treatment and to take the appropriate decisions on its management. Next, the heart rate variability study via SampEn, to characterize ventricular response and predict AF onset, is described. Through the aforementioned applications it is remarked throughout this review that SampEn can be considered as a very promising and useful tool towards the non-invasive understanding of AF.


title: Aeolian sediment transport over vegetation canopies: A wind tunnel study with live plants

abstract: Abstract This wind tunnel study examines aeolian sediment transport in live plant canopies, whereas most previous studies have used model plants for this purpose. Experiments were performed with three canopy densities of Perennial Ryegrass ( Lolium perenne ) and with bare sand surfaces. The results suggest that both total sediment mass flux Q and PM 10 concentration in the air decreased exponentially with increasing canopy density. In the large-density canopy (frontal area index λ = 0.58), Q and PM 10 concentration were reduced to 0.01% and 0.4%, respectively, compared to the unplanted configuration. In the medium-density canopy ( λ = 0.16), Q and PM 10 concentration were reduced to 6.6% and 48.5%. In the small-density canopy ( λ = 0.03), however, Q and PM 10 concentration were increased to 117.5% and 145.6%. This is attributed to elevated shear stress on the sand bed caused by flow acceleration around the tussocks and vortical structures in their lee. Furthermore, the grasses were observed to trigger erosion by oscillating movements at the ground surface. It was also found that the vertical profiles of sediment mass flux in the medium- and large-density canopy strongly deviated from the exponential decay curve of the unplanted configuration, showing a local maximum at approximately twice the canopy height.


title: A Java API for advanced faults management

abstract: The paper proposes an alternative for modeling managed resources using Java and telecommunication network management standards. It emphasizes functions related to fault management, namely: diagnostic testing and performance monitoring. Based on Java management extension (JMX/sup TM/), specific extensions are proposed to facilitate diagnostic testing and performance measurements implementation. The new API also called Java fault management extension (JFMX) consists of managed objects that model real resources being tested or monitored and support objects defined for the need of diagnostic testing and performance measurements. The paper discusses four Java implementations of a 3-tier client/server scenario focusing on the "SystemUnderTest" package of the new API to instrument a minimalist managed system scenario. These implementations are respectively built on top of the following Java based communication infrastructures: JMX/JFMX, RMI, CORBA/Java, and Voyager/sup TM/. The paper extends the Voyager implementation with JMX/JFMX and uses their dynamic and advanced features to provide a highly efficient solution. The later implementation also uses the mobile agent paradigm to overcome well-known limitations of the RPC based implementations.


title: Im2Calories: Towards an Automated Mobile Vision Food Diary

abstract: We present a system which can recognize the contents of your meal from a single image, and then predict its nutritional contents, such as calories. The simplest version assumes that the user is eating at a restaurant for which we know the menu. In this case, we can collect images offline to train a multi-label classifier. At run time, we apply the classifier (running on your phone) to predict which foods are present in your meal, and we lookup the corresponding nutritional facts. We apply this method to a new dataset of images from 23 different restaurants, using a CNN-based classifier, significantly outperforming previous work. The more challenging setting works outside of restaurants. In this case, we need to estimate the size of the foods, as well as their labels. This requires solving segmentation and depth / volume estimation from a single image. We present CNN-based approaches to these problems, with promising preliminary results.


title: Table Detection Using Deep Learning

abstract: Table detection is a crucial step in many document analysis applications as tables are used for presenting essential information to the reader in a structured manner. It is a hard problem due to varying layouts and encodings of the tables. Researchers have proposed numerous techniques for table detection based on layout analysis of documents. Most of these techniques fail to generalize because they rely on hand engineered features which are not robust to layout variations. In this paper, we have presented a deep learning based method for table detection. In the proposed method, document images are first pre-processed. These images are then fed to a Region Proposal Network followed by a fully connected neural network for table detection. The proposed method works with high precision on document images with varying layouts that include documents, research papers, and magazines. We have done our evaluations on publicly available UNLV dataset where it beats Tesseract's state of the art table detection system by a significant margin.


title: Interaction between players of mobile phone game with augmented reality (AR) interface

abstract: The main aim of our work is to explore the social and gaming attributes of multi-user collaborative mobile phone games. For these purposes, considering the performances of the contemporary converged mobile phones, we have developed and augmented reality (AR) TeamTwixT game for mobile phones. The game is characterized by shared physical space that promotes physical awareness among co-located players and provides a social user experience that leverages the desirable features of both traditional game forms such as board games and that of modern video games. We have evaluated the social interaction between team players, system performances, user interface performances by adopting the Fitts' law and the usability of mobile phones for collaborative AR. The results have shown that, despite the small screen and limited input capabilities mobile phones and 3D AR technology can be used to naturally enhance the interaction of co-located players and increase their confidence in respect to other gaming conditions.


title: Deep Learning of Image Features from Unlabeled Data for Multiple Sclerosis Lesion Segmentation

abstract: A new automatic method for multiple sclerosis (MS) lesion segmentation in multi-channel 3D MR images is presented. The main novelty of the method is that it learns the spatial image features needed for training a supervised classifier entirely from unlabeled data. This is in contrast to other current supervised methods, which typically require the user to preselect or design the features to be used. Our method can learn an extensive set of image features with minimal user effort and bias. In addition, by separating the feature learning from the classifier training that uses labeled (pre-segmented data), the feature learning can take advantage of the typically much more available unlabeled data. Our method uses deep learning for feature learning and a random forest for supervised classification, but potentially any supervised classifier can be used. Quantitative validation is carried out using 1450 T2-weighted and PD-weighted pairs of MRIs of MS patients, with 1400 pairs used for feature learning (100 of those for labeled training), and 50 for testing. The results demonstrate that the learned features are highly competitive with hand-crafted features in terms of segmentation accuracy, and that segmentation performance increases with the amount of unlabeled data used, even when the number of labeled images is fixed.


title: Happy as a lark: Morning-type younger and older adults are higher in positive affect.

abstract: There are substantial age and individual differences in chronotype, or patterns of sleep/wake activity and energy levels that are tied to time of day and governed by internal circadian and sleep drives (Horne & Ostberg, 1977; May & Hasher, 1998; Mongrain, Carrier, & Dumont, 2006; Roenneberg et al., 2007). Morning types, or “larks,” wake up early, plan activities early in the day and tend to retire early in the evening. In contrast, evening types, or “owls,” awake later and are often active until late at night. Of course, many individuals fall between these two extremes (Horne & Ostberg, 1977). Research on young adults suggests that larks and owls differ in terms of well-being and susceptibility to psychiatric illness. For example, morningness is associated with a more stable personality (DeYoung, Hasher, Djikic, Criger, & Peterson, 2007) and greater subjective well-being (Randler, 2008). In contrast, eveningness is associated with increased susceptibility to depression (Drennan, Klauber, Kripke, & Goyette, 1991; Kitamura et al., 2010) and increased alcohol and stimulant use (Wittmann, Dinich, Merrow, & Roenneberg, 2006). Evening types are also more likely to report being in fair or poor general health (Paine, Gander, & Travier, 2006). ::: ::: Central to these differences in well-being and psychopathology may be variations in emotional state: Morningness among younger adults is associated with higher positive affect across the day (Clark et al., 1989; Hasler, Allen, Sbarra, Bootzin, & Bernert, 2010). Morning types also score higher on measures of energy-alertness and lower on tiredness compared with evening types (Froberg, 1977). In contrast, negative affect does not appear to vary with chronotype (Clark et al., 1989; Hasler et al., 2010). Thus, existing evidence suggests an association between chronotype and affect, with morning types reporting greater overall experience of emotions associated with positive activation, including excitement, cheerfulness, and alertness, compared with individuals with later time of day preferences. ::: ::: This conclusion, however, is based solely on data from younger adults, and may be limited because the individuals in this age range show very different time of day preferences compared to individuals of other ages. While morning chronotypes are most common during childhood, there are more evening types in adolescence, reaching a peak at around age 19 in women and 21 in men (Kim, Dueker, Hasher, & Goldstein, 2002; Roenneberg et al., 2007). Thereafter, there are greater morningness tendencies in each subsequent age group, such that by age 60, a majority of individuals report feeling at their best in the morning (May & Hasher, 1998; Mecacci, Zani, Rocchetti, & Lucioli, 1986). These chronotype differences are thought to be linked to age-dependent changes in the concentration and timing of certain hormones, including cortisol and growth hormone, which influence the timing and quality of sleep (Roenneberg et al., 2007; Van Cauter, Leproult, & Plat, 2000). Although longitudinal evidence is lacking, retrospective self-assessments of chronotype for various stages in development support the pattern of cross-sectional differences (Roenneberg et al., 2007). Thus, older adults (ages 60 – 80) markedly differ from younger adults in terms of chronotype; their sleep–wake times are shifted earlier as is their preferred time for activities. ::: ::: Given substantial age-related differences in chronotype patterns, it is unknown whether the relationship between morningness and emotional state observed in younger adults also holds for older adults. There is evidence that positive affect and subjective well-being are higher in older adulthood (Carstensen et al., 2011; Mroczek & Kolarz, 1998; Stone, Schwartz, Broderick, & Deaton, 2010). Considering the association between morningness and age, this raises an intriguing possibility: Reports of age differences in positive affect may be tied to the greater proportion of morning-type individuals age 60 and above. In addition, given previous evidence that evening-type individuals are more likely to report being in poorer health (Paine et al., 2006), it is also possible that chronotype may contribute to individual differences in older adults’ subjective health ratings. Subjective health is an important component of well-being and is known to be a strong predictor of objective health outcomes and mortality in older people (Idler & Benyamini, 1997). ::: ::: To address the question of whether the association between morningness and well-being exists in older adults, we obtained measures of chronotype, affect and subjective health in both younger (ages 17–38) and older adults (ages 59 –79). Because older adults report both improved positive affect and a stronger tendency toward morningness, we also tested whether age differences in chronotype partially explain age-related differences in well-being.


title: The SWAL–QOL and SWAL–CARE Outcomes Tool for Oropharyngeal Dysphagia in Adults: III. Documentation of Reliability and Validity

abstract: Advances in the measurement of swallowing physiologic parameters have been clinician-driven, as has the development of intervention techniques to modify swallowing pathophysiology. However, a critical element to determining the success of such efforts will be established by the patients themselves. We conceptualized, developed, and validated the SWAL-QOL, a 93-item quality-of-life and quality-of-care outcomes tool for dysphagia researchers and clinicians. With 93 items, the SWAL-QOL was too long for practical and routine use in clinical research and practice. We used an array of psychometric techniques to reduce the 93-item instrument into two patient-centered outcomes tools: (1) the SWAL-QOL, a 44-item tool that assesses ten quality-of-life concepts, and (2) the SWAL-CARE, a 15-item tool that assesses quality of care and patient satisfaction. All scales exhibit excellent internal-consistency reliability and short-term reproducibility. The scales differentiate normal swallowers from patients with oropharyngeal dysphagia and are sensitive to differences in the severity of dysphagia as clinically defined. It is intended that the standardization and publication of the SWAL-QOL and the SWAL-CARE will facilitate their use in clinical research and clinical practice to better understand treatment effectiveness as a critical step toward improving patients' quality of life and quality of care.


title: Morphometric analysis of iatrogenic breast asymmetry secondary to operative breast shape changes in thoracic adolescent idiopathic scoliosis

abstract: Purpose ::: Spontaneous surgical alterations of the distorted surface shape of thoracic cage in adolescent idiopathic scoliosis (AIS) may relocate and remodel the bilateral breast mounds. The purpose of this study was to analyze the influence of scoliosis correction surgery on female breast morphology and to identify the risk factors for iatrogenic breast asymmetry secondary to operative breast shape changes in AIS.


title: Asynchronous Stochastic Approximation and Q-Learning

abstract: We provide some general results on the convergence of a class of stochastic approximation algorithms and their parallel and asynchronous variants. We then use these results to study the Q-learning algorithm, a reinforcement learning method for solving Markov decision problems, and establish its convergence under conditions more general than previously available.


title: A new channel assignment algorithm for wireless mesh networks

abstract: Purpose – Wireless mesh networks (WMNs) have evolved quickly during the last several years. They are widely used in a lot of fields. Channel allocation provides basic means to guarantee mesh networks’ good performance such as efficient routing. The purpose of this paper is to study channel allocation in mesh networks.Design/methodology/approach – First, the papers in channel allocation fields are surveyed, and then the limitations in existing methods noted. Graph theory is used to find a better model to represent the problem and algorithms are proposed based on this model. Simulation proved that algorithms are better than the previous conflict graph‐based approaches.Findings – The paper analyzes the conflict graph‐based model and finds its limitations, then proposes a bipartite graph‐based model. Algorithms were devised based on this model. Simulation results illustrate that the algorithms can reduce the starvation ratio and improve the bandwidth utilization, compared with previous conflict graph‐based al...


Genetic influences on impulsivity, risk taking, stress responsivity and vulnerability to drug abuse and addiction


Query Expansion using Association Matrix for Improved Information Retrieval Performance


title: Physical exercise facilitates motor processes in simple reaction time performance: An electromyographic analysis

abstract: The aim of the current study was to assess the effects of physical exercise on simple reaction time performance. Participants performed a simple reaction time task twice, one time during physical exercise and another time without exercise. Electromyographic signals were recorded from the thumb of the responding hand to fraction reaction time in pre-motor and motor time. The results showed that exercise shortened motor time but failed to affect pre-motor time. This pattern of findings is consistent with previous studies examining the effects of physical exercise on choice reaction time.


title: Immunity Measurement and Modeling of an ADC Embedded in a Microcontroller Using RFIP Technique

abstract: This paper presents a comprehensive method for the integrated circuits (ICs) immunity characterization. The resistive RF injection probe (RFIP) method is used to study the immunity of an analog-to-digital converter (ADC) embedded in a microcontroller. The provided set of immunity parameters is then exploited to construct an immunity model of the ADC according to the Integrated Circuits Immunity Model-Conducted Immunity (ICIM-CI) model. Despite some limitations, measurement and modeling results show a good potential of this method for a better understanding of ICs immunity.


title: Theory of mind

abstract: What is ‘theory of mind’? Maxi eats half his chocolate bar and puts the rest away in the kitchen cupboard. Then he goes out to play in the sun. Meanwhile Maxi’s mother comes into the kitchen, opens the cupboard and sees the chocolate bar. She puts it in the fridge. When Maxi comes back into the kitchen, where will he look for his chocolate bar? The answer to this question will seem obvious. First, Maxi doesn’t know that his mother has moved the chocolate. Second, Maxi still believes, falsely, that his chocolate is in the cupboard. That is why he looks in the cupboard. If this is how you answered the question then you have a ‘theory of mind’. We naturally explain people’s behavior on the basis of their minds: their knowledge, their beliefs and their desires, and we know that when there is a conflict between belief and reality it is the persons’ belief, not the reality that will determine their behavior. Explaining behavior in this way is called ‘having a theory of mind’ or ‘having an intentional stance.’Where will Maxi look for his chocolate? (From the collection of Uta Frith.)View Large Image | View Hi-Res Image | Download PowerPoint SlideWhat is the advantage of having a theory of mind? Through having a theory of mind we can recognize that another person’s knowledge is different from our own. I know what’s behind the rock, but he doesn’t, because, from where he is, he cannot see that there is a scorpion. Having a theory of mind allows us to manipulate other people’s behavior by manipulating their beliefs. If he is my friend I can warn him about the scorpion. If he is my enemy I can tell him it is safe. This latter is called tactical deception or Machiavellianism. Human interactions predominantly involve the dissemination of true or false knowledge for good or for ill.Who has a theory of mind? Up to the age of about five years, a child told the story of Maxi and his mother will say confidently that Maxi will look for his chocolate in the fridge. It is as if they assumed that what they know to be true everyone else knows too. Nevertheless, even three-year-olds look first at the cupboard when the question is asked, and even 15-month-olds can be shown to have an inkling of what is going on; their eye gaze pattern shows that they are surprised if Maxi looks in the fridge. But only from age five or so do children show full understanding of the situation and become able to explain exactly why Maxi has a false belief.Children with autism have a specific problem with theory of mind tasks. They expect Maxi to look for his chocolate in the fridge. They reach a mental age of about 10 years before they achieve an understanding of the Maxi task. More complex problems that involve white lies or double bluff take them even longer to learn, and they may never grasp them fully. Theory of mind difficulties can also be acquired through brain damage in frontal cortex or in the region of the temporo-parietal junction.From field studies there are accounts of a range of animals using tactical deception. But there is still argument over whether even chimpanzees show evidence of this in controlled experiments. The current view is that chimpanzees may have a rudimentary theory of mind, but monkeys (and other animals) probably do not.What is so important about deception and false belief in the study of theory of mind? Having a theory of mind enables many important human interactions other than deception, in particular teaching. But deception is important in the study of theory of mind because of its association with false beliefs. If Maxi’s belief about his chocolate were true — it was still in the cupboard — then you can correctly report where Maxi will look either by basing this prediction on what Maxi believes (because you have a theory of mind) or by basing this prediction on where the chocolate really is (not requiring having a theory of mind). Thus, when successfully solving false belief tasks, where there is a conflict between the false belief and the true state of affairs, we can deduce that theory of mind is engaged.How is theory of mind possible? In order to explain people’s behavior on the basis of their minds, we need to have some idea of what is in their minds. The ability to acquire knowledge about other peoples’ beliefs and desires is called ‘mentalizing’ or ‘mind reading’. Our understanding of the mechanisms underlying this ability remains rudimentary. In everyday speech we frequently explain behavior in terms of mental states. Maxi will look in the cupboard because that’s where he believes his chocolate is and because he wants to eat it. Maxi doesn’t know the chocolate is in the fridge. These everyday explanations of behavior in terms of mental states are referred to as folk psychology. Perhaps our ability to mentalize depends upon representations within the brain of the propositions that make up this theory of behavior (referred to as theory theory). On the other hand, perhaps the ability to mentalize is related to our capacity to empathize with other people: to put ourselves into their shoes (this is referred to as simulation theory).An influential view is that mentalizing crucially depends on the ability to form meta-representations, that is, representations that are decoupled from reality. Thus the truth of the statement, ‘Maxi believes his chocolate is in the cupboard’ does not depend upon where the chocolate is in reality. A possible starting point for developing a mechanistic account of mentalizing comes from the problem of perspective taking. The computation of what another person sees from a different point of view than yours involves translation between egocentric and allocentric spatial co-ordinates. This translation is also fundamental in spatial navigation. It is perhaps no coincidence that in young children the ability to solve spatial viewpoint problems emerges at about the same age as the ability to solve false belief tasks.What is the neural basis of mind reading? There is currently much interest in identifying a social brain: a circumscribed network of brain regions specialized for the social domain. Mentalizing is one of a number of problems confronting this social brain. When brain activity is measured during the performance of a wide range of tasks engaging theory of mind, two regions have been consistently identified: a medial prefrontal region (paracingulate cortex) and the temporo-parietal junction in the superior temporal sulcus.The medial frontal region is also engaged when subjects reflect upon their own mental states, as well as those of others with the more inferior orbital region responding especially to emotional states. The temporo-parietal junction, on the other hand, seems to have a special role in using perceptual cues to recognize the actions and intentions of biological agents. Identification of the precise role of these regions awaits the development of a mechanistic account of our remarkable ability to make inferences about the minds of others.


title: A High 2LO-RF Isolation SiGe BiCMOS Sub-Harmonic Gilbert Mixer Using Stacked-LO-Stage Topology

abstract: A 5.2 GHz SiGe BiCMOS stacked-LO-stage CMFB (Common Mode Feedback) sub-harmonic mixer is demonstrated in this letter. The stacked-LO-stage and the active loads are used to improve the 2LO-RF isolation and the conversion gain, respectively. The SiGe mixer includes five levels of transistors stacked together at the supply voltage of 3.3 V because of the low knee-voltage characteristic of the SiGe HBTs (Heterojunction Bipolar Transistors). The mixer demonstrated achieves 23 dB conversion gain and ?78 dB 2LO-RF isolation.


title: Sixty years of fear appeal research: Current state of the evidence

abstract: ear arousal is widely used in persuasive campaigns and behavioral change interventions. Yet, experimental evidence argues against the use of threatening health information. The authors reviewed the current state of empirical evidence on the effectiveness of fear appeals. Following a brief overview of the use of fear arousal in health education practice and the structure of effective fear appeals according to two main theoretical frameworks—protection motivation theory and the extended parallel process model—the findings of six meta-analytic studies in the effectiveness of fear appeals are summarized. It is concluded that coping information aimed at increasing perceptions of response effectiveness and especially self-efficacy is more important in promoting protective action than presenting threatening health information aimed at increasing risk perceptions and fear arousal. Alternative behavior change methods than fear appeals should be considered. Threatening health messages, in the social and health psychology literature referred to as fear appeals, are widely used in health communication. Members of the general population and health promoters who design these messages tend to believe in the persuasive power of fear arousal. This is best illustrated in the wide-scale use of health warnings on cigarette packages. Textual and graphic warnings on cigarette packages are typical examples of fear-inducing communications. Similar examples can be found in other health domains, such as the Australian Grim Reaper campaigns in the mid-1980s to warn of the fatal effects of human immunodeficiency virus infection/acquired immunodeficiency syndrome (HIV/AIDS), the US "this is your brain on drugs" campaign to prevent (excessive) substance use and numerous traffic safety campaigns showing the bloody consequences of traffic accidents often involving young people. These campaigns are based on the assumption that by vividly demonstrating negative and life-endangering consequences of risk behaviors, people will be motivated to reduce their current risk behavior and adopt safer alternative behaviors. Our aim in this first article of the special section on fear appeals is to discuss the use of threatening messages in health promotion campaigns. We will argue that severity information is often the most visible component of threatening health communications, but at the same time, the least persuasive component. In this article, we refer to these severity-based messages as threatening health information or fear arousal. We then illustrate the challenges involved in designing effective fear- inducing messages by referring back to meta-analyses on the effectiveness of fear-inducing persuasive messages, here referred to as fear appeals. These challenges lie in convincing the target audience that the health threat is relevant to their personal lives and in inducing a strong belief in the personal feasibility of the recommended protective action, that is, building self-efficacy in relation to the recommended health action. We conclude that the choice of fear appeals is often a poor choice because of the limited and even sometimes counterproductive effects of fear arousal and the extensive knowledge base available on more effective methods of behavior change (see Kok, Bartholomew, Parcel, Gottlieb, & Fernandez, 2014, this issue).


title: In-Cell Self-Capacitive-Type Mobile Touch System and Embedded Readout Circuit in Display Driver IC

abstract: A mobile display system, which includes a self- capacitive type in-cell touch screen panel and a display driver IC, in which the readout circuit is embedded, is developed for reduced cost, improved display performance, and superior touch performance. A touch electrode is implemented with a segmented common electrode of an LCD display. The display system of the in-cell touch panel is 5.7-in a-Si HD (720 × 1280), the number of touch electrodes is 576 (18 × 32), and the sensor pitch is approximately 4 mm. Compared to the display-only system, display performance degradation does not occur in the proposed system since in-cell technology is adopted. Despite using a self-capacitance sensing method in the panel, unlimited multitouch is available by using an all-point sensing scheme; this system shows a touch accuracy of less than 0.2 mm and signal-to-noise ratio is measured to be more than 48 dB in the entire panel without any digital filter. To enhance the touch performance, we proposed noise reduction techniques that are strongly related to the display operation, such as base line compensation, delta adding correlated double sampling, and the use of a high-voltage charge compensator. The power consumption including micro controller unit (MCU) is less than 30 mW.


title: Clustering of web search results based on the cuckoo search algorithm and Balanced Bayesian Information Criterion

abstract: The clustering of web search results - or web document clustering - has become a very interesting research area among academic and scientific communities involved in information retrieval. Web search result clustering systems, also called Web Clustering Engines, seek to increase the coverage of documents presented for the user to review, while reducing the time spent reviewing them. Several algorithms for clustering web results already exist, but results show room for more to be done. This paper introduces a new description-centric algorithm for the clustering of web results, called WDC-CSK, which is based on the cuckoo search meta-heuristic algorithm, k-means algorithm, Balanced Bayesian Information Criterion, split and merge methods on clusters, and frequent phrases approach for cluster labeling. The cuckoo search meta-heuristic provides a combined global and local search strategy in the solution space. Split and merge methods replace the original Levy flights operation and try to improve existing solutions (nests), so they can be considered as local search methods. WDC-CSK includes an abandon operation that provides diversity and prevents the population nests from converging too quickly. Balanced Bayesian Information Criterion is used as a fitness function and allows defining the number of clusters automatically. WDC-CSK was tested with four data sets (DMOZ-50, AMBIENT, MORESQUE and ODP-239) over 447 queries. The algorithm was also compared against other established web document clustering algorithms, including Suffix Tree Clustering (STC), Lingo, and Bisecting k-means. The results show a considerable improvement upon the other algorithms as measured by recall, F-measure, fall-out, accuracy and SSL"k.


Studies on the frequency of accessory tendons of m. abductor pollicis longus with reference to their significance in medical practice


title: Power-aware communication optimization for networks-on-chips with voltage scalable links

abstract: Networks-on-chip (NoC) is emerging as a practical development platform for future systems-on-chip products. We propose an energy-efficient static algorithm which optimizes the energy consumption of task communications in NoCs with voltage scalable links. In order to find optimal link speeds, the proposed algorithm (based on a genetic formulation) globally explores the design space of NoC-based systems, including task assignment, tile mapping, routing path allocation, task scheduling and link speed assignment. Experimental results show that the proposed design technique can reduce energy consumption by 28% on average compared with existing techniques.


title: Novel urushiols with human immunodeficiency virus type 1 reverse transcriptase inhibitory activity from the leaves of Rhus verniciflua

abstract: Two novel urushiols, 1 and 2, and two known urushiols, 3 and 4, were isolated from the leaves of Rhus verniciflua and were examined for their human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT) inhibitory activity. The novel urushiols were found to be 1,2-dihydroxyphenyl-3-[7'(E),9'(Z),11'(Z)-pentadecatrienyl]-14'-ol (1) and 1,2-dihydroxyphenyl-3-[8'(Z),10'(E),12'(E)-pentadecatrienyl]-14'-ol (2) by spectroscopic analyses. The absolute configuration at C-14' in 1 and 2 was determined to be a racemic mixture of (R) and (S) isomers by ozonolysis. Compound 2 (IC50: 12.6 µM) showed the highest HIV-1 RT inhibitory activity among the four urushiols, being 2.5-fold more potent than the positive control, adriamycin (IC50: 31.9 µM). Although the known urushiols were isolated from the sap and leaves of R. verniciflua, 1 was exclusively present in the leaves, and higher amounts of 2 were found in the leaves than in the sap. Present findings indicate that the leaves of R. verniciflua represent a new biological resource from which novel and known urushiols may be prepared, and the possible use of novel urushiols as bioactive products.


title: Happy Moves, Sad Grooves: Using Theories of Biological Motion and Affect to Design Shape-Changing Interfaces

abstract: The design of shape-changing interfaces to show emotions relies on craft skill with few clear guidelines. Through two experiments, we explore how to design such interfaces using theories of the relation between biological motion and affect. In the first experiment, 19 participants viewed six shape-changing behaviors that varied the velocity, fluidity, direction, and orientation of the movement of an extrusion from a small box in accordance with existing theories of affective motion. Participants were able to recognize four of the six intended basic Ekman emotions (sadness, fear, happiness, surprise) with above-chance probability. The second experiment used 36 shape-changing behaviors that systematically varied speed, regularity of motion, and direction. For each behavior, 23 participants rated valence, arousal, and dominance. Speed, direction, and orientation impacted emotion ratings significantly and in the predicted directions. These results offer an initial basis for the systematic design of emotions in shape-changing interfaces.


title: Dense Coding in Experimental Quantum Communication

abstract: Classically, sending more than one bit of information requires manipulation of more than one two-state particle. We demonstrate experimentally that one can transmit one of three messages, i.e., 1 {open_quote}{open_quote}trit{close_quote}{close_quote} {approx_equal}1.58 bit, by manipulating only one of two entangled particles. The increased channel capacity is proven by transmitting ASCII characters in five trits instead of the usual eight bits. {copyright} {ital 1996 The American Physical Society.}


title: BENCHMARKING DEEP LEARNING FRAMEWORKS FOR THE CLASSIFICATION OF VERY HIGH RESOLUTION SATELLITE MULTISPECTRAL DATA

abstract: Abstract. In this paper we evaluated deep-learning frameworks based on Convolutional Neural Networks for the accurate classification of multispectral remote sensing data. Certain state-of-the-art models have been tested on the publicly available SAT-4 and SAT-6 high resolution satellite multispectral datasets. In particular, the performed benchmark included the AlexNet, AlexNet-small and VGG models which had been trained and applied to both datasets exploiting all the available spectral information. Deep Belief Networks, Autoencoders and other semi-supervised frameworks have been, also, compared. The high level features that were calculated from the tested models managed to classify the different land cover classes with significantly high accuracy rates i.e., above 99.9%. The experimental results demonstrate the great potentials of advanced deep-learning frameworks for the supervised classification of high resolution multispectral remote sensing data.


An MRI-based Acoustic Study of Mandarin Vowels


title: A simulation analysis of double-fed induction generator for wind energy conversion using PSpice

abstract: A wind energy conversion system (WECS) differs from a conventional power system. In a conventional power plant, the power generation can be controlled. But, the power output of a WECS fluctuates with the wind. This variable power generation nature makes it difficult for analysis, design, and management of a WECS so that various approaches have been developed to study WECS behaviors. Although a transient simulation is important to understand WECS performance in a short time period, the dynamic steady-state simulation is a valuable approach to comprehend WECS characteristics in a broader spectrum. This paper explores steady-state characteristics of a typical modern variable-speed WECS that uses double-fed induction generators (DFIG), a variable-speed generator widely used in the wind power industry, as its generating facilities. The dynamic steady-state simulation model of the DFIG is developed using PSpice. Simulation analysis is performed to investigate a variety of DFIG characteristics, including torque-speed, and real and reactive-power over speed characteristics. Based on the analysis, the DFIG operating characteristics are studied.


title: Trajectory planning for car-like robots in unknown, unstructured environments

abstract: We describe a variable-velocity trajectory plan- ning algorithm for navigating car-like robots through unknown, unstructured environments along a series of possibly corrupted GPS waypoints. The trajectories are guaranteed to be kine- matically feasible, i.e., they respect the robot's acceleration and deceleration capabilities as well as its maximum steering angle and steering rate. Their costs are computed using LiDAR and camera data and depend on factors such as proximity to obstacles, curvature, changes of curvature, and slope. In a second step, velocities for the least-cost trajectory are adjusted based on the dynamics of the vehicle. When the robot is faced with an obstacle on its trajectory, the planner is restarted to compute an alternative trajectory. Our algorithm is robust against GPS error and waypoints placed in obstacle-filled areas. It was successfully used at euRathlon 2013 1 , where our autonomous vehicle MuCAR-3 took first place in the "Autonomous Navigation" scenario.


title: Agile methods and organizational culture: reflections about cultural levels

abstract: Agile methods emerge as an alternative to improve quality and performance in software development processes. However, as agile methods are essentially focused on human aspects, their application in companies depends mostly on their adequacy to the current organizational culture. This study explores the view of the organizational culture in three levels as a theoretical framework to allow early detection of problems, which could jeopardize the adoption of agile methods by a company. This article points out that many facilitators or obstacles to the adoption of an agile method can be hidden in the lower levels of the organizational culture. Additionally, the article shows that a superficial analysis of those issues can lead to a miscomprehension about the possibility of applying an agile method in a software company. This article also evidences that the interpretation of the levels of organizational culture improves the understanding of how an agile culture should be established. Copyright © 2009 John Wiley & Sons, Ltd.


title: Neurological manifestations of Ehlers-Danlos syndrome(s): A review

abstract: The term “Ehlers-Danlos syndrome” (EDS) groups together an increasing number of heritable connective tissue disorders mainly featuring joint hypermobility and related complications, dermal dysplasia with abnormal skin texture and repair, and variable range of the hollow organ and vascular dysfunctions. Although the nervous system is not considered a primary target of the underlying molecular defect, recently, increasing attention has been posed on neurological manifestations of EDSs, such as musculoskeletal pain, fatigue, headache, muscle weakness and paresthesias. Here, a comprehensive overview of neurological findings of these conditions is presented primarily intended for the clinical neurologist. Features are organized under various subheadings, including pain, fatigue, headache, stroke and cerebrovascular disease, brain and spine structural anomalies, epilepsy, muscular findings, neuropathy and developmental features. The emerging picture defines a wide spectrum of neurological manifestations that are unexpectedly common and potentially disabling. Their evaluation and correct interpretation by the clinical neurologist is crucial for avoiding superfluous investigations, wrong therapies, and inappropriate referral. A set of basic tools for patient’s recognition is offered for raising awareness among neurologists on this underdiagnosed group of hereditary disorders.


title: Evaluation of three-phase transformerless photovoltaic inverter topologies

abstract: This paper analyzes and compares three transformerless photovoltaic inverter topologies for three-phase grid connection with the main focus on the safety issues that result from the lack of galvanic isolation. A common-mode model, valid at frequencies lower than 50 kHz, is adopted to study the leakage current paths. The model is validated by both simulation and experimental results. These will be used to compare the selected topologies, and to explain the influence of system unbalance and the neutral conductor inductance on the leakage current. It will be demonstrated that the later has a crucial influence. Finally, a comparison of the selected topologies is carried out, based on the adopted modulation, connection of the neutral and its inductance, effects of unbalance conditions, component ratings, output voltage levels, and filter size.


title: Variable selection by association rules for customer churn prediction of multimedia on demand

abstract: Multimedia on demand (MOD) is an interactive system that provides a number of value-added services in addition to traditional TV services, such as video on demand and interactive online learning. This opens a new marketing and managerial problem for the telecommunication industry to retain valuable MOD customers. Data mining techniques have been widely applied to develop customer churn prediction models, such as neural networks and decision trees in the domain of mobile telecommunication. However, much related work focuses on developing the prediction models per se. Few studies consider the pre-processing step during data mining whose aim is to filter out unrepresentative data or information. This paper presents the important processes of developing MOD customer churn prediction models by data mining techniques. They contain the pre-processing stage for selecting important variables by association rules, which have not been applied before, the model construction stage by neural networks (NN) and decision trees (DT), which are widely adapted in the literature, and four evaluation measures including prediction accuracy, precision, recall, and F-measure, all of which have not been considered to examine the model performance. The source data are based on one telecommunication company providing the MOD services in Taiwan, and the experimental results show that using association rules allows the DT and NN models to provide better prediction performances over a chosen validation dataset. In particular, the DT model performs better than the NN model. Moreover, some useful and important rules in the DT model, which show the factors affecting a high proportion of customer churn, are also discussed for the marketing and managerial purpose.


title: Donna Interactive Chat-bot acting as a Personal Assistant

abstract: Chat-bots are computer programs coded to have a textual or verbal conversation which is logical or intelligent. Chat-bots are designed to make humans believe that they are talking to a human; but instead they are in fact talking to a machine. Taking advantage of this transparency property of chat-bot, an artificial character and personality can be given to a chat-bot which acts like a person of a specific profession. This paper describes an approach to the idea of implementing web-based artificially intelligent chat-bot as a personal assistant of the user, which stimulates setting and initiating meetings of user with his clients. The exchange of information happens through email conversations whereas its evaluation happens through natural language procession and natural language generation and AIML files. General Terms AIML, Artificial Intelligence, Pattern Matching.


title: Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article]

abstract: Ensemble methods use multiple models to get better performance. Ensemble methods have been used in multiple research fields such as computational intelligence, statistics and machine learning. This paper reviews traditional as well as state-of-the-art ensemble methods and thus can serve as an extensive summary for practitioners and beginners. The ensemble methods are categorized into conventional ensemble methods such as bagging, boosting and random forest, decomposition methods, negative correlation learning methods, multi-objective optimization based ensemble methods, fuzzy ensemble methods, multiple kernel learning ensemble methods and deep learning based ensemble methods. Variations, improvements and typical applications are discussed. Finally this paper gives some recommendations for future research directions.


title: Stochastic ADMM for Nonsmooth Optimization

abstract: We present a stochastic setting for optimization problems with nonsmooth convex separable objective functions over linear equality constraints. To solve such problems, we propose a stochastic Alternating Direction Method of Multipliers (ADMM) algorithm. Our algorithm applies to a more general class of nonsmooth convex functions that does not necessarily have a closed-form solution by minimizing the augmented function directly. We also demonstrate the rates of convergence for our algorithm under various structural assumptions of the stochastic functions: $O(1/\sqrt{t})$ for convex functions and $O(\log t/t)$ for strongly convex functions. Compared to previous literature, we establish the convergence rate of ADMM algorithm, for the first time, in terms of both the objective value and the feasibility violation.


title: Trust, Risk, Privacy, and Security in e- Commerce

abstract: The concepts, Trust, Risk, Privacy and Security, are widely used in various studies done by multiple disciplines, and they are often incorrectly referred to almost as synonyms. The aim is to clarify the concepts from the consumer viewpoint in e- commerce. The findings of our qualitative study suggest several relationships between the four concepts and serves as building blocks for further research.


title: Analysis of Wideband Circularly Polarized Ferrite-Loaded Antenna Based on Unidirectional Resonant Modes

abstract: We propose a wideband circularly polarized antenna that consists of a metallic patch and some parasitically coupled elements mounted on a grounded dielectric-ferrite substrate. The metallic patch is fed by a proximity-coupled feed line placed between the ferrite and the dielectric layers. The parasitically coupled elements are included to improve the impedance bandwidth of antenna and are excited through the metallic patch. The antenna utilizes the resonant modes of the structure, which rotate only in the clockwise or counter-clockwise direction. A semi-analytical solution based on the magnetic wall approximation shows that the resonance frequencies of the clock- and counter-clockwise resonant modes differ significantly when operating in the frequency range where the effective permeability of the ferrite layer is negative. Moreover, there is a good agreement between the model presented and full-wave numerical simulations. For the proposed antenna, the results show that the radiation bandwidth is more than 15.5% and the 3 dB axial ratio bandwidth is more than 50%. The proposed antenna is easy to design and fabricate, does not require a complicated feeding network, and has a wideband and wide-angle circular polarization.


title: Built-in Foreground/Background Prior for Weakly-Supervised Semantic Segmentation

abstract: Pixel-level annotations are expensive and time consuming to obtain. Hence, weak supervision using only image tags could have a significant impact in semantic segmentation. Recently, CNN-based methods have proposed to fine-tune pre-trained networks using image tags. Without additional information, this leads to poor localization accuracy. This problem, however, was alleviated by making use of objectness priors to generate foreground/background masks. Unfortunately these priors either require training pixel-level annotations/bounding boxes, or still yield inaccurate object boundaries. Here, we propose a novel method to extract markedly more accurate masks from the pre-trained network itself, forgoing external objectness modules. This is accomplished using the activations of the higher-level convolutional layers, smoothed by a dense CRF. We demonstrate that our method, based on these masks and a weakly-supervised loss, outperforms the state-of-the-art tag-based weakly-supervised semantic segmentation techniques. Furthermore, we introduce a new form of inexpensive weak supervision yielding an additional accuracy boost.


title: Degree based Classification of Harmful Speech using Twitter Data

abstract: Harmful speech has various forms and it has been plaguing the social media in different ways. If we need to crackdown different degrees of hate speech and abusive behavior amongst it, the classification needs to be based on complex ramifications which needs to be defined and hold accountable for, other than racist, sexist or against some particular group and community. This paper primarily describes how we created an ontological classification of harmful speech based on degree of hateful intent, and used it to annotate twitter data accordingly. The key contribution of this paper is the new dataset of tweets we created based on ontological classes and degrees of harmful speech found in the text. We also propose supervised classification system for recognizing these respective harmful speech classes in the texts hence.


title: Analytical model for effects of twisting on litz-wire losses

abstract: Litz wire uses complex twisting to balance currents between strands. Most models are not helpful for choosing the twisting configuration because they assume that the twisting works perfectly. A complete model that shows the effect of twisting on loss is introduced. The model can predict loss for a precise configuration, but in practice, it is difficult to achieve sufficient manufacturing precision to use this approach. More practically, the model can predict worst-case loss over the range of expected production variation. The model is useful for making design choices for the twisting configuration and the pitch of the twisting at each level of construction.


title: FocusStack: Orchestrating Edge Clouds Using Location-Based Focus of Attention

abstract: Allocating and managing resources in the Internet ofThings (IoT) presents many new challenges, including massivescale, new security issues, and new resource types that becomecritical in making orchestration decisions. In this paper, weinvestigate whether clouds of edge devices can be managed asInfrastructure-as-a-Service clouds. We describe our approach, FocusStack, that uses location based situational awareness, implemented over a multi-tier geographic addressing network, to solve the problems of inefficient awareness messaging andmixed initiative control that IoT device clouds raise for traditionalcloud management tools. We provide an extended casestudy of a shared video application as initial demonstrationand evaluation of the work and show that we effectively solvethe two key problems above.


title: Benign schwannoma of the maxillary antrum

abstract: Schwannoma also known commonly as neurilemmoma and schwann cell tumor is a benign nerve sheath tumor. About 1/3(rd) cases of schwannoma arise from the head and neck region but rarely from the nasal and paranasal sinuses. The recurrence rate in these cases has reported to be very rare. We report a rare case of schwannoma in a 60-year-old woman arising from the maxillary sinus further eroding the orbital floor and nasal bone. We have also described the clinical presentation, radiological, histological findings, and management of the case.


title: Beyond keyword search: discovering relevant scientific literature

abstract: In scientific research, it is often difficult to express information needs as simple keyword queries. We present a more natural way of searching for relevant scientific literature. Rather than a string of keywords, we define a query as a small set of papers deemed relevant to the research task at hand. By optimizing an objective function based on a fine-grained notion of influence between documents, our approach efficiently selects a set of highly relevant articles. Moreover, as scientists trust some authors more than others, results are personalized to individual preferences. In a user study, researchers found the papers recommended by our method to be more useful, trustworthy and diverse than those selected by popular alternatives, such as Google Scholar and a state-of-the-art topic modeling approach.


title: A Movie Recommendation System – An Application of Voting Theory in User Modeling

abstract: Our research agenda focuses on building software agents that can employ user modeling techniques to facilitate information access and management tasks. Personal assistant agents embody a clearly beneficial application of intelligent agent technology. A particular kind of assistant agents, recommender systems, can be used to recommend items of interest to users. To be successful, such systems should be able to model and reason with user preferences for items in the application domain. Our primary concern is to develop a reasoning procedure that can meaningfully and systematically tradeoff between user preferences. We have adapted mechanisms from voting theory that have desirable guarantees regarding the recommendations generated from stored preferences. To demonstrate the applicability of our technique, we have developed a movie recommender system that caters to the interests of users. We present issues and initial results based on experimental data of our research that employs voting theory for user modeling, focusing on issues that are especially important in the context of user modeling. We provide multiple query modalities by which the user can pose unconstrained, constrained, or instance-based queries. Our interactive agent learns a user model by gaining feedback aboutits recommended movies from the user. We also provide pro-active information gathering to make user interaction more rewarding. In the paper, we outline the current status of our implementation with particular emphasis on the mechanisms used to provide robust and effective recommendations.


title: Automated Accessibility Testing of Mobile Apps

abstract: It is important to make mobile apps accessible, so as not to exclude users with common disabilities such as blindness, low vision, or color blindness. Even when developers are aware of these accessibility needs, the lack of tool support makes the development and assessment of accessible apps challenging. Some accessibility properties can be checked statically, but user interface widgets are often created dynamically and are not amenable to static checking. Some accessibility checking frameworks analyze accessibility properties at runtime, but have to rely on existing thorough test suites. In this paper, we introduce the idea of using automated test generation to explore the accessibility of mobile apps. We present the MATE tool (Mobile Accessibility Testing), which automatically explores apps while applying different checks for accessibility issues related to visual impairment. For each issue, MATE generates a detailed report that supports the developer in fixing the issue. Experiments on a sample of 73 apps demonstrate that MATE detects more basic accessibility problems than static analysis, and many additional types of accessibility problems that cannot be detected statically at all. Comparison with existing accessibility testing frameworks demonstrates that the independence of an existing test suite leads to the identification of many more accessibility problems. Even when enabling Android's assistive features like contrast enhancement, MATE can still find many accessibility issues.


title: Ristretto: Hardware-Oriented Approximation of Convolutional Neural Networks

abstract: Convolutional neural networks (CNN) have achieved major breakthroughs in recent years. Their performance in computer vision have matched and in some areas even surpassed human capabilities. Deep neural networks can capture complex non-linear features; however this ability comes at the cost of high computational and memory requirements. State-of-art networks require billions of arithmetic operations and millions of parameters. To enable embedded devices such as smartphones, Google glasses and monitoring cameras with the astonishing power of deep learning, dedicated hardware accelerators can be used to decrease both execution time and power consumption. In applications where fast connection to the cloud is not guaranteed or where privacy is important, computation needs to be done locally. Many hardware accelerators for deep neural networks have been proposed recently. A first important step of accelerator design is hardware-oriented approximation of deep networks, which enables energy-efficient inference. We present Ristretto, a fast and automated framework for CNN approximation. Ristretto simulates the hardware arithmetic of a custom hardware accelerator. The framework reduces the bit-width of network parameters and outputs of resource-intense layers, which reduces the chip area for multiplication units significantly. Alternatively, Ristretto can remove the need for multipliers altogether, resulting in an adder-only arithmetic. The tool fine-tunes trimmed networks to achieve high classification accuracy. Since training of deep neural networks can be time-consuming, Ristretto uses highly optimized routines which run on the GPU. This enables fast compression of any given network. Given a maximum tolerance of 1%, Ristretto can successfully condense CaffeNet and SqueezeNet to 8-bit. The code for Ristretto is available.


title: Learning with Errors is easy with quantum samples

abstract: Learning with Errors is one of the fundamental problems in computational learning theory and has in the last years become the cornerstone of post-quantum cryptography. In this work, we study the quantum sample complexity of Learning with Errors and show that there exists an efficient quantum learning algorithm (with polynomial sample and time complexity) for the Learning with Errors problem where the error distribution is the one used in cryptography. While our quantum learning algorithm does not break the LWE-based encryption schemes proposed in the cryptography literature, it does have some interesting implications for cryptography: first, when building an LWE-based scheme, one needs to be careful about the access to the public-key generation algorithm that is given to the adversary; second, our algorithm shows a possible way for attacking LWE-based encryption by using classical samples to approximate the quantum sample state, since then using our quantum learning algorithm would solve LWE.


title: 3D Photogrammetric Analysis of the Nasal Tip Projection and Derotation Based on the Nasal Tip Quadripod Concept

abstract: BACKGROUND ::: Regarding septal fixation in nasal tip plasty, the nasal tip tripod theory may require modification. We performed three-dimensional (3D) photogrammetric analysis of the "Skiff graft," which is a quadripod method involving folded conchal cartilages fixated on the caudal septal angle, paired lateral crura reinforced by a derotational conchal cartilage graft, and conjoined medial crus on the columella base. We assessed the efficacy of the Skiff graft in maintaining tip projection and preventing cephalic rotation of the nasal tip. ::: ::: ::: METHODS ::: In total, 39 patients who underwent nasal tip plasty with the Skiff graft during 2012-2015 and underwent 3D photogrammetry before (T0), immediately postoperatively (T1, average of 1 week postoperatively), and an average of 215 days postoperatively (T2) were included. Nasal tip projection was objectively measured from images using a software program. ::: ::: ::: RESULTS ::: The average differences between the T0 and T1, T0 and T2, and T1 and T2 lengths were 4.81, 3.57, and 0.85 mm, respectively. Thus, 81% of the change in tip projection length that had been achieved immediately postoperatively was maintained at T2 based on 3D photogrammetry (P < 0.001). The relapse ratio of the examined technique was 19.26% (mean, 0.85 mm; P < 0.001). ::: ::: ::: CONCLUSIONS ::: Nasal tip plasty with the Skiff graft preserves septal support and is an effective primary and revision rhinoplasty technique in Asian patients. This approach may be suitable as an alternative to nasal tip plasty with the septal extension graft in terms of maintaining nasal tip projection, rotational controllability, and nasal mobility. ::: ::: ::: LEVEL OF EVIDENCE IV ::: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .


Deep Learning and Convolutional Neural Networks for Medical Image Computing


title: HMM-based script identification for OCR

abstract: While current OCR systems are able to recognize text in an increasing number of scripts and languages, typically they still need to be told in advance what those scripts and languages are. We propose an approach that repurposes the same HMM-based system used for OCR to the task of script/language ID, by replacing character labels with script class labels. We apply it in a multi-pass overall OCR process which achieves "universal" OCR over 54 tested languages in 18 distinct scripts, over a wide variety of typefaces in each. For comparison we also consider a brute-force approach, wherein a singe HMM-based OCR system is trained to recognize all considered scripts. Results are presented on a large and diverse evaluation set extracted from book images, both for script identification accuracy and for overall OCR accuracy. On this evaluation data, the script ID system provided a script ID error rate of 1.73% for 18 distinct scripts. The end-to-end OCR system with the script ID system achieved a character error rate of 4.05%, an increase of 0.77% over the case where the languages are known a priori.


title: Configuring role-based access control to enforce mandatory and discretionary access control policies

abstract: Access control models have traditionally included mandatory access control (or lattice-based access control) and discretionary access control. Subsequently, role-based access control has been introduced, along with claims that its mechanisms are general enough to simulate the traditional methods. In this paper we provide systematic constructions for various common forms of both of the traditional access control paradigms using the role-based access control (RBAC) models of Sandhu et al., commonly called RBAC96. We see that all of the features of the RBAC96 model are required, and that although for the manatory access control simulation, only one administrative role needs to be assumed, for the discretionary access control simulations, a complex set of administrative roles is required.


title: Indigenous Data Sovereignty: Toward an agenda

abstract: Overview As the global ‘data revolution’ accelerates, how can the data rights and interests of Indigenous peoples be secured? Premised on the United Nations Declaration on the Rights of Indigenous Peoples, this book argues that Indigenous peoples have inherent and inalienable rights relating to the collection, ownership and application of data about them, and about their lifeways and territories. As the first book to focus on indigenous data sovereignty, it asks: what does data sovereignty mean for indigenous peoples, and how is it being used in their pursuit of self-determination? The varied group of mostly indigenous contributors theorise and conceptualise this fast-emerging field and present case studies that illustrate the challenges and opportunities involved. These range from Indigenous communities grappling with issues of identity, governance and development, to national governments and NGOs seeking to formulate a response to Iindigenous demands for data ownership. While the book is focused on the CANZUS states of Canada, Australia, Aotearoa/New Zealand and the United States, much of the content and discussion will be of interest and practical value to a broader global audience.


title: Broadband GaN HEMT push-pull microwave power amplifier

abstract: We report a broadband, linear, push-pull amplifier that utilizes GaN-based HEMTs grown on SiC substrates. The high power density capabilities of these devices can be enhanced by the high efficiency achievable with push-pull operation. Good amplifier performance is facilitated by use of a new low-loss balun that is implemented with three symmetric coupled lines and which showed insertion loss of less than 0.5 dB per balun. The bias was injected through the baluns, thereby simplifying the amplifier design and reducing loss associated with dc decoupling capacitors. Using two 1.5 mm HEMTs with 0.35-/spl mu/m gate length, a push-pull amplifier produced a small-signal gain of 8 dB at 5 GH2, a 3 dB bandwidth of 3.5-10.5 GHz, and a PAE of 25%.


Overview of the FIRE 2017 IRLeD Track: Information Retrieval from Legal Documents


title: Cognitive and neural mechanisms of emotional memory

abstract: A highly adaptive aspect of human memory is the enhancement of explicit, consciously accessible memory for emotional stimuli. Recent findings from neuroimaging, neuropsychological, drug and neural stimulation studies indicate that emotional stimuli engage specific cognitive and neural mechanisms that enhance explicit memory. Emotional arousal influences memory via factors that act during memory encoding (attention and elaboration) and factors that modulate memory consolidation. Across studies, the amygdala has been consistently implicated as playing a key role in enhancing explicit memory for both pleasant and unpleasant emotional stimuli through modulation of encoding and consolidation processes.


title: Generating Fake but Realistic Headlines Using Deep Neural Networks

abstract: Social media platforms such as Twitter and Facebook implement filters to detect fake news as they foresee their transition from social media platform to primary sources of news. The robustness of such filters lies in the variety and the quality of the data used to train them. There is, therefore, a need for a tool that automatically generates fake but realistic news.