title: Decoding hand movement velocity from electroencephalogram signals during a drawing task

abstract: BackgroundDecoding neural activities associated with limb movements is the key of motor prosthesis control. So far, most of these studies have been based on invasive approaches. Nevertheless, a few researchers have decoded kinematic parameters of single hand in non-invasive ways such as magnetoencephalogram (MEG) and electroencephalogram (EEG). Regarding these EEG studies, center-out reaching tasks have been employed. Yet whether hand velocity can be decoded using EEG recorded during a self-routed drawing task is unclear.MethodsHere we collected whole-scalp EEG data of five subjects during a sequential 4-directional drawing task, and employed spatial filtering algorithms to extract the amplitude and power features of EEG in multiple frequency bands. From these features, we reconstructed hand movement velocity by Kalman filtering and a smoothing algorithm.ResultsThe average Pearson correlation coefficients between the measured and the decoded velocities are 0.37 for the horizontal dimension and 0.24 for the vertical dimension. The channels on motor, posterior parietal and occipital areas are most involved for the decoding of hand velocity. By comparing the decoding performance of the features from different frequency bands, we found that not only slow potentials in 0.1-4 Hz band but also oscillatory rhythms in 24-28 Hz band may carry the information of hand velocity.ConclusionsThese results provide another support to neural control of motor prosthesis based on EEG signals and proper decoding methods.


title: Everyone Likes Shopping! Multi-class Product Categorization for e-Commerce

abstract: Online shopping caters the needs of millions of users on a daily basis. To build an accurate system that can retrieve relevant products for a query like “MB252 with travel bags” one requires product and query categorization mechanisms, which classify the text as Home&Garden>Kitchen&Dining>Kitchen Appliances>Blenders. One of the biggest challenges in e-Commerce is that providers like Amazon, e-Bay, Google, Yahoo! and Walmart organize products into different product taxonomies making it hard and time-consuming for sellers to categorize goods for each shopping platform. To address this challenge, we propose an automatic product categorization mechanism, which for a given product title assigns the correct product category from a taxonomy. We conducted an empirical evaluation on445,408 product titles and used a rich product taxonomy of 319 categories organized into 6 levels. We compared performance against multiple algorithms and found that the best performing system reaches.88 f-score.


title: Expanding Domain Sentiment Lexicon through Double Propagation

abstract: In most sentiment analysis applications, the sentiment lexicon plays a key role. However, it is hard, if not impossible, to collect and maintain a universal sentiment lexicon for all application domains because different words may be used in different domains. The main existing technique extracts such sentiment words from a large domain corpus based on different conjunctions and the idea of sentiment coherency in a sentence. In this paper, we propose a novel propagation approach that exploits the relations between sentiment words and topics or product features that the sentiment words modify, and also sentiment words and product features themselves to extract new sentiment words. As the method propagates information through both sentiment words and features, we call it double propagation. The extraction rules are designed based on relations described in dependency trees. A new method is also proposed to assign polarities to newly discovered sentiment words in a domain. Experimental results show that our approach is able to extract a large number of new sentiment words. The polarity assignment method is also effective.


title: HCI and Design Research Education

abstract: This paper describes the latest insights in HCI education inspired and informed by the creative disciplines, how education is implemented, and how it could be fed back into the artistic fields. It contains examples, contrasts different methods, and discusses and concludes the findings for HCI education in general. A course on HCI is described which is supported by a creative approach, related to art, architecture and music. Experiences are described in of HCI tools and insights such as structured design methods, interaction frameworks and interface design heuristics relevant to the arts fields. 1. HCI and Education The domain of Human-Computer Interaction (HCI) is a multidisciplinary area involved in research, development and design, which should all be reflected in the education of HCI. HCI is a relatively new field combining human sciences, engineering and design. In addition to developing its own knowledge and practices, it draws knowledge from a number of more established disciplines, as shown in Fig. 1 below. On the left hand side of Fig. 1 a number of relevant human science disciplines are shown, on the right hand side relevant engineering disciplines are listed while the list on the top gives an overview of the design disciplines. In the middle it is stated what it is about in this broad approach: the interaction between people and technology. The outer circle gives what can be called the metadisciplines: philosophy, art, mathematics, and science [4]. HCI and Design Research Education 91 The three main approaches to HCI historically are from (cognitive) psychology, computer science, and design. Although many research centres and HCI courses still reflect one of these biases, in the last years particularly we have seen the field mature to a new discipline combining the different approaches. Some of the major text books in this field are written by multiple authors from a variety of backgrounds reflecting the main approaches mentioned above [8, 25]. Even though the course described in this paper is based in a Computer Science department (at the Vrije Universiteit in Amsterdam), it is set up and taught by people with various backgrounds and expertise in psychology, technology, design, and the arts. Fig. 1. An overview of related disciplines and HCI As it is increasingly understood that the design of interaction is about creating experiences, the HCI field can often borrow from the knowledge that creative disciplines have developed, such as theatre, music and video art. Methods, processes and approaches from these field inform the HCI field, while at the same time HCI teaching can help the artistic and design disciplines to develop interactions in a more user / audience centred, structured and formal way. The way the courses that are discussed in this paper are set up, reflects this interrelation between HCI and creativity.


title: Coherent Online Video Style Transfer

abstract: Training a feed-forward network for fast neural style transfer of images is proven to be successful. However, the naive extension to process video frame by frame is prone to producing flickering results. We propose the first end-to-end network for online video style transfer, which generates temporally coherent stylized video sequences in near real-time. Two key ideas include an efficient network by incorporating short-term coherence, and propagating short-term coherence to long-term, which ensures the consistency over larger period of time. Our network can incorporate different image stylization networks. We show that the proposed method clearly outperforms the per-frame baseline both qualitatively and quantitatively. Moreover, it can achieve visually comparable coherence to optimization-based video style transfer, but is three orders of magnitudes faster in runtime.


title: Behavioral Analysis of Insider Threat: A Survey and Bootstrapped Prediction in Imbalanced Data

abstract: The problem of insider threat is receiving increasing attention both within the computer science community as well as government and industry. This paper starts by presenting a broad, multidisciplinary survey of insider threat capturing contributions from computer scientists, psychologists, criminologists, and security practitioners. Subsequently, we present the behavioral analysis of insider threat ( $ \textsf{BAIT}$ ) framework, in which we conduct a detailed experiment involving 795 subjects on Amazon Mechanical Turk (AMT) in order to gauge the behaviors that real human subjects follow when attempting to exfiltrate data from within an organization. In the real world, the number of actual insiders found is very small, so supervised machine-learning methods encounter a challenge. Unlike past works, we develop bootstrapping algorithms that learn from highly imbalanced data, mostly unlabeled, and almost no history of user behavior from an insider threat perspective. We develop and evaluate seven algorithms using $ \textsf{BAIT}$ and show that they can produce a realistic (and acceptable) balance of precision and recall.


title: A case of multiple personality.

abstract: THE psychiatric manifestation called multiple personality has been extensively discussed. So too have the unicorn and the centaur. Who has not read of these legendary quadrupeds? Their pictures are, perhaps tiresomely, familiar to any schoolboy. Can one doubt that during medieval times many twilight encounters with the unicorn were convincingly reported? Surely in the days of Homer there were men of Thessaly or Beotia who had seen, or even ridden, centaurs almost as wise as Chiron. The layman who at college took a course in psychology may feel that for him dual personality, or multiple personality, is a familiar subject. Some psychiatrists' reactions suggest they are inclined to dismiss this subject as old hat. Nevertheless, like the unicorn and the centaur in some respects, multiple personality, despite vivid appearances in popularized books on psychology (2), is not commonly encountered in the full reality of life (1, 16, 17). Nearly all those perplexing reports of two or more people in one body, so to speak, that arouse a unique interest in the classroom, are reports of observations made in a relatively distant past. The most significant manifestations of this sort discussed in the current literature occurred in patients studied half a century or more ago (13, 23). It is scarcely surprising that practical psychiatrists today, never having directly observed such things as Morton Prince found in Miss Beauchamp or as Azam reported of Felida, might hold a tacitly skeptical attitude toward such archaic marvels and miracles. In the fields of internal medicine and chemistry the last, or even the middle, decades of the nineteenth century are close to us. In the relatively new field of psychopathology they are almost primeval, a dim dawn era in which we find it easy to suspect that a glimpse of a rhinoceros might have led to descriptions of the unicorn, or the sound of thunder been misinterpreted as God's literal voice. A reserved judgment toward what cannot be regularly demonstrated is not necessarily deplorable. Some current tendencies suggest that our youthful branch of medicine may not yet have emerged from its primordial and prerational phase. The discovery of orgone by one of our erstwhile leaders in the development of "psychodynamics" should not be ignored (4, 25). Enthusiastically adduced "proof" from an adult's dream that he was as an embryo significantly traumatized by fear of his father's penis, which during intercourse threatened him from his mother's vagina, is, we believe, the sort of evidence toward which our "resistance" is not without value (21). Despite Morton Prince's exquisitely thorough study of the celebrated Miss Beauchamp (23, 24) it is not surprising that decades ago McDougall should have warned us:


title: Comparison of shallow and deep neural networks for network intrusion detection

abstract: The increasing complexity and malice of modern computer and network attacks drives a need and search for more adaptive and smarter intrusion detection methods. Neural networks can provide a useful, self-learning approach to threat detection for network intrusion. After testing a variety of simple shallow and deep neural networks on the well-known NSL-KDD dataset comprised of network traffic capture containing 148,000 observations and 41 features with 22 specific attacks, we confirm the findings of previous researchers [15] that shallow neural networks are better suited for network intrusion detection than deep neural networks. Shallow networks were able to more accurately classify network data and produced lower error rates compared to deep networks.


title: View-independent recognition of hand postures

abstract: Since the human hand is highly articulated and deformable, hand posture recognition is a challenging example in the research on view-independent object recognition. Due to the difficulties of the model-based approach, the appearance-based learning approach is promising to handle large variation in visual inputs. However, the generalization of many proposed supervised learning methods to this problem often suffers from the insufficiency of labeled training data. This paper describes an approach to alleviate this difficulty by adding a large unlabeled training set. Combining supervised and unsupervised learning paradigms, a novel and powerful learning approach, the Discriminant-EM (D-EM) algorithm, is proposed in this paper to handle the case of a small labeled training set. Experiments show that D-EM outperforms many other learning methods. Based on this approach, we implement a gesture interface to recognize a set of predefined gesture commands, and it is also extended to hand detection. This algorithm can also apply to other object recognition tasks.


title: Hierarchical Recurrent Neural Hashing for Image Retrieval With Hierarchical Convolutional Features

abstract: Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.


title: Discriminatively Trained Sparse Code Gradients for Contour Detection

abstract: Finding contours in natural images is a fundamental problem that serves as the basis of many tasks such as image segmentation and object recognition. At the core of contour detection technologies are a set of hand-designed gradient features, used by most approaches including the state-of-the-art Global Pb (gPb) operator. In this work, we show that contour detection accuracy can be significantly improved by computing Sparse Code Gradients (SCG), which measure contrast using patch representations automatically learned through sparse coding. We use K-SVD for dictionary learning and Orthogonal Matching Pursuit for computing sparse codes on oriented local neighborhoods, and apply multi-scale pooling and power transforms before classifying them with linear SVMs. By extracting rich representations from pixels and avoiding collapsing them prematurely, Sparse Code Gradients effectively learn how to measure local contrasts and find contours. We improve the F-measure metric on the BSDS500 benchmark to 0.74 (up from 0.71 of gPb contours). Moreover, our learning approach can easily adapt to novel sensor data such as Kinect-style RGB-D cameras: Sparse Code Gradients on depth maps and surface normals lead to promising contour detection using depth and depth+color, as verified on the NYU Depth Dataset.


title: An intelligent system for wafer bin map defect diagnosis: An empirical study for semiconductor manufacturing

abstract: Wafer bin maps (WBMs) that show specific spatial patterns can provide clue to identify process failures in the semiconductor manufacturing. In practice, most companies rely on experienced engineers to visually find the specific WBM patterns. However, as wafer size is enlarged and integrated circuit (IC) feature size is continuously shrinking, WBM patterns become complicated due to the differences of die size, wafer rotation, the density of failed dies and thus human judgments become inconsistent and unreliable. To fill the gaps, this study aims to develop a knowledge-based intelligent system for WBMs defect diagnosis for yield enhancement in wafer fabrication. The proposed system consisted of three parts: graphical user interface, the WBM clustering solution, and the knowledge database. In particular, the developed WBM clustering approach integrates spatial statistics test, cellular neural network (CNN), adaptive resonance theory (ART) neural network, and moment invariant (MI) to cluster different patterns effectively. In addition, an interactive converse interface is developed to present the possible root causes in the order of similarity matching and record the diagnosis know-how from the domain experts into the knowledge database. To validate the proposed WBM clustering solution, twelve different WBM patterns collected in real settings are used to demonstrate the performance of the proposed method in terms of purity, diversity, specificity, and efficiency. The results have shown the validity and practical viability of the proposed system. Indeed, the developed solution has been implemented in a leading semiconductor manufacturing company in Taiwan. The proposed WBM intelligent system can recognize specific failure patterns efficiently and also record the assignable root causes verified by the domain experts to enhance troubleshooting effectively.


title: An embarrassingly simple approach to zero-shot learning

abstract: Zero-shot learning consists in learning how to recognise new concepts by just having a description of them. Many sophisticated approaches have been proposed to address the challenges this problem comprises. In this paper we describe a zero-shot learning approach that can be implemented in just one line of code, yet it is able to outperform state of the art approaches on standard datasets. The approach is based on a more general framework which models the relationships between features, attributes, and classes as a two linear layers network, where the weights of the top layer are not learned but are given by the environment. We further provide a learning bound on the generalisation error of this kind of approaches, by casting them as domain adaptation methods. In experiments carried out on three standard real datasets, we found that our approach is able to perform significantly better than the state of art on all of them, obtaining a ratio of improvement up to 17%.


title: Prevalence and determinants of early antenatal care visit among pregnant women attending antenatal care in Debre Berhan Health Institutions, Central Ethiopia.

abstract: Antenatal care (ANC), which is given to pregnant women, is widely used for prevention, early diagnosis and treatment of general medical and pregnancy-related complications. This study assessed the prevalence of early ANC visit and associated factors among pregnant women attending ANC in Debre Berhan town. An institution based cross-sectional study design was used to collect data from pregnant mothers with a face to face interview technique. Bivariate and multivariate analyses were used to identify associated factors for early ANC visit. A total of 446 pregnant women were included in the study. One hundred seventeen (26.2%) pregnant mothers started their first ANC visit early. The multivariate analysis showed that mothers with no parity before (AOR = 3.65, 95% CI: 2.14, 6.24), had good knowledge on early ANC (AOR = 3.10, 95% CI: 1.80, 5.33) and planned pregnancy (AOR = 1.66, 95% CI: 1.06, 2.61) were significantly associated with early ANC visit. The prevalence of early ANC visit was low. Awareness creation on the importance of early ANC visit needs to be emphasized at the time of service provision as well as at community levels.


title: Some reflexions on the modified Appleby procedure.

abstract: CONTEXT ::: Resection of pancreatic cancer with vascular invasion is a controversial issue, especially when the arterial trunks, such as the celiac axis, are involved. The modified Appleby procedure deals with the problem of encasement of the celiac trunk. ::: ::: ::: CASE REPORT ::: Two patients with locally advanced pancreatic cancer are reviewed: a 65-year-old female and a 78-year-old male with pancreatic cancer and involvement of the celiac trunk and hepatic artery. The male patient received neoadjuvant chemoradiation. The former underwent an extended pancreatectomy with en-bloc resection of the hepatic artery, celiac trunk, gastric serosa and adrenal gland. Liberation of arterial trunk involvement in the second patient was performed. The margins were microscopically clear of tumor (R0) in both patients. The second patient died from cholecystitis owing to arterial insufficiency. ::: ::: ::: CONCLUSIONS ::: CT vascular encasement is not always synonymous with real tumoral vascular invasion. Improvement in the quality of anesthesiological and surgical techniques has allowed vascular resections with lower morbidity. A cholecystectomy should always be performed using the modified Appleby procedure.


title: Sentiment trend analysis in social web environments

abstract: In this paper, we propose a novel method for sentiment trend analysis using Ant Colony Optimization (ACO) algorithm and SentiWordNet. We first collect social data in the form of Resource Description Framework (RDF) triples, and then use ACO algorithm to digitize the amassed RDF triples. Using ACO algorithm, we then compute pheromone values to extract the trends of the user's sentiments with the modified equations. Next, we compute the user's sentiment scores for the computed pheromone values with respect to the sentiment words with SentiWordNet. Finally, we analyze the sentiment trend of the online user by time. For verification of the proposed method, we conduct experiments, and compare the analyzed sentiment trends with their real daily lives. The results show that the proposed method performs satisfactory sentiment trend analysis on real data.


title: Shap e Recovery of Viscoelastic Deployable Structures

abstract: The paper investigates the shape recovery behavior of a simple beam and a tape spring made of LDPE under prescribed deformation history at room temperature. The linear viscoelastic material properties of LDPE were measured via creep tests. An analysis of a LDPE beam under four-point bending with an imposed history of vertical deflection and reaction force was performed. A theoretical solution was constructed by employing the Alfrey’s Correspondence Principle to the Euler-Bernoulli beam equation. The result was validated against a four-point bending experiment and a detailed nonlinear finite element simulation. Excellent agreement was obtained between theory, experiments and numerical simulations. A LDPE tape spring was fabricated and tested to provide an example of a simple deployable structure that recovers its deployed shape through a viscoelastic process for both equal sense and opposite sense folding.


title: Post-insertion Posterior Single-implant Occlusion Changes at Different Intervals: A T-Scan Computerized Occlusal Analysis

abstract: AIM ::: The aim of this prospective cohort study was to evaluate the postinsertion posterior single-implant occlusion changes at 3- and 6-month intervals using T-Scan computerized occlusal analysis. ::: ::: ::: MATERIALS AND METHODS ::: A total of 21 patients received single implant, opposed by natural dentition, in posterior regions of the maxilla or mandible (13 premolar, 8 molar) and were finally restored with cemented-retained metal-ceramic crowns. The occlusal contacts were equilibrated according to the implant-protective occlusion concept to develop light contact with heavy occlusion and no contact with light occlusion in maximum inter-cuspation. The percentage of force applied to the implant crowns (POFI) and contralateral teeth (POFT) was evaluated using T-Scan computerized occlusal analysis at prosthesis insertion, 3- and 6-month follow-up appointments. The data were statistically analyzed using Friedman test and Wilcoxon post hoc test (α = 0.05). ::: ::: ::: RESULTS ::: The POFI values at the 6- and 3-month follow-up appointments were significantly higher than those at prostheses insertion (p = 0.001 and p = 0.005 respectively). In addition, there were significant differences between the POFI at 3- and 6-month follow-up (p = 0.020). However, the POFT values at 3- and 6-month follow-up appointments were significantly lower than those at baseline (p < 0.001). ::: ::: ::: CONCLUSION ::: The intensity of occlusal contacts of implant-supported prostheses opposed by natural dentition gradually increased after prosthesis insertion. Placement of single posterior implant-supported restoration decreased the percentage of occlusal force applied to contralateral arch. ::: ::: ::: CLINICAL SIGNIFICANCE ::: A periodic occlusal adjustment of implant-supported prostheses is necessary to prevent potential overloading from the movement of opposing natural dentition.


title: The Internet of Things: Vision & challenges

abstract: The Internet of Things (IoT) was of a vision in which all physical objects are tagged and uniquely identified using RFID transponders or readers. Nowadays, research into the IoT has extended this vision to the connectivity of Things to anything, anyone, anywhere and at anytime. The IoT has grown into multiple dimensions, which encompasses various networks of applications, computers, devices, as well as physical and virtual objects, referred to as things or objects, that are interconnected together using communication technologies such as, wireless, wired and mobile networks, RFID, Bluetooth, GPS systems, and other evolving technologies. This paradigm is a major shift from an essentially computer-based network model to a fully distributed network of smart objects. This change poses serious challenges in terms of architecture, connectivity, efficiency, security and provision of services among many others. This paper studies the state-of-the art of the IoT. In addition, some major security and privacy issues are described and a new attack vector is introduced, referred to as the “automated invasion attack”.


title: Quality-Driven Face Occlusion Detection and Recovery

abstract: This paper presents a framework to automatically detect and recover the occluded facial region. We first derive a Bayesian formulation unifying the occlusion detection and recovery stages. Then a quality assessment model is developed to drive both the detection and recovery processes, which captures the face priors in both global correlation and local patterns. Based on this formulation, we further propose GraphCut-based detection and confidence-oriented sampling to attain optimal detection and recovery respectively. Compared to traditional works in image repairing, our approach is distinct in three aspects: (1) it frees the user from marking the occlusion area by incorporating an automatic occlusion detector; (2) it learns a face quality model as a criterion to guide the whole procedure; (3) it couples the detection and occlusion stages to simultaneously achieve two goals: accurate occlusion detection and high quality recovery. The comparative experiments show that our method can recover the occluded faces with both the global coherence and local details well preserved.


title: Practical oblivious storage

abstract: We study oblivious storage (OS), a natural way to model privacy-preserving data outsourcing where a client, Alice, stores sensitive data at an honest-but-curious server, Bob. We show that Alice can hide both the content of her data and the pattern in which she accesses her data, with high probability, using a method that achieves O(1) amortized rounds of communication between her and Bob for each data access. We assume that Alice and Bob exchange small messages, of size O(N1/c), for some constant c>=2, in a single round, where N is the size of the data set that Alice is storing with Bob. We also assume that Alice has a private memory of size 2N1/c. These assumptions model real-world cloud storage scenarios, where trade-offs occur between latency, bandwidth, and the size of the client's private memory.


title: Mitigating Evil Twin Attacks in 802.11

abstract: Due to the prevalence of insecure open 802.11 access points, it is currently easy for a malicious party to launch a variety of attacks such as eavesdropping and data injection. In this paper, we consider a particular threat called the evil twin attack, which occurs when an adversary clones an open access point and exploits common automatic access point selection techniques to trick a wireless client into associating with the malicious access point. We propose two lines of defense against this attack. First, we present an evil twin detection strategy called context-leashing based upon recording the nearby access points when first associating with an access point. Using this contextual information, the client determines if an adversary has setup an evil twin access point at a different location. Next, we propose an SSH-style authentication method called EAP-SWAT to perform one-way access point authentication that fits into the extensible authentication protocol (EAP) framework.


title: Deep Multispectral Semantic Scene Understanding of Forested Environments Using Multimodal Fusion

abstract: Semantic scene understanding of unstructured environments is a highly challenging task for robots operating in the real world. Deep Convolutional Neural Network architectures define the state of the art in various segmentation tasks. So far, researchers have focused on segmentation with RGB data. In this paper, we study the use of multispectral and multimodal images for semantic segmentation and develop fusion architectures that learn from RGB, Near-InfraRed channels, and depth data. We introduce a first-of-its-kind multispectral segmentation benchmark that contains 15, 000 images and 366 pixel-wise ground truth annotations of unstructured forest environments. We identify new data augmentation strategies that enable training of very deep models using relatively small datasets. We show that our UpNet architecture exceeds the state of the art both qualitatively and quantitatively on our benchmark. In addition, we present experimental results for segmentation under challenging real-world conditions. Benchmark and demo are publicly available at http://deepscene.cs.uni-freiburg.de.


title: Prosthesis implantation after radial free flap phalloplasty in patients with bladder exstrophy

abstract: Methods: Inflatable penile prosthesis were implanted into sensate radial free‐flap phalloplasties in two adult patients with bladder exstrophy.Results: Neither patient reported loss of sensation, and both were able to inflate the prosthesis and engage in sexual intercourse three months later.Conclusion: The construction of a functional neophallus is well described however has not previously been reported in exstrophy patients who are suited to this procedure due to the presence of crura, and the asbsence of a neourethra. Scarring from previous reconstructive procedures however may make implantation difficult, and long‐term follow up is required to evaluate this procedure in patients with bladder exstrophy.


A comprehensive model for code readability


title: Multi-Task Graph Autoencoders

abstract: We examine two fundamental tasks associated with graph representation learning: link prediction and node classification. We present a new autoencoder architecture capable of learning a joint representation of local graph structure and available node features for the simultaneous multi-task learning of unsupervised link prediction and semi-supervised node classification. Our simple, yet effective and versatile model is efficiently trained end-to-end in a single stage, whereas previous related deep graph embedding methods require multiple training steps that are difficult to optimize. We provide an empirical evaluation of our model on five benchmark relational, graph-structured datasets and demonstrate significant improvement over three strong baselines for graph representation learning. Reference code and data are available at https://github.com/vuptran/graph-representation-learning


An Active Short Dipole Antenna with Remote Tuning


Phase Analysis and Dielectric Properties of Oxides Obtained in the MgO—(1-x)Nb2O5—(x)Ta2O5 System.


title: Dissociation of neural networks for anticipation and consumption of monetary and social rewards

abstract: Abstract Human behaviour is generally guided by the anticipation of potential outcomes that are considered to be rewarding. Reward processing can thus be dissected into a phase of reward anticipation and a phase of reward consumption. A number of brain structures have been suggested to be involved in reward processing. However, it is unclear whether anticipation and consumption are mediated by the same or different neural networks. We examined the neural basis of these processes using functional magnetic resonance imaging (fMRI) in an incentive delay task offering either money or social approval. In both conditions participants (N = 28) were given a cue indicating potential reward. In order to receive reward a target button had to be pushed within a certain time window (adapted for individual reaction time). Cues triggering either monetary or social reward anticipation were presented sessionwise. Imaging was performed on a 1.5-Tesla Philips scanner in an event-related design. Anticipation of both reward types activated brain structures constituting the brain reward system including the ventral striatum. In contrast to the task independent activity in the anticipation phase, reward consumption evoked different patterns of activation for money and social approval, respectively. While social stimuli were mainly associated with amygdala activation, the thalamus was more strongly activated by the presentation of monetary rewards. Our results identify dissociable neural networks for the anticipation and consumption of reward. The findings implicate that the neural mechanisms underlying reward consumption are more modality-specific than those for reward anticipation, and that they are mediated by subjective reward value.


title: Contrastive Principal Component Analysis

abstract: We present a new technique called contrastive principal component analysis (cPCA) that is designed to discover low-dimensional structure that is unique to a dataset, or enriched in one dataset relative to other data. The technique is a generalization of standard PCA, for the setting where multiple datasets are available -- e.g. a treatment and a control group, or a mixed versus a homogeneous population -- and the goal is to explore patterns that are specific to one of the datasets. We conduct a wide variety of experiments in which cPCA identifies important dataset-specific patterns that are missed by PCA, demonstrating that it is useful for many applications: subgroup discovery, visualizing trends, feature selection, denoising, and data-dependent standardization. We provide geometrical interpretations of cPCA and show that it satisfies desirable theoretical guarantees. We also extend cPCA to nonlinear settings in the form of kernel cPCA. We have released our code as a python package and documentation is on Github.


title: A study of zearalenone cytotoxicity on human peripheral blood mononuclear cells.

abstract: The mycotoxin zearalenone (ZEA) is a common contaminant of all major cereal grains worldwide with estrogenic and anabolic activity. We investigated the in vitro cytopathic effects of ZEA on freshly isolated human peripheral blood mononuclear cells (PBMC) in relation to proliferation and cell death patterns of untreated and mitogen-activated cells. The higher concentration of 30microg/ml ZEA was found to totally inhibit T and B lymphocyte proliferation from the stimulation with phytohemagglutinin and pokeweed mitogen. The inhibitory effects of ZEA were further related to cell necrosis/apoptosis. Flow cytometry analysis showed a distinct necrotic effect on PBMC, irrespective of mitogen stimulation, whereas apoptotic activity was less evident. Necrosis was observed in both the lymphocyte and monocyte/granulocyte gates. Measurements of ZEA-induced intracellular calcium ion (Ca(2+)) mobilization showed an increase of both Ca(2+) levels and the number of cells with high Ca(2+) only in the monocyte/granulocyte gated cells. Using phenylmethyl sulfonyl fluoride (PMSF), a serine protease inhibitor, and ammonium chloride (NH(4)Cl), a lysosomal inhibitor, both associated with cell necrosis inhibition, we showed that PMSF at 0.05mM and NH(4)Cl at 1 and 10mM reduced the cytopathic effects induced by 30microg/ml ZEA, whereas apoptosis was less affected. Expose of PBMC to 1microg/ml ZEA did not alter the viability of the cells. Our results suggest that high ZEA concentrations in the blood may well exert cytotoxic effects that merit further investigation.


title: Beyond-Regular Typestate

abstract: We present an extension for regular typestates, called Beyond- Regular Typestate(BR-Typestate), which is expressive enough to model non-regular properties of programs and protocols over data. We model the BR-Typestate system over a dependently typed, state based, impera- tive core language, and we prove its soundness and tractability. We have implemented a prototype typechecker for the language, and we show how several important, real world non-regular properties of programs and protocols can be verified.


title: POSTER: Watch Out Your Smart Watch When Paired

abstract: We coin a new term called \textit{data transfusion} as a phenomenon that a user experiences when pairing a wearable device with the host device. A large amount of data stored in the host device (e.g., a smartphone) is forcibly copied to the wearable device (e.g., a smart watch) due to pairing while the wearable device is usually less attended. To the best of knowledge, there is no previous work that manipulates how sensitive data is transfused even without user's consent and how users perceive and behave regarding such a phenomenon for smart watches. We tackle this problem by conducting an experimental study of data extraction from commodity devices, such as in Android Wear, watchOS, and Tizen platforms, and a following survey study with 205 smart watch users, in two folds. The experimental studies have shown that a large amount of sensitive data was transfused, but there was not enough user notification. The survey results have shown that users have lower perception on smart watches for security and privacy than smartphones, but they tend to set the same passcode on both devices when needed. Based on the results, we perform risk assessment and discuss possible mitigation that involves volatile transfusion.


title: CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition

abstract: In this paper, we investigate the robustness of traffic sign recognition algorithms under challenging conditions. Existing datasets are limited in terms of their size and challenging condition coverage, which motivated us to generate the Challenging Unreal and Real Environments for Traffic Sign Recognition (CURE-TSR) dataset. It includes more than two million traffic sign images that are based on real-world and simulator data. We benchmark the performance of existing solutions in real-world scenarios and analyze the performance variation with respect to challenging conditions. We show that challenging conditions can decrease the performance of baseline methods significantly, especially if these challenging conditions result in loss or misplacement of spatial information. We also investigate the effect of data augmentation and show that utilization of simulator data along with real-world data enhance the average recognition performance in real-world scenarios. The dataset is publicly available at this https URL


title: BCD8 from 7V to 70V: a new 0.l8μm Technology Platform to Address the Evolution of Applications towards Smart Power ICs with High Logic Contents

abstract: This paper presents the 8th BCD generation integrating power and high voltage devices in a 0.18 μm technology node platform. Both dense 1.8 V and 3.3 V logic CMOS are available to realize complex monolithic solutions in the field of smart power applications.


title: Does this answer your question? towards dialogue management for restricted domain question answering systems

abstract: Interactive Restricted Domain Question Answering Systems combine the in- teractivity of dialogue systems with the information retrieval features of ques- tion answering systems. The main problem when going from task-oriented dia- logue systems to interactive restricted domain question answering systems is that the lack of task structure prohibits making simplifying assumptions as in task- oriented dialogue systems. In order to address this issue, we propose a solution that combines representations based on keywords extracted from the user utter- ances with machine learning to learn the dialogue management function. More specifically, we propose to use Support Vector Machines to classify the dialogue state containing the extracted keywords in order to determine the next action to be taken by the dialogue manager. Much of the content selection for clarification question usually found in dialogue managers is moved to an instance-based gen- eration component. The proposed method has the advantage that it does not rely on an explicit representation of task structure as is necessary for task-oriented dialogue systems.


title: SnakeFighter - Development of a Water Hydraulic Fire Fighting Snake Robot

abstract: This paper presents the SnakeFighter concept and describes the generic element within this concept in the form of a water hydraulic snake robot. Applications of a SnakeFighter system are presented with focus on fire intervention tasks. The development of a water hydraulic snake robot that demonstrates the concept is described. The robot is the first water hydraulic snake robot ever constructed. The paper identifies design challenges of a complete SnakeFighter system and describes future research on this concept.


The M / M / 1 Fork-Join Queue with Variable SubTasks


title: Construction of Extended Technology Acceptance Model of Electronic Medical Records in Jordan: The Influence of Doctors ’ Self-Efficacy and Perceived Behavioral Control

abstract: The implementation of Electronic Medical Record (EMR) in Jordan public hospitals has started since 2009.The motivation of doing this study is to observe doctors’ acceptance of EMR in Jordanian public hospitals.This study aims at constructing doctors’ acceptance model of EMR using Technology Acceptance Model (TAM) based on individual capabilities.The model will apply TAM as the basic theory and extending Self-Efficacy (SE) and Perceived Behavioral Control (PBC) as two factors of individual characteristics. Initial findings show that the main factors of the model should compose of Perceived Usefulness, Perceived Ease of Use, Behavioral Intention to Use, Self-Efficacy, and Perceived Behavioral Control.The result of the pilot test shows that all factors are ::: reliable with Cronbach Alpa 0.838.The recommendation to the top management of healthcare organization, in order to increase doctors’ acceptance of EMR, that the new deployment of EMR should focus on delivering ::: awareness and continuous training on using EMR ::: among doctors at the early stage of EMR ::: implementation .


title: 1.8 kV AlGaN/GaN HEMTs with High-k/Oxide/SiN MIS Structure

abstract: We report the fabrication of AlGaN/GaN high electron mobility transistors (HEMTs) having metal-insulator-semiconductor (MIS) gate structure with the high-A/SiN and high-k/oxide/SiN insulator structures. The SiO2 and Al2O3 were used as the oxide, and the HfO2, ZrO2 and TiO2 were used as the high-k materials. Both high-k/SiN and high-k/oxide/SiN MIS-gate HEMTs showed good DC operating characteristics. However, there was significant difference in the breakdown voltage characteristics. In the case of the high-k/oxide/SiN MIS-HEMT, the off-state drain current was significantly reduced due to the presence of the SiO2 or Al2O3 layer, and the breakdown voltage characteristics were improved. The breakdown voltage of HfO2/SiO2/SiN and ZrO2/SiO2/SiN MIS-HEMT for the gate-drain distance Lgd = 28 mum were 1.8 kV and 1.7 kV, respectively.


title: Security Concerns in Popular Cloud Storage Services

abstract: The authors provide a systematic security analysis on the sharing methods of three major cloud storage and synchronization services: Dropbox, Google Drive, and Microsoft SkyDrive. They show that all three services have security weaknesses that may result in data leakage without users' awareness.


title: Quality care outcomes following transitional care interventions for older people from hospital to home: a systematic review

abstract: BackgroundProvision of high quality transitional care is a challenge for health care providers in many western countries. This systematic review was conducted to (1) identify and synthesise research, using randomised control trial designs, on the quality of transitional care interventions compared with standard hospital discharge for older people with chronic illnesses, and (2) make recommendations for research and practice.MethodsEight databases were searched; CINAHL, Psychinfo, Medline, Proquest, Academic Search Complete, Masterfile Premier, SocIndex, Humanities and Social Sciences Collection, in addition to the Cochrane Collaboration, Joanna Briggs Institute and Google Scholar. Results were screened to identify peer reviewed journal articles reporting analysis of quality indicator outcomes in relation to a transitional care intervention involving discharge care in hospital and follow-up support in the home. Studies were limited to those published between January 1990 and May 2013. Study participants included people 60 years of age or older living in their own homes who were undergoing care transitions from hospital to home. Data relating to study characteristics and research findings were extracted from the included articles. Two reviewers independently assessed studies for risk of bias.ResultsTwelve articles met the inclusion criteria. Transitional care interventions reported in most studies reduced re-hospitalizations, with the exception of general practitioner and primary care nurse models. All 12 studies included outcome measures of re-hospitalization and length of stay indicating a quality focus on effectiveness, efficiency, and safety/risk. Patient satisfaction was assessed in six of the 12 studies and was mostly found to be high. Other outcomes reflecting person and family centred care were limited including those pertaining to the patient and carer experience, carer burden and support, and emotional support for older people and their carers. Limited outcome measures were reported reflecting timeliness, equity, efficiencies for community providers, and symptom management.ConclusionsGaps in the evidence base were apparent in the quality domains of timeliness, equity, efficiencies for community providers, effectiveness/symptom management, and domains of person and family centred care. Further research that involves the person and their family/caregiver in transitional care interventions is needed.


title: Code Injection Attacks on HTML5-based Mobile Apps: Characterization, Detection and Mitigation

abstract: Due to the portability advantage, HTML5-based mobile apps are getting more and more popular.Unfortunately, the web technology used by HTML5-based mobile apps has a dangerous feature, which allows data and code to be mixed together, making code injection attacks possible. In this paper, we have conducted a systematic study on this risk in HTML5-based mobile apps. We found a new form of code injection attack, which inherits the fundamental cause of Cross-Site Scripting attack~(XSS), but it uses many more channels to inject code than XSS. These channels, unique to mobile devices, include Contact, SMS, Barcode, MP3, etc. To assess the prevalence of the code injection vulnerability in HTML5-based mobile apps, we have developed a vulnerability detection tool to analyze 15,510 PhoneGap apps collected from Google Play. 478 apps are flagged as vulnerable, with only 2.30\% false-positive rate. We have also implemented a prototype called NoInjection as a Patch to PhoneGap in Android to defend against the attack.


title: A novel XML document structure comparison framework based-on sub-tree commonalities and label semantics

abstract: XML similarity evaluation has become a central issue in the database and information communities, its applications ranging over document clustering, version control, data integration and ranked retrieval. Various algorithms for comparing hierarchically structured data, XML documents in particular, have been proposed in the literature. Most of them make use of techniques for finding the edit distance between tree structures, XML documents being commonly modeled as Ordered Labeled Trees. Yet, a thorough investigation of current approaches led us to identify several similarity aspects, i.e., sub-tree related structural and semantic similarities, which are not sufficiently addressed while comparing XML documents. In this paper, we provide an integrated and fine-grained comparison framework to deal with both structural and semantic similarities in XML documents (detecting the occurrences and repetitions of structurally and semantically similar sub-trees), and to allow the end-user to adjust the comparison process according to her requirements. Our framework consists of four main modules for (i) discovering the structural commonalities between sub-trees, (ii) identifying sub-tree semantic resemblances, (iii) computing tree-based edit operations costs, and (iv) computing tree edit distance. Experimental results demonstrate higher comparison accuracy with respect to alternative methods, while timing experiments reflect the impact of semantic similarity on overall system performance.


title: Big data, smart cities and city planning

abstract: I define big data with respect to its size but pay particular attention to the fact that the data I am referring to is urban data, that is, data for cities that are invariably tagged to space and time. I argue that this sort of data are largely being streamed from sensors, and this represents a sea change in the kinds of data that we have about what happens where and when in cities. I describe how the growth of big data is shifting the emphasis from longer term strategic planning to short-term thinking about how cities function and can be managed, although with the possibility that over much longer periods of time, this kind of big data will become a source for information about every time horizon. By way of conclusion, I illustrate the need for new theory and analysis with respect to 6 months of smart travel card data of individual trips on Greater London’s public transport systems.


title: Communicating with Virtual Humans.

abstract: The face is a small part of a human, but it plays an essential role in cemmunication. An open hybrid system for facial animation is presented. It encapsulates a considerable amount of information regarding facial models, movements, expressions, emotions, and speech. The complex description of facial animation can be handled better by assigning multiple input accessories. These input accessories may be a simple script, a multi-input musical keyboard, a gesture dialogue from the DataGlove, or some other type of interactive physical or virtual device. Integration of all means of control offers flexibility and freedom to the animator. The scope of such an open system is tremendous. The system described is written in C with the interface built on top of the Fifth Dimension Toolkit. The various input components are independent processes running on UNIX workstations. Communication between the processes is done through sockets in stream mode using Internet protocol. A figure illustrates the system processes. (Contains 17 references.) (MAS) * Reproductions supplied by EDRS are the best that can be made * from the original document. *********************************************************************** Communicating with Virtual Humans NADIA MAGNENAT THALMANN MIRALab, University of Geneva 24, rue du General-Dufour, CH 1204 Geneva, Switzerland E-Mail: thalmann@cui.unige.ch Abstract: In this paper, we present an open hybrid system for facial animation. It encapsulates a considerable amount of information regarding facial models, movements, expressions, emotions and speech. The complex description of facial animation can be handled better by assigning multiple input accessories. These input accessories may be a simple script or a multi-input musical keyboard or a gesture dialogue from the DataGlove or some other type of interactive physical or virtual device. Integration of all means of control offers flexibility and freedom to the animator. The scope of such an open system is tremendous. Virtual worlds would be desolate indeed without things like synthetic faces that we can relate to and understand. In this paper, we present an open hybrid system for facial animation. It encapsulates a considerable amount of information regarding facial models, movements, expressions, emotions and speech. The complex description of facial animation can be handled better by assigning multiple input accessories. These input accessories may be a simple script or a multi-input musical keyboard or a gesture dialogue from the DataGlove or some other type of interactive physical or virtual device. Integration of all means of control offers flexibility and freedom to the animator. The scope of such an open system is tremendous. Virtual worlds would be desolate indeed without things like synthetic faces that we can relate to and understand.


title: Joint Information Extraction and Reasoning: A Scalable Statistical Relational Learning Approach

abstract: A standard pipeline for statistical relational learning involves two steps: one first constructs the knowledge base (KB) from text, and then performs the learning and reasoning tasks using probabilistic first-order logics. However, a key issue is that information extraction (IE) errors from text affect the quality of the KB, and propagate to the reasoning task. In this paper, we propose a statistical relational learning model for joint information extraction and reasoning. More specifically, we incorporate context-based entity extraction with structure learning (SL) in a scalable probabilistic logic framework. We then propose a latent context invention (LCI) approach to improve the performance. In experiments, we show that our approach outperforms state-of-the-art baselines over three real-world Wikipedia datasets from multiple domains; that joint learning and inference for IE and SL significantly improve both tasks; that latent context invention further improves the results.


title: Numerical Simulation of Turbulent Flows in Centrifugal Compressor Stages With Different Radial Gaps

abstract: Numerical simulation of three-dimensional flow in a one-stage centrifugal compressor with a diffuser of variable geometry has been performed using the ANSYS CFX 10 code. The computations were conducted using steady and unsteady flow formulations and employing the RANS two-equation turbulence models. Steady-state flow simulations in the compressor were done for two vaned diffuser geometries with different radial gaps. A detailed comparison with the experimental data reported in the literature for different operating points of the “Radiver” test case compressor is presented and discussed. Good agreement of the computed velocity field with the measurements data is obtained at the impeller exit. Downstream of the diffuser vane, prediction quality depends on the operating point. Transient simulations performed for the best operating point of the compressor did not improve considerably predictions of flow characteristics in the diffuser as compared to the steady-state approach.Copyright © 2007 by ANSYS Germany GmbH and GE Global Research


title: Differentially Private Generative Adversarial Network

abstract: Generative Adversarial Network (GAN) and its variants have recently attracted intensive research interests due to their elegant theoretical foundation and excellent empirical performance as generative models. These tools provide a promising direction in the studies where data availability is limited. One common issue in GANs is that the density of the learned generative distribution could concentrate on the training data points, meaning that they can easily remember training samples due to the high model complexity of deep networks. This becomes a major concern when GANs are applied to private or sensitive data such as patient medical records, and the concentration of distribution may divulge critical patient information. To address this issue, in this paper we propose a differentially private GAN (DPGAN) model, in which we achieve differential privacy in GANs by adding carefully designed noise to gradients during the learning procedure. We provide rigorous proof for the privacy guarantee, as well as comprehensive empirical evidence to support our analysis, where we demonstrate that our method can generate high quality data points at a reasonable privacy level.


title: Tooth structure removal associated with various preparation designs for posterior teeth.

abstract: The amount of tooth structure removed for various innovative and conventional preparation designs for fixed prosthodontics was quantified. Four Typodont resin teeth representing maxillary and mandibular premolars and molars were prepared in various abutment designs: adhesive, box (A2); adhesive, wing and groove (A3); mesioocclusal or distoocclusal inlay; mesio-occlusodistal inlay (13); mesio-occlusodistal onlay; partial crown; half crown (only molars); complete crown, 0.8-mm circumferential tapered chamfer (F1); complete crown, 1.0-mm circumferential rounded shoulder; and complete crown, 1.4-mm axial reduction facial shoulder, 0.7-mm lingual chamfer (F3). After tooth preparation (10 per group), the root was separated from the anatomic crown at the cementoenamel junction. Removal of tooth structure was measured by gravimetric analysis in a high-precision balance. Preparations A3 and F3 were assigned as abutments for metal-supported restorations, whereas all other preparations were used for all-ceramic restorations. When the mean structure removal of all teeth tested was compared, the adhesive and inlay abutments were the least invasive preparation designs, ranging from approximately 5.5% (A2) to 27.2% (13) tooth structure removal. Complete crowns required the most invasive preparations, ranging from 67.5% (F1) to 75.6% (F3) tooth structure removal. The tooth structure removal required for F3 retainers was almost 14 times greater than for an A2 preparation. Tooth structure removal was also influenced by the morphology of the tooth. The first comprehensive tooth preparation design classification system was introduced. The measurement system used in this study provides an accurate method of quantifying tooth structure removal for fixed prosthodontic preparations. The innovative preparation designs studied conserved significant amounts of tooth structure, yielding a better prognosis for the restored tooth.


title: Development and test of a dual-stator, spoke-type ferrite permanent magnet motor with high torque performance for direct-drive applications

abstract: The minimization or elimination of the use of rare earth magnets in electric motors has been recently recognized as a challenging problem due to the increased price and limited supply of rare earth magnetic materials. Before the emergence of new cost-effective magnetic materials with high properties, the development of rare-earth-free motors using ferrite magnets with improved motor topologies is appreciated. In the previous research, a dual-stator, spoke-type ferrite permanent magnet (PM) motor with the phase-group concentrated-coil (PGCC) windings has been reported to have high torque performance, including high torque density and low torque pulsations. However, the previous research mainly investigated the motor performance of the conceptual topologies by the aid of finite element method (FEM). In this paper, the dual-stator, spoke-type ferrite PM motor, designed based on the specifications of the referenced washer motor from LG electronics, is analyzed by considering the practical topology for manufactured prototype. To enhance torque density, the rotor-PM overhang structure, considering the length of stator end windings, is determined for the proposed motor. The effects of the misaligned angles between the outer and inner stators on motor performance are analyzed in detail. The contribution of the proposed motor with rotor-PM overhang structure is highlighted by comparing to the performance of the referenced washer motor at the washing speed case, utilizing a 3-D FEM. Finally, the performance of the proposed motor is verified by experimental test, which indicates the test results and simulation results have good agreement.


title: Online Learning to Rank in Stochastic Click Models

abstract: Online learning to rank is an important problem in machine learning, information retrieval, recommender systems, and display advertising. Many provably efficient algorithms have been developed for this problem recently, under specific click models. A click model is a model of how users click on a list of documents. Though these results are significant, the proposed algorithms have limited application because they are designed for specific click models, and do not have guarantees beyond them. To overcome this challenge, we propose a novel algorithm, which we call MergeRank, for learning to rank in a class of click models that satisfy mild technical assumptions. This class encompasses two most fundamental click models, the cascade and position-based models. We derive a gap-dependent upper bound on the expected $n$-step regret of MergeRank and evaluate it on web search queries. We observe that MergeRank performs better than ranked bandits and is more robust than CascadeKL-UCB, an existing algorithm for learning to rank in the cascade model.


title: Stock prediction using deep learning

abstract: Stock market is considered chaotic, complex, volatile and dynamic. Undoubtedly, its prediction is one of the most challenging tasks in time series forecasting. Moreover existing Artificial Neural Network (ANN) approaches fail to provide encouraging results. Meanwhile advances in machine learning have presented favourable results for speech recognition, image classification and language processing. Methods applied in digital signal processing can be applied to stock data as both are time series. Similarly, learning outcome of this paper can be applied to speech time series data. Deep learning for stock prediction has been introduced in this paper and its performance is evaluated on Google stock price multimedia data (chart) from NASDAQ. The objective of this paper is to demonstrate that deep learning can improve stock market forecasting accuracy. For this, (2D)2PCA + Deep Neural Network (DNN) method is compared with state of the art method 2-Directional 2-Dimensional Principal Component Analysis (2D)2PCA + Radial Basis Function Neural Network (RBFNN). It is found that the proposed method is performing better than the existing method RBFNN with an improved accuracy of 4.8% for Hit Rate with a window size of 20. Also the results of the proposed model are compared with the Recurrent Neural Network (RNN) and it is found that the accuracy for Hit Rate is improved by 15.6%. The correlation coefficient between the actual and predicted return for DNN is 17.1% more than RBFNN and it is 43.4% better than RNN.


title: Modeling, simulation and optimization design of PCB planar transformer

abstract: In this paper, PCB planar transformer is introduced. Based on the physical structure, a new model of this kind of planar transformer is presented. The formula to calculate the parameters of the elements in the model is given. Simulating and experimental results validate the model and the calculation methods. Based on these results, the optimization design methods are introduced to reduce the bad influence from the parasitic elements. At last the shortage of this model is analyzed


title: Chronos: a graph engine for temporal graph analysis

abstract: Temporal graphs capture changes in graphs over time and are becoming a subject that attracts increasing interest from the research communities, for example, to understand temporal characteristics of social interactions on a time-evolving social graph. Chronos is a storage and execution engine designed and optimized specifically for running in-memory iterative graph computation on temporal graphs. Locality is at the center of the Chronos design, where the in-memory layout of temporal graphs and the scheduling of the iterative computation on temporal graphs are carefully designed, so that common "bulk" operations on temporal graphs are scheduled to maximize the benefit of in-memory data locality. The design of Chronos further explores the interesting interplay among locality, parallelism, and incremental computation in supporting common mining tasks on temporal graphs. The result is a high-performance temporal-graph system that offers up to an order of magnitude speedup for temporal iterative graph mining compared to a straightforward application of existing graph engines on a series of snapshots.


title: Knowledge Transfer with Jacobian Matching

abstract: Classical distillation methods transfer representations from a"teacher"neural network to a"student"network by matching their output activations. Recent methods also match the Jacobians, or the gradient of output activations with the input. However, this involves making some ad hoc decisions, in particular, the choice of the loss function. In this paper, we first establish an equivalence between Jacobian matching and distillation with input noise, from which we derive appropriate loss functions for Jacobian matching. We then rely on this analysis to apply Jacobian matching to transfer learning by establishing equivalence of a recent transfer learning procedure to distillation. We then show experimentally on standard image datasets that Jacobian-based penalties improve distillation, robustness to noisy inputs, and transfer learning.


title: Death by self-inflicted asphyxia with helium – First case reports from Norway and review of the literature

abstract: An increasing number of asphyxia suicides by inhalation of inert gases have been reported from different parts of the world over the last decade. So far this phenomenon has not been described in our country. This article presents the first two case reports from Norway of presumed suicide by asphyxiation due to helium inhalation from a closed plastic bag over the head. In both cases a forensic autopsy, which included comprehensive toxicological analysis, was requested and performed. In the two cases death was attributed to asphyxia due to helium inhalation, and suffocation due to a plastic bag over the head and aspiration of gastric contents, respectively. Toxicological analysis revealed no findings contributing to the deaths. The absence of toxicological and autopsy findings to determine the cause of death in such cases may represent challenges of clinical and forensic significance. In contrast to the promotion of this method by euthanasia interest groups for the terminally ill reported suicides by helium asphyxiation primarily involve relatively young individuals suffering from psychiatric and/or substance use disorders, and not from terminal illness.


title: Private Information, Credit Risk and Graph Structure in P2P Lending Networks

abstract: This research investigated the potential for improving Peer-to-Peer (P2P) credit scoring by using"private information"about communications and travels of borrowers. We found that P2P borrowers' ego networks exhibit scale-free behavior driven by underlying preferential attachment mechanisms that connect borrowers in a fashion that can be used to predict loan profitability. The projection of these private networks onto networks of mobile phone communication and geographical locations from mobile phone GPS potentially give loan providers access to private information through graph and location metrics which we used to predict loan profitability. Graph topology was found to be an important predictor of loan profitability, explaining over 5.5% of variability. Networks of borrower location information explain an additional 19% of the profitability. Machine learning algorithms were applied to the data set previously analyzed to develop the predictive model and resulted in a 4% reduction in mean squared error.


title: Using Object Affordances to Improve Object Recognition

abstract: The problem of object recognition has not yet been solved in its general form. The most successful approach to it so far relies on object models obtained by training a statistical method on visual features obtained from camera images. The images must necessarily come from huge visual datasets, in order to circumvent all problems related to changing illumination, point of view, etc. We hereby propose to also consider, in an object model, a simple model of how a human being would grasp that object (its affordance). This knowledge is represented as a function mapping visual features of an object to the kinematic features of a hand while grasping it. The function is practically enforced via regression on a human grasping database. After describing the database (which is publicly available) and the proposed method, we experimentally evaluate it, showing that a standard object classifier working on both sets of features (visual and motor) has a significantly better recognition rate than that of a visual-only classifier.


Sounds Wilde. Phonetically Extended Embeddings for Author-Stylized Poetry Generation


title: The neuropsychology of prospective memory in normal aging: A componential approach

abstract: To guide understanding of the neuropsychology of prospective memory and aging, we highlight several components of prospective memory, including planning an intended action, retrieving the action at the appropriate moment, and executing the action. We posit that frontal systems are particularly important for prospective memory tasks that require planning, that require strategic monitoring to detect the appropriate moment for executing the prospective memory intention, or for which execution of the retrieved intention must be delayed briefly. Drawing from a variety of approaches, including neuroimaging (with young adults) and studies examining individual differences relating to frontal functioning, we assemble preliminary evidence that supports this hypothesis. Further, because aging especially disrupts frontal functioning, the above noted prospective memory tasks would thus be expected to display the greatest age-related decline. The available literature confirms this expectation. A second key hypothesis is that some prospective memory tasks—those requiring minimal planning and supporting spontaneous retrieval—do not rely extensively on frontal processes but instead rely on medial-temporal structures for reflexive retrieval. These prospective memory tasks tend to show minimal or no age-related decline. The literature, though sparse with regard to the neuropsychological underpinnings of this kind of prospective memory task, is consistent with the present hypothesis.


title: A 19.5mW 1.5V 10-bit pipeline ADC for DVB-H systems in 0.35 /spl mu/m CMOS

abstract: This paper describes a 10-bit analog-to-digital converter (ADC) for digital video broadcasting-handheld (DVB-H) systems. The ADC is based on a 2.5-2.5-2.5-4 bits-per-stage pipeline architecture and occupies an area of 1.3 mm/sup 2/ in a 0.35 /spl mu/m CMOS process. At the target sampling rate of 20.48 MS/s, measured results show that the converter consumes 19.5 mW from a 1.5 V power supply and achieves 56 dB SNR and 60 dB SFDR. Effective resolution bandwidth is 100 MHz and energy consumption per conversion is 0.19 pJ.


title: Full-duplex wireless based on a small-form-factor analog self-interference canceller: demo

abstract: A demonstration of a real-time full-duplex wireless link is presented, in which a pair of full-duplex transceivers perform simultaneous transmission and reception on the same frequency channel. A full-duplex transceiver is composed of a custom-designed small-form-factor analog self-interference canceller, and a digital self-interference cancellation implementation is integrated with the National Instruments Universal Software Radio Peripheral (USRP). An adaptive analog self-interference canceller tuning mechanism adjusts to environmental changes. We demonstrate the practicality and robustness of the full-duplex wireless link through the National Instruments LabVIEW interface.


title: Consciousness as Integrated Information: a Provisional Manifesto

abstract: The integrated information theory (IIT) starts from phenomenology and makes use of thought experi- ments to claim that consciousness is integrated information. Specifically: (i) the quantity of consciousness corresponds to the amount of integrated information generated by a complex of elements; (ii) the quality of experience is spec- ified by the set of informational relationships generated within that complex. Integrated information () is defined as the amount of information generated by a complex of elements, above and beyond the information generated by its parts. Qualia space (Q) is a space where each axis represents a possible state of the complex, each point is a probability distribution of its states, and arrows between points represent the informational relationships among its elements generated by causal mechanisms (connections). Together, the set of informational relationships within a complex constitute a shape in Q that completely and univo- cally specifies a particular experience. Several observations concerning the neural substrate of consciousness fall natu- rally into place within the IIT framework. Among them are the association of consciousness with certain neural systems rather than with others; the fact that neural processes un- derlying consciousness can influence or be influenced by neural processes that remain unconscious; the reduction of consciousness during dreamless sleep and generalized sei- zures; and the distinct role of different cortical architectures in affecting the quality of experience. Equating conscious- ness with integrated information carries several implications for our view of nature.


title: Convex sets which are intersections of closed balls

abstract: In this paper we aim to investigate different questions concerning the stability of the set M of all intersections of closed balls in a normed space. We are mainly concerned with: (i) the stability of M under the closure of the vector sums; (ii) the stability under the addition of balls. We prove that (i) and (ii) are different properties which have strong connections with the geometry of the space. They have interest both in finite and infinite dimension. In the former case, there is a link with linear programming theory. We also study two more stability properties related to the well-known binary intersection property. Mazur sets and Mazur spaces are introduced, as a natural family satisfying (i). We prove that every two-dimensional normed space is a Mazur space, a result which distinguishes dimension d⩽2 from dimension d⩾3. We also discuss the connections between Mazur spaces and porosity.


title: SEIPS 2.0: a human factors framework for studying and improving the work of healthcare professionals and patients

abstract: Healthcare practitioners, patient safety leaders, educators and researchers increasingly recognise the value of human factors/ergonomics and make use of the discipline's person-centred models of sociotechnical systems. This paper first reviews one of the most widely used healthcare human factors systems models, the Systems Engineering Initiative for Patient Safety (SEIPS) model, and then introduces an extended model, 'SEIPS 2.0'. SEIPS 2.0 incorporates three novel concepts into the original model: configuration, engagement and adaptation. The concept of configuration highlights the dynamic, hierarchical and interactive properties of sociotechnical systems, making it possible to depict how health-related performance is shaped at 'a moment in time'. Engagement conveys that various individuals and teams can perform health-related activities separately and collaboratively. Engaged individuals often include patients, family caregivers and other non-professionals. Adaptation is introduced as a feedback mechanism that explains how dynamic systems evolve in planned and unplanned ways. Key implications and future directions for human factors research in healthcare are discussed.


title: Discovery of Topically Coherent Sentences for Extractive Summarization

abstract: Extractive methods for multi-document summarization are mainly governed by information overlap, coherence, and content constraints. We present an unsupervised probabilistic approach to model the hidden abstract concepts across documents as well as the correlation between these concepts, to generate topically coherent and non-redundant summaries. Based on human evaluations our models generate summaries with higher linguistic quality in terms of coherence, readability, and redundancy compared to benchmark systems. Although our system is unsupervised and optimized for topical coherence, we achieve a 44.1 ROUGE on the DUC-07 test set, roughly in the range of state-of-the-art supervised models.


Compilers: Principles, Techniques, and Tools (2nd Edition)


title: Machines, computers, dialectics: A new look at human intelligence

abstract: The more recent computer developments cause us to take a new look at human intelligence. The prevailing occidental view of human intelligence represents a very one-sided, logocentric approach, so that it is becoming more urgent to look for a more complete view. In this way, specific strengths of so-called human information processing are becoming particularly evident in a new way. To provide a general substantiation for this view, some elements of a phenomenological model for a dialectical coherence of human expressions of life are briefly outlined. The starting point is the everyday experience of constantly being confronted with contradictory situations. A model of polar, or dualistic, dialectic is proposed, which attempts to systematically establish the contradictions and contingencies of human life in theoretical structures. It is assumed that formal logic fails to work — strictly speaking — when applied to real situations of human interaction. Instead, definite negations are supposed to be involved. In this way, four polarities, pairs of concepts which are mutually dependent on each other and negate each other definitely, are presented: process and structure, the individual and general (societal), acting and imagining, subject-subject-relation and subject-object-relation (love and work). The latter gives rise to the dialectic of emotion and cognition, which plays a decisive role in a comparison of human and computer-“intelligence”.


title: Telomere length is paternally inherited and is associated with parental lifespan

abstract: Telomere length (TL) is emerging as a biomarker for aging and survival. To evaluate factors influencing this trait, we measured TL in a large homogeneous population, estimated the heritability (h(2)), and tested for parental effects on TL variation. Our sample included 356 men and 551 women, aged 18-92 years, from large Amish families. Mean TL in leukocytes was measured by quantitative PCR (mean: 6,198 +/- 1,696 bp). The h(2) of TL was 0.44 +/- 0.06 (P < 0.001), after adjusting for age, sex, and TL assay batch. As expected, TL was negatively correlated with age (r = -0.40; P < 0.001). There was no significant difference in TL between men and women, consistent with our previous findings that Amish men lived as long as Amish women. There was a stronger and positive correlation and association between TL in the offspring and paternal TL (r = 0.46, P < 0.001; beta = 0.22, P = 0.006) than offspring and maternal TL (r = 0.18, P = 0.04; beta = -0.02, P = 0.4). Furthermore, we observed a positive correlation and association between daughter's TL and paternal lifespan (r = 0.20, P < 0.001; beta = 0.21, P = 0.04), but not between daughter's TL and maternal lifespan (r = -0.01, beta = 0.04; both P = not significant). Our data, which are based on one of the largest family studies of human TL, support a link between TL and aging and lifespan and suggest a strong genetic influence, possibly via an imprinting mechanism, on TL regulation.


Learning to Discover Social Circles in Ego Networks


title: The use of data mining for basketball matches outcomes prediction

abstract: Sport result prediction is nowadays very popular among fans around the world, which particularly contributed to the expansion of sports betting. This makes the problem of predicting the results of sporting events, a new and interesting challenge. Consequently systems dealing with this problem are developed every day. This paper presents one such system, which uses data mining techniques in order to predict the outcomes of basketball games in NBA (National Basketball Association) league. The problem of predicting the game result is formalized as a classification problem, where the Naive Bayes method is used. Besides actual result, for each game system calculates the spread, by using multivariate linear regression. The MVC Model 2 pattern based software system is implemented. The system was evaluated on the dataset comprising 778 games from the regular part of the 2009/2010 NBA season and it correctly predicted the winners of about 67% of matches.


title: Reverse Recognition of the Digital Images for Chinese Cultural Relics

abstract: This research used digital image processing theory, reverse image recognition and processing technology to digitize cultural relic images. To achieve it, grey theory is employed to detect the edges of ancient relic images. With a self-developed Delphi program to carry out an automated reverse recognition system, we obtained the image outline data. The data was then exported as Bezier curves by using 3D CAD software, a Solidworks' Application Programming Interface (API) technology. Case study examined in this research used an ancient bronze and jade relic from the National Palace Museum in Taiwan. Digitized outline data for these images were used to carry out totem designs. This process allows us to combine ancient relic images through the use of information technology, to assist the display of digitized designs for the digital cultural and creativity industry.


title: High-Voltage, High-Power-Density DC-DC Converter for Capacitor Charging Applications

abstract: This paper presents a DC/DC converter topology for charging high voltage bulk capacitors for pulsed load applications. The topology is a compact variant of the flyback converter with completely soft switching, variable frequency control, and optimal magnetics design. This converter is capable of efficiently transferring power from 30 V input to a capacitor charging from 0 V to 6 kV. The specific application for this converter is a pulsed sparker for a sonobouy application, but this can apply to a large number of applications, such as defibrillators, communications, pulsed weapons systems, and industrial applications. The proposed converter was experimentally tested, and found to have peak efficiency greater than 92%, with an average efficiency greater than 89% for the entire charging cycle. The power density of the prototype is greater than 1 kW/L, with the potential to achieve 2.5 kW/L.


title: Intercell busbar design for copper electrowining

abstract: The design of intercell bars is of primary relevance to control electrowinning costs. Two intercell technologies for designing these busbars are presently available: current source (CS) or voltage source. For this research project, we selected CS based on tankhouse operational data that consistently show better performance than its counterpart. Both technologies can be implemented in dogbone or central busbar configurations. Dogbone was chosen because of the better contact distance and larger heatsink area. The proposed design ensures hot swapability. Conventional busbars suffer premature aging, corrosion, and contact deterioration. Industrial samples show these phenomena. Multiphysics finite-element method modeling highlights the origin of this accelerated aging. This work provides design guidelines for S-shaped connectors to overcome previous design flaws. For completeness, the paper includes a design example for an industrial site. The resulting design surpasses standard design practices and secures hot-spot free operation.


title: Space object classification using deep neural networks

abstract: Space object classification is desired for space situational awareness to be able to discern resident space object (RSO) characteristics, behaviors, and perspective changes. Due to the limited sensing resources and observations, it is challenging for space object classification to be responsive to unfolding and unexpected events. Many machine learning algorithms are already used to classify space objects based on various sensor observations from radar and telescope. In this paper, the use of deep neural networks (DNN) is proposed to classify space objects due to DNN robust performance in many classification tasks, such as face recognition and object recognition. This paper explores DNN using light curve data. Conventional classification algorithms, such as k nearest neighbor (k-NN), are implemented and compared to the proposed DNN based classification algorithms, including the popular convolutional neural network (CNN) and the recurrent neural network (RNN), in terms of accuracy. Inherent advantages and disadvantages of the deep neural network based classification algorithms are summarized and the potential for future space object classification tasks is analyzed and postulated.


title: Bandwidth Partitioning in Decentralized Wireless Networks

abstract: This paper addresses the following question, which is of interest in the design of a multiuser decentralized network. Given a total system bandwidth of W Hz and a fixed data rate constraint of R bps for each transmission, how many frequency slots N of size W/N should the band be partitioned into in order to maximize the number of simultaneous links in the network? Dividing the available spectrum results in two competing effects. On the positive side, a larger N allows for more parallel, non- interfering communications to take place in the same area. On the negative side, a larger N increases the SINR requirement for each link because the same information rate must be achieved over less bandwidth. Exploring this tradeoff and determining the optimum value of N in terms of the system parameters is the focus of the paper. Using stochastic geometry, the optimal SINR threshold - which directly corresponds to the optimal spectral efficiency - is derived for both the low SNR (power-limited) and high SNR (interference-limited) regimes. This leads to the optimum choice of the number of frequency bands N in terms of the path loss exponent, power and noise spectral density, desired rate, and total bandwidth.


title: A Vision Based System for Monitoring the Loss of Attention in Automotive Drivers

abstract: On board monitoring of the alertness level of an automotive driver has been a challenging research in transportation safety and management. In this paper, we propose a robust real time embedded platform to monitor the loss of attention of the driver during day as well as night driving conditions. The PERcentage of eye CLOSure (PERCLOS) has been used as the indicator of the alertness level. In this approach, the face is detected using Haar like features and tracked using a Kalman Filter. The Eyes are detected using Principal Component Analysis (PCA) during day time and the block Local Binary Pattern (LBP) features during night. Finally the eye state is classified as open or closed using Support Vector Machines(SVM). In plane and off plane rotations of the drivers face have been compensated using Affine and Perspective Transformation respectively. Compensation in illumination variation is carried out using Bi Histogram Equalization (BHE). The algorithm has been cross validated using brain signals and finally been implemented on a Single Board Computer (SBC) having Intel Atom processor, 1 GB RAM, 1.66 GHz clock, x86 architecture, Windows Embedded XP operating system. The system is found to be robust under actual driving conditions.


title: BlueStreaming: towards power-efficient internet P2P streaming to mobile devices

abstract: P2P streaming applications are very popular on the Internet today. However, a mobile device in P2P streaming not only needs to continuously receive streaming data from other peers for its playback, but also needs to continuously exchange control information (e.g., buffermaps and file chunk requests) with neighboring peers and upload the downloaded streaming data to them. These lead to excessive battery power consumption on the mobile device. In this paper, we first conduct Internet experiments to study in-depth the impact of control traffic and uploading traffic on battery power consumption with several popular Internet P2P streaming applications. Motivated by measurement results, we design and implement a system called BlueStreaming that effectively utilizes the commonly existing Bluetooth interface on mobile devices. Instead of activating WiFi and Bluetooth interfaces alternatively, BlueStreaming keeps Bluetooth active all the time to transmit delay-sensitive control traffic while using WiFi for streaming data traffic. BlueStreaming trades Bluetooth's power consumption for much more significant energy saving from shaped WiFi traffic. To evaluate the performance of BlueStreaming, we have implemented prototypes on both Windows and Mac to access existing popular Internet P2P streaming services. The experimental results show that BlueStreaming can save up to 46% battery power compared to the commodity PSM scheme.


title: Future challenges and present ethical considerations in the use of personalized nutrition based on genetic advice.

abstract: N UTRITIONAL RECOMMENDATIONS DIRECTED toward the general population are based mainly on estimated average nutrient requirements for a target group. These norms intend both to meet the needs of most individuals in a community and to prevent common diseases, such as obesity, diabetes, and cardiovascular problems. Personalized nutrition guidelines are currently focused on people with metabolic disorders. Infants with inborn errors of metabolism (eg, maple syrup urine disease, phenylketonuria) or adults carrying specific genetic monogenic disorders (eg, familial hypercholesterolemia, hypolactasia) benefit from genetic-based dietary advice. Current management of nutritional and metabolic diseases is based mainly on clinical examinations and dietary advice. However, as a result of the Human Genome Project, advances in molecular biology and “omics technologies” have emerged in the last few years. This progress in genetic technologies is contributing to the extension of this practice to other diseases with gene-related backgrounds in order to individualize the dietary prescription under genetic premises. Decreased costs and increased analytical speed have boosted the commercialization of robust genetic testing for clinician and consumer use. However, important concerns must be addressed, such as validity and correlation with specific nutritional diseases, target population, and the inter-relationships of single genetic variants with unknown, compensatory genetic mechanisms or nutrient gene interactions. Nutrigenetics envisages dietary personalization through a genetic “reading” of nucleotide sequence and polymorphisms (single-nucleotide polymorphisms [SNPs]) in order to offer an individualized dietary prescription. This approach is based on the detection of those genetic variants (alleles) that can influence the patient’s development of nutritionor metabolism-related diseases. Knowledge of genetic traits can complement the data collected by the registered dietitian (RD) and other health professionals concerning anthropometrics, biochemical analyses, and dietary assessment. Also, it would contribute to more deeply customized recommendations for dietary and lifestyle modifications. Genetic variations can predict susceptibility to health problems caused by poor diet or detrimental lifestyle habits. Therefore, nutrigenetics represents a promising preventive tool in primary care by providing early information to RDs about environmental and genetic interactions influencing obesity and other nutritionally related diseases. In fact, it is an instrument for delaying or preventing disease onset through personalized dietary advice. Diverse companies are considering the potential of this applied nutrition field and a number of genetic test panels have emerged in the last 10 years. Most of them are marketed using the internet through affordable direct to consumer testing because it is considered a good medium to reach people and showcase new technology. However, this system of publicity and sale involves ethical concerns because the web is open and the seller could offer services and information with insufficient scientific accuracy or exclusively with commercially driven interest. Direct to consumer testing could also provide biased information to clients with not enough knowledge. These new tools are worthless without adequate interpretation and subsequent translation into colloquial language by trained RDs. These professionals should have the ability to assess and recommend the best genetic test by evaluating the assayed genetic variants, price, available information, quality, and other values related to nutritional interactions. The aim of this commentary is not only to provide an overview of the genetic test panels currently available in the US and European markets, but also to provide information about the analyzed genetic variants and the scientific evidence relying on these variants in order to help RDs and other nutrition and health professionals choose suitable products and use the information provided critically. To develop this search, a systematic review of the websites of genetic diagnostic laboratories was performed during 2012. Apart from general Google pages, we used specialized search engines (such as the National Center for Biotechnology Information and SNPedia) and biomedical research partnerships (eg, ASEBIO, in Spain). To interpret raw data, the matches and mismatches between genes analyzed in Spain and the United States were specifically checked.


title: Expected reciprocal rank for graded relevance

abstract: While numerous metrics for information retrieval are available in the case of binary relevance, there is only one commonly used metric for graded relevance, namely the Discounted Cumulative Gain (DCG). A drawback of DCG is its additive nature and the underlying independence assumption: a document in a given position has always the same gain and discount independently of the documents shown above it. Inspired by the "cascade" user model, we present a new editorial metric for graded relevance which overcomes this difficulty and implicitly discounts documents which are shown below very relevant documents. More precisely, this new metric is defined as the expected reciprocal length of time that the user will take to find a relevant document. This can be seen as an extension of the classical reciprocal rank to the graded relevance case and we call this metric Expected Reciprocal Rank (ERR). We conduct an extensive evaluation on the query logs of a commercial search engine and show that ERR correlates better with clicks metrics than other editorial metrics.


Searching for learner-centered, constructivist, and sociocultural components of collaborative educational learning tools


title: Application of Convolutional Neural Networks to Language Identification in Noisy Conditions

abstract: This paper proposes two novel frontends for robust language identification (LID) using a convolutional neural network (CNN) trained for automatic speech recognition (ASR). In the CNN/i-vector frontend, the CNN is used to obtain the posterior probabilities for i-vector training and extraction instead of a universal background model (UBM). The CNN/posterior frontend is somewhat similar to a phonetic system in that the occupation counts of (tied) triphone states (senones) given by the CNN are used for classification. They are compressed to a low dimensional vector using probabilistic principal component analysis (PPCA). Evaluated on heavily degraded speech data, the proposed front ends provide significant improvements of up to 50% on average equal error rate compared to a UBM/i-vector baseline. Moreover, the proposed frontends are complementary and give significant gains of up to 20% relative to the best single system when combined.


Analysis of LC-LC2 Compensated Inductive Power Transfer for High Efficiency and Load Independent Voltage Gain