abstract: k-Nearest Neighbor Classification Algorithm for Multiple Choice Sequential Sampling Yung-Kyun Noh (nohyung@snu.ac.kr) Frank Chongwoo Park (fcp@snu.ac.kr) School of Mechanical and Aerospace Engineering, Seoul National University Seoul 151-744, Korea Daniel D. Lee (ddlee@seas.upenn.edu) Department of Electrical and Systems Engineering, University of Pennsylvania Philadelphia, PA 19104, USA Abstract Decision making from sequential sampling, especially when more than two alternative choices are possible, requires appro- priate stopping criteria to maximize accuracy under time con- straints. Optimal conditions for stopping have previously been investigated for modeling human decision making processes. In this work, we show how the k-nearest neighbor classification algorithm in machine learning can be utilized as a mathemati- cal framework to derive a variety of novel sequential sampling models. We interpret these nearest neighbor models in the con- text of diffusion decision making (DDM) methods. We com- pare these nearest neighbor methods to exemplar-based models and accumulator models, such as Race and LCA. Computa- tional experiments show that the new models demonstrate sig- nificantly higher accuracy given equivalent time constraints. Keywords: sequential sampling; decision making; diffusion decision making model; k-nearest neighbor classification; evi- dence; sequential probability ratio test Introduction Whenever a faster decision is required to save time and re- sources, the decision making process should focus on choos- ing whether to proceed with a decision in light of the given in- formation or to postpone the decision in order to collect more information for a higher confidence level. In many previous and recent psychology works, various computational models have been introduced seeking to explain the speed-accuracy tradeoff and to understand the decision making process in hu- mans. However, apart from the understanding of individual models, there has been little systematic way of understand- ing these models in one mathematically unified framework. Moreover, multiple-choice problems were not discussed in- tensively in any of the methods. The optimality in decision making with sequential sam- pling is discussed with the optimality in speed-accuracy tradeoff. In other words, the objective of the present work is to seek the fastest decision with the same average accuracy or the maximum accuracy if the same average decision time is used. Sequential sampling methods such as Race (Smith & Vickers, 1988; Vickers, 1970), diffusion decision making (DDM) (Ratcliff, 1978; Ratcliff & Rouder, 2000; Shadlen, Hanks, Churchland, Kiani, & Yang, 2006; Ratcliff & Mck- oon, 2008), and leaky competing accumulator (LCA) (Usher & McClelland, 2001; Bogacz, Usher, Zhang, & McClelland, 2007) are all interested in explaining this optimality in the speed-accuracy tradeoff. In these methods, one or more vari- ables are commonly introduced for accumulating sampled in- formation, and a criterion is used to determine whether to continue collecting more information or to make a decision with given information. Here, we propose a common mathe- matical framework combining these methods and providing a systematic explanation for understanding different methods. Our framework combining sequential sampling methods is the k-nearest neighbor (NN) classification in machine learn- ing. The sequential sampling situation with multiple choices is explained as the multiway k-NN classification from the the- oretical analysis on k-NNs in the asymptotic situation. Due to this connection, we can interpret all different types of se- quential sampling methods as different methods of choosing k adaptively in k-NN classification. By further analyzing the strategy of choosing k in k-NN classification using the Se- quential Probability Ratio Test (SPRT) (Wald & Wolfowitz, 1948) and Bayesian inference, we can obtain five different ac- cumulating variable and stopping criteria for optimal tradeoff. Interestingly, all these five optimal methods are interpreted as different kinds of DDM strategies. Our work is directly applied to a recently reported neuro- scientific decision making mechanism. The proposed mech- anism considers an output neuron which sends out a decision result. By collecting Poisson spike trains from different neu- rons, the output neuron makes a decision about which neuron gives Poisson spikes at the highest rate (Shadlen & Newsome, 1998; Ma, Beck, Latham, & Pouget, 2006; Beck et al., 2008; Zhang & Bogacz, 2010). The output neuron can achieve op- timality by using our proposed strategies. The proposed method can be compared with traditional ex- emplar models which explain memory retrieval using similar- ity weighted voting based on stored exemplars. Our work is different from this line of research by using majority voting of adaptively chosen k number of NNs. We discuss the ad- vantages and disadvantages of our method when it is applied to the memory retrieval problem. The rest of the paper is organized as follows. We introduce the sequential sampling problem in Section 2 especially from the point of view of multiple-choice. In Section 3, we in- troduce problems to which sequential sampling methods can be applied, and we show how k-NN classification can be natu-


IC-CRIME : A Collaborative , Web-Based , 3 D System for the Investigation , Analysis , and Annotation of Crime Scenes


title: A Novel Weight Initialization Method for the Random Neural Network

abstract: In this paper, we propose a novel weight initialization method for the random neural network. The method relies on approximating the signal-flow equations of the network to obtain a linear system of equations with nonnegativity constraints. For the solution of the formulated linear nonnegative least squares problem we have developed a projected gradient algorithm. It is shown that supervised learning with the developed initialization method has better performance in terms of both solution quality and execution time than learning with random initialization when applied to a combinatorial optimization emergency response problem.


title: Depression in late life

abstract: Despite its prevalence and seriousness, depression in late life remains underappreciated as a source of disability and suffering for older people and their families. Despite a solid and substantial body of research, recognition of depression remains problematic and is often attributed to normal developmental changes in aging. Treatment efficacy data notwithstanding, the adequacy and appropriateness of treatment is highly variable. This paper contains a broad overview of new research developments in depression in late life and the disabilities associated with it Serving as an introduction to the specific papers that follow in this issue of Dialogues in Clinical Neuroscience, this article scans the knowledge base in basic, clinical, and health services research, identifying the highlights of current work in the area and proposing areas of needed expansion of research efforts.


title: A classification of data quality assessment and improvement methods

abstract: Data quality (DQ) assessment and improvement in larger information systems would often not be feasible without using suitable ‘DQ methods’, which are algorithms that can be automatically executed by computer systems to detect and/or correct problems in datasets. Currently, these methods are already essential, and they will be of even greater importance as the quantity of data in organisational systems grows. This paper provides a review of existing methods for both DQ assessment and improvement and classifies them according to the DQ problem and problem context. Six gaps have been identified in the classification, where no current DQ methods exist, and these show where new methods are required as a guide for future research and DQ tool development.


Scientific Errors, Atrocities and Blunders


title: Eigenspace-based anomaly detection in computer systems

abstract: We report on an automated runtime anomaly detection method at the application layer of multi-node computer systems. Although several network management systems are available in the market, none of them have sufficient capabilities to detect faults in multi-tier Web-based systems with redundancy. We model a Web-based system as a weighted graph, where each node represents a "service" and each edge represents a dependency between services. Since the edge weights vary greatly over time, the problem we address is that of anomaly detection from a time sequence of graphs.In our method, we first extract a feature vector from the adjacency matrix that represents the activities of all of the services. The heart of our method is to use the principal eigenvector of the eigenclusters of the graph. Then we derive a probability distribution for an anomaly measure defined for a time-series of directional data derived from the graph sequence. Given a critical probability, the threshold value is adaptively updated using a novel online algorithm.We demonstrate that a fault in a Web application can be automatically detected and the faulty services are identified without using detailed knowledge of the behavior of the system.


title: Manipulation Planning Among Movable Obstacles

abstract: This paper presents the resolve spatial constraints (RSC) algorithm for manipulation planning in a domain with movable obstacles. Empirically we show that our algorithm quickly generates plans for simulated articulated robots in a highly nonlinear search space of exponential dimension. RSC is a reverse-time search that samples future robot actions and constrains the space of prior object displacements. To optimize the efficiency of RSC, we identify methods for sampling object surfaces and generating connecting paths between grasps and placements. In addition to experimental analysis of RSC, this paper looks into object placements and task-space motion constraints among other unique features of the three dimensional manipulation planning domain.


title: Data2Vis: Automatic Generation of Data Visualizations Using Sequence-to-Sequence Recurrent Neural Networks

abstract: Rapidly creating effective visualizations using expressive grammars is challenging for users who have limited time and limited skills in statistics and data visualization. Even high-level, dedicated visualization tools often require users to manually select among data attributes, decide which transformations to apply, and specify mappings between visual encoding variables and raw or transformed attributes. In this paper we introduce Data2Vis, an end-to-end trainable neural translation model for automatically generating visualizations from given datasets. We formulate visualization generation as a language translation problem, where data specifications are mapped to visualization specifications in a declarative language (Vega-Lite). To this end, we train a multilayered attention-based encoder–decoder network with long short-term memory (LSTM) units on a corpus of visualization specifications. Qualitative results show that our model learns the vocabulary and syntax for a valid visualization specification, appropriate transformations (count, bins, mean), and how to use common data selection patterns that occur within data visualizations. We introduce two metrics for evaluating the task of automated visualization generation (language syntax validity, visualization grammar syntax validity) and demonstrate the efficacy of bidirectional models with attention mechanisms for this task. Data2Vis generates visualizations that are comparable to manually created visualizations in a fraction of the time, with potential to learn more complex visualization strategies at scale.


title: Semantically Enhanced Software Traceability Using Deep Learning Techniques

abstract: In most safety-critical domains the need for trace-ability is prescribed by certifying bodies. Trace links are generally created among requirements, design, source code, test cases and other artifacts; however, creating such links manually is time consuming and error prone. Automated solutions use information retrieval and machine learning techniques to generate trace links; however, current techniques fail to understand semantics of the software artifacts or to integrate domain knowledge into the tracing process and therefore tend to deliver imprecise and inaccurate results. In this paper, we present a solution that uses deep learning to incorporate requirements artifact semantics and domain knowledge into the tracing solution. We propose a tracing network architecture that utilizes Word Embedding and Recurrent Neural Network (RNN) models to generate trace links. Word embedding learns word vectors that represent knowledge of the domain corpus and RNN uses these word vectors to learn the sentence semantics of requirements artifacts. We trained 360 different configurations of the tracing network using existing trace links in the Positive Train Control domain and identified the Bidirectional Gated Recurrent Unit (BI-GRU) as the best model for the tracing task. BI-GRU significantly out-performed state-of-the-art tracing methods including the Vector Space Model and Latent Semantic Indexing.


title: Mining periodic behaviors for moving objects

abstract: Periodicity is a frequently happening phenomenon for moving objects. Finding periodic behaviors is essential to understanding object movements. However, periodic behaviors could be complicated, involving multiple interleaving periods, partial time span, and spatiotemporal noises and outliers. In this paper, we address the problem of mining periodic behaviors for moving objects. It involves two sub-problems: how to detect the periods in complex movement, and how to mine periodic movement behaviors. Our main assumption is that the observed movement is generated from multiple interleaved periodic behaviors associated with certain reference locations. Based on this assumption, we propose a two-stage algorithm, Periodica, to solve the problem. At the first stage, the notion of observation spot is proposed to capture the reference locations. Through observation spots, multiple periods in the movement can be retrieved using a method that combines Fourier transform and autocorrelation. At the second stage, a probabilistic model is proposed to characterize the periodic behaviors. For a specific period, periodic behaviors are statistically generalized from partial movement sequences through hierarchical clustering. Empirical studies on both synthetic and real data sets demonstrate the effectiveness of our method.


title: Integrating Document Clustering and Multidocument Summarization

abstract: Document understanding techniques such as document clustering and multidocument summarization have been receiving much attention recently. Current document clustering methods usually represent the given collection of documents as a document-term matrix and then conduct the clustering process. Although many of these clustering methods can group the documents effectively, it is still hard for people to capture the meaning of the documents since there is no satisfactory interpretation for each document cluster. A straightforward solution is to first cluster the documents and then summarize each document cluster using summarization methods. However, most of the current summarization methods are solely based on the sentence-term matrix and ignore the context dependence of the sentences. As a result, the generated summaries lack guidance from the document clusters. In this article, we propose a new language model to simultaneously cluster and summarize documents by making use of both the document-term and sentence-term matrices. By utilizing the mutual influence of document clustering and summarization, our method makes; (1) a better document clustering method with more meaningful interpretation; and (2) an effective document summarization method with guidance from document clustering. Experimental results on various document datasets show the effectiveness of our proposed method and the high interpretability of the generated summaries.


title: Pole-Like Road Object Detection From Mobile Lidar System Using a Coarse-to-Fine Approach

abstract: The requirements of three-dimensional (3-D) road objects have increased for various applications, such as geographic information systems and intelligent transportation systems. The use of mobile lidar systems (MLSs) running along road corridors is an effective way to collect accurate road inventories, but MLS feature extraction is challenged by the blind scanning characteristics of lidar systems and the huge amount of data involved; therefore, an automatic process for MLS data is required to improve efficiency of feature extraction. This study developed a coarse-to-fine approach for the extraction of pole-like road objects from MLS data. The major work consists of data preprocessing, coarse-to-fine segmentation, and detection. In data preprocessing, points from different trajectories were reorganized into road parts, and building facades alongside road corridors were removed to reduce their influence. Then, a coarse-to-fine computational framework for the detection of pole-like objects that segments point clouds was proposed. The results show that the pole-like object detection rate for the proposed method was about 90%, and the proposed coarse-to-fine framework was more efficient than the single-scale framework. These results indicate that the proposed method can be used to effectively extract pole-like road objects from MLS data.


title: An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets

abstract: Owing to improvements in image recognition via deep learning, machine-learning algorithms could eventually be applied to automated medical diagnoses that can guide clinical decision-making. However, these algorithms remain a ‘black box’ in terms of how they generate the predictions from the input data. Also, high-performance deep learning requires large, high-quality training datasets. Here, we report the development of an understandable deep-learning system that detects acute intracranial haemorrhage (ICH) and classifies five ICH subtypes from unenhanced head computed-tomography scans. By using a dataset of only 904 cases for algorithm training, the system achieved a performance similar to that of expert radiologists in two independent test datasets containing 200 cases (sensitivity of 98% and specificity of 95%) and 196 cases (sensitivity of 92% and specificity of 95%). The system includes an attention map and a prediction basis retrieved from training data to enhance explainability, and an iterative process that mimics the workflow of radiologists. Our approach to algorithm development can facilitate the development of deep-learning systems for a variety of clinical applications and accelerate their adoption into clinical practice. An interpretable deep-learning algorithm trained on a small dataset of computed-tomography scans of the head detects acute ICH and classifies the pathology subtypes, with a performance comparable to expert radiologists.


title: A Framework for Game-Based Security Proofs ⋆

abstract: To be accepted, a cryptographic scheme must come with a proof that it satisfies some standard security properties. However, because cryptographic schemes are based on non-trivial mathematics, proofs are error-prone and difficult to check. The main contributions of this paper are a refinement of the game-based approach to security proofs, and its implementation on top of the proof assistant Coq. The proof assistant checks that the proof is correct and deals with the mundane part of the proof. An interesting feature of our framework is that our proofs are formal enough to be mechanically checked, but still readable enough to be humanly checked. We illustrate the use of our framework by proving in a systematic way the so-called semantic security of the encryption scheme Elgamal and its hashed version.


title: The generation of hexahedral meshes for assembly geometry : survey and progress †

abstract: SUMMARY The nite element method is being used today to model component assemblies in a wide variety of application areas, including structural mechanics, uid simulations, and others. Generating hexahedral meshes for these assemblies usually requires the use of geometry decomposition, with dierent meshing algorithms applied to dierent regions. While the primary motivation for this approach remains the lack of an automatic, reliable all-hexahedral meshing algorithm, requirements in mesh quality and mesh conguration for typical analyses are also factors. For these reasons, this approach is also sometimes required when producing other types of unstructured meshes. This paper will review progress to date in automating many parts of the hex meshing process, which has halved the time to produce all-hex meshes for large assemblies. Particular issues which have been exposed due to this progress will also be discussed, along with their applicability to the general unstructured meshing problem. Published in 2001 by John Wiley & Sons, Ltd.


title: Mapping Keywords to Linked Data Resources for Automatic Query Expansion

abstract: Linked Data is a gigantic, constantly growing and extremely valuable resource, but its usage is still heavily dependent on (i) the familiarity of end users with RDF’s graph data model and its query language, SPARQL, and (ii) knowledge about available datasets and their contents. Intelligent keyword search over Linked Data is currently being investigated as a means to overcome these barriers to entry in a number of different approaches, including semantic search engines and the automatic conversion of natural language questions into structured queries. Our work addresses the specific challenge of mapping keywords to Linked Data resources, and proposes a novel method for this task. By exploiting the graph structure within Linked Data we determine which properties between resources are useful to discover, or directly express, semantic similarity. We also propose a novel scoring function to rank results. Experiments on a publicly available dataset show a 17% improvement in Mean Reciprocal Rank over the state of the art.


title: Discrete fixed-resolution representations in visual working memory

abstract: Limits on the storage capacity of working memory significantly affect cognitive abilities in a wide range of domains, but the nature of these capacity limits has been elusive. Some researchers have proposed that working memory stores a limited set of discrete, fixed-resolution representations, whereas others have proposed that working memory consists of a pool of resources that can be allocated flexibly to provide either a small number of high-resolution representations or a large number of low-resolution representations. Here we resolve this controversy by providing independent measures of capacity and resolution. We show that, when presented with more than a few simple objects, human observers store a high-resolution representation of a subset of the objects and retain no information about the others. Memory resolution varied over a narrow range that cannot be explained in terms of a general resource pool but can be well explained by a small set of discrete, fixed-resolution representations.


title: An overview of low-voltage VCO delay cells and a worst-case analysis of supply noise sensitivity

abstract: This paper presents a comparative study of the architecture and timing jitter of the delay-cells of low-voltage CMOS ring-VCOs. Design considerations, such as noise, single-ended versus differential, linearity and symmetry of load, and the output voltage swing of delay cells are examined in detail. The worst-case sensitivity of the delay time of the delay cells and that of the oscillation frequency of corresponding ring-VCO implemented in TSMC 0.18 /spl mu/m CMOS technology are analyzed using Cadence's Spectre with BSIM3v3 device models. Simulation results are presented.


title: Hand gesture to speech conversion using Matlab

abstract: Hand gesture is one of the typical methods used in sign language for non-verbal communication. Sign gestures are a non-verbal visual language, different from the spoken language, but serving the same function. It is often very difficult for the hearing impaired community to communicate their ideas and creativity to the normal humans. This paper presents a system that will not only automatically recognize the hand gestures but also convert it into corresponding speech output so that speaking impaired person can easily communicate with normal people. The gesture to speech system, G2S, has been developed using the skin colour segmentation. The system consists of camera attached to computer that will take images of hand gestures. Image segmentation & feature extraction algorithm is used to recognize the hand gestures of the signer. According to recognized hand gestures, corresponding pre-recorded sound track will be played.


Adversarial background augmentation improves object localisation using convolutional neural networks


title: Building a highperformance deduplication system

abstract: Modern deduplication has become quite effective at eliminating duplicates in data, thus multiplying the effective capacity of disk-based backup systems, and enabling them as realistic tape replacements. Despite these improvements, single-node raw capacity is still mostly limited to tens or a few hundreds of terabytes, forcing users to resort to complex and costly multi-node systems, which usually only allow them to scale to singledigit petabytes. As the opportunities for deduplication efficiency optimizations become scarce, we are challenged with the task of designing deduplication systems that will effectively address the capacity, throughput, management and energy requirements of the petascale age. ::: ::: In this paper we present our high-performance deduplication prototype, designed from the ground up to optimize overall single-node performance, by making the best possible use of a node's resources, and achieve three important goals: scale to large capacity, provide good deduplication efficiency, and near-raw-disk throughput. Instead of trying to improve duplicate detection algorithms, we focus on system design aspects and introduce novelmechanisms--thatwe combinewith careful implementations of known system engineering techniques. In particular, we improve single-node scalability by introducing progressive sampled indexing and grouped mark-and-sweep, and also optimize throughput by utilizing an event-driven, multi-threaded client-server interaction model. Our prototype implementation is able to scale to billions of stored objects, with high throughput, and very little or no degradation of deduplication efficiency.


title: Handwritten Character Recognition Using Deep-Learning

abstract: In this paper we present an innovative method for offline handwritten character detection using deep neural networks. In today world it has become easier to train deep neural networks because of availability of huge amount of data and various Algorithmic innovations which are taking place. Now-a-days the amount of computational power needed to train a neural network has increased due to the availability of GPU's and other cloud based services like Google Cloud platform and Amazon Web Services which provide resources to train a Neural network on the cloud. We have designed a image segmentation based Handwritten character recognition system. In our system we have made use of OpenCV for performing Image processing and have used Tensorflow for training a the neural Network. We have developed this system using python programming language.


title: Continuous Learning of Context-dependent Processing in Neural Networks

abstract: Deep neural networks are powerful tools in learning sophisticated but fixed mapping rules between inputs and outputs, thereby limiting their application in more complex and dynamic situations in which the mapping rules are not kept the same but change according to different contexts. To lift such limits, we developed an approach involving a learning algorithm, called orthogonal weights modification, with the addition of a context-dependent processing module. We demonstrated that with orthogonal weights modification to overcome catastrophic forgetting, and the context-dependent processing module to learn how to reuse a feature representation and a classifier for different contexts, a single network could acquire numerous context-dependent mapping rules in an online and continual manner, with as few as approximately ten samples to learn each. Our approach should enable highly compact systems to gradually learn myriad regularities of the real world and eventually behave appropriately within it. When neural networks are retrained to solve more than one problem, they tend to forget what they have learned earlier. Here, the authors propose orthogonal weights modification, a method to avoid this so-called catastrophic forgetting problem. Capitalizing on such an ability, a new module is introduced to enable the network to continually learn context-dependent processing.


title: Does timing of hemodilution influence the stress response and overall outcome?

abstract: This study was designed to assess the stress response of acute hemodilution (AH) in patients subjected to radical cystectomy. Forty adult male patients were randomly allocated into a control group (n = 10) where homologous blood transfusion was used, a preinduction AH group (n = 20) where AH was performed before lumbar epidural block and induction of anesthesia, and into a postinduction group (n = 10) where AH was performed after induction of anesthesia. Monitored variables included hemodynamic, hematological and coagulation factors, liver function tests, and serum hormones. AH performed in awake or in anesthetized patients did not result in significant hemodynamic disruption, or result in detectable end-organ or stress-hormone changes when compared to control patient outcomes after radical cystectomy. Hemodilution can be performed by protocol for patients who are undergoing this procedure without major adverse effects.


title: Deploying and managing Web services: issues, solutions, and directions

abstract: Web services are expected to be the key technology in enabling the next installment of the Web in the form of the Service Web. In this paradigm shift, Web services would be treated as first-class objects that can be manipulated much like data is now manipulated using a database management system. Hitherto, Web services have largely been driven by standards. However, there is a strong impetus for defining a solid and integrated foundation that would facilitate the kind of innovations witnessed in other fields, such as databases. This survey focuses on investigating the different research problems, solutions, and directions to deploying Web services that are managed by an integrated Web Service Management System (WSMS). The survey identifies the key features of a WSMS and conducts a comparative study on how current research approaches and projects fit in.


title: Inventory Management: A Tool of Optimizing Resources in a Manufacturing Industry A Case Study of Coca-Cola Bottling Company, Ilorin Plant

abstract: AbstractInventory constitutes the most significant part of current assets of larger majority of Nigerian manufacturing industries. Because of the relative largeness of inventories maintained by mos...


title: Non-Additive Imprecise Image Super-Resolution in a Semi-Blind Context

abstract: The most effective superresolution methods proposed in the literature require precise knowledge of the so-called point spread function of the imager, while in practice its accurate estimation is nearly impossible. This paper presents a new superresolution method, whose main feature is its ability to account for the scant knowledge of the imager point spread function. This ability is based on representing this imprecise knowledge via a non-additive neighborhood function. The superresolution reconstruction algorithm transfers this imprecise knowledge to output by producing an imprecise (interval-valued) high-resolution image. We propose some experiments illustrating the robustness of the proposed method with respect to the imager point spread function. These experiments also highlight its high performance compared with very competitive earlier approaches. Finally, we show that the imprecision of the high-resolution interval-valued reconstructed image is a reconstruction error marker.


title: Software technology in an automotive company - major challenges

abstract: The automotive industry is one of the sectors which has been affected significantly by the industrial software revolution during the last few years. Competitive challenges, which are of major relevance for the growing number of software-based automotive innovations, are presented by a dramatic growth in system complexity, rising time and cost pressure, and high quality demands. All these requirements lead to a number of software-related challenges which will be of significant competitive importance for the automotive industry in the future. Asides from the question of whether an automotive company decides to develop software in-house or whether this is carried out by suppliers, every automotive key player has to hold or to build up specific software competencies. The most important competence areas are the software development process, software quality management including supplier cooperation, the overall architecture of the in-vehicle software, as well as the ability to specify, to integrate and to test the system.


The Ehlers – Danlos syndrome , a disorder with many faces


title: Community-based archaeology in Australia

abstract: Outside the Antipodes, Australian archaeology is best known as an archaeology of the distant Palaeolithic past. However, where communities have been closely involved in developing and undertaking archaeological research programs, the focus of archaeological research has been radically different, often dealing with the archaeology of the recent, remembered past and crossing disciplinary boundaries between Aboriginal and historical archaeology. Distinguishing between 'community-based archaeology' and reactive or 'consent-based' community involvement in archaeology,this paper reviews the state of archaeology and its engagement with communities in Australia. Through several case studies in both indigenous and post-contact archaeology, it demonstrates the way in which community-based research and practise is changing what it is we think of as 'archaeology' in Australia.


title: A novel change detection framework based on deep learning for the analysis of multi-temporal polarimetric SAR images

abstract: Urban change detection is an important part of monitoring operations and disaster relief efforts. However, often sufficient ground truth data is not available to use traditional supervised machine learning techniques. In this paper, a novel Deep Learning based weakly-supervised framework for urban change detection using multi-temporal polarimetric SAR data is proposed. A modified unsupervised stacked auto-encoder stage is used to learn an efficient representation of the multi-temporal polarimetric information. Then a label aggregation is performed in the feature space before classification by a multi-layer perceptron. The proposed methodology is validated on a L-band UAVSAR dataset acquired over Los Angeles, CA and performs accurately and effectively with a low false alarm rate.


title: Feedforward Neural Network for Time Series Anomaly Detection

abstract: Time series anomaly detection is usually formulated as finding outlier data points relative to some usual data, which is also an important problem in industry and academia. To ensure systems working stably, internet companies, banks and other companies need to monitor time series, which is called KPI (Key Performance Indicators), such as CPU used, number of orders, number of online users and so on. However, millions of time series have several shapes (e.g. seasonal KPIs, KPIs of timed tasks and KPIs of CPU used), so that it is very difficult to use a simple statistical model to detect anomaly for all kinds of time series. Although some anomaly detectors have developed many years and some supervised models are also available in this field, we find many methods have their own disadvantages. In this paper, we present our system, which is based on deep feedforward neural network and detect anomaly points of time series. The main difference between our system and other systems based on supervised models is that we do not need feature engineering of time series to train deep feedforward neural network in our system, which is essentially an end-to-end system.


title: Bluewave: enabling opportunistic context sharing via bluetooth device names

abstract: Context-aware applications oftentimes require devices to share a user's context in order to provide them with relevant information and services. However, current context-sharing techniques require significant amounts of setup before they can be used, making them cumbersome when devices need to share information once or spontaneously. To address this problem, we present Bluewave, a Bluetooth-based technique that allows devices to opportunistically share context when they are nearby. With Bluewave, devices upload context to a trusted server, and extend their Bluetooth name with a URL and a set of temporary credentials. Other devices can obtain this information via Bluetooth discovery, and use it to request and receive context without having to pair. Bluewave provides a simple but effective way for users to share context through their mobile devices, supporting applications where the environment needs to collect information about the user. Our system's design has been guided by user feedback, and integrates privacy controls to let users manage how their context is being shared. In this paper, we describe Bluewave's architecture, and show how it can be used to create a wide range of "just in time" services. We argue that Bluewave's low battery consumption, combined with its speed and compatibility with existing devices, significantly reduces the cost of sharing context, and provides a practical way to create, deploy, and prototype a new generation of context-aware applications.


title: Distributed Segment-Based Anomaly Detection With Kullback–Leibler Divergence in Wireless Sensor Networks

abstract: In this paper, we focus on detecting a special type of anomaly in wireless sensor network (WSN), which appears simultaneously in a collection of neighboring nodes and lasts for a significant period of time. Existing point-based techniques, in this context, are not very effective and efficient. With the proposed distributed segment-based recursive kernel density estimation, a global probability density function can be tracked and its difference between every two periods of time is continuously measured for decision making. Kullback–Leibler (KL) divergence is employed as the measure and, in order to implement distributed in-network estimation at a lower communication cost, several types of approximated KL divergence are proposed. In the meantime, an entropic graph-based algorithm that operates in the manner of centralized computing is realized, in comparison with the proposed KL divergence-based algorithms. Finally, the algorithms are evaluated using a real-world data set, which demonstrates that they are able to achieve a comparable performance at a much lower communication cost.


title: How Art Changes Your Brain: Differential Effects of Visual Art Production and Cognitive Art Evaluation on Functional Brain Connectivity

abstract: Visual art represents a powerful resource for mental and physical well-being. However, little is known about the underlying effects at a neural level. A critical question is whether visual art production and cognitive art evaluation may have different effects on the functional interplay of the brain's default mode network (DMN). We used fMRI to investigate the DMN of a non-clinical sample of 28 post-retirement adults (63.71 years ±3.52 SD) before (T0) and after (T1) weekly participation in two different 10-week-long art interventions. Participants were randomly assigned to groups stratified by gender and age. In the visual art production group 14 participants actively produced art in an art class. In the cognitive art evaluation group 14 participants cognitively evaluated artwork at a museum. The DMN of both groups was identified by using a seed voxel correlation analysis (SCA) in the posterior cingulated cortex (PCC/preCUN). An analysis of covariance (ANCOVA) was employed to relate fMRI data to psychological resilience which was measured with the brief German counterpart of the Resilience Scale (RS-11). We observed that the visual art production group showed greater spatial improvement in functional connectivity of PCC/preCUN to the frontal and parietal cortices from T0 to T1 than the cognitive art evaluation group. Moreover, the functional connectivity in the visual art production group was related to psychological resilience (i.e., stress resistance) at T1. Our findings are the first to demonstrate the neural effects of visual art production on psychological resilience in adulthood.


title: An Overview of the ONIONS Project: Applying Ontologies to the Integration of Medical Terminologies

abstract: Abstract The paper presents a review of the ONIONS project. ONIONS is committed to developing a large-scale, axiomatized ontology library for medical terminology. The developed methodology exploits a description logic-based design for the modules in the library and makes extended use of generic theories, thus creating a stratification of the modules. Terminological knowledge is acquired by conceptual analysis and ontology integration over a set of authoritative sources. After addressing general issues about conceptual analysis and integration, the methodology is briefly described. The central part of the paper presents the investigation we have made on the 476,000 medical concepts singled out by the National Library of Medicine as the MetathesaurusTM in the UMLS project. This is followed by several case studies concerning lexical polysemy, the interface between ontologies. A section describing the current structure of the library and the generic theories reused is provided. Current results of our research include the integration of some top-level ontologies in the ON9.2 ontology library, and the formalization of the terminological knowledge in the UMLS Metathesaurus.


title: Mixed feature selection based on granulation and approximation

abstract: Feature subset selection presents a common challenge for the applications where data with tens or hundreds of features are available. Existing feature selection algorithms are mainly designed for dealing with numerical or categorical attributes. However, data usually comes with a mixed format in real-world applications. In this paper, we generalize Pawlak's rough set model into @d neighborhood rough set model and k-nearest-neighbor rough set model, where the objects with numerical attributes are granulated with @d neighborhood relations or k-nearest-neighbor relations, while objects with categorical features are granulated with equivalence relations. Then the induced information granules are used to approximate the decision with lower and upper approximations. We compute the lower approximations of decision to measure the significance of attributes. Based on the proposed models, we give the definition of significance of mixed features and construct a greedy attribute reduction algorithm. We compare the proposed algorithm with others in terms of the number of selected features and classification performance. Experiments show the proposed technique is effective.


title: Deep neural network features and semi-supervised training for low resource speech recognition

abstract: We propose a new technique for training deep neural networks (DNNs) as data-driven feature front-ends for large vocabulary continuous speech recognition (LVCSR) in low resource settings. To circumvent the lack of sufficient training data for acoustic modeling in these scenarios, we use transcribed multilingual data and semi-supervised training to build the proposed feature front-ends. In our experiments, the proposed features provide an absolute improvement of 16% in a low-resource LVCSR setting with only one hour of in-domain training data. While close to three-fourths of these gains come from DNN-based features, the remaining are from semi-supervised training.


title: Cross-layer management of a containerized NoSQL data store

abstract: Internet-scale services increasingly rely on NoSQL data store technologies for scalable, highly available data persistence. To increase resource efficiency and deployment speed, such services are adopting new deployment models based on container technologies. Emerging container management systems (CMS) offer new models of interaction with containerized applications, exposing goal-oriented management capabilities. In this paper, we study the interaction of a containerized NoSQL data store, RethinkDB, with a popular open-source CMS, Kubernetes, demonstrating that events exposed by the CMS can be leveraged to drive adaptation actions on the NoSQL data store, improving its availability and quality of service. Typical adaptation actions in data stores often involve movement of data (e.g., migrating data replicas to a new node) and thus have high overhead. In this paper we demonstrate that lower-cost adaptation actions based on targeted reconfigurations of replica groups are possible and often offer rapid response to performance degradation (such as when a node experiences a temporary resource shortage). Such reconfigurations can be exercised by a cross-layer management system that bridges between CMS and NoSQL data store. We evaluate a prototype implementation of this management system in the context of Kubernetes and RethinkDB using the Yahoo Cloud Serving Benchmark on Google Container Engine. Our results demonstrate that infrastructure-level events provided by the CMS can drive proactive, low-cost data-store adaptation actions that improve overall system manageability, availability, and performance.


title: Synthesizing Tabular Data using Generative Adversarial Networks

abstract: Generative adversarial networks (GANs) implicitly learn the probability distribution of a dataset and can draw samples from the distribution. This paper presents, Tabular GAN (TGAN), a generative adversarial network which can generate tabular data like medical or educational records. Using the power of deep neural networks, TGAN generates high-quality and fully synthetic tables while simultaneously generating discrete and continuous variables. When we evaluate our model on three datasets, we find that TGAN outperforms conventional statistical generative models in both capturing the correlation between columns and scaling up for large datasets.


title: Nanofiber-reinforced soft fluidic micro-actuators

abstract: Soft pneumatic actuators are promising candidates for micro-manipulation and delicate gripping due to their wide range of motion and ease of fabrication. While existing elastomer-based devices have attracted attention due to their compliant structures, there is a need for materials that combine flexibility, controllable actuation, and robustness. This paper bridges this capability gap by introducing a novel fabrication strategy for nanofiber-reinforced soft micro-actuators. The design and manufacturing of composite PDMS/nanofiber actuators using soft lithography and rotary jet spinning is described. We examine the impact of lamina design and fiber orientation on actuator curvature, mechanical properties, and pressurization range. Composite actuators displayed a 25.8% higher maximum pressure than pure PDMS devices. Further, the best nanofiber-reinforced laminates tested were 2.3 times tougher than the control PDMS material while maintaining comparable elongation. Finally, bending and bending-twisting are demonstrated using pristine and laser-patterned nanofiber sheets, respectively.


title: Dynamic IPC/Clock Rate Optimization

abstract: Current microprocessor designs set the functionality and clock rate of the chip at design time based on the configuration that achieves the best overall performance over a range of target applications. The result may be poor performance when running applications whose requirements are not well-matched to the particular hardware organization chosen. We present a new approach called Complexity-Adaptive Processors (CAPs) in which the IPC/clock rate tradeoff can be altered at runtime to dynamically match the changing requirements of the instruction stream. By exploiting repeater methodologies used increasingly in deep sub-micron designs, CAPs achieve this flexibility with potentially no cycle time impact compared to a fixed architecture. Our preliminary results in applying this approach to on-chip caches and instruction queues indicate that CAPs have the potential to significantly outperform conventional approaches on workloads containing both general-purpose and scientific applications.


title: Ensemble-based Classification for Author Profiling Using Various Features Notebook for PAN at CLEF 2013

abstract: This paper summarize our approach to author profiling task - a part of evaluation lab PAN'13. We have used ensemble-based classification on large features set. All the features are roughly described and experimen- tal section provides evaluation of different methods and classification ap- proaches.


title: Yeast ecology of Kombucha fermentation.

abstract: Kombucha is a traditional fermentation of sweetened tea, involving a symbiosis of yeast species and acetic acid bacteria. Despite reports of different yeast species being associated with the fermentation, little is known of the quantitative ecology of yeasts in Kombucha. Using oxytetracycline-supplemented malt extract agar, yeasts were isolated from four commercially available Kombucha products and identified using conventional biochemical and physiological tests. During the fermentation of each of the four products, yeasts were enumerated from both the cellulosic pellicle and liquor of the Kombucha. The number and diversity of species varied between products, but included Brettanomyces bruxellensis, Candida stellata, Schizosaccharomyces pombe, Torulaspora delbrueckii and Zygosaccharomyces bailii. While these yeast species are known to occur in Kombucha, the enumeration of each species present throughout fermentation of each of the four Kombucha cultures demonstrated for the first time the dynamic nature of the yeast ecology. Kombucha fermentation is, in general, initiated by osmotolerant species, succeeded and ultimately dominated by acid-tolerant species.


title: Recurring polyhedral motifs in the amorphous indium gallium zinc oxide network

abstract: The coordination polyhedra around the cations are the building blocks of ionic solids. For amorphous InGaZn oxide (a-IGZO), these coordination polyhedra are identified to investigate properties that depend on short range interactions. Therefore, in this first principles based study, a large number (10) of samples of a-IGZO were prepared by ab initio melt-and-quench molecular dynamics, so that several distinct samples of the amorphous landscape are obtained corresponding to local minima in energy. Based on a method of comparing bond angles between metal and oxygen atoms, the identified polyhedra were matched to the polyhedral motifs present in the related crystalline systems, such as, InGaZnO4, In2O3, Ga2O3, and ZnO. Consequently, we find, the a-IGZO primarily consists of the following polyhedra: a tetrahedron from space group 199 and an octahedron from space group 206 of In2O3; a tetrahedron from space group 12 and an octahedron from space group 167 of Ga2O3; a tetrahedron from space group 186 of ZnO; zinc and gallium trigonal bipyramids from c-IGZO; and one zinc fourfold, one zinc fivefold, and one indium fivefold coordination polyhedra that occur only in the amorphous phase. Thus, we were able to reduce the description of structure from 360 to 10 groups of polyhedra.


title: Partitioning 3D surface meshes using watershed segmentation

abstract: This paper describes a method for partitioning 3D surface meshes into useful segments. The proposed method generalizes morphological watersheds, an image segmentation technique, to 3D surfaces. This surface segmentation uses the total curvature of the surface as an indication of region boundaries. The surface is segmented into patches, where each patch has a relatively consistent curvature throughout, and is bounded by areas of higher, or drastically different, curvature. This algorithm has applications for a variety of important problems in visualization and geometrical modeling including 3D feature extraction, mesh reduction, texture mapping 3D surfaces, and computer aided design.


title: Prediction-based Dynamic Resource Scheduling for Virtualized Cloud Systems

abstract: Virtualization and cloud computing technologies now make it possible to consolidate multiple online services, which are packed in virtual machines (VMs), into a smaller number of physical servers. However, it is still a challenging scheduling problem for cloud provider to dynamically manage the resource for VMs in order to handle variable workloads without service level agreement (SLA) violation. In this paper, we introduce a Prediction-based Dynamic Resource Scheduling (PDRS) solution to automate elastic resource scaling for virtualized cloud systems. Unlike traditional static consolidation or threshold-driven reactive scheduling, we both consider the dynamic workload fluctuations of each VM and the resource conflict handling problem. PDRS first employs an online resource prediction, which is a VM resource demand state predictor based on the Autoregressive Integrated Moving Average (ARIMA) model, to achieve adaptive resource allocation for cloud applications on each VM. Then we propose our prediction-based dynamic resource scheduling algorithms to dynamically consolidate the VMs with adaptive resource allocation to reduce the number of physical machines. Extensive experimental results show that our scheduling is able to realize automatic elastic resource allocation with acceptable effect on SLAs.


title: The Sendai Framework for Disaster Risk Reduction: Renewing the Global Commitment to People’s Resilience, Health, and Well-being

abstract: The Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR) is the first global policy framework of the United Nations’ post-2015 agenda. It represents a step in the direction of global policy coherence with explicit reference to health, development, and climate change. To develop SFDRR, the United Nations Office for Disaster Risk Reduction (UNISDR) organized and facilitated several global, regional, national, and intergovernmental negotiations and technical meetings in the period preceding the World Conference on Disaster Risk Reduction (WCDRR) 2015 where SFDRR was adopted. UNISDR also worked with representatives of governments, UN agencies, and scientists to develop targets and indicators for SFDRR and proposed them to member states for negotiation and adoption as measures of progress and achievement in protecting lives and livelihoods. The multiple efforts of the health community in the policy development process, including campaigning for safe schools and hospitals, helped to put people’s mental and physical health, resilience, and well-being higher up the disaster risk reduction (DRR) agenda compared with the Hyogo Framework for Action 2005–2015. This article reviews the historical and contemporary policy development process that led to the SFDRR with particular reference to the development of the health theme.


On-the-fly lattice rescoring for real-time automatic speech recognition.


title: An IoT based patient monitoring system using raspberry Pi

abstract: In the recent development of, Internet of Things (IoT) makes all objects interconnected and it has been recognized as the next technical revolution. Some of the applications of Internet of Things are smart parking, smart home, smart city, smart environment, industrial places, agriculture fields and health monitoring process. One such application is in healthcare to monitor the patient health status Internet of Things makes medical equipments more efficient by allowing real time monitoring of patient health, in which sensor acquire data of patient's and reduces the human error. In Internet of Things patient's parameters get transmitted through medical devices via a gateway, where it is stored and analyzed. The significant challenges in the implementation of Internet of Things for healthcare applications is monitoring all patient's from various places. Thus Internet o Things in the medical field brings out the solution for effective patient monitoring at reduced cost and also reduces the trade-off between patient outcome and disease management. In this paper discuss about, monitoring patient's body temperature, respiration rate, heart beat and body movement using Raspberry Pi board.


title: IQA: Visual Question Answering in Interactive Environments

abstract: We introduce Interactive Question Answering (IQA), the task of answering questions that require an autonomous agent to interact with a dynamic visual environment. IQA presents the agent with a scene and a question, like: "Are there any apples in the fridge?" The agent must navigate around the scene, acquire visual understanding of scene elements, interact with objects (e.g. open refrigerators) and plan for a series of actions conditioned on the question. Popular reinforcement learning approaches with a single controller perform poorly on IQA owing to the large and diverse state space. We propose the Hierarchical Interactive Memory Network (HIMN) consisting of a factorized set of controllers, allowing the system to operate at multiple levels of temporal abstraction, reducing the diversity of the action space available to each controller and enabling an easier training paradigm. We introduce IQADATA, a new Interactive Question Answering dataset built upon AI2-THOR, a simulated photo-realistic environment of configurable indoor scenes with interactive objects. IQADATA has 75,000 questions, each paired with a unique scene configuration. Our experiments show that our proposed model outperforms popular single controller based methods on IQADATA.


title: ITS1 versus ITS2 as DNA metabarcodes for fungi

abstract: The nuclear ribosomal Internal Transcribed Spacer ITS region is widely used as a DNA metabarcoding marker to characterize the diversity and composition of fungal communities. In amplicon pyrosequencing studies of fungal diversity, one of the spacers ITS1 or ITS2 of the ITS region is normally used. In this methodological study we evaluate the usability of ITS1 vs. ITS2 as a DNA metabarcoding marker for fungi. We analyse three data sets: two comprising ITS1 and ITS2 sequences of known taxonomic affiliations and a third comprising ITS1 and ITS2 environmental amplicon pyrosequencing data. Clustering analyses of sequences with known taxonomy using the bioinformatics pipeline ClustEx revealed that a 97% similarity cut-off represent a reasonable threshold for estimating the number of known species in the data sets for both ITS1 and ITS2. However, no single threshold value worked well for all fungi at the same time within the curated UNITE database, and we found that the Operational Taxonomic Unit (OTU) concept is not easily translated into the level of species because many species are distributed over several clusters. Clustering analyses of the 134 692 ITS1 and ITS2 pyrosequences using a 97% similarity cut-off revealed a high similarity between the two data sets when it comes to taxonomic coverage. Although some groups are under- or unrepresented in the two data sets due to, e.g. primer mismatches, our results indicate that ITS1 and ITS2 to a large extent yield similar results when used as DNA metabarcodes for fungi.


title: Thai Distance English Learners and Learner Autonomy

abstract: The study reported here represents an attempt to explore learner autonomy in a distance education setting in Thailand. Autonomy is a key element in learning a language at a distance and is reflected in the number and quality of learning strategies students employ. Data for this study were collected using a questionnaire sent to students country-wide, ThinkAloud protocols and interviews. Using a number of criteria which emerged from the study, students were grouped into two kinds of language learner: self sufficient language learners, who were able to follow the course but displayed a limited degree of learner autonomy, and dynamic distance language learners, who were more proactive in their approach to learning. The outcomes from this study were used to draw conclusions about recommendations for future distance language course development and to develop theoretical work by the author relating to autonomous learning of languages at a distance


title: Evidential reasoning using stochastic simulation of causal models

abstract: Abstract Stochastic simulation is a method of computing probabilities by recording the fraction of time that events occur in a random series of scenarios generated from some causal model. This paper presents an efficient, concurrent method of conducting the simulation which guarantees that all generated scenarios will be consistent with the observed data. It is shown that the simulation can be performed by purely local computations, involving products of parameters given with the initial specification of the model. Thus, the method proposed renders stochastic simulation a powerful technique of coherent inferencing, especially suited for tasks involving complex, nondecomposable models where “ballpark” estimates of probabilities will suffice.


title: A Cost-Shaping Linear Program for Average-Cost Approximate Dynamic Programming with Performance Guarantees

abstract: We introduce a new algorithm based on linear programming for optimization of average-cost Markov decision processes (MDPs). The algorithm approximates the differential cost function of a perturbed MDP via a linear combination of basis functions. We establish a bound on the performance of the resulting policy that scales gracefully with the number of states without imposing the strong Lyapunov condition required by its counterpart in de Farias and Van Roy [de Farias, D. P., B. Van Roy. 2003. The linear programming approach to approximate dynamic programming. Oper. Res.51(6) 850--865]. We investigate implications of this result in the context of a queueing control problem.


title: The Neural Correlate of Speech Rhythm as Evidenced by Metrical Speech Processing

abstract: The present study investigates the neural correlates of rhythm processing in speech perception. German pseudosentences spoken with an exaggerated (isochronous) or a conversational (nonisochronous) rhythm were compared in an auditory functional magnetic resonance imaging experiment. The subjects had to perform either a rhythm task (explicit rhythm processing) or a prosody task (implicit rhythm processing). The study revealed bilateral activation in the supplementary motor area (SMA), extending into the cingulate gyrus, and in the insulae, extending into the right basal ganglia (neostriatum), as well as activity in the right inferior frontal gyrus (IFG) related to the performance of the rhythm task. A direct contrast between isochronous and nonisochronous sentences revealed differences in lateralization of activation for isochronous processing as a function of the explicit and implicit tasks. Explicit processing revealed activation in the right posterior superior temporal gyrus (pSTG), the right supramarginal gyrus, and the right parietal operculum. Implicit processing showed activation in the left supramarginal gyrus, the left pSTG, and the left parietal operculum. The present results indicate a function of the SMA and the insula beyond motor timing and speak for a role of these brain areas in the perception of acoustically temporal intervals. Secondly, the data speak for a specific task-related function of the right IFG in the processing of accent patterns. Finally, the data sustain the assumption that the right secondary auditory cortex is involved in the explicit perception of auditory suprasegmental cues and, moreover, that activity in the right secondary auditory cortex can be modulated by top-down processing mechanisms.


title: pH-induced conformational changes of comb-like polycarboxylate investigated by experiment and simulation

abstract: Four types of polycarboxylate ether (PCE) polymers with consistent backbone length and side-chain (poly(ethylene glycol) monomethyl ether methacrylate MAA-M2000) length but different ratios of acrylic acid (AA) to methacrylic acid (MAA) were synthesized. The ratio of the methyl group would directly affect the backbone stiffness and hydrophobicity of the PCE. Laser light scattering (LLS) and molecular dynamic (MD) simulations were used to investigate the conformations of PCE at various pH values. It was found that the conformation of PCE remained more extended with the decrease of ratio of the backbone methyl group, which resulted in a low exposed extent of carboxylic groups on the backbone at the same ionization degree. The complexation of the carboxylic oxygen atoms of PCE with Ca2+ indicated that the differences of the complexing capacity resulted from the differences in the spatial distribution of carboxylic groups, which depended on the solution conformation of PCE molecules.


title: N-path and pseudo-N-path cells for switched-current signal processing

abstract: Novel switched-current N-path and pseudo-N-path cells are proposed, which allow the transformation of a z-domain low-pass reference filter into a bandpass one. The three types of cells, a forward Euler, backward Euler, and bilinear integrator, perform the z/spl rarr/-z and z/spl rarr/-z/sup N/ transformations, which avoid clock-feedthrough noise in the realized filter passband. The pseudo-N-path cells operate on the circulating-delay principle, which avoids mirror-frequency noise due to path mismatches and require only standard biphase clocks to operate. The effects of circuit nonidealities are studied and performance comparisons are made with a conventional narrow-band bandpass filter. The robust frequency stability characteristic makes the cells suitable for narrow-band bandpass applications. Results of an HSPICE simulation example is given for a sixth-order pseudo-N-path filter.


title: LoRaWAN Bristol

abstract: The Bristol LoRaWAN Network is a low power radio network for the Internet of Things, based on LoRaWAN and utilising The Things Network. LoRaWAN is a Low Power Wide Area Network (LPWAN) specification intended for wireless battery operated Things in regional, national, or global networks. LoRaWAN targets key requirements of internet of things such as secure bi-directional communication, mobility and localisation services. A 2015 pilot programme in the city of Amsterdam aimed to cover the entire city with just 10 wireless gateways. LoRaWAN Bristol aims to replicate this experiment in the UK.


title: A Novel Dual-Stator Axial-Flux Spoke-Type Permanent Magnet Vernier Machine for Direct-Drive Applications

abstract: This paper proposes a novel double-stator axial-flux spoke-type permanent magnet vernier machine, which has a high torque density feature as well as a high-power factor at low speed for direct-drive systems. The operation principle and basic design procedure of the proposed machine are presented and discussed. The 3-D finite element method (3-D-FEM) is utilized to analyze its magnetic field and transient output performance. Furthermore, the analytical method and a simplified 2-D-FEM are also developed for the machine basic design and performance evaluation, which can effectively reduce the modeling and simulation time of the 3-D-FEM and achieve an adequate accuracy.


title: Using Business Intelligence to Support the Process of Organizational Sensemaking

abstract: Making sense of an organization overwhelmed with data becomes a problem for decision makers at all levels of business planning and operation. Although scholars have suggested several technological solutions such as business intelligence as being useful in helping busy executives to make decisions, we still know little about assisting business stakeholders in the process of understanding their organizational complexity before such decisions could even be formulated. In this paper, we investigate the opportunities in using BI technologies to make sense of a business environment. We analyze the views and opinions of developers, analysts, consultants, and users of business intelligence, who are experienced in using the technology beyond decision making to support organizational sensemaking. Our results highlight the need for creating and maintaining individual; and organizational identity and enacting this identity on the business and its environment.


title: The Current Role of Image Compression Standards in Medical Imaging

abstract: With the increasing utilization of medical imaging in clinical practice and the growing dimensions of data volumes generated by various medical imaging modalities, the distribution, storage, and management of digital medical image data sets requires data compression. Over the past few decades, several image compression standards have been proposed by international standardization organizations. This paper discusses the current status of these image compression standards in medical imaging applications together with some of the legal and regulatory issues surrounding the use of compression in medical settings.


title: Supraventricular Tachycardia Terminated by External Mechanical Stimulation: A Case of "Pothole Conversion"

abstract: Supraventricular tachycardia requires atrial or AV junctional tissue for its initiation and maintenance. Acute management of supraventricular tachycardias include vagal maneuvers, pharmacological treatment, and electrical cardioversion. We describe a case of a supraventricular tachycardias cardioverted by the jolt of the ambulance hitting a pothole. The proposed mechanism of cardioversion is an external mechanical one similar to the precordial thump. Language: en


Virginity testing: a systematic review


title: The Serial Safety Net: Efficient Concurrency Control on Modern Hardware

abstract: Concurrency control (CC) algorithms must trade off strictness for performance, with serializable schemes generally paying high cost---both in runtime overhead such as contention on lock tables, and in wasted efforts by aborting transactions---to prevent anomalies. We propose the serial safety net (SSN), a serializability-enforcing certifier for modern hardware with substantial core count and large main memory. SSN can be applied with minimal overhead on top of various CC schemes that offer higher performance but admit anomalies, e.g., snapshot isolation and read committed. We demonstrate the efficiency, accuracy and robustness of SSN using a memory-optimized OLTP engine with different CC schemes. We find that SSN is a promising approach to serializability with low abort rates and robust performance for various workloads.


title: Coverage Comparison of GPRS, NB-IoT, LoRa, and SigFox in a 7800 km² Area

abstract: In this simulation work the coverage of GPRS, Narrowband-IoT, LoRa, and SigFox is compared in a realistic scenario, covering 7800 km2 and using Telenor's commercial 2G, 3G, and 4G deployment. The target is to evaluate which of the four technologies provides the best coverage for Internet of Things devices, which may be located deep indoor. The results show that Narrowband-IoT, having the best Maximum Coupling Loss performance of 164 dB, also provides the best coverage. This is despite the fact that LoRa and SigFox deployments with omnidirectional antennas are found to provide 3 dB lower link loss on average. In the deployment 11 % of the geographical area contains devices, located both in rural and urban areas. The NB-IoT has an outage below 1 % for locations experiencing 20 dB indoor penetration loss in addition to the outdoor path loss. SigFox performs similarly, while LoRa cannot provide coverage for 2 % of those locations. For the challenging deep indoor case, where 30 dB additional penetration loss is expected, NB-IoT has 8 % outage while SigFox and LoRa is unable to cover 13 % and 20 % of the locations. The four technologies may not be deployed at all existing site locations and therefore the work also includes a study of the coverage as a function of the minimum Inter-Site Distance, where sites closer than 2, 4, and 6 km are filtered out. The results show that SigFox and NB-IoT have outage probabilities below 5 % even though sites closer than 4 km are removed from the simulations.


title: Detection of stenosis in blood vessel by using bio-impedance analysis

abstract: Bio-impedance analysis has become more extensively used for detecting various diseases such as blood and blood vessels diseases. The diseases related to the blood vessel are namely stenosis, aneurysm, occlusion, etc. The stenosis is caused by the buildup of deposits on the vessel wall. These deposits may restrict the blood flow and lead to serious health problems like heart attack. This paper presents new method for detecting the vessel stenosis non-invasively by using bio-impedance analysis. In this paper, the blood vessel is constructed and simulated using different shapes, sizes and kinds of mural deposits, and the impedance is measured at each stenosis case. Also equivalent circuit of blood vessel and mathematical equations are presented to describe the vessel stenosis. The simulation results clarify the ability of the bio impedance analysis for estimating not only the presence of deposit but also its kind and size.


title: Biologically-plausible learning algorithms can scale to large datasets

abstract: The backpropagation (BP) algorithm is often thought to be biologically implausible in the brain. One of the main reasons is that BP requires symmetric weight matrices in the feedforward and feedback pathways. To address this"weight transport problem"(Grossberg, 1987), two more biologically plausible algorithms, proposed by Liao et al. (2016) and Lillicrap et al. (2016), relax BP's weight symmetry requirements and demonstrate comparable learning capabilities to that of BP on small datasets. However, a recent study by Bartunov et al. (2018) evaluate variants of target-propagation (TP) and feedback alignment (FA) on MINIST, CIFAR, and ImageNet datasets, and find that although many of the proposed algorithms perform well on MNIST and CIFAR, they perform significantly worse than BP on ImageNet. Here, we additionally evaluate the sign-symmetry algorithm (Liao et al., 2016), which differs from both BP and FA in that the feedback and feedforward weights share signs but not magnitudes. We examine the performance of sign-symmetry and feedback alignment on ImageNet and MS COCO datasets using different network architectures (ResNet-18 and AlexNet for ImageNet, RetinaNet for MS COCO). Surprisingly, networks trained with sign-symmetry can attain classification performance approaching that of BP-trained networks. These results complement the study by Bartunov et al. (2018), and establish a new benchmark for future biologically plausible learning algorithms on more difficult datasets and more complex architectures.


title: Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing

abstract: Knowledge tracing is a sequence prediction problem where the goal is to predict the outcomes of students over questions as they are interacting with a learning platform. By tracking the evolution of the knowledge of some student, one can optimize instruction. Existing methods are either based on temporal latent variable models, or factor analysis with temporal features. We here show that factorization machines (FMs), a model for regression or classification, encompasses several existing models in the educational literature as special cases, notably additive factor model, performance factor model, and multidimensional item response theory. We show, using several real datasets of tens of thousands of users and items, that FMs can estimate student knowledge accurately and fast even when student data is sparsely observed, and handle side information such as multiple knowledge components and number of attempts at item or skill level. Our approach allows to fit student models of higher dimension than existing models, and provides a testbed to try new combinations of features in order to improve existing models.


title: Multi-Objective Binary-PSO for Improving the Performance of P300 Speller

abstract: P300 speller provides a medium of communication to patients with severe neuro-muscular disabilities. It relies on the detection of P300 peak in EEG recordings due to the occurrence of external stimulus. The patients are asked to focus their attention on the character/symbol displayed on the screen and their EEG recordings are monitored at the same time. These recorded signals are then processed and analyzed to determine the target character. Thus the patient is able to spell out the word he intended to communicate. In the process, a large amount of redundant and noisy data is also collected which reduces the overall accuracy of the speller. This paper proposes the use of multi-objective binary particle swarm optimization (MO-BPSO) technique to identify the most relevant electrode channels on an EEG headset for data collection. The trade-off between the accuracy of the character detection and the number of channels is determined using the pareto-optimal solution plot. The user can later select any of the pareto-optimal solution based on his requirement. The dataset used for this purpose is of BCI competition III. The result section also discusses the channel and thepositioning which play a major role in P300 signal detection.


title: Measuring the Effects of Data Parallelism on Neural Network Training

abstract: Recent hardware developments have made unprecedented amounts of data parallelism available for accelerating neural network training. Among the simplest ways to harness next-generation accelerators is to increase the batch size in standard mini-batch neural network training algorithms. In this work, we aim to experimentally characterize the effects of increasing the batch size on training time, as measured in the number of steps necessary to reach a goal out-of-sample error. Eventually, increasing the batch size will no longer reduce the number of training steps required, but the exact relationship between the batch size and how many training steps are necessary is of critical importance to practitioners, researchers, and hardware designers alike. We study how this relationship varies with the training algorithm, model, and data set and find extremely large variation between workloads. Along the way, we reconcile disagreements in the literature on whether batch size affects model quality. Finally, we discuss the implications of our results for efforts to train neural networks much faster in the future.


title: Verified Security for Browser Extensions

abstract: Popup blocking, form filling, and many other features of modern web browsers were first introduced as third-party extensions. New extensions continue to enrich browsers in unanticipated ways. However, powerful extensions require capabilities, such as cross-domain network access and local storage, which, if used improperly, pose a security risk. Several browsers try to limit extension capabilities, but an empirical survey we conducted shows that many extensions are over-privileged under existing mechanisms. This paper presents \ibex, a new framework for authoring, analyzing, verifying, and deploying secure browser extensions. Our approach is based on using type-safe, high-level languages to program extensions against an API providing access to a variety of browser features. We propose using Data log to specify fine-grained access control and dataflow policies to limit the ways in which an extension can use this API, thus restricting its privilege over security-sensitive web content and browser resources. We formalize the semantics of policies in terms of a safety property on the execution of extensions and develop a verification methodology that allows us to statically check extensions for policy compliance. Additionally, we provide visualization tools to assist with policy analysis, and compilers to translate extension source code to either. NET byte code or JavaScript, facilitating cross-browser deployment of extensions. We evaluate our work by implementing and verifying~\NumExt extensions with a diverse set of features and security policies. We deploy our extensions in Internet Explorer, Chrome, Fire fox, and a new experimental HTML5 platform called C3. In so doing, we demonstrate the versatility and effectiveness of our approach.


A Blockchain-based Access Control for Big Data


title: High-Frequency Transmission Lines Crosstalk Reduction Using Spacing Rules

abstract: High-frequency transmission lines crosstalk reduction using spacing rules is treated in this paper. Two of the most popular planar transmission line configurations, namely microstrip and stripline, commonly used in printed circuit boards and radio frequency/microwave integrated circuits, are considered in this work. The trace separation between two adjacent transmission lines of each type is stepwise increased as function of the trace width. The single-ended transmission line structures are numerically investigated by a frequency-based 3-D full-wave electromagnetic analysis tool. A particular case using coated microstrip transmission lines has been fabricated, along with some calibration structures, to allow direct measurement and experimental analysis of crosstalk between the single-ended transmission lines. The test structures are characterized at high-frequency (up to 20 GHz) with scattering parameters using a vector network analyzer. The experimental results are compared with the simulation data, and some conclusions and suggestions on the impact and use of spacing rules for high-frequency crosstalk reduction between single-ended transmission lines are presented. These investigations emphasize the necessity of reevaluating classical design rules for their suitability in high-frequency applications.


title: Spatial big data analytics of influenza epidemic in Vellore, India

abstract: The study objective is to develop a big spatial data model to predict the epidemiological impact of influenza in Vellore, India. Large repositories of geospatial and health data provide vital statistics on surveillance and epidemiological metrics, and valuable insight into the spatiotemporal determinants of disease and health. The integration of these big data sources and analytics to assess risk factors and geospatial vulnerability can assist to develop effective prevention and control strategies for influenza epidemics and optimize allocation of limited public health resources. We used the spatial epidemiology data of the HIN1 epidemic collected at the National Informatics Center during 2009–2010 in Vellore. We developed an ecological niche model based on geographically weighted regression for predicting influenza epidemics in Vellore, India during 2013–2014. Data on rainfall, temperature, wind speed, humidity and population are included in the geographically weighted regression analysis. We inferred positive correlations for H1N1 influenza prevalence with rainfall and wind speed, and negative correlations for H1N1 influenza prevalence with temperature and humidity. We evaluated the results of the geographically weighted regression model in predicting the spatial distribution of the influenza epidemic during 2013–2014.


Team Description Paper : Darmstadt Dribblers & Hajime Team ( KidSize ) and Darmstadt Dribblers ( TeenSize )


title: Queues don't matter when you can JUMP them!

abstract: QJUMP is a simple and immediately deployable approach to controlling network interference in datacenter networks. Network interference occurs when congestion from throughput-intensive applications causes queueing that delays traffic from latency-sensitive applications. To mitigate network interference, QJUMP applies Internet QoS-inspired techniques to datacenter applications. Each application is assigned to a latency sensitivity level (or class). Packets from higher levels are rate-limited in the end host, but once allowed into the network can "jump-the-queue" over packets from lower levels. In settings with known node counts and link speeds, QJUMP can support service levels ranging from strictly bounded latency (but with low rate) through to line-rate throughput (but with high latency variance). ::: ::: We have implemented QJUMP as a Linux Traffic Control module. We show that QJUMP achieves bounded latency and reduces in-network interference by up to 300×, outperforming Ethernet Flow Control (802.3x), ECN (WRED) and DCTCP. We also show that QJUMP improves average flow completion times, performing close to or better than DCTCP and pFabric.


title: Attribute and simile classifiers for face verification

abstract: We present two novel methods for face verification. Our first method - “attribute” classifiers - uses binary classifiers trained to recognize the presence or absence of describable aspects of visual appearance (e.g., gender, race, and age). Our second method - “simile” classifiers - removes the manual labeling required for attribute classification and instead learns the similarity of faces, or regions of faces, to specific reference people. Neither method requires costly, often brittle, alignment between image pairs; yet, both methods produce compact visual descriptions, and work on real-world images. Furthermore, both the attribute and simile classifiers improve on the current state-of-the-art for the LFW data set, reducing the error rates compared to the current best by 23.92% and 26.34%, respectively, and 31.68% when combined. For further testing across pose, illumination, and expression, we introduce a new data set - termed PubFig - of real-world images of public figures (celebrities and politicians) acquired from the internet. This data set is both larger (60,000 images) and deeper (300 images per individual) than existing data sets of its kind. Finally, we present an evaluation of human performance.


title: 3D graphics on the web: A survey

abstract: Abstract In recent years, 3D graphics has become an increasingly important part of the multimedia web experience. Following on from the advent of the X3D standard and the definition of a declarative approach to presenting 3D graphics on the web, the rise of WebGL has allowed lower level access to graphics hardware of ever increasing power. In parallel, remote rendering techniques permit streaming of high-quality 3D graphics onto a wide range of devices, and recent years have also seen much research on methods of content delivery for web-based 3D applications. All this development is reflected in the increasing number of application fields for the 3D web. In this paper, we reflect this activity by presenting the first survey of the state of the art in the field. We review every major approach to produce real-time 3D graphics rendering in the browser, briefly summarise the approaches for remote rendering of 3D graphics, before surveying complementary research on data compression methods, and notable application fields. We conclude by assessing the impact and popularity of the 3D web, reviewing the past and looking to the future.


title: Time is up! – Norms with Deadlines in Action Languages

abstract: Action Languages are simple logical formalisms to describe the properties of a domain and the behavior of an agent and to reason about it. They offer an elegant solution to the frame problem, but are inapt to reason with norms in which an obligation deadline may require the agent to adapt its behavior even though no action occurred. In this paper we extend the Action Language $\mathcal{A}$ with features that allow reasoning about norms and time in dynamic domains. Unlike previous extensions of Action Languages with norms, our resulting language is expressive enough to represent and reason with different kinds of obligations with deadlines that explicitly refer to time, as well as norm violations and even simple contrary-to-duty obligations resulting from the satisfaction or violation of an agent's obligations.


title: Modeling Semantic Relevance for Question-Answer Pairs in Web Social Communities

abstract: Quantifying the semantic relevance between questions and their candidate answers is essential to answer detection in social media corpora. In this paper, a deep belief network is proposed to model the semantic relevance for question-answer pairs. Observing the textual similarity between the community-driven question-answering (cQA) dataset and the forum dataset, we present a novel learning strategy to promote the performance of our method on the social community datasets without hand-annotating work. The experimental results show that our method outperforms the traditional approaches on both the cQA and the forum corpora.


title: Worldwide auto-mobi: Arduino IoT home automation system for IR devices

abstract: The common in use for remote control tasks achieved via specific mobile (e.g. Android or iOS) and this control done only by radio remote control systems, infrared (IR) devices, etc.and limited in the edges of office/house, but not remote control automated for electric/ electronic devices from anywhere (outside of office/house). This paper proposed an Internet of Things (IoT) home automated system in two categories, the hardware via a device named Worldwide Auto-mobi and software has been designed to automate a favorite used devices such as TV, SAT, DVD addition to any other devices that can be controlled by an IR signal, locally and anywhere (wide world) via a hybrid mobile application will reduce the number of used controllers in the house by using a single mobile software that control devices using Internet via a WiFi module ESP8266 based on Arduino UNO. This system is being connected to a cloud server and to the mobile application can support iOS/Android mobile or iPad/Tablet. The implementation of the proposed system of home automated was achieved in minimum cost (about 30$) for office/house devices and with a delay in the range (0.4–1) s. this range near real-time (within a few seconds), i.e. it provides a good solution which can be reached in anywhere and anytime based on the cloud.


title: A holistic approach to service survivability

abstract: We present SABER (Survivability Architecture: Block, Evade, React), a proposed survivability architecture that blocks, evades and reacts to a variety of attacks by using several security and survivability mechanisms in an automated and coordinated fashion. Contrary to the ad hoc manner in which contemporary survivable systems are built-using isolated, independent security mechanisms such as firewalls, intrusion detection systems and software sandboxes-SABER integrates several different technologies in an attempt to provide a unified framework for responding to the wide range of attacks malicious insiders and outsiders can launch. This coordinated multi-layer approach will be capable of defending against attacks targeted at various levels of the network stack, such as congestion-based DoS attacks, software-based DoS or code-injection attacks, and others. Our fundamental insight is that while multiple lines of defense are useful, most conventional, uncoordinated approaches fail to exploit the full range of available responses to incidents. By coordinating the response, the ability to survive successful security breaches increases substantially. We discuss the key components of SABER, how they will be integrated together, and how we can leverage on the promising results of the individual components to improve survivability in a variety of coordinated attack scenarios. SABER is currently in the prototyping stages, with several interesting open research topics.


title: A Performance Study of Big Data on Small Nodes

abstract: The continuous increase in volume, variety and velocity of Big Data exposes datacenter resource scaling to an energy utilization problem. Traditionally, datacenters employ x86-64 (big) server nodes with power usage of tens to hundreds of Watts. But lately, low-power (small) systems originally developed for mobile devices have seen significant improvements in performance. These improvements could lead to the adoption of such small systems in servers, as announced by major industry players. In this context, we systematically conduct a performance study of Big Data execution on small nodes in comparison with traditional big nodes, and present insights that would be useful for future development. We run Hadoop MapReduce, MySQL and in-memory Shark workloads on clusters of ARM big. LITTLE boards and Intel Xeon server systems. We evaluate execution time, energy usage and total cost of running the workloads on self-hosted ARM and Xeon nodes. Our study shows that there is no one size fits all rule for judging the efficiency of executing Big Data workloads on small and big nodes. But small memory size, low memory and I/O bandwidths, and software immaturity concur in canceling the lower-power advantage of ARM servers. We show that I/O-intensive MapReduce workloads are more energy-efficient to run on Xeon nodes. In contrast, database query processing is always more energy-efficient on ARM servers, at the cost of slightly lower throughput. With minor software modifications, CPU-intensive MapReduce workloads are almost four times cheaper to execute on ARM servers.


title: Off-topic Response Detection for Spontaneous Spoken English Assessment

abstract: © 2016 Association for Computational Linguistics. Automatic spoken language assessment systems are becoming increasingly important to meet the demand for English second language learning. This is a challenging task due to the high error rates of, even state-of-the-art, non-native speech recognition. Consequently current systems primarily assess fluency and pronunciation. However, content assessment is essential for full automation. As a first stage it is important to judge whether the speaker responds on topic to test questions designed to elicit spontaneous speech. Standard approaches to off-topic response detection assess similarity between the response and question based on bag-of-words representations. An alternative framework based on Recurrent Neural Network Language Models (RNNLM) is proposed in this paper. The RNNLM is adapted to the topic of each test question. It learns to associate example responses to questions with points in a topic space constructed using these example responses. Classification is done by ranking the topic-conditional posterior probabilities of a response. The RNNLMs associate a broad range of responses with each topic, incorporate sequence information and scale better with additional training data, unlike standard methods. On experiments conducted on data from the Business Language Testing Service (BULATS) this approach outperforms standard approaches.


title: A Sensitivity-Improved PFM LLC Resonant Full-Bridge DC–DC Converter With LC Antiresonant Circuitry

abstract: An LLC resonant circuit-based full-bridge dc–dc converter with an LC antiresonant tank for improving the performance of pulse frequency modulation (PFM) is proposed in this paper. The proposed resonant dc–dc converter, named as LLC-LC converter, can extend a voltage regulation area below the unity gain with a smaller frequency variation of PFM by the effect of the antiresonant tank. This advantageous property contributes for protecting overcurrent in the case of the short-circuit load condition as well as the start-up interval in the designed band of switching frequency. The circuit topology and operating principle of the proposed converter is described, after which the design procedure of the operating frequency and circuit parameters is presented. The performances on the soft switching and the steady-state PFM characteristics of the LLC-LC converter are evaluated under the open-loop control in experiment of a 2.5-kW prototype, and its actual efficiency is compared with an LLC converter prototype. For revealing the effectiveness of the LLC-LC resonant circuitry, voltages and currents of the series and antiresonant tanks are analyzed, respectively, with state-plane trajectories based on calculation and experiment, whereby the power and energy of each resonant tank are demonstrated. Finally, the feasibility of the proposed converter is evaluated from the practical point of view.


title: Beyond UVM for practical SoC verification

abstract: As the size and complexity of SoC design grow, an efficient and structured verification environment is becoming more important than ever before. It is because many engineers with different knowledge and skills are involved in SoC verification, and they have to deal with different aspects of verification. This paper looks over the diversity of SoC verification and suggests a practical application method of UVM (Universal Verification Methodology) to build an efficient and structured verification environment which meets various requirements of SoC verification. This paper shows standardized and well-organized testbench architecture that includes directory structure of testbench files, and mechanism such as interface and handles across the components. The proposed UVM application method helps testbench developers maintain consistency of testbenches and reduce the quality gap among verification works that are done by multiple verification engineers, by using standardized testbench. It ensures that IP verification engineers do their job independently and the testbenches can be reused in top level verification environment. In addition, it provides a good infrastructure to hardware designers, who have little knowledge about verification languages and methodologies, and who want to write directed test cases only. The proposed method has been validated with a set of reference testbenches developed for an application processor SoC.