title: 2007 Special Issue Learning grammatical structure with Echo State Networks

abstract: Echo State Networks (ESNs) have been shown to be effective for a number of tasks, including motor control, dynamic time series prediction, and memorizing musical sequences. However, their performance on natural language tasks has been largely unexplored until now. Simple Recurrent Networks (SRNs) have a long history in language modeling and show a striking similarity in architecture to ESNs. A comparison of SRNs and ESNs on a natural language task is therefore a natural choice for experimentation. Elman applies SRNs to a standard task in statistical NLP: predicting the next word in a corpus, given the previous words. Using a simple context-free grammar and an SRN with backpropagation through time (BPTT), Elman showed that the network was able to learn internal representations that were sensitive to linguistic processes that were useful for the prediction task. Here, using ESNs, we show that training such internal representations is unnecessary to achieve levels of performance comparable to SRNs. We also compare the processing capabilities of ESNs to bigrams and trigrams. Due to some unexpected regularities of Elman's grammar, these statistical techniques are capable of maintaining dependencies over greater distances than might be initially expected. However, we show that the memory of ESNs in this word-prediction task, although noisy, extends significantly beyond that of bigrams and trigrams, enabling ESNs to make good predictions of verb agreement at distances over which these methods operate at chance. Overall, our results indicate a surprising ability of ESNs to learn a grammar, suggesting that they form useful internal representations without learning them.


A Performance Evaluation of RPL in Contiki A Cooja Simulation based study


title: Impact of increasing number of neurons on performance of neuromorphic architecture

abstract: Pattern recognition is used to classify the input data into different classes based on extracted key features. Increasing the recognition rate of pattern recognition applications is a challenging task. The spike neural networks inspired from physiological brain architecture, is a neuromorphic hardware implementation of network of neurons. A sample of neuromorphic architecture has two layers of neurons, input and output. The number of input neurons is fixed based on the input data patterns. While the number of outputs neurons can be different. The goal of this paper is performance evaluation of neuromorphic architecture in terms of recognition rates using different numbers of output neurons. For this purpose a simulation environment of N2S3 and MNIST handwritten digits are used. Our simulation results show the recognition rate for various number of output neurons, 20, 30, 50, 100, 200, and 300 is 70%, 74%, 79%, 85%, 89%, and 91%, respectively.


title: The Pediatric Mandible: II. Management of Traumatic Injury or Fracture

abstract: Background: Fractures of the pediatric mandible are complicated by the anatomic complexity of the developing mandible, particularly by the presence of tooth buds and the eruption of deciduous and permanent teeth. Traditional methods of fracture reduction and fixation employed in adults have little applicability in the pediatric population. Methods: The authors describe the surgical techniques that have been used at their institution and those that can be used safely in the pediatric setting. Results: In most cases, “conservative” management is the preferred option, especially in the treatment of condylar fractures. In cases requiring surgical intervention, interdental wiring, drop wires in combination with circummandibular wires, and acrylic splints are suited well to specific phases of dental maturation. Conclusion: Open reduction and internal fixation using monocortical screws and microplates or resorbable plates and screws are acceptable techniques in the pediatric patient, but they require special safeguards. Algorithms are presented to simplify management of these complicated injuries.


title: Identity-Aware Textual-Visual Matching with Latent Co-attention

abstract: Textual-visual matching aims at measuring similarities between sentence descriptions and images. Most existing methods tackle this problem without effectively utilizing identity-level annotations. In this paper, we propose an identity-aware two-stage framework for the textual-visual matching problem. Our stage-1 CNN-LSTM network learns to embed cross-modal features with a novel Cross-Modal Cross-Entropy (CMCE) loss. The stage-1 network is able to efficiently screen easy incorrect matchings and also provide initial training point for the stage-2 training. The stage-2 CNN-LSTM network refines the matching results with a latent co-attention mechanism. The spatial attention relates each word with corresponding image regions while the latent semantic attention aligns different sentence structures to make the matching results more robust to sentence structure variations. Extensive experiments on three datasets with identity-level annotations show that our framework outperforms state-of-the-art approaches by large margins.


title: Tupni: automatic reverse engineering of input formats

abstract: Recent work has established the importance of automatic reverse engineering of protocol or file format specifications. However, the formats reverse engineered by previous tools have missed important information that is critical for security applications. In this paper, we present Tupni, a tool that can reverse engineer an input format with a rich set of information, including record sequences, record types, and input constraints. Tupni can generalize the format specification over multiple inputs. We have implemented a prototype of Tupni and evaluated it on ten different formats: five file formats (WMF, BMP, JPG, PNG and TIF) and five network protocols (DNS, RPC, TFTP, HTTP and FTP). Tupni identified all record sequences in the test inputs. We also show that, by aggregating over multiple WMF files, Tupni can derive a more complete format specification for WMF. Furthermore, we demonstrate the utility of Tupni by using the rich information it provides for zero-day vulnerability signature generation, which was not possible with previous reverse engineering tools.


title: An Ultra-Low-Voltage CMOS Process-Insensitive Self-Biased OTA With Rail-to-Rail Input Range

abstract: An operational-transconductance-amplifier (OTA) design for ultra-low voltage ultra-low power applications is proposed. The input stage of the proposed OTA utilizes a bulk-driven pseudo-differential pair to allow minimum supply voltage while achieving a rail-to-rail input range. All the transistors in the proposed OTA operate in the subthreshold region. Using a novel self-biasing technique to bias the OTA obviates the need for extra biasing circuitry and enhances the performance of the OTA. The proposed technique ensures the OTA robustness to process variations and increases design feasibility under ultra-low-voltage conditions. Moreover, the proposed biasing technique significantly improves the common-mode and power-supply rejection of the OTA. To further enhance the bandwidth and allow the use of smaller compensation capacitors, a compensation network based on a damping-factor control circuit is exploited. The OTA is fabricated in a 65 nm CMOS technology. Measurement results show that the OTA provides a low-frequency gain of 46 dB and rail-to-rail input common-mode range with a supply voltage as low as 0.5 V. The dc gain of the OTA is greater than 42 dB for supply voltage as low as 0.35 V. The power dissipation is 182 $\mu{\rm W}$ at $V_{DD}=0.5\ {\rm V}$ and 17 $\mu{\rm W}$ at $V_{DD}=0.35\ {\rm V}$ .


title: Mining Privacy Settings to Find Optimal Privacy-Utility Tradeoffs for Social Network Services

abstract: Privacy has been a big concern for users of social network services (SNS). On recent criticism about privacy protection, most SNS now provide fine privacy controls, allowing users to set visibility levels for almost every profile item. However, this also creates a number of difficulties for users. First, SNS providers often set most items by default to the highest visibility to improve the utility of social network, which may conflict with users' intention. It is often formidable for a user to fine-tune tens of privacy settings towards the user desired settings. Second, tuning privacy settings involves an intricate tradeoff between privacy and utility. When you turn off the visibility of one item to protect your privacy, the social utility of that item is turned off as well. It is challenging for users to make a tradeoff between privacy and utility for each privacy setting. We propose a framework for users to conveniently tune the privacy settings towards the user desired privacy level and social utilities. It mines the privacy settings of a large number of users in a SNS, e.g., Facebook, to generate latent trait models for the level of privacy concern and the level of utility preference. A tradeoff algorithm is developed for helping users find the optimal privacy settings for a specified level of privacy concern and a personalized utility preference. We crawl a large number of Facebook accounts and derive the privacy settings with a novel method. These privacy setting data are used to validate and showcase the proposed approach.


title: Energy Efficient RRAM Spiking Neural Network for Real Time Classification

abstract: Inspired by the human brain's function and efficiency, neuromorphic computing offers a promising solution for a wide set of tasks, ranging from brain machine interfaces to real-time classification. The spiking neural network (SNN), which encodes and processes information with bionic spikes, is an emerging neuromorphic model with great potential to drastically promote the performance and efficiency of computing systems. However, an energy efficient hardware implementation and the difficulty of training the model significantly limit the application of the spiking neural network. In this work, we address these issues by building an SNN-based energy efficient system for real time classification with metal-oxide resistive switching random-access memory (RRAM) devices. We implement different training algorithms of SNN, including Spiking Time Dependent Plasticity (STDP) and Neural Sampling method. Our RRAM SNN systems for these two training algorithms show good power efficiency and recognition performance on realtime classification tasks, such as the MNIST digit recognition. Finally, we propose a possible direction to further improve the classification accuracy by boosting multiple SNNs.


title: Dataset Shift Detection in Non-stationary Environments Using EWMA Charts

abstract: Dataset shift is a major challenge in the non-stationary environments wherein the input data distribution may change over time. Detecting the dataset shift point in the time-series data, where the distribution of time-series changes its properties, is of utmost interest. Dataset shift exists in a broad range of real-world systems. In such systems, there is a need for continuous monitoring of the process behavior and tracking the state of the shift so as to decide about initiating adaptive corrections in a timely manner. This paper presents an algorithm to detect the shift-point in a non-stationary time-series data. The proposed method detects the shift-point based on an exponentially weighted moving average (EWMA) control chart for auto-correlated observations. This algorithm is suitable to be run in real-time and monitors the data to detect the dataset shift. Its performance is evaluated through experiments using synthetic and real-world datasets. Results show that all the dataset-shifts are detected without the delay.


title: TLB misses: The Missing Issue of Adaptive Radix Tree?

abstract: Efficient main-memory index structures are crucial to main-memory database systems. Adaptive Radix Tree (ART) is the most recent in-memory index structure. ART is designed to avoid cache miss, leverage SIMD data parallelism, minimize branch mis-prediction, and have small memory footprint. When an in-memory index structure like ART has significantly few cache misses and branch mis-predictions, it is natural to question whether misses in Translation Lookaside Buffer (TLB) matters. In this paper, we try to confirm whether this is the case and if the answer is positive, what are the measures that we can take to alleviate that and how effective they are.


title: Are Folded-beam Suspensions Really Linear?☆

abstract: Abstract Folded-beam suspensions are designed to respond as linear springs. In this work we show that even if their design and construction is perfect, their response is linear only for static applications. We show that the dynamic response of perfect folded- beam suspensions is inherently nonlinear . Surprisingly, the nonlinearity becomes dominant when the vibration amplitudes are on the order of the width of flexure beams (far less than 10% of the beam length). We show that this nonlinearity is not associated with large deformations or with electrostatic actuation, but is rather caused by inertial effects which induce membrane stiffening. We discuss considerations that would lead to a solution of the problem, and would enable the design of a dynamically-balanced folded-beam suspension.


title: CoAP: An Application Protocol for Billions of Tiny Internet Nodes

abstract: The Constrained Application Protocol (CoAP) is a transfer protocol for constrained nodes and networks, such as those that will form the Internet of Things. Much like its older and heavier cousin HTTP, CoAP uses the REST architectural style. Based on UDP and unencumbered by historical baggage, however, CoAP aims to achieve its modest goals with considerably less complexity.


title: Real-time Voice Activity Detector Using Gammatone Filter and Modified Long-Term Signal Variability

abstract: In this paper, a real-time robust voice activity detector (VAD) is proposed. The proposed VAD adopts the gammatone filter and modify the existing long-term signal variability (LTSV) measure, i.e. known as GMLTSV in short. The proposed VAD is an improved version of existing VAD which used gammatone filter and entropy. The LTSV measure is modified to adapt to the amplitude envelopes extracted using gammatone filter by swapping entropy and variance used in LTSV measure to reduce noise effect in the extracted temporal envelopes and improve discriminative power of the extracted feature. The proposed algorithm also implements an adaptive threshold that is computed using a nonlinear filter to track short-term trend of the extracted feature in real-time. The proposed VAD using GMLTSV feature is tested against clean speech signals from TIMIT test corpus which are degraded at SNR ranged from -10dB to 20dB by non-stationary noise, eg. airport noise, babble noise, exhibition noise from Aurora-2 database, and stationary noise, eg. additive white Gaussian noise. Based on the evaluation, it is proven that the proposed GMLTSV-based VAD is robust in speech and non-speech detection even at low signal-to-noise ratio (SNR) and outperformed other existing voice activity detectors which are compared in the evaluation. The proposed VAD achieved satisfactory accuracy when compared to the impractical single frequency filtering based VAD while implementing real-time scheme for practical application.


title: Human eye sclera detection and tracking using a modified time-adaptive self-organizing map

abstract: This paper investigates the use of time-adaptive self-organizing map (TASOM)-based active contour models (ACMs) for detecting the boundaries of the human eye sclera and tracking its movements in a sequence of images. The task begins with extracting the head boundary based on a skin-color model. Then the eye strip is located with an acceptable accuracy using a morphological method. Eye features such as the iris center or eye corners are detected through the iris edge information. TASOM-based ACM is used to extract the inner boundary of the eye. Finally, by tracking the changes in the neighborhood characteristics of the eye-boundary estimating neurons, the eyes are tracked effectively. The original TASOM algorithm is found to have some weaknesses in this application. These include formation of undesired twists in the neuron chain and holes in the boundary, lengthy chain of neurons, and low speed of the algorithm. These weaknesses are overcome by introducing a new method for finding the winning neuron, a new definition for unused neurons, and a new method of feature selection and application to the network. Experimental results show a very good performance for the proposed method in general and a better performance than that of the gradient vector field (GVF) snake-based method.


title: Discriminative decorrelation for clustering and classification

abstract: Object detection has over the past few years converged on using linear SVMs over HOG features. Training linear SVMs however is quite expensive, and can become intractable as the number of categories increase. In this work we revisit a much older technique, viz. Linear Discriminant Analysis, and show that LDA models can be trained almost trivially, and with little or no loss in performance. The covariance matrices we estimate capture properties of natural images. Whitening HOG features with these covariances thus removes naturally occuring correlations between the HOG features. We show that these whitened features (which we call WHO) are considerably better than the original HOG features for computing similarities, and prove their usefulness in clustering. Finally, we use our findings to produce an object detection system that is competitive on PASCAL VOC 2007 while being considerably easier to train and test.


title: Defending distributed systems against malicious intrusions and network anomalies

abstract: Network security breaches hinder the application of distributed computing systems manifested as the grids, clusters, intranets, extranets, or P2P systems. A new integrated approach is presented for building future, network-based intrusion detection systems (NIDS). We integrate the Snort (a NIDS) with a custom-designed anomaly detection system (ADS) to yield a powerful cyber defense system, called CAIDS. This system detects known attacks through signature matching and reveals network anomalies by Internet traffic data mining. The CAIDS design integrates two different detection engines for alert correlation between intrusions and anomalies. We aim to automate signature generation into Snort database. The system was tested over an Internet trace of 24 millions of packets containing 200 attacks. Our simulation experiments result in a 75% detection rate on all attacks with a low 5% false alarm rate. The system generates alerts on both intrusive attacks to distributed resources and anomalies detected in the Internet, intranet, and extranet connections.


title: Understanding use continuance in virtual worlds: Empirical test of a research model

abstract: We decided to examine why people continue to use virtual worlds by studying a real example: the popular virtual world of Second Life. This involved building and testing a model of virtual worlds based on habit from both the habit/automaticity and instant automaticity perspectives, the latter based on utilitarian and hedonic goals. The results (for a sample of 339 users) suggested that continuance intention for the virtual world was driven by perceived usefulness, enjoyment, and both perspectives of automatic behavior, which together provide considerable explanatory power for both habit and continuance intention. We conclude with implications for practice in this new area of inquiry.


title: Exploiting the query structure for efficient join ordering in SPARQL queries

abstract: The join ordering problem is a fundamental challenge that has to be solved by any query optimizer. Since the high-performance RDF systems are often implemented as triple stores (i.e., they represent RDF data as a single table with three attributes, at least conceptually), the query optimization strategies employed by such systems are often adopted from relational query optimization. In this paper we show that the techniques borrowed from traditional SQL query optimization (such as Dynamic Programming algorithm or greedy heuristics) are not immediately capable of handling large SPARQL queries. We introduce a new join ordering algorithm that performs a SPARQL-tailored query simplification. Furthermore, we present a novel RDF statistical synopsis that accurately estimates cardinalities in large SPARQL queries. Our experiments show that this algorithm is highly superior to the state-of-the-art SPARQL optimization approaches, including the RDF-3X’s original Dynamic Programming strategy.


title: Maternal zinc restriction affects postnatal growth and glucose homeostasis in rat offspring differently depending upon adequacy of their nutrient intake

abstract: INTRODUCTION ::: We have previously investigated effects of moderate maternal zinc (Zn) restriction on growth and glucose homeostasis in offspring, but interaction between maternal Zn restriction and postnatal nutrition have not been studied. ::: ::: ::: RESULTS ::: Weight and serum Zn were lower in ZnD-IN than in ZnC-IN rats at wk 3, but ZnD-AN and ZnD-EN rats had greater weights than respective controls and higher insulin-like growth factor-1 (ZnD-AN) and leptin levels (ZnD-EN). Subsequently, both ZnD-AN and ZnD-EN pups were insulin resistant, and had evidence of elevated serum leptin and depressed insulin receptor phosphorylation with gender-specific differences up to 15 weeks. ::: ::: ::: DISCUSSION ::: Maternal Zn restriction interacted with postnatal nutritional status, resulting in divergent effects on weight gain and insulin resistance. Interaction between potential effects of fetal Zn restriction and food availability postnatally may be one factor responsible for later metabolic derangements. ::: ::: ::: METHODS ::: Rats were fed Zn restricted (ZnD, 7 μg/g) or control (ZnC, 25 μg/g) diets ad libitum from 3 wk pre-conception to 3 wk post-parturition. Postnatally, litters were culled to 13 (IN, inadequate nutrition), 7 (AN, adequate nutrition), and 4 (EN, excess nutrition) pups/dam, respectively, and nursed by their original mothers. Postweaning, pups were fed rodent diet ad libitum. Tests to assess insulin resistance were performed subsequently.


title: ContextUML: a UML-based modeling language for model-driven development of context-aware Web services

abstract: Context-aware Web services are emerging as a promising technology for the electronic businesses in mobile and pervasive environments. Unfortunately, complex context-aware services are still hard to build. In this paper, we present a modeling language for the model-driven development of context-aware Web services based on the Unified Modeling Language (UML). Specifically, we show how UML can be used to specify information related to the design of context-aware services. We present the abstract syntax and notation of the language and illustrate its usage using an example service. Our language offers significant design flexibility that considerably simplifies the development of context-aware Web services.


title: Steganography using Cryptography and Pseudo Random Numbers

abstract: When it comes to information hiding it is always a question whether to use Steganography or cryptography. Cryptography scrambles the message so that is it unrecognizable and without proper key the Encrypted message is useless. With the help of steganography messages can be passed over the network discreetly as it provides secure data hiding. It is very challenging and emerging field of research. Steganography can be simple as LSB technique or can be complicated as transform domain techniques, each technique have their own advantage. Both of these techniques are often confused with each other because in a way both are used to hide data. This paper discusses the important aspects of Steganography and Cryptography. How a simple LSB steganography technique in images can be complicated further using combination of Cryptography and Pseudo-random number generator.


title: Injectable Skin Fillers

abstract: Recent advances in soft tissue augmentation have expanded our options in the search for an ideal filling agent, and several new fillers have recently been approved by the US Food and Drug Administration (FDA). Fillers can be used aesthetically to reduce the effects of aging and to de-emphasize previous scars. With aging, there is shifting and loss of connective and subcutaneous tissue, most notably in the face, neck, and hands. Subcutaneous augmentation in appropriate areas with injectable fillers replaces this lost tissue, producing a rejuvenating effect. Fillers can work synergistically with surgical procedures (eg, facelifts) to improve results. Patients who do not want to undergo a surgical procedure can often obtain excellent results noninvasively using fillers combined with other modalities (eg, laser resurfacing, peels, botulinum toxin). Facial fillers are most useful in the lower third of the face. The gravitational effects of aging mean that tissue shifts inferiorly, resulting in accentuated nasolabial and melolabial folds. Moreover, botulinum toxin is unmatched in its ability to rejuvenate the upper third of the face. Scars from acne, surgery, or trauma that result predominantly from loss or contraction of tissue can also be improved greatly with fillers. Each type of filler has different strengths and weaknesses (Table 1). Physicians who are familiar with many fillers thus are best equipped to maximize the benefits of this class of agents and to serve


title: A Survey of Beacon-Enabled IEEE 802.15.4 MAC Protocols in Wireless Sensor Networks

abstract: IEEE 802.15.4 is the de facto standard for Wireless Sensor Networks (WSNs) that outlines the specifications of the PHY layer and MAC sub-layer in these networks. The MAC protocol is needed to orchestrate sensor nodes access to the wireless communication medium. Although distinguished by a set of strengths that contributed to its popularity in various WSNs, IEEE 802.15.4 MAC suffers from several limitations that play a role in deteriorating its performance. Also, from a practical perspective, 80.15.4-based networks are usually deployed in the vicinity of other wireless networks that operate in the same ISM band. This means that 802.15.4 MAC should be ready to cope with interference from other networks. These facts have motivated efforts to devise improved IEEE 802.15.4 MAC protocols for WSNs. In this paper we provide a survey for these protocols and highlight the methodologies they follow to enhance the performance of the IEEE 802.15.4 MAC protocol.


title: STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method

abstract: We present STRUCTURE HARVESTER (available at http://taylor0.biology.ucla.edu/structureHarvester/), a web-based program for collating results generated by the program STRUCTURE. The program provides a fast way to assess and visualize likelihood values across multiple values of K and hundreds of iterations for easier detection of the number of genetic groups that best fit the data. In addition, STRUCTURE HARVESTER will reformat data for use in downstream programs, such as CLUMPP.


title: Routine use of the Ion Torrent AmpliSeq™ Cancer Hotspot Panel for identification of clinically actionable somatic mutations

abstract: BACKGROUND ::: Somatic mutation analysis is standard of practice for solid tumors in order to identify therapeutic sensitizing and resistance mutations. Our laboratory routinely performed standalone PCR-based methods for mutations in several genes. Rapid discovery and introduction of new therapeutics has demanded additional genomic information for adequate management of the cancer patient. We evaluated a next generation sequencing assay, the Ion Torrent AmpliSeq Cancer Hotspot Panelv2 (CHPv2), capable of identifying multiple somatic mutations in 50 genes in a single assay. ::: ::: ::: METHODS ::: Accuracy, precision, limit of detection, and specificity were evaluated using DNA from well-characterized cell lines, genetically engineered cell lines fixed and embedded in paraffin, and previously tested mutation positive or negative, formalin-fixed, paraffin-embedded (FFPE) tissues. Normal kidney, tonsil and colon FFPE tissues were used as controls. ::: ::: ::: RESULTS ::: Accuracy studies showed 100% concordance in each patient sample between previous PCR results and the corresponding variants identified using the Ion Torrent panel. Precision studies gave consistent results when libraries were prepared from the same original DNA and were run on multiple 316 chips. The limit of detection was determined to be 5% for single nucleotide variants (SNVs) and 20% for insertions and deletions (indels). Specificity studies using normal FFPE tissue previously tested by PCR methods were also 100%. ::: ::: ::: CONCLUSIONS ::: We have evaluated the performance of the AmpliSeq Cancer Panel Hotspotv2 and show that it is suitable for clinical testing. This next generation sequencing panel has allowed the laboratory to consolidate a broader range of molecular oncology testing to a single platform and single assay.


title: The effects of font type and size on the legibility and reading time of online text by older adults

abstract: This study examined passages containing two serif and sans serif fonts at 12 and 14-point sizes for differences in legibility, reading time, and general preference when read by an older population. A significant main effect of size was found for font legibility in that 14-point fonts were more legible to read than 12-point fonts. A marginal interaction was also found for reading time in that participants read 12-point serif fonts significantly slower than 14-point serif or sans serif fonts. Moreover, participants significantly preferred the 14-point to the 12-point font size. Font recommendations are discussed.


title: AutoPaG: towards automated software patch generation with source code root cause identification and repair

abstract: Software patch generation is a critical phase in the life-cycle of a software vulnerability. The longer it takes to generate a patch, the higher the risk a vulnerable system needs to take to avoid from being compromised. However, in practice, it is a rather lengthy process to generate and release software patches. For example, the analysis on 10 recent Microsoft patches (MS06-045 to MS06-054) shows that, for an identified vulnerability, it took 75 days on average to generate and release the patch. In this paper, we present the design, implementation, and evaluation of AutoPaG, a system that aims at reducing the time needed for software patch generation. In our current work, we mainly focus on a common and serious type of software vulnerability: the out-of-bound vulnerability which includes buffer overflows and general boundary condition errors. Given a working out-of-bound exploit which may be previously unknown, AutoPaG is able to catch on the fly the out-of-bound violation, and then, based on data flow analysis, automatically analyzes the program source code and identifies the root cause - vulnerable source-level program statements. Furthermore, within seconds, AutoPaG generates a fine-grained source code patch to temporarily fix it without any human intervention. We have built a proof-of-concept system in Linux and the preliminary results are promising: AutoPaG is able to successfully identify the root cause and generate a source code patch within seconds for every vulnerability test in the Wilander's buffer overflow benchmark test-suite. In addition, the evaluation with a number of real-world out-of-bound exploits also demonstrates its effectiveness and practicality in automatically identifying (vulnerable) source code root causes and generating corresponding patches.


title: Large-scale damage detection using satellite imagery

abstract: Satellite imagery is a valuable source of information for assessing damages in distressed areas undergoing a calamity, such as an earthquake or an armed conflict. However, the sheer amount of data required to be inspected for this assessment makes it impractical to do it manually. To address this problem, we present a semi-supervised learning framework for large-scale damage detection in satellite imagery. We present a comparative evaluation of our framework using over 88 million images collected from 4, 665 KM2 from 12 different locations around the world. To enable accurate and efficient damage detection, we introduce a novel use of hierarchical shape features in the bags-of-visual words setting. We analyze how practical factors such as sun, sensor-resolution, satellite-angle, and registration differences impact the effectiveness our proposed representation, and compare it to five alternative features in multiple learning settings. Finally, we demonstrate through a user-study that our semi-supervised framework results in a ten-fold reduction in human annotation time at a minimal loss in detection accuracy compared to manual inspection


title: Mobile healthcare information management utilizing Cloud Computing and Android OS

abstract: Cloud Computing provides functionality for managing information data in a distributed, ubiquitous and pervasive manner supporting several platforms, systems and applications. This work presents the implementation of a mobile system that enables electronic healthcare data storage, update and retrieval using Cloud Computing. The mobile application is developed using Google's Android operating system and provides management of patient health records and medical images (supporting DICOM format and JPEG2000 coding). The developed system has been evaluated using the Amazon's S3 cloud service. This article summarizes the implementation details and presents initial results of the system in practice.


title: Students’ perceptions about evaluation and assessment in higher education: a review1

abstract: In educational contexts, understanding the student’s learning must take account of the student’s construction of reality. Reality as experienced by the student has an important additional value. This assumption also applies to a student’s perception of evaluation and assessment. Students’ study behaviour is not only determined by the examination or assessment modes that are used. Students’ perceptions about evaluation methods also play a significant role. This review aims to examine evaluation and assessment from the student’s point of view. Research findings reveal that students’ perceptions about assessment significantly influence their approaches to learning and studying. Conversely, students’ approaches to study influence the ways in which they perceive evaluation and assessment. Findings suggest that students hold strong views about different assessment and evaluation formats. In this respect students favour multiple‐choice format exams to essay type questions. However, when compared with more innovative assessment methods, students call the ‘fairness’ of these well‐known evaluation modes into question.


title: Automatic image vectorization using superpixels and random walkers

abstract: Image vectorization involves two major problems: how to extract proper geometric descriptors from the raster image and how to rasterize the vector representation for display. In this paper, we propose a novel image vectorization approach using diffusion curves as the geometric primitives. Our approach automatically extracts accurate diffusion curves from the input image without user interaction. We first segment the input image into a set of superpixels by a multi-layer algorithm. Then, boundary positions of these superpixels are explored to locate control points for diffusion curves, and color information is properly sampled to generate our double-boundary representation. To render the vector graphics, we formulate color diffusion as a random walk process. Experiments on different categories of photographs show that our approach successfully reveals detail contents in the reconstructed image, and that the rendering process can be performed nearly in realtime on a modern CPU.


title: Discovering Inconsistencies in PubMed Abstracts through Ontology-Based Information Extraction

abstract: Searching for a cure for cancer is one of the most vital pursuits in modern medicine. In that aspect microRNA research plays a key role. Keeping track of the shifts and changes in established knowledge in the microRNA domain is very important. In this paper, we introduce an Ontology-Based Information Extraction method to detect occurrences of inconsistencies in microRNA research paper abstracts. We propose a method to first use the Ontology for MIcroRNA Targets (OMIT) to extract triples from the abstracts. Then we introduce a new algorithm to calculate the oppositeness of these candidate relationships. Finally we present the discovered inconsistencies in an easy to read manner to be used by medical professionals. To our best knowledge, this study is the first ontology-based information extraction model introduced to find shifts in the established knowledge in the medical domain using research paper abstracts. We downloaded 36877 abstracts from the PubMed database. From those, we found 102 inconsistencies relevant to the microRNA domain.


title: The Compact Circularly-Polarized Hollow Rectangular Dielectric Resonator Antenna With an Underlaid Quadrature Coupler

abstract: The wideband circularly polarized (CP) dielectric resonator antenna (DRA) is investigated with an underlaid quadrature coupler. The idea is used to realize a CP hollow rectangular DRA. Since the coupler is located beneath the DRA, it does not increase the footprint of the antenna, making the system very compact. The underlaid coupler is placed entirely inside the hollow region of the DRA and, thus, it can be designed easily as if there is no overlaid DRA. Two configurations are considered in this paper. In the first configuration, an external 50-Ω load is used for the matching port of the coupler. For the second one, a strip loaded by the DRA is used to provide a load and, thus, no lumped elements are required in this configuration. In this paper, a network model is also given to aid engineers in designing the proposed integrated DRA. The reflection coefficient, axial ratio, antenna gain, and radiation pattern for each configuration are studied. It was found that wide impedance and axial-ratio bandwidths can be obtained with the proposed CP DRAs. Measurements were carried out to verify the simulations, and reasonable agreement between them was obtained.


title: Financial fragility in emerging markets: rm balance sheets and the sectoral structure

abstract: This paper builds an overlapping generation model of a two-sector small open economy in order to study the evolution of the sectoral structure and its impact on nancial fragility. Firms in the economy are subject to a borrowing constraint. It is also assumed that there is a currency mismatch in the balance sheets of the non-tradable sector. Under these two assumptions, multiple within-period equilibria associated with dieren t real exchange rates and investment levels may arise, making self-fullling balance-of-payments crises possible. The within-period crisis equilibrium exists when the non-tradable sector is large enough compared to the tradable sector and sucien tly leveraged. The paper studies the dynamics of the relative size and leverage of the non-tradable sector. It shows that their evolution leads to a nancially fragile state in economies sucien tly opened to external nance and in times of high international liquidity.


Modeling Opportunistic IoT Services in Open IoT Ecosystems


title: The Virtual Environment of Things (VEoT): A Framework for Integrating Smart Things into Networked Virtual Environments

abstract: The key concept of the Internet of Things (IoT) is linking physical smart things to the Internet to make them active participants on the Internet. The Web of Things (WoT) uses the World Wide Web platform and its associated technologies as a framework to realize IoT. Inspired by WoT, we propose a new paradigm, the Virtual Environment of Things (VEoT), to integrate real-world smart things and virtual-world avatars/objects in a computer generated virtual environment so that entities in either worlds can interact with one another in a real-time manner. To prove the VEoT concept, we design a hierarchical smart gateway, REX (Resources Exchange), to embody smart things as Restful resources that can be combined with networked virtual environments (NVEs) to realize the integration and the interaction of the virtual environments and the real world. In addition, we implement a VEoT-based NVE, called X-Campus, for guiding visitors to navigate a building of National Central University to verify the feasibility of VEoT. This implementation shows the VEoT framework works properly.


title: ADHD-200 Global Competition: diagnosing ADHD using personal characteristic data can outperform resting state fMRI measurements

abstract: Neuroimaging-based diagnostics could potentially assist clinicians to make more accurate diagnoses resulting in faster, more effective treatment. We participated in the 2011 ADHD-200 Global Competition which involved analyzing a large dataset of 973 participants including Attention deficit hyperactivity disorder (ADHD) patients and healthy controls. Each participant's data included a resting state functional magnetic resonance imaging (fMRI) scan as well as personal characteristic and diagnostic data. The goal was to learn a machine learning classifier that used a participant's resting state fMRI scan to diagnose (classify) that individual into one of three categories: healthy control, ADHD combined (ADHD-C) type, or ADHD inattentive (ADHD-I) type. We used participants' personal characteristic data (site of data collection, age, gender, handedness, performance IQ, verbal IQ, and full scale IQ), without any fMRI data, as input to a logistic classifier to generate diagnostic predictions. Surprisingly, this approach achieved the highest diagnostic accuracy (62.52%) as well as the highest score (124 of 195) of any of the 21 teams participating in the competition. These results demonstrate the importance of accounting for differences in age, gender, and other personal characteristics in imaging diagnostics research. We discuss further implications of these results for fMRI-based diagnosis as well as fMRI-based clinical research. We also document our tests with a variety of imaging-based diagnostic methods, none of which performed as well as the logistic classifier using only personal characteristic data.


title: A Review of Imaging Techniques for Plant Phenotyping

abstract: Given the rapid development of plant genomic technologies, a lack of access to plant phenotyping capabilities limits our ability to dissect the genetics of quantitative traits. Effective, high-throughput phenotyping platforms have recently been developed to solve this problem. In high-throughput phenotyping platforms, a variety of imaging methodologies are being used to collect data for quantitative studies of complex traits related to the growth, yield and adaptation to biotic or abiotic stress (disease, insects, drought and salinity). These imaging techniques include visible imaging (machine vision), imaging spectroscopy (multispectral and hyperspectral remote sensing), thermal infrared imaging, fluorescence imaging, 3D imaging and tomographic imaging (MRT, PET and CT). This paper presents a brief review on these imaging techniques and their applications in plant phenotyping. The features used to apply these imaging techniques to plant phenotyping are described and discussed in this review.


title: Dex: high-performance exploration on large graphs for information retrieval

abstract: Link and graph analysis tools are important devices to boost the richness of information retrieval systems. Internet and the existing social networking portals are just a couple of situations where the use of these tools would be beneficial and enriching for the users and the analysts. However, the need for integrating different data sources and, even more important, the need for high performance generic tools, is at odds with the continuously growing size and number of data repositories. In this paper we propose and evaluate DEX, a high performance graph database querying system that allows for the integration of multiple data sources. DEX makes graph querying possible in different flavors, including link analysis, social network analysis, pattern recognition and keyword search. The richness of DEX shows up in the experiments that we carried out on the Internet Movie Database (IMDb). Through a variety of these complex analytical queries, DEX shows to be a generic and efficient tool on large graph databases.


title: OpenML: a collaborative science platform

abstract: We present OpenML, a novel open science platform that provides easy access to machine learning data, software and results to encourage further study and application. It organizes all submitted results online so they can be easily found and reused, and features a web API which is being integrated in popular machine learning tools such as Weka, KNIME, RapidMiner and R packages, so that experiments can be shared easily.


title: Assisted Energy Management in Smart Microgrids

abstract: Demand response provides utilities with a mechanism to share with end users the stochasticity resulting from the use of renewable sources. Pricing is accordingly used to reflect energy availability, to allocate such a limited resource to those loads that value it most. However, the strictly competitive mechanism can result in service interruption in presence of competing demand. To solve this issue we investigate on the use of forward contracts, i.e., service-level agreements priced to reflect the expectation of future supply and demand curves. Given the limited resources of microgrids, service availability is an opposite objective to the one of system reactivity. We firstly design policy-based brokers and identify then a learning broker based on artificial neural networks. We show the latter being progressively minimizing the reimbursement costs and maximizing the overall profit.


title: GoWeb: a semantic search engine for the life science web

abstract: BackgroundCurrent search engines are keyword-based. Semantic technologies promise a next generation of semantic search engines, which will be able to answer questions. Current approaches either apply natural language processing to unstructured text or they assume the existence of structured statements over which they can reason.ResultsHere, we introduce a third approach, GoWeb, which combines classical keyword-based Web search with text-mining and ontologies to navigate large results sets and facilitate question answering. We evaluate GoWeb on three benchmarks of questions on genes and functions, on symptoms and diseases, and on proteins and diseases. The first benchmark is based on the BioCreAtivE 1 Task 2 and links 457 gene names with 1352 functions. GoWeb finds 58% of the functional GeneOntology annotations. The second benchmark is based on 26 case reports and links symptoms with diseases. GoWeb achieves 77% success rate improving an existing approach by nearly 20%. The third benchmark is based on 28 questions in the TREC genomics challenge and links proteins to diseases. GoWeb achieves a success rate of 79%.ConclusionGoWeb's combination of classical Web search with text-mining and ontologies is a first step towards answering questions in the biomedical domain. GoWeb is online at: http://www.gopubmed.org/goweb


title: Ontology-Based Fraud Detection

abstract: One method for detecting fraud is to check for suspicious changes in user behavior. This paper proposes a novel method, built upon ontology and ontology instance similarity. Ontology is now widely used to enable knowledge sharing and reuse, so some personality ontologies can be easily used to present user behavior. By measure the similarity of ontology instances, we can determine whether an account is defrauded. This method lows the data model cost and make the system very adaptive to different applications.


title: Evaluation of social interaction during occupational engagement.

abstract: The Evaluation of Social Interaction (ESI; Fisher & Griswold, 2008) assesses a person's performance of social interaction skills in the natural context with typical social partners during any area of occupation. We used Rasch analysis of 175 observations of 128 people, ages 4�73, to examine internal scale validity, the items� skill hierarchy and intended purpose, and the ESI's ability to differentiate between people with and without disabilities. The ESI demonstrated validity for 24 of 27 skills and six intended purposes, with a hierarchy of performance. Of the observations, 95.3% demonstrated goodness of fit to the Rasch model, indicating person response validity. People without a disability demonstrated significantly higher social skills performance than those with a known disability (t = 4.468, df = 83 p = .000). The ESI has the potential to provide a quantitative assessment of social interaction performance in the natural context of a person's desired occupation and may be useful for intervention planning and outcome measurement.


title: A Sound Type System For Secure Flow Analysis

abstract: Ensuring secure information flow within programs in the context of multiple sensitivity levels has been widely studied. Especially noteworthy is Denning’s work in secure flow analysis and the lattice model [6][7]. Until now, however, the soundness of Denning’s analysis has not been established satisfactorily. We formulate Denning’s approach as a type system and present a notion of soundness for the system that can be viewed as a form of noninterference. Soundness is established by proving, with respect to a standard programming language semantics, that all well-typed programs have this noninterference property.


title: Zika Virus Vaccine Development

abstract: The emergence of Zika virus in Brazil and its association with microcephaly and Guillain-Barré syndrome led to accelerated vaccine development efforts. Based on prior flavivirus vaccine development programs, knowledge of flavivirus particle structure, definition of E dimers as the key antigenic target, and deep understanding of neutralizing mechanisms, multiple vaccine strategies have advanced to the stage of clinical evaluation with unprecedented speed. These include nucleic acid (DNA and messenger RNA), whole-inactivated virus, live-attenuated or chimeric virus, and protein or viruslike particle vaccines. Within a year from the declaration by the World Health Organization of Zika virus as a Public Health Emergency of International Concern, multiple vaccine candidates entered clinical trials, now totaling 7 products with an additional 40-plus candidate vaccines in preclinical development. The rapid progress in vaccine development demonstrates the capacity of governments, public health organizations, and the scientific community to respond to pandemic threats when sufficient prior knowledge exists, emergency funding is made available, and interagency cooperation is achieved and serves as a paradigm for preparing for future emerging infectious diseases.


title: Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation

abstract: Multispectral pedestrian detection has attracted increasing attention from the research community due to its crucial competence for many around-the-clock applications (e.g., video surveillance and autonomous driving), especially under insufficient illumination conditions. We create a human baseline over the KAIST dataset and reveal that there is still a large gap between current top detectors and human performance. To narrow this gap, we propose a network fusion architecture, which consists of a multispectral proposal network to generate pedestrian proposals, and a subsequent multispectral classification network to distinguish pedestrian instances from hard negatives. The unified network is learned by jointly optimizing pedestrian detection and semantic segmentation tasks. The final detections are obtained by integrating the outputs from different modalities as well as the two stages. The approach significantly outperforms state-of-the-art methods on the KAIST dataset while remain fast. Additionally, we contribute a sanitized version of training annotations for the KAIST dataset, and examine the effects caused by different kinds of annotation errors. Future research of this problem will benefit from the sanitized version which eliminates the interference of annotation errors.


title: Lessons Learned from Developing Permissioned Blockchains

abstract: We have been developing permissioned blockchains (BC) since 2015 and encountered numerous issues. Some of these issues are well known, but some are unknown before. This paper summarizes these lessons learned including communication and consensus protocols. Even though communications protocols and consensus protocols have been under development and experimentation for over forty years, we still encountered issues not addressed before. These issues require innovative solutions to ensure that dependable BC will be operating at a high speed.


title: Money Laundering Detection using Synthetic Data

abstract: Criminals use money laundering to make the proceeds from their illegal activities look legitimate in the eyes of the rest of society. Current countermeasures taken by financial organizations are based on legal requirements and very basic statistical analysis. Machine Learning offers a number of ways to detect anomalous transactions. These methods can be based on supervised and unsupervised learning algorithms that improve the performance of detection of such criminal activity. In this study we present an analysis of the difficulties and considerations of applying machine learning techniques to this problem. We discuss the pros and cons of using synthetic data and problems and advantages inherent in the generation of such a data set. We do this using a case study and suggest an approach based on Multi-Agent Based Simulations (MABS).


title: A cooperative Q-learning approach for distributed resource allocation in multi-user femtocell networks

abstract: This paper studies distributed interference manage- ment for femtocells that share the same frequency band with macrocells. We propose a multi-agent learning technique based on distributed Q-learning, called subcarrier-based distributed resource allocation using Q-learning (SBDRA-Q). SBDRA-Q operates under three different learning paradigms: Independent (IL), Cooperative (CL) and Weighted Cooperative (WCL). In the IL paradigm, all femtocells learn independently from each other. In both, CL and WCL, femtocells share partial information dur- ing the learning process in order to enhance their performance. The results show that WCL outperforms both CL and IL in terms of aggregate femtocell capacity, while slightly affecting fairness. Also, the results show that CL and WCL are more robust, when compared to IL, to new femtocells being deployed during the learning process. Finally, we show SBDRA-Q achieves higher aggregate femtocell capacity under the three learning paradigms when compared to a power allocation scheme (SBDPC-Q) that was proposed in the literature.


title: Implanting Rational Knowledge into Distributed Representation at Morpheme Level

abstract: Previously, researchers paid no attention to the creation of unambiguous morpheme embeddings independent from the corpus, while such information plays an important role in expressing the exact meanings of words for parataxis languages like Chinese. In this paper, after constructing the Chinese lexical and semantic ontology based on word-formation, we propose a novel approach to implanting the structured rational knowledge into distributed representation at morpheme level, naturally avoiding heavy disambiguation in the corpus. We design a template to create the instances as pseudo-sentences merely from the pieces of knowledge of morphemes built in the lexicon. To exploit hierarchical information and tackle the data sparseness problem, the instance proliferation technique is applied based on similarity to expand the collection of pseudo-sentences. The distributed representation for morphemes can then be trained on these pseudo-sentences using word2vec. For evaluation, we validate the paradigmatic and syntagmatic relations of morpheme embeddings, and apply the obtained embeddings to word similarity measurement, achieving significant improvements over the classical models by more than 5 Spearman scores or 8 percentage points, which shows very promising prospects for adoption of the new source of knowledge.


title: The Empirical Foundations of Telemedicine Interventions for Chronic Disease Management

abstract: The telemedicine intervention in chronic disease management promises to involve patients in their own care, provides continuous monitoring by their healthcare providers, identifies early symptoms, and responds promptly to exacerbations in their illnesses. This review set out to establish the evidence from the available literature on the impact of telemedicine for the management of three chronic diseases: congestive heart failure, stroke, and chronic obstructive pulmonary disease. By design, the review focuses on a limited set of representative chronic diseases because of their current and increasing importance relative to their prevalence, associated morbidity, mortality, and cost. Furthermore, these three diseases are amenable to timely interventions and secondary prevention through telemonitoring. The preponderance of evidence from studies using rigorous research methods points to beneficial results from telemonitoring in its various manifestations, albeit with a few exceptions. Generally, the benefits include reductions in use of service: hospital admissions/readmissions, length of hospital stay, and emergency department visits typically declined. It is important that there often were reductions in mortality. Few studies reported neutral or mixed findings.


title: Multi-criteria decision making approaches for supplier evaluation and selection: A literature review

abstract: Supplier evaluation and selection problem has been studied extensively. Various decision making approaches have been proposed to tackle the problem. In contemporary supply chain management, the performance of potential suppliers is evaluated against multiple criteria rather than considering a single factor-cost. This paper reviews the literature of the multi-criteria decision making approaches for supplier evaluation and selection. Related articles appearing in the international journals from 2000 to 2008 are gathered and analyzed so that the following three questions can be answered: (i) Which approaches were prevalently applied? (ii) Which evaluating criteria were paid more attention to? (iii) Is there any inadequacy of the approaches? Based on the inadequacy, if any, some improvements and possible future work are recommended. This research not only provides evidence that the multi-criteria decision making approaches are better than the traditional cost-based approach, but also aids the researchers and decision makers in applying the approaches effectively.


title: 1 Plug-in Hybrid Electric Vehicles and Smart Grid: Investigations Based on a Micro- Simulation

abstract: The introduction of plug-in hybrid electric vehicles (PHEVs) and electric vehicles (EVs), commonly referred to as plug-in electric vehicles (PEVs), could trigger a stepwise electrification of the whole transportation sector. However, the potential impact of PEV charging on the electric grid is not fully known, yet. This paper presents an iterative approach, which integrates a PEV electricity demand model and a power system simulation to reveal potential bottlenecks in the electric grid caused by PEV energy demand. An agent-based traffic demand model is used to model the electricity demand of each vehicle over the day. An approach based on interconnected multiple energy carrier systems is used as a model for a possible future energy system. Experiments demonstrate that the model is sensitive to policy changes, e.g., changes in electricity price result in modified charging patterns. By implementing an intelligent vehicle charging solution it is demonstrated how new charging schemes can be designed and tested using the proposed framework.


title: Belief Update in CLG Bayesian Networks With Lazy Propagation

abstract: In recent years Bayesian networks (BNs) with a mixture of continuous and discrete variables have received an increasing level of attention. We present an architecture for exact belief update in Conditional Linear Gaussian BNs (CLG BNs). The architecture is an extension of lazy propagation using operations of Lauritzen & Jensen [6] and Cowell [2]. By decomposing clique and separator potentials into sets of factors, the proposed architecture takes advantage of independence and irrelevance properties induced by the structure of the graph and the evidence. The resulting benefits are illustrated by examples. Results of a preliminary empirical performance evaluation indicate a significant potential of the proposed architecture.


title: Machine learning for intelligent mobile user interfaces using TensorFlow

abstract: One key feature of TensorFlow includes the possibility to compile the trained model to run efficiently on mobile phones. This enables a wide range of opportunities for researchers and developers. In this tutorial, we teach attendees two basic steps to run neural networks on a mobile phone: Firstly, we will teach how to develop neural network architectures and train them in TensorFlow. Secondly, we show the process to run the trained models on a mobile phone.


title: Successful requirement elicitation by combining requirement engineering techniques

abstract: The role of customers and other stakeholders is becoming increasingly significant during requirement engineering activities. Methods of eliciting requirements are now more co-operative. There are many techniques to obtain requirements from customers. Selecting the right techniques according to the characteristics of the project is very important. In some complex problems, combination of requirement engineering techniques should be applied for efficient and successful requirement engineering process. In this paper, we have presented the application of combination of requirement engineering techniques for a real life complex project (Supply Chain Management) with higher requirements volatility developed in a small scale software development organization.This will help in understanding requirements elicitation for such kind of complex software and facilitate in selecting the appropriate techniques towards getting the consistent and complete requirements.


title: Shape Similarity Matching for Query-by-Example

abstract: This paper describes a unified approach for two-dimensional (2-D) shape matching and similarity ranking of objects by means of a modal representation. In particular, we propose a new shape-similarity metric in the eigenshape space for object/image retrieval from a visual database via query-by-example. This differs from prior work which performed point correspondence determination and similarity ranking of shapes in separate steps. The proposed method employs selected boundary and/or contour points of an object as a coarse-to-fine shape representation, and does not require extraction of connected boundaries or silhouettes. It is rotation-, translation- and scale-invariant, and can handle mild deformations of objects (e.g. due to partial occlusions or pose variations). Results comparing the unified method with an earlier two-step approach using B-spline-based modal matching and Hausdorff distance ranking are presented on retail and museum catalog style still-image databases.


title: Preliminary performance evaluation of IRNSS-GPS-SBAS receiver in terms of position accuracy and velocity

abstract: The positional accuracy of navigation receiver is an important factor for various applications. Based on the application, the achieved positional accuracy levels vary from meters to millimeters. IRNSS-GPS-SBAS (IGS) receiver is capable of receiving all the seven IRNSS satellite signals in L5 (1176.45MHz) and S1 (2492.028MHz) band in addition to twelve GPS satellite signals and two SBAS (GAGAN) satellite signals in L1 (1575.42 MHz) frequency band. In this paper the position accuracy is evaluated in terms of 2DRMS, 3DRMS CEP, SEP, Geometric Dilution of Precision (GDOP) and Doppler shift in various modes namely Hybrid (IRNSS L5 and GPS L1), IRNSS dual frequency (L5 and S1), IRNSS L5 only and IRNSS S1 only mode. In Hybrid mode, it is found that all the positional accuracy values are better than the corresponding other mode values.


title: Characterizing and Improving WiFi Latency in Large-Scale Operational Networks

abstract: WiFi latency is a key factor impacting the user experience of modern mobile applications, but it has not been well studied at large scale. In this paper, we design and deploy WiFiSeer, a framework to measure and characterize WiFi latency at large scale. WiFiSeer comprises a systematic methodology for modeling the complex relationships between WiFi latency and a diverse set of WiFi performance metrics, device characteristics, and environmental factors. WiFiSeer was deployed on Tsinghua campus to conduct a WiFi latency measurement study of unprecedented scale with more than 47,000 unique user devices. We observe that WiFi latency follows a long tail distribution and the 90th (99th) percentile is around 20 ms (250 ms). Furthermore, our measurement results quantitatively confirm some anecdotal perceptions about impacting factors and disapprove others. We deploy three practical solutions for improving WiFi latency in Tsinghua, and the results show significantly improved WiFi latencies. In particular, over 1,000 devices use our AP selection service based on a predictive WiFi latency model for 2.5 months, and 72% of their latencies are reduced by over half after they re-associate to the suggested APs.


NMF-based speech enhancement incorporating deep neural network.


title: CranialCloud: a cloud-based architecture to support trans-institutional collaborative efforts in neurodegenerative disorders

abstract: Purpose ::: Neurological diseases have a devastating impact on millions of individuals and their families. These diseases will continue to constitute a significant research focus for this century. The search for effective treatments and cures requires multiple teams of experts in clinical neurosciences, neuroradiology, engineering, and industry. Hence, the need to communicate a large amount of information with accuracy and precision is more necessary than ever for this specialty.


title: A distributed approach to load balance for multi-robot task allocation

abstract: In this paper we develop a distributed scheme for multiple robots engaged in task allocation. Vision-based control is extensively used in robotics, however large data volume may delay the response of robots and lead to the mismatch of actuators and sensors. We build a cascade information retrieval model and the signal vector based sensing delivery mapping to maintain a tight coupling between sensing and acting. And we propose a novel mathematical model which overcomes the problem of the existing model which has no extendibility for multi-robot task allocation. The new Markov model is composed of a set of rate equations and uses the theory of eigen-values in order to augment the number of task types and get the analytical solution of the model. In the physical experiments the results verify that the multi-robot system can split itself according to the proportions of various tasks in the environment, and show the effectiveness of distributed method for desired load balance.


title: A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down?

abstract: Autonomous driving at level five does not only means self-driving in the sunshine. Adverse weather is especially critical because fog, rain, and snow degrade the perception of the environment. In this work, current state of the art light detection and ranging (lidar) sensors are tested in controlled conditions in a fog chamber. We present current problems and disturbance patterns for four different state of the art lidar systems. Moreover, we investigate how tuning internal parameters can improve their performance in bad weather situations. This is of great importance because most state of the art detection algorithms are based on undisturbed lidar data.


title: Communication-Efficient Distributed Deep Metric Learning with Hybrid Synchronization

abstract: Deep metric learning is widely used in extreme classification and image retrieval because of its powerful ability to learn the semantic low-dimensional embedding of high-dimensional data. However, the heavy computational cost of mining valuable pair or triplet of training data and updating models frequently in existing deep metric learning approaches becomes a barrier to apply such methods to a large-scale real-world context in a distributed environment. Moreover, existing distributed deep learning framework is not designed for deep metric learning tasks, because it is difficult to implement a smart mining policy of valuable training data. In this paper, we introduce a novel distributed framework to speed up the training process of the deep metric learning using multiple machines. Specifically, we first design a distributed sampling method to find the hard-negative samples from a broader scope of candidate samples compared to the single-machine solution. Then, we design a hybrid communication pattern and implement a decentralized data-parallel framework to reduce the communication workload while the quality of the trained deep metric models is preserved. In experiments, we show excellent performance gain compared to a full spectrum of state-of-the-art deep metric learning models on multiple datasets in terms of image clustering and image retrieval tasks.


title: An Option Gate Module for Sentence Inference on Machine Reading Comprehension

abstract: In machine reading comprehension (MRC) tasks, sentence inference is an important but extremely difficult problem. Most of MRC models directly interact articles with questions from the word level, which ignores inter and intra information of sentences and cannot well focus on problems about sentence reasoning and inference, especially when the answer clues are far apart in the article. In this paper, we propose an option gate approach for reading comprehension. We consider applying a sentence-level option gate module to make the model incorporate sentence information. In our approach we (1) extract key sentences in the article to filter out noise unrelated to the question and the options, (2) encode each sentence in articles, questions and options with dot-product self-attention to obtain intra sentence representations, (3) model inter relationships between the article and the question with bilinear attention and (4) apply an option gate with sentence inference information to each option representation with the question-aware article representation. This module can help better reasoning instead of directly word matching or paraphrasing. And this module can easily supply sentence information for most of the existing reading comprehension models. Experimental results on the RACE dataset show that this easy and simple module helps outperform the baseline models by 2.5% at most (single model), and achieve state-of-the-art results on the RACE-H dataset.


title: Just a Fad? Gamification in Health and Fitness Apps

abstract: Background: Gamification has been a predominant focus of the health app industry in recent years. However, to our knowledge, there has yet to be a review of gamification elements in relation to health behavior constructs, or insight into the true proliferation of gamification in health apps. Objective: The objective of this study was to identify the extent to which gamification is used in health apps, and analyze gamification of health and fitness apps as a potential component of influence on a consumer’s health behavior. Methods: An analysis of health and fitness apps related to physical activity and diet was conducted among apps in the Apple App Store in the winter of 2014. This analysis reviewed a sample of 132 apps for the 10 effective game elements, the 6 core components of health gamification, and 13 core health behavior constructs. A regression analysis was conducted in order to measure the correlation between health behavior constructs, gamification components, and effective game elements. Results: This review of the most popular apps showed widespread use of gamification principles, but low adherence to any professional guidelines or industry standard. Regression analysis showed that game elements were associated with gamification ( P <.001). Behavioral theory was associated with gamification ( P <.05), but not game elements, and upon further analysis gamification was only associated with composite motivational behavior scores ( P <.001), and not capacity or opportunity/trigger. Conclusions: This research, to our knowledge, represents the first comprehensive review of gamification use in health and fitness apps, and the potential to impact health behavior. The results show that use of gamification in health and fitness apps has become immensely popular, as evidenced by the number of apps found in the Apple App Store containing at least some components of gamification. This shows a lack of integrating important elements of behavioral theory from the app industry, which can potentially impact the efficacy of gamification apps to change behavior. Apps represent a very promising, burgeoning market and landscape in which to disseminate health behavior change interventions. Initial results show an abundant use of gamification in health and fitness apps, which necessitates the in-depth study and evaluation of the potential of gamification to change health behaviors. [JMIR Serious Games 2014;2(2):e9]


title: Microwave synthesis Ag/reduced graphene oxide composites and enhanced antibacterial performance

abstract: A series of water-soluble silver/reduced graphene oxide (Ag/rGO) composites were prepared with rGO as a carrier. The obtained composites were characterised by energy-dispersive X-ray spectrometer (EDS), TEM, X-ray powder diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), X-ray photoelectron spectroscopy (XPS) and thermogravimetric analyses (TGA). TEM images demonstrated that well monodispersed silver nanoparticles (Ag NPs) precipitate on the rGO sheets. X-ray photoelectron spectroscopy showed that graphene oxide (GO) had been reduced to rGO in our hybrid. The antibacterial properties of Ag/rGO composites were investigated by bacteriostatic curve, and the experiments showed that the antibacterial properties of Ag/rGO antimicrobial materials were enhanced, which exhibited a superior antibacterial activity towards Escherichia coli (E. coli) because of the synergistic effect of rGO and Ag NPs. These results indicated that Ag/rGO composites attached to hierarchal hybrid supports can lead to co...


title: Tokyo Guidelines 2018: initial management of acute biliary infection and flowchart for acute cholangitis

abstract: The initial management of patients with suspected acute biliary infection starts with the measurement of vital signs to assess whether or not the situation is urgent. If the case is judged to be urgent, initial medical treatment should be started immediately including respiratory/circulatory management if required, without waiting for a definitive diagnosis. The patient's medical history is then taken; an abdominal examination is performed; blood tests, urinalysis, and diagnostic imaging are carried out; and a diagnosis is made using the diagnostic criteria for cholangitis/cholecystitis. Once the diagnosis has been confirmed, initial medical treatment should be started immediately, severity should be assessed according to the severity grading criteria for acute cholangitis/cholecystitis, and the patient's general status should be evaluated. For mild acute cholangitis, in most cases initial treatment including antibiotics is sufficient, and most patients do not require biliary drainage. However, biliary drainage should be considered if a patient does not respond to initial treatment. For moderate acute cholangitis, early endoscopic or percutaneous transhepatic biliary drainage is indicated. If the underlying etiology requires treatment, this should be provided after the patient's general condition has improved; endoscopic sphincterotomy and subsequent choledocholithotomy may be performed together with biliary drainage. For severe acute cholangitis, appropriate respiratory/circulatory management is required. Biliary drainage should be performed as soon as possible after the patient's general condition has been improved by initial treatment and respiratory/circulatory management. Free full articles and mobile app of TG18 are available at: http://www.jshbps.jp/modules/en/index.php?content_id=47 . Related clinical questions and references are also included


title: A 21nV/√Hz chopper-stabilized multipath current-feedback instrumentation amplifier with 2µV offset

abstract: Amplifiers with low offset and low 1/f noise usually employ auto-zeroing (AZ) and/or chopping. However, AZ suffers from noise aliasing, and so requires more power dissipation for a given noise specification. Chopping, although free of noise aliasing, requires notch filters [1, 2] or AZ [3] to remove the ripple caused by up-modulated offset and 1/f noise. This paper describes a chopper-stabilized multi-path current-feedback instrumentation amplifier (CFIA), whose ripple is removed by a continuous-time (CT) ripple-reduction loop (RRL) [2]. In contrast to [2], the notch created by the RRL is eliminated by the use of a multi-path architecture. This results in a CFIA with a smooth single-pole response, which also achieves low offset (2µV) and low noise (21nV/√Hz) in a power efficient manner (NEF=9.6). By appropriately connecting its inputs, the CFIA can also be used as an opamp. In this configuration, its offset is further reduced while its noise and NEF are halved.


title: Strategic purchasing, supply management, and firm performance

abstract: Abstract Purchasing has increasingly assumed a pivotal strategic role in supply-chain management. Yet, claims of the strategic role of purchasing have not been fully subjected to rigorous theoretical and empirical scrutiny. Extant research has remained largely anecdotal and theoretically under-developed. In this paper, we examine the links among strategic purchasing, supply management, and firm performance. We argue that strategic purchasing can engender sustainable competitive advantage by enabling firms to: (a) foster close working relationships with a limited number of suppliers; (b) promote open communication among supply-chain partners; and (c) develop long-term strategic relationship orientation to achieve mutual gains. Using structural equation modeling, we empirically test a number of hypothesized relationships based on a sample of 221 United States manufacturing firms. Our results provide robust support for the links between strategic purchasing, supply management, customer responsiveness, and financial performance of the buying firm. Implications for future research and managerial practice in supply-chain management are also offered.


title: Lightweight eye capture using a parametric model

abstract: Facial scanning has become ubiquitous in digital media, but so far most efforts have focused on reconstructing the skin. Eye reconstruction, on the other hand, has received only little attention, and the current state-of-the-art method is cumbersome for the actor, time-consuming, and requires carefully setup and calibrated hardware. These constraints currently make eye capture impractical for general use. We present the first approach for high-quality lightweight eye capture, which leverages a database of pre-captured eyes to guide the reconstruction of new eyes from much less constrained inputs, such as traditional single-shot face scanners or even a single photo from the internet. This is accomplished with a new parametric model of the eye built from the database, and a novel image-based model fitting algorithm. Our method provides both automatic reconstructions of real eyes, as well as artistic control over the parameters to generate user-specific eyes.


title: Improving Document Clustering for Short Texts by Long Documents via a Dirichlet Multinomial Allocation Model

abstract: Document clustering for short texts has received considerable interest. Traditional document clustering approaches are designed for long documents and perform poorly for short texts due to the their sparseness representation. To better understand short texts, we observe that words that appear in long documents can enrich short text context and improve the clustering performance for short texts. In this paper, we propose a novel model, namely DDMAfs, which (1) improves the clustering performance of short texts by sharing structural knowledge of long documents to short texts; (2) automatically identifies the number of clusters; (3) separates discriminative words from irrelevant words for long documents to obtain high quality structural knowledge. Our experiments indicate that the DDMAfs model performs well on the synthetic dataset and real datasets. Comparisons between the DDMAfs model and state-of-the-art short text clustering approaches show that the DDMAfs model is effective.


title: A Matrix-Vector Recurrent Unit Model for Capturing Compositional Semantics in Phrase Embeddings

abstract: The meaning of a multi-word phrase not only depends on the meaning of its constituent words, but also the rules of composing them to give the so-called compositional semantic. However, many deep learning models for learning compositional semantics target specific NLP tasks such as sentiment classification. Consequently, the word embeddings encode the lexical semantics, the weights of the networks are optimised for the classification task. Such models have no mechanisms to explicitly encode the compositional rules, and hence they are insufficient in capturing the semantics of phrases. We present a novel recurrent computational mechanism that specifically learns the compositionality by encoding the compositional rule of each word into a matrix. The network uses a recurrent architecture to capture the order of words for phrases with various lengths without requiring extra preprocessing such as part-of-speech tagging. The model is thoroughly evaluated on both supervised and unsupervised NLP tasks including phrase similarity, noun-modifier questions, sentiment distribution prediction, and domain specific term identification tasks. We demonstrate that our model consistently outperforms the LSTM and CNN deep learning models, simple algebraic compositions, and other popular baselines on different datasets.


Twitter Power: Tweets as Electronic Word of Mouth


title: Approach Path Generation to Scooping Position for Wheel Loader

abstract: Our group developed a method of path generation for wheel loaders as part of our ongoing research on the autonomous loading operation of wheel loaders at surface mines and construction sites. The path connecting the present vehicle position and scooping position consists of clothoid curves and straight lines. Each line segment is optimized in a path generation procedure. A simplified model of the pile shape and bucket trajectory is used to determine the scooping direction based on an estimation of the resistance force applied on the bucket during the scooping motion. We applied the proposed method in an experimental model. A stereo-vision system was used to measure the shape of the pile and the scooping direction that imposed the least moment on the bucket was selected as the scooping position. This method generates an appropriate path for the wheel loader.


title: Cytokines and glucocorticoid receptor signaling. Relevance to major depression.

abstract: Data suggest that the activation of immune responses and the release of inflammatory cytokines may play a role in the pathophysiology of major depression. One mechanism by which cytokines may contribute to depression is through their effects on the glucocorticoid receptor (GR). Altered GR function in depression has been demonstrated by neuroendocrine challenge tests that reliably reveal reduced GR sensitivity as manifested by nonsuppression of cortisol following dexamethasone administration in vivo and lack of immune suppression following administration of glucocorticoids in vitro. Relevant to the GR, cytokines have been shown to decrease GR expression, block translocation of the GR from cytoplasm to nucleus, and disrupt GR-DNA binding through nuclear protein-protein interactions. In addition, cytokines have been shown to increase the expression of the relatively inert GR beta isoform. Specific cytokine signaling molecules that have been shown to be involved in the disruption of GR activity include p38 mitogen-activated protein kinase, which is associated with reduced GR translocation, and signal transducer and activator of transcription (STAT)5, which binds to GR in the nucleus. Nuclear factor-kappaB (NF-kappaB) also has been shown to lead to GR suppression through mutually inhibitory GR-NF-kappaB nuclear interactions. Interestingly, several antidepressants have been shown to enhance GR function, as has activation of protein kinase A (PKA). Antidepressants and PKA activation have also been found to inhibit inflammatory cytokines and their signaling pathways, suggesting that drugs that target both inflammatory responses and the GR may have special efficacy in the treatment of depression.


title: Mental Health and the Paranormal

abstract: To date, there has been a dearth of work examining the relationships between paranormal experiences and mental health. After defining paranormal experience and its prevalence, I examine a number of areas related to paranormal experience and psychopathology: psi and the unconscious, dissociation and fantasy proneness, schizotypy, transliminality and reality monitoring, child abuse, reasoning and information processing, and transpersonal psychology. Finally, I discuss the clinical implications of these findings.


title: Deep Transfer Learning for Person Re-Identification

abstract: Person re-identification (Re-ID) poses an inevitable challenge to deep learning: how to learn a robust deep model with millions of parameters on a small training set of few or no labels. In this paper, two deep transfer learning methods are proposed to address the training data sparsity problem, respectively from the supervised and unsupervised settings. First, a two-stepped fine-tuning strategy with proxy classifier learning is developed to transfer knowledge from auxiliary datasets. Second, given an unlabelled Re-Iddataset, an unsupervised deep transfer learning model is proposed based on a co-training strategy. Extensive experiments show that the proposed models achieve a good performance of deep Re-ID models.


title: Three-dimensional distance field metamorphosis

abstract: Given two or more objects of general topology, intermediate objects are constructed by a distance field metamorphosis. In the presented method the interpolation of the distance field is guided by a warp function controlled by a set of corresponding anchor points. Some rules for defining a smooth least-distorting warp function are given. To reduce the distortion of the intermediate shapes, the warp function is decomposed into a rigid rotational part and an elastic part. The distance field interpolation method is modified so that the interpolation is done in correlation with the warp function. The method provides the animator with a technique that can be used to create a set of models forming a smooth transition between pairs of a given sequence of keyframe models. The advantage of the new approach is that it is capable of morphing between objects having a different topological genus where no correspondence between the geometric primitives of the models needs to be established. The desired correspondence is defined by an animator in terms of a relatively small number of anchor points


title: An Empirical Study of Factors Influencing Accounting Information Systems Adoption

abstract: This study investigates the factors that influence accounting information systems (AIS) adoption among accountants. Drawing on the unified theory of acceptance and use of technology (UTAUT), the task–technology fit (TTF) model and the institutional theory, we developed a research model for AIS adoption by accountants. Data was collected from 216 accountants and multiple linear regression was employed to test the research model. The results showed that five key factors, namely effort expectancy, perceived technology fit, facilitating conditions, self-efficacy and coercive pressure are able to influence the likelihood that accountants would adopt the AIS. This research confirms the need to integrate UTAUT, TTF and institutional theory when studying AIS adoption factors. The findings from this study are useful for senior management, technology consultants, software vendors and accounting professional bodies in promoting the adoption of AIS.


title: A survey of socially interactive robots

abstract: This paper reviews “socially interactive robots”: robots for which social human–robot interaction is important. We begin by discussing the context for socially interactive robots, emphasizing the relationship to other research fields and the different forms of “social robots”. We then present a taxonomy of design methods and system components used to build socially interactive robots. Finally, we describe the impact of these robots on humans and discuss open issues. An expanded version of this paper, which contains a survey and taxonomy of current applications, is available as a technical report [T. Fong, I. Nourbakhsh, K. Dautenhahn, A survey of socially interactive robots: concepts, design and applications, Technical Report No. CMU-RI-TR-02-29, Robotics Institute, Carnegie Mellon University, 2002]. © 2003 Elsevier Science B.V. All rights reserved.


title: Model-Driven Serious Game Development Integration of the Gamification Modeling Language GaML with Unity

abstract: The development of gamification within non-game information systems as well as serious games has recently gained an important role in a variety of business fields due to promising behavioral or psychological improvements. However, industries still struggle with the high efforts of implementing gameful affordances in non-game systems. In order to decrease factors such as project costs, development cycles, and resource consumption as well as to improve the quality of products, the gamification modeling language has been proposed in prior research. However, the language is on a descriptive level only, i.e., Cannot be used to automatically generate executable software artifacts. In this paper and based on this language, we introduce a model-driven architecture for designing as well as generating building blocks for serious games. Furthermore, we give a validation of our approach by going through the different steps of designing an achievement system in the context of an existing serious game.


title: Biclustering Algorithms: A Survey

abstract: Analysis of large scale geonomics data, notably gene expression, has initially focused on clustering methods. Recently, biclustering techniques were proposed for revealing submatrices showing unique patterns. We review some of the algorithmic approaches to biclustering and discuss their properties.


Identifying Deep Contrasting Networks from Time Series Data: Application to Brain Network Analysis.


title: Self-referential processing in our brain — A meta-analysis of imaging studies on the self

abstract: Abstract The question of the self has intrigued philosophers and psychologists for a long time. More recently, distinct concepts of self have also been suggested in neuroscience. However, the exact relationship between these concepts and neural processing across different brain regions remains unclear. This article reviews neuroimaging studies comparing neural correlates during processing of stimuli related to the self with those of non-self-referential stimuli. All studies revealed activation in the medial regions of our brains' cortex during self-related stimuli. The activation in these so-called cortical midline structures (CMS) occurred across all functional domains (e.g., verbal, spatial, emotional, and facial). Cluster and factor analyses indicate functional specialization into ventral, dorsal, and posterior CMS remaining independent of domains. Taken together, our results suggest that self-referential processing is mediated by cortical midline structures. Since the CMS are densely and reciprocally connected to subcortical midline regions, we advocate an integrated cortical–subcortical midline system underlying human self. We conclude that self-referential processing in CMS constitutes the core of our self and is critical for elaborating experiential feelings of self, uniting several distinct concepts evident in current neuroscience.


title: Detail-preserving variational surface design with multiresolution constraints

abstract: We present a variational framework for rapid shape prototyping. The modeled shape is represented as a Catmull-Clark multiresolution subdivision surface which is interactively deformed by direct user input. Free-form design goals are formulated as constraints on the shape and the modeling problem is cast into a constrained optimization one. The focus of this paper is on handling multiresolution constraints of different kinds and on preserving surface details throughout the deformation process. Our approach eliminates the need for an explicit decomposition of the input model into frequency bands and the overhead associated with saving and restoring high-frequency detail after global shape fairing. Instead, we define a deformation vector field over the model and we optimize its energy. Surface details are considered as part of the rest shape and are preserved during free-form model editing. We explore approximating the solution of the optimization problem to various degrees to balance trade-offs between interactivity and accuracy of the results.


title: Topic Discovery from Heterogeneous Texts

abstract: Recently many topic models such as Latent Dirich-let Allocation (LDA) have made important progress towards generating high-level knowledge from a large corpus. They assume that a text consists of a mixture of topics, which is usually the case for regular articles but may not hold for a short text that usually contains only one topic. In practice, a corpus may include both short texts and long texts, in this case neither methods developed for only long texts nor methods for only short texts can generate satisfying results. In this paper, we present an innovative method to discover latent topics from a heterogeneous corpus including both long and short texts. A new topic model based on collapsed Gibbs sampling algorithm is developed for modeling such heterogeneous texts. The experiments on real-world datasets validate the effectiveness of the proposed model in comparison with other state-of-the-art models.


Interactive reinforcement learning for task-oriented dialogue management


title: Unified Framework for Automated Person Re-identification and Camera Network Topology Inference in Camera Networks

abstract: Person re-identification in large-scale multi-camera networks is a challenging task because of the spatio-temporal uncertainty and high complexity due to large numbers of cameras and people. To handle these difficulties, additional information such as camera network topology should be provided, which is also difficult to automatically estimate. In this paper, we propose a unified framework which jointly solves both person re-id and camera network topology inference problems. The proposed framework takes general multi-camera network environments into account. To effectively show the superiority of the proposed framework, we also provide a new person re-id dataset with full annotations, named SLP, captured in the synchronized multi-camera network. Experimental results show that the proposed methods are promising for both person re-id and camera topology inference tasks.


title: Organizing Artworks in an Ontology-based Semantic Affective Space

abstract: In this paper, we focus on applying sentiment analysis to resources from online collections, by exploiting, as information source, tags intended as textual traces that visitors leave for commenting artworks on social platforms. Our aim is to create a semantic social space where artworks can be dynamically organized according to an ontology of emotions. We propose to tackle this issue in a semantic web setting, through the development of an ontology of emotional categories based on Plutchik’s circumplex model, a well-founded psychological model of human emotions. The ontology has been conceived for categorizing emotiondenoting words and has been populated with Italian terms. The capability of detecting emotions in artworks can foster the development of emotion-aware search engines, emotional tag clouds or interactive map of emotions, which could enable new ways of accessing and exploring art collections. First experiments on tags and artworks from the ArsMeteo Italian web portal are discussed.


Semantic , Lexical , and Geographic Cues are used in Geographic Fluency