title: Influence of Mixed Additives on the Physicochemical Properties of a 5.25% Sodium Hypochlorite Solution: An Unsupervised Multivariate Statistical Approach

abstract: INTRODUCTION ::: This article reports for the first time the effects of multiple additives (polyethylene glycol 400, Triton X-100, benzalkonium chloride, and ethyl formate) on the surface tension, pH, and viscosity of 5.25% sodium hypochlorite (NaOCl) irrigant solution. Advanced statistical approaches based on unsupervised multivariate analysis (cluster analysis and principal component analysis) were used to quantify the variability of the physicochemical properties of the modified NaOCl solution for the first time in dentistry. ::: ::: ::: METHODS ::: Solutions of 5.25% NaOCl were modified with multiple additives in various concentrations, physicochemical parameters were measured at 22°C and 37°C, and the results were statistically analyzed to group the solutions and reveal the effects of additives. ::: ::: ::: RESULTS ::: Cluster analysis and principal component analysis revealed that pH and surface tension were the significant parameters (P < .05) for grouping the modified solutions. Four principal components, accounting for 90.6% of the total variance, were associated with flow characteristics (37.3%) determined by polyethylene glycol; the wetting property (22.5% and 10.5%), which was dependent on cationic and nonionic surfactant; and the antimicrobial effect (20.3%) influenced by ethyl formate. Varimax rotation of the principal components showed that the cationic surfactant (benzalkonium chloride) had significantly decreased surface tension compared with the nonionic surfactant (Triton-X). Although ethyl formate was introduced as an odor modifier, it had a significant effect on pH decrease and the occurrence of effervescence with O2 and hypochlorous acid release. ::: ::: ::: CONCLUSIONS ::: The statistical results revealed that the 5.25% NaOCl irrigant solution should be modified with a mixture of 0.1% benzalkonium chloride, 1% ethyl formate, and 7% polyethylene glycol for obtaining a low pH and low surface tension.


title: Word Segmentation of Vietnamese Texts: a Comparison of Approaches

abstract: We present in this paper a comparison between three segmentation systems for the Vietnamese language. Indeed, the majority of Vietnamese words is built by semantic composition from about 7,000 syllables, that also have a meaning as isolated words. So the identification of word boundaries in a text is not a simple task, and ambiguities often appear. Beyond the presentation of the tested systems, we also propose a standard definition for word segmentation in Vietnamese, and introduce a reference corpus developed for the purpose of evaluating such a task. The results observed confirm that it can be relatively well treated by automatic means, although a solution needs to be found to take into account out-of-vocabulary words.


title: A secure and efficient conference key distribution system

abstract: We present practical conference key distribution systems based on public keys, which authenticate the users and which are ‘proven’ secure provided the Diffie-Hellman problem is intractable. A certain number of interactions is needed but the overall cost is low. There is a complexity tradeoff. Depending on the network used, we either have a constant (in the number of conference participants) number of rounds (exchanges) or a constant communication and computation overhead. Our technique for authentication can be extended and used as the basis for an authentication scheme which is ‘proven’ secure against any type of attack, provided the Discrete Logarithm problem is intractable.


title: A Substrate Integrated Slot Antenna Array Using Simplified Feeding Network Based on Higher Order Cavity Modes

abstract: This paper proposes a novel substrate integrated waveguide (SIW) higher order mode cavity fed $2 \times 2$ -element slot array. The higher order mode cavity feeds all of the slot elements simultaneously, and the resonant mode is excited only by a slot aperture, which helps simplify the feeding network. To enhance the bandwidth, the array is designed as an asymmetric structure with different slot sizes and locations. In this way, new resonances are introduced and thus broaden the bandwidth. Furthermore, the proposed $2 \times 2$ -element slot array is used as subarray to implement a differentially fed $4 \times 4$ -element array. The measured results demonstrate that this simple structure antenna array features wideband, high radiation efficiency, good aperture efficiency, and broadside radiation.


title: Low-Cost Alternatives for Urban Noise Nuisance Monitoring Using Wireless Sensor Networks

abstract: Noise pollution caused by vehicular traffic is a common problem in urban environments that has been shown to affect people's health and children's cognition. In the last decade, several studies have been conducted to assess this noise, by measuring the equivalent noise pressure level (called L ::: eq ::: ) to acquire an accurate sound map using wireless networks with acoustic sensors. However, even with similar values of L ::: eq ::: , people can feel the noise differently according to its frequency characteristics. Thus, indexes, which can express people's feelings by subjective measures, are required. In this paper, we analyze the suitability of using the psychoacoustic metrics given by the Zwicker's model, instead of just only considering L ::: eq ::: . The goal is to evaluate the hardware limitations of a low-cost wireless acoustic sensor network that is used to measure the annoyance, using two types of commercial and off-the-shelf sensor nodes, Tmote-Invent nodes and Raspberry Pi platforms. Moreover, to calculate the parameters using these platforms, different simplifications to the Zwicker's model based on the specific features of road traffic noise are proposed. To validate the different alternatives, the aforementioned nodes are tested in a traffic congested area of Valencia City in a vertical and horizontal network deployment. Based on the results, it is observed that the Raspberry Pi platforms are a feasible low-cost alternative to increase the spatial-temporal resolution, whereas Tmote-Invent nodes do not confirm their suitability due to their limited memory and calibration issues.


title: A FeFET based super-low-power ultra-fast embedded NVM technology for 22nm FDSOI and beyond

abstract: We show the implementation of a ferroelectric field effect transistor (FeFET) based eNVM solution into a leading edge 22nm FDSOI CMOS technology. Memory windows of 1.5 V are demonstrated in aggressively scaled FeFET cells with an area as small as 0.025 μm2 At this point program/erase endurance cycles up to 105 are supported. Complex pattern are written into 32 MBit arrays using ultrafast program/erase pulses in a 10 ns range at 4.2 V. High temperature retention up to 300 °C is achieved. It makes FeFET based eNVM a viable choice for overall low-cost and low-power IoT applications in 22nm and beyond technology nodes.


title: Multilingual Word Embeddings using Multigraphs

abstract: We present a family of neural-network--inspired models for computing continuous word representations, specifically designed to exploit both monolingual and multilingual text. This framework allows us to perform unsupervised training of embeddings that exhibit higher accuracy on syntactic and semantic compositionality, as well as multilingual semantic similarity, compared to previous models trained in an unsupervised fashion. We also show that such multilingual embeddings, optimized for semantic similarity, can improve the performance of statistical machine translation with respect to how it handles words not present in the parallel data.


title: Data fusion schemes for cooperative spectrum sensing in cognitive radio networks

abstract: Cooperative spectrum sensing has proven its efficiency to detect spectrum holes in cognitive radio network (CRN) by combining sensing information of multiple cognitive radio users. In this paper, we study different fusion schemes that can be implemented in fusion center. Simulation comparison between these schemes based on hard, quantized and soft fusion rules are conducted. It is shown through computer simulation that the soft combination scheme outperforms the hard one at the cost of more complexity; the quantized combination scheme provides a good tradeoff between detection performance and complexity. In the paper, we also analyze a quantized combination scheme based on a tree-bit quantization and compare its performance with some hard and soft combination schemes.


title: Deconvolution in Astronomy: A Review

abstract: This article reviews different deconvolution methods. The all-pervasive presence of noise is what makes deconvolution particularly difficult. The diversity of resulting algorithms reflects different ways of estimating the true signal under various idealizations of its properties. Different ways of approaching signal recovery are based on different instrumental noise models, whether the astronomical objects are pointlike or extended, and indeed on the computational resources available to the analyst. We present a number of recent results in this survey of signal restoration, including in the areas of superresolution and dithering. In particular, we show that most recent published work has consisted of incorporating some form of multiresolution in the deconvolution process.


title: Mental Rotation of Three-Dimensional Objects

abstract: The time required to recognize that two perspective drawings portray objects of the same three-dimensional shape is found to be (i) a linearly increasing function of the angular difference in the portrayed orientations of the two objects and (ii) no shorter for differences corresponding simply to a rigid rotation of one of the two-dimensional drawings in its own picture plane than for differences corresponding to a rotation of the three-dimensional object in depth.


title: Tethys: A workbench and database for passive acoustic metadata

abstract: This project proposes a community standard for the representation of passive acoustic metadata along with a freely available software implementation. Our target audience is the marine mammal community, but the concepts are general and are applicable to a wide variety of taxa. In addition, we address the need to analyze acoustic metadata in the context of other environmental and biological parameters. The implementation provides interfaces to access a wide variety of data available from external services, such solar and lunar rise/set times, sea surface temperature, chlorophyll A, etc., thus permitting extensive data exploration in a workbench environment.


title: Febrile seizures: recent developments and unanswered questions

abstract: Background ::: Febrile seizures (FS) are typically observed in infants and children affecting 2–5 % of the pediatric population and are the commonest seizures in childhood.


Recognizing Salient Entities in Shopping Queries


title: Design and Application for PV Generation System Using a Soft-Switching Boost Converter With SARC

abstract: In order to improve the efficiency of energy conversion for a photovoltaic (PV) system, a soft-switching boost converter using a simple auxiliary resonant circuit, which is composed of an auxiliary switch, a diode, a resonant inductor, and a resonant capacitor, is adopted in this paper. The conventional boost converter decreases the efficiency because of hard switching, which generates losses when the switches are turned on/off. During this interval, all switches in the adopted circuit perform zero-current switching by the resonant inductor at turn-on, and zero-voltage switching by the resonant capacitor at turn-off. This switching pattern can reduce the switching losses, voltage and current stress of the switching device. Moreover, it is very easy to control. In this paper, we have analyzed the operational principles of the adopted soft-switching boost converter, and it is designed for PV generation system. Simulation and experimental results are presented to confirm the theoretical analysis.


title: Knowledge, attitudes and practices of mothers on childhood acute respiratory infections (ARI).

abstract: A total of three hundred mothers in both rural and urban areas were interviewed and their knowledge, attitude and practice (KAP) on acute respiratory infections (ARI) were compared. It was observed that KAP on ARI of mothers in both categories was almost the same with the exception of their health care seeking practice. Most had traditional beliefs as to the cause of ARI with only a minority knowing the causes. Only one third of mothers knew signs and symptoms of pneumonia; the majority had not experienced the fatal danger sign, chest indrawing and could not recognize it. As regards health care seeking practices, private general practitioners were identified as favorite health providers in urban areas. Utilization of government health facilities was higher among rural mothers. Self medication was found to be common in both categories of mothers, with western medicine being the top of the list.


title: Comparing Biases for Minimal Network Construction with Back-Propagation

abstract: Rumelhart (1987), has proposed a method for choosing minimal or "simple" representations during learning in Back-propagation networks. This approach can be used to (a) dynamically select the number of hidden units, (b) construct a representation that is appropriate for the problem and (c) thus improve the generalization ability of Back-propagation networks. The method Rumelhart suggests involves adding penalty terms to the usual error function. In this paper we introduce Rumelhart's minimal networks idea and compare two possible biases on the weight search space. These biases are compared in both simple counting problems and a speech recognition problem. In general, the constrained search does seem to minimize the number of hidden units required with an expected increase in local minima.


title: Dreaming as mind wandering: evidence from functional neuroimaging and first-person content reports

abstract: Isolated reports have long suggested a similarity in content and thought processes across mind wandering (MW) during waking, and dream mentation during sleep. This overlap has encouraged speculation that both ‘daydreaming’ and dreaming may engage similar brain mechanisms. To explore this possibility, we systematically examined published first-person experiential reports of MW and dreaming and found many similarities: in both states, content is largely audiovisual and emotional, follows loose narratives tinged with fantasy, is strongly related to current concerns, draws on long-term memory, and simulates social interactions. Both states are also characterized by a relative lack of meta-awareness. To relate first-person reports to neural evidence, we compared meta-analytic data from numerous functional neuroimaging (PET, fMRI) studies of the default mode network (DMN, with high chances of MW) and rapid eye movement (REM) sleep (with high chances of dreaming). Our findings show large overlaps in activation patterns of cortical regions: similar to MW/DMN activity, dreaming and REM sleep activate regions implicated in self-referential thought and memory, including medial prefrontal cortex (PFC), medial temporal lobe structures, and posterior cingulate. Conversely, in REM sleep numerous PFC executive regions are deactivated, even beyond levels seen during waking MW. We argue that dreaming can be understood as an ‘intensified’ version of waking MW: though the two share many similarities, dreams tend to be longer, more visual and immersive, and to more strongly recruit numerous key hubs of the DMN. Further, whereas MW recruits fewer PFC regions than goal-directed thought, dreaming appears to be characterized by an even deeper quiescence of PFC regions involved in cognitive control and metacognition, with a corresponding lack of insight and meta-awareness. We suggest, then, that dreaming amplifies the same features that distinguish MW from goal-directed waking thought.


title: A new way to enumerate cycles in graph

abstract: In many cases, the topology of communcation systems can be abstracted and represented as graph. Graph theories and algorithms are useful in these situations. In this paper, we introduced an algorithm to enumerate all cycles in a graph. It can be applied on digraph or undirected graph. Multigraph can also be used on for this purpose. It can be used to enumerate given length cycles without enumerating all cycles. This algorithm is simple and easy to be implemented.


SRCN : Joint Sub-bands Learning for Wavelet Domain Super Resolution


title: Asynchronous Parallel Bayesian Optimisation via Thompson Sampling

abstract: We design and analyse variations of the classical Thompson sampling (TS) procedure for Bayesian optimisation (BO) in settings where function evaluations are expensive, but can be performed in parallel. Our theoretical analysis shows that a direct application of the sequential Thompson sampling algorithm in either synchronous or asynchronous parallel settings yields a surprisingly powerful result: making $n$ evaluations distributed among $M$ workers is essentially equivalent to performing $n$ evaluations in sequence. Further, by modeling the time taken to complete a function evaluation, we show that, under a time constraint, asynchronously parallel TS achieves asymptotically lower regret than both the synchronous and sequential versions. These results are complemented by an experimental analysis, showing that asynchronous TS outperforms a suite of existing parallel BO algorithms in simulations and in a hyper-parameter tuning application in convolutional neural networks. In addition to these, the proposed procedure is conceptually and computationally much simpler than existing work for parallel BO.


title: Hip Protectors — A Breakthrough in Fracture Prevention

abstract: Hip fractures are one of the most devastating and costly problems commonly faced by elderly people. Each year in the United States, more than 300,000 people 65 years old or older are hospitalized because of hip fractures,1 and about a quarter of these people survive for less than one year because of the fracture or its complications.2 Of those who survive, most have substantial reductions in their ability to function in daily life and in their ability to walk, and a sizable minority are living in long-term care institutions by the end of the year after injury.2 Most previous attempts . . .


title: Big Data Analytics for Program Popularity Prediction in Broadcast TV Industries

abstract: The precise and timely prediction of program popularity is of great value for content providers, advertisers, and broadcast TV operators. This information can be beneficial for operators in TV program purchasing decisions and can help advertisers formulate reasonable advertisement investment plans. Moreover, in terms of technical matters, a precise program popularity prediction method can optimize the whole broadcasting system, such as the content delivery network strategy and cache strategy. Several prediction models have been proposed based on video-on-demand (VOD) data from YouKu, YouTube, and Twitter. However, existing prediction methods usually require a large quantity of samples and long training time, and the prediction accuracy is poor for programs that experience a high peak or sharp decrease in popularity. This paper presents our improved prediction approach based on trend detection. First, a dynamic time warping-distance-based $K$ -medoids algorithm is applied to group programs’ popularity evolution into four trends. Then, four trend-specific prediction models are built separately using random forests regression. According to the features extracted from an electronic program guide and early viewing records, newly published programs are classified into the four trends by a gradient boosting decision tree. Finally, by combining forecasting values from the trend-specific models and the classification probability, our proposed approach achieves better prediction results. The experimental results on a massive set of real VOD data from the Jiangsu Broadcasting Corporation show that, compared with the existing prediction models, the prediction accuracy is increased by more than 20%, and the forecasting period is effectively shortened.


title: Athena: a new efficient automatic checker for security protocol analysis

abstract: We propose an efficient automatic checking algorithm, Athena, for analyzing security protocols. Athena incorporates a logic that can express security properties including authentication, secrecy and properties related to electronic commerce. We have developed an automatic procedure for evaluating well-formed formulae in this logic. For a well-formed formula, if the evaluation procedure terminates, it will generate a counter example if the formula is false, or provide a proof if the formula is true. Even when the procedure does not terminate when we allow any arbitrary configurations of the protocol execution, (for example, any number of initiators and responders), termination could be forced by bounding the number of concurrent protocol runs and the length of messages, as is done in most existing model checkers. Athena also exploits several state space reduction techniques. It is based on an extension of the Strand Space Model (Thayer et al., 1998) which captures exact causal relation information. Together with backward search and other techniques, Athena naturally avoids the state space explosion problem commonly caused by asynchronous composition and symmetry redundancy. Athena also has the advantage that it can easily incorporate results from theorem proving through unreachability theorems. By using the unreachability theorems, it can prune the state space at an early stage, hence, reduce the state space explored and increase the likelihood of termination. As shown in our experiments, these techniques dramatically reduce the state space that needs to be explored.


title: Human Interaction in Multi-User Virtual Reality

abstract: In this paper we will present an immersive multiuser environment for studying joint action and social interaction. Besides the technical challenges of immersing multiple persons into a single virtual environment, additional research questions arise: Which parameters are coordinated during a joint action transportation task? In what way does the visual absence of the interaction partner affect the coordination task? What role does haptic feedback play in a transportation task? To answer these questions and to test the new experimental environment we instructed pairs of subjects to perform a classical joint action transportation task: carrying a stretcher through an obstacle course. With this behavioral experiment we demonstrated that joint action behavior (resulting from the coordination task) is a stable process. Even though visual and haptic information about the interaction partner were reduced, humans quickly compensated for the lack of information. After a short time they did not perform significantly differently from normal joint action behavior.


title: XRec: Behavior-Based User Recognition Across Mobile Devices

abstract: As smartphones and tablets become increasingly prevalent, more customers have multiple devices. The multi-user, multi-device interactions inspire many problems worthy of investigation, among which recognizing users across devices has significant implications on recommendation, advertising and user experience. Unlike the binary classification problem in user identification on a single device, cross-device user recognition is essentially a set partition problem. The app back-end aims to divide user activities on devices hosting the app into groups each associated with one user. In this paper, we present XRec which leverages user behavioral patterns, namely when, where and how a user uses the app, to achieve the recognition. To address the user-device partition problem, we propose a classification-plus-refinement algorithm. To validate our approach, we conduct a field study with an Android app. We instrument the app to collect usage data from real users. We provide proof-of-concept experimental results to demonstrate how XRec can provide added value to mobile apps, with the ability to correctly match a user across multiple devices with 70% recall and 90% precision.


title: Extrusion of Impacted Teeth Using Mini-Implant Mechanics

abstract: a single mini-implant with dimensions of 2.3mm × 9mm or 2.3mm × 11mm is inserted in the anterior palate. A bracket abutment with a pre-ligated .016" × .022" TMA** sectional wire is screwed to the head of the mini-implant (Fig. 2). The impacted tooth is surgically exposed, and an attachment fitted with a gold chain is bonded to the tooth. To generate an extrusive force, the sec-tional wire is bent toward the chain and attached with a ligature.


title: GraphDocExplore: A Framework for the Experimental Comparison of Graph-based Document Exploration Techniques

abstract: Graphs have long been proposed as a tool to browse and navigate in a collection of documents in order to support exploratory search. Many techniques to automatically extract different types of graphs, showing for example entities or concepts and different relationships between them, have been suggested. While experimental evidence that they are indeed helpful exists for some of them, it is largely unknown which type of graph is most helpful for a specific exploratory task. However, carrying out experimental comparisons with human subjects is challenging and time-consuming. Towards this end, we present the GraphDocExplore framework. It provides an intuitive web interface for graph-based document exploration that is optimized for experimental user studies. Through a generic graph interface, different methods to extract graphs from text can be plugged into the system. Hence, they can be compared at minimal implementation effort in an environment that ensures controlled comparisons. The system is publicly available under an open-source license.


title: Deep Metric Learning for Visual Tracking

abstract: In this paper, we propose a deep metric learning (DML) approach for robust visual tracking under the particle filter framework. Unlike most existing appearance-based visual trackers, which use hand-crafted similarity metrics, our DML tracker learns a nonlinear distance metric to classify the target object and background regions using a feed-forward neural network architecture. Since there are usually large variations in visual objects caused by varying deformations, illuminations, occlusions, motions, rotations, scales, and cluttered backgrounds, conventional linear similarity metrics cannot work well in such scenarios. To address this, our proposed DML tracker first learns a set of hierarchical nonlinear transformations in the feed-forward neural network to project both the template and particles into the same feature space where the intra-class variations of positive training pairs are minimized and the interclass variations of negative training pairs are maximized simultaneously. Then, the candidate that is most similar to the template in the learned deep network is identified as the true target. Experiments on the benchmark data set including 51 challenging videos show that our DML tracker achieves a very competitive performance with the state-of-the-art trackers.


title: The multiRANSAC algorithm and its application to detect planar homographies

abstract: A RANSAC based procedure is described for detecting inliers corresponding to multiple models in a given set of data points. The algorithm we present in this paper (called multiRANSAC) on average performs better than traditional approaches based on the sequential application of a standard RANSAC algorithm followed by the removal of the detected set of inliers. We illustrate the effectiveness of our approach on a synthetic example and apply it to the problem of identifying multiple world planes in pairs of images containing dominant planar structures.


title: Modeling Temporal-Spatial Correlations for Crime Prediction

abstract: Crime prediction plays a crucial role in improving public security and reducing the financial loss of crimes. The vast majority of traditional algorithms performed the prediction by leveraging demographic data, which could fail to capture the dynamics of crimes in urban. In the era of big data, we have witnessed advanced ways to collect and integrate fine-grained urban, mobile, and public service data that contains various crime-related sources and rich temporal-spatial information. Such information provides better understandings about the dynamics of crimes and has potentials to advance crime prediction. In this paper, we exploit temporal-spatial correlations in urban data for crime prediction. In particular, we validate the existence of temporal-spatial correlations in crime and develop a principled approach to model these correlations into the coherent framework TCP for crime prediction. The experimental results on real-world data demonstrate the effectiveness of the proposed framework. Further experiments have been conducted to understand the importance of temporal-spatial correlations in crime prediction.


title: Towards SMP Challenge: Stacking of Diverse Models for Social Image Popularity Prediction

abstract: Popularity prediction on social media has attracted extensive attention nowadays due to its widespread applications, such as online marketing and economical trends. In this paper, we describe a solution of our team CASIA-NLPR-MMC for Social Media Prediction (SMP) challenge. This challenge is designed to predict the popularity of social media posts. We present a stacking framework by combining a diverse set of models to predict the popularity of images on Flickr using user-centered, image content and image context features. Several individual models are employed for scoring popularity of an image at earlier stage, and then a stacking model of Support Vector Regression (SVR) is utilized to train a meta model of different individual models trained beforehand. The Spearman's Rho of this Stacking model is 0.88 and the mean absolute error is about 0.75 on our test set. On the official final-released test set, the Spearman's Rho is 0.7927 and mean absolute error is about 1.1783. The results on provided dataset demonstrate the effectiveness of our proposed approach for image popularity prediction.


title: How to Use Information Technology for Cooperative Work: Development of Shared Technological Frames

abstract: Technological frames, participants' assumptions about information technology (IT), and in particular about the usage of the technology for everyday cooperative work, are a relevant factor for IT related behavior. Incongruent technological frames are associated with problems during the application and use of a new IT in an organization. This paper presents a field study which applies a pre---post-design in a freight forwarding company. During face-to-face discussion the participating employees of the company negotiated agreements regarding the future usage of a new mobile technology system for every day cooperative work between dispatcher agents and truck drivers. To support the development of shared technological frames the moderation technique STWT (socio-technical walkthrough) was applied. The results describe the structural changes in technological frames, and show to what extent these were shared by the participants. Based on the results possibilities to improve support for the development of shared technological frames are discussed.


title: Analyzing hidden populations online: topic, emotion, and social network of HIV-related users in the largest Chinese online community

abstract: Traditional survey methods are limited in the study of hidden populations due to the hard to access properties, including lack of a sampling frame, sensitivity issue, reporting error, small sample size, etc. The rapid increase of online communities, of which members interact with others via the Internet, have generated large amounts of data, offering new opportunities for understanding hidden populations with unprecedented sample sizes and richness of information. In this study, we try to understand the multidimensional characteristics of a hidden population by analyzing the massive data generated in the online community. By elaborately designing crawlers, we retrieved a complete dataset from the “HIV bar,” the largest bar related to HIV on the Baidu Tieba platform, for all records from January 2005 to August 2016. Through natural language processing and social network analysis, we explored the psychology, behavior and demand of online HIV population and examined the network community structure. In HIV communities, the average topic similarity among members is positively correlated to network efficiency (r = 0.70, p < 0.001), indicating that the closer the social distance between members of the community, the more similar their topics. The proportion of negative users in each community is around 60%, weakly correlated with community size (r = 0.25, p = 0.002). It is found that users suspecting initial HIV infection or first in contact with high-risk behaviors tend to seek help and advice on the social networking platform, rather than immediately going to a hospital for blood tests. Online communities have generated copious amounts of data offering new opportunities for understanding hidden populations with unprecedented sample sizes and richness of information. It is recommended that support through online services for HIV/AIDS consultation and diagnosis be improved to avoid privacy concerns and social discrimination in China.


title: Content based SMS spam filtering

abstract: In the recent years, we have witnessed a dramatic increment in the volume of spam email. Other related forms of spam are increasingly revealing as a problem of importance, specially the spam on Instant Messaging services (the so called SPIM), and Short Message Service (SMS) or mobile spam.Like email spam, the SMS spam problem can be approached with legal, economic or technical measures. Among the wide range of technical measures, Bayesian filters are playing a key role in stopping email spam. In this paper, we analyze to what extent Bayesian filtering techniques used to block email spam, can be applied to the problem of detecting and stopping mobile spam. In particular, we have built two SMS spam test collections of significant size, in English and Spanish. We have tested on them a number of messages representation techniques and Machine Learning algorithms, in terms of effectiveness. Our results demonstrate that Bayesian filtering techniques can be effectively transferred from email to SMS spam.


title: The design of an acquisitional query processor for sensor networks

abstract: We discuss the design of an acquisitional query processor for data collection in sensor networks. Acquisitional issues are those that pertain to where, when, and how often data is physically acquired (sampled) and delivered to query processing operators. By focusing on the locations and costs of acquiring data, we are able to significantly reduce power consumption over traditional passive systems that assume the a priori existence of data. We discuss simple extensions to SQL for controlling data acquisition, and show how acquisitional issues influence query optimization, dissemination, and execution. We evaluate these issues in the context of TinyDB, a distributed query processor for smart sensor devices, and show how acquisitional techniques can provide significant reductions in power consumption on our sensor devices.


title: Automatic Identification of Animal Breeds and Species Using Bioacoustics and Artificial Neural Networks

abstract: In this research endeavor, it was hypothesized that the sound produced by animals during their vocalizations can be used as identifiers of the animal breed or species even if they sound the same to unaided human ear. To test this hypothesis, three artificial neural networks (ANNs) were developed using bioacoustics properties as inputs for the respective automatic identification of 13 bird species, eight dog breeds, and 11 frog species. Recorded vocalizations of these animals were collected and processed using several known signal processing techniques to convert the respective sounds into computable bioacoustics values. The converted values of the vocalizations, together with the breed or species identifications, were used to train the ANNs following a ten-fold cross validation technique. Tests show that the respective ANNs can correctly identify 71.43\% of the birds, 94.44\% of the dogs, and 90.91\% of the frogs. This result show that bioacoustics and ANN can be used to automatically determine animal breeds and species, which together could be a promising automated tool for animal identification, biodiversity determination, animal conservation, and other animal welfare efforts.


title: Optimising agile development practices for the maintenance operation: nine heuristics

abstract: Agile methods are widely used and successful in many development situations and beginning to attract attention amongst the software maintenance community --- both researchers and practitioners. However, it should not be assumed that implementing a well-known agile method for a maintenance department is therefore a trivial endeavour - the maintenance operation differs in some, important respects from development work. Classical accounts of software maintenance emphasise more traditional software engineering processes, whereas recent research accounts of agile maintenance efforts uncritically focus on benefits. In an action research project at Aveva in Denmark we assisted with the optimisation of SCRUM, tailoring the standard process to the immediate needs of the developers. We draw on both theoretical and empirical learning to formulate seven heuristics for maintenance practitioners wishing to go agile.


title: Tracking motion context of railway passengers by fusion of low-power sensors in mobile devices

abstract: In this paper we develop StationSense, a novel mobile sensing solution for precisely tracking temporal stop-and-go patterns of railway passengers. While such motion context serves as a promising enabler of various traveler support systems, we found through experiments in a major railway network in Japan that existing accelerometer-based passenger tracking systems can poorly work in modern trains, where jolts during motion have been dramatically reduced. Towards robust motion tracking, StationSense harnesses characteristic features in ambient magnetic fields in trains to find candidates of stationary periods, and subsequently filters out false positive detections by a tailored acceleration fusion mechanism. Then it finds optimal boundaries between adjacent moving/stationary periods, employing unique signatures in accelerometer readings. Through field experiments around 16 railway lines, we show that StationSense can identify periods of train stops with accuracy of 81%, which is almost 2 times higher than the existing accelerometer-based solutions.


title: Sieving for Shortest Vectors in Lattices Using Angular Locality-Sensitive Hashing

abstract: By replacing the brute-force list search in sieving algorithms with Charikar’s angular locality-sensitive hashing (LSH) method, we get both theoretical and practical speedups for solving the shortest vector problem (SVP) on lattices. Combining angular LSH with a variant of Nguyen and Vidick’s heuristic sieve algorithm, we obtain heuristic time and space complexities for solving SVP of \(2^{0.3366n + o(n)}\) and \(2^{0.2075n + o(n)}\) respectively, while combining the same hash family with Micciancio and Voulgaris’ GaussSieve algorithm leads to an algorithm with (conjectured) heuristic time and space complexities of \(2^{0.3366n + o(n)}\). Experiments with the GaussSieve-variant show that in moderate dimensions the proposed HashSieve algorithm already outperforms the GaussSieve, and the practical increase in the space complexity is much smaller than the asymptotic bounds suggest, and can be further reduced with probing. Extrapolating to higher dimensions, we estimate that a fully optimized and parallelized implementation of the GaussSieve-based HashSieve algorithm might need a few core years to solve SVP in dimension 130 or even 140.


title: Blind phoneme segmentation with temporal prediction errors

abstract: Phonemic segmentation of speech is a critical step of speech recognition systems. We propose a novel unsupervised algorithm based on sequence prediction models such as Markov chains and recurrent neural network. Our approach consists in analyzing the error profile of a model trained to predict speech features frame-by-frame. Specifically, we try to learn the dynamics of speech in the MFCC space and hypothesize boundaries from local maxima in the prediction error. We evaluate our system on the TIMIT dataset, with improvements over similar methods.


title: Automatically verifying reachability and well-formedness in P4 Networks

abstract: P4 allows a new level of dynamism for routers beyond OpenFlow 1.4 by allowing headers and tables to be modified by software in the field. Without care, P4 can unleash a new wave of software bugs. Existing tools (e.g., VeriFlow, NetPlumber, Hassel, NoD) cannot model changes to forwarding behaviors without reprogramming tool internals or having users manually add new forwarding models. Further, a P4 network can introduce a new class of bugs (not tested for by existing tools) wherein the P4 network creates malformed packets. To attack these two problems, we provide an operational semantics for P4 constructs and use it to compile P4 to Datalog so that the verification model can be automatically updated as the network changes. We demonstrate this vision by compiling the mTag example in the P4 specification (and a new sTag security example) on a sample network and by automatically detecting forwarding bugs. Efficiently verifying (across all table entries and packet headers) that a P4 network only delivers well-formed packets takes a few seconds.


S-Net: From Answer Extraction to Answer Synthesis for Machine Reading Comprehension


title: An efficient algorithm for pothole detection using stereo vision

abstract: In this paper, a stereo vision based pothole detection system is proposed. Using the disparity map generated from an efficient disparity calculation algorithm, potholes can be detected by their distance from the fitted quadratic road surface. The system produces the size, volume and position of the potholes which allows the pothole repair to be prioritised according to its severity. The quadratic road surface model allows for camera orientation variation, road drainage and up/down hill gradients. Experimental results show robust detection in various scenarios.


title: Flexible Camera Calibration by Viewing a Plane from Unknown Orientations

abstract: Proposes a flexible new technique to easily calibrate a camera. It only requires the camera to observe a planar pattern shown at a few (at least two) different orientations. Either the camera or the planar pattern can be freely moved. The motion need not be known. Radial lens distortion is modeled. The proposed procedure consists of a closed-form solution followed by a nonlinear refinement based on the maximum likelihood criterion. Both computer simulation and real data have been used to test the proposed technique, and very good results have been obtained. Compared with classical techniques which use expensive equipment, such as two or three orthogonal planes, the proposed technique is easy to use and flexible. It advances 3D computer vision one step from laboratory environments to real-world use. The corresponding software is available from the author's Web page ( ).


title: Multiple kernels for object detection

abstract: Our objective is to obtain a state-of-the art object category detector by employing a state-of-the-art image classifier to search for the object in all possible image sub-windows. We use multiple kernel learning of Varma and Ray (ICCV 2007) to learn an optimal combination of exponential χ2 kernels, each of which captures a different feature channel. Our features include the distribution of edges, dense and sparse visual words, and feature descriptors at different levels of spatial organization.


title: Pathophysiology of New Bone Formation and Ankylosis in Spondyloarthritis

abstract: The outcome of patients suffering from spondyloarthritis is determined by chronic inflammation and new bone formation leading to ankylosis. The latter process manifests by new cartilage and bone formation leading to joint or spine fusion. This article discusses the main mechanisms of new bone formation in spondyloarthritis. It reviews the key molecules and concepts of new bone formation and ankylosis in animal models of disease and translates these findings to human disease. In addition, proposed biomarkers of new bone formation are evaluated and the translational current and future challenges are discussed with regards to new bone formation in spondyloarthritis.


title: Illumination Invariant Face Recognition Using Near-Infrared Images

abstract: Most current face recognition systems are designed for indoor, cooperative-user applications. However, even in thus-constrained applications, most existing systems, academic and commercial, are compromised in accuracy by changes in environmental illumination. In this paper, we present a novel solution for illumination invariant face recognition for indoor, cooperative-user applications. First, we present an active near infrared (NIR) imaging system that is able to produce face images of good condition regardless of visible lights in the environment. Second, we show that the resulting face images encode intrinsic information of the face, subject only to a monotonic transform in the gray tone; based on this, we use local binary pattern (LBP) features to compensate for the monotonic transform, thus deriving an illumination invariant face representation. Then, we present methods for face recognition using NIR images; statistical learning algorithms are used to extract most discriminative features from a large pool of invariant LBP features and construct a highly accurate face matching engine. Finally, we present a system that is able to achieve accurate and fast face recognition in practice, in which a method is provided to deal with specular reflections of active NIR lights on eyeglasses, a critical issue in active NIR image-based face recognition. Extensive, comparative results are provided to evaluate the imaging hardware, the face and eye detection algorithms, and the face recognition algorithms and systems, with respect to various factors, including illumination, eyeglasses, time lapse, and ethnic groups


title: Ringer: web automation by demonstration

abstract: With increasing amounts of data available on the web and a diverse range of users interested in programmatically accessing that data, web automation must become easier. Automation helps users complete many tedious interactions, such as scraping data, completing forms, or transferring data between websites. However, writing web automation scripts typically requires an expert programmer because the writer must be able to reverse engineer the target webpage. We have built a record and replay tool, Ringer, that makes web automation accessible to non-coders. Ringer takes a user demonstration as input and creates a script that interacts with the page as a user would. This approach makes Ringer scripts more robust to webpage changes because user-facing interfaces remain relatively stable compared to the underlying webpage implementations. We evaluated our approach on benchmarks recorded on real webpages and found that it replayed 4x more benchmarks than a state-of-the-art replay tool.


title: A new Kernel non-negative matrix factorization and its application in microarray data analysis

abstract: Non-negative factorization (NMF) has been a popular machine learning method for analyzing microarray data. Kernel approaches can capture more non-linear discriminative features than linear ones. In this paper, we propose a novel kernel NMF (KNMF) approach for feature extraction and classification of microarray data. Our approach is also generalized to kernel high-order NMF (HONMF). Extensive experiments on eight microarray datasets show that our approach generally outperforms the traditional NMF and existing KNMFs. Preliminary experiment on a high-order microarray data shows that our KHONMF is a promising approach given a suitable kernel function.


title: A modified Gabor filter design method for fingerprint image enhancement

abstract: Fingerprint image enhancement is an essential preprocessing step in fingerprint recognition applications. In this paper, we propose a novel filter design method for fingerprint image enhancement, primarily inspired from the traditional Gabor filter (TGF). The previous fingerprint image enhancement methods based on TGF banks have some drawbacks in their image-dependent parameter selection strategy, which leads to artifacts in some cases. To address this issue, we develop an improved version of the TGF, called the modified Gabor filter (MGF). Its parameter selection scheme is image-independent. The remarkable advantages of our MGF over the TGF consist in preserving fingerprint image structure and achieving image enhancement consistency. Experimental results indicate that the proposed MGF enhancement algorithm can reduce the FRR of a fingerprint matcher by approximately 2% at a FAR of 0.01%.


Computer security in the real world


title: Finding community structure in very large networks

abstract: The discovery and analysis of community structure in networks is a topic of considerable recent interest within the physics community, but most methods proposed so far are unsuitable for very large networks because of their computational cost. Here we present a hierarchical agglomeration algorithm for detecting community structure which is faster than many competing algorithms: its running time on a network with n vertices and m edges is O(m d log n) where d is the depth of the dendrogram describing the community structure. Many real-world networks are sparse and hierarchical, with m ~ n and d ~ log n, in which case our algorithm runs in essentially linear time, O(n log^2 n). As an example of the application of this algorithm we use it to analyze a network of items for sale on the web-site of a large online retailer, items in the network being linked if they are frequently purchased by the same buyer. The network has more than 400,000 vertices and 2 million edges. We show that our algorithm can extract meaningful communities from this network, revealing large-scale patterns present in the purchasing habits of customers.


Corruption Traps and Economic Growth


title: The case research strategy in studies of information systems

abstract: This article defines and discusses one of these qualitative methods - the case research strategy. Suggestions are provided for researchers who wish to undertake research employing this approach. Criteria for the evaluation of case research are established and several characteristics useful for categorizing the studies are identified. A sample of papers drawn from information systems journals is reviewed. The paper concludes with examples of research areas that are particularly well-suited to investigation using the case research approach.


title: SARF: Smart activity recognition framework in Ambient Assisted Living

abstract: Human activity recognition in Ambient Assisted Living (AAL) is an important application in health care systems and allows us to track regular activities or even predict these activities in order to monitor healthcare and find changes in patterns and lifestyles. A review of the literature reveals various approaches to discovering and recognizing human activities. The presence of a vast number of activity recognition issues and approaches has made it difficult to make adequate comparisons and accurate assessment. Introducing the five basic components of activity recognition in the smart homes as a famous environment to remote monitoring of patients and independent living for elderly, the present paper proposes SARF framework to classify each of activity recognition approaches and then it is evaluated based on the proposed classification by some proposed measures. Using SARF proposed framework can play an effective role in selecting the appropriate method for human activity recognition in smart homes and beneficial in analysis and evaluation of different methods for various challenges in this field.


title: Obfuscated malicious JavaScript detection by Causal Relations Finding

abstract: JavaScript code is often obfuscated; given such code, can we tell whether if it is malicious or benign? We propose Obfuscating Causal Relations Finding (OCRF), which addresses this problem. The contributions are the following: (1) careful feature extraction, using domain knowledge (2) no need for de-obfuscation, since our method can be applied to the obfuscated script directly, (3) combined obfuscation detection, with malicious obfuscated code detection (4) improved detection accuracy and significantly reduced false positives (while the average false positive rate of competitors is between 0.18 and 0.30. Our method decreases it between 0.03 and 0.1). Moreover, our method is easy to be implemented as a plug-in for Web browsers.


title: Why the Sunny Side Is Up Associations Between Affect and Vertical Position

abstract: Metaphors linking spatial location and affect (e.g., feeling up or down) may have subtle, but pervasive, effects on evaluation. In three studies, participants evaluated words presented on a computer. In Study 1, evaluations of positive words were faster when words were in the up rather than the down position, whereas evaluations of negative words were faster when words were in the down rather than the up position. In Study 2, positive evaluations activated higher areas of visual space, whereas negative evaluations activated lower areas of visual space. Study 3 revealed that, although evaluations activate areas of visual space, spatial positions do not activate evaluations. The studies suggest that affect has a surprisingly physical basis.


title: Generating retinal flow maps from structural optical coherence tomography with artificial intelligence

abstract: Despite advances in artificial intelligence (AI), its application in medical imaging has been burdened and limited by expert-generated labels. We used images from optical coherence tomography angiography (OCTA), a relatively new imaging modality that measures retinal blood flow, to train an AI algorithm to generate flow maps from standard optical coherence tomography (OCT) images, exceeding the ability and bypassing the need for expert labeling. Deep learning was able to infer flow from single structural OCT images with similar fidelity to OCTA and significantly better than expert clinicians (P < 0.00001). Our model allows generating flow maps from large volumes of previously collected OCT data in existing clinical trials and clinical practice. This finding demonstrates a novel application of AI to medical imaging, whereby subtle regularities between different modalities are used to image the same body part and AI is used to generate detailed inferences of tissue function from structure imaging.


Towards Building a SentiWordNet for Tamil.


title: Modelling, Visualising and Summarising Documents with a Single Convolutional Neural Network

abstract: Capturing the compositional process which maps the meaning of words to that of documents is a central challenge for researchers in Natural Language Processing and Information Retrieval. We introduce a model that is able to represent the meaning of documents by embedding them in a low dimensional vector space, while preserving distinctions of word and sentence order crucial for capturing nuanced semantics. Our model is based on an extended Dynamic Convolution Neural Network, which learns convolution filters at both the sentence and document level, hierarchically learning to capture and compose low level lexical features into high level semantic concepts. We demonstrate the effectiveness of this model on a range of document modelling tasks, achieving strong results with no feature engineering and with a more compact model. Inspired by recent advances in visualising deep convolution networks for computer vision, we present a novel visualisation technique for our document networks which not only provides insight into their learning process, but also can be interpreted to produce a compelling automatic summarisation system for texts.


title: Enhancing the quality and credibility of qualitative analysis.

abstract: Varying philosophical and theoretical orientations to qualitative inquiry remind us that issues of quality and credibility intersect with audience and intended research purposes. This overview examines ways of enhancing the quality and credibility of qualitative analysis by dealing with three distinct but related inquiry concerns: rigorous techniques and methods for gathering and analyzing qualitative data, including attention to validity, reliability, and triangulation; the credibility, competence, and perceived trustworthiness of the qualitative researcher; and the philosophical beliefs of evaluation users about such paradigm-based preferences as objectivity versus subjectivity, truth versus perspective, and generalizations versus extrapolations. Although this overview examines some general approaches to issues of credibility and data quality in qualitative analysis, it is important to acknowledge that particular philosophical underpinnings, specific paradigms, and special purposes for qualitative inquiry will typically include additional or substitute criteria for assuring and judging quality, validity, and credibility. Moreover, the context for these considerations has evolved. In early literature on evaluation methods the debate between qualitative and quantitative methodologists was often strident. In recent years the debate has softened. A consensus has gradually emerged that the important challenge is to match appropriately the methods to empirical questions and issues, and not to universally advocate any single methodological approach for all problems.


A fuzzy assessment model for river ecological vulnerability: A case study in Yangtze river basin


title: CCured: type-safe retrofitting of legacy code

abstract: In this paper we propose a scheme that combines type inference and run-time checking to make existing C programs type safe. We describe the CCured type system, which extends that of C by separating pointer types according to their usage. This type system allows both pointers whose usage can be verified statically to be type safe, and pointers whose safety must be checked at run time. We prove a type soundness result and then we present a surprisingly simple type inference algorithm that is able to infer the appropriate pointer kinds for existing C programs.Our experience with the CCured system shows that the inference is very effective for many C programs, as it is able to infer that most or all of the pointers are statically verifiable to be type safe. The remaining pointers are instrumented with efficient run-time checks to ensure that they are used safely. The resulting performance loss due to run-time checks is 0-150%, which is several times better than comparable approaches that use only dynamic checking. Using CCured we have discovered programming bugs in established C programs such as several SPECINT95 benchmarks.


Computational Tools for Tandem Mass Spectrometry–Based High-Throughput Quantitative Proteomics


title: Scheduling problems of automated guided vehicles in job shop, flow shop, and container terminals

abstract: Automated guided vehicles have been widely used in flexible manufacturing systems especially in a job shop and flow shop. Recently, they are included in other fields such as container terminals. This paper demonstrates a literature review of the last three decades about the scheduling problems of the automated guided vehicles in three main classes, namely job shops, flow shops, and container terminals. We classify the literature according to the scheduling problems and the solution approaches. The results of the survey are that the solution methods mainly focus on using simulation as a key performance analysis tool, whereas, the big computation times of the developed mathematical models make heuristics approaches are dominant for solving such models. After that, we discuss research gaps to the purpose of finding new research directions.


title: The impact of trust and perceived risk on internet banking adoption in India

abstract: Purpose – The main purpose of this paper is to extend the technology acceptance model (TAM) in the context of internet banking adoption in India under security and privacy threat.Design/methodology/approach – Keeping the TAM proposed by Davis as a theoretical basis, an extended TAM incorporating security‐ and privacy‐related issues for internet banking adoption is conceptualized. The authors have incorporated various inhibitors of internet banking which restrict the use of internet banking adoption under “perceived risk”, and also consider the role of the bank web site as a key determinant of perceived risk and of perceived ease of use in the context of internet banking services.Findings – The paper reveals that perceived risk has a negative impact on behavioral intention of internet banking adoption and trust has a negative impact on perceived risk. A well‐designed web site was also found to be helpful in facilitating easier use and also minimizing perceived risk concerns regarding internet banking usage...


title: Real time monocular visual odometry using Optical Flow: Study on navigation of quadrotors UAV

abstract: Navigation is an important thing in so many intelligent systems. Nowadays, without any hesitation Global Positioning System (GPS) is the most common method for navigation. Nevertheless, in certain environments, GPS will become inaccurate due to the multi-path problem. In this paper, we propose a dead-reckoning system for quadrotor using feature-based monocular visual odometry to overcome that problem. The visual odometry design will be implemented into the fix bottom camera which is the monocular camera in quadrotor. The algorithm is validated by performing several tests. The results of the tests verify that the visual odometry algorithm can determine the position and orientation with good accuracy.


Simulation and Comparison of AODV and DSR routing protocols in MANETs


title: An LTE Base-Station Magnetoelectric Dipole Antenna with Anti-Interference Characteristics and Its MIMO System Application

abstract: In this research, a wideband long-term evolution (LTE) base-station antenna with flexible band-notch characteristics is introduced by two window-type slots etched on radiation patches of the magnetoelectric dipole. The presented antenna can provide a band-notch range with an impedance bandwidth of 2.39–2.52 GHz in the band of 1.825–2.925 GHz, which well applies the wideband antenna for LTE with the good anti-interference from wireless local area network narrow communication band. The typical parameters are investigated to show that the antenna with anti-interference characteristics exhibits large scope band-notch adjustability in the whole wideband region. Based on above element, an LTE base station antenna multiple-input, multiple-output system with anti-interference characteristics is investigated and measured to show its good performances: anti-interference, high isolation, high gain, unidirectional radiation, low back radiation, and low cross-polarization.


title: Me, myself, and lie: The role of self-awareness in deception

abstract: Deception has been studied extensively but still little is known about individual differences in deception ability. We investigated the relationship between self-awareness and deception ability. We enlisted novice actors to portray varying levels of deception. Forty-two undergraduates viewed the videotaped portrayals and rated the actors believability. Actors with high private self-awareness were more effective deceivers, suggesting that high self-monitors are more effective at deceiving. Self-awareness may lead to knowledge of anothers mental state (i.e., Theory of Mind), which may improve an individuals deception ability. 2005 Elsevier Ltd. All rights reserved.


title: Nuclei Segmentation of Fluorescence Microscopy Images Using Three Dimensional Convolutional Neural Networks

abstract: Fluorescence microscopy enables one to visualize subcellular structures of living tissue or cells in three dimensions. This is especially true for two-photon microscopy using near-infrared light which can image deeper into tissue. To characterize and analyze biological structures, nuclei segmentation is a prerequisite step. Due to the complexity and size of the image data sets, manual segmentation is prohibitive. This paper describes a fully 3D nuclei segmentation method using three dimensional convolutional neural networks. To train the network, synthetic volumes with corresponding labeled volumes are automatically generated. Our results from multiple data sets demonstrate that our method can successfully segment nuclei in 3D.


title: Background subtraction: Experiments and improvements for ViBe

abstract: Motion detection plays an important role in most video based applications. One of the many possible ways to detect motion consists in background subtraction. This paper discusses experiments led for a particular background subtraction technique called ViBe. This technique models the background with a set of samples for each pixel and compares new frames, pixel by pixel, to determine if a pixel belongs to the background or to the foreground. In its original version, the scope of ViBe is limited to background modeling. In this paper, we introduce a series of modifications that alter the working of ViBe, like the inhibition of propagation around internal borders or the distinction between the updating and segmentation masks, or process the output, for example by some operations on the connected components. Experimental results obtained for video sequences provided on the workshop site validate the improvements of the proposed modifications.


title: PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection

abstract: This paper presents how we can achieve the state-of-the-art accuracy in multi-category object detection task while minimizing the computational cost by adapting and combining recent technical innovations. Following the common pipeline of "CNN feature extraction + region proposal + RoI classification", we mainly redesign the feature extraction part, since region proposal part is not computationally expensive and classification part can be efficiently compressed with common techniques like truncated SVD. Our design principle is "less channels with more layers" and adoption of some building blocks including concatenated ReLU, Inception, and HyperNet. The designed network is deep and thin and trained with the help of batch normalization, residual connections, and learning rate scheduling based on plateau detection. We obtained solid results on well-known object detection benchmarks: 83.8% mAP (mean average precision) on VOC2007 and 82.5% mAP on VOC2012 (2nd place), while taking only 750ms/image on Intel i7-6700K CPU with a single core and 46ms/image on NVIDIA Titan X GPU. Theoretically, our network requires only 12.3% of the computational cost compared to ResNet-101, the winner on VOC2012.


title: LIBLINEAR: A library for large linear classification

abstract: LIBLINEAR is an open source library for large-scale linear classification. It supports logistic regression and linear support vector machines. We provide easy-to-use command-line tools and library calls for users and developers. Comprehensive documents are available for both beginners and advanced users. Experiments demonstrate that LIBLINEAR is very efficient on large sparse data sets.


title: Fully Automatic Facial Action Recognition in Spontaneous Behavior

abstract: We present results on a user independent fully automatic system for real time recognition of facial actions from the Facial Action Coding System (FACS). The system automatically detects frontal faces in the video stream and codes each frame with respect to 20 Action units. We present preliminary results on a task of facial action detection in spontaneous expressions during discourse. Support vector machines and AdaBoost classifiers are compared. For both classifiers, the output margin predicts action unit intensity.


title: On the Use of Word Embeddings Alone to Represent Natural Language Sequences

abstract: To construct representations for natural language sequences, information from two main sources needs to be captured: (i) semantic meaning of individual words, and (ii) their compositionality. These two types of information are usually represented in the form of word embeddings and compositional functions, respectively. For the latter, Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) have been considered. There has not been a rigorous evaluation regarding the relative importance of each component to different text-representation-based tasks; i.e., how important is the modeling capacity of word embeddings alone, relative to the added value of a compositional function? In this paper, we conduct an extensive comparative study between Simple Word Embeddings-based Models (SWEMs), with no compositional parameters, relative to employing word embeddings within RNN/CNN-based models. Surprisingly, SWEMs exhibit comparable or even superior performance in the majority of cases considered. Moreover, in a new SWEM setup, we propose to employ a max-pooling operation over the learned word-embedding matrix of a given sentence. This approach is demonstrated to extract complementary features relative to the averaging operation standard to SWEMs, while endowing our model with better interpretability. To further validate our observations, we examine the information utilized by different models to make predictions, revealing interesting properties of word embeddings.


title: One Network to Solve Them All — Solving Linear Inverse Problems Using Deep Projection Models

abstract: While deep learning methods have achieved state-of-theart performance in many challenging inverse problems like image inpainting and super-resolution, they invariably involve problem-specific training of the networks. Under this approach, each inverse problem requires its own dedicated network. In scenarios where we need to solve a wide variety of problems, e.g., on a mobile camera, it is inefficient and expensive to use these problem-specific networks. On the other hand, traditional methods using analytic signal priors can be used to solve any linear inverse problem; this often comes with a performance that is worse than learning-based methods. In this work, we provide a middle ground between the two kinds of methods — we propose a general framework to train a single deep neural network that solves arbitrary linear inverse problems. We achieve this by training a network that acts as a quasi-projection operator for the set of natural images and show that any linear inverse problem involving natural images can be solved using iterative methods. We empirically show that the proposed framework demonstrates superior performance over traditional methods using wavelet sparsity prior while achieving performance comparable to specially-trained networks on tasks including compressive sensing and pixel-wise inpainting.


title: Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade

abstract: Currently available evidence strongly supports the position that the initiating event in Alzheimer's disease (AD) is related to abnormal processing of beta-amyloid (Abeta) peptide, ultimately leading to formation of Abeta plaques in the brain. This process occurs while individuals are still cognitively normal. Biomarkers of brain beta-amyloidosis are reductions in CSF Abeta(42) and increased amyloid PET tracer retention. After a lag period, which varies from patient to patient, neuronal dysfunction and neurodegeneration become the dominant pathological processes. Biomarkers of neuronal injury and neurodegeneration are increased CSF tau and structural MRI measures of cerebral atrophy. Neurodegeneration is accompanied by synaptic dysfunction, which is indicated by decreased fluorodeoxyglucose uptake on PET. We propose a model that relates disease stage to AD biomarkers in which Abeta biomarkers become abnormal first, before neurodegenerative biomarkers and cognitive symptoms, and neurodegenerative biomarkers become abnormal later, and correlate with clinical symptom severity.


Security Analysis of the edX Platform


title: Deployment of a Blockchain-Based Self-Sovereign Identity

abstract: Digital identity is unsolved: after many years of research there is still no trusted communication over the Internet. To provide identity within the context of mutual distrust, this paper presents a blockchain-based digital identity solution. Without depending upon a single trusted third party, the proposed solution achieves passport-level legally valid identity. This solution for making identities Self-Sovereign, builds on a generic provable claim model for which attestations of truth from third parties need to be collected. The claim model is then shown to be both blockchain structure and proof method agnostic. Four different implementations in support of these two claim model properties are shown to offer sub-second performance for claim creation and claim verification. Through the properties of Self-Sovereign Identity, legally valid status and acceptable performance, our solution is considered to be fit for adoption by the general public.


title: Testing AGVs in dynamic warehouse environments

abstract: Automatic Guided Vehicles (AGVs) are unmanned vehicles that can transport loads in a warehouse. AGVs are instructed by on-board AGV control software. As multiple AGVs operate in a decentralized manner in the warehouse environment, conflicts may arise. Consequently, it is crucial to test thoroughly whether the AGV control software actually handles the potential conflicts in the appropriate way. In this paper, we employ a simulated warehouse environment to test the AGV control software. The AGV control software is embedded and activated in the simulated warehouse environment. The simulated warehouse environment provides support for testing by means of (1) representing dynamism in the warehouse environment in an explicit manner, and (2) detecting conflicts of dynamism in an automated way. The approach is illustrated for the case of testing collision avoidance.


title: Breaking the habit: Measuring and predicting departures from routine in individual human mobility

abstract: Researchers studying daily life mobility patterns have recently shown that humans are typically highly predictable in their movements. However, no existing work has examined the boundaries of this predictability, where human behaviour transitions temporarily from routine patterns to highly unpredictable states. To address this shortcoming, we tackle two interrelated challenges. First, we develop a novel information-theoretic metric, called instantaneous entropy, to analyse an individual's mobility patterns and identify temporary departures from routine. Second, to predict such departures in the future, we propose the first Bayesian framework that explicitly models breaks from routine, showing that it outperforms current state-of-the-art predictors.


title: Electronic shepherd - a low-cost, low-bandwidth, wireless network system

abstract: This paper reports a new novel low-cost, wireless communication network system, called the "Electronic Shepherd" (ES). The system is innovative in the way that it supports flock behavior, meaning that a flock leader monitors the state of the other elements in the flock using low-cost radio communication equipment. The paper addresses both details of the terminal devices and communication protocols, as well as testing of the system in a real environment. The ES system was originally made to address special needs for sheep and reindeer farmers who were seeking a system to keep track of their animals during the grazing season. The system, including GPS receivers, UHF radio communication transceivers and GPRS modems, contributes a new approach for low-cost networking and service implementation, not only for the purpose of animal tracking, but also for other applications where objects are to be monitored at a low cost.


title: Residual Conv-Deconv Grid Network for Semantic Segmentation

abstract: This paper presents GridNet, a new Convolutional Neural Network (CNN) architecture for semantic image segmentation (full scene labelling). Classical neural networks are implemented as one stream from the input to the output with subsampling operators applied in the stream in order to reduce the feature maps size and to increase the receptive field for the final prediction. However, for semantic image segmentation, where the task consists in providing a semantic class to each pixel of an image, feature maps reduction is harmful because it leads to a resolution loss in the output prediction. To tackle this problem, our GridNet follows a grid pattern allowing multiple interconnected streams to work at different resolutions. We show that our network generalizes many well known networks such as conv-deconv, residual or U-Net networks. GridNet is trained from scratch and achieves competitive results on the Cityscapes dataset.


title: Closed-Loop Neuromorphic Benchmarks

abstract: Evaluating the effectiveness and performance of neuromorphic hardware is difficult. It is even more difficult when the task of interest is a closed-loop task; that is, a task where the output from the neuromorphic hardware affects some environment, which then in turn affects the hardware’s future input. However, closed-loop situations are one of the primary potential uses of neuromorphic hardware. To address this, we present a methodology for generating closed-loop benchmarks that makes use of a hybrid of real physical embodiment and a type of “minimal” simulation. Minimal simulation has been shown to lead to robust real-world performance, while still maintaining the practical advantages of simulation, such as making it easy for the same benchmark to be used by many researchers. This method is flexible enough to allow researchers to explicitly modify the benchmarks to identify specific task domains where particular hardware excels. To demonstrate the method, we present a set of novel benchmarks that focus on motor control for an arbitrary system with unknown external forces. Using these benchmarks, we show that an error-driven learning rule can consistently improve motor control performance across a randomly generated family of closed-loop simulations, even when there are up to 15 interacting joints to be controlled.


title: NFV: State of the Art, Challenges and Implementation in Next Generation Mobile Networks (vEPC)

abstract: As mobile network users look forward to the connectivity speeds of 5G networks, service providers are facing challenges in complying with connectivity demands without substantial financial investments. Network function virtualization (NFV) is introduced as a new methodology that offers a way out of this bottleneck. NFV is poised to change the core structure of telecommunications infrastructure to be more cost-efficient. In this article, we introduce an NFV framework, and discuss the challenges and requirements of its use in mobile networks. In particular, an NFV framework in the virtual environment is proposed. Moreover, in order to reduce signaling traffic and achieve better performance, this article proposes a criterion to bundle multiple functions of a virtualized evolved packet core in a single physical device or a group of adjacent devices. The analysis shows that the proposed grouping can reduce the network control traffic by 70 percent.


title: Compliance and Force Control for Computer Controlled Manipulators

abstract: Compliant motion of a manipulator occurs when the manipulator position is constrained by the task geometry. Compliant motion may be produced either by a passive mechanical compliance built in to the manipulator, or by an active compliance implemented in the control servo loop. The second method, called force control, is the subject of this paper. In particular a theory of force control based on formal models of the manipulator and the task geometry is presented. The ideal effector is used to model the manipulator, the ideal surface is used to model the task geometry, and the goal trajectory is used to model the desired behavior of the manipulator. Models are also defined for position control and force control, providing a precise semantics for compliant motion primitives in manipulation programming languages. The formalism serves as a simple interface between the manipulator and the programmer, isolating the programmer from the fundamental complexity of low-level manipulator control. A method of automatically synthesizing a restricted class of manipulator programs based on the formal models of task and goal trajectory is also provided by the formalism.


Do the eyes in the faces below Face 1 and Face 2 look the same ?


title: Titanium Cranioplasty

abstract: The technique of repairing defects of the skull with titanium is described. The skull contour can be accurately reproduced. The technique is simpler than wiring or suturing methods. The material is inert, radiolucent, and rigid.


title: A Simulated "Acquired" Imperforate Hymen Following the Genital Trauma of Sexual Abuse

abstract: Imperforate hymen, a condition in which the hymen has no aperture, usually occurs congenitally, secondary to failure of development of a lumen. A case of a documented simulated "acquired" imperforate hymen is presented in this article. The patient, a 5-year-old girl, was the victim of sexual abuse. Initial examination showed tears, scars, and distortion of the hymen, laceration of the perineal body, and loss of normal anal tone. Follow-up evaluations over the next year showed progressive healing. By 7 months after the injury, the hymen was replaced by a thick, opaque scar with no orifice. Patients with an apparent imperforate hymen require a sensitive interview and careful visual inspection of the genital and anal areas to delineate signs of injury. The finding of an apparent imperforate hymen on physical examination does not eliminate the possibility of antecedent vaginal penetration and sexual abuse.


title: How to acquire a ‘representational theory of mind’

abstract: concepts are now thought to emerge early in development. Mental state concepts, such as BELIEVE, DESIRE, and PRETEND, are among the most abstract we possess. It is striking that these concepts are routinely acquired by all normally developing children before they attend school and are even acquired by children who are mentally retarded. The verbal labels associated with these concepts are never explicitly taught, yet are typically in use around the third birthday; by contrast, words for colors, a salient sensory property, very often are explicitly taught by parents, but are typically not learned any earlier and are often learned later. Mental state concepts provide a crucial challenge to our attempts to understand 1 Acknowledgments: I am grateful to the following friends and colleagues: Eric Margolis and Susan Carey for helpful discussions, and to Jerry Fodor, Shaun Nichols, and Brian Scholl for helpful discussions and detailed comments on an earlier draft. 2 I use small caps when referring to a concept as opposed to what the concept denotes (italicized). Normally, one could simply say that the concept is a psychological entity, while what it denotes is not, e.g., DOG refers to dogs. But in the case of mental state concepts what they denote are also psychological entities.


title: On Orchestrating Virtual Network Functions in NFV

abstract: Middleboxes or network appliances like firewalls, proxies and WAN optimizers have become an integral part of today's ISP and enterprise networks. Middlebox functionalities are usually deployed on expensive and proprietary hardware that require trained personnel for deployment and maintenance. Middleboxes contribute significantly to a network's capital and operational costs. In addition, organizations often require their traffic to pass through a specific sequence of middleboxes for compliance with security and performance policies. This makes the middlebox deployment and maintenance tasks even more complicated. Network Function Virtualization (NFV) is an emerging and promising technology that is envisioned to overcome these challenges. It proposes to move packet processing from dedicated hardware middleboxes to software running on commodity servers. In NFV terminology, software middleboxes are referred to as Virtualized Network Functions (VNFs). It is a challenging problem to determine the required number and placement of VNFs that optimizes network operational costs and utilization, without violating service level agreements. We call this the VNF Orchestration Problem (VNF-OP) and provide an Integer Linear Programming (ILP) formulation with implementation in CPLEX. We also provide a dynamic programming based heuristic to solve larger instances of VNF-OP. Trace driven simulations on real-world network topologies demonstrate that the heuristic can provide solutions that are within 1.3 times of the optimal solution. Our experiments suggest that a VNF based approach can provide more than 4x reduction in the operational cost of a network.


title: Comparison of 3D interest point detectors and descriptors for point cloud fusion

abstract: The extraction and description of keypoints as salient image parts has a long tradition within processing and analysis of 2D images. Nowadays, 3D data gains more and more importance. This paper discusses the benefits and limitations of keypoints for the task of fusing multiple 3D point clouds. For this goal, several combinations of 3D keypoint detectors and descriptors are tested. The experiments are based on 3D scenes with varying properties, including 3D scanner data as well as Kinect point clouds. The obtained results indicate that the specific method to extract and describe keypoints in 3D data has to be carefully chosen. In many cases the accuracy suffers from a too strong reduction of the available points to keypoints.


title: Object-based RGBD image co-segmentation with mutex constraint

abstract: We present an object-based co-segmentation method that takes advantage of depth data and is able to correctly handle noisy images in which the common foreground object is missing. With RGBD images, our method utilizes the depth channel to enhance identification of similar foreground objects via a proposed RGBD co-saliency map, as well as to improve detection of object-like regions and provide depth-based local features for region comparison. To accurately deal with noisy images where the common object appears more than or less than once, we formulate co-segmentation in a fully-connected graph structure together with mutual exclusion (mutex) constraints that prevent improper solutions. Experiments show that this object-based RGBD co-segmentation with mutex constraints outperforms related techniques on an RGBD co-segmentation dataset, while effectively processing noisy images. Moreover, we show that this method also provides performance comparable to state-of-the-art RGB co-segmentation techniques on regular RGB images with depth maps estimated from them.


title: Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction

abstract: We propose a unified manifold learning framework for semi-supervised and unsupervised dimension reduction by employing a simple but effective linear regression function to map the new data points. For semi-supervised dimension reduction, we aim to find the optimal prediction labels F for all the training samples X, the linear regression function h(X) and the regression residue F(0) = F - h(X) simultaneously. Our new objective function integrates two terms related to label fitness and manifold smoothness as well as a flexible penalty term defined on the residue F(0). Our Semi-Supervised learning framework, referred to as flexible manifold embedding (FME), can effectively utilize label information from labeled data as well as a manifold structure from both labeled and unlabeled data. By modeling the mismatch between h(X) and F, we show that FME relaxes the hard linear constraint F = h(X) in manifold regularization (MR), making it better cope with the data sampled from a nonlinear manifold. In addition, we propose a simplified version (referred to as FME/U) for unsupervised dimension reduction. We also show that our proposed framework provides a unified view to explain and understand many semi-supervised, supervised and unsupervised dimension reduction techniques. Comprehensive experiments on several benchmark databases demonstrate the significant improvement over existing dimension reduction algorithms.


title: Designing fast architecture-sensitive tree search on modern multicore/many-core processors

abstract: In-memory tree structured index search is a fundamental database operation. Modern processors provide tremendous computing power by integrating multiple cores, each with wide vector units. There has been much work to exploit modern processor architectures for database primitives like scan, sort, join, and aggregation. However, unlike other primitives, tree search presents significant challenges due to irregular and unpredictable data accesses in tree traversal. In this article, we present FAST, an extremely fast architecture-sensitive layout of the index tree. FAST is a binary tree logically organized to optimize for architecture features like page size, cache line size, and Single Instruction Multiple Data (SIMD) width of the underlying hardware. FAST eliminates the impact of memory latency, and exploits thread-level and data-level parallelism on both CPUs and GPUs to achieve 50 million (CPU) and 85 million (GPU) queries per second for large trees of 64M elements, with even better results on smaller trees. These are 5X (CPU) and 1.7X (GPU) faster than the best previously reported performance on the same architectures. We also evaluated FAST on the Intel$^\tiny\textregistered$ Many Integrated Core architecture (Intel$^\tiny\textregistered$ MIC), showing a speedup of 2.4X--3X over CPU and 1.8X--4.4X over GPU. FAST supports efficient bulk updates by rebuilding index trees in less than 0.1 seconds for datasets as large as 64M keys and naturally integrates compression techniques, overcoming the memory bandwidth bottleneck and achieving a 6X performance improvement over uncompressed index search for large keys on CPUs.


title: Exploiting graph kernels for high performance biomedical relation extraction

abstract: BackgroundRelation extraction from biomedical publications is an important task in the area of semantic mining of text. Kernel methods for supervised relation extraction are often preferred over manual feature engineering methods, when classifying highly ordered structures such as trees and graphs obtained from syntactic parsing of a sentence. Tree kernels such as the Subset Tree Kernel and Partial Tree Kernel have been shown to be effective for classifying constituency parse trees and basic dependency parse graphs of a sentence. Graph kernels such as the All Path Graph kernel (APG) and Approximate Subgraph Matching (ASM) kernel have been shown to be suitable for classifying general graphs with cycles, such as the enhanced dependency parse graph of a sentence.In this work, we present a high performance Chemical-Induced Disease (CID) relation extraction system. We present a comparative study of kernel methods for the CID task and also extend our study to the Protein-Protein Interaction (PPI) extraction task, an important biomedical relation extraction task. We discuss novel modifications to the ASM kernel to boost its performance and a method to apply graph kernels for extracting relations expressed in multiple sentences.ResultsOur system for CID relation extraction attains an F-score of 60%, without using external knowledge sources or task specific heuristic or rules. In comparison, the state of the art Chemical-Disease Relation Extraction system achieves an F-score of 56% using an ensemble of multiple machine learning methods, which is then boosted to 61% with a rule based system employing task specific post processing rules. For the CID task, graph kernels outperform tree kernels substantially, and the best performance is obtained with APG kernel that attains an F-score of 60%, followed by the ASM kernel at 57%. The performance difference between the ASM and APG kernels for CID sentence level relation extraction is not significant. In our evaluation of ASM for the PPI task, ASM performed better than APG kernel for the BioInfer dataset, in the Area Under Curve (AUC) measure (74% vs 69%). However, for all the other PPI datasets, namely AIMed, HPRD50, IEPA and LLL, ASM is substantially outperformed by the APG kernel in F-score and AUC measures.ConclusionsWe demonstrate a high performance Chemical Induced Disease relation extraction, without employing external knowledge sources or task specific heuristics. Our work shows that graph kernels are effective in extracting relations that are expressed in multiple sentences. We also show that the graph kernels, namely the ASM and APG kernels, substantially outperform the tree kernels. Among the graph kernels, we showed the ASM kernel as effective for biomedical relation extraction, with comparable performance to the APG kernel for datasets such as the CID-sentence level relation extraction and BioInfer in PPI. Overall, the APG kernel is shown to be significantly more accurate than the ASM kernel, achieving better performance on most datasets.


title: Telemanipulation with Time Delays

abstract: In this paper we survey the development of the wave variable concept and examine wave-based teleoperation. We study the behavior of force reflecting systems under unknown but constant transmission delays, ranging from periods less than the human reaction time to several seconds. Passive transmission procedures guarantee system stability, but wave reflections and spurious dynamics may interfere with normal operation. Using wave variables for the analysis and implementation, and making appropriate design choices, a system with consistent and predictable behavior is constructed. This design methodology aims to create a virtual tool which accounts for the implicit limitations imposed by the delay. These developments also form the basis for extensions to wave-based prediction and application to variable delays, such as those inherent to Internet-based telemanipulation.


Data Mining And Strategic Marketing In The Airline Industry


Business Process Improvement Methodologies : An Overview


title: C. Andújar and J. LLuch (Editors) BRDFLab: A general system for designing BRDFs

abstract: This paper introduces a novel system for interactive modeling and designing of arbitrary BRDFs. The system is able to deal with BRDFs defined in a variety of forms, such as analytical models, measured data or data obtained by simulation. The system also allows designing BRDFs from scratch using a combination of different analytical lobes. Using the programmable graphics hardware, it then performs interactive display of the designed BRDF, and its rendering on objects lit by complex illumination. The system also allows the fitting of an input BRDF defined in any form to our analytical lobe combination, so that it can be efficiently evaluated with GPU based rendering. The idea behind this work is to make available a general system for designing, fitting and rendering BRDFs, that is intuitive and interactive in nature. We plan to use this as a tool for simulation and modeling of complex physically-based BRDFs, and thus provide access to a larger variety of material models to the rendering community.


title: Deception Detection using Real-life Trial Data

abstract: Hearings of witnesses and defendants play a crucial role when reaching court trial decisions. Given the high-stake nature of trial outcomes, implementing accurate and effective computational methods to evaluate the honesty of court testimonies can offer valuable support during the decision making process. In this paper, we address the identification of deception in real-life trial data. We introduce a novel dataset consisting of videos collected from public court trials. We explore the use of verbal and non-verbal modalities to build a multimodal deception detection system that aims to discriminate between truthful and deceptive statements provided by defendants and witnesses. We achieve classification accuracies in the range of 60-75% when using a model that extracts and fuses features from the linguistic and gesture modalities. In addition, we present a human deception detection study where we evaluate the human capability of detecting deception in trial hearings. The results show that our system outperforms the human capability of identifying deceit.