title: A 160 $\mu{\rm A}$ Biopotential Acquisition IC With Fully Integrated IA and Motion Artifact Suppression

abstract: This paper proposes a 3-channel biopotential monitoring ASIC with simultaneous electrode-tissue impedance measurements which allows real-time estimation of motion artifacts on each channel using an an external μC. The ASIC features a high performance instrumentation amplifier with fully integrated sub-Hz HPF rejecting rail-to-rail electrode-offset voltages. Each readout channel further has a programmable gain amplifier and programmable 4th order low-pass filter. Time-multiplexed 12 b SAR-ADCs are used to convert all the analog data to digital. The ASIC achieves >; 115 dB of CMRR (at 50/60 Hz), a high input impedance of >; 1 GΩ and low noise (1.3 μVrms in 100 Hz). Unlike traditional methods, the ASIC is capable of actual motion artifact suppression in the analog domain before final amplification. The complete ASIC core operates from 1.2 V with 2 V digital IOs and consumes 200 μW when all 3 channels are active.


title: Why children tell: a model of children's disclosure of sexual abuse.

abstract: OBJECTIVE: The present study investigated variables associated with delay of disclosure of child sexual abuse and tested a model of time to disclosure. METHOD: Data were obtained for 218 alleged child sexual abuse victims whose cases had been referred to District Attorneys' Offices. Five variables were posited to influence the delay between an abusive event and children's disclosure of that event to a reporting adult: child's age, gender, type of abuse experienced (intrafamilial or extrafamilial), perceived responsibility for the abuse, and fear of negative consequences of disclosure. These variables were used to create a model of factors influencing children's disclosure of sexual abuse. RESULTS: Results indicated that age, type of abuse, fear of negative consequences, and perceived responsibility all contributed to predicting time to disclosure. There was significant support for the model, suggesting that children who were older, came from incestuous families, felt greater responsibility for the abuse, and feared negative consequences of disclosure took longer to disclose. CONCLUSIONS: Children's cognitive appraisal of others' tolerance of disclosure of child sexual abuse, and their own perceptions of responsibility for the abuse, are crucial to the decision to disclose. When evaluating children for possible sexual abuse, developmental, cognitive, and socio-emotional factors need to be taken into consideration. Language: en


title: Lipreading using a comparative machine learning approach

abstract: Lipreading is the process of interpreting spoken word by observing lip movement. It plays a vital role in human communication and speech understanding, especially for hearing-impaired individuals. Automated lipreading approaches have recently been used in such applications as biometric identification, silent dictation, forensic analysis of surveillance camera capture, and communication with autonomous vehicles. However, lipreading is a difficult process that poses several challenges to human- and machine-based approaches alike. This is due to the large number of phonemes in human language that are visually represented by a smaller number of lip movements (visemes). Consequently, the same viseme may be used to represent several phonemes, which confuses any lipreader. In this paper, we present a detailed study of the machine learning approach for the real-time visual recognition of spoken words. Our focus on real-time performance is motivated by the recent trend of using lipreading in autonomous vehicles. In this paper, machine learning approaches are applied to recognize lip-reading and nine different classifiers has been implemented and tested, reporting their confusion matrices among different groups of words. The classification process went on more than one classifier but these three classifiers got the best results which are GradientBoosting, Support Vector Machine(SVM) and logistic regression with results 64.7%, 63.5% and 59.4% respectively.


Parallel News-Article Traffic Forecasting with ADMM


title: The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning

abstract: In this paper we aim to formally explain the phenomenon of fast convergence of SGD observed in modern machine learning. The key observation is that most modern learning architectures are over-parametrized and are trained to interpolate the data by driving the empirical loss (classification and regression) close to zero. While it is still unclear why these interpolated solutions perform well on test data, we show that these regimes allow for fast convergence of SGD, comparable in number of iterations to full gradient descent. ::: For convex loss functions we obtain an exponential convergence bound for {\it mini-batch} SGD parallel to that for full gradient descent. We show that there is a critical batch size $m^*$ such that: (a) SGD iteration with mini-batch size $m\leq m^*$ is nearly equivalent to $m$ iterations of mini-batch size $1$ (\emph{linear scaling regime}). (b) SGD iteration with mini-batch $m> m^*$ is nearly equivalent to a full gradient descent iteration (\emph{saturation regime}). ::: Moreover, for the quadratic loss, we derive explicit expressions for the optimal mini-batch and step size and explicitly characterize the two regimes above. The critical mini-batch size can be viewed as the limit for effective mini-batch parallelization. It is also nearly independent of the data size, implying $O(n)$ acceleration over GD per unit of computation. We give experimental evidence on real data which closely follows our theoretical analyses. ::: Finally, we show how our results fit in the recent developments in training deep neural networks and discuss connections to adaptive rates for SGD and variance reduction.


title: Environmental and physical risk factors for men to develop body dysmorphic disorder concerning penis size compared to men anxious about their penis size and men with no concerns: A cohort study

abstract: Abstract To explore the combination of environmental and physical factors associated with the development of body dysmorphic disorder (BDD) related to penis size and the differences between men who are anxious about their penis size (but do not meet criteria for BDD), and men who do not report concerns about penis size. Method: Men with BDD ( n =26) were compared to those with small penis anxiety (SPA) ( n =31) and men without concerns ( n =33), by their demographic characteristics, penile measurements, Childhood Trauma, Perception of Teasing Scale, and differential experiences including past medical conditions. Multinomial logistic regression was run to find predictors of group membership. Results: There were significant differences across the groups in which emotional and physical abuse and neglect, competency and general appearance teasing, smaller penis size, older age, and higher Body Mass Index were all identified as risk factors for developing BDD compared to those in the SPA and men without concerns. Only perceived specific genitalia teasing were identified as a risk factor in both BDD and SPA groups compared to men without concerns. Conclusions: The results have implications for our understanding of the development of BDD compared to body dissatisfaction and the prevention of psychiatric morbidity. Smaller penis size was not hypothesised to increase vulnerability to developing BDD but is consistent with being “different” during adolescence and for teasing by one's peers or a sexual partner.


title: Towards a semantic book search engine

abstract: Traditional Information Retrieval (IR) methods were initially used for searching and ranking web pages on the Web. These methods were progressively modified to exploit the peculiarities of the Web including the use of the hyperlinked structure of the Web for relevance ranking. These Web IR techniques, however, are also being applied for searching and ranking to other forms of text collections which are not inherently web documents. Books (especially in PDF form) are by nature different from web pages because they lack an explicit hypertextual structure and therefore cannot be accurately and precisely searched and ranked using traditional approaches. Books contain a highly structured content with implicit logical connections among different parts of the same book as well as to related content in other books. These book structural semantics and logical connections could be discovered and used to establish a web of books where the logical concepts, images, figures, tables, and other parts are linked with each other thus resulting in a semantic graph, which could then be exploited by a semantic book search engine for more precise and accurate indexing, searching, ranking and recommendations. Based on this hypothesis, the paper outlines a high-level architecture for one of the possible implementations of a semantic book search engine, identifies all the potential areas of research for future researchers, and reports on our work in progress in the form of the proposed model for the purpose. The proposed architecture, if implemented in its true sense, has the potential to better serve the needs of all the stakeholders including authors, publishers, readers, and librarians.


title: Random Filters for Compressive Sampling and Reconstruction

abstract: We propose and study a new technique for efficiently acquiring and reconstructing signals based on convolution with a fixed FIR filter having random taps. The method is designed for sparse and compressible signals, i.e., ones that are well approximated by a short linear combination of vectors from an orthonormal basis. Signal reconstruction involves a non-linear Orthogonal Matching Pursuit algorithm that we implement efficiently by exploiting the nonadaptive, time-invariant structure of the measurement process. While simpler and more efficient than other random acquisition techniques like Compressed Sensing, random filtering is sufficiently generic to summarize many types of compressible signals and generalizes to streaming and continuous-time signals. Extensive numerical experiments demonstrate its efficacy for acquiring and reconstructing signals sparse in the time, frequency, and wavelet domains, as well as piecewise smooth signals and Poisson processes.


title: Directly Modeling Missing Data in Sequences with RNNs: Improved Classification of Clinical Time Series

abstract: We demonstrate a simple strategy to cope with missing data in sequential inputs, addressing the task of multilabel classification of diagnoses given clinical time series. Collected from the intensive care unit (ICU) of a major urban medical center, our data consists of multivariate time series of observations. The data is irregularly sampled, leading to missingness patterns in re-sampled sequences. In this work, we show the remarkable ability of RNNs to make effective use of binary indicators to directly model missing data, improving AUC and F1 significantly. However, while RNNs can learn arbitrary functions of the missing data and observations, linear models can only learn substitution values. For linear models and MLPs, we show an alternative strategy to capture this signal. Additionally, we evaluate LSTMs, MLPs, and linear models trained on missingness patterns only, showing that for several diseases, what tests are run can be more predictive than the results themselves.


title: Minimally Supervised Number Normalization

abstract: We propose two models for verbalizing numbers, a key component in speech recognition and synthesis systems. The first model uses an end-to-end recurrent neural network. The second model, drawing inspiration from the linguistics literature, uses finite-state transducers constructed with a minimal amount of training data. While both models achieve near-perfect performance, the latter model can be trained using several orders of magnitude less data than the former, making it particularly useful for low-resource languages.


title: Pay-per-action model for online advertising

abstract: The online advertising industry is currently based on two dominant business models: the pay-per-impression model and the pay-per-click model. With the growth of sponsored search during the last few years, there has been a move toward the pay-per-click model as it decreases the risk to small advertisers. An alternative model, discussed but not widely used in the advertising industry, is pay-per-conversion, or more generally, pay-per-action. In this paper, we discuss mechanisms for the pay-per-action model and various challenges involved in designing such mechanisms.


title: Defending against sensor-sniffing attacks on mobile phones

abstract: Modern mobile phones possess three types of capabilities: computing, communication, and sensing. While these capabilities enable a variety of novel applications, they also raise serious privacy concerns. We explore the vulnerability where attackers snoop on users by sniffing on their mobile phone sensors, such as the microphone, camera, and GPS receiver. We show that current mobile phone platforms inadequately protect their users from this threat. To provide better privacy for mobile phone users, we analyze desirable uses of these sensors and discuss the properties of good privacy protection solutions. Then, we propose a general framework for such solutions and discuss various possible approaches to implement the framework's components.


title: Human balancing of an inverted pendulum: position control by small, ballistic-like, throw and catch movements

abstract: In standing, there are small sways of the body. Our interest is to use an artificial task to illuminate the mechanisms underlying the sways and to account for changes in their size. Using the ankle musculature, subjects balanced a large inverted pendulum. The equilibrium of the pendulum is unstable and quasi-regular sway was observed like that in quiet standing. By giving full attention to minimising sway subjects could systematically reduce pendulum movement. The pendulum position, the torque generated at each ankle and the soleus and tibialis anterior EMGs were recorded. Explanations about how the human inverted pendulum is balanced usually ignore the fact that balance is maintained over a range of angles and not just at one angle. Any resting equilibrium position of the pendulum is unstable and in practice temporary; movement to a different resting equilibrium position can only be accomplished by a biphasic ‘throw and catch’ pattern of torque and not by an elastic mechanism. Results showed that balance was achieved by the constant repetition of a neurally generated ballistic-like biphasic pattern of torque which can control both position and sway size. A decomposition technique revealed that there was a substantial contribution to changes in torque from intrinsic mechanical ankle stiffness; however, by itself this was insufficient to maintain balance or to control position. Minimisation of sway size was caused by improvement in the accuracy of the anticipatory torque impulses. We hypothesise that examination of centre of mass and centre of pressure data for quiet standing will duplicate these results.


title: Semi-Supervised and Unsupervised Extreme Learning Machines

abstract: Extreme learning machines (ELMs) have proven to be efficient and effective learning mechanisms for pattern classification and regression. However, ELMs are primarily applied to supervised learning problems. Only a few existing research papers have used ELMs to explore unlabeled data. In this paper, we extend ELMs for both semi-supervised and unsupervised tasks based on the manifold regularization, thus greatly expanding the applicability of ELMs. The key advantages of the proposed algorithms are as follows: 1) both the semi-supervised ELM (SS-ELM) and the unsupervised ELM (US-ELM) exhibit learning capability and computational efficiency of ELMs; 2) both algorithms naturally handle multiclass classification or multicluster clustering; and 3) both algorithms are inductive and can handle unseen data at test time directly. Moreover, it is shown in this paper that all the supervised, semi-supervised, and unsupervised ELMs can actually be put into a unified framework. This provides new perspectives for understanding the mechanism of random feature mapping, which is the key concept in ELM theory. Empirical study on a wide range of data sets demonstrates that the proposed algorithms are competitive with the state-of-the-art semi-supervised or unsupervised learning algorithms in terms of accuracy and efficiency.


title: Towards Fine-grained Citation Function Classification

abstract: We look into the problem of recognizing citation functions in scientific literature, trying to reveal authors’ rationale for citing a particular article. We introduce an annotation scheme to annotate citation functions in scientific papers with coarse-to-fine-grained categories, where the coarse-grained annotation roughly corresponds to citation sentiment and the finegrained annotation reveals more about citation functions. We implement a Maximum Entropy-based system trained on annotated data under this scheme to automatically classify citation functions in scientific literature. Using combined lexical and syntactic features, our system achieves the F-measure of 67%.


title: Intranets and Local Community: ‘Yes, an intranet is all very well, but do we still get free beer and a barbeque?’

abstract: This paper arises from a three year research project examining the development and implementation of a residential community intranet in Melbourne, Australia. At the time of writing, the level of use of the intranet by residents is low, and the paper explores possible reasons why this may be the case. These reasons include: a) the possibility that the aggregation of potential users and content is not appropriate; b) the possibility that the technology is not appropriate; c) the possibility that the conception of community relations on which the intranet is premised is not appropriate; d) the possibility that residents' perception of efforts to engineer community relations is not appropriate; and e) the possibility that the identity of the intranet as a domestic artefact has not yet been recognised by the residents. A consideration of these five possibilities using the specific case study raises issues concerning both particular community intranets, and more general socio-technical relations.


title: Probabilistic Line Searches for Stochastic Optimization

abstract: In deterministic optimization, line searches are a standard tool ensuring stability and efficiency. Where only stochastic gradients are available, no direct equivalent has so far been formulated, because uncertain gradients do not allow for a strict sequence of decisions collapsing the search space. We construct a probabilistic line search by combining the structure of existing deterministic methods with notions from Bayesian optimization. Our method retains a Gaussian process surrogate of the univariate optimization objective, and uses a probabilistic belief over the Wolfe conditions to monitor the descent. The algorithm has very low computational cost, and no user-controlled parameters. Experiments show that it effectively removes the need to define a learning rate for stochastic gradient descent.


title: Push-pull functional reactive programming

abstract: Functional reactive programming (FRP) has simple and powerful semantics, but has resisted efficient implementation. In particular, most past implementations have used demand-driven sampling, which accommodates FRP's continuous time semantics and fits well with the nature of functional programming. Consequently, values are wastefully recomputed even when inputs don't change, and reaction latency can be as high as the sampling period. This paper presents a way to implement FRP that combines data- and demand-driven evaluation, in which values are recomputed only when necessary, and reactions are nearly instantaneous. The implementation is rooted in a new simple formulation of FRP and its semantics and so is easy to understand and reason about. On the road to a new implementation, we'll meet some old friends (monoids, functors, applicative functors, monads, morphisms, and improving values) and make some new friends (functional future values, reactive normal form, and concurrent "unambiguous choice").


title: Neural Darwinism: Selection and reentrant signaling in higher brain function

abstract: Abstract Variation and selection within neural populations play key roles in the development and function of the brain. In this article, I review a population theory of the nervous system aimed at understanding the significance of these processes. Since its original formulation in 1978, considerable evidence has accumulated to support this theory of neuronal group selection. Extensive neural modeling based on the theory has provided useful insights into several outstanding neurobiological problems including those concerned with integration of cortical function, sensorimotor control, and perceptually based behavior.


title: Semantic-based lightweight ontology learning framework: a case study of intrusion detection ontology

abstract: Building ontology for wireless network intrusion detection is an emerging method for the purpose of achieving high accuracy, comprehensive coverage, self-organization and flexibility for network security. In this paper, we leverage the power of Natural Language Processing (NLP) and Crowdsourcing for this purpose by constructing lightweight semi-automatic ontology learning framework which aims at developing a semantic-based solution-oriented intrusion detection knowledge map using documents from Scopus. Our proposed framework uses NLP as its automatic component and Crowdsourcing is applied for the semi part. The main intention of applying both NLP and Crowdsourcing is to develop a semi-automatic ontology learning method in which NLP is used to extract and connect useful concepts while in uncertain cases human power is leveraged for verification. This heuristic method shows a theoretical contribution in terms of lightweight and timesaving ontology learning model as well as practical value by providing solutions for detecting different types of intrusions.


title: Automatically Selecting Answer Templates to Respond to Customer Emails

abstract: Contact center agents typically respond to email queries from customers by selecting predefined answer templates that relate to the questions present in the customer query. In this paper we present a technique to automatically select the answer templates corresponding to a customer query email. Given a set of query-response email pairs we find the associations between the actual questions and answers within them and use this information to map future questions to their answer templates. We evaluate the system on a small subset of the publicly available Pine-Info discussion list email archive and also on actual contact center data comprising customer queries, agent responses and templates.


title: Fundamental design tradeoffs in cognitive radio systems

abstract: Under the current system of spectrum allocation, rigid partitioning has resulted in vastly underutilized spectrum bands, even in urban locales. Cognitive radios have been proposed as a way to reuse this underutilized spectrum in an opportunistic manner. To achieve this reuse while guaranteeing non-interference with the primary user, cognitive radios must detect very weak primary signals. However, uncertainties in the noise+interference impose a limit on how low of a primary signal can be robustly detected. ::: In this paper, we show that the presence/absence of possible interference from other opportunistic spectrum users represents a major component of the uncertainty limiting the ability of a cognitive radio network to reclaim a band for its use. Coordination among nearby cognitive radios is required to control this uncertainty. While this coordination can take a form similar to a traditional MAC protocol for data communication, its role is different in that it aims to reduce the uncertainty about interference rather than just reducing the interference itself. ::: We show how the degree of coordination required can vary based on the coherence times and bandwidths involved, as well as the complexity of the detectors themselves. The simplest sensing strategies end up needing the most coordination, while more complex strategies involving adaptive coherent processing and interference prediction can be individually more robust and thereby reduce the need for coordination across different networks. We also show the existence of a coordination radius wall which limits secondary user densities that can be supported irrespective of coordination involved. Furthermore, local cooperation among cognitive radios for collective decision making can reduce the fading margins we need to budget for. This cooperation benefits from increased secondary user densities and hence induces a minima in the power-coordination tradeoff.


title: Digital media, anxiety, and depression in children

abstract: There are growing concerns about the impact of digital technologies on children’s emotional well-being, particularly regarding fear, anxiety, and depression. The 2 mental health categories of anxiety and depression will be discussed together because there is significant symptom overlap and comorbidity. Early research has explored the impact of traditional media (eg, television, movies) on children’s acute fears, which can result in anxieties and related sleep disturbances that are difficult to remedy. More recent research deals with the interactive nature of newer media, especially social media, and their impacts on anxiety and depression. Key topics of inquiry include the following: anxiety and depression associated with technology-based negative social comparison, anxiety resulting from lack of emotion-regulation skills because of substituted digital media use, social anxiety from avoidance of social interaction because of substituted digital media use, anxiety because of worries about being inadequately connected, and anxiety, depression, and suicide as the result of cyberbullying and related behavior. A growing body of research confirms the relationship between digital media and depression. Although there is evidence that greater electronic media use is associated with depressive symptoms, there is also evidence that the social nature of digital communication may be harnessed in some situations to improve mood and to promote health-enhancing strategies. Much more research is needed to explore these possibilities.


title: Identifying user behavior in online social networks

abstract: Online social networks pose an interesting problem: how to best characterize the different classes of user behavior. Traditionally, user behavior characterization methods, based on user individual features, are not appropriate for online networking sites. In these environments, users interact with the site and with other users through a series of multiple interfaces that let them to upload and view content, choose friends, rank favorite content, subscribe to users and do many other interactions. Different interaction patterns can be observed for different groups of users. In this paper, we propose a methodology for characterizing and identifying user behaviors in online social networks. First, we crawled data from YouTube and used a clustering algorithm to group users that share similar behavioral pattern. Next, we have shown that attributes that stem from the user social interactions, in contrast to attributes relative to each individual user, are good discriminators and allow the identification of relevant user behaviors. Finally, we present and discuss experimental results of the use of proposed methodology. A set of useful profiles, derived from the analysis of the YouTube sample is presented. The identification of different classes of user behavior has the potential to improve, for instance, recommendation systems for advertisements in online social networks.


title: Shape completion enabled robotic grasping

abstract: This work provides an architecture to enable robotic grasp planning via shape completion. Shape completion is accomplished through the use of a 3D convolutional neural network (CNN). The network is trained on our own new open source dataset of over 440,000 3D exemplars captured from varying viewpoints. At runtime, a 2.5D pointcloud captured from a single point of view is fed into the CNN, which fills in the occluded regions of the scene, allowing grasps to be planned and executed on the completed object. Runtime shape completion is very rapid because most of the computational costs of shape completion are borne during offline training. We explore how the quality of completions vary based on several factors. These include whether or not the object being completed existed in the training data and how many object models were used to train the network. We also look at the ability of the network to generalize to novel objects allowing the system to complete previously unseen objects at runtime. Finally, experimentation is done both in simulation and on actual robotic hardware to explore the relationship between completion quality and the utility of the completed mesh model for grasping.


title: A feature exploration methodology for learning based cuffless blood pressure measurement using photoplethysmography

abstract: In this work, we propose a feature exploration method for learning-based cuffless blood pressure measurement. More specifically, to efficiently explore a large feature space from the photoplethysmography signal, we have applied several analytical techniques, including random error elimination, adaptive outlier removal, maximum information coefficient and Pearson's correlation coefficient based feature assessment methods. We evaluate fifty-seven possible feature candidates and propose three separate feature sets with each containing eleven features to predict the systolic blood pressure (SBP), diastolic blood pressure (DBP) and mean blood pressure (MBP), respectively. From our experimental results on a realistic dataset, this work achieves 4.77±7.68, 3.67±5.69 and 3.85±5.87 mmHg prediction accuracy for SBP, DBP and MBP. In summary, using the proposed light-weight features, the proposed predictors can successfully achieve a Grade A in two standards proposed by the American National Standards of the Association for the Advancement of Medical Instrumentation (AAMI) and British Hypertension Society (BHS).


title: A 200-mA Digital Low Drop-Out Regulator With Coarse-Fine Dual Loop in Mobile Application Processor

abstract: This paper proposes a coarse-fine dual-loop architecture for the digital low drop-out (LDO) regulators with fast transient response and more than 200-mA load capacity. In the proposed scheme, the output voltage is coregulated by two loops, namely, the coarse loop and the fine loop. The coarse loop adopts a fast current-mirror flash analog to digital converter and supplies high output current to enhance the transient performance, while the fine loop delivers low output current and helps reduce the voltage ripples and improve the regulation accuracies. Besides, a digital controller is implemented to prevent contentions between the two loops. Fabricated in a 28-nm Samsung CMOS process, the proposed digital LDO achieves maximum load up to 200 mA when the input and the output voltages are 1.1 and 0.9 V, respectively, with a chip area of 0.021 mm2. The measured output voltage drop of around 120 mV is observed for a load step of 180 mA.


title: Why is Real-World Visual Object Recognition Hard?

abstract: Progress in understanding the brain mechanisms underlying vision requires the construction of computational models that not only emulate the brain's anatomy and physiology, but ultimately match its performance on visual tasks. In recent years, “natural” images have become popular in the study of vision and have been used to show apparently impressive progress in building such models. Here, we challenge the use of uncontrolled “natural” images in guiding that progress. In particular, we show that a simple V1-like model—a neuroscientist's “null” model, which should perform poorly at real-world visual object recognition tasks—outperforms state-of-the-art object recognition systems (biologically inspired and otherwise) on a standard, ostensibly natural image recognition test. As a counterpoint, we designed a “simpler” recognition test to better span the real-world variation in object pose, position, and scale, and we show that this test correctly exposes the inadequacy of the V1-like model. Taken together, these results demonstrate that tests based on uncontrolled natural images can be seriously misleading, potentially guiding progress in the wrong direction. Instead, we reexamine what it means for images to be natural and argue for a renewed focus on the core problem of object recognition—real-world image variation.


title: Eavesdropping with periscopes: Experimental security analysis of highly directional millimeter waves

abstract: Next generation wireless networks utilizing millimeter waves (mm-waves) achieve extremely high data rates using narrow signal beams. Featuring a high directivity and being susceptible to blockage by objects, mm-waves are often assumed to be hard to intercept. However, small-scale objects within the beam cause reflections, thus enabling eavesdroppers to receive the signal from the outside. In this paper, we practically demonstrate the vast impact that inconspicuous objects might have on mm-wave security. Experiments on our novel mm-wave software defined radio (SDR) testbed highlight that even centimeter-scale reflectors make eavesdropping from outside the signal beam possible. More sophisticated objects increase the signal strength of the reflected signal or allow the attacker to choose its location with more latitude. Modern communication devices with metal surfaces like mobile phones or laptops cause sufficient reflections for eavesdropping as well; signals will bounce off the intended receiver. With our experiments, we demonstrate empirically that reflections enable potential attackers to achieve a received signal strength as high as that of the intended receiver with only a minimal impact on the receiver's performance. For blockages that do not impact the quality of the reception, reflections decrease the secrecy capacity by 32%. When tolerating small signal blockage towards the intended receiver, the attacker overcomes any inherent security of narrow beams and reduces the secrecy capacity to zero.


title: Annotating the Predicate-Argument Structure of Chinese Nominalizations

abstract: This paper describes the Chinese NomBank Project, the goal of which is to annotate the predicate-argument structure of nominalized predicates in Chinese. The Chinese Nombank extends the general framework of the English and Chinese Proposition Banks to the annotation of nominalized predicates and adds a layer of semantic annotation to the Chinese Treebank. We first outline the scope of the work by discussing the markability of the nominalized predicates and their arguments. We then attempt to provide a categorization of the distribution of the arguments of nominalized predicates. We also discuss the relevance of the event/result distinction to the annotation of nominalized predicates and the phenomenon of incorporation. Finally we discuss some cross-linguistic differences between English and Chinese.


title: Speciation gradients and the distribution of biodiversity

abstract: Global patterns of biodiversity are influenced by spatial and environmental variations in the rate at which new species form. We relate variations in speciation rates to six key patterns of biodiversity worldwide, including the species–area relationship, latitudinal gradients in species and genetic diversity, and between-habitat differences in species richness. Although they sometimes mirror biodiversity patterns, recent rates of speciation, at the tip of the tree of life, are often highest where species richness is low. Speciation gradients therefore shape, but are also shaped by, biodiversity gradients and are often more useful for predicting future patterns of biodiversity than for interpreting the past.


title: Internal models direct dragonfly interception steering

abstract: This study tracks dragonfly head and body movements during high-velocity and high-precision prey-capture flights, and shows that the dragonfly uses predictive internal models and reactive control to build an interception trajectory that complies with biomechanical constraints.


Natural Facial Expression Recognition Using HMM and KNN


title: CourseVis: A graphical student monitoring tool for supporting instructors in web-based distance courses

abstract: This paper presents CourseVis, a system that takes a novel approach of using Web log data generated by course management systems (CMSs) to help instructors become aware of what is happening in distance learning classes. Specifically, techniques from information visualization (IV) are employed to graphically render complex, multidimensional student tracking data. Several graphical representations are generated to help distance learning instructors get a better understanding of social, behavioural, and cognitive aspects related to learners. The evaluation of CourseVis shows that it can help instructors to quickly identify tendencies in their classes and discover individuals that might need special attention. This suggests that the effectiveness of CMSs can be improved by integrating IV techniques to generate appropriate graphical representations, similar to those produced in CourseVis.


title: The Picat-SAT Compiler

abstract: SAT has become the backbone of many software systems. In order to make full use of the power of SAT solvers, a SAT compiler must encode domain variables and constraints into an efficient SAT formula. Despite many proposals for SAT encodings, there are few working SAT compilers. This paper presents Picat-SAT, the SAT compiler in the Picat system. Picat-SAT employs the sign-and-magnitude log encoding for domain variables. Log-encoding for constraints resembles the binary representation of numbers used in computer hardware, and many algorithms and optimization opportunities have been exploited by hardware design systems. This paper gives the encoding algorithms for constraints, and attempts to experimentally justify the choices of the algorithms for the addition and multiplication constraints. This paper also presents preliminary, but quite encouraging, experimental results.


title: An introduction to Docker for reproducible research, with examples from the R environment

abstract: As computational work becomes more and more integral to many aspects of scientific research, computational reproducibility has become an issue of increasing importance to computer systems researchers and domain scientists alike. Though computational reproducibility seems more straight forward than replicating physical experiments, the complex and rapidly changing nature of computer environments makes being able to reproduce and extend such work a serious challenge. In this paper, I explore common reasons that code developed for one research project cannot be successfully executed or extended by subsequent researchers. I review current approaches to these issues, including virtual machines and workflow systems, and their limitations. I then examine how the popular emerging technology Docker combines several areas from systems research - such as operating system virtualization, cross-platform portability, modular re-usable elements, versioning, and a 'DevOps' philosophy, to address these challenges. I illustrate this with several examples of Docker use with a focus on the R statistical environment.


title: Narrowband frequency scanning array antenna at 5.8 GHz for short range imaging

abstract: We present a frequency scanning array antenna for radar applications. A high angular range is obtained while requiring only a small frequency band. This is achieved by using a band-pass filter feed structure that increases the frequency-dependent phase difference between the individual array elements. An angular range of ± 50 degrees is obtained while using only 150 MHz bandwidth with 5.8 GHz center frequency corresponding to the ISM Band. This antenna type can be applied for detecting covered objects or for short range imaging.


title: Improving Software Diagnosability via Log Enhancement

abstract: Diagnosing software failures in the field is notoriously difficult, in part due to the fundamental complexity of trouble-shooting any complex software system, but further exacerbated by the paucity of information that is typically available in the production setting. Indeed, for reasons of both overhead and privacy, it is common that only the run-time log generated by a system (e.g., syslog) can be shared with the developers. Unfortunately, the ad-hoc nature of such reports are frequently insufficient for detailed failure diagnosis. This paper seeks to improve this situation within the rubric of existing practice. We describe a tool, LogEnhancer that automatically "enhances" existing logging code to aid in future post-failure debugging. We evaluate LogEnhancer on eight large, real-world applications and demonstrate that it can dramatically reduce the set of potential root failure causes that must be considered during diagnosis while imposing negligible overheads.


title: TourExplain: A Crowdsourcing Pipeline for Generating Explanations for Groups of Tourists

abstract: When a group is traveling together it is challenging to recommendan itinerary consisting of several points of interest (POIs). Thepreferences of individual group members often diverge, but it isimportant to keep everyone in the group satisfied during the entiretrip. We propose a method to consider the preferences of all thepeople in the group. Building on this method, we design expla-nations for groups of people, to help them reach a consensus forplaces to visit. However, one open question is how to best formu-late explanations for such sequences. In this paper, we introduceTourExplain, an automated crowdsourcing pipeline to generate andevaluate explanations for groups with the aim of improving ourinitial proposed explanations by relying on the wisdom of crowds.


title: What's Cookin'? Interpreting Cooking Videos using Text, Speech and Vision

abstract: We present a novel method for aligning a sequence of instructions to a video of someone carrying out a task. In particular, we focus on the cooking domain, where the instructions correspond to the recipe. Our technique relies on an HMM to align the recipe steps to the (automatically generated) speech transcript. We then refine this alignment using a state-of-the-art visual food detector, based on a deep convolutional neural network. We show that our technique outperforms simpler techniques based on keyword spotting. It also enables interesting applications, such as automatically illustrating recipes with keyframes, and searching within a video for events of interest.


title: Fast sparse matrix-vector multiplication on GPUs for graph applications

abstract: Sparse matrix-vector multiplication (SpMV) is a widely used computational kernel. The most commonly used format for a sparse matrix is CSR (Compressed Sparse Row), but a number of other representations have recently been developed that achieve higher SpMV performance. However, the alternative representations typically impose a significant preprocessing overhead. While a high preprocessing overhead can be amortized for applications requiring many iterative invocations of SpMV that use the same matrix, it is not always feasible -- for instance when analyzing large dynamically evolving graphs. This paper presents ACSR, an adaptive SpMV algorithm that uses the standard CSR format but reduces thread divergence by combining rows into groups (bins) which have a similar number of non-zero elements. Further, for rows in bins that span a wide range of non zero counts, dynamic parallelism is leveraged. A significant benefit of ACSR over other proposed SpMV approaches is that it works directly with the standard CSR format, and thus avoids significant preprocessing overheads. A CUDA implementation of ACSR is shown to outperform SpMV implementations in the NVIDIA CUSP and cuSPARSE libraries on a set of sparse matrices representing power-law graphs. We also demonstrate the use of ACSR for the analysis of dynamic graphs, where the improvement over extant approaches is even higher.


title: Shape and refractive index recovery from single-view polarisation images

abstract: In this paper, we propose an approach to the problem of simultaneous shape and refractive index recovery from multispectral polarisation imagery captured from a single viewpoint. The focus of this paper is on dielectric surfaces which diffusely polarise light transmitted from the dielectric body into the air. The diffuse polarisation of the reflection process is modelled using a Transmitted Radiance Sinusoid curve and the Fresnel transmission theory. We provide a method of estimating the azimuth angle of surface normals from the spectral variation of the phase of polarisation. Moreover, to render the problem of simultaneous estimation of surface orientation and index of refraction well-posed, we enforce a generative model on the material dispersion equations for the index of refraction. This generative model, together with the Fresnel transmission ratio, permit the recovery of the index of refraction and the zenith angle simultaneously. We show results on shape recovery and rendering for real world and synthetic imagery.


3 – Statistical Thermodynamics of Clean Surfaces


title: Walking while Performing Working Memory Tasks Changes the Prefrontal Cortex Hemodynamic Activations and Gait Kinematics

abstract: Background Increasing evidence suggests that walking while performing a concurrent task negatively influences gait performance. However, it remains unclear how higher-level cognitive processes and coordination of limb movements are altered in challenging walking environments. This study investigated the influence of cognitive task complexity and walking road condition on the neutral correlates of executive function and postural control in dual-task walking. Methods Twenty-four healthy young adults completed a series of overground walks with three walking road conditions (wide, narrow, with obstacles) with and without the concurrent n-back working memory tasks of two complexity levels (1-back and 3-back). Prefrontal brain activation was assessed by functional near-infrared spectroscopy. A three-dimensional motion analysis system was used simultaneously to measure gait performance and lower-extremity kinematics. Repeated measures analysis of variance were performed to examine the differences between the conditions. Results In comparison with standing still, participants showed lower n-back task accuracy while walking, with the worst performance from the road with obstacles. Spatiotemporal gait parameters, lower-extremity joint movements, and the relative changes in oxygenated hemoglobin (HbO) concentration levels were all significantly different across the task complexity and walking path conditions. While dual-tasking participants were found to flex their hips and knees less, leading to a slower gait speed, longer stride time, shorter step length, and greater gait variability than during normal walking. For narrow-road walking, smaller ankle dorsiflexion and larger hip flexion were observed, along with a reduced gait speed. Obstacle negotiation was mainly characterized by increased gait variability than other conditions. HbO levels appeared to be lower during dual-task walking than normal walking. Compared to wide and obstacle conditions, walking on the narrow road was found to elicit a smaller decrement in HbO levels. Conclusions The current study provided direct evidence that, in young adults, neural correlates of executive function and dynamic postural control tend to be altered in response to the cognitive load imposed by the walking environment and the concurrent task during ambulation. A shift of brain activation patterns between functionally connected networks may occur when facing challenging cognitive-motor interaction.


title: Performance Comparison of the STBC-OFDM Decoders in a Fast Fading Channel

abstract: In a fast fading channel, the orthogonality of the two consecutive STBC-OFDM symbols will be destroyed and the received signals can not be perfectly separated. As a result, the bit error rate (BER) performance will be degraded seriously. In this paper, we analyze several CCI cancellation decoders for the STBC-OFDM in a fast fading channel including the Alamouti method, SIC method, ML method, DMLD method and the DZFD method. A simple DZFD method is given to reduce the computational complexity of the DMLD method.


title: Effects of inorganic nutrients and organic matter on microbial euendolithic community composition and microbioerosion rates

abstract: Closed cages were used in Glovers Atoll, Belize to test the effects of organic matter (OM) and in- organic nutrient (N + P) additions on microbial euendo- lithic communities (bacteria, fungi, and algae) and their rates of bioerosion of Strombus gigas shells during a 49 d fertilization experiment. We hypothesized that the addi- tion of OM would release heterotrophic microborers from C-limitation and at the same time reduce light levels for euendolithic microalgae, thus changing the mi- croborers' community composition and their bioerosion rates. The addition of OM increased the abundance of heterotrophs (particularly fungi), but only when OM was added alone, not when it was combined with inorganic nutrients. In contrast, both cyanobacteria and green algae were stimulated by the addition of inorganic nutri- ents, but were not affected by OM; these taxa were 4 times more abundant in treatments with inorganic nutri- ents than in control and OM treatments. Green algae dominated the control, N + P and N + P + OM treatments (77 to 87% green algae, 6 to 8% cyanobacteria, 4 to 16% heterotrophs), whereas in the OM treatment, hetero- trophs represented nearly 50% of the total area colo- nized (52% green algae, 4% cyanobacteria, 44% het- erotrophs). Bioerosion rates in treatments with added inorganic nutrients (396 g CaCO3 m -2 yr -1 in the N + P treatment and 370 g CaCO3 m -2 yr -1 in the N + P + OM treatment) were 9-fold greater than bioerosion rates in the control and OM treatments (43 and 48 g CaCO3 m -2 yr -1 , respectively), and were not affected by OM addi- tion. We conclude that inorganic nutrients are a major factor controlling the microbioerosion rates and the abundance of euendolithis algae over fungi in carbonate substrata. Microborers act in synergy with the grazers that feed on them and with macroborers that increase the internal surfaces available for microborers to colo- nize. As a result, increased nutrients can initiate a feedback loop where bioerosion processes reinforce one another, leading to accelerated erosion of the reef framework.


title: Modeling and Fabrication of RF MEMS Switches

abstract: MicroElectroMechanical Systems (MEMS) are becoming increasingly important. The benefits of MEMS include small size, low weight, and low cost. In addition, Radio Frequency MEMS switches offer low insertion loss, high quality factor, low power, high isolation, and broadband frequency performance. Modeling of electrostatically-actuated, capacitive switches is reviewed and fabrication steps are described. A lumped element model is used in order to facilitate the use of MEMS switches in electrical circuits. Finite Element Modeling showing forces created by the voltage across the switch is also presented. Fabrication steps are described. RF MEMS switches offer significant benefits and may be incorporated into many communication devices. Understanding switch behavior and characteristics is important for designers.


title: Auditory attention decoding with EEG recordings using noisy acoustic reference signals

abstract: To decode auditory attention from electroencephalography (EEG) recordings in a cocktail-party scenario with two competing speakers a least-squares method has recently been proposed, showing a promising decoding accuracy. This method however requires the clean speech signals of both the attended and the unattended speaker to be available as reference signals, which is difficult to achieve from the noisy recorded microphone signals in practice. In addition, optimizing the parameters involved in the spatio-temporal filter design is of crucial importance in order to reach the largest possible decoding performance. In this paper, the influence of noisy acoustic reference signals and the spatio-temporal filter and regularization parameters on the decoding performance is investigated. The results show that to some extent the decoding performance is robust to noisy acoustic reference signals, depending on the noise type. Furthermore, we demonstrate the crucial influence of several parameters on the decoding performance, especially when the acoustic reference signals used for decoding have been corrupted by noise.


title: Dementia Games: A Literature Review of Dementia-Related Serious Games

abstract: Serious games find wide application in the health domain, occupying their own place in the video game industry (games for health). Currently, there is a proliferation of cognitive training, exercise and social games, targeting one of the most dangerous disease of the era: dementia, as well as its various symptoms and stages like Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD). However, the dementia-related gaming field is still uncharted. In this literature review, we list studies on serious games related to dementia, that are supported by evaluation tests on dementia, MCI and AD patients with published, peer-reviewed results. This review discusses the effects that games, which include Wii Fit, Wii Sports, Big Brain Academy, Lumosity, SmartBrain Games, MasterQuiz, MINDs et al., have on dementia-related conditions. The review leads us to the conclusions that, firstly, even though many games were developed for entertainment purposes, they are being used for health reasons (usually after technical or conceptual modification), acquiring the characteristics of serious games and, secondly, dementia games do have an effect on cognitive impaired people. If that effect is longlasting and/or transferable to the daily activities is a matter of further scientific investigation.


title: Monitoring a widespread bacterial group : in situ detection of planctomycetes with 16 s rRNA-targeted probes

abstract: Summary: The group of planctomycetes represents a separate line of descent within the domain Bacteria. Two phylum-specific 16S rRNA-targeted oligonucleotide probes for planctomycetes have been designed, optimized for in situ hybridization and used in different habitats to detect members of the group in situ. The probes, named PLA46 and PLA886, are targeting all or nearly all members of the planctomycete line of descent. Planctomycetes could be detected in almost all samples examined, e.g. a brackish water lagoon, activated sludge, and other wastewater habitats. In situ probing revealed quite uniform morphology and spatial arrangement of the detected cells but profound differences in abundance ranging from less than 0·1% to several percentage of the total cells. Single coccoid cells with diameters between 1 and 2·5 μm were dominating in most samples with the exception of the lagoon, in which rosettes of pear-shaped cells were abundant. The planctomycetes showed generally no hybridization signals with the bacterial probe EUB338, which is in accordance with base changes in their 16S rRNA sequences. A discrete ultrastructure of planctomycete cells was suggested by double staining with rRNA-targeted probes and the DNA-binding dye 4′,6-diamidino-2-phenylindole (DAPI). The probe-conferred fluorescence was distributed in a ring-shaped manner around a central DAPI spot. The two probes developed extend the existing set of group-specific rRNA-targeted probes and help to elucidate the basic composition of bacterial communities in a first step of differential analysis. In situ hybridization of environmental samples indicated widespread presence of planctomycetes in different ecosystems.


title: A survey of attack and defense techniques for reputation systems

abstract: Reputation systems provide mechanisms to produce a metric encapsulating reputation for a given domain for each identity within the system. These systems seek to generate an accurate assessment in the face of various factors including but not limited to unprecedented community size and potentially adversarial environments. We focus on attacks and defense mechanisms in reputation systems. We present an analysis framework that allows for the general decomposition of existing reputation systems. We classify attacks against reputation systems by identifying which system components and design choices are the targets of attacks. We survey defense mechanisms employed by existing reputation systems. Finally, we analyze several landmark systems in the peer-to-peer domain, characterizing their individual strengths and weaknesses. Our work contributes to understanding (1) which design components of reputation systems are most vulnerable, (2) what are the most appropriate defense mechanisms and (3) how these defense mechanisms can be integrated into existing or future reputation systems to make them resilient to attacks.


title: Online social networks and offline protest

abstract: Large-scale protests occur frequently and sometimes overthrow entire political systems. Meanwhile, online social networks have become an increasingly common component of people’s lives. We present a large-scale longitudinal study that connects online social media behaviors to offline protest. Using almost 14 million geolocated tweets and data on protests from 16 countries during the Arab Spring, we show that increased coordination of messages on Twitter using specific hashtags is associated with increased protests the following day. The results also show that traditional actors like the media and elites are not driving the results. These results indicate social media activity correlates with subsequent large-scale decentralized coordination of protests, with important implications for the future balance of power between citizens and their states.


Object Detection in Video using Faster R-CNN


title: Online Pedagogy: Beyond Digital "Chalk and Talk"

abstract: Abstract Courseware provides efficient data-management for higher education; however, less clear are the ways it serves pedagogical innovation and democratic practice. This article illustrates the challenges of creating an authentic online pedagogy through a case study of a graduate level teacher education course. While professional felt needs drive in-service teachers to achieve online interdependence, lack of proficiency with the technological side of courseware and tension between process and product pose significant challenges to developing an authentic and democratic online pedagogy. Introduction Courseware, also referred to as course management systems, is software designed for faculty and students to use in teaching and learning in higher education. Common examples include WebCT and Blackboard. Academic courseware affords students access to course content from anywhere and at anytime. Courseware is user-centered, as it affords information access and delivery outside the boundaries of time and space. Yet, as a "plug-n-play" model of delivering content to an end user, courseware by itself cannot provide meaningful learning experiences for the individual student. Posting course syllabi and reading materials online for students to access may technically qualify as an educational use of technology; however, it rarely signifies the use of technology to teach. Student-centered uses of courseware move beyond administrative uses into the more unpredictable processes that comprise teaching and learning. As an online template, courseware can provide a centralized location within the decentralized medium of the World Wide Web and provide a conventional structure that offers familiarity to novice students (and instructors) who experience unpredictability and ambiguity of an online environment. Courseware can provide instructors with formalized features to assist them in the administrative side of teaching (e.g., class roster, assignments, quizzes, grade book) yet often these features dictate a top-down management of data, rather than student-centered inquiry. There is also an inefficiency associated with the use of courseware. Katz (2003) observes a socialization curve in implementing courseware within higher education, "accompanied by a short-term loss in productivity as new tools, methods, and processes are assimilated" (p. 54). Higher education faculty cite as the main reason for using courseware to solve a pedagogical problem or challenge, yet at the same time find the use of this technology time consuming, inflexible and difficult (Morgan, 2003). Using courseware can increase an instructor's weekly workload from approximately seven hours to nearly twenty hours (Palloff & Pratt, 1999). This significant increase in workload is a high-risk investment, as the added workload does not guarantee an increase the quality or quantity of student learning. The added time and effort required to use courseware does not guarantee the creation of authentic learning contexts--teaching that is grounded in students' own lived experience and concern for the larger world of which they are a part (O' Hair, McLaughlin, & Reitzug, 2000). Given that higher education is steeped in a history of balancing pedagogy with new technology (Katz, 2003), the administrative trend of courseware poses significant challenges particularly for teacher educators responsible for preparing teachers in content knowledge, pedagogical knowledge, and practical knowledge (Sternberg & Horvath, 1995) in addition to technological proficiency (NETS-T, 2000). Faculty must pay close attention to the democratizing features of courseware (e.g., discussion board, student administrator access, e-mail) that empower students to take responsibility for their learning and secure them a position as legitimate members of a discursive community. Thus, an important question to ask is, "How can courseware support a pedagogy of authenticity? …


IMPACT OF GROUND WIRES ON THE SERIES IMPEDANCE, VOLTAGE AND CURRENT CONDITIONS OF OVERHEAD POWER LINES


title: A Survey on reconfigurable antennas

abstract: The study of reconfigurable antennas has made great progress in recent years. Compared with conventional antennas, reconfigurable antennas have more advantages and better prospects. They are lighter in weight, smaller in dimension and lower in price. Moreover, the reconfigurable antennas can provide diversity feature of operating resonant frequency, polarization, and radiation pattern. The latest researches of the reconfigurable antenna are analyzed and summarized in this paper to present the characteristics and classifications.


title: Privacy preserving event sequence data visualization using a Sankey diagram-like representation

abstract: Given the growing rates and richness of data being collected nowadays, it is non-trivial for data owners to determine a single best publishing granularity that presents the most value of the data while preserving its privacy. There have been extensive studies on privacy preserving algorithms in the data mining community, but relatively few have been done to provide a supervised control over the anonymization process. We present the design and evaluation of a visual interface that assists users to employ commonly used data anonymization techniques for making privacy preserving visualizations of the data. We focus on event sequence data due to its vulnerability to privacy concerns. Our visual interface is designed for data owners to examine potential privacy issues, obfuscate information as suggested by the algorithm, and fine-tune the results per their requests. Case studies using multiple datasets under different scenarios demonstrate the effectiveness of our design. These studies show that using visualization as an interface can help identify potential privacy issues, reveal underlying anonymization processes, and allow users to balance between data utility and privacy.


title: 60 GHz Fermi tapered slot antenna with sin-corrugation

abstract: A low-cost, high gain and broadband 60 GHz Fermi tapered slot antenna with a sin-corrugation is presented. The novel corrugation design parameters provide a high flexibility to enhance antenna characteristics. The proposed antenna provides a gain of 18.8 dB at 60 GHz, a high total radiation efficiency of ~90%, and an impedance bandwidth covers the 50-70 GHz band. With these features, this antenna is suitable for unlicensed millimeter-wave ISM-band, wireless communications, and imaging applications.


title: EEG oscillations and recognition memory: Theta correlates of memory retrieval and decision making

abstract: Studies of memory retrieval have identified electroencephalographic (EEG) correlates of a test item’s old–new status, reaction time, and memory load. In the current study, we used a multivariate analysis to disentangle the effects of these correlated variables. During retrieval, power of left-parietal theta (4–8 Hz) oscillations increased in proportion to how well a test item was remembered, and theta in central regions correlated with decision making. We also studied how these oscillatory dynamics complemented event-related potentials. These findings are the first to demonstrate that distinct patterns of theta oscillations can simultaneously relate to different aspects of


title: Artificial Intelligence and Pro-Social Behaviour

abstract: If artificial intelligence (AI) were achievable, what would the consequences be for human society? Perhaps surprisingly, the answer to this question is already at hand. We are achieving rapid and accelerating success in our quest to build AI. That very success—and the slowness with which both the academic community and the general public have come to recognise it—has shown how little we understand our own intelligence, and its role in our lives and culture. Here I attempt to address this problem of understanding, exploiting a variety of scientific evidence, including social simulation. I begin by reviewing current progress in AI, which is profound but underestimated. I suggest this lack of recognition is due to the mistaken belief that intelligence implies agency. I next examine the related question of human uniqueness: why do only we have language and extensive built culture? I use models and data to show that the propensities to use culture, share information and behave altruistically are neither unique to humans nor inexplicable to biology, but rather our uniqueness hinges on the extent of our capacities for communication and memory. Finally, I apply the impact of AI on extending our intelligence to these theories, to predict—and observe—consequences of AI on human societies and individual human lives. I make and support policy recommendations based on these predictions.


title: Fast and sensitive mapping of nanopore sequencing reads with GraphMap

abstract: Realizing the democratic promise of nanopore sequencing requires the development of new bioinformatics approaches to deal with its specific error characteristics. Here we present GraphMap, a mapping algorithm designed to analyse nanopore sequencing reads, which progressively refines candidate alignments to robustly handle potentially high-error rates and a fast graph traversal to align long reads with speed and high precision (>95%). Evaluation on MinION sequencing data sets against short- and long-read mappers indicates that GraphMap increases mapping sensitivity by 10–80% and maps >95% of bases. GraphMap alignments enabled single-nucleotide variant calling on the human genome with increased sensitivity (15%) over the next best mapper, precise detection of structural variants from length 100 bp to 4 kbp, and species and strain-specific identification of pathogens using MinION reads. GraphMap is available open source under the MIT license at https://github.com/isovic/graphmap.


title: Identifying influential reviewers for word-of-mouth marketing

abstract: The key to word-of-mouth marketing is to discover the potential influential nodes for efficiently spreading product impressions. In this paper, a framework combined with mining techniques, a modified PMI measure, and an adaptive RFM model is proposed to evaluate the influential power of online reviewers. An artificial neural network is adopted to identify the target reviewers and a well-developed trust mechanism is utilized for effectiveness evaluation. This proposed framework is verified by the data collected from Epinions.com, one of the most popular online product review websites. The experimental results show that the proposed model could accurately identify which reviewers to select to become the influential nodes. This proposed approach can be exploited in effectively carrying out online word-of-mouth marketing, which can save a lot of resources in finding customers.


title: Emotion, Motivation, and Anxiety: Brain Mechanisms and

abstract: Abstract The organization of response systems in emotion is founded on two basic motive systems, appetitive and defensive. The subcortical and deep cortical structures that determine primary motivated behavior are similar across mammalian species. Animal research has illuminated these neural systems and defined their reflex outputs. Although motivated behavior is more complex and varied in humans, the simpler underlying response patterns persist in affective expression. These basic phenomena are elucidated here in the context of affective perception. Thus, the research examines human beings watching uniquely human stimuli—primarily picture media (but also words and sounds) that prompt emotional arousal—showing how the underlying motivational structure is apparent in the organization of visceral and behavioral responses, in the priming of simple reflexes, and in the reentrant processing of these symbolic representations in the sensory cortex. Implications of the work for understanding pathological emotional states are discussed, emphasizing research on psychopathy and the anxiety disorders.


title: Normalized amplitude modulation features for large vocabulary noise-robust speech recognition

abstract: Background noise and channel degradations seriously constrain the performance of state-of-the-art speech recognition systems. Studies comparing human speech recognition performance with automatic speech recognition systems indicate that the human auditory system is highly robust against background noise and channel variabilities compared to automated systems. A traditional way to add robustness to a speech recognition system is to construct a robust feature set for the speech recognition model. In this work, we present an amplitude modulation feature derived from Teager's nonlinear energy operator that is power normalized and cosine transformed to produce normalized modulation cepstral coefficient (NMCC) features. The proposed NMCC features are compared with respect to state-of-the-art noise-robust features in Aurora-2 and a renoised Wall Street Journal (WSJ) corpus. The WSJ word-recognition experiments were performed on both a clean and artificially renoised WSJ corpus using SRI's DECIPHER large vocabulary speech recognition system. The experiments were performed under three train-test conditions: (a) matched, (b) mismatched, and (c) multi-conditioned. The Aurora-2 digit recognition task was performed using the standard HTK recognizer distributed with Aurora-2. Our results indicate that the proposed NMCC features demonstrated noise robustness in almost all the training-test conditions of renoised WSJ data and also improved digit recognition accuracies for Aurora-2 compared to the MFCCs and state-of-the-art noise-robust features


title: MOOCs for Universities and Learners - An Analysis of Motivating Factors

abstract: Massively Open Online Courses (MOOCs) are a recent introduction to the palette of educational offerings yet in a short time they have become the subject of massive interest and hype. There are those that predict that these free courses are the first ripple in the coming wave of disruption that the web and on-line education will cause to traditional universities. However University investments in producing MOOCs are increasing exponentially and at the same time learners are enthusiastically registering in their tens of thousands for these courses. This paper describes some research into the motivations for universities to create MOOCs and the motivations of learners in registering and completing them. Our results show a spectrum of motivations for universities, and suggest a need for individual universities to be clear of where they sit in that spectrum. For students we see that motivations can vary significantly across cultures.


title: The neuro vector engine: Flexibility to improve convolutional net efficiency for wearable vision

abstract: Deep Convolutional Networks (ConvNets) are currently superior in benchmark performance, but the associated demands on computation and data transfer prohibit straightforward mapping on energy constrained wearable platforms. The computational burden can be overcome by dedicated hardware accelerators, but it is the sheer amount of data transfer, and level of utilization that determines the energy-efficiency of these implementations. This paper presents the Neuro Vector Engine (NVE) a SIMD accelerator for ConvNets for visual object classification, targeting portable and wearable devices. Our accelerator is very flexible due to the usage of VLIW ISA, at the cost of instruction fetch overhead. We show that this overhead is insignificant when the extra flexibility enables advanced data locality optimizations, and improves HW utilization over ConvNet vision applications. By co-optimizing accelerator architecture and algorithm loop structure, 30 Gops is achieved with a power envelope of 54mW and only 0.26mm2 silicon footprint at TSMC 40nm technology, enabling high-end visual object recognition by portable and even wearable devices.


title: The model organism as a system: integrating 'omics' data sets

abstract: Various technologies can be used to produce genome-scale, or 'omics', data sets that provide systems-level measurements for virtually all types of cellular components in a model organism. These data yield unprecedented views of the cellular inner workings. However, this abundance of information also presents many hurdles, the main one being the extraction of discernable biological meaning from multiple omics data sets. Nevertheless, researchers are rising to the challenge by using omics data integration to address fundamental biological questions that would increase our understanding of systems as a whole.


A Study of Machine Learning in Wireless Sensor Network


title: CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks

abstract: Verifying robustness of neural network classifiers has attracted great interests and attention due to the success of deep neural networks and their unexpected vulnerability to adversarial perturbations. Although finding minimum adversarial distortion of neural networks (with ReLU activations) has been shown to be an NP-complete problem, obtaining a non-trivial lower bound of minimum distortion as a provable robustness guarantee is possible. However, most previous works only focused on simple fully-connected layers (multilayer perceptrons) and were limited to ReLU activations. This motivates us to propose a general and efficient framework, CNN-Cert, that is capable of certifying robustness on general convolutional neural networks. Our framework is general – we can handle various architectures including convolutional layers, max-pooling layers, batch normalization layer, residual blocks, as well as general activation functions; our approach is efficient – by exploiting the special structure of convolutional layers, we achieve up to 17 and 11 times of speed-up compared to the state-of-the-art certification algorithms (e.g. Fast-Lin, CROWN) and 366 times of speed-up compared to the dual-LP approach while our algorithm obtains similar or even better verification bounds. In addition, CNN-Cert generalizes state-of-the-art algorithms e.g. Fast-Lin and CROWN. We demonstrate by extensive experiments that our method outperforms state-of-the-art lowerbound-based certification algorithms in terms of both bound quality and speed.


title: Limitations of the approximation capabilities of neural networks with one hidden layer

abstract: AbstractLets≥1 be an integer andW be the class of all functions having integrable partial derivatives on [0, 1]s. We are interested in the minimum number of neurons in a neural network with a single hidden layer required in order to provide a mean approximation order of a preassignedε>0 to each function inW. We prove that this number cannot be ::: $$\mathcal{O}( \in ^{ - s} log(1/ \in ))$$ ::: if a spline-like localization is required. This cannot be improved even if one allows different neurons to evaluate different activation functions, even depending upon the target function. Nevertheless, for anyδ>0, a network with ::: $$\mathcal{O}( \in ^{ - s - \delta } )$$ ::: neurons can be constructed to provide this order of approximation, with localization. Analogous results are also valid for otherLp norms.


title: A Survey on FPGA Virtualization

abstract: FPGA accelerators are being applied in various types of systems ranging from embedded systems to cloud computing for their high performance and energy efficiency. Given the scale of deployment, there is a need for efficient application development, resource management, and scalable systems, which make FPGA virtualization extremely important. Consequently, FPGA virtualization methods and hardware infrastructures have frequently been proposed in both academia and industry for addressing multi-tenancy execution, multi-FPGA acceleration, flexibility, resource management and security. In this survey, we identify and classify the various techniques and approaches into three main categories: 1)Resource level, 2)Node level, and 3)Multi-node level. In addition, we identify current trends and developments and highlight important future directions for FPGA virtualization which require further work.


abstract: TAMPERE UNIVERSITY OF TECHNOLOGY Master‘s Degree Programme in Signal Processing CAKIR, EMRE: Multilabel Sound Event Classification with Neural Networks Master of Science Thesis, 63 pages October 2014 Major: Signal Processing Examiner: Tuomas Virtanen, Heikki Huttunen


The Future of Psychiatry


title: Changes in serum C-reactive protein during complicated and uncomplicated measles virus infections.

abstract: Serum C-reactive protein levels become elevated coincident with the onset of the rash in patients with measles. Serum C-reactive protein elevations are prolonged in measles complicated by pneumonia and may show a second elevation in measles complicated by encephalitis.


title: Hierarchical ALS Algorithms for Nonnegative Matrix and 3D Tensor Factorization

abstract: In the paper we present new Alternating Least Squares (ALS) algorithms for Nonnegative Matrix Factorization (NMF) and their extensions to 3D Nonnegative Tensor Factorization (NTF) that are robust in the presence of noise and have many potential applications, including multi-way Blind Source Separation (BSS), multi-sensory or multi-dimensional data analysis, and nonnegative neural sparse coding. We propose to use local cost functions whose simultaneous or sequential (one by one) minimization leads to a very simple ALS algorithm which works under some sparsity constraints both for an under-determined (a system which has less sensors than sources) and overdetermined model. The extensive experimental results confirm the validity and high performance of the developed algorithms, especially with usage of the multi-layer hierarchical NMF. Extension of the proposed algorithm to multidimensional Sparse Component Analysis and Smooth Component Analysis is also proposed.


title: Fast and Robust Archetypal Analysis for Representation Learning

abstract: We revisit a pioneer unsupervised learning technique called archetypal analysis, which is related to successful data analysis methods such as sparse coding and non-negative matrix factorization. Since it was proposed, archetypal analysis did not gain a lot of popularity even though it produces more interpretable models than other alternatives. Because no efficient implementation has ever been made publicly available, its application to important scientific problems may have been severely limited. Our goal is to bring back into favour archetypal analysis. We propose a fast optimization scheme using an active-set strategy, and provide an efficient open-source implementation interfaced with Matlab, R, and Python. Then, we demonstrate the usefulness of archetypal analysis for computer vision tasks, such as codebook learning, signal classification, and large image collection visualization.


title: Example-based synthesis of 3D object arrangements

abstract: We present a method for synthesizing 3D object arrangements from examples. Given a few user-provided examples, our system can synthesize a diverse set of plausible new scenes by learning from a larger scene database. We rely on three novel contributions. First, we introduce a probabilistic model for scenes based on Bayesian networks and Gaussian mixtures that can be trained from a small number of input examples. Second, we develop a clustering algorithm that groups objects occurring in a database of scenes according to their local scene neighborhoods. These contextual categories allow the synthesis process to treat a wider variety of objects as interchangeable. Third, we train our probabilistic model on a mix of user-provided examples and relevant scenes retrieved from the database. This mixed model learning process can be controlled to introduce additional variety into the synthesized scenes. We evaluate our algorithm through qualitative results and a perceptual study in which participants judged synthesized scenes to be highly plausible, as compared to hand-created scenes.


title: Enterprise Software System Integration – An Architectural Perspective

abstract: The escalating development of Information Technology enables utilities to reorganize or migrate from their existing disparate software systems towards an integrated Enterprise Software System Infrastructure (ESSI) that embraces the total organization. Integration of software systems is expected to increase competitiveness and to cut costs. However, since utilities' present ESSI is heterogeneous as to type and technical platform, overlapping with regard to both data and functionality, and relying on ad-hoc low-level middleware, integration and management of ESSIs often turn out to be hazardous.This paper presents a comparative analysis of the architectural modeling capabilities of established notations widely used by object-oriented, information engineering and structured methods, applied to typical problems found in utilities' ESSI. Architectural descriptions may be used to visualize technical risks and opportunities in a utility's current and future ESSI, as well as to improve communication between different groups within the utility, e.g. management and technical staff, and between the utility and its vendors of software systems.


title: A Sub 1-V Constant $G_{m}$– $C$ Switched-Capacitor Current Source

abstract: A switched-capacitor bias that provides a constant Gm-C characteristic over process and temperature variation is presented. The bias can be adapted for use with subthreshold circuits, or circuits in strong inversion. It uses eight transistors, five switches, and three capacitors, and performs with supply voltages less than 0.9 V. Theoretical output current is derived, and stability analysis is performed. Simulated results showing an op-amp with very consistent pulse response are presented


title: Machine learning in sentiment reconstruction of the simulated stock market

abstract: In this paper we continue the study of the simulated stock market framework defined by the driving sentiment processes. We focus on the market environment driven by the buy/sell trading sentiment process of the Markov chain type. We apply the methodology of the Hidden Markov Models and the Recurrent Neural Networks to reconstruct the transition probabilities matrix of the Markov sentiment process and recover the underlying sentiment states from the observed stock price behavior. We demonstrate that the Hidden Markov Model can successfully recover the transition probabilities matrix for the hidden sentiment process of the Markov Chain type. We also demonstrate that the Recurrent Neural Network can successfully recover the hidden sentiment states from the observed simulated stock price time series.


title: Logging user activities and sensor data on mobile devices

abstract: The goal of this work is a unified approach for collecting data about user actions on mobile devices in an appropriate granularity for user modeling. To fulfill this goal, we have designed and implemented a framework for mobile user activity logging on Windows Mobile PDAs based on the MyExperience project. We have extended this system with hardware and software sensors to monitor phone calls, messaging, peripheral devices, media players, GPS sensors, networking, personal information management, web browsing, system behavior and applications usage. It is possible to detect when, at which location and how a user employs an application or accesses certain information, for example. To evaluate our framework, we applied it in several usage scenarios. We were able to validate that our framework is able to collect meaningful information about the user. We also outline preliminary work on analyzing the logged data sets.


title: A positive association between anxiety disorders and cannabis use or cannabis use disorders in the general population- a meta-analysis of 31 studies

abstract: The aim of the current study was to investigate the association between anxiety and cannabis use/cannabis use disorders in the general population. A total of N = 267 studies were identified from a systematic literature search (any time- March 2013) of Medline and PsycInfo databases, and a hand search. The results of 31 studies (with prospective cohort or cross-sectional designs using non-institutionalised cases) were analysed using a random-effects meta-analysis with the inverse variance weights. Lifetime or past 12-month cannabis use, anxiety symptoms, and cannabis use disorders (CUD; dependence and/or abuse/harmful use) were classified according to DSM/ICD criteria or scores on standardised scales. There was a small positive association between anxiety and either cannabis use (OR = 1.24, 95% CI: 1.06-1.45, p = .006; N = 15 studies) or CUD (OR = 1.68, 95% CI: 1.23-2.31, p = .001; N = 13 studies), and between comorbid anxiety + depression and cannabis use (OR = 1.68, 95% CI: 1.17-2.40, p = .004; N = 5 studies). The positive associations between anxiety and cannabis use (or CUD) were present in subgroups of studies with ORs adjusted for possible confounders (substance use, psychiatric illness, demographics) and in studies with clinical diagnoses of anxiety. Cannabis use at baseline was significantly associated with anxiety at follow-up in N = 5 studies adjusted for confounders (OR = 1.28, 95% CI: 1.06-1.54, p = .01). The opposite relationship was investigated in only one study. There was little evidence for publication bias. Anxiety is positively associated with cannabis use or CUD in cohorts drawn from some 112,000 non-institutionalised members of the general population of 10 countries.


title: Touching an Android robot: Would you do it and how?

abstract: As the presence of robots in everyday life becomes more common, it is expected that interactions between humans and robots will include the modality of touch. To date, however, little research has been conducted on tactile interactions between humans and anthropomorphic robots. This study investigates human induced tactile interaction with an android robot. To facilitate data analysis, existing touch dictionaries were revised and adapted for the specifics of human-android interaction. By measuring the participants' personality traits and their perception of the robot, it was found that some tactile gestures are related to participants' personality traits, such as neuroticism and extroversion, and others to robot attributes such as anthropomorphism and animacy. To the best of our knowledge this is the first study to report on how people touch an android robot, and the correlation that exists between the tactile gestures used and the participants' personality traits. Possible implications are discussed.


title: Forecasting stock market short-term trends using a neuro-fuzzy based methodology

abstract: A neuro-fuzzy system composed of an Adaptive Neuro Fuzzy Inference System (ANFIS) controller used to control the stock market process model, also identified using an adaptive neuro-fuzzy technique, is derived and evaluated for a variety of stocks. Obtained results challenge the weak form of the Efficient Market Hypothesis (EMH) by demonstrating much improved and better predictions, compared to other approaches, of short-term stock market trends, and in particular the next day's trend of chosen stocks. The ANFIS controller and the stock market process model inputs are chosen based on a comparative study of fifteen different combinations of past stock prices performed to determine the stock market process model inputs that return the best stock trend prediction for the next day in terms of the minimum Root Mean Square Error (RMSE). Gaussian-2 shaped membership functions are chosen over bell shaped Gaussian and triangular ones to fuzzify the system inputs due to the lowest RMSE. Real case studies using data from emerging and well developed stock markets - the Athens and the New York Stock Exchange (NYSE) - to train and evaluate the proposed system illustrate that compared to the ''buy and hold'' strategy and several other reported methods, the proposed approach and the forecasting trade accuracy are by far superior.


Predicting Traffic Accidents Through Heterogeneous Urban Data : A Case Study


title: Hardware-Based Trusted Computing Architectures for Isolation and Attestation

abstract: Attackers target many different types of computer systems in use today, exploiting software vulnerabilities to take over the device and make it act maliciously. Reports of numerous attacks have been published, against the constrained embedded devices of the Internet of Things, mobile devices like smartphones and tablets, high-performance desktop and server environments, as well as complex industrial control systems. Trusted computing architectures give users and remote parties like software vendors guarantees about the behaviour of the software they run, protecting them against software-level attackers. This paper defines the security properties offered by them, and presents detailed descriptions of twelve hardware-based attestation and isolation architectures from academia and industry. We compare all twelve designs with respect to the security properties and architectural features they offer. The presented architectures have been designed for a wide range of devices, supporting different security properties.


title: Nanosecond-Precision Time-of-Arrival Estimation for Aircraft Signals with Low-Cost SDR Receivers

abstract: Precise Time-of-Arrival (TOA) estimations of aircraft and drone signals are important for a wide set of applications including aircraft/drone tracking, air traffic data verification, or self-localization. Our focus in this work is on TOA estimation methods that can run on low-cost software-defined radio (SDR) receivers, as widely deployed in Mode S / ADS-B crowdsourced sensor networks such as the OpenSky Network. We evaluate experimentally classical TOA estimation methods which are based on a cross-correlation with a reconstructed message template and find that these methods are not optimal for such signals. We propose two alternative methods that provide superior results for real-world Mode S / ADS-B signals captured with low-cost SDR receivers. The best method achieves a standard deviation error of 1.5 ns.


title: A further step forward in measuring journals' scientific prestige: The SJR2 indicator

abstract: A new size-independent indicator of scientific journal prestige, the SJR2 indicator, is proposed. This indicator takes into account not only the prestige of the citing scientific journal but also its closeness to the cited journal using the cosine of the angle between the vectors of the two journals’ cocitation profiles. To eliminate the size effect, the accumulated prestige is divided by the fraction of the journal's citable documents, thus eliminating the decreasing tendency of this type of indicator and giving meaning to the scores. Its method of computation is described, and the results of its implementation on the Scopus 2008 dataset is compared with those of an ad hoc Journal Impact Factor, JIF(3y), and SNIP, the comparison being made both overall and within specific scientific areas. All three, the SJR2 indicator, the SNIP indicator and the JIF distributions, were found to fit well to a logarithmic law. Although the three metrics were strongly correlated, there were major changes in rank. In addition, the SJR2 was distributed more equalized than the JIF by Subject Area and almost as equalized as the SNIP, and better than both at the lower level of Specific Subject Areas. The incorporation of the cosine increased the values of the flows of prestige between thematically close journals.


Investigation of Indian English Speech Recognition using CMU Sphinx


title: Cell Index Database (CELLX): A Web Tool for Cancer Precision Medicine

abstract: The Cell Index Database, (CELLX) (http://cellx.sourceforge.net) provides a computational framework for integrating expression, copy number variation, mutation, compound activity, and meta data from cancer cells. CELLX provides the computational biologist a quick way to perform routine analyses as well as the means to rapidly integrate data for offline analysis. Data is accessible through a web interface which utilizes R to generate plots and perform clustering, correlations, and statistical tests for associations within and between data types for ~20,000 samples from TCGA, CCLE, Sanger, GSK, GEO, GTEx, and other public sources. We show how CELLX supports precision oncology through indications discovery, biomarker evaluation, and cell line screening analysis.


title: A Deep Architecture for Matching Short Texts

abstract: Many machine learning problems can be interpreted as learning for matching two types of objects (e.g., images and captions, users and products, queries and documents, etc.). The matching level of two objects is usually measured as the inner product in a certain feature space, while the modeling effort focuses on mapping of objects from the original space to the feature space. This schema, although proven successful on a range of matching tasks, is insufficient for capturing the rich structure in the matching process of more complicated objects. In this paper, we propose a new deep architecture to more effectively model the complicated matching relations between two objects from heterogeneous domains. More specifically, we apply this model to matching tasks in natural language, e.g., finding sensible responses for a tweet, or relevant answers to a given question. This new architecture naturally combines the localness and hierarchy intrinsic to the natural language problems, and therefore greatly improves upon the state-of-the-art models.


title: Unification: a multidisciplinary survey

abstract: The unification problem and several variants are presented. Various algorithms and data structures are discussed. Research on unification arising in several areas of computer science is surveyed; these areas include theorem proving, logic programming, and natural language processing. Sections of the paper include examples that highlight particular uses of unification and the special problems encountered. Other topics covered are resolution, higher order logic, the occur check, infinite terms, feature structures, equational theories, inheritance, parallel algorithms, generalization, lattices, and other applications of unification. The paper is intended for readers with a general computer science background—no specific knowledge of any of the above topics is assumed.


title: Dental Extrusion with Orthodontic Miniscrew Anchorage: A Case Report Describing a Modified Method

abstract: In recent years, the skeletal anchorage through miniscrews has expanded the treatment options in orthodontics (Yamaguchi et al., 2012). We hereby present a modified method for tooth extrusion for cases where crown-lengthening surgery is contraindicated for aesthetic reasons. This modified method uses three orthodontic appliances: a mini-implant, an orthodontic wire, and a bracket. The aim of this case report was to increase the length of the clinical crown of a fractured tooth (tooth 23) by means of an orthodontic extrusion with the modified method of Roth and Diedrich.


title: Tap-Tap and Pay (TTP): Preventing Man-In-The-Middle Attacks in NFC Payment Using Mobile Sensors

abstract: • Using ambient sensors: to measure the characteristics of the surrounding environment, such as light, sound, temperature and GPS. The underlying assumption is that the malicious and honest readers should be in two different locations with different environments. • Problem: All these solutions will not work if the two readers are located in the same environment (e.g., in two nearby stalls in the same mall). In this work, we present TTP as a countermeasure that defends against the reader-and-ghost attack even when the malicious and honest readers are in the same environment. Our solution is based on the following observation: As the result of physical tapping between a pair of sensor-rich devices (say two NFC-enabled phones acting as a card and a reader), the tapping event causes unpredictable but strongly correlated vibrations at both sides, which are hard to reproduce and can be reliably measured by accelerometer sensors.