title: Studying just-in-time defect prediction using cross-project models

abstract: Unlike traditional defect prediction models that identify defect-prone modules, Just-In-Time (JIT) defect prediction models identify defect-inducing changes. As such, JIT defect models can provide earlier feedback for developers, while design decisions are still fresh in their minds. Unfortunately, similar to traditional defect models, JIT models require a large amount of training data, which is not available when projects are in initial development phases. To address this limitation in traditional defect prediction, prior work has proposed cross-project models, i.e., models learned from other projects with sufficient history. However, cross-project models have not yet been explored in the context of JIT prediction. Therefore, in this study, we empirically evaluate the performance of JIT models in a cross-project context. Through an empirical study on 11 open source projects, we find that while JIT models rarely perform well in a cross-project context, their performance tends to improve when using approaches that: (1) select models trained using other projects that are similar to the testing project, (2) combine the data of several other projects to produce a larger pool of training data, and (3) combine the models of several other projects to produce an ensemble model. Our findings empirically confirm that JIT models learned using other projects are a viable solution for projects with limited historical data. However, JIT models tend to perform best in a cross-project context when the data used to learn them are carefully selected.


title: Video Visual Relation Detection

abstract: As a bridge to connect vision and language, visual relations between objects in the form of relation triplet $langle subject,predicate,object\rangle$, such as "person-touch-dog'' and "cat-above-sofa'', provide a more comprehensive visual content understanding beyond objects. In this paper, we propose a novel vision task named Video Visual Relation Detection (VidVRD) to perform visual relation detection in videos instead of still images (ImgVRD). As compared to still images, videos provide a more natural set of features for detecting visual relations, such as the dynamic relations like "A-follow-B'' and "A-towards-B'', and temporally changing relations like "A-chase-B'' followed by "A-hold-B''. However, VidVRD is technically more challenging than ImgVRD due to the difficulties in accurate object tracking and diverse relation appearances in video domain. To this end, we propose a VidVRD method, which consists of object tracklet proposal, short-term relation prediction and greedy relational association. Moreover, we contribute the first dataset for VidVRD evaluation, which contains 1,000 videos with manually labeled visual relations, to validate our proposed method. On this dataset, our method achieves the best performance in comparison with the state-of-the-art baselines.


title: A New Angle on L2 Regularization

abstract: Imagine two high-dimensional clusters and a hyperplane separating them. Consider in particular the angle between: the direction joining the two clusters' centroids and the normal to the hyperplane. In linear classification, this angle depends on the level of L2 regularization used. Can you explain why?


title: A hybrid system architecture for the readout of a printed physical unclonable function

abstract: Uncontrollable manufacturing variations in electrical hardware circuits can be exploited as Physical Unclonable Functions (PUFs). Herein, we present a Printed Electronics (PE)-based PUF system architecture. Our proposed Differential Circuit PUF (DiffC-PUF) is a hybrid system, combining silicon-based and PE-based electronic circuits. The novel approach of the DiffC-PUF architecture is to provide a specially designed real hardware system architecture, that enables the automatic readout of interchangeable printed DiffC-PUF core circuits. The silicon-based addressing and evaluation circuit supplies and controls the printed PUF core and ensures seamless integration into silicon-based smart systems. Major objectives of our work are interconnected applications for the Internet of Things (IoT).


title: A new approach for robot motion planning using rapidly-exploring Randomized Trees

abstract: In the last few years, car-like robots became increasingly important. Thus, motion planning algorithms for this kind of problem are needed more than ever. Unfortunately, this problem is computational difficult and so probabilistic approaches like Probabilistic Roadmaps or Rapidly-exploring Randomized Trees are often used in this context. This paper introduces a new concept for robot motion planning especially for car-like robots based on Rapidly-exploring Randomized Trees. In contrast to the conventional method, the presented approach uses a pre-computed auxiliary path to improve the distribution of random states. The main contribution of this approach is the significantly increased quality of the computed path. A proof-of-concept implementation evaluates the quality and performance of the proposed concept.


title: Universal Dependency Parsing for Hindi-English Code-switching

abstract: Code-switching is a phenomenon of mixing grammatical structures of two or more languages under varied social constraints. The code-switching data differ so radically from the benchmark corpora used in NLP community that the application of standard technologies to these data degrades their performance sharply. Unlike standard corpora, these data often need to go through additional processes such as language identification, normalization and/or back-transliteration for their efficient processing. In this paper, we investigate these indispensable processes and other problems associated with syntactic parsing of code-switching data and propose methods to mitigate their effects. In particular, we study dependency parsing of code-switching data of Hindi and English multilingual speakers from Twitter. We present a treebank of Hindi-English code-switching tweets under Universal Dependencies scheme and propose a neural stacking model for parsing that efficiently leverages part-of-speech tag and syntactic tree annotations in the code-switching treebank and the preexisting Hindi and English treebanks. We also present normalization and back-transliteration models with a decoding process tailored for code-switching data. Results show that our neural stacking parser is 1.5% LAS points better than the augmented parsing model and our decoding process improves results by 3.8% LAS points over the first-best normalization and/or back-transliteration.


title: Deep Learning: A Tool for Computational Nuclear Physics

abstract: In recent years, several successful applications of the Artificial Neural Networks (ANNs) have emerged in nuclear physics and high-energy physics, as well as in biology, chemistry, meteorology, and other fields of science. A major goal of nuclear theory is to predict nuclear structure and nuclear reactions from the underlying theory of the strong interactions, Quantum Chromodynamics (QCD). With access to powerful High Performance Computing (HPC) systems, several ab initio approaches, such as the No-Core Shell Model (NCSM), have been developed to calculate the properties of atomic nuclei. However, to accurately solve for the properties of atomic nuclei, one faces immense theoretical and computational challenges. The present study proposes a feed-forward ANN method for predicting the properties of atomic nuclei like ground state energy and ground state point proton root-mean-square (rms) radius based on NCSM results in computationally accessible basis spaces. The designed ANNs are sufficient to produce results for these two very different observables in 6Li from the ab initio NCSM results in small basis spaces that satisfy the theoretical physics condition: independence of basis space parameters in the limit of extremely large matrices. We also provide comparisons of the results from ANNs with established methods of estimating the results in the infinite matrix limit.


title: Elastic Structure Preserving (ESP) Control for Compliantly Actuated Robots

abstract: Physical compliance can be considered one of the key technical properties a robot should exhibit to increase its mechanical robustness. In addition, the accompanying temporal energy-storing capabilities enable explosive and energy efficient cyclic motions. But these advantages come at a price, as compliance introduces unwanted intrinsic oscillatory dynamics, underactuation, and reduces the natural frequency of the plant. These aspects make control of the link configuration variables a challenging task. This paper presents two novel control methods for implementing link-side motion tracking capabilities and injecting a desired damping characteristic to suppress link vibrations along the reference trajectory for compliantly actuated robots with nonlinear elastic characteristics. We prove their uniform global asymptotic stability by invoking a theorem by Matrosov. Both approaches, namely elastic structure preserving (ESP) and ESP+, have in common that they preserve the link-side inertial properties and the elastic structure of the original plant dynamics, hence the name ESP control. Apart from that, ESP control focuses on preserving the inertial properties of motor dynamics. While ESP+ control aims at minimizing the dynamic shaping on the motor side. The performance of the feedback control laws have been evaluated on the Hand Arm System from the German Aerospace Center (DLR), a variable stiffness robot arm, where the stiffness in each of its joints is highly nonlinear. To the best of our knowledge, this is the first experimentally validated tracking controller for compliantly actuated, multijoint robots with nonlinear elastic elements.


title: Practical considerations of series-series and series-parallel compensation topologies in wireless power transfer system application

abstract: Wireless power transfer (WPT) system has the advantages of safety, reliability, convenience and so on. In order to improve the output power and reduce the input capacity of the system, the energy transfer coils should be compensated by electrical measures in practical applications. In this paper, series-series (SS) and series-parallel (SP) compensation topologies of primary and secondary coils were respectively analyzed based on the theory of mutual inductance in detail. The relationship was obtained between output power, transmission efficiency and the load, frequency and coupling coefficient by Matlab simulation. The simulation results show that the output power is more sensitive to the change of frequency, and SS compensation topology is more suitable for small load system while SP is appropriate for large load system. For a fixed transmission distance, there is an optimal load to maximize the output power and transmission efficiency. And the smaller the load, the frequency splitting is more easily for SS topology. In contrary, the larger the load, the frequency splitting is more easily for SP topology. In addition, the transmission efficiency increases with the decrease of transmission distance. So this paper can give a guideline for selecting the suitable compensation topology under the consideration of practical applications.


title: Analysis of Various Sentiment Classification Techniques

abstract: Sentiment analysis is an ongoing research area in the field of text mining. People post their review in form of unstructured data so opinion extraction provides overall opinion of reviews so it does best job for customer, people, organization etc. The main aim of this paper is to find out approaches that generate output with good accuracy. This paper presents recent updates on papers related to classification of sentiment analysis of implemented various approaches and algorithms. The main contribution of this paper is to give idea about that careful feature selection and existing classification approaches can give better accuracy.


title: Monolithic CMOS MEMS Oscillator Circuit for Sensing in the Attogram Range

abstract: This letter presents the design, fabrication, and demonstration of a CMOS/microelectromechanical system (MEMS) electrostatically self-excited resonator based on a submicrometer-scale cantilever with ~1 ag/Hz mass sensitivity. The mechanical resonator is the frequency-determining element of an oscillator circuit monolithically integrated and implemented in a commercial 0.35 mum CMOS process. The oscillator is based on a Pierce topology adapted for the MEMS resonator that presents a mechanical resonance frequency of ~6 MHz, a relative low quality factor of 100, and a large motional resistance of ~25 M. The MEMS oscillator has a frequency stability of ~1.6 Hz resulting in a mass resolution of ~1 ag (1 ag = 10-18 g in air conditions.


Simultaneous Generalizations of the Theorems of Ceva and Menelaus


title: Solar ECG acquisition system

abstract: Cardiovascular (CVD) disease causes many people dying every year and these are sudden events hence can cause immediate death if not diagnosed early. Electrocardiogram (ECG) is the best available option today for diagnosis of heart and problems related to it. But conventional methods for ECG monitoring are based on line voltage so, it can contain noise and ECG signals are very sensitive to noise. It should be accurate for the diagnosis of the problems related to heart. Hence the system proposed here is based on solar power. The line voltage noise can be removed by this and the accuracy of signal increases. Today also there are many areas present in the world where electricity is not present. Such remote areas are not accessible for the advanced equipments like ECG monitoring device. Also, the tests with these equipments are so expensive, that poor people cannot afford it. Hence the system proposed here will provide the solution for this problem, that anyone can observe their own ECG. The system mainly consists of solar cell and batteries which provides the power to the acquisition system. The system output has been checked on digital CRO. It gives the ECG signal waveform. Testing of the system is done with digital CRO later the output can be provide to the mobile phone and further study of the ECG can be done with that. The initial stages of heart attack or heart problems can be detected with this and accordingly the help can be provided.


A S TUDY OF NON-LINEAR COMPLEMENTARY FILTER DESIGN FOR KINEMATIC SYSTEMS ON THE SPECIAL ORTHOGONAL GROUP


title: Compact UWB Bandpass Filter With Ultra Narrow Notched Band

abstract: A compact ultra-wideband (UWB) bandpass filter with an ultra narrow notched band is proposed using a hybrid microstrip and coplanar waveguide (CPW) structure. The CPW detached-mode resonator (DMR) composed of a quarter-wavelength (?/4) nonuniform CPW resonator with a short-stub and a ?/4 single-mode CPW resonator (SMCR) can allocate three split frequencies at the lower end, middle, higher end of the UWB passband. The conventional broadside-coupled microstrip/CPW structure is introduced to improve the bandwidth enhancement around the split frequencies, which leads to good UWB operation. To avoid the interferences such as WLAN signals, the ?/4 meander slot-line structure embedded in the DMR is employed to obtain the notched band inside the UWB passband. The design is then verified by experiment. Good passband and stopband performances are achieved. Specifically, the fabricated filter has a 10 dB notched fractional bandwidth (FBW) of 2.06% at the notched center frequency of 5.80 GHz.


title: Soft Rasterizer: Differentiable Rendering for Unsupervised Single-View Mesh Reconstruction

abstract: Rendering is the process of generating 2D images from 3D assets, simulated in a virtual environment, typically with a graphics pipeline. By inverting such renderer, one can think of a learning approach to predict a 3D shape from an input image. However, standard rendering pipelines involve a fundamental discretization step called rasterization, which prevents the rendering process to be differentiable, hence suitable for learning. We present the first non-parametric and truly differentiable rasterizer based on silhouettes. Our method enables unsupervised learning for high-quality 3D mesh reconstruction from a single image. We call our framework `soft rasterizer' as it provides an accurate soft approximation of the standard rasterizer. The key idea is to fuse the probabilistic contributions of all mesh triangles with respect to the rendered pixels. When combined with a mesh generator in a deep neural network, our soft rasterizer is able to generate an approximated silhouette of the generated polygon mesh in the forward pass. The rendering loss is back-propagated to supervise the mesh generation without the need of 3D training data. Experimental results demonstrate that our approach significantly outperforms the state-of-the-art unsupervised techniques, both quantitatively and qualitatively. We also show that our soft rasterizer can achieve comparable results to the cutting-edge supervised learning method and in various cases even better ones, especially for real-world data.


title: Revised Adomian decomposition method for solving systems of ordinary and fractional differential equations

abstract: A modification of the Adomian decomposition method applied to systems of linear/nonlinear ordinary and fractional differential equations, which yields a series solution with accelerated convergence, has been presented. Illustrative examples have been given.


title: On Task Assignment for Real-Time Reliable Crowdsourcing

abstract: With the rapid growth of mobile smartphone users, several commercial mobile companies have exploited crowd sourcing as an effective approach to collect and analyze data, to improve their services. In a crowd sourcing system, "human workers" are enlisted to perform small tasks, that are difficult to be automated, in return for some monetary compensation. This paper presents our crowd sourcing system that seeks to address the challenge of determining the most efficient allocation of tasks to the human crowd. The goal of our algorithm is to efficiently determine the most appropriate set of workers to assign to each incoming task, so that the real-time demands are met and high quality results are returned. We empirically evaluate our approach and show that our system effectively meets the requested demands, has low overhead and can improve the number of tasks processed under the defined constraints over 71% compared to traditional approaches.


title: Physicomimetics for Mobile Robot Obstacle Avoidance

abstract: The ability of robots to promptly avoid obstacles and reach a goal while accurately maintaining a formation is extremely important for many swarm robotic tasks such as terrain analysis, search and rescue, and chemical plume source tracing. In prior work, using simulation, we established how Physicomimetics can be used to maintain swarms of mobile robots in formations that move towards a goal through an obstacle field. In this paper, we demonstrate the obstacle avoidance capability of Physicomimetics control algorithm using two different mobile robot platforms with limited hardware, Maxelbot and X80Pro.


title: Improve collaborative filtering through bordered block diagonal form matrices

abstract: Collaborative Filtering-based recommendation algorithms have achieved widespread success on the Web, but little work has been performed to investigate appropriate user-item relationship structures of rating matrices. This paper presents a novel and general collaborative filtering framework based on (Approximate) Bordered Block Diagonal Form structure of user-item rating matrices. We show formally that matrices in (A)BBDF structures correspond to community detection on the corresponding bipartite graphs, and they reveal relationships among users and items intuitionally in recommendation tasks. By this framework, general and special interests of a user are distinguished, which helps to improve prediction accuracy in collaborative filtering tasks. Experimental results on four real-world datasets, including the Yahoo! Music dataset, which is currently the largest, show that the proposed framework helps many traditional collaborative filtering algorithms, such as User-based, Item-based, SVD and NMF approaches, to make more accurate rating predictions. Moreover, by leveraging smaller and denser submatrices to make predictions, this framework contributes to the scalability of recommender systems.


title: ARMlock: Hardware-based Fault Isolation for ARM

abstract: Software fault isolation (SFI) is an effective mechanism to confine untrusted modules inside isolated domains to protect their host applications. Since its debut, researchers have proposed different SFI systems for many purposes such as safe execution of untrusted native browser plugins. However, most of these systems focus on the x86 architecture. Inrecent years, ARM has become the dominant architecture for mobile devices and gains in popularity in data centers.Hence there is a compellingneed for an efficient SFI system for the ARM architecture. Unfortunately, existing systems either have prohibitively high performance overhead or place various limitations on the memory layout and instructions of untrusted modules. In this paper, we propose ARMlock, a hardware-based fault isolation for ARM. It uniquely leverages the memory domain support in ARM processors to create multiple sandboxes. Memory accesses by the untrusted module (including read, write, and execution) are strictly confined by the hardware,and instructions running inside the sandbox execute at the same speed as those outside it. ARMlock imposes virtually no structural constraints on untrusted modules. For example, they can use self-modifying code, receive exceptions, and make system calls. Moreover, system calls can be interposed by ARMlock to enforce the policies set by the host. We have implemented a prototype of ARMlock for Linux that supports the popular ARMv6 and ARMv7 sub-architecture. Our security assessment and performance measurement show that ARMlock is practical, effective, and efficient.


title: Probabilistic rectangular-shape estimation for extended object tracking

abstract: This paper presents new methods for the representation of a vehicle's contour by an oriented rectangle, also known as the bounding box. The parameters of this bounding box are originally modeled probabilistically by a single multivariate Gaussian distribution. This approach incorporates the sensor uncertainties, where the problem of estimating the parameters of this distribution from range measurements is addressed. Additionally, a transformation of the parameters into the measurement space is introduced. This representation is used to perform probabilistic updates by new measurements. The proposed method can handle strong parameter changes which might be affected by object occlusion. Experiments on real-world data demonstrate the robustness and accuracy of the probabilistic approach integrated in a tracking framework incorporating the Doppler measurements of automotive radars and laser measurements.


title: Populous: a tool for building OWL ontologies from templates

abstract: BackgroundOntologies are being developed for the life sciences to standardise the way we describe and interpret the wealth of data currently being generated. As more ontology based applications begin to emerge, tools are required that enable domain experts to contribute their knowledge to the growing pool of ontologies. There are many barriers that prevent domain experts engaging in the ontology development process and novel tools are needed to break down these barriers to engage a wider community of scientists.ResultsWe present Populous, a tool for gathering content with which to construct an ontology. Domain experts need to add content, that is often repetitive in its form, but without having to tackle the underlying ontological representation. Populous presents users with a table based form in which columns are constrained to take values from particular ontologies. Populated tables are mapped to patterns that can then be used to automatically generate the ontology's content. These forms can be exported as spreadsheets, providing an interface that is much more familiar to many biologists.ConclusionsPopulous's contribution is in the knowledge gathering stage of ontology development; it separates knowledge gathering from the conceptualisation and axiomatisation, as well as separating the user from the standard ontology authoring environments. Populous is by no means a replacement for standard ontology editing tools, but instead provides a useful platform for engaging a wider community of scientists in the mass production of ontology content.


title: A more powerful exact test of noninferiority from binary matched-pairs data.

abstract: Assessing the therapeutic noninferiority of one medical treatment compared with another is often based on the difference in response rates from a matched binary pairs design. This paper develops a new exact unconditional test for noninferiority that is more powerful than available alternatives. There are two new elements presented in this paper. First, we introduce the likelihood ratio statistic as an alternative to the previously proposed score statistic of Nam (Biometrics 1997; 53:1422-1430). Second, we eliminate the nuisance parameter by estimation followed by maximization as an alternative to the partial maximization of Berger and Boos (Am. Stat. Assoc. 1994; 89:1012-1016) or traditional full maximization. Based on an extensive numerical study, we recommend tests based on the score statistic, the nuisance parameter being controlled by estimation followed by maximization.


title: Virtual Reality Exposure Therapy for World Trade Center Post-traumatic Stress Disorder: A Case Report

abstract: Done properly by experienced therapists, re-exposure to memories of traumatic events via imaginal exposure therapy can lead to a reduction of Post-traumatic Stress Disorder (PTSD) symptoms. Exposure helps the patient process and habituate to memories and strong emotions associated with the traumatic event: memories and emotions they have been carefully avoiding. But many patients are unwilling or unable to self-generate and re-experience painful emotional images. The present case study describes the treatment of a survivor of the World Trade Center (WTC) attack of 9-11-01 who had developed acute PTSD. After she failed to improve with traditional imaginal exposure therapy, we sought to increase emotional engagement and treatment success using virtual reality (VR) exposure therapy. Over the course of six 1-h VR exposure therapy sessions, we gradually and systematically exposed the PTSD patient to virtual planes flying over the World Trade Center, jets crashing into the World Trade Center with animated explo...


title: Effect of three cueing devices for people with Parkinson's disease with gait initiation difficulties

abstract: Abstract Background Freezing of gait (FOG) remains one of the most common debilitating aspects of Parkinson's disease and has been linked to injuries, falls and reduced quality of life. Although commercially available portable cueing devices exist claiming to assist with overcoming freezing; their immediate effectiveness in overcoming gait initiation failure is currently unknown. This study investigated the effects of three different types of cueing device in people with Parkinson's disease who experience freezing. Methods Twenty participants with idiopathic Parkinson's disease who experienced freezing during gait but who were able to walk short distances indoors independently were recruited. At least three attempts at gait initiation were recorded using a 10 camera Qualisys motion analysis system and four force platforms. Test conditions were; Laser Cane, sound metronome, vibrating metronome, walking stick and no intervention. Results During testing 12 of the 20 participants had freezing episodes, from these participants 100 freezing and 91 non-freezing trials were recorded. Clear differences in the movement patterns were seen between freezing and non-freezing episodes. The Laser Cane was most effective cueing device at improving the forwards/backwards and side to side movement and had the least number of freezing episodes. The walking stick also showed significant improvements compared to the other conditions. The vibration metronome appeared to disrupt movement compared to the sound metronome at the same beat frequency. Conclusion This study identified differences in the movement patterns between freezing episodes and non-freezing episodes, and identified immediate improvements during gait initiation when using the Laser Cane over the other interventions.


title: On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms

abstract: Outlier detection is a fundamental issue in data mining, specifically in fraud detection, network intrusion detection, network monitoring, etc. SmartSifter is an outlier detection engine addressing this problem from the viewpoint of statistical learning theory. This paper provides a theoretical basis for SmartSifter and empirically demonstrates its effectiveness. SmartSifter detects outliers in an on-line process through the on-line unsupervised learning of a probabilistic model (using a finite mixture model) of the information source. Each time a datum is input SmartSifter employs an on-line discounting learning algorithm to learn the probabilistic model. A score is given to the datum based on the learned model with a high score indicating a high possibility of being a statistical outlier. The novel features of SmartSifter are: (1) it is adaptive to non-stationary sources of datas (2) a score has a clear statistical/information-theoretic meanings (3) it is computationally inexpensives and (4) it can handle both categorical and continuous variables. An experimental application to network intrusion detection shows that SmartSifter was able to identify data with high scores that corresponded to attacks, with low computational costs. Further experimental application has identified a number of meaningful rare cases in actual health insurance pathology data from Australia's Health Insurance Commission.


title: Isolation and characterization of polymorphic microsatellite loci from aerial yam (Dioscorea bulbifera L.)

abstract: Dioscorea bulbifera L. is widely distributed in pantropical regions along the equator. The taxonomic treatment of this species is ambiguous due to its extreme polymorphic morphological characters. In order to provide tools to facilitate the study of genetic diversity, population structure, patterns of gene flow, and the mating system of this species, and to assess intraspecific variability and relationships in D. bulbifera, 14 novel polymorphic microsatellite loci were developed using the dual- suppression PCR technique. The number of alleles per locus ranged from 4 to 17, with an average of 9.93. The mean observed heterozygosities were 0.7327 and 0.7223, and the mean Shannon-Wiener indices were 1.6431 and 1.811 in the Nanjing and Nanchong populations, respectively. All novel microsatellite loci showed high levels of polymorphism, indicating that these markers offer great potential significance and profound influence for future studies of this species.


title: [Psychosocial problems in the elderly in Rotterdam and point of departure for prevention].

abstract: The aim of this study is to examine the prevalence of psycho-social problems among the elderly in order to plan prevention-programs. A questionnaire was sent in 1990 to a sample of 1989 inhabitants of the city of Rotterdam aged 65-79 years living on their own, drawn at random from the municipal registrar's office (response 58%). The results show that one third of the respondents experiences psycho-social problems. Groups at risk are: women, men who live alone, those with a low socio-economic status, people who live on their own but receive limited support from institutions. These socio-demographic variables explain only 5% of the variance in psycho-social well-being. Risk-indicators for psycho-social problems are problems in the performance of activities of daily living (ADL) and household-activities (HHA) as well as decreased social support. The relationship between satisfaction with living environment and psycho-social problems is affected by these risk-indicators. Elderly experiencing only psycho-social problems and those experiencing only ADL/HHA-problems visited their general practitioner during the last three months just frequently. Elderly experiencing both psycho-social and ADL/HHA-problems most frequently visited their GP. More elderly in Rotterdam compared to elderly in some other Dutch cities and to normscores seem to have psycho-social problems.


title: Predicting the demographics of twitter users from website traffic data

abstract: Understanding the demographics of users of online social networks has important applications for health, marketing, and public messaging. In this paper, we predict the demographics of Twitter users based on whom they follow. Whereas most prior approaches rely on a supervised learning approach, in which individual users are labeled with demographics, we instead create a distantly labeled dataset by collecting audience measurement data for 1,500 websites (e.g., 50% of visitors to gizmodo.com are estimated to have a bachelor's degree). We then fit a regression model to predict these demographics using information about the followers of each website on Twitter. The resulting average held-out correlation is .77 across six different variables (gender, age, ethnicity, education, income, and child status). We additionally validate the model on a smaller set of Twitter users labeled individually for ethnicity and gender, finding performance that is surprisingly competitive with a fully supervised approach.


title: The logic of Simpson’s paradox

abstract: There are three distinct questions associated with Simpson’s paradox. (i) Why or in what sense is Simpson’s paradox a paradox? (ii) What is the proper analysis of the paradox? (iii) How one should proceed when confronted with a typical case of the paradox? We propose a “formal” answer to the first two questions which, among other things, includes deductive proofs for important theorems regarding Simpson’s paradox. Our account contrasts sharply with Pearl’s causal (and questionable) account of the first two questions. We argue that the “how to proceed question?” does not have a unique response, and that it depends on the context of the problem. We evaluate an objection to our account by comparing ours with Blyth’s account of the paradox. Our research on the paradox suggests that the “how to proceed question” needs to be divorced from what makes Simpson’s paradox “paradoxical.”


title: A Review of the Effects of Physical Activity and Exercise on Cognitive and Brain Functions in Older Adults

abstract: Studies supporting the notion that physical activity and exercise can help alleviate the negative impact of age on the body and the mind abound. This literature review provides an overview of important findings in this fast growing research domain. Results from cross-sectional, longitudinal, and intervention studies with healthy older adults, frail patients, and persons suffering from mild cognitive impairment and dementia are reviewed and discussed. Together these finding suggest that physical exercise is a promising nonpharmaceutical intervention to prevent age-related cognitive decline and neurodegenerative diseases.


title: SCADA system cyber security — A comparison of standards

abstract: Cyber security of Supervisory Control And Data Acquisition (SCADA) systems has become very important. SCADA systems are vital for operation and control of critical infrastructures, such as the electrical power system. Therefore, a number of standards and guidelines have been developed to support electric power utilities in their cyber security efforts.


Automated spatiotemporal and semantic information extraction for hazards


title: Search engine coverage bias: evidence and possible causes

abstract: Commercial search engines are now playing an increasingly important role in Web information dissemination and access. Of particular interest to business and national governments is whether the big engines have coverage biased towards the US or other countries. In our study we tested for national biases in three major search engines and found significant differences in their coverage of commercial Web sites The US sites were much better covered than the others in the study: sites from China, Taiwan and Singapore. We then examined the possible technical causes of the differences and found that the language of a site does not affect its coverage by search engines. However, the visibility of a site, measured by the number of links to it, affects its chance to be covered by search engines. We conclude that the coverage bias does exist but this is due not to deliberate choices of the search engines but occurs as a natural result of cumulative advantage effects of US sites on the Web. Nevertheless, the bias remains a cause for international concern.


title: Fast Boosting Based Detection Using Scale Invariant Multimodal Multiresolution Filtered Features

abstract: In this paper we propose a novel boosting-based sliding window solution for object detection which can keep up with the precision of the state-of-the art deep learning approaches, while being 10 to 100 times faster. The solution takes advantage of multisensorial perception and exploits information from color, motion and depth. We introduce multimodal multiresolution filtering of signal intensity, gradient magnitude and orientation channels, in order to capture structure at multiple scales and orientations. To achieve scale invariant classification features, we analyze the effect of scale change on features for different filter types and propose a correction scheme. To improve recognition we incorporate 2D and 3D context by generating spatial, geometric and symmetrical channels. Finally, we evaluate the proposed solution on multiple benchmarks for the detection of pedestrians, cars and bicyclists. We achieve competitive results at over 25 frames per second.


Hybrid Dialogue State Tracking for Real World Human-to-Human Dialogues.


title: User Based Aggregation for Biterm Topic Model

abstract: Biterm Topic Model (BTM) is designed to model the generative process of the word co-occurrence patterns in short texts such as tweets. However, two aspects of BTM may restrict its performance: 1) user individualities are ignored to obtain the corpus level words co-occurrence patterns; and 2) the strong assumptions that two co-occurring words will be assigned the same topic label could not distinguish background words from topical words. In this paper, we propose Twitter-BTM model to address those issues by considering user level personalization in BTM. Firstly, we use user based biterms aggregation to learn user specific topic distribution. Secondly, each user’s preference between background words and topical words is estimated by incorporating a background topic. Experiments on a large-scale real-world Twitter dataset show that Twitter-BTM outperforms several stateof-the-art baselines.


title: DATA MINING SYSTEM AND APPLICATIONS: A REVIEW

abstract: In the Information Technology era information plays vital role in every sphere of the human life. It is very important to gather data from different data sources, store and maintain the data, generate information, generate knowledge and disseminate data, information and knowledge to every stakeholder. Due to vast use of computers and electronics devices and tremendous growth in computing power and storage capacity, there is explosive growth in data collection. The storing of the data in data warehouse enables entire enterprise to access a reliable current database. To analyze this vast amount of data and drawing fruitful conclusions and inferences it needs the special tools called data mining tools. This paper gives overview of the data mining systems and some of its applications.


title: Entropy loss in PUF-based key generation schemes: The repetition code pitfall

abstract: One of the promising usages of Physically Unclonable Functions (PUFs) is to generate cryptographic keys from PUFs for secure storage of key material. This usage has attractive properties such as physical unclonability and enhanced resistance against hardware attacks. In order to extract a reliable cryptographic key from a noisy PUF response a fuzzy extractor is used to convert non-uniform random PUF responses into nearly uniform randomness. Bosch et al. in 2008 proposed a fuzzy extractor suitable for efficient hardware implementation using two-stage concatenated codes, where the inner stage is a conventional error correcting code and the outer stage is a repetition code. In this paper we show that the combination of PUFs with repetition code approaches is not without risk and must be approached carefully. For example, PUFs with min-entropy lower than 66% may yield zero leftover entropy in the generated key for some repetition code configurations. In addition, we find that many of the fuzzy extractor designs in the literature are too optimistic with respect to entropy estimation. For high security applications, we recommend a conservative estimation of entropy loss based on the theoretical work of fuzzy extractors and present parameters for generating 128-bit keys from memory based PUFs.


title: Easy to Draw, but Hard to Trace?: On the Observability of Grid-based (Un)lock Patterns

abstract: We performed a systematic evaluation of the shoulder surfing susceptibility of the Android pattern (un)lock. The results of an online study (n=298) enabled us to quantify the influence of pattern length, line visibility, number of knight moves, number of overlaps and number of intersections on observation resistance. The results show that all parameters have a highly significant influence, with line visibility and pattern length being most important. We discuss implications for real-world patterns and present a linear regression model that can predict the observability of a given pattern. The model can be used to provide proactive security measurements for (un)lock patterns, in analogy to password meters.


title: Arabic sentiment analysis: Lexicon-based and corpus-based

abstract: The emergence of the Web 2.0 technology generated a massive amount of raw data by enabling Internet users to post their opinions, reviews, comments on the web. Processing this raw data to extract useful information can be a very challenging task. An example of important information that can be automatically extracted from the users' posts and comments is their opinions on different issues, events, services, products, etc. This problem of Sentiment Analysis (SA) has been studied well on the English language and two main approaches have been devised: corpus-based and lexicon-based. This paper addresses both approaches to SA for the Arabic language. Since there is a limited number of publically available Arabic dataset and Arabic lexicons for SA, this paper starts by building a manually annotated dataset and then takes the reader through the detailed steps of building the lexicon. Experiments are conducted throughout the different stages of this process to observe the improvements gained on the accuracy of the system and compare them to corpus-based approach.


title: Approaches to Semantic Web Services: an Overview and Comparisons

abstract: The next Web generation promises to deliver Semantic Web Services (SWS); services that are self-described and amenable to automated discovery, composition and invocation. A prerequisite to this, however, is the emergence and evolution of the Semantic Web, which provides the infrastructure for the semantic interoperability of Web Services. Web Services will be augmented with rich formal descriptions of their capabilities, such that they can be utilized by applications or other services without human assistance or highly con-strained agreements on interfaces or protocols. Thus, Semantic Web Services have the potential to change the way knowledge and business services are consumed and provided on the Web. In this paper, we survey the state of the art of current enabling technologies for Semantic Web Services. In addition, we characterize the infrastructure of Semantic Web Services along three orthogonal dimensions: activities, architecture and service ontology. Further, we examine and contrast three current approaches to SWS according to the proposed dimensions.


title: Real-time Dynamic MRI Reconstruction using Stacked Denoising Autoencoder

abstract: In this work we address the problem of real-time dynamic MRI reconstruction. There are a handful of studies on this topic; these techniques are either based on compressed sensing or employ Kalman Filtering. These techniques cannot achieve the reconstruction speed necessary for real-time reconstruction. In this work, we propose a new approach to MRI reconstruction. We learn a non-linear mapping from the unstructured aliased images to the corresponding clean images using a stacked denoising autoencoder (SDAE). The training for SDAE is slow, but the reconstruction is very fast - only requiring a few matrix vector multiplications. In this work, we have shown that using SDAE one can reconstruct the MRI frame faster than the data acquisition rate, thereby achieving real-time reconstruction. The quality of reconstruction is of the same order as a previous compressed sensing based online reconstruction technique.


title: Analysis of wing twist effects on hover flight dynamics of a single rotor aerial craft

abstract: Advancements in control theory and the miniaturization of microelectronics has led to significant improvements in the aerial characteristics and performance of single rotor aerial crafts. These single rotor aerial crafts are of particular interest as they can be considered true hybrids between traditional fixed-wing crafts and the maneuverable multi-rotor platforms, allowing both VTOL capabilities as well as extended endurance. Currently, the dynamic models used to describe these novel crafts are hindered by the lack of an optimized wing design that caters to the single rotor aerial craft's unique flight envelope. Hence, this paper seeks to investigate and explore wing models with specific twist angles across the wingspan so as to improve the craft's hover performance and in turn, its endurance. Simulation results suggest that for the wing twist characteristic, a reciprocal function model outperforms linear function models in terms of induced power loss without incurring any significant drawbacks to existing design setups. With the advent of digital fabrication, such customized and intricate wing designs can be more easily realized.


title: High-performing feature selection for text classification

abstract: This paper reports a controlled study on a large number of filter feature selection methods for text classification. Over 100 variants of five major feature selection criteria were examined using four well-known classification algorithms: a Naive Bayesian (NB) approach, a Rocchio-style classifier, a k-nearest neighbor (kNN) method and a Support Vector Machine (SVM) system. Two benchmark collections were chosen as the testbeds: Reuters-21578 and small portion of Reuters Corpus Version 1 (RCV1), making the new results comparable to published results. We found that feature selection methods based on chi2 statistics consistently outperformed those based on other criteria (including information gain) for all four classifiers and both data collections, and that a further increase in performance was obtained by combining uncorrelated and high-performing feature selection methods.The results we obtained using only 3% of the available features are among the best reported, including results obtained with the full feature set.


TRANSPORTATION NETWORK ANALYSIS BY USING REMOTE SENSING AND GIS A REVIEW


title: Components of aesthetic experience: aesthetic fascination, aesthetic appraisal, and aesthetic emotion

abstract: In this paper aesthetic experience is defined as an experience qualitatively different from everyday experience and similar to other exceptional states of mind. Three crucial characteristics of aesthetic experience are discussed: fascination with an aesthetic object (high arousal and attention), appraisal of the symbolic reality of an object (high cognitive engagement), and a strong feeling of unity with the object of aesthetic fascination and aesthetic appraisal. In a proposed model, two parallel levels of aesthetic information processing are proposed. On the first level two sub-levels of narrative are processed, story (theme) and symbolism (deeper meanings). The second level includes two sub-levels, perceptual associations (implicit meanings of object's physical features) and detection of compositional regularities. Two sub-levels are defined as crucial for aesthetic experience, appraisal of symbolism and compositional regularities. These sub-levels require some specific cognitive and personality dispositions, such as expertise, creative thinking, and openness to experience. Finally, feedback of emotional processing is included in our model: appraisals of everyday emotions are specified as a matter of narrative content (eg, empathy with characters), whereas the aesthetic emotion is defined as an affective evaluation in the process of symbolism appraisal or the detection of compositional regularities.


title: Graffito: crowd-based performative interaction at festivals

abstract: Crowd-based events are generating new forms of crowd-based performative interaction. Nightclubs and festivals are at the cutting edge of crowd-based interaction with ubiquitous computing. The social capital of crowd-based interaction is not well understood and is usually limited to one-off events. Our intention is to explore the possibility for generating a lifelong contextual footprint of crowd-based performative interaction. In this paper, we present and discuss two case studies of designing applications for crowd-based performative interaction at two large-scale festivals and reflect on their implications for design.


Fault-Tolerant Trajectory Tracking Control of a Quadrotor Helicopter Using Gain-Scheduled PID and Model Reference Adaptive Control


title: Fast and accurate link prediction in social networking systems

abstract: Online social networks (OSNs) recommend new friends to registered users based on local-based features of the graph (i.e. based on the number of common friends that two users share). However, OSNs do not exploit all different length paths of the network. Instead, they consider only pathways of maximum length 2 between a user and his candidate friends. On the other hand, there are global-based approaches, which detect the overall path structure in a network, being computationally prohibitive for huge-sized social networks. In this paper we provide friend recommendations, also known as the link prediction problem, by traversing all paths of a limited length, based on the ''algorithmic small world hypothesis''. As a result, we are able to provide more accurate and faster friend recommendations. We also derive variants of our method that apply to different types of networks (directed/undirected and signed/unsigned). We perform an extensive experimental comparison of the proposed method against existing link prediction algorithms, using synthetic and three real data sets (Epinions, Facebook and Hi5). We also show that a significant accuracy improvement can be gained by using information about both positive and negative edges. Finally, we discuss extensively various experimental considerations, such as a possible MapReduce implementation of FriendLink algorithm to achieve scalability.


title: A practical scalable distributed B-tree

abstract: Internet applications increasingly rely on scalable data structures that must support high throughput and store huge amounts of data. These data structures can be hard to implement efficiently. Recent proposals have overcome this problem by giving up on generality and implementing specialized interfaces and functionality (e.g., Dynamo [4]). We present the design of a more general and flexible solution: a fault-tolerant and scalable distributed B-tree. In addition to the usual B-tree operations, our B-tree provides some important practical features: transactions for atomically executing several operations in one or more B-trees, online migration of B-tree nodes between servers for load-balancing, and dynamic addition and removal of servers for supporting incremental growth of the system. ::: ::: Our design is conceptually simple. Rather than using complex concurrency and locking protocols, we use distributed transactions to make changes to B-tree nodes. We show how to extend the B-tree and keep additional information so that these transactions execute quickly and efficiently. Our design relies on an underlying distributed data sharing service, Sinfonia [1], which provides fault tolerance and a light-weight distributed atomic primitive. We use this primitive to commit our transactions. We implemented our B-tree and show that it performs comparably to an existing open-source B-tree and that it scales to hundreds of machines. We believe that our approach is general and can be used to implement other distributed data structures easily.


title: Semantic description of process modules Towards an open implementation for plug and produce in process plants

abstract: The reduction of the effort required for integration of a module into a production plant is the fundamental aim of modular plant architectures. Effective module integration can be achieved, for instance, by shifting engineering efforts from the classical site-engineering to the engineering of plant modules. The basis of this approach is the formal description of various aspects of a module in a module type package (MTP). The core elements of this MTP are presented in this paper and discussed in terms of use in a standard module description. In addition, this paper presents a transformation of this concept to the standardized format AutomationML.


title: Another Look at Moral Foundations Theory: Do Authoritarianism and Social Dominance Orientation Explain Liberal-Conservative Differences in “Moral” Intuitions?

abstract: Moral foundations theorists propose that the moral domain should include not only “liberal” ethics of justice and care but also ostensibly “conservative” concerns about the virtues of ingroup loyalty, obedience to authority, and enforcement of purity standards. This proposal clashes with decades of research in political psychology connecting the latter set of characteristics to “the authoritarian personality.” We demonstrate that liberal-conservative differences in moral intuitions are statistically mediated by authoritarianism and social dominance orientation, so that conservatives’ greater valuation of ingroup, authority, and purity concerns is attributable to higher levels of authoritarianism, whereas liberals’ greater valuation of fairness and harm avoidance is attributable to lower levels of social dominance. We also find that ingroup, authority, and purity concerns are positively associated with intergroup hostility and support for discrimination, whereas concerns about fairness and harm avoidance are negatively associated with these variables. These findings might lead some to question the wisdom and appropriateness of efforts to “broaden” scientific conceptions of morality in such a way that preferences based on authoritarianism and social dominance are treated as moral—rather than amoral or even immoral—and suggest that the explicit goal of incorporating conservative ideology into the study of moral psychology (in order to increase ideological diversity) may lead researchers astray.


SOLATING S OURCES OF D ISENTANGLEMENT IN V ARIATIONAL A UTOENCODERS


title: LUCIA: An Open Source 3D Expressive Avatar for Multimodal h.m.i.

abstract: LUCIA is an MPEG-4 facial animation system developed at ISTC-CNR. It works on standard Facial Animation Parameters and speaks with the Italian version of FESTIVAL TTS. To achieve an emotive/expressive talking head LUCIA was built from real human data physically extracted by ELITE optic-tracking movement analyzer. LUCIA can copy a real human being by reproducing the movements of passive markers positioned on his face and recorded by the ELITE device or can be driven by an emotional XML tagged input text, thus realizing true audio/visual emotive/expressive synthesis. Synchronization between visual and audio data is very important in order to create the correct WAV and FAP files needed for the animation. LUCIA’s voice is based on the ISTC Italian version of FESTIVAL-MBROLA packages, modified by means of an appropriate APML/VSML tagged language. LUCIA is available in two different versions: an open source framework and the “work in progress” WebGL.


title: Bayesian Occupancy Filtering for Multitarget Tracking: An Automotive Application

abstract: Reliable and efficient perception and reasoning in dynamic and densely cluttered environments are still major challenges for driver assistance systems. Most of today's systems use target tracking algorithms based on object models. They work quite well in simple environments such as freeways, where few potential obstacles have to be considered. However, these approaches usually fail in more complex environments featuring a large variety of potential obstacles, as is usually the case in urban driving situations. In this paper, we propose a new approach for robust perception and risk assessment in highly dynamic environments. This approach is called Bayesian occupancy filtering; it basically combines a four-dimensional occupancy grid representation of the ecobstacle state space with Bayesian filtering techniques.


title: Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey

abstract: Internet of Things (IoT) has been growing rapidly due to recent advancements in communications and sensor technologies. Meanwhile, with this revolutionary transformation, researchers, implementers, deployers, and users are faced with many challenges. IoT is a complicated, crowded, and complex field; there are various types of devices, protocols, communication channels, architectures, middleware, and more. Standardization efforts are plenty, and this chaos will continue for quite some time. What is clear, on the other hand, is that IoT deployments are increasing with accelerating speed, and this trend will not stop in the near future. As the field grows in numbers and heterogeneity, “intelligence” becomes a focal point in IoT. Since data now becomes “big data,” understanding, learning, and reasoning with big data is paramount for the future success of IoT. One of the major problems in the path to intelligent IoT is understanding “context,” or making sense of the environment, situation, or status using data from sensors, and then acting accordingly in autonomous ways. This is called “context-aware computing,” and it now requires both sensing and, increasingly, learning, as IoT systems get more data and better learning from this big data. In this survey, we review the field, first, from a historical perspective, covering ubiquitous and pervasive computing, ambient intelligence, and wireless sensor networks, and then, move to context-aware computing studies. Finally, we review learning and big data studies related to IoT. We also identify the open issues and provide an insight for future study areas for IoT researchers.


Simulation Assessment of a Near-Optimal Control Algorithm for Central Cooling Plants with Chilled Water Storage


Assessing the conservation status of habitat 6210(*) Semi- natural dry grasslands and scrubland facies on calcareous substrates (Festuco-Brometalia) in Italy


title: A dyadic multi-resolution deep convolutional neural wavelet network for image classification

abstract: For almost the past four decades, image classification has gained a lot of attention in the field of pattern recognition due to its application in various fields. Given its importance, several approaches have been proposed up to now. In this paper, we will present a dyadic multi-resolution deep convolutional neural wavelets’ network approach for image classification. This approach consists of performing the classification of one class versus all the other classes of the dataset by the reconstruction of a Deep Convolutional Neural Wavelet Network (DCNWN). This network is based on the Neural Network (NN) architecture, the Fast Wavelet Transform (FWT) and the Adaboost algorithm. It consists, first, of extracting features using the FWT based on the Multi-Resolution Analysis (MRA). These features are used to calculate the inputs of the hidden layer. Second, those inputs are filtered by using the Adaboost algorithm to select the best ones corresponding to each image. Third, we create an AutoEncoder (AE) using wavelet networks of all images. Finally, we apply a pooling for each hidden layer of the wavelet network to obtain a DCNWN that permits the classification of one class and rejects all other classes of the dataset. Classification rates given by our approach show a clear improvement compared to those cited in this article.


title: Visually induced motion sickness estimation and prediction in virtual reality using frequency components analysis of postural sway signal

abstract: The paper proposes a method for estimating and predicting visually induced motion sickness (VIMS) occurring in a navigation task in a 3D immersive virtual environment, by extracting features from the body postural sway signals in both the time and frequency domains. Past research showed that the change in the body postural sway may be an element for characterizing VIMS. Therefore, we conducted experiments in a 3D virtual environment where the task was simply a translational movement with different navigation speeds. By measuring the evolution of the body's center of gravity (COG), the analysis of the sway signals in the time domain showed a dilation of the COG's area, as well as a change in the shape of the area. Frequency Components Analysis (FCA) of the sway signal gave an efficient feature to estimate and predict the level of VIMS. The results provide promising insight to better monitor sickness in a virtual reality application.


title: Effective Fusion of Multi-Modal Remote Sensing Data in a Fully Convolutional Network for Semantic Labeling

abstract: In recent years, Fully Convolutional Networks (FCN) have led to a great improvement of semantic labeling for various applications including multi-modal remote sensing data. Although different fusion strategies have been reported for multi-modal data, there is no in-depth study of the reasons of performance limits. For example, it is unclear, why an early fusion of multi-modal data in FCN does not lead to a satisfying result. In this paper, we investigate the contribution of individual layers inside FCN and propose an effective fusion strategy for the semantic labeling of color or infrared imagery together with elevation (e.g., Digital Surface Models). The sensitivity and contribution of layers concerning classes and multi-modal data are quantified by recall and descent rate of recall in a multi-resolution model. The contribution of different modalities to the pixel-wise prediction is analyzed explaining the reason of the poor performance caused by the plain concatenation of different modalities. Finally, based on the analysis an optimized scheme for the fusion of layers with image and elevation information into a single FCN model is derived. Experiments are performed on the ISPRS Vaihingen 2D Semantic Labeling dataset (infrared and RGB imagery as well as elevation) and the Potsdam dataset (RGB imagery and elevation). Comprehensive evaluations demonstrate the potential of the proposed approach.


title: Dynamic and Sensitivity Analysis of KNTU CDRPM: A Cable Driven Redundant Parallel Manipulator

abstract: KNTU CDRPM is a cable driven redundant parallel manipulator, which is under investigation for possible implementation of large workspace applications. This newly developed manipulators have several advantages compared to the conventional parable mechanisms. In this paper, the governing dynamic equation of motion of such structure is derived using the Newton-Euler formulation. Next, the dynamic equations of the system are used in simulations. It is shown that on the contrary to serial manipulators, dynamic equations of motion of cable-driven parallel manipulators can be only represented implicitly, and only special integration routines can be used for their simulations. In order to verify the accuracy and integrity of the derived dynamic equations, open- and closed-loop simulations for the system is performed and analyzed. Also, the effects of mechanical assembly tolerances on the closed-loop control performance of a cable driven parallel robot are studied in detail, and the sensitivity analysis of the precision in the construction and assembly of the system on the closed-loop behavior of the KNTU CDRPM is performed.


title: Attentive Tensor Product Learning for Language Generation and Grammar Parsing.

abstract: This paper proposes a new architecture - Attentive Tensor Product Learning (ATPL) - to represent grammatical structures in deep learning models. ATPL is a new architecture to bridge this gap by exploiting Tensor Product Representations (TPR), a structured neural-symbolic model developed in cognitive science, aiming to integrate deep learning with explicit language structures and rules. The key ideas of ATPL are: 1) unsupervised learning of role-unbinding vectors of words via TPR-based deep neural network; 2) employing attention modules to compute TPR; and 3) integration of TPR with typical deep learning architectures including Long Short-Term Memory (LSTM) and Feedforward Neural Network (FFNN). The novelty of our approach lies in its ability to extract the grammatical structure of a sentence by using role-unbinding vectors, which are obtained in an unsupervised manner. This ATPL approach is applied to 1) image captioning, 2) part of speech (POS) tagging, and 3) constituency parsing of a sentence. Experimental results demonstrate the effectiveness of the proposed approach.


title: PiCANet: Learning Pixel-wise Contextual Attention in ConvNets and Its Application in Saliency Detection

abstract: Context plays an important role in many computer vision tasks. Previous models usually construct contextual information from the whole context region. However, not all context locations are helpful and some of them may be detrimental to the final task. To solve this problem, we propose a novel pixel-wise contextual attention network, i.e., the PiCANet, to learn to selectively attend to informative context locations for each pixel. Specifically, it can generate an attention map over the context region for each pixel, where each attention weight corresponds to the contextual relevance of each context location w.r.t. the specified pixel location. Thus, an attended contextual feature can be constructed by using the attention map to aggregate the contextual features. We formulate PiCANet in a global form and a local form to attend to global contexts and local contexts, respectively. Our designs for the two forms are both fully differentiable. Thus they can be embedded into any CNN architectures for various computer vision tasks in an end-to-end manner. We take saliency detection as an example application to demonstrate the effectiveness of the proposed PiCANets. Specifically, we embed global and local PiCANets into an encoder-decoder Convnet hierarchically. Thorough analyses indicate that the global PiCANet helps to construct global contrast while the local PiCANets help to enhance the feature maps to be more homogenous, thus making saliency detection results more accurate and uniform. As a result, our proposed saliency model achieves state-of-the-art results on 4 benchmark datasets.


title: Emergent Language in a Multi-Modal, Multi-Step Referential Game

abstract: Inspired by previous work on emergent language in referential games, we propose a novel multi-modal, multi-step referential game, where the sender and receiver have access to distinct modalities of an object, and their information exchange is bidirectional and of arbitrary duration. The multi-modal multi-step setting allows agents to develop an internal language significantly closer to natural language, in that they share a single set of messages, and that the length of the conversation may vary according to the difficulty of the task. We examine these properties empirically using a dataset consisting of images and textual descriptions of mammals, where the agents are tasked with identifying the correct object. Our experiments indicate that a robust and efficient communication protocol emerges, where gradual information exchange informs better predictions and higher communication bandwidth improves generalization.


title: Building Systematic Reviews Using Automatic Text Classification Techniques

abstract: The amount of information in medical publications continues to increase at a tremendous rate. Systematic reviews help to process this growing body of information. They are fundamental tools for evidence-based medicine. In this paper, we show that automatic text classification can be useful in building systematic reviews for medical topics to speed up the reviewing process. We propose a per-question classification method that uses an ensemble of classifiers that exploit the particular protocol of a systematic review. We also show that when integrating the classifier in the human workflow of building a review the per-question method is superior to the global method. We test several evaluation measures on a real dataset.


title: Interactive design of bonsai tree models

abstract: Because of their complexity, plant models used in computer graphics are commonly created with proceduralmethods. A difficult problem is the user control of these models: a small number of parameters is insufficient tospecify plant characteristics in detail, while large numbers of parameters are tedious to manipulate and difficultto comprehend. To address this problem, we propose a method for managing parameters involved in plant modelmanipulation. Specifically, we introduce decomposition graphs as multiscale representations of plant structuresand present interactive tools for designing trees that operate on decomposition graphs. The supported operationsinclude browsing of the parameter space, editing of generalized parameters (scalars, functions, and branchingsystem silhouettes), and the definition of dependencies between parameters. We illustrate our method by creatingmodels of bonsai trees.


title: Ground Truth Accuracy and Performance of the Matching Pipeline

abstract: Feature matching quality strongly influences the accuracy of most computer vision tasks. This led to impressive advances in keypoint detection, descriptor calculation, and feature matching itself. To compare different approaches and evaluate their quality, datasets from related tasks are used. Unfortunately, none of these datasets actually provide ground truth (GT) feature matches. Thus, matches can only be approximated due to repeatability errors of keypoint detectors and inaccuracies of GT. In this paper, we introduce ground truth matches (GTM) for several well known datasets. Based on the provided spacial ground truth, we automatically generate them using popular feature types. Currently, feature matching evaluation is typically performed using precision and recall. The introduced GTM additionally enable evaluation with accuracy and fall-out. The datasets were manually annotated, on the one hand to evaluate the precision and unambiguousness of the GTM, and on the other hand to determine the accuracy of the ground truth provided with the datasets. Using GTM, we present an evaluation of multiple state-of-the-art keypoint-descriptor combinations as well as matching algorithms.


title: The priority factor model for customer relationship management system success

abstract: As the market competition becomes keen, constructing a customer relationship management system is coming to the front for winning over new customers, developing service and products for customer satisfaction and retaining existing customers. However, decisions for CRM implementation have been hampered by inconsistency between information technology and marketing strategies, and the lack of conceptual bases necessary to develop the success measures. Using a structural equation analysis, this study explores the CRM system success model that consists of CRM initiatives: process fit, customer information quality, and system support; intrinsic success: efficiency and customer satisfaction; and extrinsic success: profitability. These constructs underlie much of the existing literature on information system success and customer satisfaction perspectives. We found the empirical support for CRM implementation decision-making from 253 respondents of 14 companies which have implemented the CRM system. These findings should be of great interest to both researchers and practitioners.


title: A Unified Foundational Ontology and some Applications of it in Business Modeling

abstract: Foundational ontologies provide the basic concepts upon which any domain-specific ontology is built. This paper presents a new foundational ontology, UFO, and shows how it can be used as a guideline in business modeling and for evaluating business modeling methods. UFO is derived from a synthesis of two other foundational ontologies, GFO/GOL and OntoClean/DOLCE. While their main areas of application are natural sciences and linguistics/cognitive engineering, respectively, the main purpose of UFO is to provide a foundation for conceptual modeling, including business modeling.


title: Learning Subjective Language

abstract: Subjectivity in natural language refers to aspects of language used to express opinions, evaluations, and speculations. There are numerous natural language processing applications for which subjectivity analysis is relevant, including information extraction and text categorization. The goal of this work is learning subjective language from corpora. Clues of subjectivity are generated and tested, including low-frequency words, collocations, and adjectives and verbs identified using distributional similarity. The features are also examined working together in concert. The features, generated from different data sets using different procedures, exhibit consistency in performance in that they all do better and worse on the same data sets. In addition, this article shows that the density of subjectivity clues in the surrounding context strongly affects how likely it is that a word is subjective, and it provides the results of an annotation study assessing the subjectivity of sentences with high-density features. Finally, the clues are used to perform opinion piece recognition (a type of text categorization and genre detection) to demonstrate the utility of the knowledge acquired in this article.


Question Answering


title: Fast Searchable Encryption With Tunable Locality

abstract: Searchable encryption (SE) allows a client to outsource a dataset to an untrusted server while enabling the server to answer keyword queries in a private manner. SE can be used as a building block to support more expressive private queries such as range/point and boolean queries, while providing formal security guarantees. To scale SE to big data using external memory, new schemes with small locality have been proposed, where locality is defined as the number of non-continuous reads that the server makes for each query. Previous space-efficient SE schemes achieve optimal locality by increasing the read efficiency-the number of additional memory locations (false positives) that the server reads per result item. This can hurt practical performance. In this work, we design, formally prove secure, and evaluate the first SE scheme with tunable locality and linear space. Our first scheme has optimal locality and outperforms existing approaches (that have a slightly different leakage profile) by up to 2.5 orders of magnitude in terms of read efficiency, for all practical database sizes. Another version of our construction with the same leakage as previous works can be tuned to have bounded locality, optimal read efficiency and up to 60x more efficient end-to-end search time. We demonstrate that our schemes work fast in in-memory as well, leading to search time savings of up to 1 order of magnitude when compared to the most practical in-memory SE schemes. Finally, our construction can be tuned to achieve trade-offs between space, read efficiency, locality, parallelism and communication overhead.


title: UHD AMOLED Driving Scheme of Compensation Pixel and Gate Driver Circuits Achieving High-Speed Operation

abstract: This paper presents a new driving scheme for active-matrix organic light-emitting diode displays. A pixel circuit based on this driving scheme was developed to compensate for the threshold voltage and mobility variations of low-temperature polycrystalline-silicon thin-film transistors (TFTs), eliminate image flickering and current-resistance voltage drops, and suit ultrahigh-definition (UHD) resolution applications. A new gate driver with a concise 6T1C structure is also proposed to realize the driving scheme in practice. Fabricated TFTs and OLEDs were measured to establish models. Simulations using these models demonstrate that the proposed pixel circuit achieves uniform OLED current regardless of undesirable influences at UHD resolution.


title: A 65nm 1Mb nonvolatile computing-in-memory ReRAM macro with sub-16ns multiply-and-accumulate for binary DNN AI edge processors

abstract: Many artificial intelligence (AI) edge devices use nonvolatile memory (NVM) to store the weights for the neural network (trained off-line on an AI server), and require low-energy and fast I/O accesses. The deep neural networks (DNN) used by AI processors [1,2] commonly require p-layers of a convolutional neural network (CNN) and q-layers of a fully-connected network (FCN). Current DNN processors that use a conventional (von-Neumann) memory structure are limited by high access latencies, I/O energy consumption, and hardware costs. Large working data sets result in heavy accesses across the memory hierarchy, moreover large amounts of intermediate data are also generated due to the large number of multiply-and-accumulate (MAC) operations for both CNN and FCN. Even when binary-based DNN [3] are used, the required CNN and FCN operations result in a major memory I/O bottleneck for AI edge devices.


title: Discriminative Training for Large-Vocabulary Speech Recognition Using Minimum Classification Error

abstract: The minimum classification error (MCE) framework for discriminative training is a simple and general formalism for directly optimizing recognition accuracy in pattern recognition problems. The framework applies directly to the optimization of hidden Markov models (HMMs) used for speech recognition problems. However, few if any studies have reported results for the application of MCE training to large-vocabulary, continuous-speech recognition tasks. This article reports significant gains in recognition performance and model compactness as a result of discriminative training based on MCE training applied to HMMs, in the context of three challenging large-vocabulary (up to 100 k word) speech recognition tasks: the Corpus of Spontaneous Japanese lecture speech transcription task, a telephone-based name recognition task, and the MIT Jupiter telephone-based conversational weather information task. On these tasks, starting from maximum likelihood (ML) baselines, MCE training yielded relative reductions in word error ranging from 7% to 20%. Furthermore, this paper evaluates the use of different methods for optimizing the MCE criterion function, as well as the use of precomputed recognition lattices to speed up training. An overview of the MCE framework is given, with an emphasis on practical implementation issues


title: Green logistic vehicle routing problem: Routing light delivery vehicles in urban areas using a neuro-fuzzy model

abstract: Today's growth in the level of traffic in cities is leading to both congestion and environmental pollution (exhaust emissions and noise), as well as increased costs. Traffic congestion makes cities less pleasant places to live in, a particular problem being the negative impact on health as a result of increased exhaust emissions. In addition to these emissions, another major effect of transport which can lead to serious health problems is noise (EEA, 2013a, 2013b). There is a strong tendency in the world towards the development of ''clean'' motor vehicles that do not pollute the environment, that is, that do not emit harmful substances in their exhaust fumes and which create less noise without causing other types of pollution. The growth in the influence of transport on the environment has resulted in planners formulating procedures which take into account the effect of traffic on the quality of life in urban areas. This paper presents a model for the routing of light delivery vehicles by logistics operators. The model presented takes into account the fact that logistics operators have a limited number of environmentally friendly vehicles (EFV) available to them. When defining a route, EFV vehicles and environmentally unfriendly vehicles (EUV) are considered separately. For solving the problem of routing in the model, an adaptive neural network was used which was trained by a simulated annealing algorithm. An adaptive neural network was used for assessing the performance of the network branches. The input parameters of the neural network were the logistics operating costs and environmental parameters (exhaust emissions and noise) for the given vehicle route. Each of the input parameters of the neural network was thoroughly examined. The input parameters were broken down into elements which further describe the state of the environment, noise and logistics operating costs. After obtaining the performance of the network links for calculating the route for EFV and EUV vehicles a modified Clark-Wright algorithm was used. The proposed model was tested on a network which simulates the conditions in the very centre of Belgrade. All of the input parameters of the model were obtained on the basis of 40 automatic measuring stations for monitoring the air quality (SEA, 2012).


title: Automated Modeling of Building HVAC Systems for MPC

abstract: Model predictive control (MPC) offers a tremendous scope in optimizing the consumption of energy by building HVAC systems. This paper presents an automated real-time procedure for the development of linear parametric models of building air-conditioning systems through system identification for the implementation of the MPC algorithms. The procedure is used to decide on the various aspects of system identification such as selecting the model structure, the inputs to the system, the interaction of the systems with their neighbors, and the updating of the model coefficients in real-time. The effectiveness of the procedure is demonstrated by modeling the various components air-conditioning systems of a real building. The root mean squared error was used as a performance metric to gauge the models. The paper also demonstrates that a 15 minute sampling interval is sufficient to model the dynamics of the air-handling unit and the room temperatures, but a faster sampling rate may be required to model the VAV boxes.© 2014 ASME


title: Key Technologies in the Context of Future Networks: Operational and Management Requirements

abstract: The concept of Future Networks is based on the premise that current infrastructures require enhanced control, service customization, self-organization and self-management capabilities to meet the new needs in a connected society, especially of mobile users. In order to provide a high-performance mobile system, three main fields must be improved: radio, network, and operation and management. In particular, operation and management capabilities are intended to enable business agility and operational sustainability, where the addition of new services does not imply an excessive increase in capital or operational expenditures. In this context, a set of key-enabled technologies have emerged in order to aid in this field. Concepts such as Software Defined Network (SDN), Network Function Virtualization (NFV) and Self-Organized Networks (SON) are pushing traditional systems towards the next 5G network generation.This paper presents an overview of the current status of these promising technologies and ongoing works to fulfill the operational and management requirements of mobile infrastructures. This work also details the use cases and the challenges, taking into account not only SDN, NFV, cloud computing and SON but also other paradigms.


title: An Ergonomics Study of Menu-Operation on Mobile Phone Interface

abstract: The studies on user and mobile phone interaction have been an important problem in small screen interface design. The present study was intend to investigate the impact of three common mobile phone menu displays: matrix, tree, and page-to-page - and differential organizations of their sub-menus and functions by reaction time experiment. We also used rating scale to investigate users' preference of different mobile phone menu displays. Reaction time for accurately completing these operations was fastest when participants were presented with the matrix menu and fastest when participants were presented organizations emphasizing logical categorization rather than non-logical categorization. The result also showed consistency between users' operating efficiency and mobile phone users' interface preference. Finally, the implications of these findings for mobile phone design are discussed.


AtlanticWave-SDX : An International SDX to Support Science Data Applications


title: A positive tensions PID controller for a planar cable robot: An experimental study

abstract: In this paper design and control of planar cable-driven parallel robots are studied in an experimental prospective. Since in this class of manipulators, cable tensionability conditions must be met, feedback control of such robots becomes more challenging than for conventional robots. To meet these conditions, internal force control structure is introduced and used in addition to a PID control scheme to ensure that all cables remain in tension. A robust PID controller is proposed for partial knowledge of the robot, to keep the tracking errors bounded. Finally, the effectiveness of the proposed control algorithm is examined through experiments on K.N. Toosi planar cable-driven robot and it is shown that the proposed control structure is able to provide suitable performance in practice.


title: The Evaluation Criteria of Workflow Metamodels

abstract: This paper defines the evaluation framework of workflow metamodels, based on a list of evaluation criteria. The presented evaluation criteria combine domain specific evaluation approach and some of the existing quality metrics, defined in the field of software. Well established evaluation criteria of workflow metamodels should enable their comparison, selection and proper use of methods and tool built upon them. The final result is a list of nine categories of evaluation criteria, decomposed into more than forty subcategories, but the list of subcategories is extendable. The practical validation of proposed evaluation highlighted some topics for future research.


title: Attention is not Explanation

abstract: Attention mechanisms have seen wide adoption in neural NLP models. In addition to improving predictive performance, these are often touted as affording transparency: models equipped with attention provide a distribution over attended-to input units, and this is often presented (at least implicitly) as communicating the relative importance of inputs. However, it is unclear what relationship exists between attention weights and model outputs. In this work, we perform extensive experiments across a variety of NLP tasks that aim to assess the degree to which attention weights provide meaningful `explanations' for predictions. We find that they largely do not. For example, learned attention weights are frequently uncorrelated with gradient-based measures of feature importance, and one can identify very different attention distributions that nonetheless yield equivalent predictions. Our findings show that standard attention modules do not provide meaningful explanations and should not be treated as though they do. Code for all experiments is available at this https URL.


title: Weighted logistic regression for large-scale imbalanced and rare events data

abstract: Latest developments in computing and technology, along with the availability of large amounts of raw data, have led to the development of many computational techniques and algorithms. Concerning binary data classification in particular, analysis of data containing rare events or disproportionate class distributions poses a great challenge to industry and to the machine learning community. Logistic Regression (LR) is a powerful classifier. The combination of LR and the truncated-regularized iteratively re-weighted least squares (TR-IRLS) algorithm, has provided a powerful classification method for large data sets. This study examines imbalanced data with binary response variables containing many more non-events (zeros) than events (ones). It has been established in the literature that these variables are difficult to predict and explain. This research combines rare events corrections to LR with truncated Newton methods. The proposed method, Rare Event Weighted Logistic Regression (RE-WLR), is capable of processing large imbalanced data sets at relatively the same processing speed as the TR-IRLS, however, with higher accuracy.


title: Perceptual and Cognitive Characteristics of Common Playing Cards

abstract: We examined the perceptual and cognitive characteristics of the playing cards commonly used in the Western world. Specifically, we measured their visibility, memorability, likability, and verbal and visual accessibility. Based on visibility and memorability, four groups of cards were distinguished: the Ace of Spades, other Aces, number cards, and face cards. Within each of these groups, there were few differences due to value or suit. Based on likability and accessibility, three additional groups were distinguished: the Ace of Hearts, Queen of Hearts, and King of Hearts. Several interesting relations were found between how people remember, like, and access cards; some of these were similar to effects found in studies of visual perception, while others seemed entirely new. Our results demonstrate that rigorous examination of real-world stimuli can shed light on the perception of ordinary objects, as well as help us understand why magic works in the mind.


title: PIXHAWK: A system for autonomous flight using onboard computer vision

abstract: We provide a novel hardware and software system for micro air vehicles (MAV) that allows high-speed, low-latency onboard image processing. It uses up to four cameras in parallel on a miniature rotary wing platform. The MAV navigates based on onboard processed computer vision in GPS-denied in- and outdoor environments. It can process in parallel images and inertial measurement information from multiple cameras for multiple purposes (localization, pattern recognition, obstacle avoidance) by distributing the images on a central, low-latency image hub. Furthermore the system can utilize low-bandwith radio links for communication and is designed and optimized to scale to swarm use. Experimental results show successful flight with a range of onboard computer vision algorithms, including localization, obstacle avoidance and pattern recognition.


title: gvnn: Neural Network Library for Geometric Computer Vision

abstract: We introduce gvnn, a neural network library in Torch aimed towards bridging the gap between classic geometric computer vision and deep learning. Inspired by the recent success of Spatial Transformer Networks, we propose several new layers which are often used as parametric transformations on the data in geometric computer vision. These layers can be inserted within a neural network much in the spirit of the original spatial transformers and allow backpropagation to enable end-to-end learning of a network involving any domain knowledge in geometric computer vision. This opens up applications in learning invariance to 3D geometric transformation for place recognition, end-to-end visual odometry, depth estimation and unsupervised learning through warping with a parametric transformation for image reconstruction error.


title: Deep Distributed Fusion Network for Air Quality Prediction

abstract: Accompanying the rapid urbanization, many developing countries are suffering from serious air pollution problem. The demand for predicting future air quality is becoming increasingly more important to government's policy-making and people's decision making. In this paper, we predict the air quality of next 48 hours for each monitoring station, considering air quality data, meteorology data, and weather forecast data. Based on the domain knowledge about air pollution, we propose a deep neural network (DNN)-based approach (entitled DeepAir), which consists of a spatial transformation component and a deep distributed fusion network. Considering air pollutants' spatial correlations, the former component converts the spatial sparse air quality data into a consistent input to simulate the pollutant sources. The latter network adopts a neural distributed architecture to fuse heterogeneous urban data for simultaneously capturing the factors affecting air quality, e.g. meteorological conditions. We deployed DeepAir in our AirPollutionPrediction system, providing fine-grained air quality forecasts for 300+ Chinese cities every hour. The experimental results on the data from three-year nine Chinese-city demonstrate the advantages of DeepAir beyond 10 baseline methods. Comparing with the previous online approach in AirPollutionPrediction system, we have 2.4%, 12.2%, 63.2% relative accuracy improvements on short-term, long-term and sudden changes prediction, respectively.


title: Design of Simulink Model to denoise ECG signal using various IIR & FIR filters

abstract: Electrocardiogram ECG signal is usually corrupted by several artifacts and these must be removed before diagnosis. This paper therefore presents the design of various FIR filters like Kaiser, Rectangular, Hamming, Hanning, Gaussian and Bartllet window techniques and FIR Equiripple Filter. IIR filters like Butterworth, Chebychev I & II and Elliptic Filters are also explored to remove the artifacts in ECG signal. To verify the usability of designed filters, noisy ECG signal is generated by adding random noise, white noise and 50 Hz interference (hum) to the 100m.mat ECG data sample taken from MIT-BIH database. Signal to noise ratio (SNR) is calculated to compare the performance of different filtering methods using Simulink Model. Results obtained show that FIR Equiripple filter gives a SNR of 7.71 for a 3rd order filter and IIR Elliptic filter has SNR of for first order filter and are therefore recommended.


title: A High-Slew Rate SiGe BiCMOS Operational Amplifier for Operation Down to Deep Cryogenic Temperatures

abstract: We investigate, for the first time, the design and implementation of a high-slew rate op-amp in SiGe BiCMOS technology capable of operation across very wide temperature ranges, and down to deep cryogenic temperatures. We achieve the first monolithic op-amp (for any material system) capable of operating reliably down to 4.3 K. Two variants of the SiGe BiCMOS op-amp were implemented using alternative biasing schemes, and the effects of temperature on these biasing schemes, and their impact on the overall op-amp performance, is investigated.


Cyber Insider Threats Situation Awareness Using Game Theory and Information Fusion-based User Behavior Predicting Algorithm


title: Detecting Novel Network Intrusions Using Bayes

abstract: From the first appearance of network attacks, the internet worm, to the most recent one in which the servers of several famous e-business companies were paralyzed for several hours, causing huge financial losses, network-based attacks have been increasing in frequency and severity. As a powerful weapon to protect networks, intrusion detection has been gaining a lot of attention. Traditionally, intrusion detection techniques are classified into two broad categories: misuse detection and anomaly detection. Misuse detection aims to detect well-known attacks as well as slight variations of them, by characterizing the rules that govern these attacks. Due to its nature, misuse detection has low false alarms but it is unable to detect any attacks that lie beyond its knowledge. Anomaly detection is designed to capture any deviations from the established profiles of users and systems normal behavior pattern. Although in principle, anomaly detection has the ability to detect new attacks, in practice this is far from easy. Anomaly detection has the potential to generate too many false alarms, and it is very time consuming and labor expensive to sift true intrusions from the false alarms. As new network attacks emerge, the need for intrusion detection systems to detect novel attacks becomes pressing. As we stated before, this is one of the hardest tasks to accomplish, since no knowledge about the novel attacks is available. However, if we view the problem from another angle, we can find a solution. Attacks do something that is different from normal activities: if we have comprehensive knowledge about normal activities and their normal deviations, then all activities ∗This work has been funded by AFRL Rome Labs under the contract F 30602-00-2-0512. †All the authors are at George Mason University, Center for Secure Information Systems Fairfax, VA 22303