title: Injectable Poly-l-Lactic Acid: A Novel Sculpting Agent for the Treatment of Dermal Fat Atrophy After Severe Acne

abstract: Acne vulgaris affects up to 80% of people 11 to 30 years of age, and scarring can occur for up to 95% of these patients. Scarring may be pitted or hypertrophic in nature, although in most cases it is atrophic. Atrophic acne scarring follows dermal collagen and fat loss after moderate to severe acne infection. Injectable poly-L-acid (PLLA) is a biocompatible, biodegradable, synthetic polymer device that is hypothesized to enhance dermal volume via the endogenous production of fibroblasts and, subsequently, collagen. The gradual improvements in cutaneous volume observed after treatment with injectable PLLA have been noted to last up to 2 years. The case studies presented describe the use of injectable PLLA to correct dermal fat loss in macular atrophic acne scarring of the cheeks. Two female patients underwent three treatment sessions with injectable PLLA over a 12-week period. At each treatment session, the reconstituted product was injected into the deep dermis under the depressed portion of the scar. Both patients were extremely pleased with their results at, respectively, 1- and 4-year follow-up evaluations. Patients experienced minimal swelling and redness after injection and no product-related adverse events such as papule and/or nodule formation. The author believes these data suggest that injectable PLLA is a good treatment option for the correction of macular atropic scarring with thin dermis (off-label use), particularly compared with other injectable fillers currently used for this indication that have shorter durations of effect.


title: Sparse nonparametric density estimation in high dimensions using the rodeo

abstract: We consider the problem of estimating the joint density of a d-dimensional random vector X = (X1,X2, ...,Xd) when d is large. We assume that the density is a product of a parametric component and a nonparametric component which depends on an unknown subset of the variables. Using a modification of a recently developed nonparametric regression framework called rodeo (regularization of derivative expectation operator), we propose a method to greedily select bandwidths in a kernel density estimate. It is shown empirically that the density rodeo works well even for very high dimensional problems. When the unknown density function satisfies a suitably defined sparsity condition, and the parametric baseline density is smooth, the approach is shown to achieve near optimal minimax rates of convergence, and thus avoids the curse of dimensionality.


title: SPOT the Drug! An Unsupervised Pattern Matching Method to Extract Drug Names from Very Large Clinical Corpora

abstract: Although structured electronic health records are becoming more prevalent, much information about patient health is still recorded only in unstructured text. """"Understanding"""" these texts has been a focus of natural language processing research for many years, with some remarkable successes. Knowing the drugs patients take is not only critical for understanding patient health (e.g., for drug-drug interactions or drug-enzyme interaction), but also for secondary uses, such as research on treatment effectiveness. Several drug dictionaries have been curated, such as RxNorm or FDA's Orange Book, with a focus on prescription drugs. Developing these dictionaries is a challenge, but even more challenging is keeping these dictionaries up-to-date in the face of a rapidly advancing field. To discover other, new adverse drug interactions, a large number of patient histories often need to be examined, necessitating not only accurate but also fast algorithms to identify pharmacological substances. We propose a new algorithm, SPOT, which identifies drug names that can be used as new dictionary entries from a large corpus, where a """"drug"""" is defined as a substance intended for use in the diagnosis, cure, mitigation, treatment, or prevention of disease. Measured against a manually annotated gold-standard corpus, we present precision and recall values for SPOT. SPOT is language and syntax independent, can be run efficiently to keep dictionaries up-to-date and to also suggest words and phrases which may be misspellings or uncatalogued synonyms of a known drug. We show how SPOT's lack of reliance on NLP tools makes it robust in analyzing clinical medical text. SPOT is a generalized bootstrapping algorithm, seeded with a known dictionary and automatically extracting the context within which each drug is mentioned. We define three features of such context: support, confidence and prevalence. We present the performance tradeoffs depending on the thresholds chosen for these features.


title: IoT-O, a Core-Domain IoT Ontology to Represent Connected Devices Networks

abstract: Smart objects are now present in our everyday lives, and the Internet of Things is expanding both in number of devices and in volume of produced data. These devices are deployed in dynamic ecosystems, with spatial mobility constraints, intermittent network availability depending on many parameters e.g. battery level or duty cycle, etc. To capture knowledge describing such evolving systems, open, shared and dynamic knowledge representations are required. These representations should also have the ability to adapt over time to the changing state of the world. That is why we propose IoT-O, a core-domain modular IoT ontology proposing a vocabulary to describe connected devices and their relation with their environment. First, existing IoT ontologies are described and compared to requirements an IoT ontology should be compliant with. Then, after a detailed description of its modules, IoT-O is instantiated in a home automation use case to illustrate how it supports the description of evolving systems.


title: Development of a Spherical Ultrasonic Motor with an Attitude Sensing System using Optical Fibers

abstract: We present a spherical ultrasonic motor (SUSM) and an attitude sensing system using optical fibers for the SUSM. The SUSM is constructed from three ring-shaped stators and a spherical rotor, and has three degrees of freedom (DOF). It has good responsiveness, high positioning accuracy, and strong magnetic field compatibility. In the attitude sensing system, a flat mirror is built in the spherical rotor. Light is emitted to the mirror, and the reflection is caught by optical fibers. The light intensities captured by the optical fibers are processed by a neural network and are converted to attitude information for the spherical rotor. We describe a system comprising the SUSM and the attitude sensing system with its design derived from mechanics and optics. The prototype SUSM is 26mm in diameter and was actuated by the attitude sensing system. Position errors with respect to x and y axes are less than 0.5deg.


title: Multi-robot long-term persistent coverage with fuel constrained robots

abstract: In this paper, we present an algorithm to solve the Multi-Robot Persistent Coverage Problem (MRPCP). Here, we seek to compute a schedule that will allow a fleet of agents to visit all targets of a given set while maximizing the frequency of visitation and maintaining a sufficient fuel capacity by refueling at depots. We also present a heuristic method to allow us to compute bounded suboptimal results in real time. The results produced by our algorithm will allow a team of robots to efficiently cover a given set of targets or tasks persistently over long periods of time, even when the cost to transition between tasks is dynamic.


title: Highly testable finite state machines based on EXOR logic

abstract: It is well-known that AND/EXOR circuits are more easily testable than AND/OR circuits. Therefore, in this paper, we primarily propose to use AND/EXOR realizations for implementation of the combinational logic parts of finite state machines. Then, we investigate the effect of different state assignments (i.e. one-hot, grey-code, etc.) and that of using different types of registers (i.e. D-type, JK-type, etc) on the testability of finite state machines. As the basis of our measurements, we considered two easily testable AND/EXOR realizations; one for EXOR sum-of-products expressions and the other for generalized Reed-Muller expressions. We make comparisons of these realizations in terms of area and the number of test patterns as we change the state assignment and the type of registers. We also show that 2-level AND/EXOR realizations can yield less area than 2-level AND/OR realizations in the implementation of finite state machines.


title: A review of dynamic vehicle routing problems

abstract: A number of technological advances have led to a renewed interest in dynamic vehicle routing problems. This survey classifies routing problems from the perspective of information quality and evolution. After presenting a general description of dynamic routing, we introduce the notion of degree of dynamism, and present a comprehensive review of applications and solution methods for dynamic vehicle routing problems.


title: Differential Angular Imaging for Material Recognition

abstract: Material recognition for real-world outdoor surfaces has become increasingly important for computer vision to support its operation "in the wild." Computational surface modeling that underlies material recognition has transitioned from reflectance modeling using in-lab controlled radiometric measurements to image-based representations based on internet-mined images of materials captured in the scene. We propose to take a middle-ground approach for material recognition that takes advantage of both rich radiometric cues and flexible image capture. We realize this by developing a framework for differential angular imaging, where small angular variations in image capture provide an enhanced appearance representation and significant recognition improvement. We build a large-scale material database, Ground Terrain in Outdoor Scenes (GTOS) database, geared towards real use for autonomous agents. The database consists of over 30,000 images covering 40 classes of outdoor ground terrain under varying weather and lighting conditions. We develop a novel approach for material recognition called a Differential Angular Imaging Network (DAIN) to fully leverage this large dataset. With this novel network architecture, we extract characteristics of materials encoded in the angular and spatial gradients of their appearance. Our results show that DAIN achieves recognition performance that surpasses single view or coarsely quantized multiview images. These results demonstrate the effectiveness of differential angular imaging as a means for flexible, in-place material recognition.


title: Statistics of Speckle in Ultrasound B-Scans

abstract: the of the magnitude, i.e., intensity, of the field.) It is shown that Rayleigh statistics govern the fist-order behavior of the magnitude; and the autocorrelation of the resulting image speckle is obtained by the methodof Middleton. The corresponding power spectrum follows immediately by Fourier transformation. Theoretical and experimentally determined autocorrelation functions and power spectra derived from B-scans of a scattering phantom containing many scatterers per resolution cell are presented. These functions lead naturally to the definition of the average speckle spot or cell sue, and this inturn is comparable to the resolution cell. Each independent speckle servesas a degreeof freedom that determines the number of samples of tissue available over a target.As the speckle cell size decreases this number increases in a manner predictable from the physical parameters of the cell size. However, it is found that the speckle cellis broadened, the degrees of freedom diminished, when the object structureis correlated. This yields the possibilityof deducing information about the object structure from the second-order statistics of the speckle texture, in addition to that obtainable from the fiistorder statistics.


title: Online Education in Computer and Digital Forensics: A Case Study

abstract: Computer forensics is a relatively new, but growing, field of study at the undergraduate college and university level. This paper describes some of the course design aspects of teaching computer forensics in an online environment. The learning theories and pedagogies that provide the guiding principles for course design are presented, along with specific issues related to adult education. The paper then presents a detailed description of the design of an introductory computer forensics course, with particular attention to the issue of hands-on assignments in the online environment. Finally, a small study about the efficacy of the online courses is presented


title: Individual differences in adult decision-making competence (A-DMC

abstract: The authors evaluated the reliability and validity of a set of 7 behavioral decision-making tasks, measuring different aspects of the decision-making process. The tasks were administered to individuals from diverse populations. Participants showed relatively consistent performance within and across the 7 tasks, which were then aggregated into an Adult Decision-Making Competence (A-DMC) index that showed good reliability. The validity of the 7 tasks and of overall A-DMC emerges in significant relationships with measures of socioeconomic status, cognitive ability, and decision-making styles. Participants who performed better on the A-DMC were less likely to report negative life events indicative of poor decision making, as measured by the Decision Outcomes Inventory. Significant predictive validity remains when controlling for demographic measures, measures of cognitive ability, and constructive decision-making styles. Thus, A-DMC appears to be a distinct construct relevant to adults' real-world decisions.


title: Crowdsourcing Open Interactive Narrative

abstract: Interactive narrative is a form of digital interactive experience in which users influence a dramatic storyline through their actions. Artificial intelligence approaches to interactive narrative use a domain model to determine how the narrative should unfold based on user actions. However, domain models for interactive narrative require artificial intelligence and knowledge representation expertise. We present open interactive narrative, the problem of generating an interactive narrative experience about any possible topic. We present an open interactive narrative system— Scherazade IF—that learns a domain model from crowdsourced example stories so that the player can perform different actions and still receive a coherent story experience. We report on an evaluation of our system showing near-human level authoring.


title: Pedestrian detection using dense LDB descriptor combined with HOG

abstract: Pedestrian detection plays a vital role in numerous vision-based safety and security applications in recent days. Given an image, a pedestrian detector computes features from it and works on the features to classify if there is pedestrian. This paper presents a new feature set for pedestrian detection where a modified version of the local difference binary features are combined with the histogram of oriented gradients features. The linear support vector machine is used as the classifier. The performance of the proposed detector is presented in terms of miss-rate versus FPPI and miss-rate versus FPPW, and is compared with available pedestrian detectors of similar type. The computational efficiency of the detector is also studied.


WarpGAN : Automatic Caircature Generation


title: The COMpliance PraxiS Survey (COMPASS): a multidimensional instrument to monitor compliance for patients on antihypertensive medication

abstract: Low compliance is suspected as a major reason for treatment failure in hypertensive patients. To identify patients with low compliance at the commencement of antihypertensive treatment, the compliance praxis survey (COMPASS) was utilised. A total of 161 physicians identified 2389 hypertensive patients treated by ACE-inhibitor. The physicians rated the compliance of the patients at baseline and at 6-month follow-up prospectively. Standard care was given. The mean age of patients was 64.5 years (54.4% women). The baseline overall score of compliance was medium to high in 97.3% of patients. A quarter of all patients (24.7%) were described as having difficulties to follow lifestyle changes, 22.3% lacked sufficient social support, and 31.0% were unwilling to obtain additional information about illness and treatment. Patients who had received prior treatment for cardiovascular disease were less compliant than those who received first treatment within this study (P=0.05). Younger (P<0.01), male patients (P<0.01), and those without prior cardiovascular disease (P<0.001) were significantly more likely to stop the antihypertensive treatment without a doctor's recommendation. Doctors' rating of compliance at baseline correlated well with ongoing treatment at 6-month follow-up. In conclusion, the COMPASS survey questionnaire is a useful instrument for doctors to differentiate between patients who lack resources and to then counsel patients based on their individual needs.


title: Evaluation of Two WiFi Positioning Systems Based on Autonomous Crowdsourcing of Handheld Devices for Indoor Navigation

abstract: Current WiFi positioning systems (WPSs) require databases – such as locations of WiFi access points and propagation parameters, or a radio map – to assist with positioning. Typically, procedures for building such databases are time-consuming and labour-intensive. In this paper, two autonomous crowdsourcing systems are proposed to build the databases on handheld devices by using our designed algorithms and an inertial navigation solution from a Trusted Portable Navigator (T-PN). The proposed systems, running on smartphones, build and update the database autonomously and adaptively to account for the dynamic environment. To evaluate the performance of automatically generated databases, two improved WiFi positioning schemes (fingerprinting and trilateration) corresponding to these two database building systems, are also discussed. The main contribution of the paper is the proposal of two crowdsourcing-based WPSs that eliminate the various limitations of current crowdsourcing-based systems which (a) require a floor plan or GPS, (b) are suitable only for specific indoor environments, and (c) implement a simple MEMS-based sensors’ solution. In addition, these two WPSs are evaluated and compared through field tests. Results in different test scenarios show that average positioning errors of both proposed systems are all less than 5.75 m.


A Real-time Physical Human-Robot Interaction Framework with Robust Background Invariant Hand Gesture Detection


title: Accelerating Scientific Data Exploration via Visual Query Systems

abstract: The increasing availability of rich and complex data in a variety of scientific domains poses a pressing need for tools to enable scientists to rapidly make sense of and gather insights from data. One proposed solution is to design visual query systems (VQSs) that allow scientists to search for desired patterns in their datasets. While many existing VQSs promise to accelerate exploratory data analysis by facilitating this search, they are unfortunately not widely used in practice. Through a year-long collaboration with scientists in three distinct domains---astronomy, genetics, and material science---we study the impact of various features within VQSs that can aid rapid visual data analysis, and how VQSs fit into a scientists' analysis workflow. Our findings offer design guidelines for improving the usability and adoption of next-generation VQSs, paving the way for VQSs to be applied to a variety of scientific domains.


title: Hydraulically coupled power‐plants commitment within short‐term operation planning in mixed hydro‐thermal power systems

abstract: A new method for short-term operation planning and power exchange scheduling of interconnected hydro-thermal power systems is proposed in the paper. The criterion function is the total variable profit obtained by sales of energy to customers within the system, exchange of energy with other systems, diminished by the cost of system operation. The method is based on Lagrangian relaxation, decomposition technique and successive solution of dual and primal problems. The dual and primal problems are decomposed and solved using appropriate optimization methods, while Lagrangian multipliers are corrected using subgradient method. Dual hydro subproblems are solved using linearization and network flow method. Special modification of the algorithm is made so as to enable commitment of hydro power plants, taking into account bilogical constraints on minimal flow of water downstream of the hydro power plant. The efficiency of the method is illustrated on a test problem.


Embodied Construction Grammar


title: Learning image representations equivariant to ego-motion.

abstract: Understanding how images of objects and scenes behave in response to specific ego-motions is a crucial aspect of proper visual development, yet existing visual learning methods are conspicuously disconnected from the physical source of their images. We propose to exploit proprioceptive motor signals to provide unsupervised regularization in convolutional neural networks to learn visual representations from egocentric video. Specifically, we enforce that our learned features exhibit equivariance i.e. they respond systematically to transformations associated with distinct ego-motions. With three datasets, we show that our unsupervised feature learning system significantly outperforms previous approaches on visual recognition and next-best-view prediction tasks. In the most challenging test, we show that features learned from video captured on an autonomous driving platform improve large-scale scene recognition in a disjoint domain.


title: Fourth Workshop and Tutorial on Practical Use of Coloured Petri Nets and the CPN Tools

abstract: This booklet contains the proceedings of the Fourth Workshop on Practical Use of Coloured Petri Nets and the CPN Tools, August 28-30, 2002. The workshop is organised by the CPN group at the Department of Computer Science, University of Aarhus, Denmark. The papers are also available in electronic form via the web pages: http://www.daimi.au.dk/CPnets/workshop02


title: Ensemble of feature sets and classification algorithms for sentiment classification

abstract: In this paper, we make a comparative study of the effectiveness of ensemble technique for sentiment classification. The ensemble framework is applied to sentiment classification tasks, with the aim of efficiently integrating different feature sets and classification algorithms to synthesize a more accurate classification procedure. First, two types of feature sets are designed for sentiment classification, namely the part-of-speech based feature sets and the word-relation based feature sets. Second, three well-known text classification algorithms, namely [email protected]?ve Bayes, maximum entropy and support vector machines, are employed as base-classifiers for each of the feature sets. Third, three types of ensemble methods, namely the fixed combination, weighted combination and meta-classifier combination, are evaluated for three ensemble strategies. A wide range of comparative experiments are conducted on five widely-used datasets in sentiment classification. Finally, some in-depth discussion is presented and conclusions are drawn about the effectiveness of ensemble technique for sentiment classification.


title: Learning approximate neural estimators for wireless channel state information

abstract: Estimation is a critical component of synchronization in wireless and signal processing systems. There is a rich body of work on estimator derivation, optimization, and statistical characterization from analytic system models which are used pervasively today. We explore an alternative approach to building estimators which relies principally on approximate regression using large datasets and large computationally efficient artificial neural network models capable of learning non-linear function mappings which provide compact and accurate estimates. For single carrier PSK modulation, we explore the accuracy and computational complexity of such estimators compared with the current gold-standard analytically derived alternatives. We compare performance in various wireless operating conditions and consider the trade offs between the two different classes of systems. Our results show the learned estimators can provide improvements in areas such as short-time estimation and estimation under non-trivial real world channel conditions such as fading or other non-linear hardware or propagation effects.


Assembly Classifier Approach to Analyze Intrusion Detection Dataset in Networks by Using Data Mining Techniques


title: Implementing Efficient All Solutions SAT Solvers

abstract: All solutions SAT (AllSAT for short) is a variant of the propositional satisfiability problem. AllSAT has been relatively unexplored compared to other variants despite its significance. We thus survey and discuss major techniques of AllSAT solvers. We accurately implemented them and conducted comprehensive experiments using a large number of instances and various types of solvers including a few publicly available software. The experiments revealed the solvers’ characteristics. We made our implemented solvers publicly available so that other researchers can easily develop their solvers by modifying our code and comparing it with existing methods.


Elicitation and Prioritization of Software requirements


title: Hand gestures to control infotainment equipment in cars

abstract: Controlling multiple devices while driving steals drivers' attention from the road and is becoming the cause of accidents in 1 out of 3 cases. Many research efforts are being dedicated to design, manufacture and test Human-Machine Interfaces that allow operating car devices without distracting the drivers' attention. A complete system for controlling the infotainment equipment through hand gestures is explained in this paper. The system works with a visible-infrared camera mounted on the ceiling of the car and pointing to the shift-stick area, and is based in a combination of some new and some well-known computer vision algorithms. The system has been tested by 23 volunteers on a car simulator and a real vehicle and the results show that the users slightly prefer this system to an equivalent one based on a touch-screen interface.


title: You are your Metadata: Identification and Obfuscation of Social Media Users using Metadata Information

abstract: Metadata are associated to most of the information we produce in our daily interactions and communication in the digital world. Yet, surprisingly, metadata are often still catergorized as non-sensitive. Indeed, in the past, researchers and practitioners have mainly focused on the problem of the identification of a user from the content of a message. In this paper, we use Twitter as a case study to quantify the uniqueness of the association between metadata and user identity and to understand the effectiveness of potential obfuscation strategies. More specifically, we analyze atomic fields in the metadata and systematically combine them in an effort to classify new tweets as belonging to an account using different machine learning algorithms of increasing complexity. We demonstrate that through the application of a supervised learning algorithm, we are able to identify any user in a group of 10,000 with approximately 96.7% accuracy. Moreover, if we broaden the scope of our search and consider the 10 most likely candidates we increase the accuracy of the model to 99.22%. We also found that data obfuscation is hard and ineffective for this type of data: even after perturbing 60% of the training data, it is still possible to classify users with an accuracy higher than 95%. These results have strong implications in terms of the design of metadata obfuscation strategies, for example for data set release, not only for Twitter, but, more generally, for most social media platforms.


title: GPU Performance vs. Thread-Level Parallelism: Scalability Analysis and a Novel Way to Improve TLP

abstract: Graphics Processing Units (GPUs) leverage massive thread-level parallelism (TLP) to achieve high computation throughput and hide long memory latency. However, recent studies have shown that the GPU performance does not scale with the GPU occupancy or the degrees of TLP that a GPU supports, especially for memory-intensive workloads. The current understanding points to L1 D-cache contention or off-chip memory bandwidth. In this article, we perform a novel scalability analysis from the perspective of throughput utilization of various GPU components, including off-chip DRAM, multiple levels of caches, and the interconnect between L1 D-caches and L2 partitions. We show that the interconnect bandwidth is a critical bound for GPU performance scalability. ::: ::: For the applications that do not have saturated throughput utilization on a particular resource, their performance scales well with increased TLP. To improve TLP for such applications efficiently, we propose a fast context switching approach. When a warp/thread block (TB) is stalled by a long latency operation, the context of the warp/TB is spilled to spare on-chip resource so that a new warp/TB can be launched. The switched-out warp/TB is switched back when another warp/TB is completed or switched out. With this fine-grain fast context switching, higher TLP can be supported without increasing the sizes of critical resources like the register file. Our experiment shows that the performance can be improved by up to 47% and a geometric mean of 22% for a set of applications with unsaturated throughput utilization. Compared to the state-of-the-art TLP improvement scheme, our proposed scheme achieves 12% higher performance on average and 16% for unsaturated benchmarks.


title: The Minimum Rank with Hysteresis Objective Function

abstract: The Routing Protocol for Low-Power and Lossy Networks (RPL) constructs ::: routes by using Objective Functions that optimize or constrain the ::: routes it selects and uses. This specification describes the Minimum ::: Rank with Hysteresis Objective Function (MRHOF), an Objective Function ::: that selects routes that minimize a metric, while using hysteresis to ::: reduce churn in response to small metric changes. MRHOF works with ::: additive metrics along a route, and the metrics it uses are determined ::: by the metrics that the RPL Destination Information Object (DIO) ::: messages advertise. [STANDARDS-TRACK]


title: Behavioral, Neural, and Computational Principles of Bodily Self-Consciousness

abstract: Recent work in human cognitive neuroscience has linked self-consciousness to the processing of multisensory bodily signals (bodily self-consciousness [BSC]) in fronto-parietal cortex and more posterior temporo-parietal regions. We highlight the behavioral, neurophysiological, neuroimaging, and computational laws that subtend BSC in humans and non-human primates. We propose that BSC includes body-centered perception (hand, face, and trunk), based on the integration of proprioceptive, vestibular, and visual bodily inputs, and involves spatio-temporal mechanisms integrating multisensory bodily stimuli within peripersonal space (PPS). We develop four major constraints of BSC (proprioception, body-related visual information, PPS, and embodiment) and argue that the fronto-parietal and temporo-parietal processing of trunk-centered multisensory signals in PPS is of particular relevance for theoretical models and simulations of BSC and eventually of self-consciousness.


title: A Tensor Spectral Approach to Learning Mixed Membership Community Models

abstract: Detecting hidden communities from observed interactions is a classical problem. Theoretical analysis of community detection has so far been mostly limited to models with non-overlapping communities such as the stochastic block model. In this paper, we provide guaranteed community detection for a family of probabilistic network models with overlapping communities, termed as the mixed membership Dirichlet model, first introduced in Airoldi et al. (2008). This model allows for nodes to have fractional memberships in multiple communities and assumes that the community memberships are drawn from a Dirichlet distribution. Moreover, it contains the stochastic block model as a special case. We propose a unified approach to learning communities in these models via a tensor spectral decomposition approach. Our estimator uses low-order moment tensor of the observed network, consisting of 3-star counts. Our learning method is based on simple linear algebraic operations such as singular value decomposition and tensor power iterations. We provide guaranteed recovery of community memberships and model parameters, and present a careful finite sample analysis of our learning method. Additionally, our results match the best known scaling requirements for the special case of the (homogeneous) stochastic block model.


title: Adaptive degraded document . . .

abstract: This paper presents a new adaptive approach for the binarization and enhancement of degraded documents. The proposed method does not require any parameter tuning by the user and can deal with degradations which occur due to shadows, non-uniform illumination, low contrast, large signal-dependent noise, smear and strain. We follow several distinct steps: a pre-processing procedure using a low-pass Wiener filter, a rough estimation of foreground regions, a background surface calculation by interpolating neighboring background intensities, a thresholding by combining the calculated background surface with the original image while incorporating image up-sampling and finally a post-processing step in order to improve the quality of text regions and preserve stroke connectivity. After extensive experiments, our method demonstrated superior performance against four (4) well-known techniques on numerous degraded document images.


title: Spectral conversion based on maximum likelihood estimation considering global variance of converted parameter

abstract: The paper describes a novel spectral conversion method for voice transformation. We perform spectral conversion between speakers using a Gaussian mixture model (GMM) on the joint probability density of source and target features. A smooth spectral sequence can be estimated by applying maximum likelihood (ML) estimation to the GMM-based mapping using dynamic features. However, there is still degradation of the converted speech quality due to an over-smoothing of the converted spectra, which is inevitable in conventional ML-based parameter estimation. In order to alleviate the over-smoothing, we propose an ML-based conversion taking account of the global variance of the converted parameter in each utterance. Experimental results show that the performance of the voice conversion can be improved by using the global variance information. Moreover, it is demonstrated that the proposed algorithm is more effective than spectral enhancement by postfiltering.


title: Cryptanalysis and Improvement of Authentication and Key Agreement Protocols for Telecare Medicine Information Systems

abstract: Recently, many authentication protocols have been presented using smartcard for the telecare medicine information system (TMIS). In 2014, Xu et al. put forward a two-factor mutual authentication with key agreement protocol using elliptic curve cryptography (ECC). However, the authors have proved that the protocol is not appropriate for practical use as it has many problems (1) it fails to achieve strong authentication in login and authentication phases; (2) it fails to update the password correctly in the password change phase; (3) it fails to provide the revocation of lost/stolen smartcard; and (4) it fails to protect the strong replay attack. We then devised an anonymous and provably secure two-factor authentication protocol based on ECC. Our protocol is analyzed with the random oracle model and demonstrated to be formally secured against the hardness assumption of computational Diffie-Hellman problem. The performance evaluation demonstrated that our protocol outperforms from the perspective of security, functionality and computation costs over other existing designs.


title: Characterizing Botnets from Email Spam Records

abstract: We develop new techniques to map botnet membership using traces of spam email. To group bots into botnets we look for multiple bots participating in the same spam email campaign. We have applied our technique against a trace of spam email from Hotmail Web mail services. In this trace, we have successfully identified hundreds of botnets. We present new findings about botnet sizes and behavior while also confirming other researcher's observations derived by different methods [1, 15].


title: Annotating Anaphoric Shell Nouns with their Antecedents

abstract: Anaphoric shell nouns such as this issue and this fact conceptually encapsulate complex pieces of information (Schmid, 2000). We examine the feasibility of annotating such anaphoric nouns using crowdsourcing. In particular, we present our methodology for reliably annotating antecedents of such anaphoric nouns and the challenges we faced in doing so. We also evaluated the quality of crowd annotation using experts. The results suggest that most of the crowd annotations were good enough to use as training data for resolving such anaphoric nouns.


title: Spaceship Launch: Designing a Collaborative Exergame for Families

abstract: Parents play a critical role in facilitating children's physical activity, as they are an important source of modeling and support. While Human-Computer Interaction (HCI) researchers have explored exergame design for children or adults separately, an important open area of work is identifying design guidelines for family exergames. One question that researchers have increasingly posed is, how can exergames be designed to avoid potential negative consequences of competition? To address these questions we designed Spaceship Launch, an exergame for parents and kids in lower income neighborhoods, where obesity is most prevalent. We describe our iterative design process: the formative study to identify design opportunities, our resulting system, and our field evaluation of the tool. Our findings highlight the impact of SL on physical activity intentions, and how parental preferences for in-game competition were aligned with the psychological needs of relatedness and competence. We conclude with design recommendations for future family-focused exergames.


title: Boosted local structured HOG-LBP for object localization

abstract: Object localization is a challenging problem due to variations in object's structure and illumination. Although existing part based models have achieved impressive progress in the past several years, their improvement is still limited by low-level feature representation. Therefore, this paper mainly studies the description of object structure from both feature level and topology level. Following the bottom-up paradigm, we propose a boosted Local Structured HOG-LBP based object detector. Firstly, at feature level, we propose Local Structured Descriptor to capture the object's local structure, and develop the descriptors from shape and texture information, respectively. Secondly, at topology level, we present a boosted feature selection and fusion scheme for part based object detector. All experiments are conducted on the challenging PASCAL VOC2007 datasets. Experimental results show that our method achieves the state-of-the-art performance.


title: Towards Explainable Music Emotion Recognition: The Route via Mid-level Features

abstract: Emotional aspects play an important part in our interaction with music. However, modelling these aspects in MIR systems have been notoriously challenging since emotion is an inherently abstract and subjective experience, thus making it difficult to quantify or predict in the first place, and to make sense of the predictions in the next. In an attempt to create a model that can give a musically meaningful and intuitive explanation for its predictions, we propose a VGG-style deep neural network that learns to predict emotional characteristics of a musical piece together with (and based on) human-interpretable, mid-level perceptual features. We compare this to predicting emotion directly with an identical network that does not take into account the mid-level features and observe that the loss in predictive performance of going through the mid-level features is surprisingly low, on average. The design of our network allows us to visualize the effects of perceptual features on individual emotion predictions, and we argue that the small loss in performance in going through the mid-level features is justified by the gain in explainability of the predictions.


title: AOD-Net: All-in-One Dehazing Network

abstract: This paper proposes an image dehazing model built with a convolutional neural network (CNN), called All-in-One Dehazing Network (AOD-Net). It is designed based on a re-formulated atmospheric scattering model. Instead of estimating the transmission matrix and the atmospheric light separately as most previous models did, AOD-Net directly generates the clean image through a light-weight CNN. Such a novel end-to-end design makes it easy to embed AOD-Net into other deep models, e.g., Faster R-CNN, for improving high-level tasks on hazy images. Experimental results on both synthesized and natural hazy image datasets demonstrate our superior performance than the state-of-the-art in terms of PSNR, SSIM and the subjective visual quality. Furthermore, when concatenating AOD-Net with Faster R-CNN, we witness a large improvement of the object detection performance on hazy images.


title: MMD GAN: Towards Deeper Understanding of Moment Matching Network

abstract: Generative moment matching network (GMMN) is a deep generative model that differs from Generative Adversarial Network (GAN) by replacing the discriminator in GAN with a two-sample test based on kernel maximum mean discrepancy (MMD). Although some theoretical guarantees of MMD have been studied, the empirical performance of GMMN is still not as competitive as that of GAN on challenging and large benchmark datasets. The computational efficiency of GMMN is also less desirable in comparison with GAN, partially due to its requirement for a rather large batch size during the training. In this paper, we propose to improve both the model expressiveness of GMMN and its computational efficiency by introducing adversarial kernel learning techniques, as the replacement of a fixed Gaussian kernel in the original GMMN. The new approach combines the key ideas in both GMMN and GAN, hence we name it MMD GAN. The new distance measure in MMD GAN is a meaningful loss that enjoys the advantage of weak topology and can be optimized via gradient descent with relatively small batch sizes. In our evaluation on multiple benchmark datasets, including MNIST, CIFAR- 10, CelebA and LSUN, the performance of MMD-GAN significantly outperforms GMMN, and is competitive with other representative GAN works.


title: Survey of Stochastic Computing

abstract: Stochastic computing (SC) was proposed in the 1960s as a low-cost alternative to conventional binary computing. It is unique in that it represents and processes information in the form of digitized probabilities. SC employs very low-complexity arithmetic units which was a primary design concern in the past. Despite this advantage and also its inherent error tolerance, SC was seen as impractical because of very long computation times and relatively low accuracy. However, current technology trends tend to increase uncertainty in circuit behavior and imply a need to better understand, and perhaps exploit, probability in computation. This article surveys SC from a modern perspective where the small size, error resilience, and probabilistic features of SC may compete successfully with conventional methodologies in certain applications. First, we survey the literature and review the key concepts of stochastic number representation and circuit structure. We then describe the design of SC-based circuits and evaluate their advantages and disadvantages. Finally, we give examples of the potential applications of SC and discuss some practical problems that are yet to be solved.


title: Selecting a set of appropriate metrics for detecting code clones

abstract: Most of the software systems consist of a number of code clones. Although it makes the task of software development easy but at the same time code cloning may cause several maintenance and cost related problems. A number of clone detection techniques have been proposed so far. In this paper, an approach for selecting a set of appropriate metrics from a list of large number of metrics is presented. The proposed approach evaluates a set of independent metrics on the basis of their precision and recall values in clone detection starting from all combinations of one metric and then gradually increasing the number of metrics in the metrics combinations until the complete set of metrics involved in the approach are evaluated. The result of implementing the proposed approach on a C language software system is provided as example.


title: Adherence factors to paperless primary healthcare: a focus group study

abstract: Background: Information and communication technology (ICT) and paperless practices have been shown to improve “existing processes in the workplace” “as well as being an important component of modern primary healthcare”. The aim of our study was to analyse the attitudes of health-care professionals and patients with regard to paperless practice and the most frequently used information and communication technology tools in Slovenian primary healthcare. Methods and participants: Qualitative methodology using focus groups of 22 primary care physicians, 14 nurses and 18 patients. Results: The areas recognised by all participants as important for further information and communication technology development were: computer-supported decision making, accessibility and completeness of personal e-health data, emergency cases, support for chronic disease management, ICT related time savings, e-prescriptions and e-discharge letters. The most important identified barriers impeding the use of ICT were: the heavy workload of primary care physicians and nurses, health insurance reimbursement rules and duplication of work using both paper and electronic health records. Conclusions: This study highlighted a number of strengths of ICT use in primary care as well as numerous areas where changes in procedures and improvement of ICT tools to support them are needed.


title: Adalimumab for the Treatment of Moderate to Severe Hidradenitis Suppurativa

abstract: Background: Hidradenitis suppurativa (HS) is a chronic, painful skin disease characterized by abscesses, nodules, and draining fistulas in the axilla and groin of young adults. Objective: To evaluate the efficacy and safety of adalimumab, an anti-tumor necrosis factor-αantibody, in patients with moderate to severe HS. Design: Phase 2, parallel, randomized, placebo-controlled trial consisting of a blinded 16-week period (period 1) and an open-label 36-week period (period 2). All study personnel, investigators, and patients remained blinded to treatment group throughout the study. (ClinicalTrials.gov: NCT00918255) Setting: 26 academic and private practice medical centers in the United States and Europe. Patients: 154 adult patients with moderate to severe HS who were unresponsive or intolerant to oral antibiotics. Intervention: Patients were assigned in a 1:1:1 ratio to adalimumab, 40 mg/wk; adalimumab, 40 mg every other week (EOW); or placebo. All patients received adalimumab, 40 mg EOW, at the beginning of period 2 but switched to weekly dosing if the response was suboptimal (HS Physician's Global Assessment [PGA] score of moderate or worse) at weeks 28 or 31. Measurements: The primary outcome measure (clinical response) was the proportion of patients achieving an HS-PGA score of clear, minimal, or mild with at least a 2-grade improvement relative to baseline at week 16. Results: At week 16, 3.9% of placebo patients (2 of 51), 9.6% of EOW patients (5 of 52), and 17.6% of weekly patients (9 of 51) achieved clinical response (EOW vs. placebo strata-adjusted difference, 5.6% [95% CI,- 4.0% to 15.3%]; P = 0.25; weekly vs. placebo strata-adjusted difference, 13.7% [CI, 1.7% to 25.7%]; P = 0.025). Serious adverse event rates were 3.9%, 5.8%, and 7.8% for placebo, EOW, and weekly patients, respectively (EOW vs. placebo difference, 1.8% [CI,- 6.4% to 10.1%]; weekly vs. placebo difference, 3.9% [CI,- 5.2% to 13.0%]). Significantly greater improvements in patient-reported outcomes and pain were seen in the weekly dosing group than in the placebo group. A decrease in response was seen after the switch from weekly to EOW dosing in period 2. Limitations: Weeks 16 to 52 of the study were open-label. The study was not powered to assess the risk for known serious adverse effects of adalimumab, such as tuberculosis, other serious infections, and demyelinating disorders. Conclusion: Adalimumab dosed once per week alleviates moderate to severe HS. Primary Funding Source: Abbott Laboratories.


title: 1 Monocular Scene Reconstruction for Reliable Obstacle Detection and Robot Navigation

abstract: In this paper, we present a feature based approach for monocular scene reconstruction based on extended Kalman filters (EKF). Our method processes a sequence of images taken by a single camera mounted frontal on a mobile robot. Using different techniques, we are able to produce a precise reconstruction that is free from outliers and therefore can be used for reliable obstacle detection. In real-world field-tests we show that the presented approach is able to detect obstacles that are not seen by other sensors, such as laser-range-finder s. Furthermore, we show that visual obstacle detection combined with a laser-range-finder can increase the detection rate of obstacles considerably allowing the autonomous use of mobile robots in complex public environments.


title: Children raised in fatherless families from infancy: a follow-up of children of lesbian and single heterosexual mothers at early adolescence.

abstract: BACKGROUND ::: An increasing number of lesbian women and single heterosexual women are bringing up children with no male involvement. This study follows up to adolescence a sample of children raised in fatherless families from birth or early infancy. ::: ::: ::: METHODS ::: Twenty-five lesbian mother families and 38 families headed by a single heterosexual mother were compared with 38 two-parent heterosexual families. The quality of parenting by the mother, and the social and emotional development of the child, were assessed using standardised interview and questionnaire measures administered to mothers, children and teachers. ::: ::: ::: RESULTS ::: Children in fatherless families experienced more interaction with their mother, and perceived her as more available and dependable than their peers from father-present homes. However, there were no group differences in maternal warmth towards the children. Mothers raising their child without a father reported more severe disputes with their child than did mothers in father-present families. The children's social and emotional development was not negatively affected by the absence of a father, although boys in father-absent families showed more feminine but no less masculine characteristics of gender role behaviour. No major differences in parenting or child development were identified between families headed by lesbian and single heterosexual mothers. ::: ::: ::: CONCLUSIONS ::: The presence or absence of a father in the home from the outset does appear to have some influence on adolescents' relationships with their mothers. However, being without a resident father from infancy does not seem to have negative consequences for children. In addition, there is no evidence that the sexual orientation of the mother influences parent-child interaction or the socioemotional development of the child.


title: A study on improvement of internet traffic measurement and analysis using Hadoop system

abstract: By using conventional database management tools, data explosion is one of the most regular triggers for the development of Big Data. This results in increase difficulty for database management and larger datasets from existing applications. The organizations face the challenge to capture, manage and analyze the data in an acceptable period of time due to the increasing size of data sets, ranging from several terabytes to multiple petabytes. The proposed research aims at resolving the problem faced in internet traffic measurement and depth analysis. As there is a wide growth in the internet traffic and high speed access, the system is facing a scalability problem. To overcome this, we are introducing a Hadoop based traffic monitoring system which performs analysis of multi-terabytes (IP, HTTP, TCP, NetFlow) of the internet traffic system in a scalable manner and for in-depth analysis of the problem, the paper shows the different features.


title: Modified versions of SLIC algorithm for generating superpixels in hyperspectral images

abstract: This paper aims at assessing the performance of the Simple Linear Iterative Clustering (SLIC) superpixel generating algorithm on hyperspectral images. Two modified versions of SLIC algorithm have been proposed. In the first, the HyperSLIC version, modifications were made to the basic algorithm in order to work with higher dimensions. In the second, the FD-SLIC version, a more complex distance measure, the fractional distance, already successfully used in the unmixing procedure was introduced. HyperSLIC was also applied on the abundance maps that are produced by the endmembers of the hyperspectral image. Algorithms have been applied on two images. Evaluation was based on visual inspection, NSE metric and “danger” maps. It has been shown that whole hyperspectral volume and fractional distance metric improves SLIC performance.


title: Understanding Abuse: A Typology of Abusive Language Detection Subtasks

abstract: As the body of research on abusive language detection and analysis grows, there is a need for critical consideration of the relationships between different subtasks that have been grouped under this label. Based on work on hate speech, cyberbullying, and online abuse we propose a typology that captures central similarities and differences between subtasks and we discuss its implications for data annotation and feature construction. We emphasize the practical actions that can be taken by researchers to best approach their abusive language detection subtask of interest.


Dual Transfer Learning for Neural Machine Translation with Marginal Distribution Regularization


title: Parameter identification problems in the modelling of cell motility

abstract: We present a novel parameter identification algorithm for the estimation of parameters in models of cell motility using imaging data of migrating cells. Two alternative formulations of the objective functional that measures the difference between the computed and observed data are proposed and the parameter identification problem is formulated as a minimisation problem of nonlinear least squares type. A Levenberg-Marquardt based optimisation method is applied to the solution of the minimisation problem and the details of the implementation are discussed. A number of numerical experiments are presented which illustrate the robustness of the algorithm to parameter identification in the presence of large deformations and noisy data and parameter identification in three dimensional models of cell motility. An application to experimental data is also presented in which we seek to identify parameters in a model for the monopolar growth of fission yeast cells using experimental imaging data.


Estimating Customer Reviews in Recommender Systems Using Sentiment Analysis Methods


Bi-LSTM Neural Networks for Chinese Grammatical Error Diagnosis.


title: TeamPhone: Networking SmartPhones for Disaster Recovery

abstract: In this paper, we investigate how to network smartphones for providing communications in disaster recovery. By bridging the gaps among different kinds of wireless networks, we have designed and implemented a system called TeamPhone, which provides smartphones the capabilities of communications in disaster recovery. Specifically, TeamPhone consists of two components: A messaging system and a self-rescue system. The messaging system integrates cellular networking, ad-hoc networking, and opportunistic networking seamlessly, and enables communications among rescue workers. The self-rescue system groups, schedules, and positions the smartphones of trapped survivors. Such a group of smartphones can cooperatively wake up and send out emergency messages in an energy-efficient manner with their location and position information so as to assist rescue operations. We have implemented TeamPhone as a prototype application on the Android platform and deployed it on off-the-shelf smartphones. Experimental results demonstrate that TeamPhone can properly fulfill communication requirements and greatly facilitate rescue operations in disaster recovery.


title: Respiratory Rate and Flow Waveform Estimation from Tri-axial Accelerometer Data

abstract: There is a strong medical need for continuous, unobstrusive respiratory monitoring, and many shortcomings to existing methods. Previous work shows that MEMS accelerometers worn on the torso can measure inclination changes due to breathing, from which a respiratory rate can be obtained. There has been limited validation of these methods. The problem of practical continuous monitoring, in which patient movement disrupts the measurements and the axis of interest changes, has also not been addressed. We demonstrate a method based on tri-axial accelerometer data from a wireless sensor device, which tracks the axis of rotation and obtains angular rates of breathing motion. The resulting rates are validated against gyroscope measurements and show high correlation to flow rate measurements using a nasal cannula. We use a movement detection method to classify periods in which the patient is static and breathing signals can be observed accurately. Within these periods we obtain a close match to cannula measurements, for both the flow rate waveform and derived respiratory rates, over multi-hour datasets obtained from wireless sensor devices on hospital patients. We discuss future directions for improvement and potential methods for estimating absolute airflow rate and tidal volume.


title: Deploying and Evaluating Pufferfish Privacy for Smart Meter Data

abstract: Realizing a Smart Grid without sacrificing the privacy of consumers is a challenging problem. Data-centric approaches like Puffer fish ensure privacy by transforming data so that certain user-specified information, so-called secrets, cannot be inferred. Deploying Puffer fish on smart-meter data requires application-specific decisions, i.e., A general definition of secrets in time series. We investigate how to perturb energy consumption data in this manner, and we quantify the trade off between privacy and utility.


title: Wafer level system packaging and integration for solid state lighting (SSL)

abstract: In this paper, wafer level system packaging and integration for solid state lighting (SSL) is presented, which includes wafer level phosphor coating, wafer level LED chip encapsulation, wafer level optics manufacturing, reconfiguration (or reconstitution) of LED/silicon wafers, through silicon vias (TSV) formations between LED and silicon/ceramics/polymer wafers, wafer-to-wafer or wafer-to-chip bonding and stacking, and wafer level bumping technologies. A variety of wafer level bumping technologies is introduced, such as ball on I/O (BON), ball on polymer (BOP), redistribution dielectric layer (RDL) process, and copper post bumping process. The reliability improvement among different bumping technologies and the implications in SSL systems are discussed.


Using Non-Functional Requirements to Systematically Select Among Alternatives in Architectural Design


title: Motion Interchange Patterns for Action Recognition in Unconstrained Videos

abstract: Action Recognition in videos is an active research field that is fueled by an acute need, spanning several application domains. Still, existing systems fall short of the applications' needs in real-world scenarios, where the quality of the video is less than optimal and the viewpoint is uncontrolled and often not static. In this paper, we consider the key elements of motion encoding and focus on capturing local changes in motion directions. In addition, we decouple image edges from motion edges using a suppression mechanism, and compensate for global camera motion by using an especially fitted registration scheme. Combined with a standard bag-of-words technique, our methods achieves state-of-the-art performance in the most recent and challenging benchmarks.


title: Precision grasping and manipulation of small objects from flat surfaces using underactuated fingers

abstract: In this paper we demonstrate an underactuated finger design and grasping method for precision grasping and manipulation of relatively small objects. Taking a cue from human manipulation, we introduce the flip-and-pinch task, in which the hand picks up thin objects from a table surface by flipping it into a stable configuration. Despite the fact that finger motions are not fully constrained by the hand actuators, we demonstrate that the hand and fingers can be configured with the table surface to produce a set of constraints that result in a repeatable quasi-static motion trajectory. This approach is shown to be robust for a variety of object sizes, even when utilizing identical open-loop kinematic playback. Experimental results suggest that the advantages of underactuated, adaptive robot hands can be carried over to dexterous, precision tasks as well.


title: ComboGAN: Unrestrained Scalability for Image Domain Translation

abstract: This year alone has seen unprecedented leaps in the area of learning-based image translation, namely CycleGAN, by Zhu et al. But experiments so far have been tailored to merely two domains at a time, and scaling them to more would require an quadratic number of models to be trained. And with two-domain models taking days to train on current hardware, the number of domains quickly becomes limited by the time and resources required to process them. In this paper, we propose a multi-component image translation model and training scheme which scales linearly - both in resource consumption and time required - with the number of domains. We demonstrate its capabilities on a dataset of paintings by 14 different artists and on images of the four different seasons in the Alps. Note that 14 data groups would need (14 choose 2) = 91 different CycleGAN models: a total of 182 generator/discriminator pairs; whereas our model requires only 14 generator/discriminator pairs.


title: Marital processes predictive of later dissolution: behavior, physiology, and health.

abstract: Seventy-three married couples were studied in 1983 and 1987. To identify marital processes associated with dissolution, a balance theory of marriage was used to generate 1 variable for dividing couples into regulated and nonregulated groups. For studying the precursors of divorce, a "cascade" model of marital dissolution, which forms a Guttman-like scale, received preliminary support. Compared with regulated couples, nonregulated couples had (a) marital problems rated as more severe (Time 1); (b) lower marital satisfaction (Time 1 and Time 2); (c) poorer health (Time 2); (d) smaller finger pulse amplitudes (wives); (e) more negative ratings for interactions; (f) more negative emotional expression; (g) less positive emotional expression; (h) more stubbornness and withdrawal from interaction; (i) greater defensiveness; and (j) greater risk for marital dissolution (lower marital satisfaction and higher incidence of consideration of dissolution and of actual separation).


Combining Social Network Analysis with Semi-supervised Clustering : a case study on fraud detection


title: Ensuring data integrity in storage: techniques and applications

abstract: Data integrity is a fundamental aspect of storage security and reliability. With the advent of network storage and new technology trends that result in new failure modes for storage, interesting challenges arise in ensuring data integrity. In this paper, we discuss the causes of integrity violations in storage and present a survey of integrity assurance techniques that exist today. We describe several interesting applications of storage integrity checking, apart from security, and discuss the implementation issues associated with techniques. Based on our analysis, we discuss the choices and trade-offs associated with each mechanism. We then identify and formalize a new class of integrity assurance techniques that involve logical redundancy. We describe how logical redundancy can be used in today's systems to perform efficient and seamless integrity assurance.


title: Neurocognitive Aging and the Compensation Hypothesis

abstract: The most unexpected and intriguing result from functional brain imaging studies of cognitive aging is evidence for age-related overactivation: greater activation in older adults than in younger adults, even when performance is age-equivalent. Here we examine the hypothesis that age-related overactivation is compensatory and discuss the compensation-related utilization of neural circuits hypothesis (CRUNCH). We review evidence that favors a compensatory account, discuss questions about strategy differences, and consider the functions that may be served by overactive brain areas. Future research directed at neurocognitively informed training interventions may augment the potential for plasticity that persists into the later years of the human lifespan.


title: X-band GaN HEMT advanced power amplifier unit for compact active phased array antennas

abstract: A compact power amplifier unit for X-band solid-state Active Phased Array Antennas (APAAs) has been developed. Its major features are, (1) high power GaN (Gallium Nitride) HEMT is employed, (2)16 transmit MIC modules are arrayed in line on a thin flat printed wiring board (PWB), (3) output power is 30W (pulse) at each transmit-output port of the unit, hence 480 W in total, and (4) other necessary functions for phased array antennas, such as digital phase-shifters and RF dividers, are all included in this PWB. Resulting electrical measured data are presented in detail in the paper, proving that further power and frequency range increase for APAAs would become feasible by the concepts employed in this unit design.


Network Protocols


title: A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms

abstract: Stereo matching is one of the most active research areas in computer vision. While a large number of algorithms for stereo correspondence have been developed, relatively little work has been done on characterizing their performance. In this paper, we present a taxonomy of dense, two-frame stereo methods designed to assess the different components and design decisions made in individual stereo algorithms. Using this taxonomy, we compare existing stereo methods and present experiments evaluating the performance of many different variants. In order to establish a common software platform and a collection of data sets for easy evaluation, we have designed a stand-alone, flexible C++ implementation that enables the evaluation of individual components and that can be easily extended to include new algorithms. We have also produced several new multiframe stereo data sets with ground truth, and are making both the code and data sets available on the Web.


title: Recent Development in Big Data Analytics for Business Operations and Risk Management

abstract: "Big data" is an emerging topic and has attracted the attention of many researchers and practitioners in industrial systems engineering and cybernetics. Big data analytics would definitely lead to valuable knowledge for many organizations. Business operations and risk management can be a beneficiary as there are many data collection channels in the related industrial systems (e.g., wireless sensor networks, Internet-based systems, etc.). Big data research, however, is still in its infancy. Its focus is rather unclear and related studies are not well amalgamated. This paper aims to present the challenges and opportunities of big data analytics in this unique application domain. Technological development and advances for industrial-based business systems, reliability and security of industrial systems, and their operational risk management are examined. Important areas for future research are also discussed and revealed.


title: A Survey of Fog Computing and Communication: Current Researches and Future Directions

abstract: In this survey, we discuss the evolution of distributed computing from the utility computing to the fog computing, various research challenges for the development of fog computing environments, the current status on fog computing research along with a taxonomy of various existing works in this direction. Then, we focus on the architectures of fog computing systems, technologies for enabling fog, fog computing features, security and privacy of fog, the QoS parameters, applications of fog, and give critical insights of various works done on this domain. Lastly, we briefly discuss about different fog computing associations that closely work on the development of fog based platforms and services, and give a summary of various types of overheads associated with fog computing platforms. Finally, we provide a thorough discussion on the future scopes and open research areas in fog computing as an enabler for the next generation computing paradigm.


title: Modeling Past and Future for Neural Machine Translation

abstract: Existing neural machine translation systems do not explicitly model what has been translated and what has not during the decoding phase. To address this problem, we propose a novel mechanism that s...


title: Different patterns of autobiographical memory loss in semantic dementia and medial temporal lobe amnesia: a challenge to consolidation theory.

abstract: Temporally graded retrograde memory loss, with a disproportionate impairment of recent relative to remote memories, is considered a hallmark of medial temporal lobe amnesia. According to consolidation theory, the hippocampal complex, which includes the hippocampal formation, parahippocampal gyrus, the entorhinal and perirhinal cortex, plays a time-limited role in memory, needed only until consolidation in the neocortex is complete (Squire, Psychological Review 1992; 99: 195-231). Recent support for this theory comes from findings of a reverse gradient in people with semantic dementia with neocortical degeneration but a relatively preserved hippocampal complex (Hodges and Graham, Neuropsychologia 1998; 36: 803-25). Consolidation theory is challenged by evidence that remote autobiographical memory is not always spared in amnesia (Nadel and Moscovitch, Current Opinion in Neurobiology 1997; 7: 217-27) and that semantic memory becomes highly personalized in semantic dementia (Snowden et al., Memory 1995; 3: 225-46). According to Nadel and Moscovitch, the hippocampal complex is needed to retain and retrieve detailed memories of autobiographical episodes no matter how old they are. To test consolidation theory against the opposing view, we investigated the role of the hippocampal complex in recent and remote autobiographical and personal semantic memory by contrasting the memory of a semantic dementia patient, EL, with that of an amnesic patient, KC, using family photographs as recall cues. KC demonstrated a complete loss of autobiographical episodes with a sparing of autobiographical facts; EL demonstrated well-preserved memory for episodes with a reverse gradient for personally relevant names. The influence of autobiographical significance on memory for names of public figures was examined further by comparing the effect that familiarity and recollection had on recognition of names of famous people and famous places. EL's memory was influenced by autobiographical significance, whereas KC's was not. We propose that the hippocampal complex plays a permanent role in the storage and retrieval of autobiographical episodes and that autobiographical significance may affect semantic representations.


title: A novel proposal to improve reliability of spoke-type BLDC motor using ferrite permanent magnet

abstract: This paper proposes a new design for a spoke-type BLDC motor using a ferrite permanent magnet (Fe-PM) to improve the reliability of the motor. First, the design process for a neodymium permanent magnet free spoke-type BLDC motor without an inserted additional assistant permanent magnet (PM) is presented. The proposed motor is designed by taking into account the demagnetization phenomenon. Second, the effect of the armature reaction on the PM is analyzed by using the finite element method in order to evaluate the robustness of the proposed motor with Fe-PM. Finally, a prototype motor is manufactured based on the analyzed motor. The output characteristic is verified through an experiment.


title: Buck converter MPPT control of a photovoltaic system

abstract: This work concerns the behavior study of a MPPT algorithm based on the incremental conductance. An open loop analysis of a photovoltaic chain, modeled in matlab-simulink, allows to extract the maximum power of the photovoltaic panel. The principle is based on the determination instantaneously of the conductance and its tendency materialized by a signed increment. A buck step down converter is used to adapt the voltage to its appropriate value to reach a maximal power extraction. This novel analysis method applied to the photovoltaic system is made under different atmospheric parameters. The performances are shown using Matlab/Simulink software.


title: A practical analysis of smartphone security

abstract: Recent developments in mobile technologies have produced a new kind of device, a programmable mobile phone, the smartphone. Generally, smartphone users can program any application which is customized for needs. Furthermore, they can share these applications in online market. Therefore, smartphone and its application are now most popular keywords in mobile technology. However, to provide these customized services, smartphone needs more private information and this can cause security vulnerabilities. Therefore, in this work, we analyze security of smartphone based on its environments and describe countermeasures.


title: A Reading Comprehension Style Question Answering Model Based On Attention Mechanism

abstract: In recent years, research on reading-compr question and answering has drawn intense attention in Language Processing. However, it is still a key issue to the high-level semantic vector representation of quest paragraph. Drawing inspiration from DrQA [1], wh question and answering system proposed by Facebook, tl proposes an attention-based question and answering 11 adds the binary representation of the paragraph, the par; attention to the question, and the question's attentioi paragraph. Meanwhile, a self-attention calculation m proposed to enhance the question semantic vector reption. Besides, it uses a multi-layer bidirectional Lon: Term Memory(BiLSTM) networks to calculate the h semantic vector representations of paragraphs and q Finally, bilinear functions are used to calculate the pr of the answer's position in the paragraph. The expe results on the Stanford Question Answering Dataset(SQl development set show that the F1 score is 80.1% and tl 71.4%, which demonstrates that the performance of the is better than that of the model of DrQA, since they inc 2% and 1.3% respectively.


title: Deep Learning for Household Load Forecasting—A Novel Pooling Deep RNN

abstract: The key challenge for household load forecasting lies in the high volatility and uncertainty of load profiles. Traditional methods tend to avoid such uncertainty by load aggregation (to offset uncertainties), customer classification (to cluster uncertainties) and spectral analysis (to filter out uncertainties). This paper, for the first time, aims to directly learn the uncertainty by applying a new breed of machine learning algorithms—deep learning. However, simply adding layers in neural networks will cap the forecasting performance due to the occurrence of over-fitting. A novel pooling-based deep recurrent neural network is proposed in this paper which batches a group of customers’ load profiles into a pool of inputs. Essentially the model could address the over-fitting issue by increasing data diversity and volume. This paper reports the first attempts to develop a bespoke deep learning application for household load forecasting and achieved preliminary success. The developed method is implemented on Tensorflow deep learning platform and tested on 920 smart metered customers from Ireland. Compared with the state-of-the-art techniques in household load forecasting, the proposed method outperforms ARIMA by 19.5%, SVR by 13.1% and classical deep RNN by 6.5% in terms of RMSE.


title: www.elsevier.com/locate/tcs k-Center problems with minimum coverage

abstract: In this work, we study an extension of the k-center facility location problem, where centers are required to service a minimum of clients. This problem is motivated by requirements to balance the workload of centers while allowing each center to cater to a spread of clients. We study three variants of this problem, all of which are shown to be N P-hard. In-approximation hardness and approximation algorithms with factors equal or close to the best lower bounds are provided. Generalizations, including vertex costs and vertex weights, are also studied.


Running head : Ternary Eye Movement Classification Automated Classification and Scoring of Smooth Pursuit Eye Movements in Presence of Fixations and Saccades


title: Non-Convex Weighted Lp Minimization based Group Sparse Representation Framework for Image Denoising

abstract: Nonlocal image representation or group sparsity has attracted considerable interest in various low-level vision tasks and has led to several state-of-the-art image denoising techniques, such as BM3D, learned simultaneous sparse coding. In the past, convex optimization with sparsity-promoting convex regularization was usually regarded as a standard scheme for estimating sparse signals in noise. However, using convex regularization cannot still obtain the correct sparsity solution under some practical problems including image inverse problems. In this letter, we propose a nonconvex weighted $\ell _p$ minimization based group sparse representation framework for image denoising. To make the proposed scheme tractable and robust, the generalized soft-thresholding algorithm is adopted to solve the nonconvex $\ell _p$ minimization problem. In addition, to improve the accuracy of the nonlocal similar patch selection, an adaptive patch search scheme is proposed. Experimental results demonstrate that the proposed approach not only outperforms many state-of-the-art denoising methods such as BM3D and weighted nuclear norm minimization, but also results in a competitive speed.


title: Extending a C-like language for portable SIMD programming

abstract: SIMD instructions are common in CPUs for years now. Using these instructions effectively requires not only vectorization of code, but also modifications to the data layout. However, automatic vectorization techniques are often not powerful enough and suffer from restricted scope of applicability; hence, programmers often vectorize their programs manually by using intrinsics: compiler-known functions that directly expand to machine instructions. They significantly decrease programmer productivity by enforcing a very error-prone and hard-to-read assembly-like programming style. Furthermore, intrinsics are not portable because they are tied to a specific instruction set. In this paper, we show how a C-like language can be extended to allow for portable and efficient SIMD programming. Our extension puts the programmer in total control over where and how control-flow vectorization is triggered. We present a type system and a formal semantics of our extension and prove the soundness of the type system. Using our prototype implementation IVL that targets Intel's MIC architecture and SSE instruction set, we show that the generated code is roughly on par with handwritten intrinsic code.


title: Development of Dual-Axis Solar Tracking using Arduino with Lab VIEW

abstract: As the energy demand and the environmental problems increase, the natural energy sources have become very important as an alternative to the conventional energy sources. The renewable energy sector is fast gaining ground as a new growth area for numerous countries with the vast potential it presents environmentally and economically. Solar energy plays an important role as a primary source of energy, especially for rural area. This paper aims at the development of process to track the sun and attain maximum efficiency using Arduino uno and LabVIEW for real time monitoring. The project is divided into two stages, which are hardware and software development. In hardware development, four light dependent resistor (LDR) has been used for capturing maximum light source. Two DC motors have been used to move the solar panel at maximum light source location sensing by LDR. The GUI is constructed by using LabVIEW. The performance of the system has been tested and compared with static solar panel. This paper describes the design of a low cost, solar tracking system.


title: Detecting anomalies in graphs with numeric labels

abstract: This paper presents Yagada, an algorithm to search labelled graphs for anomalies using both structural data and numeric attributes. Yagada is explained using several security-related examples and validated with experiments on a physical Access Control database. Quantitative analysis shows that in the upper range of anomaly thresholds, Yagada detects twice as many anomalies as the best-performing numeric discretization algorithm. Qualitative evaluation shows that the detected anomalies are meaningful, representing a combination of structural irregularities and numerical outliers.


title: Data centre temperature monitoring with ESP8266 based Wireless Sensor Network and cloud based dashboard with real time alert system

abstract: The Internet of Things (IoT) system proposed in this paper is an advanced solution for monitoring the temperature at different points of location in a data centre, making this temperature data visible over internet through cloud based dashboard and sending SMS and email alerts to predefined recipients when temperature rises above the safe operating zone and reaches certain high values. This helps the datacenter management team to take immediate action to rectify this temperature deviation. Also this can be monitored from anywhere anytime over online dashboard by the senior level professionals who are not present in the data centre at any point in time. This Wireless Sensor Network (WSN) based monitoring system consists of temperature sensors, ESP8266 and Wi-Fi router. ESP8266 is a low power, highly integrated Wi-Fi solution from Espress if. The ESP8266 here, in this prototype, connects to ‘Ubidots’ cloud through its API for posting temperature data to the cloud dashboard on real time and the cloud event management system generates alerts whenever the high temperature alert event is fired. Cloud events need to be configured for different alerts beforehand through the user friendly user interface of the platform. It's to be noted that the sensor used here can be leveraged to monitor the relative humidity of the data center environment as well along with the temperature of the data center. But for this prototype solution focus is kept entirely on the temperature monitoring.


title: Voice Interfaces in Everyday Life

abstract: Voice User Interfaces (VUIs) are becoming ubiquitously available, being embedded both into everyday mobility via smartphones, and into the life of the home via 'assistant' devices. Yet, exactly how users of such devices practically thread that use into their everyday social interactions remains underexplored. By collecting and studying audio data from month-long deployments of the Amazon Echo in participants' homes-informed by ethnomethodology and conversation analysis-our study documents the methodical practices of VUI users, and how that use is accomplished in the complex social life of the home. Data we present shows how the device is made accountable to and embedded into conversational settings like family dinners where various simultaneous activities are being achieved. We discuss how the VUI is finely coordinated with the sequential organisation of talk. Finally, we locate implications for the accountability of VUI interaction, request and response design, and raise conceptual challenges to the notion of designing 'conversational' interfaces.


title: Visual scanning of faces in autism.

abstract: The visual scanpaths of five high-functioning adult autistic males and five adult male controls were recorded using an infrared corneal reflection technique as they viewed photographs of human faces. Analyses of the scanpath data revealed marked differences in the scanpaths of the two groups. The autistic participants viewed nonfeature areas of the faces significantly more often and core feature areas of the faces (i.e., eyes, nose, and mouth) significantly less often than did control participants. Across both groups of participants, scanpaths generally did not differ as a function of the instructions given to the participants (i.e., “Please look at the faces in any manner you wish.” vs. “Please identify the emotions portrayed in these faces.”). Autistic participants showed a deficit in emotion recognition, but this effect was driven primarily by deficits in the recognition of fear. Collectively, these results indicate disorganized processing of face stimuli in autistic individuals and suggest a mechanism that may subserve the social information processing deficits that characterize autism spectrum disorders.


title: Cycle-Consistent Deep Generative Hashing for Cross-Modal Retrieval

abstract: In this paper, we propose a novel deep generative approach to cross-modal retrieval to learn hash functions in the absence of paired training samples through the cycle consistency loss. Our proposed approach employs adversarial training scheme to learn a couple of hash functions enabling translation between modalities while assuming the underlying semantic relationship. To induce the hash codes with semantics to the input-output pair, cycle consistency loss is further delved into the adversarial training to strengthen the correlation between the inputs and corresponding outputs. Our approach is generative to learn hash functions, such that the learned hash codes can maximally correlate each input–output correspondence and also regenerate the inputs so as to minimize the information loss. The learning to hash embedding is thus performed to jointly optimize the parameters of the hash functions across modalities as well as the associated generative models. Extensive experiments on a variety of large-scale cross-modal data sets demonstrate that our proposed method outperforms the state of the arts.


title: Combating Human Trafficking with Deep Multimodal Models

abstract: Human trafficking is a global epidemic affecting millions of people across the planet. Sex trafficking, the dominant form of human trafficking, has seen a significant rise mostly due to the abundance of escort websites, where human traffickers can openly advertise among at-will escort advertisements. In this paper, we take a major step in the automatic detection of advertisements suspected to pertain to human trafficking. We present a novel dataset called Trafficking-10k, with more than 10,000 advertisements annotated for this task. The dataset contains two sources of information per advertisement: text and images. For the accurate detection of trafficking advertisements, we designed and trained a deep multimodal model called the Human Trafficking Deep Network (HTDN).


title: Reconstruction of Dorsal and/or Caudal Nasal Septum Deformities With Septal Battens or by Septal Replacement: An Overview and Comparison of Techniques

abstract: OBJECTIVES ::: The objectives of this study were to describe and compare two techniques used to correct nasal septum deviations located in the dorsal and/or caudal septum. ::: ::: ::: STUDY DESIGN ::: The authors conducted a retrospective clinical chart review. ::: ::: ::: METHODS ::: The authors conducted a comparison of functional and technical results between surgery in the L-strut of the septum in 114 patients with septal battens or by septal replacement by subjective self-evaluation and by examination of the position of the septum during follow up. ::: ::: ::: RESULTS ::: There was subjective improvement in nasal breathing in 86% of the septal batten group and in 94% of the septal replacement group. This difference was not statistically significant. The technical result was judged by examining the position of the septum during follow up as midline, slightly deviated, or severely deviated. The septum was significantly more often located in the midline during follow up in the septal replacement group than in the septal batten group. ::: ::: ::: CONCLUSION ::: Treatment of deformities located in the structurally important L-strut of the septum may be technically challenging and many functional, structural, and esthetic considerations must be taken into account. On the basis of this series, both septal battens and septal replacement techniques may be considered for correction of deviations in this area. The functional improvement rates were not significantly different between the techniques, although during follow up, the septum appeared to be significantly more often located in the midline in the septal replacement group. The techniques are described and their respective advantages and potential drawbacks are discussed.


title: Shielding Google's language toxicity model against adversarial attacks

abstract: Lack of moderation in online communities enables participants to incur in personal aggression, harassment or cyberbullying, issues that have been accentuated by extremist radicalisation in the contemporary post-truth politics scenario. This kind of hostility is usually expressed by means of toxic language, profanity or abusive statements. Recently Google has developed a machine-learning-based toxicity model in an attempt to assess the hostility of a comment; unfortunately, it has been suggested that said model can be deceived by adversarial attacks that manipulate the text sequence of the comment. In this paper we firstly characterise such adversarial attacks as using obfuscation and polarity transformations. The former deceives by corrupting toxic trigger content with typographic edits, whereas the latter deceives by grammatical negation of the toxic content. Then, we propose a two--stage approach to counter--attack these anomalies, bulding upon a recently proposed text deobfuscation method and the toxicity scoring model. Lastly, we conducted an experiment with approximately 24000 distorted comments, showing how in this way it is feasible to restore toxicity of the adversarial variants, while incurring roughly on a twofold increase in processing time. Even though novel adversary challenges would keep coming up derived from the versatile nature of written language, we anticipate that techniques combining machine learning and text pattern recognition methods, each one targeting different layers of linguistic features, would be needed to achieve robust detection of toxic language, thus fostering aggression--free digital interaction.


title: Decapitation as a result of suicidal hanging.

abstract: A report is presented on a 47 year old man who committed suicide by hanging himself from a staircase bannister of an apartment house. The man, weighing 144 kg jumped with the noose of a 2 cm thick and 2 m long hemp rope around his neck and was completely decapitated. Death from typical "normal" suicidal hanging is usually due to cerebral ischaemia caused by compression of the carotid (and vertebral) arteries. Except for bleeding at the clavicular insertions of the sternocleidomastoid muscles there are only occasional injuries to the cervical soft parts or hyoid bone and/or laryngeal cartilage. A fall with a noose around the neck, on the other hand, is associated with more frequent injuries to cervical structures through additional axial traction and radial shearing forces of the tightening noose. Complete decapitation can occur in rare cases under extreme conditions (heavy body weight, inelastic and/or thin rope material, fall from a great height).


title: Multimodal Residual Learning for Visual QA

abstract: Deep neural networks continue to advance the state-of-the-art of image recognition tasks with various methods. However, applications of these methods to multimodality remain limited. We present Multimodal Residual Networks (MRN) for the multimodal residual learning of visual question-answering, which extends the idea of the deep residual learning. Unlike the deep residual learning, MRN effectively learns the joint representation from vision and language information. The main idea is to use element-wise multiplication for the joint residual mappings exploiting the residual learning of the attentional models in recent studies. Various alternative models introduced by multimodality are explored based on our study. We achieve the state-of-the-art results on the Visual QA dataset for both Open-Ended and Multiple-Choice tasks. Moreover, we introduce a novel method to visualize the attention effect of the joint representations for each learning block using back-propagation algorithm, even though the visual features are collapsed without spatial information.


title: ON BODY CONCEALED WEAPON DETECTION USING A PHASED ANTENNA ARRAY

abstract: The detection and identiflcation of metal items and, in particular weapons, of linear size ‚ 10cm, concealed upon the human body, is demonstrated as being entirely feasible by using a phased array of suitably ultra wide band transceivers. The complex natural resonances and especially the fundamental resonance, are excited by ultra wide band, stepped frequency continuous wave illumination of the target, using a phased array of antennae to focus the radiation. Broadband illumination of the target with microwave radiation of suitable frequency range (Typically 0.3{3GHz for handgun sized objects) excites low order complex natural resonances and the late time response of the concealed item can be spatially located using phased array imaging techniques. Further processing of the late time response enables classiflcation of the concealed object, based on the complex natural resonant frequencies of the object, so that threat items such as handguns and knives can be difierentiated from benign items such as mobile phone handsets and cameras.


title: Dialogue Act Tagging For Instant Messaging Chat Sessions

abstract: Instant Messaging chat sessions are real-time text-based conversations which can be analyzed using dialogue-act models. We describe a statistical approach for modelling and detecting dialogue acts in Instant Messaging dialogue. This involved the collection of a small set of task-based dialogues and annotating them with a revised tag set. We then dealt with segmentation and synchronisation issues which do not arise in spoken dialogue. The model we developed combines naive Bayes and dialogue-act n-grams to obtain better than 80% accuracy in our tagging experiment.


title: A general solution to the P4P problem for camera with unknown focal length

abstract: This paper presents a general solution to the determination of the pose of a perspective camera with unknown focal length from images of four 3D reference points. Our problem is a generalization of the P3P and P4P problems previously developed for fully calibrated cameras. Given four 2D-to-3D correspondences, we estimate camera position, orientation and recover the camera focal length. We formulate the problem and provide a minimal solution from four points by solving a system of algebraic equations. We compare the Hidden variable resultant and Grobner basis techniques for solving the algebraic equations of our problem. By evaluating them on synthetic and on real-data, we show that the Grobner basis technique provides stable results.


title: Participatory Evolving Fuzzy Modeling

abstract: This paper introduces an approach to develop evolving fuzzy rule-based models based on the idea of participatory learning. Participatory learning is a means to learn and revise beliefs based on what is already known or believed. Participatory learning naturally induces unsupervised dynamic fuzzy clustering algorithms and provides an effective alternative construct evolving functional fuzzy models and adaptive fuzzy systems. Evolving participatory learning is used to forecast average weekly inflows for hydroelectric generation purposes and compared with eTS, an evolving modeling technique that uses the notion of potential to dynamically cluster data.