title: Multiband Sierpinski fractal antenna

abstract: A fractal monopole antenna based on the Sierpinski gasket is studied in this paper. The monopole antenna based on the Sierpinski gasket constructed through three iterations displays a multiband behaviour with three bands that are log-periodically spaced by a factor of 2, the same scale factor that defines the geometrical self-similarity of the Sierpinski fractal. The simulated as well as measured input return loss and radiation patterns all display multi-band behaviour. The geometrical scale factor of the Sierpinski fractal is then changed to see whether the bands are shifted according to the new scale factor or not. The simulated results showed that the band positions could be controlled by changing the scale factor but on account of poor input matching. This poor input matching of Sierpinski fractal monopole antenna with modified scale factor is rectified using microstrip feed technique. The simulated as well as measured results of the antenna are in good agreement.


title: On the development of the Agile Eye

abstract: The "Agile Eye" is a high-performance mechanism capable of orienting a camera within a workspace larger than that of a human eye and with velocities and accelerations larger than those of the human eye. The mechanical design, control issues, and experimental results are presented.


title: Changes in Achievement Goal Orientations, Perceived Academic Competence, and Grades across the Transition to Middle-Level Schools

abstract: Goal orientation theory was used to examine changes in student motivation during the transition from elementary to middle school. Surveys were given to 341 students in the fifth grade in elementary and again in sixth grade in middle school. Students were more oriented to task goals (wanting to improve their competency), perceived a greater emphasis on task goals during instruction, and felt more academically competent in fifth grade in elementary school than in sixth grade in middle school. They perceived a greater emphasis on performance goals (an emphasis on relative ability and right answers) in middle school than in elementary school. Several interactions emerged between year (fifth grade, sixth grade), and both student level of ability (higher, lower, based on standardized achievement tests) and subject domain (math, English).


title: Learning Visual Features from Large Weakly Supervised Data

abstract: Convolutional networks trained on large supervised datasets produce visual features which form the basis for the state-of-the-art in many computer-vision problems. Further improvements of these visual features will likely require even larger manually labeled data sets, which severely limits the pace at which progress can be made. In this paper, we explore the potential of leveraging massive, weakly-labeled image collections for learning good visual features. We train convolutional networks on a dataset of 100 million Flickr photos and comments, and show that these networks produce features that perform well in a range of vision problems. We also show that the networks appropriately capture word similarity and learn correspondences between different languages.


title: A Probabilistic Exclusion Principle for Tracking Multiple Objects

abstract: Tracking multiple targets is a challenging problem, especially when the targets are “identical”, in the sense that the same model is used to describe each target. In this case, simply instantiating several independent 1-body trackers is not an adequate solution, because the independent trackers tend to coalesce onto the best-fitting target. This paper presents an observation density for tracking which solves this problem by exhibiting a probabilistic exclusion principle. Exclusion arises naturally from a systematic derivation of the observation density, without relying on heuristics. Another important contribution of the paper is the presentation of partitioned sampling, a new sampling method for multiple object tracking. Partitioned sampling avoids the high computational load associated with fully coupled trackers, while retaining the desirable properties of coupling.


title: Literature mining, ontologies and information visualization for drug repurposing.

abstract: The immense growth of MEDLINE coupled with the realization that a vast amount of biomedical knowledge is recorded in free-text format, has led to the appearance of a large number of literature mining techniques aiming to extract biomedical terms and their inter-relations from the scientific literature. Ontologies have been extensively utilized in the biomedical domain either as controlled vocabularies or to provide the framework for mapping relations between concepts in biology and medicine. Literature-based approaches and ontologies have been used in the past for the purpose of hypothesis generation in connection with drug discovery. Here, we review the application of literature mining and ontology modeling and traversal to the area of drug repurposing (DR). In recent years, DR has emerged as a noteworthy alternative to the traditional drug development process, in response to the decreased productivity of the biopharmaceutical industry. Thus, systematic approaches to DR have been developed, involving a variety of in silico, genomic and high-throughput screening technologies. Attempts to integrate literature mining with other types of data arising from the use of these technologies as well as visualization tools assisting in the discovery of novel associations between existing drugs and new indications will also be presented.


title: Bandwidth formula for Linear FMCW radar waveforms

abstract: The International Telecommunications Union provides recommendations regarding spectral emission bounds for primary radar systems. These bounds are currently in review and are defined in terms of spectral occupancy, necessary bandwidth, 40dB bandwidth and out-of-band roll-off rates. Here we derive out-of-band domain spectral envelopes, bandwidth formula and roll-off rates, for various Linear FMCW radar waveforms including sawtooth (LFMCW), Quadratic Phase Coded LFMCW, LFM Pulse Train, and Hann amplitude tapered LFMCW.


How I Do It A Punch Technique for Gingival Incisional Biopsy


Inferring Discourse Relations In Context


title: Anafora: A Web-based General Purpose Annotation Tool

abstract: Anafora is a newly-developed open source web-based text annotation tool built to be lightweight, flexible, easy to use and capable of annotating with a variety of schemas, simple and complex. Anafora allows secure web-based annotation of any plaintext file with both spanned (e.g. named entity or markable) and relation annotations, as well as adjudication for both types of annotation. Anafora offers automatic set assignment and progress-tracking, centralized and humaneditable XML annotation schemas, and filebased storage and organization of data in a human-readable single-file XML format.


title: An analysis of facial expression recognition under partial facial image occlusion

abstract: In this paper, an analysis of the effect of partial occlusion on facial expression recognition is investigated. The classification from partially occluded images in one of the six basic facial expressions is performed using a method based on Gabor wavelets texture information extraction, a supervised image decomposition method based on Discriminant Non-negative Matrix Factorization and a shape-based method that exploits the geometrical displacement of certain facial features. We demonstrate how partial occlusion affects the above mentioned methods in the classification of the six basic facial expressions, and indicate the way partial occlusion affects human observers when recognizing facial expressions. An attempt to specify which part of the face (left, right, lower or upper region) contains more discriminant information for each facial expression, is also made and conclusions regarding the pairs of facial expressions misclassifications that each type of occlusion introduces, are drawn.


title: Cyber reasoning with argumentation: Abstracting from incomplete and contradictory evidence

abstract: Information given to system administrators is often incomplete and contradictory. Even worse, administrators are required to adhere to organizational policies, which frequently contain conflicting goals. While prior work in security has sought to alleviate these concerns, much of it strives to identify attacks and intrusions with approaches that require complete knowledge for analysis. In this paper, we present a framework to addresses the challenges facing administrators by using formal argumentation to generate big-picture conclusions regarding the system. Unlike other schemes, argumentation excels in situations where information is incomplete and knowledge is contradictory. To motivate our approach, we detail a scenario inspired by real-world data taken from the U.C. Davis environment.


title: An ultra-low-power 400 MHz VCO for MICS band application

abstract: This work presents the design and simulation of an ultra-low-power voltage-controlled oscillator (VCO) for MICS (Medical Implant Communication Service) band applications. The VCO is based on a single ended CMOS inverter ring oscillator. For the voltage control, current starving has been employed. Current for the oscillator is provided by a beta multiplier reference (BMR) which is highly insensitive to supply noise. A digital current control is also implemented in this BMR to make the oscillator tunable over a wide range of 400 MHz to 935 MHz. Besides these, the oscillator circuit has an additional control for binary frequency shift keying. The oscillator along with the reference circuit consumes only about 2 µW with 1 V supply at 400 MHz frequency which is very suitable for low-power biomedical applications.


title: KeyphraseDS: Automatic generation of survey by exploiting keyphrase information

abstract: Abstract In this paper, we present a novel document summarization mechanism called KeyphraseDS that can organize the scientific articles into multi-aspect and informative scientific survey by exploiting keyphrases. Keyphrases describe text's salience and central focus, which can serve as the component of aspects under specific topic. KeyphraseDS consists of three steps: keyphrase graph construction, semantic aspect generation and content selection. Keyprhases are firstly extracted through CRF-based model exploiting various features, such as syntactic features, correlation features, etc. Spectral clustering is then performed on keyphrase graph to generate different aspects, where the semantic relatedness between keyphrases is computed through knowledge-based similarity and topic-based similarity. The proposed semantic relatedness can not only utilize the statistical text signals efficiently but also overcome the data sparsity problem. Significant sentences are then selected with respect to the generated aspects through integer linear programming (ILP), which takes semantic relevance, semantic diversity, and keyphrase salience into consideration. Extensive experiments, measured by automatic evaluation and human evaluation, demonstrate the effectiveness of our mechanism for generating scientific survey.


title: You Are Where You Go: Inferring Demographic Attributes from Location Check-ins

abstract: User profiling is crucial to many online services. Several recent studies suggest that demographic attributes are predictable from different online behavioral data, such as users' "Likes" on Facebook, friendship relations, and the linguistic characteristics of tweets. But location check-ins, as a bridge of users' offline and online lives, have by and large been overlooked in inferring user profiles. In this paper, we investigate the predictive power of location check-ins for inferring users' demographics and propose a simple yet general location to profile (L2P) framework. More specifically, we extract rich semantics of users' check-ins in terms of spatiality, temporality, and location knowledge, where the location knowledge is enriched with semantics mined from heterogeneous domains including both online customer review sites and social networks. Additionally, tensor factorization is employed to draw out low dimensional representations of users' intrinsic check-in preferences considering the above factors. Meanwhile, the extracted features are used to train predictive models for inferring various demographic attributes. We collect a large dataset consisting of profiles of 159,530 verified users from an online social network. Extensive experimental results based upon this dataset validate that: 1) Location check-ins are diagnostic representations of a variety of demographic attributes, such as gender, age, education background, and marital status; 2) The proposed framework substantially outperforms compared models for profile inference in terms of various evaluation metrics, such as precision, recall, F-measure, and AUC.


title: Contrasting the Spread of Misinformation in Online Social Networks

abstract: The emergence of online social networks has revolutionized the way people seek and share information. Nowadays, popular online social sites as Twitter, Facebook and Google+ are among the major news sources as well as the most effective channels for viral marketing. However, these networks also became the most effective channel for spreading misinformation, accidentally or maliciously. The widespread diffusion of inaccurate information or fake news can lead to undesirable and severe consequences, such as widespread panic, libelous campaigns and conspiracies. In order to guarantee the trustworthiness of online social networks it is a crucial challenge to find effective strategies to contrast the spread of the misinformation in the network. ::: ::: In this paper we concentrate our attention on two problems related to the diffusion of misinformation in social networks: identify the misinformation sources and limit its diffusion in the network. We consider a social network where some nodes have already been infected from misinformation. We first provide an heuristics to recognize the set of most probable sources of the infection. Then, we provide an heuristics to place a few monitors in some network nodes in order to control information diffused by the suspected nodes and block misinformation they injected in the network before it reaches a large part of the network. To verify the quality and efficiency of our suggested solutions, we conduct experiments on several real-world networks. Empirical results indicate that our heuristics are among the most effective known in literature.


title: Two-photon targeted patching and electroporation in vivo.

abstract: By combining patch-clamp methods with two-photon microscopy, it is possible to target recordings to specific classes of neurons in vivo. Here we describe methods for imaging and recording from the soma and dendrites of neurons identified using genetically encoded probes such as green fluorescent protein (GFP) or functional indicators such as Oregon Green BAPTA-1. Two-photon targeted patching can also be adapted for use with wild-type brains by perfusing the extracellular space with a membrane-impermeable dye to visualize the cells by their negative image and target them for electrical recordings, a technique termed "shadowpatching." We discuss how these approaches can be adapted for single-cell electroporation to manipulate specific cells genetically. These approaches thus permit the recording and manipulation of rare genetically, morphologically, and functionally distinct subsets of neurons in the intact nervous system.


title: A Ka-Band Broadband Integrated Transition of Air-Filled Waveguide to Laminated Waveguide

abstract: In this letter, a novel compact and broadband integrated transition between a laminated waveguide and an air-filled rectangular waveguide operating in Ka band is proposed. A three-pole filter equivalent circuit model is employed to interpret the working mechanism and to predict the performance of the transition. A back-to-back prototype of the proposed transition is designed and fabricated for proving the concept. Good agreement of the measured and simulated results is obtained. The measured result shows that the insertion loss of better than 0.26 dB from 34.8 to 37.8 GHz can be achieved.


title: Automatic Geometric Processing for Very High Resolution Optical Satellite Data Based on Vector Roads and Orthophotos

abstract: In response to the increasing need for fast satellite image processing SPACE-SI developed STORM—a fully automatic image processing chain that performs all processing steps from the input optical images to web-delivered map-ready products for various sensors. This paper focuses on the automatic geometric corrections module and its adaptation to very high resolution (VHR) multispectral images. In the automatic ground control points (GCPs) extraction sub-module a two-step algorithm that utilizes vector roads as a reference layer and delivers GCPs for high resolution RapidEye images with near pixel accuracy was initially implemented. Super-fine positioning of individual GCPs onto an aerial orthophoto was introduced for VHR images. The enhanced algorithm is capable of achieving accuracy of approximately 1.5 pixels on WorldView-2 data. In the case of RapidEye images the accuracies of the physical sensor model reach sub-pixel values at independent check points. When compared to the reference national aerial orthophoto the accuracies of WorldView-2 orthoimages automatically produced with the rational function model reach near-pixel values. On a heterogeneous set of 41 RapidEye images the rate of automatic processing reached 97.6%. Image processing times remained under one hour for standard-size images of both sensor types.


title: Design and implementation of an active multibeam antenna system with 64 RF channels and 256 antenna elements for massive MIMO application in 5G wireless communications

abstract: This paper focuses on the design and implementation of an active multibeam antenna system for massive MIMO applications in 5G wireless communications. The highly integrated active multibeam antenna system is designed and implemented at 5.8 GHz with 64 RF Channels and 256 antenna elements. The 64-channel highly integrated active multibeam antenna system provides a verification platform for digital beamforming algorithm and massive MIMO channel estimation for next generation wireless communications.


title: Implementing moving target IPv6 defense to secure 6LoWPAN in the internet of things and smart grid

abstract: The growing momentum of the Internet of Things (IoT) has shown an increase in attack vectors within the security research community. We propose adapting a recent new approach of frequently changing IPv6 address assignment to add an additional layer of security to the Internet of Things. We examine implementing Moving Target IPv6 Defense (MT6D) in IPv6 over Low-Powered Wireless Personal Area Networks (6LoWPAN); a protocol that is being used in wireless sensors found in home automation systems and smart meters. 6LoWPAN allows the Internet of Things to extend into the world of wireless sensor networks. We propose adapting Moving-Target IPv6 Defense for use with 6LoWPAN in order to defend against network-side attacks such as Denial-of-Service and Man-In-The-Middle while maintaining anonymity of client-server communications. This research aims in providing a moving-target defense for wireless sensor networks while maintaining power efficiency within the network.


title: Module-Level DC/DC Conversion for Photovoltaic Systems: The Delta-Conversion Concept

abstract: Photovoltaic (PV) systems are increasingly used to generate electrical energy from solar irradiation incident on PV modules. PV modules are formed by placing many PV cells in series. The PV system is then formed by placing a number of PV modules in series in a string. In practical cases, differences will exist between output powers of the PV cells in the various PV modules, e.g., due to (part of) the modules being temporarily shaded or pollution on one or more PV cells. Due to the current-source-type behavior of PV cells and their series connection, these differences will lead to a relatively large drop in PV-system output power. This paper addresses this problem by adding dc/dc converters on PV-module level. The so-called delta-conversion concept is introduced that aims at averaging out differences in output power between groups of PV cells within modules and between modules inside the PV system. All groups of PV cells can then output their maximum available power, such that a drop in output power of the total system is prevented. This paper describes implementation details, compares the delta-conversion concept with other state-of-the-art module-level power-conversion concepts, and presents first measurement results obtained with a demonstrator system.


title: OmniUnpack: Fast, Generic, and Safe Unpacking of Malware

abstract: Malicious software (or malware) has become a growing threat as malware writers have learned that signature- based detectors can be easily evaded by "packing" the malicious payload in layers of compression or encryption. State-of-the-art malware detectors have adopted both static and dynamic techniques to recover the pay- load of packed malware, but unfortunately such techniques are highly ineffective. In this paper we propose a new technique, called OmniUnpack, to monitor the execution of a program in real-time and to detect when the program has removed the various layers of packing. OmniUnpack aids malware detection by directly providing to the detector the unpacked malicious payload. Experimental results demonstrate the effectiveness of our approach. OmniUnpack is able to deal with both known and unknown packing algorithms and introduces a low overhead (at most 11% for packed benign programs).


title: Nursing Bedside Handover does not Influence Cardiovascular Surgery Patients' Participation in Nursing Care Decision-Making Process: Results Three Months after Implementation

abstract: Background: Nursing bedside handover has been reported as a me thod to foster patients’ participation in their care. However, to date, no study has assessed the e ffect of implementing nursing bedside handover on p atients’ perception of being involved in the decision-making process of their care and the side effects of nurs ing bedside handover. Aim: This study aims to evaluate the effect of nursing bedside handover on patients’ perception of shareddecision making in nursing care and the side effect s of nursing bedside handover. Method: Single-centre, non-experimental study. Results: There were no statistically significant difference s regarding patients’ perception of decision-making aspects. Before and after implementation of nursing bedside handover, most patients perceived the styl e of the decision-making process about their nursing care as paternalistic. No side effects of nursing bedside handover implementation could be detected. During implementation of nursing bedside handover nurses expressed d istress with and showed a defensive attitude toward nursing bedside handover. Conclusion: The implementation of nursing bedside handover has no influence on patients’ perception of shared-decision making in nursing care and side eff ects in a cardiovascular surgery patient population . Evaluation of nursing bedside handover should be co nducted over a period longer than three months.


title: Multidimensional Constellations for Uplink SCMA Systems—A Comparative Study

abstract: Sparse code multiple access (SCMA) is a class of non-orthogonal multiple access (NOMA) that is proposed to support uplink machine-type communication services. In an SCMA system, designing multidimensional constellation plays an important role in the performance of the system. Since the behavior of multidimensional constellations highly depends on the type of the channel, it is crucial to employ a constellation that is suitable for a certain application. In this paper, we first highlight and review the key performance indicators (KPIs) of multidimensional constellations that should be considered in their design process for various channel scenarios. We then provide a survey on the known multidimensional constellations in the context of SCMA systems with their design criteria. The performance of some of those constellations are evaluated for uncoded, high-rate, and low-rate LTE turbo-coded SCMA systems under different channel conditions through extensive simulations. All turbo-coded comparisons are performed for bit-interleaved coded modulation, with a concatenated detection and decoding scheme. Simulation results confirm that multidimensional constellations that satisfy KPIs of a certain channel scenario outperform others. Moreover, the bit error rate performance of uncoded systems, and the performance of the coded systems are coupled to their bit-labeling. The performance of the systems also remarkably depends on the behavior of the multiuser detector at different signal-to-noise ratio regions.


title: FDMO: Feature Assisted Direct Monocular Odometry

abstract: Visual Odometry (VO) can be categorized as being either direct or feature based. When the system is calibrated photometrically, and images are captured at high rates, direct methods have shown to outperform feature-based ones in terms of accuracy and processing time; they are also more robust to failure in feature-deprived environments. On the downside, Direct methods rely on heuristic motion models to seed the estimation of camera motion between frames; in the event that these models are violated (e.g., erratic motion), Direct methods easily fail. This paper proposes a novel system entitled FDMO (Feature assisted Direct Monocular Odometry), which complements the advantages of both direct and featured based techniques. FDMO bootstraps indirect feature tracking upon the sub-pixel accurate localized direct keyframes only when failure modes (e.g., large baselines) of direct tracking occur. Control returns back to direct odometry when these conditions are no longer violated. Efficiencies are introduced to help FDMO perform in real time. FDMO shows significant drift (alignment, rotation & scale) reduction when compared to DSO & ORB SLAM when evaluated using the TumMono and EuroC datasets.


title: Total parser combinators

abstract: A monadic parser combinator library which guarantees termination of parsing, while still allowing many forms of left recursion, is described. The library's interface is similar to those of many other parser combinator libraries, with two important differences: one is that the interface clearly specifies which parts of the constructed parsers may be infinite, and which parts have to be finite, using dependent types and a combination of induction and coinduction; and the other is that the parser type is unusually informative. The library comes with a formal semantics, using which it is proved that the parser combinators are as expressive as possible. The implementation is supported by a machine-checked correctness proof.


title: Sarcoidosis: a rare cause of Kleine-Levine-Critchley Syndrome

abstract: Hypothalamic sarcoidosis is a rare entity that can alter the hypothalamic-pituitary axis and induce various combinations of endocrine changes.We present a case of neurosarcoidosis with uncommon features of hypersomnolense and hyperphagia. Current strategies to increase awareness and prevention of the harmful effects of obesity require clinicians to be cognizant of potential disorders that produce these features The mechanism, differential diagnosis and therapeutic options of this organic etiology are reviewed.


title: A Review of the Pathophysiology, Etiology, and Treatment of Attention-Deficit Hyperactivity Disorder (ADHD)

abstract: OBJECTIVE ::: To review the pathophysiology, etiology, and treatment of attention-deficit hyperactivity disorder (ADHD). ::: ::: ::: DATA SOURCES AND DATA EXTRACTION ::: A literature search was conducted in PubMed and EMBASE using the terms attention deficit hyperactive disorder, ADHD, pathophysiology, etiology, and neurobiology. Limits applied were the following: published in the past 10 years (January 2003 to August 2013), humans, review, meta-analysis, and English language. These yielded 63 articles in PubMed and 74 in EMBASE. After removing duplicate/irrelevant articles, 86 articles and their relevant reference citations were reviewed. ::: ::: ::: DATA SYNTHESIS ::: ADHD is a neurological disorder that affects children, but symptoms may persist into adulthood. Individuals suffering from this disorder exhibit hyperactivity, inattention, impulsivity, and problems in social interaction and academic performance. Medications used to treat ADHD such as methylphenidate, amphetamine, and atomoxetine indicate a dopamine/norepinephrine deficit as the neurochemical basis of ADHD, but the etiology is more complex. Moreover, these agents have poor adverse effect profiles and a multitude of drug interactions. Because these drugs are also dispensed to adults who may have concomitant conditions or medications, a pharmacist needs to be aware of these adverse events and drug interactions. This review, therefore, focuses on the pathophysiology, etiology, and treatment of ADHD and details the adverse effects and drug interaction profiles of the drugs used to treat it. ::: ::: ::: CONCLUSIONS ::: Published research shows the benefit of drug therapy for ADHD in children, but given the poor adverse effect and drug interaction profiles, these must be dispensed with caution.


Security-enabled NFC Tag with Flexible Architecture Supporting Asymmetric Cryptography


title: Pedestrian detection and direction estimation by cascade detector with multi-classifiers utilizing feature interaction descriptor

abstract: This paper proposes a pedestrian detection and direction estimation method by the cascade approach with multiclassifiers using the Feature Interaction Descriptor (FIND). FIND describes the high-level properties of an object's appearance by computing pair-wise interactions of adjacent regionlevel features. To perform efficient and accurate detection using FIND, we employ the cascade approach with multiclassifiers specialized in both the direction of a pedestrian and the distance of the pedestrian from a camera. Using this framework, the developed system can improve the detection performance and provide information of the direction of a pedestrian simultaneously. The experimental results show that superior detection performance and direction estimation results were obtained by our method.


title: Hybrid control trajectory optimization under uncertainty

abstract: Trajectory optimization is a fundamental problem in robotics. While optimization of continuous control trajectories is well developed, many applications require both discrete and continuous, i.e. hybrid controls. Finding an optimal sequence of hybrid controls is challenging due to the exponential explosion of discrete control combinations. Our method, based on Differential Dynamic Programming (DDP), circumvents this problem by incorporating discrete actions inside DDP: we first optimize continuous mixtures of discrete actions, and, subsequently force the mixtures into fully discrete actions. Moreover, we show how our approach can be extended to partially observable Markov decision processes (POMDPs) for trajectory planning under uncertainty. We validate the approach in a car driving problem where the robot has to switch discrete gears and in a box pushing application where the robot can switch the side of the box to push. The pose and the friction parameters of the pushed box are initially unknown and only indirectly observable.


title: Comparison of granger causality and phase slope index

abstract: The present invention provides an output signal whose pulse width may be adjusted with respect to the pulse width of an incoming input signal. In particular, a plurality of signals is generated in response to the input signal. One of the plurality of signals is selected for controlling when the output signal transitions from a first logic state to a second logic state, and one of the plurality of signals is selected for controlling when the output signal transitions from a second logic state to a first logic state wherein the output signal has a pulse width being a function of the selection of the plurality of signals.


title: Gut Microbiome and Brain-Gut Axis in Autism — Aberrant Development of Gut-Brain Communication and Reward Circuitry

abstract: The function of the gut microbiome and the bidirectional communication between the gastrointestinal tract (GIT) and the brain is increasingly recognized in health and disease and disruption in its composition is not unique to the autistic pathology. However, the bidirectional communication between the gut and the brain, “the gut-brain/brain-gut axis” in autism has been relatively understudied. In general, this communication between gut and brain occurs through a direct neuronal pathway via the vagus nerve, the hormonal pathway of several hormones involved in the regulation of food intake, such as cholecystokinin (CCK), ghrelin, leptin and insulin, and by the immunological signaling pathway involving cytokines. Recent studies indicate that the vagus nerve is involved in immunomodulation as suggested by its ability to attenuate the production of proinflammatory cytokines in experimental models of inflammation (de Jonge and Ullola, 2007). Furthermore, the gut microbiome emerges as a major player not only in the maturation of GIT tissue and the gut brain axis but also in brain maturation, through its effect on both the immune and endocrine systems. Many toxins, toxicants, infectious agents, diet or stress, affect an individual’s gut microbiome, which may be especially sensitive during the critical developmental period. Disruption of the developing microbiome may have profound consequences on the developing gut-brain axis including the brain as well as long-term effects on both the physical and psychological development.


title: Incremental light bundle adjustment for robotics navigation

abstract: This paper presents a new computationally-efficient method for vision-aided navigation (VAN) in autonomous robotic applications. While many VAN approaches are capable of processing incoming visual observations, incorporating loop-closure measurements typically requires performing a bundle adjustment (BA) optimization, that involves both all the past navigation states and the observed 3D points. Our approach extends the incremental light bundle adjustment (LBA) method, recently developed for structure from motion [10], to information fusion in robotics navigation and in particular for including loop-closure information. Since in many robotic applications the prime focus is on navigation rather then mapping, and as opposed to traditional BA, we algebraically eliminate the observed 3D points and do not explicitly estimate them. Computational complexity is further improved by applying incremental inference. To maintain highrate performance over time, consecutive IMU measurements are summarized using a recently-developed technique and navigation states are added to the optimization only at camera rate. If required, the observed 3D points can be reconstructed at any time based on the optimized robot's poses. The proposed method is compared to BA both in terms of accuracy and computational complexity in a statistical simulation study.


title: A quantitative performance analysis model for GPU architectures

abstract: We develop a microbenchmark-based performance model for NVIDIA GeForce 200-series GPUs. Our model identifies GPU program bottlenecks and quantitatively analyzes performance, and thus allows programmers and architects to predict the benefits of potential program optimizations and architectural improvements. In particular, we use a microbenchmark-based approach to develop a throughput model for three major components of GPU execution time: the instruction pipeline, shared memory access, and global memory access. Because our model is based on the GPU's native instruction set, we can predict performance with a 5–15% error. To demonstrate the usefulness of the model, we analyze three representative real-world and already highly-optimized programs: dense matrix multiply, tridiagonal systems solver, and sparse matrix vector multiply. The model provides us detailed quantitative analysis on performance, allowing us to understand the configuration of the fastest dense matrix multiply implementation and to optimize the tridiagonal solver and sparse matrix vector multiply by 60% and 18% respectively. Furthermore, our model applied to analysis on these codes allows us to suggest architectural improvements on hardware resource allocation, avoiding bank conflicts, block scheduling, and memory transaction granularity.


title: Turtle Guard: Helping Android Users Apply Contextual Privacy Preferences

abstract: Current mobile platforms provide privacy management interfaces to regulate how applications access sensitive data. Prior research has shown how these interfaces are insufficient from a usability standpoint: they do not account for context. In allowing for more contextual decisions, machine-learning techniques have shown great promise for designing systems that automatically make privacy decisions on behalf of the user. However, if such decisions are made automatically, then feedback mechanisms are needed to empower users to both audit those decisions and correct any errors. ::: ::: In this paper, we describe our user-centered approach towards designing a fully functional privacy feedback interface for the Android platform. We performed two large-scale user studies to research the usability of our design. Our second, 580-person validation study showed that users of our new interface were significantly more likely to both understand and control the selected set of circumstances under which applications could access sensitive data when compared to the default Android privacy settings interface.


title: Mining fuzzy association rules in databases

abstract: Data mining is the discovery of previously unknown, potentially useful and hidden knowledge in databases. In this paper, we concentrate on the discovery of association rules. Many algorithms have been proposed to find association rules in databases with binary attributes. We introduce the fuzzy association rules of the form, 'If X is A then Y is B', to deal with quantitative attributes. X, Y are set of attributes and A, B are fuzzy sets which describe X and Y respectively. Using the fuzzy set concept, the discovered rules are more understandable to human. Moreover, fuzzy sets handle numerical values better than existing methods because fuzzy sets soften the effect of sharp boundaries.


title: Evolutionary optimization in uncertain environments-a survey

abstract: Evolutionary algorithms often have to solve optimization problems in the presence of a wide range of uncertainties. Generally, uncertainties in evolutionary computation can be divided into the following four categories. First, the fitness function is noisy. Second, the design variables and/or the environmental parameters may change after optimization, and the quality of the obtained optimal solution should be robust against environmental changes or deviations from the optimal point. Third, the fitness function is approximated, which means that the fitness function suffers from approximation errors. Fourth, the optimum of the problem to be solved changes over time and, thus, the optimizer should be able to track the optimum continuously. In all these cases, additional measures must be taken so that evolutionary algorithms are still able to work satisfactorily. This paper attempts to provide a comprehensive overview of the related work within a unified framework, which has been scattered in a variety of research areas. Existing approaches to addressing different uncertainties are presented and discussed, and the relationship between the different categories of uncertainties are investigated. Finally, topics for future research are suggested.


title: Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising Networks

abstract: Demosaicking and denoising are among the most crucial steps of modern digital camera pipelines and their joint treatment is a highly ill-posed inverse problem where at-least two-thirds of the information are missing and the rest are corrupted by noise. This poses a great challenge in obtaining meaningful reconstructions and a special care for the efficient treatment of the problem is required. While there are several machine learning approaches that have been recently introduced to deal with joint image demosaicking-denoising, in this work we propose a novel deep learning architecture which is inspired by powerful classical image regularization methods and large-scale convex optimization techniques. Consequently, our derived network is more transparent and has a clear interpretation compared to alternative competitive deep learning approaches. Our extensive experiments demonstrate that our network outperforms any previous approaches on both noisy and noise-free data. This improvement in reconstruction quality is attributed to the principled way we design our network architecture, which also requires fewer trainable parameters than the current state-of-the-art deep network solution. Finally, we show that our network has the ability to generalize well even when it is trained on small datasets, while keeping the overall number of trainable parameters low.


title: Spectral meta-learner for regression (SMLR) model aggregation: Towards calibrationless brain-computer interface (BCI)

abstract: To facilitate the transition of brain-computer interface (BCI) systems from laboratory settings to real-world application, it is very important to minimize or even completely eliminate the subject-specific calibration requirement. There has been active research on calibrationless BCI systems for classification applications, e.g., P300 speller. To our knowledge, there is no literature on calibrationless BCI systems for regression applications, e.g., estimating the continuous drowsiness level of a driver from EEG signals. This paper proposes a novel spectral meta-learner for regression (SMLR) approach, which optimally combines base regression models built from labeled data from auxiliary subjects to label offline EEG data from a new subject. Experiments on driver drowsiness estimation from EEG signals demonstrate that SMLR significantly outperforms three state-of-the-art regression model fusion approaches. Although we introduce SMLR as a regression model fusion in the BCI domain, we believe its applicability is far beyond that.


title: Determination of poly(epsilon-caprolactone) solubility parameters: application to solvent substitution in a microencapsulation process.

abstract: The evolution of regulation on chemical substances (i.e. REACH regulation) calls for the progressive substitution of toxic chemicals in formulations when suitable alternatives have been identified. In this context, the method of Hansen solubility parameters was applied to identify an alternative solvent less toxic than methylene chloride used in a microencapsulation process. During the process based on a multiple emulsion (W/O/W) with solvent evaporation/extraction method, the solvent has to dissolve a polymer, poly(epsilon-caprolactone) (PCL), which forms a polymeric matrix encapsulating or entrapping a therapeutic protein as the solvent is extracted. Therefore the three partial solubility parameters of PCL have been determined by a group contribution method, swelling experiments and turbidimetric titration. The results obtained allowed us to find a solvent, anisole, able to solubilize PCL and to form a multiple emulsion with aqueous solutions. A feasibility test was conducted under standard operating conditions and allowed the production of PCL microspheres.


title: Long-term correlation tracking

abstract: In this paper, we address the problem of long-term visual tracking where the target objects undergo significant appearance variation due to deformation, abrupt motion, heavy occlusion and out-of-view. In this setting, we decompose the task of tracking into translation and scale estimation of objects. We show that the correlation between temporal context considerably improves the accuracy and reliability for translation estimation, and it is effective to learn discriminative correlation filters from the most confident frames to estimate the scale change. In addition, we train an online random fern classifier to re-detect objects in case of tracking failure. Extensive experimental results on large-scale benchmark datasets show that the proposed algorithm performs favorably against state-of-the-art methods in terms of efficiency, accuracy, and robustness.


title: FMCW radar system with additional phase evaluation for high accuracy range detection

abstract: A radar based approach of range detection depends mainly on the utilized radar principle which allows either high accuracy or high unambiguous ranges. For the most widely used FMCW radar the entire distance is not restricted in the meter range whereas the achievable accuracy directly depends on the bandwidth. An improvement is only possible if system parameters are changed or a smart signal processing is applied. In this paper an approach is described which extends the FMCW radar with an additional phase evaluation to facilitate also high accuracy using low-cost radar components and none intensive processing methods. Therefore requirements for frequency and phase evaluation must be kept which are investigated and solved using special algorithms. The performance of these proceedings is confirmed with measurements using a developed radio frequency circuit for the K-band. Moreover the possibility to combine the frequency and phase information for range detection is described and also validated by measurements.


title: Speed control of PMBLDC motor using MATLAB/Simulink and effects of load and inertia changes

abstract: Modeling and simulation of electromechanical systems with machine drives are essential steps at the design stage of such systems. This paper describes the procedure of deriving a model for the brushless dc motor with 120-degree inverter system and its validation in the MATLAB/Simulink platform. The discussion arrives at a closed-loop speed control, in which PI algorithm is adopted and the position-pulse determination is done through current control for a standard trapezoidal BLDC motors. The simulation results for BLDC motor drive systems confirm the validity of the proposed method.


title: Design of Kernels in Convolutional Neural Networks for Image Classification

abstract: Despite the effectiveness of convolutional neural networks (CNNs) for image classification, our understanding of the effect of shape of convolution kernels on learned representations is limited. In this work, we explore and employ the relationship between shape of kernels which define receptive fields (RFs) in CNNs for learning of feature representations and image classification. For this purpose, we present a feature visualization method for visualization of pixel-wise classification score maps of learned features. Motivated by our experimental results, and observations reported in the literature for modeling of visual systems, we propose a novel design of shape of kernels for learning of representations in CNNs.


title: Don't Wait!: How Timing Affects Coordination of Crowdfunding Donations

abstract: Crowdfunding sites often impose deadlines for projects to receive their requested funds. This deadline structure creates a difficult decision for potential donors. Donors can donate early to a project to help it reach its goal and to signal to other donors that the project is worthwhile. But donors may also want to wait for a similar signal from others. We conduct an experimental simulation of a crowdfunding website to explore how potential donors to projects make this decision. We find evidence for both strategies in our experiment; some donate early while others wait till the last second. However, we also find that making an early donation is usually a better strategy for donors because the amount of donations made early in a project's campaign is often the only difference between that project being funded or not. This finding suggests that crowdfunding sites need to develop designs, policies and incentives that encourage people to make immediate donations so that the site can most efficiently fund projects.


AUDIO-VISUAL SPEECH RECOGNITION


title: Personality determinants of online shopping : Explaining online purchase intentions using a hierarchical approach

abstract: What determines whether one purchases products online or through another channel? Just as retailers seek to develop online retail websites as profitable channels of distribution, researchers have pursued answers to this very question. In pursuing this line of research, several approaches have been utilized including those based upon behavioral economics, lifestyle analysis, and merchandising effects. While some of this work identifies the potential moderation of personality traits most of it focuses on factors related to time, costs and benefits, and shopping context. Following the hierarchical approach to personality developed by Mowen (Mowen J. The 3M Model of Motivation and Personality. Norwell, MA: Kluwer Academic Press, 2000.), this study seeks to understand online purchase intent using personality constructs. The present study uses data from an online consumer panel to develop a hierarchical model of personality useful for predicting consumer intentions to purchase products and services online.


title: Financial Trading as a Game: A Deep Reinforcement Learning Approach

abstract: An automatic program that generates constant profit from the financial market is lucrative for every market practitioner. Recent advance in deep reinforcement learning provides a framework toward end-to-end training of such trading agent. In this paper, we propose an Markov Decision Process (MDP) model suitable for the financial trading task and solve it with the state-of-the-art deep recurrent Q-network (DRQN) algorithm. We propose several modifications to the existing learning algorithm to make it more suitable under the financial trading setting, namely 1. We employ a substantially small replay memory (only a few hundreds in size) compared to ones used in modern deep reinforcement learning algorithms (often millions in size.) 2. We develop an action augmentation technique to mitigate the need for random exploration by providing extra feedback signals for all actions to the agent. This enables us to use greedy policy over the course of learning and shows strong empirical performance compared to more commonly used epsilon-greedy exploration. However, this technique is specific to financial trading under a few market assumptions. 3. We sample a longer sequence for recurrent neural network training. A side product of this mechanism is that we can now train the agent for every T steps. This greatly reduces training time since the overall computation is down by a factor of T. We combine all of the above into a complete online learning algorithm and validate our approach on the spot foreign exchange market.


title: A Survey of Cloud Computing Detection Techniques against DDoS Attacks

abstract: A Distributed Denial of Service Attack (DDoS) is an attack in which multiple systems compromised by a Trojan are maliciously used to target a single system. The attack leads to the denial of a certain service on the target system. In a DDoS attack, both the target system and the systems used to perform the attack are all victims of the attack. The compromised systems are also called Botnets. These attacks occur on networked systems, among them the cloud computing facet. Scholars have tried coming up with separate mechanisms for detecting and preventing such attacks long before they occur. However, as technology progresses in advancement so do the attack mechanisms. In cloud computing, security issues affect various stakeholders who plan on cloud adoption. DDoS attacks are such serious concerns that require mitigation in the cloud. This paper presents a survey of the various mechanisms, both traditional and modern, that are applied in detecting cloud-based DDoS attacks.


title: An Inconvenient Truth: Algorithmic Transparency & Accountability in Criminal Intelligence Profiling

abstract: In the hopes of making law enforcement more effective and efficient, police and intelligence analysts are increasingly relying on algorithms underpinning technologybased and data-driven policing. To achieve these objectives, algorithms must also be accurate, unbiased and just. In this paper, we examine how European data protection law regulates automated profiling and how this regulation impacts police and intelligence algorithms and algorithmic discrimination. In particular, we assess to what extent the regulatory frameworks address the challenges of algorithmic transparency and accountability. We argue that while the law regulates both algorithms and their discriminatory effects, the framework is insufficient in addressing the complex interactions that must take place between system developers, users, oversight and profiled individuals to fully guarantee algorithmic transparency and accountability.


title: Group Sparse Regularization for Deep Neural Networks

abstract: In this paper, we address the challenging task of simultaneously optimizing (i) the weights of a neural network, (ii) the number of neurons for each hidden layer, and (iii) the subset of active input features (i.e., feature selection). While these problems are traditionally dealt with separately, we propose an efficient regularized formulation enabling their simultaneous parallel execution, using standard optimization routines. Specifically, we extend the group Lasso penalty, originally proposed in the linear regression literature, to impose group-level sparsity on the networks connections, where each group is defined as the set of outgoing weights from a unit. Depending on the specific case, the weights can be related to an input variable, to a hidden neuron, or to a bias unit, thus performing simultaneously all the aforementioned tasks in order to obtain a compact network. We carry out an extensive experimental evaluation, in comparison with classical weight decay and Lasso penalties, both on a toy dataset for handwritten digit recognition, and multiple realistic mid-scale classification benchmarks. Comparative results demonstrate the potential of our proposed sparse group Lasso penalty in producing extremely compact networks, with a significantly lower number of input features, with a classification accuracy which is equal or only slightly inferior to standard regularization terms.


title: SAFE: Spectral Evolution Analysis Feature Extraction for Non-Stationary Time Series Prediction

abstract: This paper presents a practical approach for detecting non-stationarity in time series prediction. This method is called SAFE and works by monitoring the evolution of the spectral contents of time series through a distance function. This method is designed to work in combination with state-of-the-art machine learning methods in real time by informing the online predictors to perform necessary adaptation when a non-stationarity presents. We also propose an algorithm to proportionally include some past data in the adaption process to overcome the Catastrophic Forgetting problem. To validate our hypothesis and test the effectiveness of our approach, we present comprehensive experiments in different elements of the approach involving artificial and real-world datasets. The experiments show that the proposed method is able to significantly save computational resources in term of processor or GPU cycles while maintaining high prediction performances.


title: Parametric Shape Grammar Formalism for Moorish Geometric Design Analysis and Generation

abstract: The goal of this article is to propose a modeling method to automatically generate original and new forms of periodic Moorish geometric patterns. The proposed method is based on the symmetry-based approach and the shape grammar formalism. The symmetry-based approach consists of constructing a periodic pattern by using isometric transformations of its template motif. The main contribution of this article is to propose a parametric shape grammar method to generate the template motif. The process used consists first in identifying the shapes vocabulary characterizing the Moorish style. By identifying its common features by the analysis of a dataset of historical Moorish patterns, parametric shape grammar (PSG) rules are then determined. Furthermore, the use of PSGs implies additional mechanisms, such as the consideration of suitable constraints and goals. These mechanisms are imposed by the grid, which is the underlying geometric structure used traditionally by artisans to design sophisticated geometric patterns. The performance of the proposed method is measured by its capability to create original and new motifs of periodic Moorish patterns. This method preserves the authenticity of the style considered, because the generated patterns follow a parametric shape grammar based on a priori domain knowledge and extract one from representative images of Moorish style.


title: The mind of expert motor performance is cool and focused

abstract: Extraordinary motor skills required for expert athletic or music performance require longstanding and intensive practice leading to two critical skills, a level of maximal performance that far exceeds that of non-experts and a degree of privileged focus on motor performance that excludes intrusions. This study of motor planning in expert golfers demonstrated their brain activation during their pre-shot routine to be radically different than in novices. The posterior cingulate, the amygdala–forebrain complex, and the basal ganglia were active only in novices, whereas experts had activation primarily in the superior parietal lobule, the dorsal lateral premotor area, and the occipital area. The fact that these differences are apparent before the golfer swings the club suggests that the disparity between the quality of the performance of novice and expert golfers lies at the level of the organization of neural networks during motor planning. In particular, we suggest that extensive practice over a long period of time leads experts to develop a focused and efficient organization of task-related neural networks, whereas novices have difficulty filtering out irrelevant information.


title: Profiling Users in GUI Based Systems for Masquerade Detection

abstract: Masquerading or impersonation attack refers to the illegitimate activity on a computer system when one user impersonates another user. Masquerade attacks are serious in nature due to the fact that they are mostly carried by insiders and thus are extremely difficult to detect. Detection of these attacks is done by monitoring significant changes in user's behavior based on his/her profile. Currently, such profiles are based mostly on the user command line data and do not represent his/her complete behavior in a graphical user interface (GUI) based system and hence are not sufficient to quickly detect such masquerade attacks. In this paper, we present a new framework for creating a unique feature set for user behavior on GUI based systems. We have collected real user behavior data from live systems and extracted parameters to construct these feature vectors. These vectors contain user information such as mouse speed, distance, angles and amount of clicks during a user session. We model our technique of user identification and masquerade detection as a binary classification problem and use support vector machine (SVM) to learn and classify these feature vectors. We show that our technique can provide detection rates of up to 96% with few false positives based on these feature vectors. We have tested our technique with various feature vector parameters and conclude that these feature vectors can provide unique and comprehensive user behavior information and are powerful enough to detect masqueraders


title: Non-Invasive Glucose Measurement Technologies: An Update from 1999 to the Dawn of the New Millennium

abstract: There are three main issues in non-invasive (NI) glucose measurements: namely, specificity, compartmentalization of glucose values, and calibration. There has been progress in the use of near-infrared and mid-infrared spectroscopy. Recently new glucose measurement methods have been developed, exploiting the effect of glucose on erythrocyte scattering, new photoacoustic phenomenon, optical coherence tomography, thermo-optical studies on human skin, Raman spectroscopy studies, fluorescence measurements, and use of photonic crystals. In addition to optical methods, in vivo electrical impedance results have been reported. Some of these methods measure intrinsic properties of glucose; others deal with its effect on tissue or blood properties. Recent studies on skin from individuals with diabetes and its response to stimuli, skin thermo-optical response, peripheral blood flow, and red blood cell rheology in diabetes shed new light on physical and physiological changes resulting from the disease that can affect ...


title: Edible mushrooms as a source of valuable nutritive constituents

abstract: An overview of Polish and foreign literature concerning the chemical composition of edible mushrooms both cultivated and harvested in natural sites in Poland and abroad is presented. 100 g of fresh mushrooms contains 5.3-14.8 g dry matter, 1.5-6.7 g of carbohydrates, 1.5-3.0 g of protein and 0.3-0.4 g of fat. Mushrooms are a high valued source of mineral constituents, particularly potassium, phosphorus and magnesium and of vitamins of the B group, chiefly vitamins B2 and B3 and also vitamin D. The aroma of the discussed raw materials is based on about 150 aromatic compounds. The mushrooms can be a source of heavy metals and radioactive substances. They are also characterized by the occurrence of numerous enzymes.


title: Automated construction by contour crafting-related robotics and information technologies

abstract: Although automation has advanced in manufacturing, the growth of automation in construction has been slow. Conventional methods of manufacturing automation do not lend themselves to construction of large structures with internal features. This may explain the slow rate of growth in construction automation. Contour crafting (CC) is a recent layered fabrication technology that has a great potential in automated construction of whole structures as well as subcomponents. Using this process, a single house or a colony of houses, each with possibly a different design, may be automatically constructed in a single run, imbedded in each house all the conduits for electrical, plumbing and air-conditioning. Our research also addresses the application of CC in building habitats on other planets. CC will most probably be one of the very few feasible approaches for building structures on other planets, such as Moon and Mars, which are being targeted for human colonization before the end of the new century.


title: Automatic Query Expansion Using SMART : TREC 3

abstract: The Smart information retrieval project emphasizes completely automatic approaches to the understanding and retrieval of large quantities of text. We continue our work in TREC 3, performing runs in the routing, ad-hoc, and foreign language environments. Our major focus is massive query expansion : adding from 300 to 530 terms to each query. These terms come from known relevant documents in the case of routing, and from just the top retrieved documents in the case of ad-hoc and Spanish. This approach improves effectiveness from 7% to 25% in the various experiments. Other ad-hoc work extends our investigations into combining global similarities, giving an overall indication of how a document matches a query, with local similarities identifying a smaller part of the document which matches the query. Using an overlapping text window definition of local, we achieve a 16% improvement.


title: SimApp: A Framework for Detecting Similar Mobile Applications by Online Kernel Learning

abstract: With the popularity of smart phones and mobile devices, the number of mobile applications (a.k.a. "apps") has been growing rapidly. Detecting semantically similar apps from a large pool of apps is a basic and important problem, as it is beneficial for various applications, such as app recommendation, app search, etc. However, there is no systematic and comprehensive work so far that focuses on addressing this problem. In order to fill this gap, in this paper, we explore multi-modal heterogeneous data in app markets (e.g., description text, images, user reviews, etc.), and present "SimApp" -- a novel framework for detecting similar apps using machine learning. Specifically, it consists of two stages: (i) a variety of kernel functions are constructed to measure app similarity for each modality of data; and (ii) an online kernel learning algorithm is proposed to learn the optimal combination of similarity functions of multiple modalities. We conduct an extensive set of experiments on a real-world dataset crawled from Google Play to evaluate SimApp, from which the encouraging results demonstrate that SimApp is effective and promising.


title: Carbon Storage by Carex stricta Tussocks: A Restorable Ecosystem Service?

abstract: Tussock-forming plants are globally widespread and enhance ecosystem services. We hypothesized that tussocks of Carex stricta store carbon (C) in addition to enhancing microtopography and biodiversity. We characterized tussock size, composition, and carbon pools associated with three undisturbed C. stricta-dominated tussock meadows in the Upper Midwest, USA. Remnant meadow tussocks were tall (17.2 cm), voluminous (4,113 cm3), and largely organic (95 %), indicating their ability to accumulate organic matter and store carbon. Tussocks were the second largest C pool (next to soil) in these ecosystems; they comprised 41–62 % of total biomass C. Using bomb 14C dating, we estimated that reference-site tussocks were over 50 years old. Their long-term persistence is consistent with lower leaf decomposition rates on tussocks (k = 0.26 years−1) than in tussock interspaces (k = 0.39 years−1). An urban tussock meadow had tussocks that were shorter than those of remnant sites, but less dense than a restored meadow. The restored meadow (≤15 years) had smaller, structurally distinct tussocks that stored less C. Among the five sites, C stocks were lowest in the urban and restored meadows, supporting the need to conserve existing C stores in remnant meadows and to restore tussock sedge for multiple ecosystem services.


title: A Survey of Personality Computing

abstract: Personality is a psychological construct aimed at explaining the wide variety of human behaviors in terms of a few, stable and measurable individual characteristics. In this respect, any technology involving understanding, prediction and synthesis of human behavior is likely to benefit from Personality Computing approaches, i.e. from technologies capable of dealing with human personality. This paper is a survey of such technologies and it aims at providing not only a solid knowledge base about the state-of-the-art, but also a conceptual model underlying the three main problems addressed in the literature, namely Automatic Personality Recognition (inference of the true personality of an individual from behavioral evidence), Automatic Personality Perception (inference of personality others attribute to an individual based on her observable behavior) and Automatic Personality Synthesis (generation of artificial personalities via embodied agents). Furthermore, the article highlights the issues still open in the field and identifies potential application areas.


title: A numerical study of spin-dependent organization of alkali-metal atomic clusters using density-functional method

abstract: We calculate the different geometric isomers of spin clusters composed of a small number of alkali-metal atoms using the UB3LYP density-functional method. The electron density distribution of clusters changes according to the value of total spin. Steric structures as well as planar structures arise when the number of atoms increases. The lowest spin state is the most stable and Li n , Na n , K n , Rb n , and Cs n with n = 2–8 can be formed in higher spin states. In the highest spin state, the preparation of clusters depends on the kind and the number of constituent atoms. The interaction energy between alkali-metal atoms and rare-gas atoms is smaller than the binding energy of spin clusters. Consequently, it is possible to self-organize the alkali-metal-atom clusters on a non-wetting substrate coated with rare-gas atoms.


title: Manipulating Attributes of Natural Scenes via Hallucination

abstract: In this study, we explore building a two-stage framework for enabling users to directly manipulate high-level attributes of a natural scene. The key to our approach is a deep generative network that can hallucinate images of a scene as if they were taken in a different season (e.g., during winter), weather condition (e.g., on a cloudy day), or at a different time of the day (e.g., at sunset). Once the scene is hallucinated with the given attributes, the corresponding look is then transferred to the input image while preserving the semantic details intact, giving a photo-realistic manipulation result. As the proposed framework hallucinates what the scene will look like, it does not require any reference style image as commonly utilized in most of the appearance or style transfer approaches. Moreover, it allows to simultaneously manipulate a given scene according to a diverse set of transient attributes within a single model, eliminating the need of training multiple networks per each translation task. Our comprehensive set of qualitative and quantitative results demonstrates the effectiveness of our approach against the competing methods.


title: Ushio: Analyzing News Media and Public Trends in Twitter

abstract: In this information age, Social Networking Services contribute a significant amount of contents in creating a knowledge based society. Nowadays, there are more than 500 million tweets sent per day in Twitter. Such drastic growth of contents brings new opportunities for human beings to discover their surroundings more effectively in a timely manner. Moreover, these types of services evolve not only in a perspective of scalability, but also in the view of indicating more meaningful information regarding what happens in the world. Numerous news agencies are broadcasting breaking news via Twitter and people would like to leave comments with their own opinions as well. However, there are differences between events that news media are more willing to cover and news stories that people are more interested in. Furthermore, as people are becoming the largest sensor network, trending topics are not only led by media, but also by the public, and hence it is worth pondering how they affect each other. In this paper, we focus on studying these concerns by building a system, Ushio, analyzing Twitter streams in both the tweets updated by multiple news agencies and those appearing in the public timeline. We describe our design and implementation of this system, which extracts named entities from the Twitter streams and generates corresponding statistics with its relational model. We then show how we use these data to find trending topics and real focus from both media and the public, as well as discover their related topics along with the correlation indicating the leading role between them for assorted topics.


title: Understanding Neural Networks through Representation Erasure

abstract: While neural networks have been successfully applied to many natural language processing tasks, they come at the cost of interpretability. In this paper, we propose a general methodology to analyze and interpret decisions from a neural model by observing the effects on the model of erasing various parts of the representation, such as input word-vector dimensions, intermediate hidden units, or input words. We present several approaches to analyzing the effects of such erasure, from computing the relative difference in evaluation metrics, to using reinforcement learning to erase the minimum set of input words in order to flip a neural model's decision. In a comprehensive analysis of multiple NLP tasks, including linguistic feature classification, sentence-level sentiment analysis, and document level sentiment aspect prediction, we show that the proposed methodology not only offers clear explanations about neural model decisions, but also provides a way to conduct error analysis on neural models.


title: On-Chip High-Voltage Generation in Integrated Circuits Using an Improved Multiplier Technique

abstract: An improved oped for generating +40 voltage multiplier technique has been develV internally in p-channel MNOS integrated circuits to enrtble them to be operated from standard +5and : 12-V supply rails. With this technique, the multiplication efficiency and current driving capabtilty are both independent of the number of multiplier stages. A mathematical model and simple equivalent circuit have been developed for the multiplier and the predicted performance agrees well with measured results. A multiplier has already been incorporated into a TTL compatible nonvolatile quad-latch, in which it occupies a chip area of 600 ~m X 240 pm. It is operated with a clock frequency of 1 MHz and can supManuscript received December 9, 1975; revised February 18, 1976. This paper is based on part of a presentation entitled “A non-volatile MNOS quad-latch,” which was presented at the First European SolidState Circuits Conference, Canterbury, England, September 2-5, 1975. The author is with the Allen Clark Research Centre, The Plessey Company Ltd., CasweJl, Towcester, Northants., England. MNOS Voltage ply a maximum load current of about 10 NA. The output impedance is 3.2 Mfl. INTRODUCTION ALTHOUGH MNOS technology is now well established for fabricating nonvolatile memory circuits, the relatively high potentials necessary to write or erase information, typically 30-40 V, are an obvious disadvantage. In many applications, the need to generate these voltages has prevented the use of MNOS devices being economically viable, especially when only a few bits of nonvolatile data are required. To overcome this problem, a method of on-chip high-voltage generation using a new voltage multiplier technique has been developed, enabling MNOS circuits to be operated with standard DICKSON: ON-CHIP HIGH-VOLTAGEGENERATION 375


title: Efficiency analysis of a high frequency PS-ZVS isolated unidirectional full-bridge DC-DC converter based on SiC MOSFETs

abstract: In this work, the static and dynamic characteristics of a new double-trench SiC MOSFET are experimentally investigated and compared to two different available commercial SiC MOSFETs. The SiC devices are applied in a ZVZCS Phase-Shifted Full-Bridge converter which uses the parasitic leakage inductance of the transformer to achieve soft switching. A practical method to break down the converter losses based on separating the measured semiconductor and magnetic losses is proposed and compared to an analytical loss model. The experimental results show several advantages of using SiC MOSFETs as well as ZVZCS in the converter. The main results can be summarized as elimination of the freewheeling circulating current mode, clamping the overshoot voltage across the rectifier diodes and achieving soft switching in a wide power load range without additional auxiliary circuits or resonant inductor. Experimental results for a 10kW, (100-250) kHz prototype show an efficiency of up to 98,1% for the whole converter.


title: Unsupervised Methods for Determining Object and Relation Synonyms on the Web

abstract: The task of identifying synonymous relations and objects, or synonym resolution, is critical for high-quality information extraction. This paper investigates synonym resolution in the context of unsupervised information extraction, where neither hand-tagged training examples nor domain knowledge is available. The paper presents a scalable, fully-implemented system that runs in O(KN log N) time in the number of extractions, N, and the maximum number of synonyms per word, K. The system, called RESOLVER, introduces a probabilistic relational model for predicting whether two strings are co-referential based on the similarity of the assertions containing them. On a set of two million assertions extracted from the Web, RESOLVER resolves objects with 78% precision and 68% recall, and resolves relations with 90% precision and 35% recall. Several variations of RESOLVER's probabilistic model are explored, and experiments demonstrate that under appropriate conditions these variations can improve F1 by 5%. An extension to the basic RESOLVER system allows it to handle polysemous names with 97% precision and 95% recall on a data set from the TREC corpus.


title: Prioritization methods for accelerating MDP solvers

abstract: The performance of value and policy iteration can be dramatically improved by eliminating redundant or useless backups, and by backing up states in the right order. We study several methods designed to accelerate these iterative solvers, including prioritization, partitioning, and variable reordering. We generate a family of algorithms by combining several of the methods discussed, and present extensive empirical evidence demonstrating that performance can improve by several orders of magnitude for many problems, while preserving accuracy and convergence guarantees.


title: Compressed air energy storage: Thermodynamic and economic review

abstract: Compressed air energy storage (CAES) is one of the most promising mature electrical energy storage (EES) technologies. In this paper, recent technological and thermodynamic advances in CAES are examined. This review includes an examination of the three major thermodynamic approaches to CAES, an overview of air and thermal storage systems, and an examination of CAES in a distributed application. An economic analysis is then offered in order to determine the commercial viability of this technology.


title: Electronic circuits modeling using artificial neural networks

abstract: In this paper artificial neural networks (ANN) are applied to modeling of electronic circuits. ANNs are used for application of the black-box modeling concept in the time domain. Modeling process is described, so the topology of the ANN, the testing signal used for excitation, together with the complexity of ANN are considered. The procedure is first exemplified in modeling of resistive circuits. MOS transistor, as a four-terminal device, is modeled. Then nonlinear negative resistive characteristic is modeled in order to be used as a piece-wise linear resistor in Chua's circuit. Examples of modeling nonlinear dynamic circuits are given encompassing a variety of modeling problems. A nonlinear circuit containing quartz oscillator is considered for modeling. Verification of the concept is performed by verifying the ability of the model to generalize i.e. to create acceptable responses to excitations not used during training. Implementation of these models within a behavioral simulator is exemplified. Every model is implemented in realistic surrounding in order to show its interaction, and of course, its usage and purpose.


title: Knowledge management in organizations : examining the interaction between technologies , techniques , and people

abstract: Argues that the knowledge management process can be categorized into knowledge creation, knowledge validation, knowledge presentation, knowledge distribution, and knowledge application activities. To capitalize on knowledge, an organization must be swift in balancing its knowledge management activities. In general, such a balancing act requires changes in organizational culture, technologies, and techniques. A number of organizations believe that by focusing exclusively on people, technologies, or techniques, they can manage knowledge. However, that exclusive focus on people, technologies, or techniques does not enable a firm to sustain its competitive advantages. It is, rather, the interaction between technology, techniques, and people that allow an organization to manage its knowledge effectively. By creating a nurturing and “learning‐by‐doing” kind of environment, an organization can sustain its competitive advantages.


title: Business Intelligence Maturity Models: An Overview

abstract: In order to identify and explore the strengths and weaknesses of business intelligence (BI) initiatives, managers need to assess the maturity of their BI efforts. For this, a wide range of maturity models has been developed. In this paper, we present an overview of BI maturity models and elaborate their methodical and conceptual characteristics. This overview helps to understand the topic of BI maturity and can be used to guide the selection of an appropriate BI maturity model.


title: When Cognitive Radio meets the Internet of Things?

abstract: Internet of Things (IoT) is a world wide network of interconnected objects. IoT capable objects will be interconnected through wired and wireless communication technologies. However, cost-effectiveness issues and accessibility to remote users make wireless communication as a feasible solution. A majority of possibilities have been proposed but many of these suffer from vulnerabilities to dynamic environmental conditions, ease of access, bandwidth allocation and utilization, and cost to purchase spectrum. Thus trends are shifting to the adaptability of Cognitive Radio Networks (CRNs) into IoT. Additionally, ubiquitous objects with cognitive capabilities will be able to make intelligent decisions to achieve interference-free and on-demand services. The main goal of this paper is to discuss how CR technology can be helpful for the IoT paradigm. More precisely, in this paper, we highlight CR functionalities, specially spectrum sensing in conjunction with cloud services to serve as self-reconfigurable IoT solutions for a number of applications.


title: An Ensemble-of-Classifiers Based Approach for Early Diagnosis of Alzheimer's Disease: Classification Using Structural Features of Brain Images

abstract: Structural brain imaging is playing a vital role in identification of changes that occur in brain associated with Alzheimer's disease. This paper proposes an automated image processing based approach for the identification of AD from MRI of the brain. The proposed approach is novel in a sense that it has higher specificity/accuracy values despite the use of smaller feature set as compared to existing approaches. Moreover, the proposed approach is capable of identifying AD patients in early stages. The dataset selected consists of 85 age and gender matched individuals from OASIS database. The features selected are volume of GM, WM, and CSF and size of hippocampus. Three different classification models (SVM, MLP, and J48) are used for identification of patients and controls. In addition, an ensemble of classifiers, based on majority voting, is adopted to overcome the error caused by an independent base classifier. Ten-fold cross validation strategy is applied for the evaluation of our scheme. Moreover, to evaluate the performance of proposed approach, individual features and combination of features are fed to individual classifiers and ensemble based classifier. Using size of left hippocampus as feature, the accuracy achieved with ensemble of classifiers is 93.75%, with 100% specificity and 87.5% sensitivity.


title: A Comparative Study of Computational Methods for Compressed Sensing Reconstruction of EMG Signal

abstract: Wearable devices offer a convenient means to monitor biosignals in real time at relatively low cost, and provide continuous monitoring without causing any discomfort. Among signals that contain critical information about human body status, electromyography (EMG) signal is particular useful in monitoring muscle functionality and activity during sport, fitness, or daily life. In particular surface electromyography (sEMG) has proven to be a suitable technique in several health monitoring applications, thanks to its non-invasiveness and ease to use. However, recording EMG signals from multiple channels yields a large amount of data that increases the power consumption of wireless transmission thus reducing the sensor lifetime. Compressed sensing (CS) is a promising data acquisition solution that takes advantage of the signal sparseness in a particular basis to significantly reduce the number of samples needed to reconstruct the signal. As a large variety of algorithms have been developed in recent years with this technique, it is of paramount importance to assess their performance in order to meet the stringent energy constraints imposed in the design of low-power wireless body area networks (WBANs) for sEMG monitoring. The aim of this paper is to present a comprehensive comparative study of computational methods for CS reconstruction of EMG signals, giving some useful guidelines in the design of efficient low-power WBANs. For this purpose, four of the most common reconstruction algorithms used in practical applications have been deeply analyzed and compared both in terms of accuracy and speed, and the sparseness of the signal has been estimated in three different bases. A wide range of experiments are performed on real-world EMG biosignals coming from two different datasets, giving rise to two different independent case studies.


title: Updating An NLP System To Fit New Domains: An Empirical Study On The Sentence Segmentation Problem

abstract: Statistical machine learning algorithms have been successfully applied to many natural language processing (NLP) problems. Compared to manually constructed systems, statistical NLP systems are often easier to develop and maintain since only annotated training text is required. From annotated data, the underlying statistical algorithm can build a model so that annotations for future data can be predicted. However, the performance of a statistical system can also depend heavily on the characteristics of the training data. If we apply such a system to text with characteristics different from that of the training data, then performance degradation will occur. In this paper, we examine this issue empirically using the sentence boundary detection problem. We propose and compare several methods that can be used to update a statistical NLP system when moving to a different domain.


title: Automatic Adaptive Center of Pupil Detection Using Face Detection and CDF Analysis

abstract: This paper presents a novel adaptive algorithm to detect the center of pupil in frontal view faces. This algorithm, at first, employs the viola-Jones face detector to find the approximate location of face in an image. The knowledge of the face structure is exploited to detect the eye region. The histogram of the detected region is calculated and its CDF is employed to extract the eyelids and iris region in an adaptive way. The center of this region is considered as the pupil center. The experimental results show ninety one percent's accuracy in detecting pupil center.


title: Road Scene Segmentation from a Single Image

abstract: Road scene segmentation is important in computer vision for different applications such as autonomous driving and pedestrian detection. Recovering the 3D structure of road scenes provides relevant contextual information to improve their understanding. ::: ::: In this paper, we use a convolutional neural network based algorithm to learn features from noisy labels to recover the 3D scene layout of a road image. The novelty of the algorithm relies on generating training labels by applying an algorithm trained on a general image dataset to classify on–board images. Further, we propose a novel texture descriptor based on a learned color plane fusion to obtain maximal uniformity in road areas. Finally, acquired (off–line) and current (on–line) information are combined to detect road areas in single images. ::: ::: From quantitative and qualitative experiments, conducted on publicly available datasets, it is concluded that convolutional neural networks are suitable for learning 3D scene layout from noisy labels and provides a relative improvement of 7% compared to the baseline. Furthermore, combining color planes provides a statistical description of road areas that exhibits maximal uniformity and provides a relative improvement of 8% compared to the baseline. Finally, the improvement is even bigger when acquired and current information from a single image are combined.


title: Seeing Your Face Is Not Enough: An Inertial Sensor-Based Liveness Detection for Face Authentication

abstract: Leveraging built-in cameras on smartphones and tablets, face authentication provides an attractive alternative of legacy passwords due to its memory-less authentication process. However, it has an intrinsic vulnerability against the media-based facial forgery (MFF) where adversaries use photos/videos containing victims' faces to circumvent face authentication systems. In this paper, we propose FaceLive, a practical and robust liveness detection mechanism to strengthen the face authentication on mobile devices in fighting the MFF-based attacks. FaceLive detects the MFF-based attacks by measuring the consistency between device movement data from the inertial sensors and the head pose changes from the facial video captured by built-in camera. FaceLive is practical in the sense that it does not require any additional hardware but a generic front-facing camera, an accelerometer, and a gyroscope, which are pervasively available on today's mobile devices. FaceLive is robust to complex lighting conditions, which may introduce illuminations and lead to low accuracy in detecting important facial landmarks; it is also robust to a range of cumulative errors in detecting head pose changes during face authentication.


title: The Application of SHESN on Financial Time Series Prediction

abstract: Echo State Network (ESN) has been widely applied to all kinds of time series prediction tasks because of their advantages in modeling nonlinear systems and strong memory capacity. The improved scale-free highly clustered echo state network (SHESN), whose reservoir has both small-world and scale-free characteristics, has better echo state property and can achieve more accurate prediction results compared with the conventional ESN. In this paper, the extended application of the scale-free highly clustered echo state network model on financial time series prediction tasks is researched. The two widely used financial series SaP500 index and US/Euro exchange rate are chosen here. The experimental results show that the SHESN model has better prediction performance and demonstrate the scale-free highly clustered topology has stronger computational capability.


title: HS-SLA: A Hierarchical Self-Healing SLA Model for Cloud Computing

abstract: The service level agreement (SLA) is a mutual contract between the service provider and consumer which determines the agreed service level objective (SLO). The common SLA is enforced as a plain documental agreement without any relation to other upper and lower level agreements among different layers of cloud computing. Hence, the cloud computing environment needs the hierarchical and autonomic SLA. This paper proposes the HS-SLA model to enforce the hierarchical self-healing SLA in cloud computing. The self-healing ability includes the SLA monitoring, violation detecting and violation reacting processes. In HS-SLA, the dependent SLAs have a connection to each other hierarchically. This model enables the SLA to check its QoS attributes and notify the current status to dependent SLAs. Additionally, HS-SLA could prevent or propagate the notified violations by an urgent reaction. So, the service providers have a great chance to prevent the violated SLA before sensing by end users. The HSSLA model is simulated and the experiment results have shown the violation detection and reaction capabilities of the proposed model in a hierarchical cloud environment. Furthermore, in HS-SLA the end users meet the lesser violated SLAs in comparison with the common SLA structure.


title: A Modular Framework for Digital Painting

abstract: While there has been tremendous research in the simulation of natural media painting, little academic work has been written to understand how all these contributions interrelate and to use this knowledge to direct future work. In this paper, we survey the set of interesting artistic tools to categorize their effects and motivate a modular framework for digital painting that can reproduce those effects in a loosely coupled way. We use this framework as a lens through which we survey the literature and classify the achievements of previous efforts. We examine our own contributions in the field in more detail, discussing how the framework motivated those results and how it impacted our accomplishments. Finally, we discuss the open challenges that remain for the research community, and how the framework can help to make contributions towards those challenges.


title: Spreadsheet Validation and Analysis through Content Visualization

abstract: Visualizing spreadsheet content provides analytic insight and visual validation of large amounts of spreadsheet data. Oculus Excel Visualizer is a point and click data visualization experiment which directly visualizes Excel data and re-uses the layout and formatting already present in the spreadsheet.


title: Leadership skills development for engineers

abstract: Abstract:Leadership must be a key element advancing for the engineering profession to remain relevant and connected in an era of heightened outsourcing and global competition. Companies intent on m...


title: Enlargement of vulnerable web applications for testing

abstract: There are two main kinds of vulnerable web applications, usual applications developed with a specific aim and applications which are vulnerable by design. On one hand, the usual applications are those that are used everywhere and on a daily basis, and where vulnerabilities are detected, and often mended, such as online banking systems, newspaper sites, or any other Web site. On the other hand, vulnerable by design web applications are developed for proper evaluation of web vulnerability scanners and for training in detecting web vulnerabilities. The main drawback of vulnerable by design web applications is that they used to include just a short set of well-known types of vulnerabilities, usually from famous classifications like the OWASP Top Ten. They do not include most of the types of web vulnerabilities. In this paper, an analysis and assessment of vulnerable web applications is conducted in order to select the applications that include the larger set of types of vulnerabilities. Then those applications are enlarged with more types of web vulnerabilities that vulnerable web applications do not include. Lastly, the new vulnerable web applications have been analyzed to check whether web vulnerability scanners are able to detect the new added vulnerabilities, those vulnerabilities that vulnerable by design web applications do not include. The results show that the tools are not very successful in detecting those vulnerabilities, less than well-known vulnerabilities.


title: ROI Based Medical Image Watermarking with Zero Distortion and Enhanced Security

abstract: Medical Image Watermarking has been considered as an appropriate method for enhancing the data security and content verification in medical science. For the purpose of achieving better security it is recommended to hide the data in the medical image itself that will improve the reliability of medical information system. In our paper, the work is divided in two main stages, first of all image is divided into two parts: Region of Interest that is defined as informational part of image and Region of Non Interest that is defined as noninformational part. In our work we have used Region of Non Interest to hide the data so that the informational part of the image kept safe. Watermark is encrypted by using RSA. A Discrete Wavelet Transform based approach is used for embedding the encrypted data in Region of Non Interest part of image for enhancing data security. The experimental result shows that watermark embedded by the proposed algorithm provides better security and can survive successfully under different attacks.


title: Technologies for Trust in Electronic Commerce

abstract: Lack of consumer trust in e-commerce merchants, e-commerce technology, and the social, financial and legal infrastructures of the e-commerce environment, poses a major challenge to the large-scale uptake of business to consumer e-commerce. Most traditional cues for assessing trust in the physical world are not available online. This paper gives an overview of some of the work being done to devise alternative methods for assessing, communicating and establishing trust in this environment. Examples are drawn from a wide range of disciplines including human–computer interaction, usability, marketing, information technology, mathematics, linguistics and law. Industry, self-regulatory and government initiatives aimed at building consumer trust and confidence in e-commerce are also discussed.