title: Weakly Paired Multimodal Fusion for Object Recognition

abstract: The ever-growing development of sensor technology has led to the use of multimodal sensors to develop robotics and automation systems. It is therefore highly expected to develop methodologies capable of integrating information from multimodal sensors with the goal of improving the performance of surveillance, diagnosis, prediction, and so on. However, real multimodal data often suffer from significant weak-pairing characteristics, i.e., the full pairing between data samples may not be known, while pairing of a group of samples from one modality to a group of samples in another modality is known. In this paper, we establish a novel projective dictionary learning framework for weakly paired multimodal data fusion. By introducing a latent pairing matrix, we realize the simultaneous dictionary learning and the pairing matrix estimation, and therefore improve the fusion effect. In addition, the kernelized version and the optimization algorithms are also addressed. Extensive experimental validations on some existing data sets are performed to show the advantages of the proposed method. Note to Practitioners —In many industrial environments, we usually use multiple heterogeneous sensors, which provide multimodal information. Such multimodal data usually lead to two technical challenges. First, different sensors may provide different patterns of data. Second, the full-pairing information between modalities may not be known. In this paper, we develop a unified model to tackle such problems. This model is based on a projective dictionary learning method, which efficiently produces the representation vector for the original data by an explicit form. In addition, the latent pairing relation between samples can be learned automatically and be used to improve the classification performance. Such a method can be flexibly used for multimodal fusion with full-pairing, partial-pairing and weak-pairing cases.


title: Reduction Mammoplasty in a Developing Country: A 10-year Review (2001-2010) at the National Orthopaedic Hospital, Enugu.

abstract: CONTEXT ::: Large breast is a major problem because of associated symptomatology and aesthetic concerns. Reduction mammoplasty (RM) resolves the symptom and at the same time improves the aesthetic appearance of the breast, hence improving self-esteem and social integration. ::: ::: ::: AIMS ::: To describe the pattern of RM in a hospital in the developing world and its impact on postgraduate surgical training. ::: ::: ::: SETTINGS AND DESIGN ::: A retrospective review of all the RMs done in the National Orthopaedic Hospital, Enugu (a major plastic surgery training center in Nigeria) over a ten-year period (2001-2010), in the developing country of Nigeria. ::: ::: ::: SUBJECTS AND METHODS ::: All RMs done in the hospital were reviewed after retrieving their records from operation register and medical records department. Fifteen (15) cases were retrieved and analyzed. ::: ::: ::: DATA ANALYSIS ::: Data was analyzed with Microsoft excel 2007. ::: ::: ::: RESULTS ::: Average age of female patients who had RM was 26.5 years and 83.3% were single. The most common complaint was abnormally large breast (macromastia). Inferior pedicle technique was commonly used. ::: ::: ::: CONCLUSIONS ::: The results of RM are remarkable as it impact positively on the quality of life of the patients. However, the level of awareness about the availability of this service is still low in the region as shown by few cases done over the period of review and this impacts negatively on the training. The need for public awareness cannot be overemphasized.


title: Ouroboros: A Provably Secure Proof-of-Stake Blockchain Protocol

abstract: We present “Ouroboros”, the first blockchain protocol based on proof of stake with rigorous security guarantees. We establish security properties for the protocol comparable to those achieved by the bitcoin blockchain protocol. As the protocol provides a “proof of stake” blockchain discipline, it offers qualitative efficiency advantages over blockchains based on proof of physical resources (e.g., proof of work). We also present a novel reward mechanism for incentivizing Proof of Stake protocols and we prove that, given this mechanism, honest behavior is an approximate Nash equilibrium, thus neutralizing attacks such as selfish mining.


title: Endothelium-Restricted Overexpression of Human Endothelin-1 Causes Vascular Remodeling and Endothelial Dysfunction

abstract: Background— Endothelin (ET)-1 is a potent vasoconstrictor that contributes to vascular remodeling in hypertension and other cardiovascular diseases. Endogenous ET-1 is produced predominantly by vascular endothelial cells. To directly test the role of endothelium-derived ET-1 in cardiovascular pathophysiology, we specifically targeted expression of the human preproET-1 gene to the endothelium by using the Tie-2 promoter in C57BL/6 mice. Methods and Results— Ten-week-old male C57BL/6 transgenic (TG) and nontransgenic (wild type; WT) littermates were studied. TG mice exhibited 3-fold higher vascular tissue ET-1 mRNA and 7-fold higher ET-1 plasma levels than did WT mice but no significant elevation in blood pressure. Despite the absence of significant blood pressure elevation, TG mice exhibited marked hypertrophic remodeling and oxidant excess-dependent endothelial dysfunction of resistance vessels, altered ET-1 and ET-3 vascular responses, and significant increases in ETB expression compared with WT litterma...


title: Fault ride through of DFIG wind turbines during symmetrical voltage dip with crowbar or stator current feedback solution

abstract: Low Voltage Ride Through is an important feature for wind turbine systems to fulfill grid code requirements. In case of wind turbine technologies using doubly fed induction generators the reaction to grid voltage disturbances is sensitive. Hardware or software protection must be implemented to protect the converter from tripping during severe grid voltage faults. In this paper two methods for low voltage ride through of symmetrical grid voltage dips are investigated. As a basis, an analysis of the rotor voltages during grid fault is given. First, the conventional hardware method using a crowbar is introduced. Then the stator current reference feedback solution is presented. Both methods are investigated and compared by simulation results using 2 MW wind turbine system parameters. Measurement results on a 22 kW laboratory DFIG test bench show the effectiveness of the proposed control technique.


title: Fine-grained named entity recognition using conditional random fields for question answering

abstract: In many QA systems, fine-grained named entities are extracted by coarse-grained named entity recognizer and fine-grained named entity dictionary. In this paper, we describe a fine-grained Named Entity Recognition using Conditional Random Fields (CRFs) for question answering. We used CRFs to detect boundary of named entities and Maximum Entropy (ME) to classify named entity classes. Using the proposed approach, we could achieve an 83.2% precision, a 74.5% recall, and a 78.6% Fl for 147 fined-grained named entity types. Moreover, we reduced the training time to 27% without loss of performance compared to a baseline model. In the question answering, The QA system with passage retrieval and AIU archived about 26% improvement over QA with passage retrieval. The result demonstrated that our approach is effective for QA.


title: Knowledge Constructive Jigsaw as an Adaptive Learning Framework: Its Design Principles and Network Supports

abstract: I will report our current endeavor to create a learning community aiming for better quality education, by changing classroom practices from teacher-centric to learner-centric. Our efforts are focused on creating intellectually challenging collaborative learning based on a framework called "constructive interaction, " which has been identified as a basic skill of human beings to learn from social interaction. The design is called the "Knowledge Constructive Jigsaw, " which encourages the learners engage in actively explaining what each learner knows to integrate them and create an answer to the shared problem. In this presentation, I will explain the basic framework and how it can be implemented in regular classroom activities, with some outcomes. I will then touch upon how this renovation effort opens new research fields, including the formation of a networked, sustainable learning community.


title: Therapeutic interventions in endothelial dysfunction: Endothelium as a target organ

abstract: Endothelial dysfunction is recognized as the initial step in the atherosclerotic process. To date, most interventions attempting to improve endothelial dysfunction have targeted one or more of the numerous risk factors that can cause endothelial damage: hypertension (angiotensin-converting enzyme inhibitors and calcium antagonists), hypercholesterolemia (lipid-lowering agents), cigarette smoking (cessation), sedentary lifestyle (increased physical activity), menopause (estrogen replacement therapy), and diabetes mellitus (control of associated metabolic abnormalities). Interventions targeted specifically to the endothelium remain speculative, as the precise mechanisms of endothelial dysfunction are still being elucidated. Several pharmacologic agents have been suggested to achieve vascular protection through mechanisms that go beyond their primary therapeutic (e.g., hypotensive or hypocholesterolemic) actions; examples of these are angiotensin-converting enzyme inhibitors or HMG-CoA reductase inhibitors. Beneficial changes to the endothelium might result from promotion of vasorelaxation, inhibition of vasoconstriction, reduction in the production of free radicals, or other mechanisms that protect the endothelium from injury.


title: Big ideas paper: Policy-driven middleware for a legally-compliant Internet of Things

abstract: Internet of Things (IoT) applications, systems and services are subject to law. We argue that for the IoT to develop lawfully, there must be technical mechanisms that allow the enforcement of specified policy, such that systems align with legal realities. The audit of policy enforcement must assist the apportionment of liability, demonstrate compliance with regulation, and indicate whether policy correctly captures legal responsibilities. As both systems and obligations evolve dynamically, this cycle must be continuously maintained. This poses a huge challenge given the global scale of the IoT vision. The IoT entails dynamically creating new services through managed and flexible data exchange. Data management is complex in this dynamic environment, given the need to both control and share information, often across federated domains of administration. We see middleware playing a key role in managing the IoT. Our vision is for a middleware-enforced, unified policy model that applies end-to-end, throughout the IoT. This is because policy cannot be bound to things, applications, or administrative domains, since functionality is the result of composition, with dynamically formed chains of data flows. We have investigated the use of Information Flow Control (IFC) to manage and audit data flows in cloud computing; a domain where trust can be well-founded, regulations are more mature and associated responsibilities clearer. We feel that IFC has great potential in the broader IoT context. However, the sheer scale and the dynamic, federated nature of the IoT pose a number of significant research challenges.


title: Grit, conscientiousness, and the transtheoretical model of change for exercise behavior

abstract: Grit and the Big Five Inventory (BFI) Conscientiousness dimension were examined with respect to the transtheoretical model (TTM) stages of change for exercise behavior. Participants (N = 1171) completed an online survey containing exercise-related TTM staging questions, the Short Grit Scale and BFI Conscientiousness. Ordinal regression analyses showed that grit significantly predicted high intensity and moderate intensity exercise TTM stage while BFI Conscientiousness did not. The results suggest that grit is a potentially important differentiator of TTM stage for moderate and high intensity exercise.


title: THz Imaging Radar for Standoff Personnel Screening

abstract: A summary of the NASA Jet Propulsion Laboratory's 675 GHz imaging radar is presented, with an emphasis on several key design aspects that enable fast, reliable through-clothes imaging of person-borne concealed objects. Using the frequency-modulated continuous-wave (FMCW) radar technique with a nearly 30 GHz bandwidth, sub-centimeter range resolution is achieved. To optimize the radar's range resolution, a reliable software calibration procedure compensates for signal distortion from radar waveform nonlinearities. Low-noise, high dynamic range detection comes from the radar's heterodyne RF architecture, low-noise chirp source, and high-performance 675 GHz transceiver. The radar's optical design permits low-distortion fast beam scanning for single-pixel imaging, and a real-time radar image frame rate of 1 Hz is now possible. Still faster speeds are on the horizon as multi-beam THz transceivers are developed.


title: Decontaminating eukaryotic genome assemblies with machine learning

abstract: BACKGROUND ::: High-throughput sequencing has made it theoretically possible to obtain high-quality de novo assembled genome sequences but in practice DNA extracts are often contaminated with sequences from other organisms. Currently, there are few existing methods for rigorously decontaminating eukaryotic assemblies. Those that do exist filter sequences based on nucleotide similarity to contaminants and risk eliminating sequences from the target organism. ::: ::: ::: RESULTS ::: We introduce a novel application of an established machine learning method, a decision tree, that can rigorously classify sequences. The major strength of the decision tree is that it can take any measured feature as input and does not require a priori identification of significant descriptors. We use the decision tree to classify de novo assembled sequences and compare the method to published protocols. ::: ::: ::: CONCLUSIONS ::: A decision tree performs better than existing methods when classifying sequences in eukaryotic de novo assemblies. It is efficient, readily implemented, and accurately identifies target and contaminant sequences. Importantly, a decision tree can be used to classify sequences according to measured descriptors and has potentially many uses in distilling biological datasets.


title: Crowded environments reduce spatial memory in older but not younger adults

abstract: Previous studies have reported an age-related decline in spatial abilities. However, little is known about whether the presence of other, task-irrelevant stimuli during learning further affects spatial cognition in older adults. Here we embedded virtual environments with moving crowds of virtual human pedestrians (Experiment 1) or objects (Experiment 2) whilst participants learned a route and landmarks embedded along that route. In subsequent test trials we presented clips from the learned route and measured spatial memory using three different tasks: a route direction task (i.e. whether the video clip shown was a repetition or retracing of the learned route); an intersection direction task; and a task involving identity of the next landmark encountered. In both experiments, spatial memory was tested in two separate sessions: first following learning of an empty maze environment and second using a different maze which was populated. Older adults performed worse than younger adults in all tasks. Moreover, the presence of crowds during learning resulted in a cost in performance to the spatial tasks relative to the ‘no crowds’ condition in older adults but not in younger adults. In contrast, crowd distractors did not affect performance on the landmark sequence task. There was no age-related cost on performance with object distractors. These results suggest that crowds of human pedestrians selectively capture older adults’ attention during learning. These findings offer further insights into how spatial memory is affected by the ageing process, particularly in scenarios which are representative of real-world situations.


title: Optic Fiber Sensing IOT Technology and Application Research

abstract: The growth of the Internet of Things (IOT) industry has become a new mark of the communication domain. As the development of the technology of the IOT and the fiber-optical sensor, the combination of the both is a big question to be discussed, and the fiber-optical IOT also has a good development prospect. This article first introduces IOT's current status, the key technology, the theoretical frame and the applications. Then, it discusses the classification of the optical fiber sensor as well as the development and its application's situation. Lastly, it puts the optical fiber sensing technology into the IOT, and introduces a specific application which is used in the mine safety based on the fiber-optical IOT. Copyright © 2014 IFSA Publishing, S. L.


title: Robust and secure image hashing

abstract: Image hash functions find extensive applications in content authentication, database search, and watermarking. This paper develops a novel algorithm for generating an image hash based on Fourier transform features and controlled randomization. We formulate the robustness of image hashing as a hypothesis testing problem and evaluate the performance under various image processing operations. We show that the proposed hash function is resilient to content-preserving modifications, such as moderate geometric and filtering distortions. We introduce a general framework to study and evaluate the security of image hashing systems. Under this new framework, we model the hash values as random variables and quantify its uncertainty in terms of differential entropy. Using this security framework, we analyze the security of the proposed schemes and several existing representative methods for image hashing. We then examine the security versus robustness tradeoff and show that the proposed hashing methods can provide excellent security and robustness.


title: Toward a Unified Catalog of Implemented Cognitive Architectures

abstract: This work is a review of the online Comparative Table of Cognitive Architectures (the version that was available at http://bicasymposium.com/cogarch on September 20, 2010). This continuously updated online resource is a collective product of many researchers and developers of cognitive architectures. Names of its contributors (sorted alphabetically by the represented architecture name) are: James S. Albus (4D/RCS), Christian Lebiere and Andrea Stocco (ACT-R), Stephen Grossberg (ART), Brandon Rohrer (BECCA), Balakrishnan Chandrasekaran and Unmesh Kurup (biSoar), Raul Arrabales (CERA-CRANIUM), Fernand Gobet and Peter Lane (CHREST), Ron Sun (CLARION), Ben Goertzel (CogPrime), Frank Ritter and Rick Evertsz (CoJACK), George Tecuci (Disciple), Shane Mueller (EPIC), Susan L. Epstein (FORR), Stuart C. Shapiro (GLAIR), Alexei Samsonovich (GMU BICA), Jeff Hawkins (HTM), David C. Noelle (Leabra), Stan Franklin (LIDA), Pei Wang (NARS), Akshay Vashist and Shoshana Loeb (Nexting), Cyril Brom (Pogamut), Nick Cassimatis (Polyscheme), L. Andrew Coward (Recommendation Architecture), Ashok Goel, J. William Murdock and Spencer Rugaber (REM), John Laird (Soar), and Kristinn Thorisson (Ymir). All these contributions are summarized in this work in a form that makes the described architectures easy to compare against each other.


title: Caspase Inhibitor Infusion Protects an Avian Song Control Circuit from Seasonal-Like Neurodegeneration

abstract: Sex steroids such as androgens and estrogens have trophic effects on the brain and can ameliorate neurodegeneration, and the withdrawal of circulating steroids induces neurodegeneration in several hormone-sensitive brain areas. Very little is known about the underlying molecular mechanisms that mediate neuronal regression caused by hormone-withdrawal, however. Here we show that reduction of programmed cell death by local infusion of caspase inhibitors rescues a telencephalic nucleus in the adult avian song control system from neurodegeneration that is induced by hormone withdrawal. This treatment also has trans-synaptic effects that provide some protection of an efferent target region. We found that unilateral infusion of caspase inhibitors in vivo in adult white-crowned sparrows rescued neurons within the hormone-sensitive song nucleus HVC (used as a proper name) from programmed cell death for as long as seven days after withdrawal of testosterone and a shift to short-day photoperiod and that the activation of caspase-3 was reduced by 59% on average in the ipsilateral HVC compared with the unmanipulated contralateral HVC. Caspase inhibitor infusion near HVC was sufficient to preserve neuron size ipsilaterally in a downstream nucleus, the robust nucleus of the arcopallium. This is the first report that sustained local application of caspase inhibitors can protect a telencephalic brain area from neurodegeneration in vivo and that a degenerating neural circuit rescued with caspase inhibitors produces sufficient trophic support to protect attributes of a downstream target that would otherwise degenerate. These results strengthen the case for the possible therapeutic use of caspase inhibitors under certain neurodegenerative conditions.


title: Hearing at low and infrasonic frequencies

abstract: The human perception of sound at frequencies below 200 Hz is reviewed. Knowledge about our perception of this frequency range is important, since much of the sound we are exposed to in our everyday environment contains significant energy in this range. Sound at 20-200 Hz is called low-frequency sound, while for sound below 20 Hz the term infrasound is used. The hearing becomes gradually less sensitive for decreasing frequency, but despite the general understanding that infrasound is inaudible, humans can perceive infrasound, if the level is sufficiently high. The ear is the primary organ for sensing infrasound, but at levels somewhat above the hearing threshold it is possible to feel vibrations in various parts of the body. The threshold of hearing is standardized for frequencies down to 20 Hz, but there is a reasonably good agreement between investigations below this frequency. It is not only the sensitivity but also the perceived character of a sound that changes with decreasing frequency. Pure tones become gradually less continuous, the tonal sensation ceases around 20 Hz, and below 10 Hz it is possible to perceive the single cycles of the sound. A sensation of pressure at the eardrums also occurs. The dynamic range of the auditory system decreases with decreasing frequency. This compression can be seen in the equal-loudness-level contours, and it implies that a slight increase in level can change the perceived loudness from barely audible to loud. Combined with the natural spread in thresholds, it may have the effect that a sound, which is inaudible to some people, may be loud to others. Some investigations give evidence of persons with an extraordinary sensitivity in the low and infrasonic frequency range, but further research is needed in order to confirm and explain this phenomenon.


title: Natural image statistics and efficient coding

abstract: Natural images contain characteristic statistical regularities that set them apart from purely random images. Understanding what these regularities are can enable natural images to be coded more efficiently. In this paper, we describe some of the forms of structure that are contained in natural images, and we show how these are related to the response properties of neurons at early stages of the visual system. Many of the important forms of structure require higher-order (i.e. more than linear, pairwise) statistics to characterize, which makes models based on linear Hebbian learning, or principal components analysis, inappropriate for finding efficient codes for natural images. We suggest that a good objective for an efficient coding of natural scenes is to maximize the sparseness of the representation, and we show that a network that learns sparse codes of natural scenes succeeds in developing localized, oriented, bandpass receptive fields similar to those in the mammalian striate cortex.


title: Learning Visual Odometry with a Convolutional Network

abstract: We present an approach to predicting velocity and direction changes from visual information (”visual odometry”) using an end-to-end, deep learning-based architecture. The architecture uses a single type of computational module and learning rule to extract visual motion, depth, and finally odometry information from the raw data. Representations of depth and motion are extracted by detecting synchrony across time and stereo channels using network layers with multiplicative interactions. The extracted representations are turned into information about changes in velocity and direction using a convolutional neural network. Preliminary results show that the architecture is capable of learning the resulting mapping from video to egomotion.


title: Separating fact from fiction: An examination of deceptive self-presentation in online dating profiles. Personality Social Psych

abstract: This study examines self-presentation in online dating profiles using a novel cross-validation technique for establishing accuracy. Eighty online daters rated the accuracy of their online self-presentation. Information about participants' physical attributes was then collected (height, weight, and age) and compared with their online profile, revealing that deviations tended to be ubiquitous but small in magnitude. Men lied more about their height, and women lied more about their weight, with participants farther from the mean lying more. Participants' self-ratings of accuracy were significantly correlated with observed accuracy, suggesting that inaccuracies were intentional rather than self-deceptive. Overall, participants reported being the least accurate about their photographs and the most accurate about their relationship information. Deception patterns suggest that participants strategically balanced the deceptive opportunities presented by online self-presentation (e.g., the editability of profiles)...


title: Classification on ADHD with Deep Learning

abstract: Effective discrimination of attention deficit hyperactivity disorder (ADHD) using imaging and functional biomarkers would have fundamental influence on public health. In usual, the discrimination is based on the standards of American Psychiatric Association. In this paper, we modified one of the deep learning method on structure and parameters according to the properties of ADHD data, to discriminate ADHD on the unique public dataset of ADHD-200. We predicted the subjects as control, combined, inattentive or hyperactive through their frequency features. The results achieved improvement greatly compared to the performance released by the competition. Besides, the imbalance in datasets of deep learning model influenced the results of classification. As far as we know, it is the first time that the deep learning method has been used for the discrimination of ADHD with fMRI data.


title: Learning attention map from images

abstract: While bottom-up and top-down processes have shown effectiveness during predicting attention and eye fixation maps on images, in this paper, inspired by the perceptual organization mechanism before attention selection, we propose to utilize figure-ground maps for the purpose. So as to take both pixel-wise and region-wise interactions into consideration when predicting label probabilities for each pixel, we develop a context-aware model based on multiple segmentation to obtain final results. The MIT attention dataset [14] is applied finally to evaluate both new features and model. Quantitative experiments demonstrate that figure-ground cues are valid in predicting attention selection, and our proposed model produces improvements over baseline method.


title: Validity of the Brief Patient Health Questionnaire Mood Scale (PHQ-9) in the general population.

abstract: OBJECTIVE ::: The aim of this study was to assess the validity of the Patient Health Questionnaire depression module (PHQ-9). It has been subject to studies in medical settings, but its validity as a screening for depression in the general population is unknown. ::: ::: ::: METHOD ::: A representative population sample (2,066 subjects, 14-93 years) filled in the PHQ-9 for diagnosis [major depressive disorder, other depressive disorder, depression screen-positive (DS+) and depression screen-negative (DS-)] and other measures for distress (GHQ-12), depression (Brief-BDI) and subjective health perception (EuroQOL; SF-36). ::: ::: ::: RESULTS ::: A prevalence rate of 9.2% of a current PHQ depressive disorder (major depression 3.8%, subthreshold other depressive disorder 5.4%) was identified. The two depression groups had higher Brief-BDI and GHQ-12 scores, and reported lower health status (EuroQOL) and health-related quality of life (SF-36) than did the DS- group (P's < .001). Strong associations between PHQ-9 depression severity and convergent variables were found (with BDI r = .73, with GHQ-12 r = .59). ::: ::: ::: CONCLUSION ::: The results support the construct validity of the PHQ depression scale, which seems to be a useful tool to recognize not only major depression but also subthreshold depressive disorder in the general population.


title: Non-Computability of Consciousness

abstract: With the great success in simulating many intelligent behaviors using computing devices, there has been an ongoing debate whether all conscious activities are computational processes. In this paper, the answer to this question is shown to be no. A certain phenomenon of consciousness is demonstrated to be fully represented as a computational process using a quantum computer. Based on the computability criterion discussed with Turing machines, the model constructed is shown to necessarily involve a non-computable element. The concept that this is solely a quantum effect and does not work for a classical case is also discussed.


title: An Unsupervised Model for Text Message Normalization

abstract: Cell phone text messaging users express themselves briefly and colloquially using a variety of creative forms. We analyze a sample of creative, non-standard text message word forms to determine frequent word formation processes in texting language. Drawing on these observations, we construct an unsupervised noisy-channel model for text message normalization. On a test set of 303 text message forms that differ from their standard form, our model achieves 59% accuracy, which is on par with the best supervised results reported on this dataset.


title: Stretchable Carbon Nanotube/Ion–Gel Supercapacitors with High Durability Realized through Interfacial Microroughness

abstract: A critical problem with stretchable supercapacitors developed to date has been evaporation of a volatile component of their electrolyte, causing failure. In this work, we demonstrated successful use of an ionic-liquid-based nonvolatile gel (ion–gel) electrolyte in carbon nanotube (CNT)-based stretchable supercapacitors. The CNT/ion–gel supercapacitors showed high capacitance retention (96.6%) over 3000 stretch cycles at 20% strain. The high durability against stretch cycles was achieved by introducing microroughness at the interfaces between different materials. The microroughness was produced by the simple process of imprinting the surface microstructure of office paper onto a poly(dimethylsiloxane) substrate; the surface texture is reproduced in successive current collector and CNT layers. Adhesion between the different layers was strengthened by this roughness and prevented delamination over repeated stretch cycles. The addition of a CNT layer decreased the sensitivity of electrical characteristics to ...


title: Efficient Exploratory Learning of Inverse Kinematics on a Bionic Elephant Trunk

abstract: We present an approach to learn the inverse kinematics of the “bionic handling assistant”—an elephant trunk robot. This task comprises substantial challenges including high dimensionality, restrictive and unknown actuation ranges, and nonstationary system behavior. We use a recent exploration scheme, online goal babbling, which deals with these challenges by bootstrapping and adapting the inverse kinematics on the fly. We show the success of the method in extensive real-world experiments on the nonstationary robot, including a novel combination of learning and traditional feedback control. Simulations further investigate the impact of nonstationary actuation ranges, drifting sensors, and morphological changes. The experiments provide the first substantial quantitative real-world evidence for the success of goal-directed bootstrapping schemes, moreover with the challenge of nonstationary system behavior. We thereby provide the first functioning control concept for this challenging robot platform.


title: A review: Security challenges in cognitive radio networks

abstract: Cognitive Radio (CR) is a technology that envisions the concurrence of legitimate and unauthorized users by permitting them to use the licensed bands and to avoid interference in process. As CR networks are growing rapidly with huge popularity but on the flip side, a number of challenges are being explored. The most common but the most important one of them is security, and it is the ultimate focus of this paper. In this paper, we investigate all the attacks on different layers of CR network in between 2010–2016, and presented all this information in a refined taxonomy. Also, we provide appropriate explanation about the consequences of these attacks that could be faced by any user or system. At the end, we provide the counter-measures that should be taken to prevent and handle these attacks.


title: Inkjet-printed sensors on paper substrate for agricultural applications

abstract: In this paper, recent progresses in inkjet-printed sensors on paper substrate is introduced. The advantages of inkjet-printing technology and paper substrate for agricultural applications are discussed. Inkjet-printed RFID-enabled sensor, inkjet-printed retro-directive dual band transponder and inkjet-printed sensor platform are presented as design examples.


title: SmartPDR: Smartphone-Based Pedestrian Dead Reckoning for Indoor Localization

abstract: Indoor pedestrian tracking extends location-based services to indoor environments where GPS signal is rarely detected. Typical indoor localization method is Wi-Fi-based positioning system, which is practical showing accuracy and extending coverage. However, it involves significant costs of installing and managing wireless access points. A practical indoor pedestrian-tracking approach should consider the absence of any infrastructure or pretrained database. In this paper, we present a smartphone-based pedestrian dead reckoning, SmartPDR, which tracks pedestrians through typical dead reckoning approach using data from inertial sensors embedded in smartphones. SmartPDR does not require any complex and expensive additional device or infrastructure that most existing pedestrian tracking systems rely on. The proposed system was implemented on off-the-shelf smartphones and the performance was evaluated in several buildings. Despite inherent localization errors from low-cost noisy sensors and complicated human movements, SmartPDR successfully tracks indoor user's location, which is confirmed from the experimental results with reasonable location accuracy. Indoor pedestrian tracking system using smartphone inertial sensors can be a promising methodology validating its practical usage through real deployment.


title: Auditing journal entries using self-organizing map

abstract: A considerable body of regulatory pronouncements attests to the significance of auditing journal entries for ensuring that financial statements are free of material misstatements; however, existing ...


title: Boafft: Distributed Deduplication for Big Data Storage in the Cloud

abstract: As data progressively grows within data centers, the cloud storage systems continuously face challenges in saving storage capacity and providing capabilities necessary to move big data within an acceptable time frame. In this paper, we present the Boafft, a cloud storage system with distributed deduplication. The Boafft achieves scalable throughput and capacity using multiple data servers to deduplicate data in parallel, with a minimal loss of deduplication ratio. Firstly, the Boafft uses an efficient data routing algorithm based on data similarity that reduces the network overhead by quickly identifying the storage location. Secondly, the Boafft maintains an in-memory similarity indexing in each data server that helps avoid a large number of random disk reads and writes, which in turn accelerates local data deduplication. Thirdly, the Boafft constructs hot fingerprint cache in each data server based on access frequency, so as to improve the data deduplication ratio. Our comparative analysis with EMC’s stateful routing algorithm reveals that the Boafft can provide a comparatively high deduplication ratio with a low network bandwidth overhead. Moreover, the Boafft makes better usage of the storage space, with higher read/write bandwidth and good load balance.


title: Internet use, videogame playing and cell phone use as predictors of children's body mass index (BMI), body weight, academic performance, and social and overall self-esteem

abstract: In this research we examined the prediction of children's body mass index (BMI), body weight, academic performance, social self-esteem and overall self-esteem from their IT (information technology) use, specifically, their Internet use, cell phone use and videogame playing, after controlling for the effects of gender, race, age and household income on these measures. Participants were 482 children whose average age was 12years old. One-third was African American and two-thirds were Caucasian American. Results indicated that IT use did not predict BMI or body weight, contrary to one previous survey and the widespread belief that screen time is responsible for the obesity epidemic among our nation's children. Instead, BMI and body weight were higher for African Americans, older children and children from lower income households. The sole and strong positive predictor of visual-spatial skills was videogame playing, which also predicted lower grade point averages (GPAs). Gender and Internet use predicted standardized test scores in reading skills. Females and children who used the Internet more had better reading skills than did males and children who used the Internet less, respectively. Implications of these findings for future research on the benefits and liabilities of IT use are discussed.


title: Sparse logistic regression with supervised selectivity for predictors selection in credit scoring

abstract: Credit scoring is a fundamental and one of the most complex tasks that financial institutions have to deal with. Commonly this problem is considered as default probability prediction for the lenders counterparts. Knowing predictors that significantly contribute to default prediction has recently emerged as a crucial issue of credit risk analysis. Default prediction and default predictor selection are two related issues, but many existing approaches address them separately. In this paper a unified procedure is proposed that is based on the regularization approach with logistic regression as an underlying model, which simultaneously selects the default predictors and optimizes all the parameters within the model. We give the strong probabilistic statement of shrinkage criterion for features selection. The proposed regularization do not corrupt the relevant predictors, can select correlated predictors into model, gives stable subset of relevant features. Experimental results show that the proposed framework is competitive on both artificial data and publicly available data sets.


title: Cheating husbands and other stories: A case study of knowledge, action, and communication

abstract: The relationship between knowledge and action is a fundamental one: a processor in a computer network (or a robot or a person, for that matter) should base its actions on the knowledge (or information) it has. One of the main uses of communication is passing around information that may eventually be required by the receiver in order to decide upon subsequent actions. Understanding the relationship between knowledge, action, and communication is fundamental to the design of computer network protocols, intelligent robots, etc. By looking at a number of variants of thecheating husbands puzzle, we illustrate the subtle relationship between knowledge, communication, and action in a distributed environment.


title: Recurrent Neural Networks for Noise Reduction in Robust ASR

abstract: Recent work on deep neural networks as acoustic models for automatic speech recognition (ASR) have demonstrated substantial performance improvements. We introduce a model which uses a deep recurrent auto encoder neural network to denoise input features for robust ASR. The model is trained on stereo (noisy and clean) audio features to predict clean features given noisy input. The model makes no assumptions about how noise affects the signal, nor the existence of distinct noise environments. Instead, the model can learn to model any type of distortion or additive noise given sufficient training data. We demonstrate the model is competitive with existing feature denoising approaches on the Aurora2 task, and outperforms a tandem approach where deep networks are used to predict phoneme posteriors directly.


title: Smart Cities' Data: Challenges and Opportunities for Semantic Technologies

abstract: How can we innovate smart systems for smart cities, to make data available homogeneously, inexpensively, and flexibly while supporting an array of applications that have yet to exist or be specified?


title: K.: Developing a Framework for Multi Channel Strategies. An Analysis of Cases from the Grocery Retail Industry

abstract: Based on ongoing research in multi channel management, we are proposing a simple taxonomy of multi channel strategies. Cases from the grocery retail industry are not only used to illustrate four core strategies but also to study contingencies for the choice of the respective strategies. Furthermore, the cases are used to analyze the alignment of corporate marketing and online channel strategies.


title: Quantized H∞ control for stochastic systems with packet losses

abstract: In this paper, the quantized H ∞ feedback control problem is investigated for a class of stochastic networked systems with quantization and probabilistic data missing. The parameter uncertainties are time-varying norm-bounded and appear in both the state and input matrices. Input-delay approach is employed to model the networked control systems where the state and control signal are quantized by two logarithmic quantizers, respectively. By assuming that the network packet loss rate and employing the probability distribution characteristic of packet dropouts, the considered stochastic networked system with stochastic input-delay is transformed into a system with stochastic parameter. Based on that, robust mean-square exponential stability of the system with an H ∞ performance is guaranteed and quantized H ∞ controller design procedure is proposed. Finally, a numerical simulation example is exploited to show the effectiveness of the results derived.


title: Cruise Control for Pedestrians: Controlling Walking Direction using Electrical Muscle Stimulation

abstract: Pedestrian navigation systems require users to perceive, interpret, and react to navigation information. This can tax cognition as navigation information competes with information from the real world. We propose actuated navigation, a new kind of pedestrian navigation in which the user does not need to attend to the navigation task at all. An actuation signal is directly sent to the human motor system to influence walking direction. To achieve this goal we stimulate the sartorius muscle using electrical muscle stimulation. The rotation occurs during the swing phase of the leg and can easily be counteracted. The user therefore stays in control. We discuss the properties of actuated navigation and present a lab study on identifying basic parameters of the technique as well as an outdoor study in a park. The results show that our approach changes a user's walking direction by about 16°/m on average and that the system can successfully steer users in a park with crowded areas, distractions, obstacles, and uneven ground.


title: Enabling Personal Genomics with an Explicit Test of Epistasis

abstract: One goal of personal genomics is to use information about genomic variation to predict who is at risk for various common diseases. Technological advances in genotyping have spawned several personal genetic testing services that market genotyping services directly to the consumer. An important goal of consumer genetic testing is to provide health information along with the genotyping results. This has the potential to integrate detailed personal genetic and genomic information into healthcare decision making. Despite the potential importance of these advances, there are some important limitations. One concern is that much of the literature that is used to formulate personal genetics reports is based on genetic association studies that consider each genetic variant independently of the others. It is our working hypothesis that the true value of personal genomics will only be realized when the complexity of the genotype-to-phenotype mapping relationship is embraced, rather than ignored. We focus here on complexity in genetic architecture due to epistasis or nonlinear gene-gene interaction. We have previously developed a multifactor dimensionality reduction (MDR) algorithm and software package for detecting nonlinear interactions in genetic association studies. In most prior MDR analyses, the permutation testing strategy used to assess statistical significance was unable to differentiate MDR models that captured only interaction effects from those that also detected independent main effects. Statistical interpretation of MDR models required post-hoc analysis using entropy-based measures of interaction information. We introduce here a novel permutation test that allows the effects of nonlinear interactions between multiple genetic variants to be specifically tested in a manner that is not confounded by linear additive effects. We show using simulated nonlinear interactions that the power using the explicit test of epistasis is no different than a standard permutation test. We also show that the test has the appropriate size or type I error rate of approximately 0.05. We then apply MDR with the new explicit test of epistasis to a large genetic study of bladder cancer and show that a previously reported nonlinear interaction between is indeed significant, even after considering the strong additive effect of smoking in the model. Finally, we evaluated the power of the explicit test of epistasis to detect the nonlinear interaction between two XPD gene polymorphisms by simulating data from the MDR model of bladder cancer susceptibility. The results of this study provide for the first time a simple method for explicitly testing epistasis or gene-gene interaction effects in genetic association studies. Although we demonstrated the method with MDR, an important advantage is that it can be combined with any modeling approach. The explicit test of epistasis brings us a step closer to the type of routine gene-gene interaction analysis that is needed if we are to enable personal genomics.


title: A brief history of scanning

abstract: Incessant scanning of hosts by attackers looking for vulnerable servers has become a fact of Internet life. In this paper we present an initial study of the scanning activity observed at one site over the past 12.5 years. We study the onset of scanning in the late 1990s and its evolution in terms of characteristics such as the number of scanners, targets and probing patterns. While our study is preliminary in many ways, it provides the first longitudinal examination of a now ubiquitous Internet phenomenon.


Survey A survey of timed automata for the development of real-time systems


title: A Corpus of Preposition Supersenses

abstract: We present the first corpus annotated with preposition supersenses, unlexicalized categories for semantic functions that can be marked by English prepositions (Schneider et al., 2015). The preposition supersenses are organized hierarchically and designed to facilitate comprehensive manual annotation. Our dataset is publicly released on the web. 1


title: Distributed community detection in delay tolerant networks

abstract: Community is an important attribute of Pocket Switched Networks (PSN), because mobile devices are carried by people who tend to belong to communities. We analysed community structure from mobility traces and used for forwarding algorithms [12], which shows significant impact of community. Here, we propose and evaluate three novel distributed community detection approaches with great potential to detect both static and temporal communities. We find that with suitable configuration of the threshold values, the distributed community detection can approximate their corresponding centralised methods up to 90% accuracy.


title: Airblue: a system for cross-layer wireless protocol development

abstract: Over the past few years, researchers have developed many cross-layer wireless protocols to improve the performance of wireless networks. Experimental evaluations of these protocols have been carried out mostly using software-defined radios, which are typically two to three orders of magnitude slower than commodity hardware. FPGA-based platforms provide much better speeds but are quite difficult to modify because of the way high-speed designs are typically implemented. Experimenting with cross-layer protocols requires a flexible way to convey information beyond the data itself from lower to higher layers, and a way for higher layers to configure lower layers dynamically and within some latency bounds. One also needs to be able to modify a layer's processing pipeline without triggering a cascade of changes. We have developed Airblue, an FPGA-based software radio platform, that has all these properties and runs at speeds comparable to commodity hardware. We discuss the design philosophy underlying Airblue that makes it relatively easy to modify it, and present early experimental results.


title: Ka-Band Phased Array Antenna for High-Data-Rate SATCOM

abstract: The general issue of this letter deals with the design of a phased array antenna for high-data-rate SATCOM. A final demonstrator antenna could be installed on an unmanned aerial vehicle (UAV) to communicate with a satellite in Ka-band. First, a compact reflection-type phase shifter is designed and realized. Second, the conception of a phased array antenna prototype is detailed. Third, a new calibration method is involved that can provide the bias voltage to be applied to each phase shifter in order to scan the beam in the desired direction.


title: Modelling input texts: from Tree Kernels to Deep Learning

abstract: One of the core questions when designing modern Natural Language Processing (NLP) systems is how to model input textual data such that the learning algorithm is provided with enough information to estimate accurate decision functions. The mainstream approach is to represent input objects as feature vectors where each value encodes some of their aspects, e.g., syntax, semantics, etc. Feature-based methods have demonstrated state-of-the-art results on various NLP tasks. However, designing good features is a highly empirical-driven process, it greatly depends on a task requiring a significant amount of domain expertise. Moreover, extracting features for complex NLP tasks ::: often requires expensive pre-processing steps running a large number of linguistic tools while relying on external knowledge sources that are often not available or hard to get. Hence, this process is not cheap and often constitutes one of the major challenges when attempting a new task or adapting to a different language or domain. ::: The problem of modelling input objects is even more acute in cases when the input examples are not just single objects but pairs of objects, such as in various learning to rank problems in Information Retrieval and Natural Language processing. An alternative to feature-based methods is using kernels which are essentially non-linear functions mapping input examples into some high dimensional space thus allowing for learning decision functions with higher discriminative power. Kernels implicitly generate a very large number of features computing similarity between input examples in that implicit space. A well-designed kernel function can greatly reduce the effort to design a large set of manually designed features often leading to superior results. However, in the recent years, the use of kernel methods in NLP has been greatly under-estimated primarily due to the following reasons: (i) learning with kernels is slow as it requires to carry out optimization in the dual space leading to quadratic complexity; (ii) applying kernels to the input objects encoded with vanilla structures, e.g., generated by syntactic parsers, often yields minor improvements over carefully designed feature-based methods. ::: In this thesis, we adopt the kernel learning approach for solving complex NLP tasks and primarily focus on solutions to the aforementioned problems posed by the use of kernels. In particular, we design novel learning algorithms for training Support Vector Machines with structural kernels, e.g., tree kernels, considerably speeding up the training over the conventional SVM training methods. We show that using the training algorithms developed in this thesis allows for training tree kernel models on large-scale datasets containing millions of instances, which was not possible before. Next, we focus on the problem of designing input structures that are fed to tree kernel functions to automatically generate a large set of tree-fragment features. We demonstrate that previously used plain structures generated by syntactic parsers, e.g., syntactic or dependency trees, are often a poor choice thus compromising the expressivity offered by a tree kernel learning framework. We propose several effective design patterns of the input tree structures for various NLP tasks ranging from sentiment analysis to answer passage reranking. The central idea is to inject additional semantic information relevant for the task directly into the tree nodes and let the expressive kernels generate rich feature spaces. For the opinion mining tasks, the additional semantic information injected into tree nodes can be word polarity labels, while for more complex tasks of modelling text pairs the relational information about overlapping words in a pair appears to significantly improve the accuracy of the resulting models. Finally, we observe that both feature-based and kernel methods typically treat words as atomic units where matching different yet semantically similar words is problematic. Conversely, the idea of distributional approaches to model words as vectors is much more effective in establishing a semantic match between words and phrases. While tree kernel functions do allow for a more flexible matching between phrases and sentences through matching their syntactic contexts, their representation can not be tuned on the training set as it is possible with distributional approaches. Recently, deep learning approaches have been applied to generalize the distributional word matching problem to matching sentences taking it one step further by learning the optimal sentence representations for a given task. Deep neural networks have already claimed state-of-the-art performance in many computer vision, speech recognition, and natural language tasks. Following this trend, this thesis also explores the virtue of deep learning architectures for modelling input texts and text pairs where we build on some of the ideas to model input objects proposed within the tree kernel learning framework. In particular, we explore the idea of relational linking (proposed in the preceding chapters to encode text pairs using linguistic tree structures) to design a state-of-the-art deep learning architecture for modelling text pairs. We compare the proposed deep learning models that require even less manual intervention in the feature design process then previously described tree kernel methods that already offer a very good trade-off between the feature-engineering effort and the expressivity of the resulting representation. Our deep learning models demonstrate the state-of-the-art performance on a recent benchmark for Twitter Sentiment Analysis, Answer Sentence Selection and Microblog retrieval.


title: SCFS: a shared cloud-backed file system

abstract: Despite of their rising popularity, current cloud storage services and cloud-backed storage systems still have some limitations related to reliability, durability assurances and inefficient file sharing. We present SCFS, a cloud-backed file system that addresses these issues and provides strong consistency and near-POSIX semantics on top of eventually-consistent cloud storage services. SCFS provides a pluggable backplane that allows it to work with various storage clouds or a cloud-of-clouds (for added dependability). It also exploits some design opportunities inherent in the current cloud services through a set of novel ideas for cloud-backed file systems: always write and avoid reading, modular coordination, private name spaces and consistency anchors.


title: Data warehouse design approaches from social media: review and comparison

abstract: With the rise of social media in our life, several decision makers have worked on these networks to make better decisions. In order to benefit from the data issued from these media, many researchers focused on helping companies understand how to perform a social media competitive analysis and transform these data into knowledge for decision makers. A high number of users interact at any time on different ways in social media such as by expressing their opinions about products, services or transaction related to the organization which can prove very helpful for making better projections. In this paper, we provide a literature review on data warehouse design approaches from social media. More precisely, we start by introducing the main concepts of data warehouse and social media. We also propose two classes of data warehouse design approaches from social media (behavior analysis and integration of sentiment analysis in data warehouse schema) and expose for each one the most representative existing works. Afterward, we propose a comparative study of the existing works.


title: Lifelong Planning A*

abstract: Heuristic search methods promise to find shortest paths for path-planning problems faster than uninformed search methods. Incremental search methods, on the other hand, promise to find shortest paths for series of similar path-planning problems faster than is possible by solving each path-planning problem from scratch. In this article, we develop Lifelong Planning A* (LPA*), an incremental version of A* that combines ideas from the artificial intelligence and the algorithms literature. It repeatedly finds shortest paths from a given start vertex to a given goal vertex while the edge costs of a graph change or vertices are added or deleted. Its first search is the same as that of a version of A* that breaks ties in favor of vertices with smaller g-values but many of the subsequent searches are potentially faster because it reuses those parts of the previous search tree that are identical to the new one. We present analytical results that demonstrate its similarity to A* and experimental results that demonstrate its potential advantage in two different domains if the path-planning problems change only slightly and the changes are close to the goal.


title: Life-after-death beliefs and self motivations

abstract: AbstractAn experimental study was designed to ascertain whether three self-motives (death-thoughts avoidance, self-uncertainty reduction, and need-for-control) are irreducible motives or examples of a more basic primary motive with regard to their relationship to life-after-death (LAD) beliefs. The study explored the impact of the three self-motives on adherence to LAD beliefs through three mediators: self-esteem, feelings of disquiet, and thoughts-accessibility (thoughts of death, need-for-control, and uncertainty). Participants were randomly assigned to write about their own death, a time they felt uncertain, a time they felt powerless, or about a toothache episode. The mediators (accessibility of disturbing thoughts, feelings of disquiet-uncertainty, and self-esteem) were measured next. The dependent variable selected was endorsement of LAD beliefs. Results showed that the cognitive and emotional responses produced by the activation of the three motives were similar. The three motives increased the acc...


title: A nonlinear diffusion for salt and pepper noise removal

abstract: We present a method to remove salt-and-pepper noise for single images. The method consists of two stages, noise detection and noise removal. In the first stage, a detector identifies corrupted pixels; in the second stage, an algorithm employs a nonlinear isotropic diffusion to suppression noise, which diffusion is only for those corrupted pixels. We apply our method to a test set containing five images. Experimental results show that the method is powerful for salt-and-pepper noise removal.


title: Compact MMIC CPW and asymmetric CPS branch-line couplers and Wilkinson dividers using shunt and series stub loading

abstract: Circuit size may be reduced by up to 60% by embedding series and shunt uniplanar stubs inside the main uniplanar line since this reuses physical space in the longitudinal direction. This size-efficient stub loading of the main uniplanar line results in robust options for designing compact K-band 90/spl deg/ hybrid couplers and Wilkinson dividers. The approach is confirmed by experimental results that agree well with theory up to at least 30 GHz using both coplanar waveguide (CPW) and asymmetric coplanar stripline (ACPS). Furthermore, these couplers and dividers do not use lumped inductors or capacitors and, consequently, have excellent design accuracy even at millimeter-wave frequencies. Additionally, it is shown that it is possible to design an ACPS 90/spl deg/ branch-line coupler without the conventional CPW quarter wavelength transformers used to match the input and output port characteristic impedances.


title: Design of an anthropomorphic dual-arm robot with biologically inspired 8-DOF arms

abstract: From the perspective of kinematics, dual-arm manipulation in robots differs from single-arm manipulation in that it requires high dexterity in a specific region of the manipulator's workspace. This feature has motivated research on the specialized design of manipulators for dual-arm robots. These recently introduced robots often utilize a shoulder structure with a tilted angle of some magnitude. The tilted shoulder yields better kinematic performance for dual-arm manipulation, such as a wider common workspace for each arm. However, this method tends to reduce total workspace volume, which results in lower kinematic performance for single-arm tasks in the outer region of the workspace. To overcome this trade-off, the authors of this study propose a design for a dual-arm robot with a biologically inspired four degree-of-freedom shoulder mechanism. This study analyzes the kinematic performance of the proposed design and compares it with that of a conventional dual-arm robot from the perspective of workspace and single-/dual-arm manipulability. The comparative analysis revealed that the proposed structure can significantly enhance single- and dual-arm kinematic performance in comparison with conventional dual-arm structures. This superior kinematic performance was verified through experiments, which showed that the proposed method required shorter settling time and trajectory-following performance than the conventional dual-arm robot.


title: Explore Efficient Local Features from RGB-D Data for One-Shot Learning Gesture Recognition

abstract: Availability of handy RGB-D sensors has brought about a surge of gesture recognition research and applications. Among various approaches, one shot learning approach is advantageous because it requires minimum amount of data. Here, we provide a thorough review about one-shot learning gesture recognition from RGB-D data and propose a novel spatiotemporal feature extracted from RGB-D data, namely mixed features around sparse keypoints (MFSK). In the review, we analyze the challenges that we are facing, and point out some future research directions which may enlighten researchers in this field. The proposed MFSK feature is robust and invariant to scale, rotation and partial occlusions. To alleviate the insufficiency of one shot training samples, we augment the training samples by artificially synthesizing versions of various temporal scales, which is beneficial for coping with gestures performed at varying speed. We evaluate the proposed method on the Chalearn gesture dataset (CGD). The results show that our approach outperforms all currently published approaches on the challenging data of CGD, such as translated, scaled and occluded subsets. When applied to the RGB-D datasets that are not one-shot (e.g., the Cornell Activity Dataset-60 and MSR Daily Activity 3D dataset), the proposed feature also produces very promising results under leave-one-out cross validation or one-shot learning.


title: The osseointegration behavior of titanium–zirconium implants in ovariectomized rabbits

abstract: Objective ::: The aim of this study is to compare the osseointegration behavior of titanium (Ti) and titanium–zirconium (TiZr) implants in ovariectomized (OVX) rabbits. ::: ::: Materials and methods ::: Twelve ovariectomized New Zealand rabbits submitted to a hypocalcic diet and 12 sham-aged rabbits were used. After the bone mass loss induction period, TiZr or Ti implants both benefiting from a SLActive surface treatment were randomly inserted in the tibia and femur of each animal. The total of 24 rabbits were divided in four groups (n = 6): SHAM + Ti, OVX + Ti, SHAM + TiZr,OVX + TiZr. The animals were respectively sacrificed 3 and 6 weeks after dental implant placement. Six implants in each group at each time point were subjected to removal torque testing, and the remaining implants were prepared for histomorphometric analysis. ::: ::: Results ::: At the end of the healing period, all implants were osseointegrated. The removal torque value of the SHAM-TiZr group was significantly higher than those of the SHAM-Ti group (P < 0.001). The removal torque of the OVX-Ti group was significantly lower than those of the OVX-TiZr group. All groups demonstrated an increase in the peak torque value after 6 weeks: 46.0 and 50.8 Ncm for the OVX and the SHAM animals, respectively, in the case where Ti implants were used. When TiZr implants were inserted, the values reached 60.7 and 76.2 Ncm with a similar group configuration. The BIC and the BA/TA analysis showed an increase between week 3 and 6 in the case of nonovariectomized animals. From week 3 to 6, the BIC went from 37.1 ± 14.3 to 47.7 ± 8.7 for the SHAM + Ti group and from 37.6 ± 10.9 to 50.4 ± 11 for the SHAM + TiZr group. The BIC values were not significantly different between groups. ::: ::: Conclusion ::: The parameters intended to be representative of the bone morphology (BIC & BATA) did not help to discriminate between Ti and TiZr which appeared to behave similarly in this experimental model. However, the removal torque values for the TiZr group were statistically higher than those of the Ti group in both the SHAM and the ovariectomized animals. That likely reflected an increased quality of bone around the TiZr implants.


title: Current Evidence on the Socket-Shield Technique: A Systematic Review.

abstract: The recently popularized socket-shield technique involves intentional retention of a section of the remnant root at the time of immediate implant placement, thereby preserving the buccal/proximal bone from resorption. The objective of this systematic review was to assess the literature available on the socket-shield technique and weigh its biological plausibility and long-term clinical prognosis. A systematic search was performed on PubMed-Medline, Embase, Web of Knowledge, Google Scholar, and Cochrane Central for clinical/animal studies from January 1970 to April 2017. Twenty-three studies were assessed: 1 clinical case-control study, 4 animal histological reports, 1 clinical abstract, and 17+2* case reports. Eighteen out of the 23 studies had a duration of ≤12 months. A quality assessment of 5 studies (4 animal histologic and 1 clinical case-control) performed using the modified Animal Research: Reporting of In Vivo Experiments guidelines revealed that 4/5 studies had low scores. Fifty-eight out of 70 (82.86%) implants from 4 animal histological studies had complications; buccal/crestal bone loss (54.55%) and failure of osseointegration (27.27%) were the most common. Thirty-three out of 136 (24.26%) implants from 19+2 (2 studies had both histologic and clinical components, which are assessed separately) clinical studies had complications; buccal/crestal bone loss (78.78%) and shield exposure/failure (12.12%) were the most common. Other complications recorded were periodontal ligament and cementum formation on implant surfaces, pocket formation, inflammation, mucositis, and peri-implantitis. However, some clinical reports indicated stable results at 12 months. It would be difficult to predict the long-term success of this technique until high-quality evidence becomes available. A video abstract is available for viewing at https://youtu.be/lNMeUxj2XPA?list=PLvRxNhB9EJqbqjcYMbwKbwi8Xpbb0YuHI .


title: Color and emotion: effects of hue, saturation, and brightness.

abstract: Previous studies on emotional effects of color often failed to control all the three perceptual dimensions of color: hue, saturation, and brightness. Here, we presented a three-dimensional space of chromatic colors by independently varying hue (blue, green, red), saturation (low, medium, high), and brightness (dark, medium, bright) in a factorial design. The 27 chromatic colors, plus 3 brightness-matched achromatic colors, were presented via an LED display. Participants (N = 62) viewed each color for 30 s and then rated their current emotional state (valence and arousal). Skin conductance and heart rate were measured continuously. The emotion ratings showed that saturated and bright colors were associated with higher arousal. The hue also had a significant effect on arousal, which increased from blue and green to red. The ratings of valence were the highest for saturated and bright colors, and also depended on the hue. Several interaction effects of the three color dimensions were observed for both arousal and valence. For instance, the valence ratings were higher for blue than for the remaining hues, but only for highly saturated colors. Saturated and bright colors caused significantly stronger skin conductance responses. Achromatic colors resulted in a short-term deceleration in the heart rate, while chromatic colors caused an acceleration. The results confirm that color stimuli have effects on the emotional state of the observer. These effects are not only determined by the hue of a color, as is often assumed, but by all the three color dimensions as well as their interactions.


title: Distributed Cycle Detection and Removal

abstract: In this paper, we provide distributed algorithms to detect and remove cycles in a directed relational graph by exploiting the underlying undirected communication graph ; the relational graph models a relation among the agents, e.g., a pairwise ordering, while the communication graph captures how information can be shared among them. Specifically, we provide a synchronous distributed algorithm to detect cycles in the relational graph, by exploiting the fact that nodes with zero in-degree or zero out-degree can not be part of a cycle, and can be iteratively removed without creating new loops in the relational topology. The proposed algorithm considerably improves transmission efficiency (the number of messages and bandwidth required) compared to the state of the art. We demonstrate that the problem of making the relational graph acyclic by swapping the orientation of a minimum number of edges is NP-Complete and APX-Hard; for this reason, we develop an efficient, yet suboptimal, distributed algorithm to remove the cycles by swapping the direction of some of the links. The methodology provided in this paper finds application in several distributed control problems where the agents must be interconnected via a directed acyclic graph, such as cluster consensus, formation control or multiple leader election. Extensive numerical analysis emphasizes the effectiveness of the proposed solution with respect to the state of the art.


title: Consensus of Hybrid Multi-Agent Systems

abstract: In this paper, we consider the consensus problem of hybrid multi-agent system. First, the hybrid multi-agent system is proposed which is composed of continuous-time and discrete-time dynamic agents. Then, three kinds of consensus protocols are presented for hybrid multi-agent system. The analysis tool developed in this paper is based on the matrix theory and graph theory. With different restrictions of the sampling period, some necessary and sufficient conditions are established for solving the consensus of hybrid multi-agent system. The consensus states are also obtained under different protocols. Finally, simulation examples are provided to demonstrate the effectiveness of our theoretical results.


title: PaloBoost: An Overfitting-robust TreeBoost with Out-of-Bag Sample Regularization Techniques

abstract: Stochastic Gradient TreeBoost is often found in many winning solutions in public data science challenges. Unfortunately, the best performance requires extensive parameter tuning and can be prone to overfitting. We propose PaloBoost, a Stochastic Gradient TreeBoost model that uses novel regularization techniques to guard against overfitting and is robust to parameter settings. PaloBoost uses the under-utilized out-of-bag samples to perform gradient-aware pruning and estimate adaptive learning rates. Unlike other Stochastic Gradient TreeBoost models that use the out-of-bag samples to estimate test errors, PaloBoost treats the samples as a second batch of training samples to prune the trees and adjust the learning rates. As a result, PaloBoost can dynamically adjust tree depths and learning rates to achieve faster learning at the start and slower learning as the algorithm converges. We illustrate how these regularization techniques can be efficiently implemented and propose a new formula for calculating feature importance to reflect the node coverages and learning rates. Extensive experimental results on seven datasets demonstrate that PaloBoost is robust to overfitting, is less sensitivity to the parameters, and can also effectively identify meaningful features.


title: ATR4S: Toolkit with State-of-the-art Automatic Terms Recognition Methods in Scala

abstract: Automatically recognized terminology is widely used for various domain-specific texts processing tasks, such as machine translation, information retrieval or ontology construction. However, there is still no agreement on which methods are best suited for particular settings and, moreover, there is no reliable comparison of already developed methods. We believe that one of the main reasons is the lack of state-of-the-art method implementations, which are usually non-trivial to recreate—mostly, in terms of software engineering efforts. In order to address these issues, we present ATR4S, an open-source software written in Scala that comprises 13 state-of-the-art methods for automatic terminology recognition (ATR) and implements the whole pipeline from text document preprocessing, to term candidates collection, term candidate scoring, and finally, term candidate ranking. It is highly scalable, modular and configurable tool with support of automatic caching. We also compare 13 state-of-the-art methods on 7 open datasets by average precision and processing time. Experimental comparison reveals that no single method demonstrates best average precision for all datasets and that other available tools for ATR do not contain the best methods.


title: Learning to Refine an Automatically Extracted Knowledge Base Using Markov Logic

abstract: A number of text mining and information extraction projects such as Text Runner and NELL seek to automatically build knowledge bases from the rapidly growing amount of information on the web. In order to scale to the size of the web, these projects often employ ad hoc heuristics to reason about uncertain and contradictory information rather than reasoning jointly about all candidate facts. In this paper, we present a Markov logic-based system for cleaning an extracted knowledge base. This allows a scalable system such as NELL to take advantage of joint probabilistic inference, or, conversely, allows Markov logic to be applied to a web scale problem. Our system uses only the ontological constraints and confidence values of the original system, along with human-labeled data if available. The labeled data can be used to calibrate the confidence scores from the original system or learn the effectiveness of individual extraction patterns. To achieve scalability, we introduce a neighborhood grounding method that only instantiates the part of the network most relevant to the given query. This allows us to partition the knowledge cleaning task into tractable pieces that can be solved individually. In experiments on NELL's knowledge base, we evaluate several variants of our approach and find that they improve both F1 and area under the precision-recall curve.


title: A Descriptive, Cross-sectional Survey of Turkish Nurses' Knowledge of Pressure Ulcer Risk, Prevention, and Staging.

abstract: Nurses' knowledge of pressure ulcer (PU) prevention and management is an important first step in the provision of optimal care. To evaluate PU prevention/risk, staging, and wound description knowledge, a descriptive, cross-sectional survey was conducted among nurses working in an acute care Turkish hospital. The survey instrument was a modified and translated version of the Pieper Pressure Ulcer Knowledge Test (PUKT), and its validity and reliability were established. Nurses completed a Personal Characteristics Form, including sociodemographic information and exposure to educational presentations and information about and experience with PUs, followed by the 49-item modified PUKT which includes 33 prevention/risk items, 9 staging items, and 7 wound description items. All items are true/false questions with an I don't know option (scoring: minimum 0, maximum 49). Correct answers received 1 point and incorrect/unknown answers received 0 points. The paper-pencil questionnaires were distributed by 2 researchers to all nurses in the participating hospital and completed by those willing to be included. Responses were analyzed using descriptive statistics. Pearson's correlation test was used to examine the relationship between quantitative variables, and mean scores were compared using the Mann-Whitney U and Kruskal-Wallis tests. Among the 308 participating nurses (mean age 29.5 ± 8.1 [range 19-56] years) most were women (257, 83.4%) with 7.3 ± 7.8 (range 1-36) years of experience. The mean knowledge score for the entire sample was 29.7 ± 6.7 (range 8-42). The overall percentage of correct answers was 60.6% to 61.8% for PU prevention/risk assessment, 60% for wound description, and 56.6% for PU staging. Knowledge scores were significantly (P <.05) higher for participants who attended at least 1 lecture/conference/course on PUs in the last year, read articles/books about PUs, cared for patients with PUs, or believed their patients were at risk for PU development. Most participants (180, 58.4%) scored 60% or more correct; 8 (2.6%) correctly answered 80% or more of the items. The lowest number of correct answers was for the item, "Bunny boots and gel pads relieve pressure on the heels" (22, 7.1%). The results of this study suggest education and experience caring for patients who are at risk for or have a PU affect nurses' knowledge. This study, and additional research examining nurse knowledge, will help the development of much-needed education programs.


Stochastic Predictive Control of Autonomous Vehicles in Uncertain Environments


title: Ranging and tuning based particle swarm optimization with bat algorithm for task scheduling in cloud computing

abstract: Cloud computing is the new technology offering services to build new application through virtualization. Virtualization improves the usage of resource utilization in cloud environment. Recently research in Task Scheduling problem has received more attention because the customers want to maximize the utilization of resources in a cheaper way. In this paper an enhanced particle swarm optimization (PSO) algorithm for improving the efficiency in the task scheduling has been proposed. A ranging function and tuning function based PSO (RTPSO) based on data locality is introduced in this paper for solving the inertia weight assignment problem in existing PSO algorithm for task scheduling. The large inertia weight and small inertia weight will assist a global search and local search respectively. In addition, we have combined the RTPSO with Bat algorithm (RTPSO-B) to improve the optimization. Cloudsim is used in this paper to simulate the task scheduling in cloud environment. The proposed RTPSO-B based task scheduling is compared with various existing task scheduling algorithms such as GA, ACO, ordinary PSO. Simulation results proved proposed RTPSO-B based task scheduling method reduces makespan, cost and increases the utilization of resources.


title: A novel wireless vein finder

abstract: The process of venous access for drawing blood or intravenous drug delivery is a crucial one, upon which the number of successful attempts and quality of specimens depend. In this paper, we present a simple and cost effective design for a vein imaging system with wireless communication capability assisting in vein finding procedure performed on patients. The vein imaging is based on the differential pattern of Infrared (IR) light absorption by blood. A serial JPEG camera module interfaced with an Xbee pair wirelessly transmits the images to a central image processing unit which suitably highlights the superficially located veins thereby reducing the error involved in puncturing the right vein by the phlebotomist. Our results indicate that this could be transferred to the clinic for real world applications.


title: Overlapping correlation clustering

abstract: We introduce a new approach for finding overlapping clusters given pairwise similarities of objects. In particular, we relax the problem of correlation clustering by allowing an object to be assigned to more than one cluster. At the core of our approach is an optimization problem in which each data point is mapped to a small set of labels, representing membership in different clusters. The objective is to find a mapping so that the given similarities between objects agree as much as possible with similarities taken over their label sets. The number of labels can vary across objects. To define a similarity between label sets, we consider two measures: (i) a 0–1 function indicating whether the two label sets have non-zero intersection and (ii) the Jaccard coefficient between the two label sets. The algorithm we propose is an iterative local-search method. The definitions of label set similarity give rise to two non-trivial optimization problems, which, for the measures of set-intersection and Jaccard, we solve using a greedy strategy and non-negative least squares, respectively. We also develop a distributed version of our algorithm based on the BSP model and implement it using a Pregel framework. Our algorithm uses as input pairwise similarities of objects and can thus be applied when clustering structured objects for which feature vectors are not available. As a proof of concept, we apply our algorithms on three different and complex application domains: trajectories, amino-acid sequences, and textual documents.


title: A Printed Circularly Polarized Y-Shaped Monopole Antenna

abstract: In this letter, a printed circularly polarized omnidirectional Y-shaped monopole antenna for possible satellite and terrestrial applications is presented. The proposed antenna incorporates the desirable features of wire turnstile antennas while eliminating external coupler networks used in those antennas. Furthermore, the antenna structure is extremely simple and durable, occupies relatively small space, and resists breakage. The antenna is matched to a 50-Ω coaxial cable without any requirement for an external matching. The fabricated antenna operates over the frequency band of 2250-2350 MHz and provides both senses of circular polarization simultaneously resulting in a superior performance over the frequency band of operation.


European Macroprudential Database


title: Robust image registration using log-polar transform

abstract: This paper describes a hierarchical image registration algorithm for affine motion recovery. The algorithm estimates the affine transformation parameters necessary to register any two digital images misaligned due to rotation, scale, shear, and translation. The parameters are computed iteratively in a coarse-to-fine hierarchical framework using a variation of the Levenberg-Marquadt nonlinear least squares optimization method. This approach yields a robust solution that precisely registers images with subpixel accuracy. A log-polar registration module is introduced to accommodate arbitrary rotation angles and a wide range of scale changes. This serves to furnish a good initial estimate for the optimization-based affine registration stage. We demonstrate the hybrid algorithm on pairs of digital images subjected to large affine motion.


title: Underdifferentiation of English lexical stress contrasts by L2 taiwan speakers.

abstract: Learning the stress patterns of English words presents a challenge for L1 speakers from syllable-timed and/or tone languages. Realization of stress contrasts in previous studies has been measured in a variety of ways. This study adapts and extends Pairwise Variability Index (PVI), a method generally used to measure duration as a property of speech rhythm, to compare F0 and amplitude contrasts across L1 and L2 production of stressed and unstressed syllables in English multisyllabic words. L1 North American English and L1 Taiwan-Mandarin English speech data were extracted from the AESOP-ILAS corpus. Results of acoustic analysis show that overall, stress contrasts were realized most robustly by L1 English speakers. A general pattern of contrast underdifferentiation was found in L2 speakers with respect to F0, duration and intensity, with the most striking difference found in F0. These results corroborate our earlier findings on L1 Mandarin speakers’ production of on-focus/post-focus contrasts in their realization of English narrow focus. Taken together, these results demonstrate that underdifferentiation of prosodic contrasts at both the lexical and phrase levels is a major prosodic feature of Taiwan English; future research will determine whether it can also be found in the L2 English of other syllable-timed or tone language speakers.


A Semi-Automatic Approach to Handle Data Warehouse Schema Evolution using Ontology


title: Blood cell segmentation from microscopic blood images

abstract: Blood cell segmentation is a critical innovation for automatic differential blood counting, classification and analysis in clinical examination. In color blood cell images segmentation and recognition are two essential issues in the field of biomedical cell morphology. This paper approaches methods to segment the blood cells from microscopic thin blood images. This data is the premise to perform higher level tasks for example, automatic differential blood counting, detection of different diseases such as Malaria, Babesia, Chagas disease, Anemia, Leukemia etc. A noteworthy necessity of an automated, real-time, computer vision-based cell segmentation system is an efficient method for segmenting different blood component such as Red Blood Cells (RBCs), White Blood Cells (WBCs), Platelets from input images for blood count as well as to detect the parasites present in blood cells. Input images are captured by connecting digital camera to microscope. Captured images are enhanced and segmented using K-mean clustering as well as global threshold. Overlapping cells are separated using Sobel edge detector and Watershed transform.


title: Multi-criteria decision making methods for Third-Party Logistics evaluation

abstract: Much has been written in recent year about the outsourcing logistics. This phenomenon can be defined as an activity entrusted to a Third-Party Logistics provider (3PL) instead of being achieved internally. The studies on this field are mostly of empirical type and focused on reasons, benefits, and risks of working with 3PL as well as the role of those on supply chain management. This paper focuses on 3PL selection and evaluation problem and presents a literature analysis of multi-criteria decision making approaches for solving this problem. Related articles published during 1993–2010 period in reputable international journals are studied to identify the mainly approaches applied and their evaluating criteria in measuring the performance of 3PL.


title: Lowered leptin slims immune response

abstract: The decrease in leptin induced by starvation causes immune suppression, further demonstrating that falling and rising leptin levels have major, and quite distinct, physiologic consequences.


title: Distraction-Based Neural Networks for Document Summarization

abstract: Distributed representation learned with neural networks has recently shown to be effective in modeling natural languages at fine granularities such as words, phrases, and even sentences. Whether and how such an approach can be extended to help model larger spans of text, e.g., documents, is intriguing, and further investigation would still be desirable. This paper aims to enhance neural network models for such a purpose. A typical problem of document-level modeling is automatic summarization, which aims to model documents in order to generate summaries. In this paper, we propose neural models to train computers not just to pay attention to specific regions and content of input documents with attention models, but also distract them to traverse between different content of a document so as to better grasp the overall meaning for summarization. Without engineering any features, we train the models on two large datasets. The models achieve the state-of-the-art performance, and they significantly benefit from the distraction modeling, particularly when input documents are long.


title: A full-duplex wireless integrated transceiver for implant-to-air data communications

abstract: This paper presents a novel fully integrated full-duplex data transceiver to support bidirectional high-data rate neural interfaces (electrical stimulation and neural recording). The transmitter (TX) and the receiver (RX) are designed to share a single implantable antenna. The TX generates IR-UWB based on edge combining, and the RX uses a novel ISM-2.4-GHz narrow-band OOK receiver. Separation between the TX and RX path is implemented by: 1) properly shaping the transmitted pulse, so its spectrum falls between 3.1 and 7 GHz UWB subband, and 2) carefully filtering the spectrum of the received waveform directly in the receiver low-noise amplifier (LNA), to avoid using a circulator or a diplexer (found in most full duplex systems). The receiver is designed to support downlink telemetry of neural stimulation applications with a data rate as high as 100 Mbps within a power budget of 5 mW. The transmitter is designed to support uplink back telemetry of neural recording applications with a data rate of up to 500 Mbps for power consumption of 5.4 mW and 10.8 mW for OOK and BPSK modulations, respectively (10.8 pJ/b). Measurement results obtained with biological tissues confirm full functionality of the fabricated full duplex transceiver. The total size of the chip is 0.8 mm2.


title: Consumer Opportunity, Ability and Motivation as a Framework for Electronic Market Research

abstract: Past research has focused on transaction cost economics as a framework for electronic market (e-market) research. This takes a simplistic view of consumers as economic agents whose primary decision-making criterion is the minimization of transaction costs. We propose that the factors that potentially determine e-market success or failure can be better identified using the Ability-Motivation-Opportunity framework from consumer behavior. This looks at additional factors not considered by transaction cost-based research and takes a less simplistic view of consumers. We hypothesize that factors that increase consumers' opportunity to participate in e-markets, their ability to do so, and their motivation to purchase through the on-line channel, will determine the ultimate success of e-markets. This provides a new direction for electronic market research.


title: An unusual case of postaxial polydactyly of the feet

abstract: Polydactyly is a fairly common congenital anomaly characterized by additional fingers and/or toes, generally divided into three groups: medial ray (preaxial) and central and lateral ray (postaxial). We present a case of postaxial (fibular) polydactyly of the feet, assaying its clinical and radiological features and the method of surgical correction. Clinical outcomes after initial surgery were excellent in both feet. This surgical case shows that a careful individual preoperative planning is necessary to achieve good functional and cosmetic outcome.


title: “Why Does Rain Fall?”: Children Prefer to Learn From an Informant Who Uses Noncircular Explanations

abstract: These two studies explored 3- and 5-year-olds' evaluation of noncircular and circular explanations, and their use of such explanations to determine informant credibility. Although 5-year-olds demonstrated a selective preference for noncircular over circular explanations (Experiment 1: Long Explanations; Experiment 2: Short Explanations), 3-year-olds only demonstrated a preference for the noncircular when the explanations were shortened (Experiment 2). Children's evaluation of the explanations extended to their inferences about the informants' future credibility. Both age groups demonstrated a selective preference for learning novel explanations from an informant who had previously provided noncircular explanations—although only 5-year-olds also preferred to learn novel labels from her. The implications and scope of children's ability to monitor the quality of an informant's explanation are discussed.


title: Left ventrolateral prefrontal cortex and the cognitive control of memory

abstract: Cognitive control mechanisms permit memory to be accessed strategically, and so aid in bringing knowledge to mind that is relevant to current goals and actions. In this review, we consider the contribution of left ventrolateral prefrontal cortex (VLPFC) to the cognitive control of memory. Reviewed evidence supports a two-process model of mnemonic control, supported by a double dissociation among rostral regions of left VLPFC. Specifically, anterior VLPFC (∼BA 47; inferior frontal gyrus pars orbitalis) supports controlled access to stored conceptual representations, whereas mid-VLPFC (∼BA 45; inferior frontal gyrus pars triangularis) supports a domain-general selection process that operates post-retrieval to resolve competition among active representations. We discuss the contribution of these control mechanisms across a range of mnemonic domains, including semantic retrieval, recollection of contextual details about past events, resolution of proactive interference in working memory, and task switching. Finally, we consider open directions for future research into left VLPFC function and the cognitive control of memory. © 2007 Elsevier Ltd. All rights reserved.


title: Learning goal-oriented hierarchical tasks from situated interactive instruction

abstract: Our research aims at building interactive robots and agents that can expand their knowledge by interacting with human users. In this paper, we focus on learning goal-oriented tasks from situated interactive instructions. Learning the structure of novel tasks and how to execute them is a challenging computational problem requiring the agent to acquire a variety of knowledge including goal definitions and hierarchical control information. We frame acquisition of novel tasks as an explanation-based learning (EBL) problem and propose an interactive learning variant of EBL for a robotic agent. We show that our approach can exploit information in situated instructions along with the domain knowledge to demonstrate fast generalization on several tasks. The knowledge acquired transfers across structurally similar tasks. Finally, we show that our approach seamlessly combines agent-driven exploration with instructions for mixed-initiative learning.


[On the uptake of labeled glucose by the mucous membranes of the nose, mouth, and middle ear].


A Tool for the Analysis of DMN Decision Tables