title: Pharmacological importance of Clitoria ternatea - A review

abstract: Clitoria ternatea contained tannins, phlobatannin, carbohydrates, saponins, triterpenoids, phenols, flavanoids, flavonol glycosides, proteins, alkaloids, antharaquinone, anthocyanins, cardiac glycosides, Stigmast- 4-ene-3,6-dione, volatile oils and steroids. The plant showed many pharmacological effects including antioxidant, hypolipidemic, anticancer, anti-inflammatory, analgesic, antipyretic, antidiabetic, CNS, antimicrobial, gastro-intestinal antiparasitic, insecticidal and many other pharmacological effects. This Review will highlight the chemical constituents and pharmacological effects of Clitoria ternatea. I. INTRODUCTION A large and increasing number of patients in the world use medicinal plants and herbs for health purpose. Therefore, scientific scrutiny of their therapeutic potential, biological properties, and safety will be useful in making wise decisions about their use (1-2) . There are hundreds of significant drugs and biologically active compounds developed from the traditional medicinal plants. Plant showed wide range of pharmacological activities including antimicrobial, antioxidant, anticancer, hypolipidemic, cardiovascular, central nervous, respiratory, immunological, anti-inflammatory, analgesic antipyretic and many other pharmacological effects (3- 40 . The preliminary phytochemical screening showed that Clitoria ternatea contained tannins, phlobatannin, carbohydrates, saponins, triterpenoids, phenols, flavanoids, flavonol glycosides, proteins, alkaloids, antharaquinone, anthocyanins, cardiac glycosides, Stigmast-4-ene-3,6-dione, volatile oils and steroids. The plant showed many pharmacological effects including antioxidant, hypolipidemic, anticancer, anti- inflammatory, analgesic, antipyretic, antidiabetic, CNS, antimicrobial, gastro-intestinal antiparasitic, insecticidal and many other pharmacological effects. This Review will highlight the chemical constituents and pharmacological effects of Clitoria ternatea. Plant profile: Synonyms: Clitoria albiflora Mattei, Clitoria bracteata Poir., Clitoria mearnsii De Wild., Clitoria tanganicensis Micheli, Clitoria zanzibarensis Vatke (41) .


title: Considerations for restoring temperate forests of tomorrow: forest restoration, assisted migration, and bioengineering

abstract: Tomorrow’s forests face extreme pressures from contemporary climate change, invasive pests, and anthropogenic demands for other land uses. These pressures, collectively, demand land managers to reassess current and potential forest management practices. We discuss three considerations, functional restoration, assisted migration, and bioengineering, which are currently being debated in the literature and have the potential to be applied independently or concurrently across a variety of scales. The emphasis of functional restoration is to reestablish or maintain functions provided by the forest ecosystem, such as water quality, wildlife habitat, or carbon sequestration. Maintaining function may call upon actions such as assisted migration—moving tree populations within a species current range to aid adaptation to climate change or moving a species far outside its current range to avoid extinction—and we attempt to synthesize an array of assisted migration terminology. In addition, maintenance of species and the functions they provide may also require new technologies, such as genetic engineering, which, compared with traditional approaches to breeding for pest resistance, may be accomplished more rapidly to meet and overcome the challenges of invasive insect and disease pests. As managers develop holistic adaptive strategies to current and future anthropogenic stresses, functional restoration, assisted migration, and bioengineering, either separately or in combinations, deserve consideration, but must be addressed within the context of the restoration goal.


title: Pattern synonyms

abstract: Pattern matching has proven to be a convenient, expressive way of inspecting data. Yet this language feature, in its traditional form, is limited: patterns must be data constructors of concrete data types. No computation or abstraction is allowed. The data type in question must be concrete, with no ability to enforce any invariants. Any change in this data type requires all clients to update their code. This paper introduces pattern synonyms, which allow programmers to abstract over patterns, painting over all the shortcomings listed above. Pattern synonyms are assigned types, enabling a compiler to check the validity of a synonym independent of its definition. These types are intricate; detailing how to assign a type to a pattern synonym is a key contribution of this work. We have implemented pattern synonyms in the Glasgow Haskell Compiler, where they have enjoyed immediate popularity, but we believe this feature could easily be exported to other languages that support pattern matching.


title: Control of and experimentation on an active dual-mode twisted string actuation mechanism

abstract: In this paper, we present the control of and experimentation on an active dual-mode twisted string actuation mechanism for automatic transmission. This mechanism has a two-stage transmission ratio and is implemented by changing the twisted radius of the string in each mode (force mode or speed mode). Control algorithms for implementing different transmission ratios are proposed and experimentally verified. Moreover, this mechanism was able to change its compliance characteristics depending on the mode and it was also experimentally confirmed by impulse testing. Since this is a compact and simple transmission mechanism, it can be simply applied to small robotic system and provide a wide operating range and variable compliance.


LEARNING USING A SIAMESE CONVOLUTIONAL NEURAL NETWORK


Exploratory Factor Analysis ; Concepts and Theory


title: Juvéderm Volbella with Lidocaine for Lip and Perioral Enhancement: A Prospective, Randomized, Controlled Trial

abstract: Lip fullness and definition are key facial aesthetic features associated with attractiveness and youth. However, lips are prone to multiple factors that can dramatically change their shape over time, resulting in a narrower, pale, and flat appearance.1–3 Temporary dermal fillers containing hyaluronic acid are commonly used to enhance overall lip fullness and the vermilion border, minimize perioral lines, and provide lip definition.4–7 Juvederm Volbella with Lidocaine (Allergan, Santa Barbara, Calif.) is a moldable filler that uses the Vycross (Allergan, Irvine, Calif.) technology platform, which combines low- and high-molecular-weight hyaluronic acid with a novel cross-linking process.7 These properties increase cross-linking efficiency, resulting in a tightly cross-linked hyaluronic acid network, which increases product duration of action and produces a higher-viscosity gel with greater lift capacity.7 A small quantity of non–cross-linked hyaluronic acid is included to reduce extrusion force and allow injection through a 30-gauge, half-inch needle. ::: ::: The present study evaluated the safety and effectiveness of Juvederm Volbella with Lidocaine for lip volume enhancement versus Restylane-L (Medicis Aesthetics, Scottsdale, Ariz.), another lidocaine-containing hyaluronic acid dermal filler approved for lip augmentation. The FACE-Q—a new subject-reported outcome questionnaire8—was used to assess treatment outcomes.


title: Maximum Margin Clustering

abstract: We propose a new method for clustering based on finding maximum margin hyperplanes through data. By reformulating the problem in terms of the implied equivalence relation matrix, we can pose the problem as a convex integer program. Although this still yields a difficult computational problem, the hard-clustering constraints can be relaxed to a soft-clustering formulation which can be feasibly solved with a semidefinite program. Since our clustering technique only depends on the data through the kernel matrix, we can easily achieve nonlinear clusterings in the same manner as spectral clustering. Experimental results show that our maximum margin clustering technique often obtains more accurate results than conventional clustering methods. The real benefit of our approach, however, is that it leads naturally to a semi-supervised training method for support vector machines. By maximizing the margin simultaneously on labeled and unlabeled training data, we achieve state of the art performance by using a single, integrated learning principle.


title: Enhancement of wind energy conversion systems active and reactive power control via flywheel energy storage systems integration

abstract: The penetration level of wind energy conversion systems is increasing steadily. The random and stochastic nature of wind energy may lead to serious problems onto the electrical system Stability, Controllability, and Power quality. Generally, Energy Storage Systems (ESS) can improve wind generation grid interconnection in terms of power quality, and System Controllability. Flywheel Energy Storage Systems (FESS) have long life span, and high number of charging and discharging cycles that make them a perfect companion to wind integration as compared to other energy storage technologies. In this paper, low speed PMSM based flywheel energy storage system is integrated with Direct-Drive (DD) variable speed PMSG based Wind Energy Conversion System. This integration is proposed in two schemes named as First Topology (Modified STATCOM), and Second Topology (ON DC LINK). Matlab / Simulink model is conducted for the both configurations. Simulation results will show effectiveness of the integrated system in Active Power Smoothing while maximizing the harvested wind power through Optimal Torque Control Maximum Power Point Tracking, and Dynamic VAR Compensation as well. A comparative study is performed between the two topologies to highlight the merits, demerits, and suitability of each one.


title: FDUQA on TREC2005 QA Track

abstract: In this year’s QA Track, we participant in the main and document ranking task and do not take part in the relation task. We put the most effort in factoid and definition questions, and very little on list questions and document ranking task. For factoid questions, we use three QA systems: system 1, system 2 and system 3. System 1 is very similar to our last year’s system [Wu et al, 2004] except two main modifications. One is adding an answer validation-feedback scheme. The other is an improved answer projection module. System 2 is a classic QA system that does not use Web. System 3 is a pattern-based system that we used in TREC 2002 evaluation. The main contribution for factoid question is two improvements for Web-based QA system and the system combination. For definition question, we attempt to utilize both the existing definitions in the Web knowledge bases and the automatically generated structured patterns. Effective methods are adopted to make full use of these resources, and they promise high quality response to definition questions. For list questions, we use a pattern-based method to find more answers other than those found in the processing of factoid question. For document ranking task, we only collect the outputs from document searching or answer projection module. In the following, Section 2, 3, 4 will describe our algorithms to factoid, list and definition questions separately. Section 5 will present our results in TREC 2005.


title: Discussion of “Multivariate functional outlier detection”

abstract: This interesting paper adds on the authors long and outstanding trajectory in robust statistics and , more specifically, on robust functional data analysis. We congratulate Mia Hubert, Peter Rousseeuw and Pieter Segaert for this important contribution. As the authors point out, most of the literature to date on functional outlier detection deals with univariate functional data (one curve observed by individual). This work considers the case of p-variate functional data (p curves observed by individual). The paper discusses carefully the problem of outlier detection in this setting and starts by establishing a classification of different outlying behaviours. Then, several p-variate functional depths and distance functions are defined by integrating over time the existing or newly defined p-variate counterparts. Finally, by combining these measures, several graphical diagnostic tools are proposed. We would like to contribute to the discussion by focusing on two aspects. Firstly, we will compare the proposed taxonomy of functional outliers with the classification currently adopted in the literature. Secondly, we will comment on the differences between the proposed collection of methods and the outliergram (Arribas-Gil and Romo 2014), the recent procedure to detect shape outliers. We compare it with the proposed methodology in several examples.


title: Smooth muscle and adenoma-like renal tumor: a previously unreported variant of mixed epithelial stromal tumor or a distinctive renal neoplasm?

abstract: We describe 6 cases of a biphasic renal neoplasm, which we designate smooth muscle and adenoma-like renal tumor, which do not cleanly fit any category as currently defined. There were 4 females and 2 males (age, 27-70 years); neither male had a history of hormone exposure. All 5 neoplasms with available history were discovered incidentally on imaging studies with sizes ranging from 4 to 20 cm. The stroma was composed of smooth muscle fascicles alternating with looser, edematous areas; none of the cases contained ovarian-like stroma. The complex but cytologically benign epithelial component consisted of tubulopapillary nodules, branching tubules, clefts, and large cysts. The stroma of all of the cases labeled diffusely for desmin. Estrogen receptor labeling was absent in 4 cases with only minimal (<10%) weak labeling in the remaining 2. The epithelial component of each case labeled diffusely for cytokeratin 7 and was patchy for α-methyl-CoA racemase (P504S). Carbonic anhydrase IX, HMB45, WT-1, and inhibin were negative. None of the 5 cases tested demonstrated trisomies of chromosome 7 or 17 by fluorescence in situ hybridization. Two patients with significant follow-up are disease free at 18.5 and 2.5 years. Smooth muscle and adenoma-like renal tumor could potentially represent a variant of mixed epithelial stromal tumor, which would expand its reported spectrum. However, the absence of clinical history of hormone exposure, predominance of smooth muscle with lack of ovarian-like stroma, prominence of epithelial nodules, and typical absence of estrogen receptor labeling suggest that it may represent a distinct entity.


title: Quantitation of β-lapachone and 3-hydroxy-β-lapachone in human plasma samples by reversed-phase high-performance liquid chromatography

abstract: Abstract β-Lapachone is an o -naphthoquinone found to have in vitro cytotoxicity in cancer cells, type I human immunodeficiency virus, and fungi. Analytical methods for evaluating β-lapachone in biological fluids using high-performance liquid chromatography (HPLC) have not been published. The reversed-phase HPLC method described in this report utilizes liquid extraction of a 0.5-ml plasma sample with average recoveries of 67±10.8% and 70±10.3% for β-lapachone and 3-hydroxy-β-lapachone, respectively. Sensitivity of the assay using ultraviolet (UV) detection at 256 nm is 15 ng ml −1 from a 100 μl injection. Plasma standards for β-lapachone and 3-hydroxy-β-lapachone are linear with no significant difference in slope between the compounds. The retention times of 2.7 min for 3-hydroxy-β-lapachone and 5.9 min for β-lapachone result in a clean separation permitting use of the same assay procedure without modification for both compounds. This assay offers the advantage that either β-lapachone or 3-hydroxy-β-lapachone can serve as the internal standard, depending on which compound is being analyzed.


title: A Neural Representation of Prior Information during Perceptual Inference

abstract: Summary Perceptual inference is biased by foreknowledge about what is probable or possible. How prior expectations are neurally represented during visual perception, however, remains unknown. We used functional magnetic resonance imaging to measure brain activity in humans judging simple visual stimuli. Perceptual decisions were either biased in favor of a single alternative (A/∼A decisions) or taken without bias toward either choice (A/B decisions). Extrastriate and anterior temporal lobe regions were more active during A/∼A than A/B decisions, suggesting multiple representations of prior expectations within the visual hierarchy. Forward connectivity was increased when expected and observed perception diverged ("prediction error" signals), whereas prior expectations fed backward from higher to lower regions. Finally, the coincidence between expected and observed perception activated orbital prefrontal regions, perhaps reflecting the reinforcement of prior expectations. These data support computational and quantitative models proposing that a visual percept emerges from converging bottom-up and top-down signals.


title: A comparison of fuzzy logic and PID controller for a single-axis solar tracking system

abstract: Proportional integral derivative controllers are widely used in industrial processes because of their simplicity and effectiveness for linear and nonlinear systems. The fuzzy controller is the most suitable for the human decision-making mechanism, providing the operation of an electronic system with decisions of experts. In addition, using the fuzzy controller for a nonlinear system allows for a reduction of uncertain effects in the system control. In this study, a proportional integral derivative controller and a fuzzy logic controller are designed and compared for a single-axis solar tracking system using an Atmel microcontroller. According to the angle of solar energy, a solar panel is oriented to the side where light intensity is greatest by being designed for the related supervisory controllers. Thus, the aim is to increase the energy obtained from solar panels by providing the specular reflection of the sun’s rays to a solar panel. At the same time, a maximum efficient processing system has been determined by taking account of two controllers for the designed system.


title: Electronic and Optical Properties of Pristine and Vertical and Lateral Heterostructures of Janus MoSSe and WSSe

abstract: On the basis of electron–electron self-energy corrections, quasiparticle band structures of Janus MoSSe and WSSe are identified, and the excitonic effects are demonstrated to play a dominate role in the optical response. Combining together MoSSe and WSSe monolayers to form vertical heterostructures (VHTs) and lateral heterostructures (LHTs) rarely leads to a simple arithmetic sum of their properties, giving rise to novel and unexpected behaviors. In particular, Rashba polarization can be enhanced in VHTs due to improved out-of-plane electric polarity. In the case of LHTs, photoresponse and absorption coefficients show optical activity in a wide visible light range. It is of interest that both VHTs and LHTs reveal type-II band alignment, enabling the separation of excitons. Besides, grain boundaries (GBs) of large angle (60°) in Janus MoSSe due to chalcogen effects behave as one-dimensional (1D) metallic quantum wires, suggesting the possible formation of 1D electron or hole gas in such electronic heterost...


title: The Construct and Criterion Validity of Emotional Intelligence and Its Potential Utility for Management Studies.

abstract: In this study, the authors reviewed the definition of emotional intelligence (EI) and argued that El is conceptually distinct from personality. In Study 1, the authors showed that El was related to yet distinct from personality dimensions and that it had incremental predictive power on life satisfaction. The authors examined the construct validity of self-reports and others' ratings of EI using two samples in Study 2. In a student sample, parents' ratings explained additional variance in the students' life satisfaction and feelings of powerlessness after controlling for the Big Five personality dimensions. In the work sample, peer ratings were found to be significant predictors of job performance ratings provided by supervisors after controlling for the Big Five personality dimensions. Other implications for future research on El are discussed.


A constrained graph algebra for semantic parsing with AMRs


title: FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling

abstract: The graph convolutional networks (GCN) recently proposed by Kipf and Welling are an effective graph model for semi-supervised learning. This model, however, was originally designed to be learned with the presence of both training and test data. Moreover, the recursive neighborhood expansion across layers poses time and memory challenges for training with large, dense graphs. To relax the requirement of simultaneous availability of test data, we interpret graph convolutions as integral transforms of embedding functions under probability measures. Such an interpretation allows for the use of Monte Carlo approaches to consistently estimate the integrals, which in turn leads to a batched training scheme as we propose in this work---FastGCN. Enhanced with importance sampling, FastGCN not only is efficient for training but also generalizes well for inference. We show a comprehensive set of experiments to demonstrate its effectiveness compared with GCN and related models. In particular, training is orders of magnitude more efficient while predictions remain comparably accurate.


An Improved Performance of the SRM Drives Using Z-Source Inverter with the Simplified Fuzzy Logic Rule Base


A look at the topology of convolutional neural networks.


title: Decoupled Classification Refinement: Hard False Positive Suppression for Object Detection

abstract: In this paper, we analyze failure cases of state-of-the-art detectors and observe that most hard false positives result from classification instead of localization and they have a large negative impact on the performance of object detectors. We conjecture there are three factors: (1) Shared feature representation is not optimal due to the mismatched goals of feature learning for classification and localization; (2) multi-task learning helps, yet optimization of the multi-task loss may result in sub-optimal for individual tasks; (3) large receptive field for different scales leads to redundant context information for small objects. We demonstrate the potential of detector classification power by a simple, effective, and widely-applicable Decoupled Classification Refinement (DCR) network. In particular, DCR places a separate classification network in parallel with the localization network (base detector). With ROI Pooling placed on the early stage of the classification network, we enforce an adaptive receptive field in DCR. During training, DCR samples hard false positives from the base detector and trains a strong classifier to refine classification results. During testing, DCR refines all boxes from the base detector. Experiments show competitive results on PASCAL VOC and COCO without any bells and whistles. Our codes are available at: this https URL.


title: NAIST Openhand M2S: A Versatile Two-Finger Gripper Adapted for Pulling and Tucking Textile

abstract: When manipulating textiles and thin objects, one challenging task is to push (tuck) of textiles into small openings. Further, for a number of manufacturing tasks it poses an obstacle to automation. However, current robot grippers are almost exclusively designed for grasping objects, or imitate the human hand with very fine mechanisms that break easily, so that they cannot withstand the loads required by the pushing task. Further, as textiles are very thin, even grippers with pressure pads cannot easily confirm if a textile was grasped successfully or if the gripper is empty. In this research, we present a gripper design that can detect the successful grasping of thin objects via active perception, can sustain significant pushing loads in order to perform tucking tasks and can perform dexterous grasping and in-hand manipulation. The gripper is open-source and can be 3D printed. We demonstrate the gripper's performance experimentally, its precision when controlling its grasp force, and the maximum grasping force.


title: ARTIFICIAL INTELLIGENCE 333 Planning for Conjunctive Goals*

abstract: Abstract The problem of achieving conjunctive goals has been central to domain-independent planning research; the nonlinear constraint-posting approach has been most successful. Previous planners of this type have been complicated, heuristic, and ill-defined. I have combined and distilled the state of the art into a simple, precise, implemented algorithm (TWEAK) which I have proved correct and complete. I analyze previous work on domain-independent conjunctive planning; in retrospect it becomes clear that all conjunctive planners, linear and nonlinear, work the same way. The efficiency and correctness of these planners depends on the traditional add/delete-list representation for actions, which drastically limits their usefulness. I present theorems that suggest that efficient general purpose planning with more expressive action representations is impossible, and suggest ways to avoid this problem.


title: EnCore: exploiting system environment and correlation information for misconfiguration detection

abstract: As software systems become more complex and configurable, failures due to misconfigurations are becoming a critical problem. Such failures often have serious functionality, security and financial consequences. Further, diagnosis and remediation for such failures require reasoning across the software stack and its operating environment, making it difficult and costly. We present a framework and tool called EnCore to automatically detect software misconfigurations. EnCore takes into account two important factors that are unexploited before: the interaction between the configuration settings and the executing environment, as well as the rich correlations between configuration entries. We embrace the emerging trend of viewing systems as data, and exploit this to extract information about the execution environment in which a configuration setting is used. EnCore learns configuration rules from a given set of sample configurations. With training data enriched with the execution context of configurations, EnCore is able to learn a broad set of configuration anomalies that spans the entire system. EnCore is effective in detecting both injected errors and known real-world problems - it finds 37 new misconfigurations in Amazon EC2 public images and 24 new configuration problems in a commercial private cloud. By systematically exploiting environment information and by learning correlation rules across multiple configuration settings, EnCore detects 1.6x to 3.5x more misconfiguration anomalies than previous approaches.


title: Predictive positioning and quality of service ridesharing for campus mobility on demand systems

abstract: Autonomous Mobility On Demand (MOD) systems can utilize fleet management strategies to properly position vehicles and allocate customers in order to maintain a high customer quality of service (QoS). Previous works on autonomous MOD systems have developed methods for rebalancing single capacity vehicles, where QoS is maintained through large fleet sizing. This work focuses on MOD systems utilizing a small number of vehicles, such as those found on a campus, where additional vehicles cannot be introduced as demand for rides increases. A predictive positioning method is presented for improving customer QoS in small-fleet MOD systems by identifying key locations which minimize expected customer wait time. Ridesharing is introduced as a means for improving customer QoS as arrival rates increase. However, with ridesharing perceived QoS is dependent on an often unknown customer preference. To address this challenge, a customer ratings model, which learns customer preference from a 5-star rating, is developed and incorporated directly into a ridesharing algorithm. The predictive positioning and ridesharing methods are applied to simulation of a real-world campus MOD system. A combined predictive positioning and ridesharing approach is shown to reduce customer service times by up to 29%. and the customer ratings model is shown to provide the best overall MOD fleet management performance over a range of customer preferences.


title: Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagery

abstract: The trend towards higher resolution remote sensing imagery facilitates a transition from land-use classification to object-level scene understanding. Rather than relying purely on spectral content, appearance-based image features come into play. In this work, deep convolutional neural networks (CNNs) are applied to semantic labelling of high-resolution remote sensing data. Recent advances in fully convolutional networks (FCNs) are adapted to overhead data and shown to be as effective as in other domains. A full-resolution labelling is inferred using a deep FCN with no downsampling, obviating the need for deconvolution or interpolation. To make better use of image features, a pre-trained CNN is fine-tuned on remote sensing data in a hybrid network context, resulting in superior results compared to a network trained from scratch. The proposed approach is applied to the problem of labelling high-resolution aerial imagery, where fine boundary detail is important. The dense labelling yields state-of-the-art accuracy for the ISPRS Vaihingen and Potsdam benchmark data sets.


title: Analysis of WEKA Data Mining Algorithm REPTree, Simple Cart and RandomTree for Classification of Indian News

abstract: The amount of data in the world and in our lives seems everincreasing and there’s no end to it. The Weka workbench is an organized collection of state-of-the-art machine learning algorithms and data pre-processing tools. The basic way of interacting with these methods is by invoking them from the command line. However, convenient interactive graphical user interfaces are provided for data exploration, for setting up largescale experiments on distributed computing platforms, and for designing configurations for streamed data processing. These interfaces constitute an advanced environment for experimental data mining. Classification is an important data mining technique with broad applications. It classifies data of various kinds. This paper has been carried out to make a performance evaluation of REPTree, Simple Cart and RandomTree classification algorithm. The paper sets out to make comparative evaluation of classifiers REPTree, Simple Cart and RandomTree in the context of dataset of Indian news to maximize true positive rate and minimize false positive rate. For processing Weka API were used. The results in the paper on dataset of Indian news also show that the efficiency and accuracy of RandomTree is good than REPTree, and Simple Cart.


Effective Function Recovery for COTS Binaries using Interface Verification


title: A note on heroin epidemics

abstract: We show that the steady states of the White and Comiskey [E. White, C. Comiskey, Heroin epidemics, treatment and ODE modelling, Math. Biosci. 208 (2007) 312–324.] model of heroin epidemics are stable.


title: Bayesian network based extreme learning machine for subjectivity detection

abstract: Abstract Subjectivity detection is a task of natural language processing that aims to remove ‘factual’ or ‘neutral’ content, i.e., objective text that does not contain any opinion, from online product reviews. Such a pre-processing step is crucial to increase the accuracy of sentiment analysis systems, as these are usually optimized for the binary classification task of distinguishing between positive and negative content. In this paper, we extend the extreme learning machine (ELM) paradigm to a novel framework that exploits the features of both Bayesian networks and fuzzy recurrent neural networks to perform subjectivity detection. In particular, Bayesian networks are used to build a network of connections among the hidden neurons of the conventional ELM configuration in order to capture dependencies in high-dimensional data. Next, a fuzzy recurrent neural network inherits the overall structure generated by the Bayesian networks to model temporal features in the predictor. Experimental results confirmed the ability of the proposed framework to deal with standard subjectivity detection problems and also proved its capacity to address portability across languages in translation tasks.


title: High Reliability and Efficiency Single-Phase Transformerless Inverter for Grid-Connected Photovoltaic Systems

abstract: This paper presents a high-reliability single-phase transformerless grid-connected inverter that utilizes superjunction MOSFETs to achieve high efficiency for photovoltaic applications. The proposed converter utilizes two split ac-coupled inductors that operate separately for positive and negative half grid cycles. This eliminates the shoot-through issue that is encountered by traditional voltage source inverters, leading to enhanced system reliability. Dead time is not required at both the high-frequency pulsewidth modulation switching commutation and the grid zero-crossing instants, improving the quality of the output ac-current and increasing the converter efficiency. The split structure of the proposed inverter does not lead itself to the reverse-recovery issues for the main power switches and as such superjunction MOSFETs can be utilized without any reliability or efficiency penalties. Since MOSFETs are utilized in the proposed converter high efficiency can be achieved even at light load operations achieving a high California energy commission (CEC) or European union efficiency of the converter system. It also has the ability to operate at higher switching frequencies while maintaining high efficiency. The higher operating frequencies with high efficiency enables reduced cooling requirements and results in system cost savings by shrinking passive components. With two additional ac-side switches conducting the currents during the freewheeling phases, the photovoltaic array is decoupled from the grid. This reduces the high-frequency common-mode voltage leading to minimized ground loop leakage current. The operation principle, common-mode characteristic and design considerations of the proposed transformerless inverter are illustrated. The total losses of the power semiconductor devices of several existing transformerless inverters which utilize MOSFETs as main switches are evaluated and compared. The experimental results with a 5 kW prototype circuit show 99.0% CEC efficiency and 99.3% peak efficiency with a 20 kHz switching frequency. The high reliability and efficiency of the proposed converter makes it very attractive for single-phase transformerless photovoltaic inverter applications.


title: Social comparisons, appearance related comments, contingent self-esteem and their relationships with body dissatisfaction and eating disturbance among women

abstract: This study examined social comparisons, appearance related comments and contingent self-esteem, and their relationships with body dissatisfaction and eating disturbance in young adult women. Importantly, the role of both positive and negative appearance related comments, and upward and downward comparisons, were investigated. A self-report questionnaire assessing each of these variables was completed by one hundred and ninety-six women aged 18–35. A higher frequency of negative comments and contingent self-esteem were associated with higher upward comparisons, and more positive comments were associated with higher downward comparisons. Overall, social comparisons were shown to be more important than verbal commentary and contingent self-esteem. More upward comparisons and less downward comparisons uniquely predicted higher body dissatisfaction and eating disturbance. In addition, negative appearance comments were found to be more salient than positive comments. Negative comments and contingent self-esteem uniquely predicted more eating disturbance but positive comments were not a predictor of body dissatisfaction or eating disturbance. Longitudinal studies are now required to establish the direction of these relationships and to more fully examine the interplay among the factors. In addition, given that our study only assessed self-reported social comparisons, our findings need to be validated against experimental methods.


title: The impact of emotion on perception, attention, memory, and decision-making

abstract: Summary Reason and emotion have long been considered opposing forces. However, recent psychological and neuroscientific research has revealed that emotion and cognition are closely intertwined. Cognitive processing is needed to elicit emotional responses. At the same time, emotional responses modulate and guide cognition to enable adaptive responses to the environment. Emotion determines how we perceive our world, organise our memory, and make important decisions. In this review, we provide an overview of current theorising and research in the Affective Sciences. We describe how psychological theories of emotion conceptualise the interactions of cognitive and emotional processes. We then review recent research investigating how emotion impacts our perception, attention, memory, and decision-making. Drawing on studies with both healthy participants and clinical populations, we illustrate the mechanisms and neural substrates underlying the interactions of cognition and emotion.


title: Neural network based language models for highly inflective languages

abstract: Speech recognition of inflectional and morphologically rich languages like Czech is currently quite a challenging task, because simple n-gram techniques are unable to capture important regularities in the data. Several possible solutions were proposed, namely class based models, factored models, decision trees and neural networks. This paper describes improvements obtained in recognition of spoken Czech lectures using language models based on neural networks. Relative reductions in word error rate are more than 15% over baseline obtained with adapted 4-gram backoff language model using modified Kneser-Ney smoothing.


title: Blending Aboriginal and Western healing methods to treat intergenerational trauma with substance use disorder in Aboriginal peoples who live in Northeastern Ontario, Canada

abstract: As with many Indigenous groups around the world, Aboriginal communities in Canada face significant challenges with trauma and substance use. The complexity of symptoms that accompany intergenerational trauma and substance use disorders represents major challenges in the treatment of both disorders. There appears to be an underutilization of substance use and mental health services, substantial client dropout rates, and an increase in HIV infections in Aboriginal communities in Canada. The aim of this paper is to explore and evaluate current literature on how traditional Aboriginal healing methods and the Western treatment model “Seeking Safety” could be blended to help Aboriginal peoples heal from intergenerational trauma and substance use disorders. A literature search was conducted using the keywords: intergenerational trauma, historical trauma, Seeking Safety, substance use, Two-Eyed Seeing, Aboriginal spirituality, and Aboriginal traditional healing. Through a literature review of Indigenous knowledge, most Indigenous scholars proposed that the wellness of an Aboriginal community can only be adequately measured from within an Indigenous knowledge framework that is holistic, inclusive, and respectful of the balance between the spiritual, emotional, physical, and social realms of life. Their findings indicate that treatment interventions must honour the historical context and history of Indigenous peoples. Furthermore, there appears to be strong evidence that strengthening cultural identity, community integration, and political empowerment can enhance and improve mental health and substance use disorders in Aboriginal populations. In addition, Seeking Safety was highlighted as a well-studied model with most populations, resulting in healing. The provided recommendations seek to improve the treatment and healing of Aboriginal peoples presenting with intergenerational trauma and addiction. Other recommendations include the input of qualitative and quantitative research as well as studies encouraging Aboriginal peoples to explore treatments that could specifically enhance health in their respective communities.


3 D laser road profiling for the automated measurement of road surface conditions and geometry .


title: A Short Introduction to Probabilistic Soft Logic

abstract: Probabilistic soft logic (PSL) is a framework for collective, probabilistic reasoning in relational domains. PSL uses first order logic rules as a template language for graphical models over random variables with soft truth values from the interval [0, 1]. Inference in this setting is a continuous optimization task, which can be solved efficiently. This paper provides an overview of the PSL language and its techniques for inference and weight learning. An implementation of PSL is available at http://psl.umiacs.umd.edu/.


title: Identifying cyber risk hotspots : A framework for measuring temporal variance in computer network risk

abstract: Modern computer networks generate significant volume of behavioural system logs on a daily basis. Such networks comprise many computers with Internet connectivity, and many users who access the Web and utilise Cloud services make use of numerous devices connected to the network on an ad-hoc basis. Measuring the risk of cyber attacks and identifying the most recent modus-operandi of cyber criminals on large computer networks can be difficult due to the wide range of services and applications running within the network, the multiple vulnerabilities associated with each application, the severity associated with each vulnerability, and the ever-changing attack vector of cyber criminals. In this paper we propose a framework to represent these features, enabling real-time network enumeration and traffic analysis to be carried out, in order to produce quantified measures of risk at specific points in time. We validate the approach using data from a University network, with a data collection consisting of 462,787 instances representing threats measured over a 144 hour period. Our analysis can be generalised to a variety of other contexts.


title: Detecting Cyberbullying using Latent Semantic Indexing

abstract: Cyberbullying has proven consequential to youth Internet users and previous methods relied heavily on the use of manually developed dictionaries. This project describes preliminary results for a system that uses Latent Semantic Indexing (LSI) for the detection of cyberbullying in a labeled collection of posts from Formspring.me. After preprocessing to account for variations in spelling and use of emoticons, a search system was developed. Our system significantly outperforms the baseline with a very simple query and is not dependent on a dictionary of bullying terms.


title: Material Recognition from Local Appearance in Global Context

abstract: Recognition of materials has proven to be a challenging problem due to the wide variation in appearance within and between categories. Global image context, such as where the material is or what object it makes up, can be crucial to recognizing the material. Existing methods, however, operate on an implicit fusion of materials and context by using large receptive fields as input (i.e., large image patches). Many recent material recognition methods treat materials as yet another set of labels like objects. Materials are, however, fundamentally different from objects as they have no inherent shape or defined spatial extent. Approaches that ignore this can only take advantage of limited implicit context as it appears during training. We instead show that recognizing materials purely from their local appearance and integrating separately recognized global contextual cues including objects and places leads to superior dense, per-pixel, material recognition. We achieve this by training a fully-convolutional material recognition network end-to-end with only material category supervision. We integrate object and place estimates to this network from independent CNNs. This approach avoids the necessity of preparing an impractically-large amount of training data to cover the product space of materials, objects, and scenes, while fully leveraging contextual cues for dense material recognition. Furthermore, we perform a detailed analysis of the effects of context granularity, spatial resolution, and the network level at which we introduce context. On a recently introduced comprehensive and diverse material database \cite{Schwartz2016}, we confirm that our method achieves state-of-the-art accuracy with significantly less training data compared to past methods.


title: Reactive sputtering of AlScN thin Ulms with variable Sc content on 200 mm wafers

abstract: Sc-doped AlN polycrystalline films are attractive active layers for high frequency (GHz range) acoustic resonators owing to the significant enlargement of the AlN piezoelectric activity with the increasing Sc content. To sputter homogenously doped AlScN films on 200 mm Si wafers we use a configurable cathode containing a variable number of embedded Sc pellets to fine tuning the Sc content in the films. The method was implemented in an Endeavor-AT™ cluster tool from OEM Group, adapted for sputtering on 200 mm wafers. 1 μm thick AlScN films with uniform Sc content (around 7 at.%), high crystal quality and good piezoelectric response have been sputtered over 200 mm production-level wafers.


title: An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face Recognition

abstract: We present a fully automatic face recognition algorithm and demonstrate its performance on the FRGC v2.0 data. Our algorithm is multimodal (2D and 3D) and performs hybrid (feature based and holistic) matching in order to achieve efficiency and robustness to facial expressions. The pose of a 3D face along with its texture is automatically corrected using a novel approach based on a single automatically detected point and the Hotelling transform. A novel 3D spherical face representation (SFR) is used in conjunction with the scale-invariant feature transform (SIFT) descriptor to form a rejection classifier, which quickly eliminates a large number of candidate faces at an early stage for efficient recognition in case of large galleries. The remaining faces are then verified using a novel region-based matching approach, which is robust to facial expressions. This approach automatically segments the eyes- forehead and the nose regions, which are relatively less sensitive to expressions and matches them separately using a modified iterative closest point (ICP) algorithm. The results of all the matching engines are fused at the metric level to achieve higher accuracy. We use the FRGC benchmark to compare our results to other algorithms that used the same database. Our multimodal hybrid algorithm performed better than others by achieving 99.74 percent and 98.31 percent verification rates at a 0.001 false acceptance rate (FAR) and identification rates of 99.02 percent and 95.37 percent for probes with a neutral and a nonneutral expression, respectively.


title: Comprehensive review and comparison of DC fast charging converter topologies: Improving electric vehicle plug-to-wheels efficiency

abstract: The commercial success of electric vehicles (EVs) relies heavily on the presence of high-efficiency charging stations. This paper provides an overview and a comprehensive performance comparison of the present status and future implementation plans for DC fast charging infrastructures and converter topologies. The paper also discusses critical consequences of DC fast charging stations on the AC grid. Different power converter topologies for DC fast charging are presented, compared, and evaluated, based on the power level requirements, efficiency, cost, and technical performance specifications. The paper focuses specifically on Level-3 DC fast charging converter topologies and their performance comparison. Finally, the paper presents a detailed well-to-wheels (WTW) analysis from an energy-efficiency standpoint. The most important part of this analysis focuses on the effect of usage of various charging levels and charger topologies on the all-important plug-to-battery (P2B) energy-efficiency within the overall context of WTW energy cycle efficiency.


title: Toward successful project management in global software development

abstract: Abstract Project management in the context of global software development (GSD) is challenging due to a number of issues. This paper has a two-fold objective: (1) to identify the factors from the literature related to the successful project management in GSD and to validate the identified factors in the real-world practice; (2) to map the identified factors to 10 project management knowledge areas of PMBOK. Our results show a positive correlation between the ranks obtained from the literature and the survey. The results of t -test (i.e., t = 1.979, p = 0.061 > 0.05) show that there is no significant difference between the findings of the literature and survey. Our mapping shows that most of the success factors are related to human resource knowledge area. It is anticipated that the identified success factors can be helpful to practitioners for developing strategic implementation of project management activities in GSD environment.


Secure Directional Modulation Using the Symmetrical Multi-Carrier Frequency Diverse Array with Logarithmical Frequency Increment


title: A fully planar antenna for millimeter-wave and 5G communications based on a new CSRR-enhanced substrate-integrated waveguide

abstract: We present a fully planar, easy to fabricate antenna for millimeter-wave communications, based on a new substrate-integrated waveguide (SIW). The SIW itself is entirely planar, since it is designed using series of side-by-side complementary split-ring resonators (CSRR), instead of vias, with the CSRR being etched on top and bottom metal ground surfaces that cover the dielectric substrate. This metamaterial-inspired structure provides a single-negative effective material parameter behavior that blocks wave propagation to the perpendicular direction. Hence, two parallel structures of this kind can be used to design an SIW, with propagation losses comparable to the conventional one. The antenna structure is consequently designed, by the use of proper cavities within the substrate and radiating slots on the ground structure and provides an easy to fabricate alternative for millimeter-wave and 5G communications.


title: Opening the Black Box of Deep Neural Networks via Information

abstract: Despite their great success, there is still no comprehensive theoretical understanding of learning with Deep Neural Networks (DNNs) or their inner organization. Previous work proposed to analyze DNNs in the \textit{Information Plane}; i.e., the plane of the Mutual Information values that each layer preserves on the input and output variables. They suggested that the goal of the network is to optimize the Information Bottleneck (IB) tradeoff between compression and prediction, successively, for each layer. ::: In this work we follow up on this idea and demonstrate the effectiveness of the Information-Plane visualization of DNNs. Our main results are: (i) most of the training epochs in standard DL are spent on {\emph compression} of the input to efficient representation and not on fitting the training labels. (ii) The representation compression phase begins when the training errors becomes small and the Stochastic Gradient Decent (SGD) epochs change from a fast drift to smaller training error into a stochastic relaxation, or random diffusion, constrained by the training error value. (iii) The converged layers lie on or very close to the Information Bottleneck (IB) theoretical bound, and the maps from the input to any hidden layer and from this hidden layer to the output satisfy the IB self-consistent equations. This generalization through noise mechanism is unique to Deep Neural Networks and absent in one layer networks. (iv) The training time is dramatically reduced when adding more hidden layers. Thus the main advantage of the hidden layers is computational. This can be explained by the reduced relaxation time, as this it scales super-linearly (exponentially for simple diffusion) with the information compression from the previous layer.


title: Event detection and recognition for semantic annotation of video

abstract: Research on methods for detection and recognition of events and actions in videos is receiving an increasing attention from the scientific community, because of its relevance for many applications, from semantic video indexing to intelligent video surveillance systems and advanced human-computer interaction interfaces. Event detection and recognition requires to consider the temporal aspect of video, either at the low-level with appropriate features, or at a higher-level with models and classifiers than can represent time. In this paper we survey the field of event recognition, from interest point detectors and descriptors, to event modelling techniques and knowledge management technologies. We provide an overview of the methods, categorising them according to video production methods and video domains, and according to types of events and actions that are typical of these domains.


title: Isolation Improvement in UWB MIMO Antenna System Using Carbon Black Film

abstract: A carbon black film was used to enhance the isolation of ultrawideband multiple-input–multiple-output (MIMO) antenna system, which could absorb the interference between two or more antenna elements. We investigated the proportion of carbon black film, coated position and area, and the properties of MIMO antenna system to enhance the isolation in MIMO system. The properties of the carbon black film-coated antenna are as follows: at 2.5–11 GHz, $S_{11}$ $S_{21}$ 2.11 dBi; and envelope correlation coefficients <0.02. Results indicate that the film can be applied to various MIMO systems.


Variational Recurrent Neural Networks for Speech Separation


Relational Language and Relational Thought


Beyond Data Mining : Integrative Machine Learning for Health Informatics


title: Augmented vehicle tracking under occlusions for decision-making in autonomous driving

abstract: This paper reports on an algorithm to support autonomous vehicles in reasoning about occluded regions of their environment to make safe, reliable decisions. In autonomous driving scenarios, other traffic participants are often occluded from sensor measurements by buildings or large vehicles like buses or trucks, which makes tracking dynamic objects challenging.We present a method to augment standard dynamic object trackers with means to 1) estimate the occluded state of other traffic agents and 2) robustly associate the occluded estimates with new observations after the tracked object reenters the visible region of the sensor horizon. We perform occluded state estimation using a dynamics model that accounts for the driving behavior of traffic agents and a hybrid Gaussian mixture model (hGMM) to capture multiple hypotheses over discrete behavior, such as driving along different lanes or turning left or right at an intersection. Upon new observations, we associate them to existing estimates in terms of the Kullback-Leibler divergence (KLD). We evaluate the proposed method in simulation and using a real-world traffic-tracking dataset from an autonomous vehicle platform. Results show that our method can handle significantly prolonged occlusions when compared to a standard dynamic object tracking system.


title: Designing a Non-contact Wearable Tactile Display Using Airflows

abstract: Traditional wearable tactile displays transfer tactile stimulations through a firm contact between the stimulator and the skin. We conjecture that a firm contact may not be always possible and acceptable. Therefore, we explored the concept of a non-contact wearable tactile display using an airflow, which can transfer information without a firm contact. To secure an empirical ground for the design of a wearable airflow display, we conducted a series of psychophysical experiments to estimate the intensity thresholds, duration thresholds, and distance thresholds of airflow perception on various body locations, and report the resulting empirical data in this paper. We then built a 4-point airflow display, compared its performance with that of a vibrotactile display, and could show that the two tactile displays are comparable in information transfer performance. User feedback was also positive and revealed many unique expressions describing airflow-based tactile experiences. Lastly, we demonstrate the feasibility of an airflow-based wearable tactile display with a prototype using micro-fans.


title: Survey on Various Gesture Recognition Techniques for Interfacing Machines Based on Ambient Intelligence

abstract: Gesture recognition is mainly apprehensive on analyzing the functionality of human wits. The main goal of gesture recognition is to create a system which can recognize specific human gestures and use them to convey information or for device control. Hand gestures provide a separate complementary modality to speech for expressing ones ideas. Information associated with hand gestures in a conversation is degree, discourse structure, spatial and temporal structure. The approaches present can be mainly divided into Data-Glove Based and Vision Based approaches. An important face feature point is the nose tip. Since nose is the highest protruding point from the face. Besides that, it is not affected by facial expressions. Another important function of the nose is that it is able to indicate the head pose. Knowledge of the nose location will enable us to align an unknown 3D face with those in a face database. Eye detection is divided into eye position detection and eye contour detection. Existing works in eye detection can be classified into two major categories: traditional image-based passive approaches and the active IR based approaches. The former uses intensity and shape of eyes for detection and the latter works on the assumption that eyes have a reflection under near IR illumination and produce bright/dark pupil effect. The traditional methods can be broadly classified into three categories: template based methods, appearance based methods and feature based methods. The purpose of this paper is to compare various human Gesture recognition systems for interfacing machines directly to human wits without any corporeal media in an ambient environment.


title: Moving gradients: a path-based method for plausible image interpolation

abstract: We describe a method for plausible interpolation of images, with a wide range of applications like temporal up-sampling for smooth playback of lower frame rate video, smooth view interpolation, and animation of still images. The method is based on the intuitive idea, that a given pixel in the interpolated frames traces out a path in the source images. Therefore, we simply move and copy pixel gradients from the input images along this path. A key innovation is to allow arbitrary (asymmetric) transition points, where the path moves from one image to the other. This flexible transition preserves the frequency content of the originals without ghosting or blurring, and maintains temporal coherence. Perhaps most importantly, our framework makes occlusion handling particularly simple. The transition points allow for matches away from the occluded regions, at any suitable point along the path. Indeed, occlusions do not need to be handled explicitly at all in our initial graph-cut optimization. Moreover, a simple comparison of computed path lengths after the optimization, allows us to robustly identify occluded regions, and compute the most plausible interpolation in those areas. Finally, we show that significant improvements are obtained by moving gradients and using Poisson reconstruction.


title: CHREST models of implicit learning and board game interpretation

abstract: A general theory of intelligence must include learning, the process of converting experiences into retrievable memories. We present two CHREST models to illustrate the effects of learning across two different time scales (minutes and years, respectively). The first is an illustration of implicit learning, checking the validity of strings drawn from an artificial grammar. The second looks at the interpretation of boardgame positions. The same learning and retrieval mechanisms are used in both cases, and we argue that CHREST can be used by an artificial general intelligence to construct and access declarative memory.


title: MARSS: a full system simulator for multicore x86 CPUs

abstract: We present MARSS, an open source, fast, full system simulation tool built on QEMU to support cycle-accurate simulation of superscalar homogeneous and heterogeneous multicore x86 processors. MARSS includes detailed models of coherent caches, interconnections, chipsets, memory and IO devices. MARSS simulates the execution of all software components in the system, including unmodified binaries of applications, OS and libraries.


title: Iterative reconstruction methods in X-ray CT

abstract: Abstract Iterative reconstruction (IR) methods have recently re-emerged in transmission x-ray computed tomography (CT). They were successfully used in the early years of CT, but given up when the amount of measured data increased because of the higher computational demands of IR compared to analytical methods. The availability of large computational capacities in normal workstations and the ongoing efforts towards lower doses in CT have changed the situation; IR has become a hot topic for all major vendors of clinical CT systems in the past 5 years. This review strives to provide information on IR methods and aims at interested physicists and physicians already active in the field of CT. We give an overview on the terminology used and an introduction to the most important algorithmic concepts including references for further reading. As a practical example, details on a model-based iterative reconstruction algorithm implemented on a modern graphics adapter (GPU) are presented, followed by application examples for several dedicated CT scanners in order to demonstrate the performance and potential of iterative reconstruction methods. Finally, some general thoughts regarding the advantages and disadvantages of IR methods as well as open points for research in this field are discussed.


title: Large Scale Language Modeling: Converging on 40GB of Text in Four Hours

abstract: Recent work has shown how to train Convolutional Neural Networks (CNNs) rapidly on large image datasets, then transfer the knowledge gained from these models to a variety of tasks. Following [Radford 2017], in this work, we demonstrate similar scalability and transfer for Recurrent Neural Networks (RNNs) for Natural Language tasks. By utilizing mixed precision arithmetic and a 32k batch size distributed across 128 NVIDIA Tesla V100 GPUs, we are able to train a character-level 4096-dimension multiplicative LSTM (mLSTM) for unsupervised text reconstruction over 3 epochs of the 40 GB Amazon Reviews dataset in four hours. This runtime compares favorably with previous work taking one month to train the same size and configuration for one epoch over the same dataset. Converging large batch RNN models can be challenging. Recent work has suggested scaling the learning rate as a function of batch size, but we find that simply scaling the learning rate as a function of batch size leads either to significantly worse convergence or immediate divergence for this problem. We provide a learning rate schedule that allows our model to converge with a 32k batch size. Since our model converges over the Amazon Reviews dataset in hours, and our compute requirement of 128 Tesla V100 GPUs, while substantial, is commercially available, this work opens up large scale unsupervised NLP training to most commercial applications and deep learning researchers. A model can be trained over most public or private text datasets overnight.


title: Structure Learning for Relational Logistic Regression: An Ensemble Approach

abstract: We consider the problem of learning Relational Logistic Regression (RLR). Unlike standard logistic regression, the features of RLRs are first-order formulae with associated weight vectors instead of scalar weights. We turn the problem of learning RLR to learning these vector-weighted formulae and develop a learning algorithm based on the recently successful functional-gradient boosting methods for probabilistic logic models. We derive the functional gradients and show how weights can be learned simultaneously in an efficient manner. Our empirical evaluation on standard and novel data sets demonstrates the superiority of our approach over other methods for learning RLR.


title: Multimodal integration learning of robot behavior using deep neural networks

abstract: For humans to accurately understand the world around them, multimodal integration is essential because it enhances perceptual precision and reduces ambiguity. Computational models replicating such human ability may contribute to the practical use of robots in daily human living environments; however, primarily because of scalability problems that conventional machine learning algorithms suffer from, sensory-motor information processing in robotic applications has typically been achieved via modal-dependent processes. In this paper, we propose a novel computational framework enabling the integration of sensory-motor time-series data and the self-organization of multimodal fused representations based on a deep learning approach. To evaluate our proposed model, we conducted two behavior-learning experiments utilizing a humanoid robot; the experiments consisted of object manipulation and bell-ringing tasks. From our experimental results, we show that large amounts of sensory-motor information, including raw RGB images, sound spectrums, and joint angles, are directly fused to generate higher-level multimodal representations. Further, we demonstrated that our proposed framework realizes the following three functions: (1) cross-modal memory retrieval utilizing the information complementation capability of the deep autoencoder; (2) noise-robust behavior recognition utilizing the generalization capability of multimodal features; and (3) multimodal causality acquisition and sensory-motor prediction based on the acquired causality. Novel computational framework for sensory-motor integration learning.Cross-modal memory retrieval utilizing a deep autoencoder.Noise-robust behavior recognition utilizing acquired multimodal features.Multimodal causality acquisition and sensory-motor prediction.


title: Spring over muscle (SOM) actuator for rehabilitation devices

abstract: For people affected by stroke, frequent physical therapy has been shown to be an effective form of rehabilitation. To this goal, several home therapy devices have been developed. Many of these devices may benefit from the use of a bidirectional pneumatic muscle actuator. This work presents the concept and design of the double-acting, compliant, spring over muscle (SOM) actuator. The principle design uses a spring in parallel with a pneumatic muscle actuator. This concept is economical, and easily scalable. Additionally, a design proposal for an ankle rehabilitation device, which incorporates the SOM actuator, is discussed.


title: AVEC 2013: the continuous audio/visual emotion and depression recognition challenge

abstract: Mood disorders are inherently related to emotion. In particular, the behaviour of people suffering from mood disorders such as unipolar depression shows a strong temporal correlation with the affective dimensions valence and arousal. In addition, psychologists and psychiatrists take the observation of expressive facial and vocal cues into account while evaluating a patient's condition. Depression could result in expressive behaviour such as dampened facial expressions, avoiding eye contact, and using short sentences with flat intonation. It is in this context that we present the third Audio-Visual Emotion recognition Challenge (AVEC 2013). The challenge has two goals logically organised as sub-challenges: the first is to predict the continuous values of the affective dimensions valence and arousal at each moment in time. The second sub-challenge is to predict the value of a single depression indicator for each recording in the dataset. This paper presents the challenge guidelines, the common data used, and the performance of the baseline system on the two tasks.


title: A Benchmark for Betweenness Centrality Approximation Algorithms on Large Graphs

abstract: Betweenness centrality quantifies the importance of graph nodes in a variety of applications including social, biological and communication networks. Its computation is very costly for large graphs; therefore, many approximate methods have been proposed. Given the lack of a golden standard, the accuracy of most approximate methods is evaluated on tiny graphs and is not guaranteed to be representative of realistic datasets that are orders of magnitude larger. In this paper, we develop BeBeCA, a benchmark for betweenness centrality approximation methods on large graphs. Specifically: (i) We generate a golden standard by deploying a parallel implementation of Brandes algorithm using 96,000 CPU cores on a supercomputer to compute exact betweenness centrality values for several large graphs with up to 126M edges. (ii) We propose an evaluation methodology to assess various aspects of approximation accuracy, such as average error and quality of node ranking. (iii) We survey a large number of existing approximation methods and compare their performance and accuracy using our benchmark. (iv) We publicly share our benchmark, which includes the golden standard exact betweenness centrality values together with the scripts that implement our evaluation methodology; for researchers to compare their own algorithms and practitioners to select the appropriate algorithm for their application and data.


Indexing by Latent Semantic Analysis


title: DiffAni: Visualizing Dynamic Graphs with a Hybrid of Difference Maps and Animation

abstract: Visualization of dynamically changing networks (graphs) is a significant challenge for researchers. Previous work has experimentally compared animation, small multiples, and other techniques, and found trade-offs between these. One potential way to avoid such trade-offs is to combine previous techniques in a hybrid visualization. We present two taxonomies of visualizations of dynamic graphs: one of non-hybrid techniques, and one of hybrid techniques. We also describe a prototype, called DiffAni, that allows a graph to be visualized as a sequence of three kinds of tiles: diff tiles that show difference maps over some time interval, animation tiles that show the evolution of the graph over some time interval, and small multiple tiles that show the graph state at an individual time slice. This sequence of tiles is ordered by time and covers all time slices in the data. An experimental evaluation of DiffAni shows that our hybrid approach has advantages over non-hybrid techniques in certain cases.


title: Tagsplanations: explaining recommendations using tags

abstract: While recommender systems tell users what items they might like, explanations of recommendations reveal why they might like them. Explanations provide many benefits, from improving user satisfaction to helping users make better decisions. This paper introduces tagsplanations, which are explanations based on community tags. Tagsplanations have two key components: tag relevance, the degree to which a tag describes an item, and tag preference, the user's sentiment toward a tag. We develop novel algorithms for estimating tag relevance and tag preference, and we conduct a user study exploring the roles of tag relevance and tag preference in promoting effective tagsplanations. We also examine which types of tags are most useful for tagsplanations.


title: Finding shortest paths on real road networks: the case for A*

abstract: The problem of identifying the shortest path along a road network is a fundamental problem in network analysis, ranging from route guidance in a navigation system to solving spatial allocation problems. Since this type of problem is solved so frequently, it is important to craft an approach that is as efficient as possible. Based upon past research, it is generally accepted that several efficient implementations of the Dijkstra algorithm are the fastest at optimally solving the 'one-to-one' shortest path problem (Cherkassky et al. 1996). We show that the most efficient state-of-the-art implementations of Dijkstra can be improved by taking advantage of network properties associated with GIS-sourced data. The results of this paper, derived from tests of different algorithmic approaches on real road networks, will be extremely valuable for application developers and researchers in the GIS community.


title: COBRAS-TS: A new approach to Semi-Supervised Clustering of Time Series

abstract: Clustering is ubiquitous in data analysis, including analysis of time series. It is inherently subjective: different users may prefer different clusterings for a particular dataset. Semi-supervised clustering addresses this by allowing the user to provide examples of instances that should (not) be in the same cluster. This paper studies semi-supervised clustering in the context of time series. We show that COBRAS, a state-of-the-art semi-supervised clustering method, can be adapted to this setting. We refer to this approach as COBRAS-TS. An extensive experimental evaluation supports the following claims: (1) COBRAS-TS far outperforms the current state of the art in semi-supervised clustering for time series, and thus presents a new baseline for the field; (2) COBRAS-TS can identify clusters with separated components; (3) COBRAS-TS can identify clusters that are characterized by small local patterns; (4) a small amount of semi-supervision can greatly improve clustering quality for time series; (5) the choice of the clustering algorithm matters (contrary to earlier claims in the literature).


title: Using Dependency-Based Features to Take the “ Para-farce ” out of Paraphrase

abstract: As research in text-to-text paraphrase generation progresses, it has the potential to improve the quality of generated text. However, the use of paraphrase generation methods creates a secondary problem. We must ensure that generated novel sentences are not inconsistent with the text from which it was generated. We propose a machine learning approach be used to filter out inconsistent novel sentences, or False Paraphrases. To train such a filter, we use the Microsoft Research Paraphrase corpus and investigate whether features based on syntactic dependencies can aid us in this task. Like Finch et al. (2005), we obtain a classification accuracy of 75.6%, the best known performance for this corpus. We also examine the strengths and weaknesses of dependency based features and conclude that they may be useful in more accurately classifying cases of False Paraphrase.


title: Source code pattern as anchored abstract syntax tree

abstract: For a software engineer, well-organized source code conveys the intention of the authors, as if it were an informal specification. An experienced engineer often grasps the “pseudo” meaning of source code first by identifying semantic chunks of various granularities in it, then by weaving them into a hypothetically consistent semantics. We call the specific part in source code that realizes a semantic chunk a code pattern. Tool support for locating already defined code pattern would help engineer's understanding, but it has been practically difficult. One reason is that a code pattern may appear as a lot of slightly different variations. This becomes an obstacle to define a code pattern. In this paper, a framework for an implicit representation of a code pattern is presented. In the framework, a code pattern is defined by an enumeration of instances which is tagged either “positive” of “negative” by a human. From these sets, the system incrementally infers a hypothetical explicit (syntactic) definition of the code pattern. Each instance is represented as an anchored AST (abstract syntax tree. An anchored AST is an AST with a tuple of AST nodes of interest.


title: Trajectory generation for swing-free maneuvers of a quadrotor with suspended payload: A dynamic programming approach

abstract: In this paper, we address the problem of agile swing-free trajectory tracking of a quadrotor with a suspended load. This problem has great practical significance in many UAV applications. However, it has received little attention in the literature so far. Flying with a suspended load can be a very challenging and sometimes hazardous task as the suspended load significantly alters the flight characteristics of the quadrotor. In order to deal with this problem, we propose a technique based on dynamic programming which ensures swing-free trajectory tracking. We start by presenting the mathematical model of a quadrotor with suspended load dynamics and kinematics. A high-level planner is used to provide desired waypoints, and then a dynamic programming approach is used to generate the swing-free trajectory for the quadrotor carrying a suspended load. Effectiveness of this method is demonstrated by numerical simulations and experiments.


EACH-USP Ensemble Cross-domain Authorship Attribution: Notebook for PAN at CLEF 2018


title: Cost Efficient Resource Management in Fog Computing Supported Medical Cyber-Physical System

abstract: With the recent development in information and communication technology, more and more smart devices penetrate into people’s daily life to promote the life quality. As a growing healthcare trend, medical cyber-physical systems (MCPSs) enable seamless and intelligent interaction between the computational elements and the medical devices. To support MCPSs, cloud resources are usually explored to process the sensing data from medical devices. However, the high quality-of-service of MCPS challenges the unstable and long-delay links between cloud data center and medical devices. To combat this issue, mobile edge cloud computing, or fog computing, which pushes the computation resources onto the network edge (e.g., cellular base stations), emerges as a promising solution. We are thus motivated to integrate fog computation and MCPS to build fog computing supported MCPS (FC-MCPS). In particular, we jointly investigate base station association, task distribution, and virtual machine placement toward cost-efficient FC-MCPS. We first formulate the problem into a mixed-integer non-linear linear program and then linearize it into a mixed integer linear programming (LP). To address the computation complexity, we further propose an LP-based two-phase heuristic algorithm. Extensive experiment results validate the high-cost efficiency of our algorithm by the fact that it produces near optimal solution and significantly outperforms a greedy algorithm.


title: Learning from Collective Intelligence: Feature Learning Using Social Images and Tags

abstract: Feature representation for visual content is the key to the progress of many fundamental applications such as annotation and cross-modal retrieval. Although recent advances in deep feature learning offer a promising route towards these tasks, they are limited in application domains where high-quality and large-scale training data are expensive to obtain. In this article, we propose a novel deep feature learning paradigm based on social collective intelligence, which can be acquired from the inexhaustible social multimedia content on the Web, in particular, largely social images and tags. Differing from existing feature learning approaches that rely on high-quality image-label supervision, our weak supervision is acquired by mining the visual-semantic embeddings from noisy, sparse, and diverse social image collections. The resultant image-word embedding space can be used to (1) fine-tune deep visual models for low-level feature extractions and (2) seek sparse representations as high-level cross-modal features for both image and text. We offer an easy-to-use implementation for the proposed paradigm, which is fast and compatible with any state-of-the-art deep architectures. Extensive experiments on several benchmarks demonstrate that the cross-modal features learned by our paradigm significantly outperforms others in various applications such as content-based retrieval, classification, and image captioning.


title: Power coordinates: a geometric construction of barycentric coordinates on convex polytopes

abstract: We present a full geometric parameterization of generalized barycentric coordinates on convex polytopes. We show that these continuous and non-negative coefficients ensuring linear precision can be efficiently and exactly computed through a power diagram of the polytope's vertices and the evaluation point. In particular, we point out that well-known explicit coordinates such as Wachspress, Discrete Harmonic, Voronoi, or Mean Value correspond to simple choices of power weights. We also present examples of new barycentric coordinates, and discuss possible extensions such as power coordinates for non-convex polygons and smooth shapes.


title: Atomic In-place Updates for Non-volatile Main Memories with Kamino-Tx

abstract: Data structures for non-volatile memories have to be designed such that they can be atomically modified using transactions. Existing atomicity methods require data to be copied in the critical path which significantly increases the latency of transactions. These overheads are further amplified for transactions on byte-addressable persistent memories where often the byte ranges modified for data structure updates are significantly smaller compared to the granularity at which data can be efficiently copied and logged. We propose Kamino-Tx that provides a new way to perform transactional updates on non-volatile byte-addressable memories (NVM) without requiring any copying of data in the critical path. Kamino-Tx maintains an additional copy of data off the critical path to achieve atomicity. But in doing so Kamino-Tx has to overcome two important challenges of safety and minimizing NVM storage overhead. We propose a more dynamic approach to maintaining the additional copy of data to reduce storage overheads. To further mitigate the storage overhead of using Kamino-Tx in a replicated setting, we develop Kamino-Tx-Chain, a variant of Chain Replication where replicas perform in-place updates and do not maintain data copies locally; replicas in Kamino-Tx-Chain leverage other replicas as copies to roll back or forward for atomicity. Our results show that using Kamino-Tx increases throughput by up to 9.5x for unreplicated systems and up to 2.2x for replicated settings.


title: Finding better active learners for faster literature reviews

abstract: Literature reviews can be time-consuming and tedious to complete. By cataloging and refactoring three state-of-the-art active learning techniques from evidence-based medicine and legal electronic discovery, this paper finds and implements FASTREAD, a faster technique for studying a large corpus of documents, combining and parametrizing the most efficient active learning algorithms. This paper assesses FASTREAD using datasets generated from existing SE literature reviews (Hall, Wahono, Radjenović, Kitchenham et al.). Compared to manual methods, FASTREAD lets researchers find 95% relevant studies after reviewing an order of magnitude fewer papers. Compared to other state-of-the-art automatic methods, FASTREAD reviews 20–50% fewer studies while finding same number of relevant primary studies in a systematic literature review.


title: Loss of expression of the recycling receptor, FcRn, promotes tumor cell growth by increasing albumin consumption

abstract: Tumor cells rely on high concentrations of amino acids to support their growth and proliferation. Although increased macropinocytic uptake and lysosomal degradation of the most abundant serum protein, albumin, in Ras-transformed cells can meet these demands, it is not understood how the majority of tumor cells that express wild type Ras achieve this. In the current study we reveal that the neonatal Fc receptor, FcRn, regulates tumor cell proliferation through the ability to recycle its ligand, albumin. By contrast with normal epithelial cells, we show that human FcRn is present at very low or undetectable levels in the majority of tumor cell lines analyzed. Remarkably, shRNA-mediated ablation of FcRn expression in an FcRn-positive tumor cell line results in a substantial growth increase of tumor xenografts, whereas enforced expression of this receptor by lentiviral transduction has the reverse effect. Moreover, intracellular albumin and glutamate levels are increased by the loss of FcRn-mediated recycling of albumin, combined with hypoalbuminemia in tumor-bearing mice. These studies identify a novel role for FcRn as a suppressor of tumor growth and have implications for the use of this receptor as a prognostic indicator and therapeutic target.


title: 3-D Convolutional Recurrent Neural Networks With Attention Model for Speech Emotion Recognition

abstract: Speech emotion recognition (SER) is a difficult task due to the complexity of emotions. The SER performances are heavily dependent on the effectiveness of emotional features extracted from the speech. However, most emotional features are sensitive to emotionally irrelevant factors, such as the speaker, speaking styles, and environment. In this letter, we assume that calculating the deltas and delta-deltas for personalized features not only preserves the effective emotional information but also reduces the influence of emotionally irrelevant factors, leading to reduce misclassification. In addition, SER often suffers from the silent frames and emotionally irrelevant frames. Meanwhile, attention mechanism has exhibited outstanding performances in learning relevant feature representations for specific tasks. Inspired by this, we propose a three-dimensional attention-based convolutional recurrent neural networks to learn discriminative features for SER, where the Mel-spectrogram with deltas and delta-deltas are used as input. Experiments on IEMOCAP and Emo-DB corpus demonstrate the effectiveness of the proposed method and achieve the state-of-the-art performance in terms of unweighted average recall.


title: Part-Based Player Identification Using Deep Convolutional Representation and Multi-scale Pooling

abstract: This paper addresses the problem of automatic player identification in broadcast sports videos filmed with a single side-view medium distance camera. Player identification in this setting is a challenging task because visual cues such as faces and jersey numbers are not clearly visible. Thus, this task requires sophisticated approaches to capture distinctive features from players to distinguish them. To this end, we use Convolutional Neural Networks (CNN) features extracted at multiple scales and encode them with an advanced pooling, called Fisher vector. We leverage it for exploring representations that have sufficient discriminatory power and ability to magnify subtle differences. We also analyze the distinguishing parts of the players and present a part based pooling approach to use these distinctive feature points. The resulting player representation is able to identify players even in difficult scenes. It achieves state-of-the-art results up to 96% on NBA basketball clips.


title: A Pilot Study to Assess Designer’s Mental Stress Using Eye Gaze System and Electroencephalogram

abstract: The study of mental stress is of great importance to design in that it enhances our understanding of designer’s cognitive model during the creative design process, among others. As the first step of this effort, this paper focuses on the assessment of mental stress based on the analysis of electroencephalogram (EEG) signals and the eye related data. The stress stimuli used is the computer based Stroop test with six difficulty levels. By using different parameters such as EEG power bands, and other eye behavior data, human mental stresses were assessed. Results indicate a strong correlation between the recorded physiological signals and the emotional state of the designers. This study provides a baseline for the further analysis of designer’s and users mental stresses during design-related tasks. 1 BACKGROUND


title: Mining time-changing data streams

abstract: Most statistical and machine-learning algorithms assume that the data is a random sample drawn from a stationary distribution. Unfortunately, most of the large databases available for mining today violate this assumption. They were gathered over months or years, and the underlying processes generating them changed during this time, sometimes radically. Although a number of algorithms have been proposed for learning time-changing concepts, they generally do not scale well to very large databases. In this paper we propose an efficient algorithm for mining decision trees from continuously-changing data streams, based on the ultra-fast VFDT decision tree learner. This algorithm, called CVFDT, stays current while making the most of old data by growing an alternative subtree whenever an old one becomes questionable, and replacing the old with the new when the new becomes more accurate. CVFDT learns a model which is similar in accuracy to the one that would be learned by reapplying VFDT to a moving window of examples every time a new example arrives, but with O(1) complexity per example, as opposed to O(w), where w is the size of the window. Experiments on a set of large time-changing data streams demonstrate the utility of this approach.


title: A phase-shift self-oscillating stereo class-D amplifier for battery-powered applications

abstract: This paper presents a highly efficient stereo audio amplifier, based on a self-oscillating modulator. This modulation scheme has been analyzed and shows to have a higher bandwidth and error correction than standard Pulse Width Modulation (PWM). A practical implementation in CMOS 0.25um technology has been done to validate our theoretical and simulation results, based on a modified PWM amplifier. Our amplifier demonstrated a Total Harmonic Distortion plus Noise (THD+N) as low as 0.007%, current consumption is 3.88mA at 3.6V (stereo) and the efficiency reaches 88% into 8Ohms. The integrated circuit measures 1.6×1.6mm and is able to work at supply voltages from 2.3V to 5.5V.


title: Thirty years later: lessons from the Multics security evaluation

abstract: Almost thirty years ago a vulnerability assessment of Multics identified significant vulnerabilities, despite the fact that Multics was more secure than other contemporary (and current) computer systems. Considerably more important than any of the individual design and implementation flaws was the demonstration of subversion of the protection mechanism using malicious software (e.g., trap doors and Trojan horses). A series of enhancements were suggested that enabled Multics to serve in a relatively benign environment. These included addition of "mandatory access controls" and these enhancements were greatly enabled by the fact the Multics was designed from the start for security. However, the bottom-line conclusion was that "restructuring is essential" around a verifiable "security kernel" before using Multics (or any other system) in an open environment (as in today's Internet) with the existence of well-motivated professional attackers employing subversion. The lessons learned from the vulnerability assessment are highly applicable today as governments and industry strive (unsuccessfully) to "secure" today's weaker operating systems through add-ons, "hardening", and intrusion detection schemes.


title: Exploiting visual search theory to infer social interactions

abstract: In this paper we propose a new method to infer human social interactions using typical techniques adopted in literature for visual search and information retrieval. The main piece of information we use to discriminate among different types of interactions is provided by proxemics cues acquired by a tracker, and used to distinguish between intentional and casual interactions. The proxemics information has been acquired through the analysis of two different metrics: on the one hand we observe the current distance between subjects, and on the other hand we measure the O-space synergy between subjects. The obtained values are taken at every time step over a temporal sliding window, and processed in the Discrete Fourier Transform (DFT) domain. The features are eventually merged into an unique array, and clustered using the K-means algorithm. The clusters are reorganized using a second larger temporal window into a Bag Of Words framework, so as to build the feature vector that will feed the SVM classifier.


title: Stochastic Model for Traffic Flow Prediction and Its Validation

abstract: This research presents and explores a stochastic model to predict traﬃc ﬂow along a highway and provides a framework to investigate some potential applications, including dynamic congestion pricing. Traﬃc congestion in urban areas is posing many challenges, and a traﬃc ﬂow model that accurately predicts traﬃc conditions can be useful in re-sponding to them. Traditionally, deterministic partial diﬀerential equations, like the LWR model and its extensions, have been used to capture traﬃc ﬂow conditions. However, a traﬃc system is subject to many stochastic factors, like erratic driver behavior, weather conditions etc. A deterministic model may fail to capture the many eﬀects of randomness, and thus may not adequately predict the traﬃc conditions. The model proposed in this research uses a stochastic partial diﬀerential equation to describe the evolution of traﬃc ﬂow on the highway. The model as calibrated and validated by real data shows that it has better predictive power than the deterministic model. Such a model has many uses, including dynamic toll pricing based on congestion, estimating the loss of time due to congestion, etc.


title: The Anatomy of Motivation: An Evolutionary-Ecological Approach

abstract: There have been few attempts to bring evolutionary theory to the study of human motivation. From this perspective motives can be considered psychological mechanisms to produce behavior that solves evolutionarily important tasks in the human niche. From the dimensions of the human niche we deduce eight human needs: optimize the number and survival of gene copies; maintain bodily integrity; avoid external threats; optimize sexual, environmental, and social capital; and acquire reproductive and survival skills. These needs then serve as the foundation for a necessary and sufficient list of 15 human motives, which we label: lust, hunger, comfort, fear, disgust, attract, love, nurture, create, hoard, affiliate, status, justice, curiosity, and play. We show that these motives are consistent with evidence from the current literature. This approach provides us with a precise vocabulary for talking about motivation, the lack of which has hampered progress in behavioral science. Developing testable theories about the structure and function of motives is essential to the project of understanding the organization of animal cognition and learning, as well as for the applied behavioral sciences.


title: Trust-aware crowdsourcing with domain knowledge

abstract: The rise of social network and crowdsourcing platforms makes it convenient to take advantage of the collective intelligence to estimate true labels of questions of interest. However, input from workers is often noisy and even malicious. Trust is used to model workers in order to better estimate true labels of questions. We observe that questions are often not independent in real life applications. Instead, there are logical relations between them. Similarly, workers that provide answers are not independent of each other either. Answers given by workers with similar attributes tend to be correlated. Therefore, we propose a novel unified graphical model consisting of two layers. The top layer encodes domain knowledge which allows users to express logical relations using first-order logic rules and the bottom layer encodes a traditional crowdsourcing graphical model. Our model can be seen as a generalized probabilistic soft logic framework that encodes both logical relations and probabilistic dependencies. To solve the collective inference problem efficiently, we have devised a scalable joint inference algorithm based on the alternating direction method of multipliers. Finally, we demonstrate that our model is superior to state-of-the-art by testing it on multiple real-world datasets.


title: Monte Carlo Sampling Methods for Approximating Interactive POMDPs

abstract: Partially observable Markov decision processes (POMDPs) provide a principled framework for sequential planning in uncertain single agent settings. An extension of POMDPs to multiagent settings, called interactive POMDPs (I-POMDPs), replaces POMDP belief spaces with interactive hierarchical belief systems which represent an agent's belief about the physical world, about beliefs of other agents, and about their beliefs about others' beliefs. This modification makes the difficulties of obtaining solutions due to complexity of the belief and policy spaces even more acute. We describe a general method for obtaining approximate solutions of I-POMDPs based on particle filtering (PF). We introduce the interactive PF, which descends the levels of the interactive belief hierarchies and samples and propagates beliefs at each level. The interactive PF is able to mitigate the belief space complexity, but it does not address the policy space complexity. To mitigate the policy space complexity -- sometimes also called the curse of history -- we utilize a complementary method based on sampling likely observations while building the look ahead reachability tree. While this approach does not completely address the curse of history, it beats back the curse's impact substantially. We provide experimental results and chart future work.