title: An Exact Dual Decomposition Algorithm for Shallow Semantic Parsing with Constraints

abstract: We present a novel technique for jointly predicting semantic arguments for lexical predicates. The task is to find the best matching between semantic roles and sentential spans, subject to structural constraints that come from expert linguistic knowledge (e.g., in the FrameNet lexicon). We formulate this task as an integer linear program (ILP); instead of using an off-the-shelf tool to solve the ILP, we employ a dual decomposition algorithm, which we adapt for exact decoding via a branch-and-bound technique. Compared to a baseline that makes local predictions, we achieve better argument identification scores and avoid all structural violations. Runtime is nine times faster than a proprietary ILP solver.


title: Adaptive rate stream processing for smart grid applications on clouds

abstract: Pervasive smart meters that continuously measure power usage by consumers within a smart (power) grid are providing utilities and power systems researchers with unprecedented volumes of information through streams that need to be processed and analyzed in near realtime. We introduce the use of Cloud platforms to perform scalable, latency sensitive stream processing for eEngineering applications in the smart grid domain. One unique aspect of our work is the use of adaptive rate control to throttle the rate of generation of power events by smart meters, which meets accuracy requirements of smart grid applications while consuming 50% lesser bandwidth resources in the Cloud.


Co-robots : The synergy of humans and robots operating as partners The confluence of engineering-based robotics and human-centered application domains


title: Managing Technological Change in the Digital Age: The Role of Architectural Frames

abstract: Inspired by Herbert Simon’s notion of nearly decomposable systems, researchers have examined modularity as a powerful approach to manage technological change in product innovation. We articulate this approach as the hierarchy-of-parts architecture and explain how it emphasizes decomposition of a design into loosely coupled parts and subsequent aggregation of these into an industrial product. To realize the scale benefits of modularity, firms successively freeze design specifications before production and therefore only allow limited windows of functionality design and redesign. This makes it difficult to take advantage of the increased speed by which digitized products can be developed and modified. To address this problem, we draw on Christopher Alexander’s notion of design patterns to introduce a complementary approach to manage technological change that is resilient to digital technology. We articulate this approach as the network-of-patterns architecture and explain how it emphasizes generalization of ideas into patterns and subsequent specialization of patterns for different design purposes. In response to the increased digitization of industrial products, we demonstrate the value of complementing hierarchy-of-parts thinking with network-of-patterns thinking through a case study of infotainment architecture at an automaker. As a result, we contribute to the literature on managing products in the digital age: we highlight the properties of digital technology that increase the speed by which digitized products can be redesigned; we offer the notion of architectural frames and propose hierarchy-of-parts and network-of-patterns as frames to support innovation of digitized products; and, we outline an agenda for future research that reconsiders the work of Simon and Alexander as well as their followers to address key challenges in innovating digitized products.


title: The tritone paradox revisited: Effects of musical training, envelope peak, and response mode

abstract: Eighteen musicians and nine nonmusicians judged the direction of tritone intervals between pairs of octave‐complex (Shepard) tones. The tones in each pair had the same spectral envelope centered on either C or F♯ in one of three octaves. Nine of the musicians sang the intervals on one‐half of the trial blocks instead of making the usual up/down judgments. Monte Carlo analysis revealed that 83% of the musicians and 33% of the nonmusicians showed a significant direction bias for regions of the chroma circle. This corroborates the notion of a chroma‐circle template of definite orientation [Deutsch et al., Mus. Perc. 5 (1987)], but suggests that musical training may play a greater role in either its establishment or access. Biases in upward judgments were toward the C‐chroma region, consistent with Deutsch’s [J. Acoust. Soc. Am. Suppl. 1 88, S139 (1990)] findings for native Californians as opposed to British. With the exception of one musician, envelope peak position had little effect on the results. In gener...


title: Overview of High-Step-Up Coupled-Inductor Boost Converters

abstract: High-step-up, high-efficiency, and cost-effective dc–dc converters, serving as an interfacing cell to boost the low-voltage output of renewable sources to the utility voltage level, are an important part in renewable energy systems. Over the past few years, there has been a substantial amount of studies devoted to high-step-up dc–dc converters. Among them, the category of coupled-inductor boost converters is widely researched and considered to be a promising solution for high-step-up applications. In this paper, these converters are categorized into five groups according to the major topological features. The derivation process, advantages, and disadvantages of these converters are systematically discussed, compared, and scrutinized. This paper aims to provide an introduction, review, and framework for the category of high-step-up coupled-inductor boost converters. General structures for the topologies are proposed to clarify the topological derivation process and to show potential gaps. Furthermore, challenges or directions are presented in this paper for deriving new topologies in this field.


title: A Time-Aware Personalized Point-of-Interest Recommendation via High-Order Tensor Factorization

abstract: Recently, location-based services (LBSs) have been increasingly popular for people to experience new possibilities, for example, personalized point-of-interest (POI) recommendations that leverage on the overlapping of user trajectories to recommend POI collaboratively. POI recommendation is yet challenging as it suffers from the problems known for the conventional recommendation tasks such as data sparsity and cold start, and to a much greater extent. In the literature, most of the related works apply collaborate filtering to POI recommendation while overlooking the personalized time-variant human behavioral tendency. In this article, we put forward a fourth-order tensor factorization-based ranking methodology to recommend users their interested locations by considering their time-varying behavioral trends while capturing their long-term preferences and short-term preferences simultaneously. We also propose to categorize the locations to alleviate data sparsity and cold-start issues, and accordingly new POIs that users have not visited can thus be bubbled up during the category ranking process. The tensor factorization is carefully studied to prune the irrelevant factors to the ranking results to achieve efficient POI recommendations. The experimental results validate the efficacy of our proposed mechanism, which outperforms the state-of-the-art approaches significantly.


title: Product Classification in E-Commerce using Distributional Semantics

abstract: Product classification is the task of automatically predicting a taxonomy path for a product in a predefined taxonomy hierarchy given a textual product description or title. For efficient product classification we require a suitable representation for a document (the textual description of a product) feature vector and efficient and fast algorithms for prediction. To address the above challenges, we propose a new distributional semantics representation for document vector formation. We also develop a new two-level ensemble approach utilizing (with respect to the taxonomy tree) a path-wise, node-wise and depth-wise classifiers for error reduction in the final product classification. Our experiments show the effectiveness of the distributional representation and the ensemble approach on data sets from a leading e-commerce platform and achieve better results on various evaluation metrics compared to earlier approaches.


title: AR Spectral Analysis Technique for Human PPG, ECG and EEG Signals

abstract: In this study, Fast Fourier transform (FFT) and autoregressive (AR) methods were selected for processing the photoplethysmogram (PPG), electrocardiogram (ECG), electroencephalogram (EEG) signals recorded in order to examine the effects of pulsed electromagnetic field (PEMF) at extremely low frequency (ELF) upon the human electrophysiological signal behavior. The parameters in the autoregressive (AR) method were found by using the least squares method. The power spectra of the PPG, ECG, and EEG signals were obtained by using these spectral analysis techniques. These power spectra were then used to compare the applied methods in terms of their frequency resolution and the effects in extraction of the features representing the PPG, ECG, and EEG signals. Some conclusions were drawn concerning the efficiency of the FFT and least squares AR methods as feature extraction methods used for representing the signals under study.


title: Improving the kinematic performance of the SCARA-Tau PKM

abstract: One well acknowledged drawback of traditional parallel kinematic machines (PKMs) is that the ratio of accessible workspace to robot footprint is small for these structures. This is most likely a contributing reason why relatively few PKMs are used in industry today. The SCARA-Tau structure is a parallel robot concept designed with the explicit goal of overcoming this limitation and developing a PKM with a workspace similar to that of a serial type robot of the same size. This paper shows for the first time how a proposed variant of the SCARA-Tau PKM can improve the usability of this robot concept further by significantly reducing the dependence between tool platform position and orientation of the original concept. The inverse kinematics of the proposed variant is derived and a comparison is made between this structure and the original SCARA-Tau concept, both with respect to platform orientation changes and workspace.


title: Internet of things enabled real time water quality monitoring system

abstract: Smart solutions for water quality monitoring are gaining importance with advancement in communication technology. This paper presents a detailed overview of recent works carried out in the field of smart water quality monitoring. Also, a power efficient, simpler solution for in-pipe water quality monitoring based on Internet of Things technology is presented. The model developed is used for testing water samples and the data uploaded over the Internet are analyzed. The system also provides an alert to a remote user, when there is a deviation of water quality parameters from the pre-defined set of standard values.


title: Planar Monopole With a Coupling Feed and an Inductive Shorting Strip for LTE/GSM/UMTS Operation in the Mobile Phone

abstract: A planar monopole having a small size yet providing two wide bands for covering the eight-band LTE/GSM/UMTS operation in the mobile phone is presented. The small-size yet wideband operation is achieved by exciting the antenna's wide radiating plate using a coupling feed and short-circuiting it to the system ground plane of the mobile phone through a long meandered strip as an inductive shorting strip. The coupling feed leads to a wide operating band to cover the frequency range of 1710-2690 MHz for the GSM1800/1900/UMTS/LTE2300/2500 operation. The inductive shorting strip results in the generation of a wide operating band to cover the frequency range of 698-960 MHz for the LTE700/GSM850/900 operation. The planar monopole can be directly printed on the no-ground portion of the system circuit board of the mobile phone and is promising to be integrated with a practical loudspeaker. The antenna's radiating plate can also be folded into a thin structure (3 mm only) to occupy a small volume of 3 × 6 × 40 mm3 (0.72 cm3) for the eight-band LTE/GSM/UMTS operation; in this case, including the 8-mm feed gap, the antenna shows a low profile of 14 mm to the ground plane of the mobile phone. The proposed antenna, including its planar and folded structures, are suitable for slim mobile phone applications.


title: Power Integrity Analysis of a 28 nm Dual-Core ARM Cortex-A57 Cluster Using an All-Digital Power Delivery Monitor

abstract: This paper presents a power delivery monitor (PDM) peripheral integrated in a flip-chip packaged 28 nm system-on-chip (SoC) for mobile computing. The PDM is composed entirely of digital standard cells and consists of: 1) a fully integrated VCO-based digital sampling oscilloscope; 2) a synthetic current load; and 3) an event engine for triggering, analysis, and debug. Incorporated inside an SoC, it enables rapid, automated analysis of supply impedance, as well as monitoring supply voltage droop of multi-core CPUs running full software workloads and during scan-test operations. To demonstrate these capabilities, we describe a power integrity case study of a dual-core ARM Cortex-A57 cluster in a commercial 28 nm mobile SoC. Measurements are presented of power delivery network (PDN) electrical parameters, along with waveforms of the CPU cluster running test cases and benchmarks on bare metal and Linux OS. The effect of aggressive power management techniques, such as power gating on the dominant resonant frequency and peak impedance, is highlighted. Finally, we present measurements of supply voltage noise during various scan-test operations, an often-neglected aspect of SoC power integrity.


title: Analysis of polymer flow in a conical coating unit: a power law approach

abstract: Abstract Polymer flow during wire coating using a conical unit has been simulated where the flow is dependent on the wire velocity, geometry of the unit and the viscosity of the polymer. When a solid continuum is pulled through an orifice filled with a viscous fluid, shearing takes place at the interface of the fluid and the solid surface. If the fluid film is converging in the direction of pull then, in addition to the shear force, a hydrodynamic pressure is generated. This hydrodynamic pressure can assist coating process of wire, strip and tube. In this paper a mathematical model has been developed in cylindrical coordinates for the velocity and pressure distribution within a tapered unit. Results are also obtained for different wire speeds in terms of the changes in viscosity, shear rate, shear stress and drawing force distributions within the unit.


title: The state of electronic word-of-mouth research: a literature analysis

abstract: This study evaluates the current status of electronic word-of-mouth (eWOM) research through an exhaustive literature review of relevant articles. We have identified a total of 83 eWOM research articles published from 2001 through 2010. Published research into eWOM first appeared in peerreviewed journals about ten years ago, and research has been steadily increasing. Among research topic area, the impact of eWOM communication was the most researched topic in the last decade. We also found that individual and message were the two mostly used unit of analysis in eWOM studies. Survey, secondary data analysis, and mathematical modeling were the three main streams of research method. Finally, we found diverse theoretical approaches in understanding eWOM communication. We conclude this paper by identifying important trends in the eWOM literature to provide future research directions.


title: PUBLIC KEY ENCRYPTION WITH CONJUNCTIVE FIELD FREE KEYWORD SEARCH SCHEME

abstract: Searchable encryption allows a remote server to search over encrypted documents without knowing the sensitive data contents. Prior searchable symmetric encryption schemes focus on single keyword search. Conjunctive Keyword Searches (CKS) schemes improve system usability by retrieving the matched documents. In this type of search, the user has to repeatedly perform the search protocol for many times. Most of existent (CKS) schemes use conjunctive keyword searches with fixed position keyword fields, this type of search is not useful for many applications, such as unstructured text. In our paper, we propose a new public key encryption scheme based on bilinear pairings, the scheme supports conjunctive keyword search queries on encrypted data without needing to specify the positions of the keywords where the keywords can be in any arbitrary order. Instead of giving the server one trapdoor for each keyword in the conjunction set, we use a bilinear map per a set of combined keywords to make them regarded as one keyword. In another meaning, the proposed method will retrieve the data in one round of communication between the user and server. Furthermore, the search process could not reveal any information about the number of keywords in the query expression. Through analysis section we determine how such scheme could be used to guarantee fast and secure access to the database.


title: An assignment method for IPUs in distributed systems

abstract: In a distributed system, one of the most important things is to establish an assignment method for distributing tasks. It is assumed that a distributed system does not have a central administrator, all independent processing units in this system want to cooperate for the best results, but they cannot know the conditions of one another. So in order to undertake the tasks in admirable proportions, they have to adjust their undertaking tasks only by self-learning. In this paper, the performance of this system is analyzed by Markov chains, and a robust method of self-learning for independent processing units in this kind of systems is presented. This method can lead the tasks of the system to be distributed very well among all the independent processing units, and can also be used to solve the general assignment problem.


title: Traffic Signs Detection Based on Faster R-CNN

abstract: In this paper, we use a advanced method called Faster R-CNN to detect traffic signs. This new method represents the highest level in object recognition, which don't need to extract image feature manually anymore and can segment image to get candidate region proposals automatically. Our experiment is based on a traffic sign detection competition in 2016 by CCF and UISEE company. The mAP(mean average precision) value of the result is 0.3449 that means Faster R-CNN can indeed be applied in this field. Even though the experiment did not achieve the best results, we explore a new method in the area of the traffic signs detection. We believe that we can get a better achievement in the future.


title: Requirements of Software Visualization Tools: A Literature Survey

abstract: Our objective is to identify requirements (i.e., quality attributes and functional requirements) for software visualization tools. We especially focus on requirements for research tools that target the domains of visualization for software maintenance, reengineering, and reverse engineering. The requirements are identified with a comprehensive literature survey based on relevant publications in journals, conference proceedings, and theses. The literature survey has identified seven quality attributes (i.e., rendering scalability, information scalability, interoperability, customizability, interactivity, usability, and adoptability) and seven functional requirements (i.e., views, abstraction, search, filters, code proximity, automatic layouts, and undo/history). The identified requirements are useful for researchers in the software visualization field to build and evaluate tools, and to reason about the domain of software visualization.


title: Quicksilver: Fast Predictive Image Registration - a Deep Learning Approach

abstract: This paper introduces Quicksilver, a fast deformable image registration method. Quicksilver registration for image-pairs works by patch-wise prediction of a deformation model based directly on image appearance. A deep encoder-decoder network is used as the prediction model. While the prediction strategy is general, we focus on predictions for the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the momentum-parameterization of LDDMM, which facilitates a patch-wise prediction strategy while maintaining the theoretical properties of LDDMM, such as guaranteed diffeomorphic mappings for sufficiently strong regularization. We also provide a probabilistic version of our prediction network which can be sampled during the testing time to calculate uncertainties in the predicted deformations. Finally, we introduce a new correction network which greatly increases the prediction accuracy of an already existing prediction network. We show experimental results for uni-modal atlas-to-image as well as uni-/multi-modal image-to-image registrations. These experiments demonstrate that our method accurately predicts registrations obtained by numerical optimization, is very fast, achieves state-of-the-art registration results on four standard validation datasets, and can jointly learn an image similarity measure. Quicksilver is freely available as an open-source software.


title: Collaborative multidisciplinary design optimization: A framework applied on aircraft conceptual system design

abstract: In a product development process, it is crucial to understand and evaluate multiple and synergic aspects of systems such as performance, cost, reliability, and safety. These aspects are mainly considered during later stages of the design process. However, in order to improve the foundations for decision-making, this article presents methods that are intended to increase the engineering knowledge in the early design phases. In complex products, different systems from a multitude of engineering disciplines have to work tightly together. Collaborative design is described as a process where a product is designed through the collective and joint efforts of domain experts. A collaborative multidisciplinary design optimization process is therefore proposed in the conceptual design phase in order to increase the likelihood of more accurate decisions being taken early on. The performance of the presented framework is demonstrated in an industrial application to design aircraft systems in the conceptual phase.


45 The Permanente Journal / Fall


title: Thermally Stable Enhancement-Mode GaN Metal-Isolator-Semiconductor High-Electron-Mobility Transistor With Partially Recessed Fluorine-Implanted Barrier

abstract: Al2O3/AlGaN/GaN enhancement-mode metal-isolator-semiconductor high-electron-mobility transistor (MIS-HEMT) featuring a partially recessed (Al) GaN barrier was realized by a fluorine plasma implantation/etch technique. By properly adjusting the RF power driving the fluorine plasma, the fluorine plasma is able to produce two desirable results: 1) a well-controlled slow dry etching for gate recess and 2) implanting fluorine ions into the AlGaN barrier. The fluorine ions become negatively charged in the barrier layer and induce a positive shift in the threshold voltage. The proposed MIS-HEMT exhibits a threshold voltage ( $V_{\mathrm {\mathbf {TH}}})$ of +0.6 V at a drain current of $10~\mu \text{A}$ /mm, a maximum drive current of 730 mA/mm, an ON-resistance of $7.07~\Omega \,\cdot \,$ mm, and an OFF-state breakdown voltage of 703 V at an OFF-state drain leakage current of $1~\mu \text{A}$ /mm. From room temperature to 200 °C, the device exhibits a small negative shift of $V_{\mathrm {\mathbf {TH}}}$ ( $\sim 0.5$ V) that is attributed to the high-quality dielectric/F-implanted-(Al) GaN interface and the partially recessed barrier.


title: Understanding and predicting classes of college students who use pornography

abstract: Despite the widespread acceptance and use of pornography, much remains unknown about the heterogeneity among consumers of pornography. Using a sample of 457 college students from a mid-western university in the United States, a latent profile analysis was conducted to identify unique classifications of pornography users considering motivations of pornography use, level of pornography use, age of user, degree of pornography acceptance, and religiosity. Results indicated three classes of pornography users: Porn Abstainers (n=285), Auto-Erotic Porn Users (n=85), and Complex Porn Users (n=87). These three classes of pornography use are carefully defined. The odds of membership in these three unique classes of pornography users was significantly distinguished by relationship status, self-esteem, and gender. These results expand what is known about pornography users by providing a more person-centered approach that is more nuanced in understanding pornography use. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Three classes of pornography users were delineated using a latent profile analysis.Porn Abstainers (62%) had small porn acceptance, use, and motivations for porn use.Complex Porn Users (19%) had high reports of use and many reasons of porn use.Auto-Erotic Porn Users (19%) had high reports of use and used it for masturbation.


title: Leveraging Co-authorship and Biographical Information for Author Ambiguity Resolution in DBLP

abstract: Many authors can share the same name and this constitutes a serious problem that affects the relevancy of retrieval results and constitutes our motivation of finding such approach to cover this issue at the author names entity level. Solving such a problem may return with positive gain at the level of document retrieval, web search and the quality of data. This entity resolution task can be tackled as an unsupervised problem, where there are set of features that can be employed for the resolution job, or as supervised problem to compute the similarities among two citations and then classify if they are the same or not. Recent approaches usually utilize features such as: co-author, venue, topic similarity, affiliations and title of publications to deal with author ambiguity. In this paper, three attributes are used to treat this problem sequentially. The co-authorship firstly which is a well-known attribute, and then the topic and affiliation extracted from biographies, which can be found inside the publication, and this is our novelty frame in this paper.


Recurrent Attention Network on Memory for Aspect Sentiment Analysis


title: The Use of Computer Decision Support Systems for the Critical Care Environment

abstract: Abstract : As the workload of primary care providers continues to increase, the use of automation in the intensive care unit (ICU) environment has been touted as a way to increase effectiveness of patient care and standardize practice, while reducing the number of complications associated with the use of manually implemented approaches.1 4 Medical computer decision support (CDS), or computer decision assist, applications are a type of information technology system that embed the expertise of experienced care providers and/or standards of care into a computerized system. They can be implemented at any care level including prehospital, emergency departments, operating rooms, and ICUs. These systems provide information on patient care issues as an adjunct to standard treatments used during the patient s stay. For example, a typical CDS system may provide alerts when a combination of patient parameters fall out of range and recommend changes in drug rates to the care provider to bring the patient parameters within acceptable limits. By providing clinicians with these types of applications, patient care is improved in 2 ways. First, they provide a tool for learning and quality improvement. These systems provide the ability to push the expertise of more experienced care providers to clinicians who may not be fully trained or may be unfamiliar with current evidence based practice (EBP) guidelines. Second, as a decision support adjunct, these systems provide additional information that may not be otherwise available or easily obtainable to experienced care providers. Computer decision support systems improve the provider s ability to manage multiple sources of data, allow for trending analysis, and provide the ability to fuse data from multiple sources. Furthermore, these tools allow for advanced information processing that can be used in predictive modeling of patient outcomes.


title: Supervised Neural Models Revitalize the Open Relation Extraction

abstract: Open relation extraction (ORE) remains a challenge to obtain a semantic representation by discovering arbitrary relation tuples from the un-structured text. However, perhaps due to limited data, previous extractors use unsupervised or semi-supervised methods based on pattern matching, which heavily depend on manual work or syntactic parsers and are inefficient or error-cascading. Their development has encountered bottlenecks. Although a few people try to use neural network based models to improve the ORE task performance recently, it is always intractable for ORE to produce supervised systems based on various neural architectures. We analyze and review the neural ORE methods. Further, we construct a large-scale automatically tagging training set and design a tagging scheme to frame ORE as a supervised sequence tagging task. A hybrid neural sequence tagging model (NST) is proposed which combines BiLSTM, CNN and CRF to capture the contextual temporal information, local spatial information, and sentence level tag information of the sequence by using the word and part-of-speech embeddings. Experiments on multiple datasets show that our method is better than most of the existing pattern-based methods and other neural networks based models.


title: Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space

abstract: Most existing deep reinforcement learning (DRL) frameworks consider either discrete action space or continuous action space solely. Motivated by applications in computer games, we consider the scenario with discrete-continuous hybrid action space. To handle hybrid action space, previous works either approximate the hybrid space by discretization, or relax it into a continuous set. In this paper, we propose a parametrized deep Q-network (P- DQN) framework for the hybrid action space without approximation or relaxation. Our algorithm combines the spirits of both DQN (dealing with discrete action space) and DDPG (dealing with continuous action space) by seamlessly integrating them. Empirical results on a simulation example, scoring a goal in simulated RoboCup soccer and the solo mode in game King of Glory (KOG) validate the efficiency and effectiveness of our method.


title: A deep learning approach to monitoring and detecting atrial fibrillation using wearable technology

abstract: Atrial Fibrillation (AF) is the most common cardiac arrhythmia in clinical practice, with a prevalence of 2% in the community. Not only it is associated with reduced quality of life, but also increased risk of stroke and myocardial infarction. Unfortunately, many cases of AF are clinically silent and undiagnosed, but long-term monitoring is difficult. Nonetheless, efforts at monitoring at-risk individuals and detecting clinically silent AF may yield significant public health benefit, as individuals with new-onset, asymptomatic AF would receive preventive therapies with anticoagulants and beta-blockers, for example. Wearables have enormous potential to provide low-risk and low-cost long-term monitoring of AF, but signals from such devices suffer from significant movement related noise that resembles AF. This work presents a robust approach to AF detection in a sequence of short windows with significant movement artifact. Pulsatile photoplethysmographic (PPG) data and triaxial accelerometry from 98 subjects (45 with AF and 53 with other rhythms) were captured using a multichannel wrist-worn device. A single channel electrocardiogram (ECG) was recorded (for rhythm verification only) simultaneously. A novel deep neural network approach to classify AF from wrist-worn PPG signals was developed on this data. A continuous wavelet transform was applied to the PPG data and a convolutional neural network (CNN) was trained on the derived spectrograms to detect AF. Combining the output of the CNN with features calculated based on beat-to-beat variability and signal quality provided a significant accuracy boost. Leave-one-out cross validation resulted in a pooled AUC of 0.95 (Accuracy=91.8%). The proposed approach resulted in a novel robust and accurate algorithm for detection of AF from PPG data, which is scalable and likely to improve in accuracy as the dataset size continues to expand.


title: VulHunter: Toward Discovering Vulnerabilities in Android Applications

abstract: With the prosperity of the Android app economy, many apps have been published and sold in various markets. However, short development cycles and insufficient security development guidelines have led to many vulnerable apps. Although some systems have been developed for automatically discovering specific vulnerabilities in apps, their effectiveness and efficiency are usually restricted because of the exponential growth of paths to examine and simplified assumptions. In this article, the authors propose a new static-analysis framework for facilitating security analysts to detect vulnerable apps from three aspects. First, they propose an app property graph (APG), a new data structure containing detailed and precise information from apps. Second, by modeling app-related vulnerabilities as graph traversals, the authors conduct graph traversals over APGs to identify vulnerable apps for easing the identification process. Third, they reduce the workload of manual verification by removing infeasible paths and generating attack inputs whenever possible. They have implemented the framework in a system named VulHunter with 9,145 lines of Java code and modeled five types of vulnerabilities. Checking 557 popular apps that are randomly collected from Google Play and have at least 1 million installations, the authors found that 375 apps (67.3 percent) have at least one vulnerability.


title: The Expression of Conflict in Computer-Mediated and Face-To-Face Groups

abstract: This study integrated the research streams of computer-mediated communication (CMC) and group conflict by comparing the expression of different types of conflict in CMC groups and face-to-face (FTF) groups over time. The main aim of the study was to compare the cues-filtered-out approach against the social information processing theory. A laboratory study was conducted with 39 groups (19 CMC and 20 FTF) in which members were required to work together over three sessions. The frequencies of task, process, and relationship conflict were analyzed. Findings supported the social information processing theory. There was more process and relationship conflict in CMC groups compared to FTF groups on Day 1. However, this difference disappeared on Days 2 and 3. There was no difference between CMC and FTF groups in the amount of task conflict expressed on any day.


title: Exponential stability of impulsive systems with application to uncertain sampled-data systems

abstract: We establish exponential stability of nonlinear time-varying impulsive systems by employing Lyapunov functions with discontinuity at the impulse times. Our stability conditions have the property that when specialized to linear impulsive systems, the stability tests can be formulated as Linear Matrix Inequalities (LMIs). Then we consider LTI uncertain sampled-data systems in which there are two sources of uncertainty: the values of the process parameters can be unknown while satisfying a polytopic condition and the sampling intervals can be uncertain and variable. We model such systems as linear impulsive systems and we apply our theorem to the analysis and state-feedback stabilization. We find a positive constant which determines an upper bound on the sampling intervals for which the stability of the closed loop is guaranteed. The control design LMIs also provide controller gains that can be used to stabilize the process. We also consider sampled-data systems with constant sampling intervals and provide results that are less conservative than the ones obtained for variable sampling intervals.


title: The Raid-6 Liber8Tion Code

abstract: Large centralized and networked storage systems have grown to the point where the single fault tolerance provided by RAID-5 is no longer enough. RAID-6 storage systems protect k disks of data with two parity disks so that the system of k + 2 disks may tolerate the failure of any two disks. Coding techniques for RAID-6 systems are varied, but an important class of techniques are those with minimum density, featuring an optimal combination of encoding, decoding and modification complexity. The word size of a code has an impact on both how the code is laid out on each disk's sectors and how large k can be. Word sizes which are powers of two are especially important, since they fit precisely into file system blocks. Minimum density codes exist for many word sizes with the notable exception of eight. This paper fills that gap by describing a new code called The RAID-6 Liber8tion Code for this important word size. The description includes performance properties as well as details of the discovery process.


title: Mining problem-solving strategies from HCI data

abstract: Can we learn about users' problem-solving strategies by observing their actionsq This article introduces a data mining system that extracts complex behavioral patterns from logged user actions to discover users' high-level strategies. Our application domain is an HCI study aimed at revealing users' strategies in an end-user debugging task and understanding how the strategies relate to gender and to success. We cast this problem as a sequential pattern discovery problem, where user strategies are manifested as sequential behavior patterns. Problematically, we found that the patterns discovered by standard data mining algorithms were difficult to interpret and provided limited information about high-level strategies. To help interpret the patterns as strategies, we examined multiple ways of clustering the patterns into meaningful groups. This collectively led to interesting findings about users' behavior in terms of both gender differences and debugging success. These common behavioral patterns were novel HCI findings about differences in males' and females' behavior with software, and were verified by a parallel study with an independent data set on strategies. As a research endeavor into the interpretability issues faced by data mining techniques, our work also highlights important research directions for making data mining more accessible to non-data-mining experts.


Lemmatization and Morphological Tagging in German and Latin: A Comparison and a Survey of the State-of-the-art.


title: Anaerobic Capacity: A Maximal Anaerobic Running Test Versus the Maximal Accumulated Oxygen Deficit

abstract: The present investigation evaluates a maximal anaerobic running test (MART) against the maximal accumulated oxygen deficit (MAOD) for the determination of anaerobic capacity. Essentially, this involved comparing 18 male students performing two randomly assigned supramaximal runs to exhaustion on separate days. Post warm-up and 1, 3, and 6 min postexercise capillary blood samples were taken during both tests for plasma blood lactate (BLa) determination. In the MART only, blood ammonia (BNH3) concentration was measured, while capillary blood samples were additionally taken after every second sprint for BLa determination. Anaerobic capacity, measured as oxygen equivalents in the MART protocol, averaged 112.2 +/- 5.2 ml.kg-1.min-1. Oxygen deficit, representing the anaerobic capacity in the MAOD test, was an average of 74.6 +/- 7.3 ml.kg-1. There was a significant correlation between the MART and MAOD (r = .83, p < .001). BLa values obtained over time in the two tests showed no significant difference, nor was there any difference in the peak BLa recorded. Peak BNH3 concentration recorded was significantly increased from resting levels at exhaustion during the MART.


title: Real-Time Multivehicle Truckload Pickup and Delivery Problems

abstract: In this paper we formally introduce a generic real-time multivehicle truckload pickup and delivery problem. The problem includes the consideration of various costs associated with trucks' empty travel distances, jobs' delayed completion times, and job rejections. Although very simple, the problem captures most features of the operational problem of a real-world trucking fleet that dynamically moves truckloads between different sites according to customer requests that arrive continuously.We propose a mixed-integer programming formulation for the offline version of the problem. We then consider and compare five rolling horizon strategies for the real-time version. Two of the policies are based on a repeated reoptimization of various instances of the offline problem, while the others use simpler local (heuristic) rules. One of the reoptimization strategies is new, while the other strategies have recently been tested for similar real-time fleet management problems.The comparison of the policies is done under a general simulation framework. The analysis is systematic and considers varying traffic intensities, varying degrees of advance information, and varying degrees of flexibility for job-rejection decisions. The new reoptimization policy is shown to systematically outperform the others under all these conditions.


title: A SET OF PRINCIPLES FOR CONDUCTING AND EVALUATING INTERPRETIVE FIELD STUDIES IN INFORMATION SYSTEMS 1

abstract: This article discusses the conduct and evaluatoin of interpretive research in information systems. While the conventions for evaluating information systems case studies conducted according to the natural science model of social science are now widely accepted, this is not the case for interpretive field studies. A set of principles for the conduct and evaluation of interpretive field research in information systems is proposed, along with their philosophical rationale. The usefulness of the principles is illustrated by evaluating three published interpretive field studies drawn from the IS research literature. The intention of the paper is to further reflect and debate on the important subject of grounding interpretive research methodology.


ZigBee Technology


title: LaSTUS/TALN+INCO @ CL-SciSumm 2018 - Using Regression and Convolutions for Cross-document Semantic Linking and Summarization of Scholarly Literature

abstract: Comunicacio presentada al congres BIRNDL 2018, 3rd Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries que va tenir lloc el 21 de juliol de 2018 a Ann Arbor, Estats Units.


title: Measuring Similarity between Ontologies

abstract: Ontologies now play an important role for many knowledge-intensive applications for which they provide a source of precisely defined terms. However, with their wide-spread usage there come problems concerning their proliferation. Ontology engineers or users frequently have a core ontology that they use, e.g., for browsing or querying data, but they need to extend it with, adapt it to, or compare it with the large set of other ontologies. For the task of detecting and retrieving relevant ontologies, one needs means for measuring the similarity between ontologies. We present a set of ontology similarity measures and a multiple-phase empirical evaluation.


title: Deep Convolution Networks for Compression Artifacts Reduction

abstract: Lossy compression introduces complex compression artifacts, particularly blocking artifacts, ringing effects and blurring. Existing algorithms either focus on removing blocking artifacts and produce blurred output, or restore sharpened images that are accompanied with ringing effects. Inspired by the success of deep convolutional networks (DCN) on superresolution, we formulate a compact and efficient network for seamless attenuation of different compression artifacts. To meet the speed requirement of real-world applications, we further accelerate the proposed baseline model by layer decomposition and joint use of large-stride convolutional and deconvolutional layers. This also leads to a more general CNN framework that has a close relationship with the conventional Multi-Layer Perceptron (MLP). Finally, the modified network achieves a speed up of 7.5 times with almost no performance loss compared to the baseline model. We also demonstrate that a deeper model can be effectively trained with features learned in a shallow network. Following a similar "easy to hard" idea, we systematically investigate three practical transfer settings and show the effectiveness of transfer learning in low-level vision problems. Our method shows superior performance than the state-of-the-art methods both on benchmark datasets and a real-world use case.


title: Ultra-Wideband Chip Attenuator for Precise Noise Measurements at Cryogenic Temperatures

abstract: A 20-dB chip attenuator designed for cryogenic noise measurements from dc up to 40 GHz is presented. The chip is based on the use of temperature-stable tantalum-nitride thin-film resistors, a high thermal conductivity substrate such as crystal quartz (z-cut), and a suitable design that avoids inner conductor thermal heating, which is an important limiting factor for the precision of cryogenic noise measurements. A high-accuracy temperature sensor installed inside the attenuator module provides precise temperature characterization close to the chip location. The high thermal conductivity of the chip substrate in the designed attenuator assures a negligible temperature gradient between the resistive elements in the chip and the sensor, thus improving the measurement accuracy. The attenuator also shows an excellent electrical performance with insertion losses of 19.9 dB ± 0.65 dB and return losses better than 20.6 dB in the whole frequency range at 296 K. The insertion loss change when cooled to 15 K is less than 0.25 dB, which demonstrates its temperature stability.


title: The Rise of Social Bots

abstract: Today's social bots are sophisticated and sometimes menacing. Indeed, their presence can endanger online ecosystems as well as our society.


title: CAD-based pose estimation for random bin-picking of multiple objects using a RGB-D camera

abstract: In this paper, we propose a CAD-based 6-DOF pose estimation design for random bin-picking of multiple different objects using a Kinect RGB-D sensor. 3D CAD models of objects are constructed via a virtual camera, which generates a point cloud database for object recognition and pose estimation. A voxel grid filter is suggested to reduce the number of 3D point cloud of objects for reducing computing time of pose estimation. A voting-scheme method was adopted for the 6-DOF pose estimation a swell as object recognition of different type objects in the bin. Furthermore, an outlier filter is designed to filter out bad matching poses and occluded ones, so that the robot arm always picks up the upper object in the bin to increase pick up success rate. A series of experiments on a Kuka 6-axis robot revels that the proposed system works satisfactorily to pick up all random objects in the bin. The average recognition rate of three different type objects is 93.9% and the pickup success rate is 89.7%.


Design and Analysis Double Wishbone Suspension System using Finite Element Analysis


title: Gender recognition from body

abstract: This paper studies the problem of recognizing gender from full body images. This problem has not been addressed before, partly because of the variant nature of human bodies and clothing that can bring tough difficulties. However, gender recognition has high application potentials, e.g. security surveillance and customer statistics collection in restaurants, supermarkets, and even building entrances. In this paper, we build a system of recognizing gender from full body images, taken from frontal or back views. Our contributions are three-fold. First, to handle the variety of human body characteristics, we represent each image by a collection of patch features, which model different body parts and provide a set of clues for gender recognition. To combine the clues, we build an ensemble learning algorithm from those body parts to recognize gender from fixed view body images (frontal or back). Second, we relax the fixed view constraint and show the possibility to train a flexible classifier for mixed view images with the almost same accuracy as the fixed view case. At last, our approach is shown to be robust to small alignment errors, which is preferred in many applications.


title: Mining data streams: a review

abstract: The recent advances in hardware and software have enabled the capture of different measurements of data in a wide range of fields. These measurements are generated continuously and in a very high fluctuating data rates. Examples include sensor networks, web logs, and computer network traffic. The storage, querying and mining of such data sets are highly computationally challenging tasks. Mining data streams is concerned with extracting knowledge structures represented in models and patterns in non stopping streams of information. The research in data stream mining has gained a high attraction due to the importance of its applications and the increasing generation of streaming information. Applications of data stream analysis can vary from critical scientific and astronomical applications to important business and financial ones. Algorithms, systems and frameworks that address streaming challenges have been developed over the past three years. In this review paper, we present the state-of-the-art in this growing vital field.


title: Simulation Model of Hydro Power Plant Using Matlab/Simulink

abstract: Hydropower has now become the best source of electricity on earth. It is produced due to the energy provided by moving or falling water. History proves that the cost of this electricity remains constant over the year. Because of the many advantages, most of the countries now have hydropower as the source of major electricity producer. The most important advantage of hydropower is that t is green energy, which mean that no air or water pollutants are produced, also no greenhouse gases like carbon dioxide are produced which makes this source of energy environment-friendly. It prevents us from the danger of global warming. This paper describes a generalized model which can be used to simulate a hydro power plant using MATLAB/SIMULINK. The plant consists of hydro turbine connected to synchronous generator, which is connected to public grid. Simulation of hydro turbine and synchronous generator can be done using various simulation tools, In this work, SIMULINK/MATLAB is favored over other tools in modeling the dynamics of a hydro turbine and synchronous machine. The SIMULINK program in MATLAB is used to obtain a schematic model of the hydro plant by means of basic function blocks. This approach is pedagogically better than using a compilation of program code as in other software programs .The library of SIMULINK software programs includes function blocks which can be linked and edited to model. The main objectives of this model are aimed to achieve some operating modes of the hydro plant and some operating tests.


title: A Psychometric Evaluation of the Facial Action Coding System for Assessing Spontaneous Expression

abstract: The Facial Action Coding System (FACS) (Ekman & Friesen, 1978) is a comprehensive and widely used method of objectively describing facial activity. Little is known, however, about inter-observer reliability in coding the occurrence, intensity, and timing of individual FACS action units. The present study evaluated the reliability of these measures. Observational data came from three independent laboratory studies designed to elicit a wide range of spontaneous expressions of emotion. Emotion challenges included olfactory stimulation, social stress, and cues related to nicotine craving. Facial behavior was video-recorded and independently scored by two FACS-certified coders. Overall, we found good to excellent reliability for the occurrence, intensity, and timing of individual action units and for corre- sponding measures of more global emotion-specified combinations.


title: An Overview on XML Semantic Disambiguation from Unstructured Text to Semi-Structured Data: Background, Applications, and Ongoing Challenges

abstract: Since the last two decades, XML has gained momentum as the standard for web information management and complex data representation. Also, collaboratively built semi-structured information resources, such as Wikipedia, have become prevalent on the Web and can be inherently encoded in XML. Yet most methods for processing XML and semi-structured information handle mainly the syntactic properties of the data, while ignoring the semantics involved. To devise more intelligent applications, one needs to augment syntactic features with machine-readable semantic meaning. This can be achieved through the computational identification of the meaning of data in context, also known as (a.k.a.) automated semantic analysis and disambiguation, which is nowadays one of the main challenges at the core of the Semantic Web. This survey paper provides a concise and comprehensive review of the methods related to XML-based semi-structured semantic analysis and disambiguation. It is made of four logical parts. First, we briefly cover traditional word sense disambiguation methods for processing flat textual data. Second, we describe and categorize disambiguation techniques developed and extended to handle semi-structured and XML data. Third, we describe current and potential application scenarios that can benefit from XML semantic analysis, including: data clustering and semantic-aware indexing, data integration and selective dissemination, semantic-aware and temporal querying, web and mobile services matching and composition, blog and social semantic network analysis, and ontology learning. Fourth, we describe and discuss ongoing challenges and future directions, including: the quantification of semantic ambiguity, expanding XML disambiguation context, combining structure and content, using collaborative/social information sources, integrating explicit and implicit semantic analysis, emphasizing user involvement, and reducing computational complexity.


title: Big Data Reduction Methods: A Survey

abstract: Research on big data analytics is entering in the new phase called fast data where multiple gigabytes of data arrive in the big data systems every second. Modern big data systems collect inherently complex data streams due to the volume, velocity, value, variety, variability, and veracity in the acquired data and consequently give rise to the 6Vs of big data. The reduced and relevant data streams are perceived to be more useful than collecting raw, redundant, inconsistent, and noisy data. Another perspective for big data reduction is that the million variables big datasets cause the curse of dimensionality which requires unbounded computational resources to uncover actionable knowledge patterns. This article presents a review of methods that are used for big data reduction. It also presents a detailed taxonomic discussion of big data reduction methods including the network theory, big data compression, dimension reduction, redundancy elimination, data mining, and machine learning methods. In addition, the open research issues pertinent to the big data reduction are also highlighted.


title: Transfer Feature Learning with Joint Distribution Adaptation

abstract: Transfer learning is established as an effective technology in computer vision for leveraging rich labeled data in the source domain to build an accurate classifier for the target domain. However, most prior methods have not simultaneously reduced the difference in both the marginal distribution and conditional distribution between domains. In this paper, we put forward a novel transfer learning approach, referred to as Joint Distribution Adaptation (JDA). Specifically, JDA aims to jointly adapt both the marginal distribution and conditional distribution in a principled dimensionality reduction procedure, and construct new feature representation that is effective and robust for substantial distribution difference. Extensive experiments verify that JDA can significantly outperform several state-of-the-art methods on four types of cross-domain image classification problems.


title: Approximation theory of the MLP model in neural networks

abstract: In this survey we discuss various approximation-theoretic problems that arise in the multilayer feedforward perceptron (MLP) model in neural networks. The MLP model is one of the more popular and practical of the many neural network models. Mathematically it is also one of the simpler models. Nonetheless the mathematics of this model is not well understood, and many of these problems are approximation-theoretic in character. Most of the research we will discuss is of very recent vintage. We will report on what has been done and on various unanswered questions. We will not be presenting practical (algorithmic) methods. We will, however, be exploring the capabilities and limitations of this model.


title: A Framework for Controlling and Managing Hybrid Cloud Service Integration

abstract: In this paper, we first describe the challenges and pain points of adopting off-premise cloud-based computing services by enterprise users. To address these challenges, we have developed a hybrid cloud architectural framework for controlling and managing network of integrated computing services in on- and off-premise cloud environments. We identify three types of integration patterns that are commonly observed and describe support in the hybrid cloud framework for such patterns. The framework allows creation, modification, and management of integrated hybrid cloud services. Using this framework, we describe how solutions can be designed for policy-based access to cloud services from on-premise environments and for policy-based secure access to on premise data from off-premise cloud based services. The framework offers capabilities for (i) on-demand capacity expansion or cloud-bursting, (ii) service composition and management across multiple cloud domains, (iii) unification and customization of service offerings from multiple cloud service providers, (iv) seamless integration of common workload management services such monitoring, metering, and security, and (v) unified governance of IT operation across the hybrid cloud. We then describe a realization of this architecture that has served as the basis of IBM's hybrid cloud solution offerings and describe how the hybrid cloud framework described here mitigates some of cloud adoption risks and lowers or eliminates the inhibitors.


title: Association between the Five Factor personality traits and perceived stress: is the effect mediated by general self-efficacy?

abstract: Abstract Ill-health resulting from chronic stress is influenced by personality traits leading to different ways of appraising and coping with life's daily hassles. Using a large population sample the study aimed to investigate possible associations between perceived stress and the personality dimensions of neuroticism, extroversion, openness, agreeableness, and conscientiousness, and to explore the role of general self-efficacy (GSE). A population-based cross-sectional study was conducted at the Research Centre for Prevention and Health, Denmark, in 2006–2008. Men and women (N=3471) aged 18–69, were randomly sampled in the suburbs of Copenhagen. We used the NEO Five-Factor Inventory (NEO-FFI), Cohen's Perceived Stress Scale and the General Self-Efficacy Scale. Negative associations were found between perceived stress and extroversion, conscientiousness, agreeableness, and openness – the latter initially non-significant – whilst neuroticism had a positive association. The associations with agreeableness an...


title: The 1918 Influenza Pandemic: Insights for the 21st Century

abstract: The 1918-1919 H1N1 influenza pandemic was among the most deadly events in recorded human history, killing an estimated 50-100 million persons. Because recent H5N1 avian epizootics have been associated with sporadic human fatalities, concern has been raised that a new pandemic, as fatal as the pandemic of 1918, or more so, could be developing. Understanding the events and experiences of 1918 is thus of great importance. However, despite the genetic sequencing of the entire genome of the 1918 virus, many questions about the 1918 pandemic remain. In this review we address several of these questions, concerning pandemic-virus origin, unusual epidemiologic features, and the causes and demographic patterns of fatality. That none of these questions can yet be fully answered points to the need for continued pandemic vigilance, basic and applied research, and pandemic preparedness planning that emphasizes prevention, containment, and treatment with antiviral medications and hospital-based intensive care.


title: The Strong Story Hypothesis and the Directed Perception Hypothesis

abstract: I ask why humans are smarter than other primates, and I hypothesize that an important part of the answer lies in what I call the Strong Story Hypothesis, which holds that story telling and understanding have a central role in human intelligence. Next, I introduce another hypothesis, the Directed Perception Hypothesis, which holds that we derive much of our commonsense, including the commonsense required in story understanding, by deploying our perceptual apparatus on real and imagined events. Then, after discussing methodology, I describe the representations and methods embodied in the Genesis system, a story-understanding system that analyzes stories ranging


title: Motor control and aging: Links to age-related brain structural, functional, and biochemical effects

abstract: Although connections between cognitive deficits and age-associated brain differences have been elucidated, relationships with motor performance are less well understood. Here, we broadly review age-related brain differences and motor deficits in older adults in addition to cognition-action theories. Age-related atrophy of the motor cortical regions and corpus callosum may precipitate or coincide with motor declines such as balance and gait deficits, coordination deficits, and movement slowing. Correspondingly, degeneration of neurotransmitter systems—primarily the dopaminergic system—may contribute to age-related gross and fine motor declines, as well as to higher cognitive deficits. In general, older adults exhibit involvement of more widespread brain regions for motor control than young adults, particularly the prefrontal cortex and basal ganglia networks. Unfortunately these same regions are the most vulnerable to age-related effects, resulting in an imbalance of “supply and demand”. Existing exercise, pharmaceutical, and motor training interventions may ameliorate motor deficits in older adults.


title: Towards optimal bag-of-features for object categorization and semantic video retrieval

abstract: Bag-of-features (BoF) deriving from local keypoints has recently appeared promising for object and scene classification. Whether BoF can naturally survive the challenges such as reliability and scalability of visual classification, nevertheless, remains uncertain due to various implementation choices. In this paper, we evaluate various factors which govern the performance of BoF. The factors include the choices of detector, kernel, vocabulary size and weighting scheme. We offer some practical insights in how to optimize the performance by choosing good keypoint detector and kernel. For the weighting scheme, we propose a novel soft-weighting method to assess the significance of a visual word to an image. We experimentally show that the proposed soft-weighting scheme can consistently offer better performance than other popular weighting methods. On both PASCAL-2005 and TRECVID-2006 datasets, our BoF setting generates competitive performance compared to the state-of-the-art techniques. We also show that the BoF is highly complementary to global features. By incorporating the BoF with color and texture features, an improvement of 50% is reported on TRECVID-2006 dataset.


title: A design for an electronically-steerable holographic antenna with polarization control

abstract: We present a design for an electronic-steerable holographic antenna with polarization control composed of a radial array of Ku-band, electronically-steerable, surface-wave waveguide (SWG) artificial-impedance-surface antennas (AISA). The antenna operates by launching surface waves into each of the SWGs via a central feed network. The surface-wave impedance is electronically controlled with varactor-tuned impedance patches. The impedance is adjusted to scan the antenna in elevation, azimuth and polarization. The radial symmetry allows for 360° azimuthal steering. If constructed with previously-demonstrated SWG AISAs, it is capable of scanning in elevation from −75° to 75° with gain variation of less than 3 dB. The polarization can be switched among V-Pol, H-Pol, LHCP and RHCP at will.


title: Jael’s Syndrome: Facial Impalement

abstract: Penetrating injuries to the face or neck can cause difficult airway issues. There is a relative dearth of literature to define the best approach to these patients. Impalement injuries are uncommon, and survivable injuries are most commonly confined to the abdomen or thorax. We report the case of a patient with an obviously difficult airway due to a facial impalement (Jael’s Syndrome) injured at a local construction site.


title: Shortcut Sequence Tagging

abstract: Deep stacked RNNs are usually hard to train. Adding shortcut connections across different layers is a common way to ease the training of stacked networks. However, extra shortcuts make the recurrent step more complicated. To simply the stacked architecture, we propose a framework called shortcut block, which is a marriage of the gating mechanism and shortcuts, while discarding the self-connected part in LSTM cell. We present extensive empirical experiments showing that this design makes training easy and improves generalization. We propose various shortcut block topologies and compositions to explore its effectiveness. Based on this architecture, we obtain a 6% relatively improvement over the state-of-the-art on CCGbank supertagging dataset. We also get comparable results on POS tagging task.


2 vec : Citation-Driven Document Exploration via Word Embeddings


title: Multiresolution feature extraction for unstructured meshes

abstract: We present a framework to extract mesh features from unstructured two-manifold surfaces. Our method computes a collection of piecewise linear curves describing the salient features of surfaces, such as edges and ridge lines. We extend these basic techniques to a multiresolution setting which improves the quality of the results and accelerates the extraction process. The extraction process is semi-automatic, that is, the user is required to input a few control parameters and to select the operators to be applied to the input surface. Our mesh feature extraction algorithm can be used as a preprocessor for a variety of applications in geometric modeling including mesh fairing, subdivision and simplification.


title: Relating Anatomical and Social Connectivity: White Matter Microstructure Predicts Emotional Empathy

abstract: Understanding cues to the internal states of others involves a widely distributed network of brain regions. Although white matter (WM) connections are likely crucial for communication between these regions, the role of anatomical connectivity in empathic processing remains unexplored. The present study tested for a relationship between anatomical connectivity and empathy by assessing the WM microstructural correlates of affective empathy, which promotes interpersonal understanding through emotional reactions, and cognitive empathy, which does so via perspective taking. Associations between fractional anisotropy (FA) and the emotional (empathic concern, EC) and cognitive (perspective taking, PT) dimensions of empathy as assessed by the Interpersonal Reactivity Index were examined. EC was positively associated with FA in tracts providing communicative pathways within the limbic system, between perception and action-related regions, and between perception and affect-related regions, independently of individual differences in age, gender, and other dimensions of interpersonal reactivity. These findings provide a neuroanatomical basis for the rapid, privileged processing of emotional sensory information and the automatic elicitation of responses to the affective displays of others.


title: Design WSN node for protection of forest trees against poaching based on ZigBee

abstract: Smuggling/theft of most important trees such as sandal wood in forests, poses a serious threat to forest resources, causes significant economic damage and ultimately has quite a devastating effect on the environment all over the world. This paper propose a microcontroller based anti poaching system employing WSN technology, which is capable of detecting theft by monitoring the vibrations produced by the cutting of trees/branches using a 3 axis MEMS accelerometer. A low power MSP430F5529 microcontroller is used along with Xbee RF modules based on IEEE 802.15.4 Zigbee standards to communicate to a central server from a remote place. WSN is widely used technology in remote monitoring applications. The embedded system architecture and the hardware/software designs are described in detail. Vibration data collected by various tests on wood and simulated using Labview.


title: Semi-supervised Log Pattern Detection and Exploration Using Event Concurrence and Contextual Information

abstract: Process mining offers a variety of techniques for analyzing process execution event logs. Although process discovery algorithms construct end-to-end process models, they often have difficulties dealing with the complexity of real-life event logs. Discovered models may contain either complex or over-generalized fragments, the interpretation of which is difficult, and can result in misleading insights. Detecting and visualizing behavioral patterns instead of creating model structures can reduce complexity and give more accurate insights into recorded behaviors. Unsupervised detection techniques, based on statistical properties of the log only, generate a multitude of patterns and lack domain context. Supervised pattern detection requires a domain expert to specify patterns manually and lacks the event log context. In this paper, we reconcile supervised and unsupervised pattern detection. We visualize the log and help users extract patterns of interest from the log or obtain patterns through unsupervised learning automatically. Pattern matches are visualized in the context of the event log (also showing concurrency and additional contextual information). Earlier patterns can be extended or modified based on the insights. This enables an interactive and iterative approach to identify complex and concrete behavioral patterns in event logs. We implemented our approach in the ProM framework and evaluated the tool using both the BPI Challenge 2012 log of a loan application process and an insurance claims log from a major Australian insurance company.


title: Acceptance is in the eye of the beholder: self-esteem and motivated perceptions of acceptance from the opposite sex.

abstract: Social risk elicits self-esteem differences in signature social motivations and behaviors during the relationship-initiation process. In particular, the present research tested the hypothesis that lower self-esteem individuals' (LSEs) motivation to avoid rejection leads them to self-protectively underestimate acceptance from potential romantic partners, whereas higher self-esteem individuals' (HSEs) motivation to promote new relationships leads them to overestimate acceptance. The results of 5 experiments supported these predictions. Social risk increased activation of avoidance goals for LSEs on a word-recall task but increased activation of approach goals for HSEs, as evidenced by their increased use of likeable behaviors. Consistent with these patterns of goal activation, even though actual acceptance cues were held constant across all participants, social risk decreased the amount of acceptance that LSEs perceived from their interaction partner but increased the amount of acceptance that HSEs perceived from their interaction partner. It is important to note that such self-esteem differences in avoidance goals, approach behaviors, and perceptions of acceptance were completely eliminated when social risk was removed.


title: Intuitive theories as grammars for causal inference

abstract: This chapter considers a set of questions at the interface of the study of intuitive theories, causal knowledge, and problems of inductive inference. By an intuitive theory, we mean a cognitive structure that in some important ways is analogous to a scientific theory. It is becoming broadly recognized that intuitive theories play essential roles in organizing our most basic knowledge of the world, particularly for causal structures in physical, biological, psychological or social domains (Atran, 1995; Carey, 1985a; Kelley, 1973; McCloskey, 1983; Murphy & Medin, 1985; Nichols & Stich, 2003). A principal function of intuitive theories in these domains is to support the learning of new causal knowledge: generating and constraining people’s hypotheses about possible causal relations, highlighting variables, actions and observations likely to be informative about those hypotheses, and guiding people’s interpretation of the data they observe (Ahn & Kalish, 2000; Pazzani, 1987; Pazzani, Dyer & Flowers, 1986; Waldmann, 1996). Leading accounts of cognitive development argue for the importance of intuitive theories in children’s mental lives and frame the major transitions of cognitive development as instances of theory change (Carey, 1985a; Gopnik & Meltzoff, 1997; Inagaki & Hatano 2002; Wellman & Gelman, 1992). Here we attempt to lay out some prospects for understanding the structure, function, and acquisition of intuitive theories from a rational computational perspective. From this viewpoint, theory-like representations are not just a convenient way of summarizing certain aspects of human knowledge. They provide crucial foundations for successful learning and reasoning, and we want to understand how they do so. With this goal in mind, we focus on


title: Policy Gradient Methods for Robotics

abstract: The acquisition and improvement of motor skills and control policies for robotics from trial and error is of essential importance if robots should ever leave precisely pre-structured environments. However, to date only few existing reinforcement learning methods have been scaled into the domains of high-dimensional robots such as manipulator, legged or humanoid robots. Policy gradient methods remain one of the few exceptions and have found a variety of applications. Nevertheless, the application of such methods is not without peril if done in an uninformed manner. In this paper, we give an overview on learning with policy gradient methods for robotics with a strong focus on recent advances in the field. We outline previous applications to robotics and show how the most recently developed methods can significantly improve learning performance. Finally, we evaluate our most promising algorithm in the application of hitting a baseball with an anthropomorphic arm


title: A novel collaborative filtering approach for recommending ranked items

abstract: In recent years, Collaborative Filtering (CF) has proven to be one of the most successful techniques used in recommendation systems. Since current CF systems estimate the ratings of not-yet-rated items based on other items' ratings, these CF systems fail to recommend products when users' preferences are not expressed in numbers. In many practical situations, however, users' preferences are represented by ranked lists rather than numbers, such as lists of movies ranked according to users' preferences. Therefore, this study proposes a novel collaborative filtering methodology for product recommendation when the preference of each user is expressed by multiple ranked lists of items. Accordingly, a four-staged methodology is developed to predict the rankings of not-yet-ranked items for the active user. Finally, a series of experiments is performed, and the results indicate that the proposed methodology produces high-quality recommendations.


title: A compliant underactuated hand with suction flow for underwater mobile manipulation

abstract: Fingertip suction is investigated using a compliant, underactuated, tendon-driven hand designed for underwater mobile manipulation. Tendon routing and joint stiffnesses are designed to provide ease of closure while maintaining finger rigidity, allowing the hand to pinch small objects, as well as secure large objects, without diminishing strength. While the hand is designed to grasp a range of objects, the addition of light suction flow to the fingertips is especially effective for small, low-friction (slippery) objects. Numerical simulations confirm that changing suction parameters can increase the object acquisition region, providing guidelines for future versions of the hand.


title: TimeBench: A Data Model and Software Library for Visual Analytics of Time-Oriented Data

abstract: Time-oriented data play an essential role in many Visual Analytics scenarios such as extracting medical insights from collections of electronic health records or identifying emerging problems and vulnerabilities in network traffic. However, many software libraries for Visual Analytics treat time as a flat numerical data type and insufficiently tackle the complexity of the time domain such as calendar granularities and intervals. Therefore, developers of advanced Visual Analytics designs need to implement temporal foundations in their application code over and over again. We present TimeBench, a software library that provides foundational data structures and algorithms for time-oriented data in Visual Analytics. Its expressiveness and developer accessibility have been evaluated through application examples demonstrating a variety of challenges with time-oriented data and long-term developer studies conducted in the scope of research and student projects.


title: Multiple-Goal Heuristic Search

abstract: This paper presents a new framework for anytime heuristic search where the task is to achieve as many goals as possible within the allocated resources. We show the inadequacy of traditional distance-estimation heuristics for tasks of this type and present alternative heuristics that are more appropriate for multiple-goal search. In particular, we introduce the marginal-utility heuristic, which estimates the cost and the benefit of exploring a subtree below a search node. We developed two methods for online learning of the marginal-utility heuristic. One is based on local similarity of the partial marginal utility of sibling nodes, and the other generalizes marginal-utility over the state feature space. We apply our adaptive and non-adaptive multiple-goal search algorithms to several problems, including focused crawling, and show their superiority over existing methods.


title: Information retrieval with concept-based pseudo-relevance feedback in MEDLINE

abstract: Although using domain specific knowledge sources for information retrieval yields more accurate results compared to pure keyword-based methods, more improvements can be achieved by considering both relations between concepts in an ontology and also their statistical dependencies over the corpus. In this paper, an innovative approach named concept-based pseudo-relevance feedback is introduced for improving accuracy of biomedical retrieval systems. Proposed method uses a hybrid retrieval algorithm for discovering relevancy between queries and documents which is based on a combination of keyword- and concept-based approaches. It also uses a pseudo-relevance feedback mechanism for expanding initial queries with auxiliary biomedical concepts extracted from top-ranked results of hybrid information retrieval. Using concept-based similarities makes it possible for the system to detect related documents to users’ queries, which are semantically close to each other while not necessarily sharing common keywords. In addition, expanding initial queries with concepts introduced by pseudo-relevance feedback captures those relations between queries and documents, which rely on statistical dependencies between concepts they contain. As a matter of fact, these relations may remain undetected, examining merely existing links between concepts in an external knowledge source. Proposed approach is evaluated using OHSUMED test collection and standard evaluation methods from text retrieval conference (TREC). Experimental results on MEDLINE documents (in OHSUMED collection) show 21% improvement over keyword-based approach in terms of mean average precision, which is a noticeable gain.


title: Lane Detection Using Steerable Filters and FPGA-based Implementation

abstract: Vision-based lane detection is a key component for Driver-Assistance (DA) systems. It is still a challenging task in road scenes with complex shadows. This paper presents a novel local edge detector, using vanishing point position as a high level information to guide the use of steerable flters in lane detection, and its implementation on a Field Programmable Gate Array (FPGA) device. The FPGA technology has the advantages of high-performances for digital image processing and low cost, both of which are the requirements of DA systems. The main contributions of this work are twofold: 1) an edge extraction algorithm for lane detection is proposed, using the estimated vanishing point as high-level information to detect lanes. Firstly, a rough estimation of the vanishing point is used for calculating the expected local edge orientations. Secondly, a steerable flter is tuned to the expected direction for edge response. 2) a framework on FPGA is designed to implement the proposed algorithm. The framework is designed by using multi-engine technology, so it works in parallel for any order of steerable flters. Experiments and comparisons show that the proposed algorithm is very effcient in dealing with the complex shadow conditions, and works in real-time on FPGA device.


Presence: Experiments in the Psychology of Virtual Environments


title: Unleashing Mayhem on Binary Code

abstract: In this paper we present Mayhem, a new system for automatically finding exploitable bugs in binary (i.e., executable) programs. Every bug reported by Mayhem is accompanied by a working shell-spawning exploit. The working exploits ensure soundness and that each bug report is security-critical and actionable. Mayhem works on raw binary code without debugging information. To make exploit generation possible at the binary-level, Mayhem addresses two major technical challenges: actively managing execution paths without exhausting memory, and reasoning about symbolic memory indices, where a load or a store address depends on user input. To this end, we propose two novel techniques: 1) hybrid symbolic execution for combining online and offline (concolic) execution to maximize the benefits of both techniques, and 2) index-based memory modeling, a technique that allows Mayhem to efficiently reason about symbolic memory at the binary level. We used Mayhem to find and demonstrate 29 exploitable vulnerabilities in both Linux and Windows programs, 2 of which were previously undocumented.


SportVis: Discovering Meaning in Sports Statistics through Information Visualization


title: Impact of aluminum wire and ribbon bonding technologies on D2PAK package reliability during thermal cycling applications

abstract: Abstract This paper highlights the impact of bonding techniques (Al wire and Al ribbon) on the aging of Schottky diodes, assembled in D 2 PAK package, subjected to temperature cycling. The experimental test results coupled with failure analyses exhibit a better robustness and a lifetime about 2.3 times higher for the ribbon bonding assembly. A higher contact surface, the low loop profile and stiffness of ribbons allow slowing down crack initiation and propagation between the Al bond and Al metallization on the top of the silicon die.


title: Unsupervised Semantic Parsing

abstract: We present the first unsupervised approach to the problem of learning a semantic parser, using Markov logic. Our USP system transforms dependency trees into quasi-logical forms, recursively induces lambda forms from these, and clusters them to abstract away syntactic variations of the same meaning. The MAP semantic parse of a sentence is obtained by recursively assigning its parts to lambda-form clusters and composing them. We evaluate our approach by using it to extract a knowledge base from biomedical abstracts and answer questions. USP substantially outperforms TextRunner, DIRT and an informed baseline on both precision and recall on this task.


title: A Blockchain-Based Location Privacy Protection Incentive Mechanism in Crowd Sensing Networks

abstract: Crowd sensing is a perception mode that recruits mobile device users to complete tasks such as data collection and cloud computing. For the cloud computing platform, crowd sensing can not only enable users to collaborate to complete large-scale awareness tasks but also provide users for types, social attributes, and other information for the cloud platform. In order to improve the effectiveness of crowd sensing, many incentive mechanisms have been proposed. Common incentives are monetary reward, entertainment & gamification, social relation, and virtual credit. However, there are rare incentives based on privacy protection basically. In this paper, we proposed a mixed incentive mechanism which combined privacy protection and virtual credit called a blockchain-based location privacy protection incentive mechanism in crowd sensing networks. Its network structure can be divided into three parts which are intelligence crowd sensing networks, confusion mechanism, and blockchain. We conducted the experiments in the campus environment and the results shows that the incentive mechanism proposed in this paper has the efficacious effect in stimulating user participation.


title: A performance evaluation of local descriptors

abstract: In this paper, we compare the performance of descriptors computed for local interest regions, as, for example, extracted by the Harris-Affine detector [Mikolajczyk, K and Schmid, C, 2004]. Many different descriptors have been proposed in the literature. It is unclear which descriptors are more appropriate and how their performance depends on the interest region detector. The descriptors should be distinctive and at the same time robust to changes in viewing conditions as well as to errors of the detector. Our evaluation uses as criterion recall with respect to precision and is carried out for different image transformations. We compare shape context [Belongie, S, et al., April 2002], steerable filters [Freeman, W and Adelson, E, Setp. 1991], PCA-SIFT [Ke, Y and Sukthankar, R, 2004], differential invariants [Koenderink, J and van Doorn, A, 1987], spin images [Lazebnik, S, et al., 2003], SIFT [Lowe, D. G., 1999], complex filters [Schaffalitzky, F and Zisserman, A, 2002], moment invariants [Van Gool, L, et al., 1996], and cross-correlation for different types of interest regions. We also propose an extension of the SIFT descriptor and show that it outperforms the original method. Furthermore, we observe that the ranking of the descriptors is mostly independent of the interest region detector and that the SIFT-based descriptors perform best. Moments and steerable filters show the best performance among the low dimensional descriptors.


title: Enhancing WPA2-PSK four-way handshaking after re-authentication to deal with de-authentication followed by brute-force attack a novel re-authentication protocol

abstract: The nature of wireless network transmission and the emerging attacks are continuously creating or exploiting more vulnerabilities. Despite the fact that the security mechanisms and protocols are constantly upgraded and enhanced, the Small Office/Home Office (SOHO) environments that cannot afford a separate authentication system, and generally adopt the IEEE 802.11 Wi-Fi-Protected-Access-2/Pre-Shared-Key (WPA2-PSK) are still exposed to some attack categories such as de-authentication attacks that aim to push wireless client to re-authenticate to the Access Point (AP) and try to capture the keys exchanged during the handshake to compromise the network security. This kind of attack is impossible to detect or prevent in spite of having an Intrusion Detection and Prevention System (IDPS) installed on the client or on the AP, especially when the attack is not repetitive and is targeting only one client. This paper proposes a novel method which can mitigate and eliminate the risk of exposing the PSK to be captured during the re-authentication process by introducing a novel re-authentication protocol relying on an enhanced four-way handshake which does not require any hardware upgrade or heavy-weight cryptography affecting the network flexibility and performances.


title: 60-GHz Circularly Polarized U-Slot Patch Antenna Array on LTCC

abstract: This communication presents a 60-GHz wideband circularly polarized (CP) U-slot patch antenna array of 4 × 4 elements on low temperature cofired ceramic (LTCC). A CP U-slot patch antenna is used as the array element to enhance the impedance bandwidth and a stripline sequential rotation feeding scheme is applied to achieve wide axial ratio (AR) bandwidth. Meanwhile, a grounded coplanar waveguide (GCPW) to stripline transition is designed for probe station measurement. The fabricated antenna array has a dimension of 14 ×16 × 1.1 mm3 . The simulated and measured impedance bandwidths, AR bandwidths, and radiation patterns are investigated and compared. Measured results show that the proposed antenna array has a wide impedance bandwidth from 50.5 GHz to 67 GHz for |S11| <; -10 dB, and a wide AR bandwidth from 54 GHz to 65.5 GHz for AR <; 3 dB. In addition, it exhibits a peak gain of 16 dBi and a beam-shaped pattern with 3-dB beam width of 20°. Moreover, its AR keeps below 3 dB within the 3-dB beam width.


title: Modal analysis and transcription of strokes of the mridangam using non-negative matrix factorization

abstract: In this paper we use a Non-negative Matrix Factorization (NMF) based approach to analyze the strokes of the mridangam, a South Indian hand drum, in terms of the normal modes of the instrument. Using NMF, a dictionary of spectral basis vectors are first created for each of the modes of the mridangam. The composition of the strokes are then studied by projecting them along the direction of the modes using NMF. We then extend this knowledge of each stroke in terms of its basic modes to transcribe audio recordings. Hidden Markov Models are adopted to learn the modal activations for each of the strokes of the mridangam, yielding up to 88.40% accuracy during transcription.


title: The representation of polysemy through vectors: some building blocks for constructing models and applications with LSA

abstract: The problem of the multiplicity of word meanings has preoccupied so many researches from the linguistics, psychology or computational linguistic. In this paper, we revised how LSA represents the polysemous words and we explain some bias related with the meaning generation and revised some constraint-satisfaction models which introduce into the equation some dynamic mechanisms. The idea of these models is to take the amalgamated word vector from LSA and embed it into its discourse and semantic context, and by means of a dynamic mechanism, the appropriate features of it is are selected. To illustrate our arguments, we present some networks, providing evidence that polysemous words have separated representations for each sense only in presence of the linguistic context that involved it. We also present an example of how these mechanisms also contribute to support the visual heuristic searches in the visual information retrieval interfaces (VIRIs).


Novel SLC 12 A 1 ( NKCC 2 ) Mutations in Two Families with Bartter Syndrome Type 1


title: Using Wikipedia knowledge to improve text classification

abstract: Text classification has been widely used to assist users with the discovery of useful information from the Internet. However, traditional classification methods are based on the “Bag of Words” (BOW) representation, which only accounts for term frequency in the documents, and ignores important semantic relationships between key terms. To overcome this problem, previous work attempted to enrich text representation by means of manual intervention or automatic document expansion. The achieved improvement is unfortunately very limited, due to the poor coverage capability of the dictionary, and to the ineffectiveness of term expansion. In this paper, we automatically construct a thesaurus of concepts from Wikipedia. We then introduce a unified framework to expand the BOW representation with semantic relations (synonymy, hyponymy, and associative relations), and demonstrate its efficacy in enhancing previous approaches for text classification. Experimental results on several data sets show that the proposed approach, integrated with the thesaurus built from Wikipedia, can achieve significant improvements with respect to the baseline algorithm.


title: An Introduction to Kernel and Nearest Neighbor Nonparametric Regression

abstract: Abstract Nonparametric regression is a set of techniques for estimating a regression curve without making strong assumptions about the shape of the true regression function. These techniques are therefore useful for building and checking parametric models, as well as for data description. Kernel and nearest-neighbor regression estimators are local versions of univariate location estimators, and so they can readily be introduced to beginning students and consulting clients who are familiar with such summaries as the sample mean and median.


title: Simplify or help?: text simplification strategies for people with dyslexia

abstract: We present a user study for two different automatic strategies that simplify text content for people with dyslexia. The strategies considered are the standard one (replacing a complex word with the most simpler synonym) and a new one that presents several synonyms for a complex word if the user requests them. We compare texts transformed by both strategies with the original text and to a gold standard manually built. The study was undertook by 96 participants, 47 with dyslexia plus a control group of 49 people without dyslexia. To show device independence, for the new strategy we used three different reading devices. Overall, participants with dyslexia found texts presented with the new strategy significantly more readable and comprehensible. To the best of our knowledge, this is the largest user study of its kind.


title: From rule-based to data-driven lexical entrainment models in spoken dialog systems

abstract: HighlightsNew methods for the integration of lexical entrainment in spoken dialog systems.Rule based method for entrainment implemented in two different systems.Data-driven method for lexical entrainment in SDSs.Tests reveal better performance with entrainment policy. This paper presents uses a data-driven approach to improve Spoken Dialog System (SDS) performance by automatically finding the most appropriate terms to be used in system prompts. The literature shows that speakers use one another's terms (entrain) when trying to create common ground during a spoken dialog. Those terms are commonly called "primes", since they influence the interlocutors' linguistic decision-making. This approach emulates human interaction, with a system built to propose primes to the user and accept the primes that the user proposes. These primes are chosen on the fly during the interaction, based on a set of features that indicate good candidate primes. A good candidate is one that we know is easily recognized by the speech recognizer, and is also a normal word choice given the context. The system is trained to follow the user's choice of prime if system performance is not negatively affected. When system performance is affected, the system proposes a new prime. In our previous work we have shown how we can identify the prime candidates and how the system can select primes using rules. In this paper we go further, presenting a data-driven method to perform the same task. Live tests with this method show that use of on-the-fly entrainment reduces out-of-vocabulary and word error rate, and also increases the number of correctly transferred concepts.


title: Planning by Incremental Dynamic Programming

abstract: Abstract This paper presents the basic results and ideas of dynamic programming as they relate most directly to the concerns of planning in AI. These form the theoretical basis for the incremental planning methods used in the integrated architecture Dyna. These incremental planning methods are based on continually updating an evaluation function and the situation-action mapping of a reactive system. Actions are generated by the reactive system and thus involve minimal delay, while the incremental planning process guarantees that the actions and evaluation function will eventually be optimal—no matter how extensive a search is required. These methods are well suited to stochastic tasks and to tasks in which a complete and accurate model is not available. For tasks too large to implement the situation-action mapping as a table, supervised-learning methods must be used, and their capabilities remain a significant limitation of the approach.