title: Big five dimensions and ADHD symptoms: Links between personality traits and clinical symptoms

abstract: Attention-deficit/hyperactivity disorder (ADHD) in adulthood is conceptualized as originating in childhood. Despite considerable theoretical interest, little is known about how ADHD symptoms relate to normal personality traits in adults. In 6 studies, the Big Five personality dimensions were related to ADHD symptoms that adults both recalled from childhood and reported concurrently (total N = 1,620). Substantial effects emerged that were replicated across samples. First, the ADHD symptom cluster of inattention-disorganization was substantially related to low Conscientiousness and, to a lesser extent, Neuroticism. Second, ADHD symptom clusters of hyperactivity-impulsivity and oppositional childhood and adult behaviors were associated with low Agreeableness. Results were replicated with self-reports and observer reports of personality in community and clinical samples. Findings support theoretical connections between personality traits and ADHD symptoms.


title: Single or multiple frequency generators in on‐going brain activity: A mechanistic whole‐brain model of empirical MEG data

abstract: Abstract During rest, envelopes of band-limited on-going MEG signals co-vary across the brain in consistent patterns, which have been related to resting-state networks measured with fMRI. To investigate the genesis of such envelope correlations, we consider a whole-brain network model assuming two distinct fundamental scenarios: one where each brain area generates oscillations in a single frequency, and a novel one where each brain area can generate oscillations in multiple frequency bands. The models share, as a common generator of damped oscillations, the normal form of a supercritical Hopf bifurcation operating at the critical border between the steady state and the oscillatory regime. The envelopes of the simulated signals are compared with empirical MEG data using new methods to analyse the envelope dynamics in terms of their phase coherence and stability across the spectrum of carrier frequencies. Considering the whole-brain model with a single frequency generator in each brain area, we obtain the best fit with the empirical MEG data when the fundamental frequency is tuned at 12 Hz. However, when multiple frequency generators are placed at each local brain area, we obtain an improved fit of the spatio-temporal structure of on-going MEG data across all frequency bands. Our results indicate that the brain is likely to operate on multiple frequency channels during rest, introducing a novel dimension for future models of large-scale brain activity.


title: Boosting as a Regularized Path to a Maximum Margin Classifier

abstract: In this paper we study boosting methods from a new perspective. We build on recent work by Efron et al. to show that boosting approximately (and in some cases exactly) minimizes its loss criterion with an l1 constraint on the coefficient vector. This helps understand the success of boosting with early stopping as regularized fitting of the loss criterion. For the two most commonly used criteria (exponential and binomial log-likelihood), we further show that as the constraint is relaxed---or equivalently as the boosting iterations proceed---the solution converges (in the separable case) to an "l1-optimal" separating hyper-plane. We prove that this l1-optimal separating hyper-plane has the property of maximizing the minimal l1-margin of the training data, as defined in the boosting literature. An interesting fundamental similarity between boosting and kernel support vector machines emerges, as both can be described as methods for regularized optimization in high-dimensional predictor space, using a computational trick to make the calculation practical, and converging to margin-maximizing solutions. While this statement describes SVMs exactly, it applies to boosting only approximately.


title: LadderNet: Multi-path networks based on U-Net for medical image segmentation

abstract: We are correcting some important errors and working on a new version now. Will release the updated version in the future.


title: Enhanced Inter-Cell Interference Coordination Challenges in Heterogeneous Networks

abstract: 3GPP LTE-Advanced has recently been investigating heterogeneous network (HetNet) deployments as a cost effective way to deal with the unrelenting traffic demand. HetNets consist of a mix of macrocells, remote radio heads, and low-power nodes such as picocells, femtocells, and relays. Leveraging network topology, increasing the proximity between the access network and the end users, has the potential to provide the next significant performance leap in wireless networks, improving spatial spectrum reuse and enhancing indoor coverage. Nevertheless, deployment of a large number of small cells overlaying the macrocells is not without new technical challenges. In this article, we present the concept of heterogeneous networks and also describe the major technical challenges associated with such network architecture. We focus in particular on the standardization activities within the 3GPP related to enhanced intercell interference coordination.


title: Depth first search and linear graph algorithms

abstract: The value of depth-first search or “backtracking” as a technique for solving problems is illustrated by two examples. An improved version of an algorithm for finding the strongly connected components of a directed graph and at algorithm for finding the biconnected components of an undirect graph are presented. The space and time requirements of both algorithms are bounded by $k_1 V + k_2 E + k_3 $ for some constants $k_1 ,k_2 $, and $k_3 $, where V is the number of vertices and E is the number of edges of the graph being examined.


title: Culture and institutions

abstract: Part I A growing body of empirical work measuring different types of cultural traits has shown that culture matters for a variety of economic outcomes. This paper focuses on one specific aspect of the relevance of culture: its relationship to institutions. We review work with a theoretical, empirical, and historical bent to assess the presence of a two-way causal effect between culture and institutions. The first part of the paper provides the main definitions and discusses the difficulties associated with the quantitative analysis of this effect. It also features a map of cultural traits used in economics and of their correlations.


title: Verovio: A library for Engraving MEI Music Notation into SVG

abstract: Rendering symbolic music notation is a common component of many MIR applications, and many tools are available for this task. There is, however, a need for a tool that can natively render the Music Encoding Initiative (MEI) notation encodings that are increasingly used in music research projects. In this paper, we present Verovio, a library and toolkit for rendering MEI. A significant advantage of Verovio is that it implements MEI’s structure internally, making it the best suited solution for rendering features that make MEI unique. Verovio is designed as a fast, portable, lightweight tool written in pure standard C++ with no dependencies on third-party frameworks or libraries. It can be used as a command-line rendering tool, as a library, or it can be compiled to JavaScript using the Emscripten LLVM-to-JavaScript compiler. This last option is particularly interesting because it provides a complete in-browser music MEI typesetter. The SVG output from Verovio is organized in such a way that the MEI structure is preserved as much as possible. Since every graphic in SVG is an XML element that is easily addressable, Verovio is particularly well-suited for interactive applications, especially in web browsers. Verovio is available under the GPL open-source license.


title: Solving the Search for Source Code

abstract: Programmers frequently search for source code to reuse using keyword searches. The search effectiveness in facilitating reuse, however, depends on the programmer's ability to specify a query that captures how the desired code may have been implemented. Further, the results often include many irrelevant matches that must be filtered manually. More semantic search approaches could address these limitations, yet existing approaches are either not flexible enough to find approximate matches or require the programmer to define complex specifications as queries. We propose a novel approach to semantic code search that addresses several of these limitations and is designed for queries that can be described using a concrete input/output example. In this approach, programmers write lightweight specifications as inputs and expected output examples. Unlike existing approaches to semantic search, we use an SMT solver to identify programs or program fragments in a repository, which have been automatically transformed into constraints using symbolic analysis, that match the programmer-provided specification. We instantiated and evaluated this approach in subsets of three languages, the Java String library, Yahooe Pipes mashup language, and SQL select statements, exploring its generality, utility, and trade-offs. The results indicate that this approach is effective at finding relevant code, can be used on its own or to filter results from keyword searches to increase search precision, and is adaptable to find approximate matches and then guide modifications to match the user specifications when exact matches do not already exist. These gains in precision and flexibility come at the cost of performance, for which underlying factors and mitigation strategies are identified.


title: Dual-wideband log-periodic antennas

abstract: Two log-periodic (LP) antenna configurations able to suppress a specific frequency band are introduced in this paper. LP antennas belong to the class of frequency independent antennas and are able to achieve multi-decade wide bandwidths. We start with a parametric study of a traditional planar curved tooth LP antenna aimed in determining the means for achieving specific bandwidth. Two techniques for accomplishing consistent near/far-field dual-band performance are introduced next. The first is a LP antenna integrated with a dual-wideband feed line, and the second one is a LP with removed resonant teeth. Antennas and feeds are designed on a microstrip substrate and method of moments modeling within FEKO is used to demonstrate their performance. Required dual-band operation is 1.8-5 GHz and 7-11 GHz.


title: Two UK suicides using nicotine extracted from tobacco employing instructions available on the Internet.

abstract: Tobacco is one of the most easily accessible and commonly abused drugs world-wide. Nicotine, one of its principal constituents, can cause serious or fatal overdoses. Whilst the deliberate ingestion of this substance appears to be relatively rare, often the important signs of its consumption are not recognised, sometimes with fatal results. Here we describe two cases of intentional fatal ingestion of nicotine. The nicotine was extracted from tobacco using instructions available on the Internet. The first case involved a male aged 19 who died in 2008. The post-mortem blood and urine levels of nicotine were 5.5mg/l and >80 mg/l respectively; the blood level is in line with the generally recognised fatal level of >5mg/l. The levels of nicotine's main metabolite, cotinine, for this case were 2.5mg/l and 7.9 mg/l for blood and urine respectively. A comparative case in 1999 involved a 32 year-old male. The level of post-mortem nicotine in his blood was 1.0mg/l. These are believed to be the first UK suicides by nicotine using instructions from the Internet reported in the literature. Information that nicotine was the agent responsible only became apparent some time after death. There may be more deaths due to this cause that go unrecognised because quantification of nicotine and cotinine levels is not often conducted, due to the wide prevalence of smoking. It is important that all evidence at the scene of a sudden and unexplained death is carefully evaluated, including potential clues on PCs and lap-tops.


title: Overlap between autism and specific language impairment: comparison of Autism Diagnostic Interview and Autism Diagnostic Observation Schedule scores

abstract: Autism and specific language impairment (SLI) are developmental disorders that, although distinct by definition, have in common some features of both language and social behavior. The goal of this study was to further explore the extent to which specific clinical features of autism are seen in SLI. The children with the two disorders, matched for non-verbal IQ, were compared on the Autism Diagnostic Interview—Revised (ADI-R) and the Autism Diagnostic Observation Schedule (ADOS). In the SLI group, 41% met autism or autism spectrum cut-offs for social or communication domains either on the ADI or ADOS or both. No relationship was found between the language deficits exhibited by the children with SLI and their scores on the ADI and ADOS. These findings contribute to evidence that there is some overlap in social and communicative deficits between autism and SLI, supporting the view that autism and SLI share etiologic factors. This continuum of pathology between SLI and autism appears to range from structural language abnormalities as seen in individuals with SLI to individuals with SLI with both structural and social abnormalities to individuals with autism with pragmatic impairment and language abnormalities.


title: Improved Knowledge Graph Embedding using Background Taxonomic Information

abstract: Knowledge graphs are used to represent relational information in terms of triples. To enable learning about domains, embedding models, such as tensor factorization models, can be used to make predictions of new triples. Often there is background taxonomic information (in terms of subclasses and subproperties) that should also be taken into account. We show that existing fully expressive (a.k.a. universal) models cannot provably respect subclass and subproperty information. We show that minimal modifications to an existing knowledge graph completion method enables injection of taxonomic information. Moreover, we prove that our model is fully expressive, assuming a lower-bound on the size of the embeddings. Experimental results on public knowledge graphs show that despite its simplicity our approach is surprisingly effective.


title: HASBE: A Hierarchical Attribute-Based Solution for Flexible and Scalable Access Control in Cloud Computing

abstract: Cloud computing has emerged as one of the most influential paradigms in the IT industry in recent years. Since this new computing technology requires users to entrust their valuable data to cloud providers, there have been increasing security and privacy concerns on outsourced data. Several schemes employing attribute-based encryption (ABE) have been proposed for access control of outsourced data in cloud computing; however, most of them suffer from inflexibility in implementing complex access control policies. In order to realize scalable, flexible, and fine-grained access control of outsourced data in cloud computing, in this paper, we propose hierarchical attribute-set-based encryption (HASBE) by extending ciphertext-policy attribute-set-based encryption (ASBE) with a hierarchical structure of users. The proposed scheme not only achieves scalability due to its hierarchical structure, but also inherits flexibility and fine-grained access control in supporting compound attributes of ASBE. In addition, HASBE employs multiple value assignments for access expiration time to deal with user revocation more efficiently than existing schemes. We formally prove the security of HASBE based on security of the ciphertext-policy attribute-based encryption (CP-ABE) scheme by Bethencourt and analyze its performance and computational complexity. We implement our scheme and show that it is both efficient and flexible in dealing with access control for outsourced data in cloud computing with comprehensive experiments.


title: CASTIN: a system for comprehensive analysis of cancer-stromal interactome

abstract: Background ::: Cancer microenvironment plays a vital role in cancer development and progression, and cancer-stromal interactions have been recognized as important targets for cancer therapy. However, identifying relevant and druggable cancer-stromal interactions is challenging due to the lack of quantitative methods to analyze whole cancer-stromal interactome.


title: Model-based development of dynamically adaptive software

abstract: Increasingly, software should dynamically adapt its behavior at run-time in response to changing conditions in the supporting computing and communication infrastructure, and in the surrounding physical environment. In order for an adaptive program to be trusted, it is important to have mechanisms to ensure that the program functions correctly during and after adaptations. Adaptive programs are generally more difficult to specify, verify, and validate due to their high complexity. Particularly, when involving multi-threaded adaptations, the program behavior is the result of the collaborative behavior of multiple threads and software components. This paper introduces an approach to create formal models for the behavior of adaptive programs. Our approach separates the adaptation behavior and non-adaptive behavior specifications of adaptive programs, making the models easier to specify and more amenable to automated analysis and visual inspection. We introduce a process to construct adaptation models, automatically generate adaptive programs from the models, and verify and validate the models. We illustrate our approach through the development of an adaptive GSM-oriented audio streaming protocol for a mobile computing application.


title: GPU-acceleration for Large-scale Tree Boosting

abstract: In this paper, we present a novel massively parallel algorithm for accelerating the decision tree building procedure on GPUs (Graphics Processing Units), which is a crucial step in Gradient Boosted Decision Tree (GBDT) and random forests training. Previous GPU based tree building algorithms are based on parallel multi-scan or radix sort to find the exact tree split, and thus suffer from scalability and performance issues. We show that using a histogram based algorithm to approximately find the best split is more efficient and scalable on GPU. By identifying the difference between classical GPU-based image histogram construction and the feature histogram construction in decision tree training, we develop a fast feature histogram building kernel on GPU with carefully designed computational and memory access sequence to reduce atomic update conflict and maximize GPU utilization. Our algorithm can be used as a drop-in replacement for histogram construction in popular tree boosting systems to improve their scalability. As an example, to train GBDT on epsilon dataset, our method using a main-stream GPU is 7-8 times faster than histogram based algorithm on CPU in LightGBM and 25 times faster than the exact-split finding algorithm in XGBoost on a dual-socket 28-core Xeon server, while achieving similar prediction accuracy.


title: Community detection in networks: Structural communities versus ground truth

abstract: Algorithms to find communities in networks rely just on structural information and search for cohesive subsets of nodes. On the other hand, most scholars implicitly or explicitly assume that structural communities represent groups of nodes with similar (non-topological) properties or functions. This hypothesis could not be verified, so far, because of the lack of network datasets with information on the classification of the nodes. We show that traditional community detection methods fail to find the metadata groups in many large networks. Our results show that there is a marked separation between structural communities and metadata groups, in line with recent findings. That means that either our current modeling of community structure has to be substantially modified, or that metadata groups may not be recoverable from topology alone.


title: C-ARM DISTORTION CORRECTION USING PATIENT CT AS A FIDUCIAL

abstract: X-ray imaging is an extensively used intra-operative imaging modality because of its low cost and portability. These images have to be corrected for geometric distortion in order to use them for quantitative analysis. Current distortion correction techniques, which use a grid pattern phantom, are not user friendly, for example the phantom interferes with the patient anatomy being imaged. We present a novel method to estimate the c-arm distortion parameters along with the patient pose using patient CT as a fiducial. This method could be very useful in applications such as 3D reconstruction and surgical navigation. In this method, we characterize the c-arm distortion patterns statistically using principal component analysis. The distortion correction method optimizes the distortion parameters by comparing the fluoroscopic images and the projections of the registered patient CT. Our simulation experiments show that the distortion parameters can be recovered up to an average accuracy of 0.5117 mm with a pose error of about 0.1692 mm in translation, 0.2112 deg in rotation


title: Optimization of an Aircraft Power Distribution Subsystem

abstract: A mathematical optimization problem is formulated to design several components of an electrical power distribution system for next-generation aircraft. A simple interconnected system consisting of an input e lter and a dc‐dc buck converter is used as the prototype for the optimization demonstration. The components are designed for minimum weight, subject to performance, stability, and peak voltage constraints. Optimized designs for each of the individual subsystems and for the integrated system are presented. It is shown that there is an interaction between thee lterdesign problem and theconverterdesign problem and thatweightimprovementscanbeobtained by considering these interactions. The optimization methodology described enables designers to obtain optimized electric power subsystem designs in a time-efe cient manner.


title: Understanding Color Image Processing by Machine Vision for Biological Materials

abstract: Handling (Post harvest) process of fruits is completed in several steps: washing, sorting, grading, packing, transporting and storage. The fruits sorting and grading are considered the most important steps of handling. Product quality and quality evaluation are important aspects of fruit and vegetable production. Sorting and grading are major processing tasks associated with the production of fresh-market fruit types. Considerable effort and time have been invested in the area of automation.


title: Implementation and Customization of a Smart Mirror through a Facial Recognition Authentication and a Personalized News Recommendation Algorithm

abstract: In recent years the advancement of technologies of information and communication (technology ICTs) have helped to improve the quality of people's lives. The paradigm of internet of things (IoT, Internet of things) presents innovative solutions that are changing the style of life of the people. Because of this proposes the implementation of a smart mirror as part of a system of home automation, with which we intend to optimize the time of people as they prepare to start their day. This device is constructed from a reflective glass, LCD monitor, a Raspberry Pi 3, a camera and a platform IoT oriented cloud computing, where the information is obtained to show in the mirror, through the consumption of web services. The information is customizable thanks to a mobile application, which in turn allows the user photos to access the mirror, using authentication with facial recognition and user information to predict the news to show according to your profile. In addition, as part of the idea of providing the user a personalized experience, the Smart Mirror incorporates a news recommendation algorithm, implemented using a predictive model, which uses the algorithm, naive bayes.


title: Canadian Cardiovascular Society atrial fibrillation guidelines 2010: prevention of stroke and systemic thromboembolism in atrial fibrillation and flutter.

abstract: The stroke rate in atrial fibrillation is 4.5% per year, with death or permanent disability in over half. The risk of stroke varies from under 1% to over 20% per year, related to the risk factors of congestive heart failure, hypertension, age, diabetes, and prior stroke or transient ischemic attack (TIA). Major bleeding with vitamin K antagonists varies from about 1% to over 12% per year and is related to a number of risk factors. The CHADS(2) index and the HAS-BLED score are useful schemata for the prediction of stroke and bleeding risks. Vitamin K antagonists reduce the risk of stroke by 64%, aspirin reduces it by 19%, and vitamin K antagonists reduce the risk of stroke by 39% when directly compared with aspirin. Dabigatran is superior to warfarin for stroke prevention and causes no increase in major bleeding. We recommend that all patients with atrial fibrillation or atrial flutter, whether paroxysmal, persistent, or permanent, should be stratified for the risk of stroke and for the risk of bleeding and that most should receive antithrombotic therapy. We make detailed recommendations as to the preferred agents in various types of patients and for the management of antithrombotic therapies in the common clinical settings of cardioversion, concomitant coronary artery disease, surgical or diagnostic procedures with a risk of major bleeding, and the occurrence of stroke or major bleeding. Alternatives to antithrombotic therapies are briefly discussed.


title: EEG dynamics in patients with Alzheimer's disease

abstract: Alzheimer's disease (AD) is the most common neurodegenerative disorder characterized by cognitive and intellectual deficits and behavior disturbance. The electroencephalogram (EEG) has been used as a tool for diagnosing AD for several decades. The hallmark of EEG abnormalities in AD patients is a shift of the power spectrum to lower frequencies and a decrease in coherence of fast rhythms. These abnormalities are thought to be associated with functional disconnections among cortical areas resulting from death of cortical neurons, axonal pathology, cholinergic deficits, etc. This article reviews main findings of EEG abnormalities in AD patients obtained from conventional spectral analysis and nonlinear dynamical methods. In particular, nonlinear alterations in the EEG of AD patients, i.e. a decreased complexity of EEG patterns and reduced information transmission among cortical areas, and their clinical implications are discussed. For future studies, improvement of the accuracy of differential diagnosis and early detection of AD based on multimodal approaches, longitudinal studies on nonlinear dynamics of the EEG, drug effects on the EEG dynamics, and linear and nonlinear functional connectivity among cortical regions in AD are proposed to be investigated. EEG abnormalities of AD patients are characterized by slowed mean frequency, less complex activity, and reduced coherences among cortical regions. These abnormalities suggest that the EEG has utility as a valuable tool for differential and early diagnosis of AD.


INTEGRATED COUPLED-INDUCTOR AND DIODE-CAPACITOR FOR A HIGH VOLTAGE GAIN DC-DC CONVERTER


title: Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning

abstract: Accurate and efficient analysis of multivariate spatio-temporal data is critical in climatology, geology, and sociology applications. Existing models usually assume simple inter-dependence among variables, space, and time, and are computationally expensive. We propose a unified low rank tensor learning framework for multivariate spatio-temporal analysis, which can conveniently incorporate different properties in spatio-temporal data, such as spatial clustering and shared structure among variables. We demonstrate how the general framework can be applied to cokriging and forecasting tasks, and develop an efficient greedy algorithm to solve the resulting optimization problem with convergence guarantee. We conduct experiments on both synthetic datasets and real application datasets to demonstrate that our method is not only significantly faster than existing methods but also achieves lower estimation error.


title: Pervasive Computing: A Paradigm for the 21st Century

abstract: Pervasive computing promises to make life simpler via digital environments that sense, adapt, and respond to human needs. Yet we still view computers as machines that run programs in a virtual environment. Pervasive computing presumes a different vision. A device can be a portal into an application-data space, not just a repository of custom software a user must manage. An application is a means by which a user performs a task, not software written to exploit a device's capabilities. And a computing environment is an information-enhanced physical space, not a virtual environment that exists to store and run software. Pervasive computing is close to technical and economic viability.


Patent Claim Translation based on Sublanguage-specific Sentence Structure


title: Toward an understanding of flow in video games

abstract: In the domain of computer games, research into the interaction between player and game has centred on 'enjoyment', often drawing in particular on optimal experience research and Csikszentmihalyi's 'Flow theory'. Flow is a well-established construct for examining experience in any setting and its application to game-play is intuitive. Nevertheless, it's not immediately obvious how to translate between the flow construct and an operative description of game-play. Previous research has attempted this translation through analogy. In this article we propose a practical, integrated approach for analysis of the mechanics and aesthetics of game-play, which helps develop deeper insights into the capacity for flow within games. The relationship between player and game, characterized by learning and enjoyment, is central to our analysis. We begin by framing that relationship within Cowley's user-system-experience (USE) model, and expand this into an information systems framework, which enables a practical mapping of flow onto game-play. We believe this approach enhances our understanding of a player's interaction with a game and provides useful insights for games' researchers seeking to devise mechanisms to adapt game-play to individual players.


title: Keyword and Keyphrase Extraction using Newton's Law of Universal Gravitation

abstract: In current times, there has been a surge in the amount of collected data from computational systems. The vast amount of data can be useful in many applications and fields, particularly so in Big Data Analytics. However with a large collection of data there is a difficulty discovering important information. Automatic Document Summarization (ADS) systems are suitable for the task of outlining useful data. The ADS system model takes a text document as input, and outputs a semantically-relevant summary of this information. This information can be further separated and outlined as keywords, or keyphrases. This paper proposes a novel unsupervised approach for automatic keyword and keyphrase generation system using Newton's Law of Universal Gravitation. This approach allows for a complete capture of meaningful text, incorporating the physical structure of a document and discovered relationships between highly related words. Our model uses a new weighting method that combines both the character length of a word, and frequency of a word within a document to simulate a mass. Our model then computes the force of attraction and ranks the word-pair-force as a means of keyword and keyphrase extraction. Experimental results on several text documents demonstrated that the proposed approach improves on the state-of-the-art models.


title: A Modified Single-Phase Quasi-Z-Source AC–AC Converter

abstract: A modified single-phase quasi-Z-source ac-ac converter is proposed in this paper. The proposed converter has the main features in that the output voltage can be bucked or boosted and be both in-phase and out-of-phase with the input voltage. The input voltage and output voltage share the same ground, the size of a converter is reduced, and it operates in a continuous current mode. A safe-commutation strategy for the modified single-phase quasi-Z-source ac-ac converter is used instead of a snubber circuit. The operating principles and a steady-state analysis are presented. A laboratory prototype, tested using a resistive load, a passive load, and a nonlinear load, was constructed that used an input voltage of 70 Vrms /60 Hz in order to verify the performance of the modified single-phase quasi-Z-source ac-ac converter. The experimental results verified that the converter has a lower input current total harmonic distortion, a higher input power factor, and a higher efficiency in comparison to a conventional single-phase Z-source ac-ac converter. In addition, the experimental results show that the use of the safe-commutation strategy is a significant improvement, as it makes it possible to avoid voltage spikes on the switches.


title: Pick from here!: an interactive mobile cart using in-situ projection for order picking

abstract: Order Picking is not only one of the most important but also most mentally demanding and error-prone tasks in the industry. Both stationary and wearable systems have been introduced to facilitate this task. Existing stationary systems are not scalable because of the high cost and wearable systems have issues being accepted by the workers. In this paper, we introduce a mobile camera-projector cart called OrderPickAR, which combines the benefits of both stationary and mobile systems to support order picking through Augmented Reality. Our system dynamically projects in-situ picking information into the storage system and automatically detects when a picking task is done. In a lab study, we compare our system to existing approaches, i.e, Pick-by-Paper, Pick-by-Voice, and Pick-by-Vision. The results show that using the proposed system, order picking is almost twice as fast as other approaches, the error rate is decreased up to 9 times, and mental demands are reduced up to 50%.


title: Maze exploration using a fungal search algorithm: Part 1 - Algorithm model

abstract: This paper proposes a new maze exploration algorithm based on a fungal search algorithm (FSA). The proposed maze exploration fungal search algorithm (MEFSA) was tested using randomly generated 100 cell mazes. Results indicate that the MEFSA can successfully be applied to solve mazes. The contribution made in this paper is the application of the FSA to the problem of maze exploration.


title: A K-band planar slot array antenna on a single layer ridge gap waveguide

abstract: A K-band slot array antenna is developed by using ridge gap waveguide technology in a single layer metal structure. A power divider of 1:4 is used to feed the ridge gap waveguide and the slots. The power divided itself is fed with a waveguide transition. Detail design of the antenna is presented. The antenna has the impedance bandwidth of 9% at the center frequency of 38.5 GHz, The antenna gain is 21 dBi and the side lobe level is less than 12 dB.


title: Privacy and Efficiency Tradeoffs for Multiword Top K Search with Linear Additive Rank Scoring

abstract: This paper proposes a privacy-aware ranking scheme with linear additive scoring for efficient top keyword search on modest-sized cloud datasets. This scheme strikes for tradeoffs between privacy and efficiency by proposing single-round client-server collaboration with server-side partial ranking based on blinded feature weights with random masks. Client-side preprocessing includes query decomposition with chunked postings to facilitate earlier range intersection and fast access of server-side key-value stores, and server-side query processing deals with feature vector sparsity through optional feature matching and enables result filtering for queries that yield too many matched documents. This paper provides details on indexing and run-time conjunctive query processing and presents an evaluation that assesses the accuracy, efficiency, and privacy tradeoffs of this scheme through five datasets with various sizes.


title: A high-resolution spontaneous 3D dynamic facial expression database

abstract: Facial expression is central to human experience. Its efficient and valid measurement is a challenge that automated facial image analysis seeks to address. Most publically available databases are limited to 2D static images or video of posed facial behavior. Because posed and un-posed (aka “spontaneous”) facial expressions differ along several dimensions including complexity and timing, well-annotated video of un-posed facial behavior is needed. Moreover, because the face is a three-dimensional deformable object, 2D video may be insufficient, and therefore 3D video archives are needed. We present a newly developed 3D video database of spontaneous facial expressions in a diverse group of young adults. Well-validated emotion inductions were used to elicit expressions of emotion and paralinguistic communication. Frame-level ground-truth for facial actions was obtained using the Facial Action Coding System. Facial features were tracked in both 2D and 3D domains using both person-specific and generic approaches. The work promotes the exploration of 3D spatiotemporal features in subtle facial expression, better understanding of the relation between pose and motion dynamics in facial action units, and deeper understanding of naturally occurring facial action.


title: BICP: Block-Incremental CP Decomposition with Update Sensitive Refinement

abstract: With many applications relying on multi-dimensional datasets for decision making, tensors (or multi-dimensional arrays) are emerging as a popular data representation to support diverse types of data, such as sensor streams and social networks. Consequently, tensor decomposition forms the basis for many data analysis and knowledge discovery tasks, from clustering, trend detection, anomaly detection, to correlation analysis. In applications where data evolves over time and the tensor-based analysis results need to be continuously maintained, re-computation of the whole tensor decomposition with each update will cause high computational costs and incur large memory overheads. In this paper, we propose a two-phase block-incremental CP-based tensor decomposition technique, BICP, that efficiently and effectively maintains tensor decomposition results in the presence of dynamically evolving tensor data. In its first phase, instead of repeatedly conducting ALS on each sub-tensor, BICP only revises the decompositions of the tensors that contain updated data. Moreover, when updates are relatively small with respect to the block size, BICP relies on a incremental factor tracking to avoid re-decomposition the updated sub-tensor. In its second phase, BICP limits the block-centric refinement process to only those blocks that are critical given the update. Experiment results show that the proposed method significantly reduces the execution time while assuring high accuracy.


CharNER: Character-Level Named Entity Recognition


title: Us and them: identifying cyber hate on Twitter across multiple protected characteristics

abstract: Hateful and antagonistic content published and propagated via the World Wide Web has the potential to cause harm and suffering on an individual basis, and lead to social tension and disorder beyond cyber space. Despite new legislation aimed at prosecuting those who misuse new forms of communication to post threatening, harassing, or grossly offensive language - or cyber hate - and the fact large social media companies have committed to protecting their users from harm, it goes largely unpunished due to difficulties in policing online public spaces. To support the automatic detection of cyber hate online, specifically on Twitter, we build multiple individual models to classify cyber hate for a range of protected characteristics including race, disability and sexual orientation. We use text parsing to extract typed dependencies, which represent syntactic and grammatical relationships between words, and are shown to capture ‘othering’ language - consistently improving machine classification for different types of cyber hate beyond the use of a Bag of Words and known hateful terms. Furthermore, we build a data-driven blended model of cyber hate to improve classification where more than one protected characteristic may be attacked (e.g. race and sexual orientation), contributing to the nascent study of intersectionality in hate crime.


title: Kinematics and workspace analysis for a novel 6-PSS parallel manipulator

abstract: In this paper, a novel 6-PSS parallel manipulator is designed and the three dimensional structure model is obtained with the Solidworks. The inverse kinematics analysis is performed based on the designed geometric parameters. And according to this analysis, we obtain the theoretical simulation results of virtual structures with different positions and orientations by using MATLAB software. These simulation structures verify the feasibility of this novel 6-PSS parallel manipulator. With a given orientation, a numerical search method is adopted in finding the reachable workspace with the judgment conditions of physical constraints, and the relationship between the reachable workspace and the size of the structure is studied in virtual simulation. Therefore, the range including the largest reachable workspace is achieved and a possible further application of this novel 6-PSS parallel manipulator is proposed, especially in some fields of different structure size requirements.


title: Evaluating the Genre Classification Performance of Lyrical Features Relative to Audio, Symbolic and Cultural Features

abstract: T his paper describes experimental research investigating the genre classification utility of combining features extracted from lyrical, audio, symbolic and cultural sources of musical information. It was found that cultural features consisting of information extracted from both web searches and mined listener tags were particularly effective, with the result that classification accuracies were achieved that compare favorably with the current state of the art of musical genre classification. It was also found that features extracted from lyrics were less effective than the other feature types. Finally, it was found that, with some exceptions, combining feature types does improve classification performance. The new lyricFetcher and jLyrics software are also presented as tools that can be used as a framework for developing more effective classification methodologies based on lyrics in the future.


title: Detecting sentiment embedded in Arabic social media – A lexicon-based approach

abstract: Sentiment analysis aims at extracting sentiment embedded mainly in text reviews. The prevalence of semantic web technologies has encouraged users of the web to become authors as well as readers. People write on a wide range of topics. These writings embed valuable information for organizations and industries. This paper introduces a novel framework for sentiment detection in Arabic tweets. The heart of this framework is a sentiment lexicon. This lexicon was built by translating the SentiStrength English sentiment lexicon into Arabic and afterwards the lexicon was expanded using Arabic thesauri. To assess the viability of the suggested framework, the authors have collected and manually annotated a set of 4400 Arabic tweets. These tweets were classified according to their sentiment into positive or negative tweets using the proposed framework. The results reveal that lexicons are helpful for sentiment detection. The overall results are encouraging and open venues for future research.


title: Novel dual Marx Generator for microplasma applications

abstract: Micrometer size plasmas, or microplasmas, find applications in pollution control, reduction, and prevention. The required nonthermal plasmas can be generated by either an electron beam or an electric discharge. The pulse widths and voltages necessary to generate these nonthermal plasmas are 10/sup -10/-10/sup -8/ s, and 10/sup 3/-10/sup 4/ V, respectively, depending on the application. The required energy is typically in the low 10/sup -3/ J range. This paper presents a novel circuit design to generate high-voltage pulses with variable pulse widths and pulse rise and fall times in the low 10/sup -9/ s regime. The circuit employs two parallel Marx Generators utilizing bipolar junction transistors (BJTs) as closing switches. The BJTs are operated in the avalanche mode to yield fast rise times. The design allows for positive or negative polarity pulses, and can easily be changed to yield higher or lower output voltage.


title: Real-Time Detection and Tracking for Augmented Reality on Mobile Phones

abstract: In this paper, we present three techniques for 6DOF natural feature tracking in real time on mobile phones. We achieve interactive frame rates of up to 30 Hz for natural feature tracking from textured planar targets on current generation phones. We use an approach based on heavily modified state-of-the-art feature descriptors, namely SIFT and Ferns plus a template-matching-based tracker. While SIFT is known to be a strong, but computationally expensive feature descriptor, Ferns classification is fast, but requires large amounts of memory. This renders both original designs unsuitable for mobile phones. We give detailed descriptions on how we modified both approaches to make them suitable for mobile phones. The template-based tracker further increases the performance and robustness of the SIFT- and Ferns-based approaches. We present evaluations on robustness and performance and discuss their appropriateness for Augmented Reality applications.


title: Automated segmentation of optic disc and optic cup in fundus images for glaucoma diagnosis

abstract: The vertical Cup-to-Disc Ratio (CDR) is an important indicator in the diagnosis of glaucoma. Automatic segmentation of the optic disc (OD) and optic cup is crucial towards a good computer-aided diagnosis (CAD) system. This paper presents a statistical model-based method for the segmentation of the optic disc and optic cup from digital color fundus images. The method combines knowledge-based Circular Hough Transform and a novel optimal channel selection for segmentation of the OD. Moreover, we extended the method to optic cup segmentation, which is a more challenging task. The system was tested on a dataset of 325 images. The average Dice coefficient for the disc and cup segmentation is 0.92 and 0.81 respectively, which improves significantly over existing methods. The proposed method has a mean absolute CDR error of 0.10, which outperforms existing methods. The results are promising and thus demonstrate a good potential for this method to be used in a mass screening CAD system.


title: Tailoring persuasive health games to gamer type

abstract: Persuasive games are an effective approach for motivating health behavior, and recent years have seen an increase in games designed for changing human behaviors or attitudes. However, these games are limited in two major ways: first, they are not based on theories of what motivates healthy behavior change. This makes it difficult to evaluate why a persuasive approach works. Second, most persuasive games treat players as a monolithic group. As an attempt to resolve these weaknesses, we conducted a large-scale survey of 642 gamers' eating habits and their associated determinants of healthy behavior to understand how health behavior relates to gamer type. We developed seven different models of healthy eating behavior for the gamer types identified by BrainHex. We then explored the differences between the models and created two approaches for effective persuasive game design based on our results. The first is a one-size-fits-all approach that will motivate the majority of the population, while not demotivating any players. The second is a personalized approach that will best motivate a particular type of gamer. Finally, to make our approaches actionable in persuasive game design, we map common game mechanics to the determinants of healthy behavior.


title: Spectral Network Embedding: A Fast and Scalable Method via Sparsity

abstract: Network embedding aims to learn low-dimensional representations of nodes in a network, while the network structure and inherent properties are preserved. It has attracted tremendous attention recently due to significant progress in downstream network learning tasks, such as node classification, link prediction, and visualization. However, most existing network embedding methods suffer from the expensive computations due to the large volume of networks. In this paper, we propose a $10\times \sim 100\times$ faster network embedding method, called Progle, by elegantly utilizing the sparsity property of online networks and spectral analysis. In Progle, we first construct a \textit{sparse} proximity matrix and train the network embedding efficiently via sparse matrix decomposition. Then we introduce a network propagation pattern via spectral analysis to incorporate local and global structure information into the embedding. Besides, this model can be generalized to integrate network information into other insufficiently trained embeddings at speed. Benefiting from sparse spectral network embedding, our experiment on four different datasets shows that Progle outperforms or is comparable to state-of-the-art unsupervised comparison approaches---DeepWalk, LINE, node2vec, GraRep, and HOPE, regarding accuracy, while is $10\times$ faster than the fastest word2vec-based method. Finally, we validate the scalability of Progle both in real large-scale networks and multiple scales of synthetic networks.


title: A continental-scale tool for acoustic identification of European bats

abstract: Summary 1. Acoustic methods are used increasingly to survey and monitor bat populations. However, the use of acoustic methods at continental scales can be hampered by the lack of standardized and objective methods to identify all species recorded. This makes comparable continent-wide monitoring difficult, impeding progress towards developing biodiversity indicators, transboundary conservation programmes and monitoring species distribution changes. 2. Here we developed a continental-scale classifier for acoustic identification of bats, which can be used throughout Europe to ensure objective, consistent and comparable species identifications. We selected 1350 full-spectrum reference calls from a set of 15 858 calls of 34 European species, from EchoBank, a global echolocation call library. We assessed 24 call parameters to evaluate how well they distinguish between species and used the 12 most useful to train a hierarchy of ensembles of artificial neural networks to distinguish the echolocation calls of these bat species. 3. Calls are first classified to one of five call-type groups, with a median accuracy of 976%. The median species-level classification accuracy is 837%, providing robust classification for most European species, and an estimate of classification error for each species. 4. These classifiers were packaged into an online tool, iBatsID, which is freely available, enabling anyone to classify European calls in an objective and consistent way, allowing standardized acoustic identification across the continent. 5. Synthesis and applications. iBatsID is the first freely available and easily accessible continental-scale bat call classifier, providing the basis for standardized, continental acoustic bat monitoring in Europe. This method can provide key information to managers and conservation planners on distribution changes and changes in bat species activity through time.


title: A new skin-surface device for measuring the curvature and global and segmental ranges of motion of the spine: reliability of measurements and comparison with data reviewed from the literature

abstract: There is an increasing awareness of the risks and dangers of exposure to radiation associated with repeated radiographic assessment of spinal curvature and spinal movements. As such, attempts are continuously being made to develop skin-surface devices for use in examining the progression and response to treatment of various spinal disorders. However, the reliability and validity of measurements recorded with such devices must be established before they can be recommended for use in the research or clinical environment. The aim of this study was to examine the reliability of measurements using a newly developed skin-surface device, the Spinal Mouse. Twenty healthy volunteers (mean age 41±12 years, nine males, 11 females) took part. On 2 separate days, spinal curvature was measured with the Spinal Mouse during standing, full flexion, and full extension (each three times by each of two examiners). Paired t-tests, intraclass correlation coefficients (ICC), and standard errors of measurement (SEM) with 95% confidence intervals were used to characterise between-day and interexaminer reliability for: standing sacral angle, lumbar lordosis, thoracic kyphosis, and ranges of motion (flexion, extension) of the thoracic spine, lumbar spine, hips, and trunk. The between-day reliability for segmental ranges of flexion was also determined for each motion segment from T1-2 to L5-S1. The majority of parameters measured for the ‘global regions’ (thoracic, lumbar, or hips) showed good between-day reliability. Depending on the parameter of interest, between-day ICCs ranged from 0.67 to 0.92 for examiner 1 (average 0.82) and 0.57 to 0.95 for examiner 2 (average 0.83); for 70% of the parameters measured, the ICCs were greater than 0.8 and generally highest for the lumbar spine and whole trunk measures. For lumbar spine range of flexion, the SEM was approximately 3°. The ICCs were also good for the interexaminer comparisons, ranging from 0.62 to 0.93 on day 1 (average 0.81) and 0.70 to 0.94 on day 2 (average 0.86), although small systematic differences were sometimes observed in their mean values. The latter were still evident even if both examiners used the same skin markings. For segmental ranges of flexion, the ICCs varied between vertebral levels but overall were lower than for the global measures (average for all levels in all analyses, ICC 0.6). For each examiner, the average between-day SEM over all vertebral levels was approximately 2°. For ‘global’ regions of the spine, the Spinal Mouse delivered consistently reliable values for standing curvatures and ranges of motion which compared well with those reported in the literature. This suggests that the device can be reliably implemented for in vivo studies of the sagittal profile and range of motion of the spine. As might be expected for the smaller angles being measured, the segmental ranges of flexion showed lower reliability. Their usefulness with regard to the interpretation of individual results and the detection of ‘real change’ on an individual basis thus remains questionable. Nonetheless, the group mean values showed few between-day differences, suggesting that the device may still be of use in providing clinically interesting data on segmental motion when examining groups of individuals with a given spinal pathology or undergoing some type of intervention.


title: Exploring Risk and Fraud Scenarios in Affiliate Marketing Technologies from the Advertisers perspective

abstract: Affiliate Marketing (AM) has become an important and cost effective tool for e-commerce. There are numerous risks and vulnerabilities that are typically associated with AM. Though a well-planned AM model can greatly benefit the e-commerce strategies of an enterprise, a haphazardly implemented system can expose a business enterprise to major risks and vulnerabilities, which can lead to great financial losses through fraudulent activities. This research-in-progress has identified some of the risks and the technical background of those scenarios. The research will now move on to build a functional prototype of an AM network to design and test solutions to control the identified risks.


title: A high-voltage pulse generator for corona plasma generation

abstract: This paper discusses a high-voltage pulse generator for producing corona plasma. The generator consists of three resonant charging circuits, a transmission line transformer, and a triggered spark-gap switch. Voltage pulses in the order of 30-100 kV with a rise time of 10-20 ns, a pulse duration of 100-200 ns, a pulse repetition rate of 1-900 pps, an energy per pulse of 0.5-12 J, and the average power of up to 10 kW have been achieved with total energy conversion efficiency of 80%-90%. Moreover, the system has been used in four industrial demonstrations on volatile organic compounds removal, odor emission control, and biogas conditioning.


title: Original article Savannah: mobile gaming and learning?

abstract: This paper reports a study that attempts to explore how using mobile technologies in direct physical interaction with space and with other players can be combined with principles of engagement and self-motivation to create a powerful and engaging learning experience. We developed a mobile gaming experience designed to encourage the development of children's conceptual understanding of animal behaviour. Ten children (five boys and five girls) aged between 11 and 12 years played and explored the game. The findings from this study offer interesting insights into the extent to which mobile gaming might be employed as a tool for supporting learning. It also highlights a number of major challenges that this format raises for the organisation of learning within schools and the design of such resources.


title: Accelerating Deep Neural Networks with Spatial Bottleneck Modules

abstract: This paper presents an efficient module named spatial bottleneck for accelerating the convolutional layers in deep neural networks. The core idea is to decompose convolution into two stages, which first reduce the spatial resolution of the feature map, and then restore it to the desired size. This operation decreases the sampling density in the spatial domain, which is independent yet complementary to network acceleration approaches in the channel domain. Using different sampling rates, we can tradeoff between recognition accuracy and model complexity. As a basic building block, spatial bottleneck can be used to replace any single convolutional layer, or the combination of two convolutional layers. We empirically verify the effectiveness of spatial bottleneck by applying it to the deep residual networks. Spatial bottleneck achieves 2x and 1.4x speedup on the regular and channel-bottlenecked residual blocks, respectively, with the accuracies retained in recognizing low-resolution images, and even improved in recognizing high-resolution images.


title: Adaptive Recommender Systems for Travel Planning

abstract: Conversational recommender systems have been introduced in Travel and Tourism applications in order to support interactive dialogues which assist users in acquiring their goals, e.g., travel planning in a dynamic packaging system. Notwithstanding this increased interactivity, these systems employ an interaction strategy that is specified a priori (at design time) and is followed quite rigidly during the interaction. In this paper we illustrate a new type of conversational recommender system which uses Reinforcement Learning techniques in order to autonomously learn an adaptive interaction strategy. After a successful validation in an off-line experiment (with simulated users), the approach is now applied within an online recommender system which is supported by the Austrian Tourism portal (Austria.info). In this paper, we present the methodology behind the adaptive conversational recommender system and a summarization of the most important issues which have been addressed in order to validate the approach in an online context with real users.


title: Enhanced brain connectivity in long-term meditation practitioners

abstract: Abstract Very little is currently known about the cerebral characteristics that underlie the complex processes of meditation as only a limited number of studies have addressed this topic. Research exploring structural connectivity in meditation practitioners is particularly rare. We thus acquired diffusion tensor imaging (DTI) data of high angular and spatial resolution and used atlas-based tract mapping methods to investigate white matter fiber characteristics in a well-matched sample of long-term meditators and controls (n = 54). A broad field mapping approach estimated the fractional anisotropy (FA) for twenty different fiber tracts (i.e., nine tracts in each hemisphere and two inter-hemispheric tracts) that were subsequently used as dependent measures. Results showed pronounced structural connectivity in meditators compared to controls throughout the entire brain within major projection pathways, commissural pathways, and association pathways. The largest group differences were observed within the corticospinal tract, the temporal component of the superior longitudinal fasciculus, and the uncinate fasciculus. While cross-sectional studies represent a good starting point for elucidating possible links between meditation and white matter fiber characteristics, longitudinal studies will be necessary to determine the relative contribution of nature and nurture to enhanced structural connectivity in long-term meditators.


title: Editorial: Brand loyalty programs: Are they shams? Marketing Sci

abstract: Brand loyalty and the more modern topics of computing customer lifetime value and structuring loyalty programs remain the focal point for a remarkable number of research articles. At first, this research appears consistent with firm practices. However, close scrutiny reveals disaffirming evidence. Many current so-called loyalty programs appear unrelated to the cultivation of customer brand loyalty and the creation of customer assets. True investments are up-front expenditures that produce much greater future returns. In contrast, many so-called loyalty programs are shams because they produce liabilities (e.g., promises of future rewards or deferred rebates) rather than assets. These programs produce short-term revenue from customers while producing substantial future obligations to those customers. Rather than showing trust by committing to the customer, the firm asks the customer to trust the firm--that is, trust that future rewards are indeed forthcoming. The entire idea is antithetical to the concept of a customer asset. Many modern loyalty programs resemble old-fashioned trading stamps or deferred rebates that promise future benefits for current patronage. A true loyalty program invests in the customer (e.g., provides free up-front training, allows familiarization or customization) with the expectation of greater future revenue. Alternative motives for extant programs are discussed.


title: Intelligent plant disease detection system using wireless multimedia sensor networks

abstract: Wireless multimedia sensor networks (WMSNs) attracts significant attention in the field of agriculture where disease detection plays an important role. To improve the cultivation yield of plants it is necessary to detect the onset of diseases in plants and provide advice to farmers who will act based on the received suggestion. Due to the limitations of WMSN, it is necessary to design a simple system which can provide higher accuracy with less complexity. In this paper a novel disease detection system (DDS) is proposed to detect and classify the diseases in leaves. Statistical based thresholding strategy is proposed for segmentation which is less complex compared to k-means clustering method. The features extracted from the segmented image will be transmitted through sensor nodes to the monitoring site where the analysis and classification is done using Support Vector Machine classifier. The performance of the proposed DDS has been evaluated in terms of accuracy and is compared with the existing k-means clustering technique. The results show that the proposed method provides an overall accuracy of around 98%. The transmission energy is also analyzed in real time using TelosB nodes.


title: Exploiting Feature and Class Relationships in Video Categorization with Regularized Deep Neural Networks

abstract: In this paper, we study the challenging problem of categorizing videos according to high-level semantics such as the existence of a particular human action or a complex event. Although extensive efforts have been devoted in recent years, most existing works combined multiple video features using simple fusion strategies and neglected the utilization of inter-class semantic relationships. This paper proposes a novel unified framework that jointly exploits the feature relationships and the class relationships for improved categorization performance. Specifically, these two types of relationships are estimated and utilized by imposing regularizations in the learning process of a deep neural network (DNN). Through arming the DNN with better capability of harnessing both the feature and the class relationships, the proposed regularized DNN (rDNN) is more suitable for modeling video semantics. We show that rDNN produces better performance over several state-of-the-art approaches. Competitive results are reported on the well-known Hollywood2 and Columbia Consumer Video benchmarks. In addition, to stimulate future research on large scale video categorization, we collect and release a new benchmark dataset, called FCVID, which contains 91,223 Internet videos and 239 manually annotated categories.


title: Performance comparison of FPGA, GPU and CPU in image processing

abstract: Many applications in image processing have high inherent parallelism. FPGAs have shown very high performance in spite of their low operational frequency by fully extracting the parallelism. In recent micro processors, it also becomes possible to utilize the parallelism using multi-cores which support improved SIMD instructions, though programmers have to use them explicitly to achieve high performance. Recent GPUs support a large number of cores, and have a potential for high performance in many applications. However, the cores are grouped, and data transfer between the groups is very limited. Programming tools for FPGA, SIMD instructions on CPU and a large number of cores on GPU have been developed, but it is still difficult to achieve high performance on these platforms. In this paper, we compare the performance of FPGA, GPU and CPU using three applications in image processing; two-dimensional filters, stereo-vision and k-means clustering, and make it clear which platform is faster under which conditions.


title: Map Matching with Inverse Reinforcement Learning

abstract: We study map-matching, the problem of estimating the route that is traveled by a vehicle, where the points observed with the Global Positioning System are available. A state-of-the-art approach for this problem is a Hidden Markov Model (HMM). We propose a particular transition probability between latent road segments by the use of the number of turns in addition to the travel distance between the latent road segments. We use inverse reinforcement learning to estimate the importance of the number of turns relative to the travel distance. This estimated importance is incorporated in the transition probability of the HMM. We show, through numerical experiments, that the error of map-matching can be reduced substantially with the proposed transition probability.


title: Analogy Events: How Examples are Used During Problem Solving

abstract: In attempting to fit a model of analogical problem solving to protocol data of students solving physics problems, several unexpected observations were made. Analogies between examples and exercises (a form of case-based reasoning) consisted of two distinct types of events. During an initialization event, the solver retrieved an example, set up a mapping between it and the problem, and decided whether the example was useful. During a transfer event, the solver inferred something about the problem's solution. Many different types of initialization and transfer events were observed. Poor solvers tended to follow the example verbatim, copying each solution line over to the problem. Good solvers tried to solve the problem themselves, but referred to the example when they got stuck, or wanted to check a step, or wanted to avoid a detailed calculation. Rather than learn from analogies, both Good and Poor solvers tended to repeat analogies at subsequent similar situations. A revised version of the model is proposed (but not yet implemented) that appears to be consistent with all the findings observed in this and other studies of the same subjects.


title: Intermediation in a Sharing Economy: Insurance, Moral Hazard, and Rent Extraction

abstract: Electronic intermediaries have become pervasive in sales transactions for many durables, such as cars, power tools, and apartments. Yet only recently have they successfully tackled the challenge of enabling parties to share such goods. A key impediment to sharing is a lender's concern about damage due to unobservable actions by a renter, usually resulting in moral hazard. This paper shows how an intermediary can eliminate the moral-hazard problem by providing optimal insurance to the lender and first-best-incentives to the renter to exert care, as long as market participants are risk-neutral. The solution is illustrated for the collaborative housing market but applies in principle to any sharing market with vertically differentiated goods. A population of renters, heterogeneous both in their preferences for housing quality and with respect to the amounts of care they exert in a rental situation, faces a choice between collaborative housing and staying at a local hotel. The private hosts choose their prices strategically and the intermediary sets commission rates on both sides of the market as well as insurance terms for the rental agreement. The latter are set so as to eliminate moral hazard. The intermediary is able to extract the gains the hosts would earn compared to transacting directly. Finally, even if hotels set their prices at the outset so as to maximize collusive profits, we find that collaborative housing persists at substantial market shares, regardless of the difference between the efficiencies of hosts and hotels to reduce renters' cost of effort. The aggregate of hosts, intermediary, and hotels benefits from (a variety in) these effort costs, which indicates that the intermediated sharing of goods is an economically viable, robust phenomenon.


title: The usage-based theory of language acquisition

abstract: ‘Meaning is use’ represents an approach to the functional or semantic dimension of linguistic communication. It originated with Wittgenstein (1953) and other pragmatically based philosophers of language, who wanted to combat the idea that meanings are things and instead focus on howpeople use linguistic conventions to achieve social ends. ‘Structure emerges from use’ represents an approach to the structural or grammatical dimension of linguistic communication. It is implicit in the work on grammaticalization and language change of many historical linguists, and has been made explicit by Langacker (1987, 2000) and other usage-based linguists, whowant to combat the idea of a wholly formal grammar devoid of meaning and instead focus on how meaning-based grammatical constructions emerge from individual acts of language use. Drawing on the work of many other researchers, Tomasello (2003) proposes a usage-based theory of language acquisition. Paralleling the two aphorisms above, the proposal is that children come to the process of language acquisition, at around one year of age, equipped with two sets of cognitive skills, both evolved for other, more general functions before linguistic communication emerged in the human species:


title: LUMBAR DISC HERNIATION

abstract: Lumbar disc herniation is the most common diagnosis among the degenerative abnormalities of the lumbar spine (affecting 2 to 3% of the population), and is the principal cause of spinal surgery among the adult population. The typical clinical picture includes initial lumbalgia, followed by progressive sciatica. The natural history of disc herniation is one of rapid resolution of the symptoms (four to six weeks). The initial treatment should be conservative, managed through medication and physiotherapy, sometimes associated with percutaneous nerve root block. Surgical treatment is indicated if pain control is unsuccessful, if there is a motor deficit greater than grade 3, if there is radicular pain associated with foraminal stenosis, or if cauda equina syndrome is present. The latter represents a medical emergency. A refined surgical technique, with removal of the extruded fragment and preservation of the ligamentum flavum, resolves the sciatic symptoms and reduces the risk of recurrence over the long term.


title: Cultural diversity among nursing students: Reanalysis of the cultural awareness scale

abstract: Nurses are educated to provide culturally competent care. Cultural competence begins with cultural awareness, a concept previously measured with the Cultural Awareness Scale (CAS). The purpose of this study was to reanalyze the CAS to determine construct validity and differences in cultural awareness among students of varying educational levels and experiences. The sample consisted of 150 nursing students (92% female, 33.6% racial minorities). Confirmatory factor analysis yielded three factors (CFI = 0.868, TLI = 0.854, RMSEA = 0.065, and SRMR = 0.086). Cronbach's alpha ranged from 0.70 to 0.89. There were significant differences among educational levels, with lower division BSN students generally scoring higher than upper division and master's of science in nursing students. Students who had taken courses on cultural diversity or global health generally outscored those who had not taken such courses. Findings support the validity of the CAS and its applicability to research studies of cultural awareness in nursing.


title: Position Control of a Sensorless Stepper Motor

abstract: In this paper, the experimental results of position control of the hybrid stepper motor without a mechanical sensor are exhibited. Use of the steady-state extended Kalman filter to estimate the mechanical variables of the motor is shown. With this method the computing time is reduced. The initial rotor position is estimated by the impulse voltage technique. For position control, a simple state feedback control that can compensate the load torque variations was designed. The robustness against the motor parameters variation was also studied. A field-oriented control strategy is chosen. It is known that the mechanical position is crucially important to achieve this strategy. Finally, favorable experimental results are shared.


title: A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains

abstract: Voxel-based-morphometry (VBM) is a whole-brain, unbiased technique for characterizing regional cerebral volume and tissue concentration differences in structural magnetic resonance images. We describe an optimized method of VBM to examine the effects of age on grey and white matter and CSF in 465 normal adults. Global grey matter volume decreased linearly with age, with a significantly steeper decline in males. Local areas of accelerated loss were observed bilaterally in the insula, superior parietal gyri, central sulci, and cingulate sulci. Areas exhibiting little or no age effect (relative preservation) were noted in the amygdala, hippocampi, and entorhinal cortex. Global white matter did not decline with age, but local areas of relative accelerated loss and preservation were seen. There was no interaction of age with sex for regionally specific effects. These results corroborate previous reports and indicate that VBM is a useful technique for studying structural brain correlates of ageing through life in humans.


title: A Comparative Analysis of Histogram Equalization based Techniques for Contrast Enhancement and Brightness Preserving

abstract: Histogram Equalization (HE) is a simple and effective image enhancement technique.But, it tends to change the mean brightness of the image to the middle level of the permitted range, and hence is not a very suitable for consumer product. While preserving the original brightness is essential to avoid annoying artefacts. To preserve brightness and to enhance contrast of images, numerous methods are introduced, but many of them present unwanted artefacts such as intensity saturation, over-enhancement and noise amplification. In the present paper, available histogram equalization based methods are reviewed and compared with image quality measurement (IQM)tools such as Absolute Mean Brightness Error (AMBE) to assess brightness preserving and Peak Signal-to-Noise Ratio (PSNR) to evaluate contrast enhancement.


title: Structural modeling and analysis of dengue-mediated inhibition of interferon signaling pathway.

abstract: Dengue virus (DENV) belongs to the family Flaviviridae and can cause major health problems worldwide, including dengue fever and dengue shock syndrome. DENV replicon in human cells inhibits interferon alpha and beta with the help of its non-structural proteins. Non-structural protein 5 (NS5) of DENV is responsible for the proteasome-mediated degradation of signal transducer and activator of transcription (STAT) 2 protein, which has been implicated in the development of resistance against interferon-mediated antiviral effect. This degradation of STAT2 primarily occurs with the help of E3 ubiquitin ligases. Seven in absentia homologue (SIAH) 2 is a host protein that can mediate the ubiquitination of proteins and is known for its interaction with NS5. In this study, comprehensive computational analysis was performed to characterize the protein-protein interactions between NS5, SIAH2, and STAT2 to gain insight into the residues and sites of interaction between these proteins. The objective of the study was to structurally characterize the NS5-STAT2, SIAH2-STAT2, and NS5-SIAH2 interactions along with the determination of the possible reaction pattern for the degradation of STAT2. Docking and physicochemical studies indicated that DENV NS5 may first interact with the host SIAH2, which can then proceed towards binding with STAT2 from the side of SIAH2. These implications are reported for the first time and require validation by wet-lab studies.


title: A review of genetic algorithms applied to training radial basis function networks

abstract: The problems associated with training feedforward artificial neural networks (ANNs) such as the multilayer perceptron (MLP) network and radial basis function (RBF) network have been well documented. The solutions to these problems have inspired a considerable amount of research, one particular area being the application of evolutionary search algorithms such as the genetic algorithm (GA). To date, the vast majority of GA solutions have been aimed at the MLP network. This paper begins with a brief overview of feedforward ANNs and GAs followed by a review of the current state of research in applying evolutionary techniques to training RBF networks.


title: Shaping Agility through Digital Options: Reconceptualizing the Role of Information Technology in Contemporary Firms

abstract: Agility is vital to the innovation and competitive performance of firms in contemporary business environments. Firms are increasingly relying on information technologies, including process, knowledge, and communication technologies, to enhance their agility. The purpose of this paper is to broaden understanding about the strategic role of IT by examining the nomological network of influences through which IT impacts firm performance. By drawing upon recent thinking in the strategy, entrepreneurship, and IT management literatures, this paper uses a multitheoretic lens to argue that information technology investments and capabilities influence firm performance through three significant organizational capabilities (agility, digital options, and entrepreneurial alertness) and strategic processes (capability-building, entrepreneurial action, and coevolutionary adaptation). We also propose that these dynamic capabilities and strategic processes impact the ability of firms to launch many and varied competitive actions and that, in turn, these competitive actions are a significant antecedent of firm performance. Through our theorizing, we draw attention to a significant and reframed role of IT as a digital options generator in contemporary firms.


title: Clinical Use of HIV Integrase Inhibitors: A Systematic Review and Meta-Analysis

abstract: Background ::: Optimal regimen choice of antiretroviral therapy is essential to achieve long-term clinical success. Integrase inhibitors have swiftly been adopted as part of current antiretroviral regimens. The purpose of this study was to review the evidence for integrase inhibitor use in clinical settings. ::: ::: ::: Methods ::: MEDLINE and Web-of-Science were screened from April 2006 until November 2012, as were hand-searched scientific meeting proceedings. Multiple reviewers independently screened 1323 citations in duplicate to identify randomized controlled trials, nonrandomized controlled trials and cohort studies on integrase inhibitor use in clinical practice. Independent, duplicate data extraction and quality assessment were conducted. ::: ::: ::: Results ::: 48 unique studies were included on the use of integrase inhibitors in antiretroviral therapy-naive patients and treatment-experienced patients with either virological failure or switching to integrase inhibitors while virologically suppressed. On the selected studies with comparable outcome measures and indication (n = 16), a meta-analysis was performed based on modified intention-to-treat (mITT), on-treatment (OT) and as-treated (AT) virological outcome data. In therapy-naive patients, favorable odds ratios (OR) for integrase inhibitor-based regimens were observed, (mITT OR 0.71, 95% CI 0.59–0.86). However, integrase inhibitors combined with protease inhibitors only did not result in a significant better virological outcome. Evidence further supported integrase inhibitor use following virological failure (mITT OR 0.27; 95% CI 0.11–0.66), but switching to integrase inhibitors from a high genetic barrier drug during successful treatment was not supported (mITT OR 1.43; 95% CI 0.89–2.31). Integrase inhibitor-based regimens result in similar immunological responses compared to other regimens. A low genetic barrier to drug-resistance development was observed for raltegravir and elvitegravir, but not for dolutegravir. ::: ::: ::: Conclusion ::: In first-line therapy, integrase inhibitors are superior to other regimens. Integrase inhibitor use after virological failure is supported as well by the meta-analysis. Careful use is however warranted when replacing a high genetic barrier drug in treatment-experienced patients switching successful treatment.


title: Comparative Image Fusion Analysais

abstract: Image fusion is and will be an integral part of many existing and future surveillance systems. However, little or no systematic attempt has been made up to now on studying the relative merits of various fusion techniques and their effectiveness on real multi-sensor imagery. In this paper we provide a method for evaluating the performance of image fusion algorithms. We define a set of measures of effectiveness for comparative performance analysis and then use them on the output of a number of fusion algorithms that have been applied to a set of real passive infrared (IR) and visible band imagery.


title: Acceleration of Macroscopic Particle to Hypervelocity by High-Intensity Beams

abstract: Hypervelocity (~1000-km/s) impact of a macroscopic particle (macron) has profound influences in high energy density physics and inertial fusion energy researches. As the charge-mass ratio of macrons is too low, the length of an electrostatic accelerator can reach hundreds to thousands of kilometers, rendering macron acceleration impractical. To reduce the accelerator length, a much higher electric field than what the most powerful klystrons can provide is desired. One practical choice may be the high-intensity charged particle beam ldquoblowing-piperdquo approach. In this approach, a high-intensity (~10-kA) medium-energy (0.5-2-MeV) long-pulse (10-1000-mus) positively charged ion beam shots to a heavily charged millimeter-size macron to create a local high-strength electric field (~1010 V/m), accelerating the macron efficiently. We will discuss the physics and challenges involved in this concept and give an illustrative simulation.


title: A Three-Winding Coupled-Inductor DC–DC Converter Topology With High Voltage Gain and Reduced Switch Stress

abstract: This paper presents a dc–dc boost converter topology for low input and high output voltage applications, such as photovoltaic systems, fuel cell systems, high-intensity discharge lamp, and electric vehicles. The suggested configuration consists of a three-winding coupled-inductor, a single switch and two hybrid voltage multiplier cells. Furthermore, two independent hybrid voltage multiplier cells are in parallel when the single switch S is turned on , and they are in series when the switch S is turned off . So, the advantages of the proposed converter structure are summarized as follows: 1) A coupled inductor with three windings is introduced in the presented converter structure. The two secondary windings of the coupled inductor are, respectively, used to form a hybrid multiplier cell on the one hand, on the other hand, it increases the control freedom of the voltage gain, enhances the utility rate of magnetic core and power density, and reduces the stress of power components to provide a stable constant dc output voltage. 2) The two hybrid multiplier cells can absorb synchronously the energy of stray inductance, which not only reduces the current stress of corresponding diodes, but also greatly alleviates the spike voltage of the main switch, which improves the efficiency. 3) The two hybrid multiplier cells are connected in series to supply power energy for the load, so the voltage gain is extended greatly due to this particular structure. Thus, low-voltage low-conduction-loss devices can be selected and the reverse-recovery currents within the diodes are inhibited. The operating principles and the steady state analyses of the proposed converter are discussed in detail. Finally, a test prototype has been implemented in the laboratory, and the simulated and experimental results show satisfactory agreement with the theoretical analysis.


title: Design of simple DC-to-DC Wireless Power Transfer via inductive coupling

abstract: This research presents a wireless DC-to-DC power transfer over a short distance. Distance of remotely located target from the source coil and received voltage at the load are the major concern in the Wireless Power Transfer (WPT) applications. The inversely proportional behavior of these parameters degrades the system performance. In order to optimize these values and achieving transfer efficiency, microelectronic circuit is developed and simulated using NI Multisim. The transmitter and receiver modules are constructed and their individual blocks are simulated and analyzed for wireless power transfer. Two inductively coupled coils are designed and used in both transmitter and receiver sections. The inverter is designed using H-bridge with five CMOSFET (IRF520NS, IRF5210S) and its gate signal is generated using NE555 timer IC with two output clock signal where one was inverted using an NMOS inverter. Through simulation, it is observed that with such a design, the power transfer has a limited range, and the range will be smaller for smaller receiving coils and improper alignment. A simulated model is proposed and implemented in this paper. The simulation outputs of each unit is plotted and analyzed separately. In addition, transient plot of load resistance versus output voltage are illustrated to analyze the power of dc-dc power transfer circuit. It is observed that the output voltage is increased correspondingly with increasing load resistance and determined good output voltage is harvested with the optimized load resistance is at 1 kΩ and is 4.25 V. Simulated results shows that the proposed system can transfer power with high efficiency. This proposed system could be made commercially viable through further research work.


Mallows and Generalized Mallows Model for


title: The cognitive neuroscience of working memory.

abstract: For more than 50 years, psychologists and neuroscientists have recognized the importance of a working memory to coordinate processing when multiple goals are active and to guide behavior with information that is not present in the immediate environment. In recent years, psychological theory and cognitive neuroscience data have converged on the idea that information is encoded into working memory by allocating attention to internal representations, whether semantic long-term memory (e.g., letters, digits, words), sensory, or motoric. Thus, information-based multivariate analyses of human functional MRI data typically find evidence for the temporary representation of stimuli in regions that also process this information in nonworking memory contexts. The prefrontal cortex (PFC), on the other hand, exerts control over behavior by biasing the salience of mnemonic representations and adjudicating among competing, context-dependent rules. The “control of the controller” emerges from a complex interplay between PFC and striatal circuits and ascending dopaminergic neuromodulatory signals.


title: Asymmetric Transitivity Preserving Graph Embedding

abstract: Graph embedding algorithms embed a graph into a vector space where the structure and the inherent properties of the graph are preserved. The existing graph embedding methods cannot preserve the asymmetric transitivity well, which is a critical property of directed graphs. Asymmetric transitivity depicts the correlation among directed edges, that is, if there is a directed path from u to v, then there is likely a directed edge from u to v. Asymmetric transitivity can help in capturing structures of graphs and recovering from partially observed graphs. To tackle this challenge, we propose the idea of preserving asymmetric transitivity by approximating high-order proximity which are based on asymmetric transitivity. In particular, we develop a novel graph embedding algorithm, High-Order Proximity preserved Embedding (HOPE for short), which is scalable to preserve high-order proximities of large scale graphs and capable of capturing the asymmetric transitivity. More specifically, we first derive a general formulation that cover multiple popular high-order proximity measurements, then propose a scalable embedding algorithm to approximate the high-order proximity measurements based on their general formulation. Moreover, we provide a theoretical upper bound on the RMSE (Root Mean Squared Error) of the approximation. Our empirical experiments on a synthetic dataset and three real-world datasets demonstrate that HOPE can approximate the high-order proximities significantly better than the state-of-art algorithms and outperform the state-of-art algorithms in tasks of reconstruction, link prediction and vertex recommendation.


Intensification of Intimacy in Computer-Mediated Communication From Perception to Behavior : Disclosure Reciprocity and the


title: Balancing control actions for cascaded H-bridge converters with integrated battery energy storage

abstract: This paper investigates the ways to handle active power components for control and balancing purposes in three-phase Cascaded H-Bridge converters with embedded battery energy storage systems. The split storage elements are interfaced to the converter modules through a non-isolated DC/DC stage, in order to eliminate the inherent second-order harmonic of the module output current. Both the cases of star and delta converter configurations are analyzed. The topology-specific degrees of freedom are exploited, control algorithms are developed and limitations are established. Potential grid asymmetries are taken into consideration through the utilization of symmetrical components. Several modes of operation are studied, such as independent load feeding, State of Charge balancing as well as converter operation under grid faults.


title: Learning Relaxed Deep Supervision for Better Edge Detection

abstract: Wepropose using relaxed deep supervision (RDS) within convolutional neural networks for edge detection. The conventional deep supervision utilizes the general groundtruth to guide intermediate predictions. Instead, we build hierarchical supervisory signals with additional relaxed labels to consider the diversities in deep neural networks. We begin by capturing the relaxed labels from simple detectors (e.g. Canny). Then we merge them with the general groundtruth to generate the RDS. Finally we employ the RDS to supervise the edge network following a coarse-to-fine paradigm. These relaxed labels can be seen as some false positives that are difficult to be classified. Weconsider these false positives in the supervision, and are able to achieve high performance for better edge detection. Wecompensate for the lack of training images by capturing coarse edge annotations from a large dataset of image segmentations to pretrain the model. Extensive experiments demonstrate that our approach achieves state-of-the-art performance on the well-known BSDS500 dataset (ODS F-score of .792) and obtains superior cross-dataset generalization results on NYUD dataset.


title: Review of Semantic-Free Utterances in Social Human–Robot Interaction

abstract: As a young and emerging field in social human–robot interaction (HRI), semantic-free utterances (SFUs) research has been receiving attention over the last decade. SFUs are an auditory interaction means for machines that allow emotion and intent expression, which are composed of vocalizations and sounds without semantic content or language dependence. Currently, SFUs are most commonly utilized in animation movies (e.g., R2-D2, WALL-E, Despicable Me), cartoons (e.g., “Teletubbies,” “Morph,” “La Linea”), and computer games (e.g., The Sims) and hold significant potential for applications in HRI. SFUs are categorized under four general types: Gibberish Speech (GS), Non-Linguistic Utterances (NLUs), Musical Utterances (MU), and Paralinguistic Utterances (PU). By introducing the concept of SFUs and bringing multiple sets of studies in social HRI that have never been analyzed jointly before, this article addresses the need for a comprehensive study of the existing literature for SFUs. It outlines the current gran...


title: Modeling and Analysis of State-of-the-art VM-based Cloud Management Platforms

abstract: Virtualization is a key aspect to achieve scalability and flexibility in a cloud. Many solutions have been proposed to monitor and deploy Virtual Machines (VM) in resource pool of cloud. However, most of the cloud management systems, such as Amazon EC2 are proprietary. In the said perspective, many open source VM-based platforms have tossed for general users to research. The existing work has mainly focused on the discussion of architecture, feature-set, and performance analysis. Other important aspects, such as formal analysis, modeling, and verification are usually ignored. In this paper, we provide formal analysis, modeling, and verification of three open source state-of-the-art VM-based cloud platforms: (a) Eucalyptus, (b) Open Nebula, and (c) Nimbus. We used High-Level Petri Nets (HLPN) to model and analyze the structural and behavioral properties of the systems. Moreover, to verify the models, we have used Satisfiability Modulo Theories Library (SMT-Lib) and Z3 Solver. We modeled about 100 VM to verify the correctness and feasibility of our models. The results reveal that the models are functioning correctly. Moreover, the increase in the number of VM does not affect the working of the models that indicates the practicability of the models in a highly scalable and flexible environment.


title: SemEval-2015 Task 17: Taxonomy Extraction Evaluation (TExEval)

abstract: This paper describes the first shared task on Taxonomy Extraction Evaluation organised as part of SemEval-2015. Participants were asked to find hypernym-hyponym relations between given terms. For each of the four selected target domains the participants were provided with two lists of domainspecific terms: a WordNet collection of terms and a well-known terminology extracted from an online publicly available taxonomy. A total of 45 taxonomies submitted by 6 participating teams were evaluated using standard structural measures, the structural similarity with a gold standard taxonomy, and through manual quality assessment of sampled novel relations.


title: Implementing a Wall-In Building Placement in StarCraft with Declarative Programming

abstract: In real-time strategy games like StarCraft, skilled players often block the entrance to their base with buildings to prevent the opponent's units from getting inside. This technique, called "walling-in", is a vital part of player's skill set, allowing him to survive early aggression. However, current artificial players (bots) do not possess this skill, due to numerous inconveniences surfacing during its implementation in imperative languages like C++ or Java. In this text, written as a guide for bot programmers, we address the problem of finding an appropriate building placement that would block the entrance to player's base, and present a ready to use declarative solution employing the paradigm of answer set programming (ASP). We also encourage the readers to experiment with different declarative approaches to this problem.