title: Zero-Shot Activity Recognition with Verb Attribute Induction

abstract: In this paper, we investigate large-scale zero-shot activity recognition by modeling the visual and linguistic attributes of action verbs. For example, the verb "salute" has several properties, such as being a light movement, a social act, and short in duration. We use these attributes as the internal mapping between visual and textual representations to reason about a previously unseen action. In contrast to much prior work that assumes access to gold standard attributes for zero-shot classes and focuses primarily on object attributes, our model uniquely learns to infer action attributes from dictionary definitions and distributed word representations. Experimental results confirm that action attributes inferred from language can provide a predictive signal for zero-shot prediction of previously unseen activities.


title: Optimality guarantees for distributed statistical estimation

abstract: Large data sets often require performing distributed statistical estimation, with a full data set split across multiple machines and limited communication between machines. To study such scenarios, we define and study some refinements of the classical minimax risk that apply to distributed settings, comparing to the performance of estimators with access to the entire data. Lower bounds on these quantities provide a precise characterization of the minimum amount of communication required to achieve the centralized minimax risk. We study two classes of distributed protocols: one in which machines send messages independently over channels without feedback, and a second allowing for interactive communication, in which a central server broadcasts the messages from a given machine to all other machines. We establish lower bounds for a variety of problems, including location estimation in several families and parameter estimation in different types of regression models. Our results include a novel class of quantitative data-processing inequalities used to characterize the effects of limited communication.


title: Optimization of photovoltaic energy production through an efficient switching matrix

abstract: This work presents a preliminary study on the implementation of a new system for power output maximization of photovoltaic generators under non-homogeneous conditions. The study evaluates the performance of an efficient switching matrix and the relevant automatic reconfiguration control algorithms. The switching matrix is installed between the PV generator and the inverter, allowing a large number of possible module configurations. PV generator, switching matrix and the intelligent controller have been simulated in Simulink. The proposed reconfiguration system improved the energy extracted by the PV generator under non-uniform solar irradiation conditions. Short calculation times of the proposed control algorithms allow its use in real time applications even where a higher number of PV modules is required.


title: Automatic assessment of student reading comprehension from short summaries

abstract: This paper describes our research on automatically scoring students’ summaries for comprehension using not only text specific quantitative and qualitative features, but also more complex features based on the computational indices of cohesion available via Coh-Metrix and on Information Content (IC, a measure of text informativeness). We assessed whether human rated summary scores could be predicted by indices of text complexity and IC. The IC metric of the summaries was a better predictor of human scores than word count or any of the CohMetrix text complexity dimensions. This finding may justify the implementation of IC in future automated summary rating tools to rate short summaries.


title: Newer Understanding of Specific Anatomic Targets in the Aging Face as Applied to Injectables

abstract: Summary:Logical correction of aging contour changes of the face is based on understanding its structure and the processes involved in the aging appearance. Aging changes are seen at all tissue levels between the skin and bone although the relative contribution of each component to the overall change


title: Breast Cancer Subtype Approximated by Estrogen Receptor, Progesterone Receptor, and HER-2 Is Associated With Local and Distant Recurrence After Breast-Conserving Therapy

abstract: Purpose To determine whether breast cancer subtype is associated with outcome after breast-conserving therapy (BCT) consisting of lumpectomy and radiation therapy. Patients and Methods We studied 793 consecutive patients with invasive breast cancer who received BCT from July 1998 to December 2001. Among them, 97% had pathologically negative margins of resection, and 90% received adjuvant systemic therapy. No patient received adjuvant trastuzumab. Receptor status was used to approximate subtype: estrogen receptor (ER) or progesterone receptor (PR) positive and human epidermal growth factor receptor 2 negative = luminal A; ER+ or PR+ and HER-2+ = luminal B; ER–and PR –and HER-2+ = HER-2; and ER–and PR –and HER-2–= basal. Competing risks methodology was used to analyze time to local recurrence and distant metastases. Results Median follow-up was 70 months. The overall 5-year cumulative incidence of local recurrence was 1.8% (95% CI, 1.0 to 3.1); 0.8% (0.3, 2.2) for luminal A, 1.5% (0.2, 10) for luminal B, 8....


title: Assessing Structural Diversity in Managed Forests

abstract: Many spatial scales are recognised within multi-objective forest management, but relatively little is known about the effects of current management regimes on biodiversity at a stand level in British forests. This is important, as most management operations are carried out at this level within commercial forests. This paper describes one aspect of a project to assess forest biodiversity at the stand level, namely the description of vertical stand structure. The Project aims to assess levels of biodiversity associated with plantation stands, through the evaluation of new field assessment methods, the collection of baseline data, and an appraisal of the effects of management through the complete range of structural classes generated by clearfelling and replanting silvicultural methods. In other countries, chronosequences have been examined for this purpose.


title: Morse theory for filtrations and efficient computation of persistent homology, in preparation

abstract: We introduce an efficient preprocessing algorithm to reduce the number of cells in a filtered cell complex while preserving its persistent homology groups. The technique is based on an extension of combinatorial Morse theory from complexes to filtrations.


title: The development of mobile applications for patient education

abstract: The International Centre for Digital Content explores the relationship of technology to societyothrough present and emerging applications. This paper outlines ICDCis rapid application development methodology, focusing specifically on a current project partnership with Healthcare Services. The aim of this project is to provide the Primary Care Group (PCG) healthcare specialists with a suite of interoperable devices that can provide breast cancer patients with personalized educational information at any point throughout the course of their treatment. The first phase of the project focuses on the development of a Personal Digital Assistant (PDA) prototype. The patient will have access to educational multimedia content relating to their condition at any time and in any context.


title: Learning a Probabilistic Model of Event Sequences From Internet Weblog Stories

abstract: One of the central problems in building broad-coverage story understanding systems is generating expectations about event sequences, i.e. predicting what happens next given some arbitrary narrative context. In this paper, we describe how a large corpus of stories extracted from Internet weblogs was used to learn a probabilistic model of event sequences using statistical language modeling techniques. Our approach was to encode weblog stories as sequences of events, one per sentence in the story, where each event was represented as a pair of descriptive key words extracted from the sentence. We then applied statistical language modeling techniques to each of the event sequences in the corpus. We evaluated the utility of the resulting model for the tasks of narrative event ordering and event prediction.


title: An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting

abstract: The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series. Indeed, both service interruptions and resource waste can be reduced with the implementation of an effective forecasting system. Significant research has thus been devoted to the design and development of methodologies for short term load forecasting over the past decades. A class of mathematical models, called Recurrent Neural Networks, are nowadays gaining renewed interest among researchers and they are replacing many practical implementation of the forecasting systems, previously based on static methods. Despite the undeniable expressive power of these architectures, their recurrent nature complicates their understanding and poses challenges in the training procedures. Recently, new important families of recurrent architectures have emerged and their applicability in the context of load forecasting has not been investigated completely yet. In this paper we perform a comparative study on the problem of Short-Term Load Forecast, by using different classes of state-of-the-art Recurrent Neural Networks. We test the reviewed models first on controlled synthetic tasks and then on different real datasets, covering important practical cases of study. We provide a general overview of the most important architectures and we define guidelines for configuring the recurrent networks to predict real-valued time series.


title: OpenHAR: A Matlab Toolbox for Easy Access to Publicly Open Human Activity Data Sets

abstract: This study introduces OpenHAR, a free Matlab toolbox to combine and unify publicly open data sets. It provides an easy access to accelerometer signals of ten publicly open human activity data sets. Data sets are easy to access as OpenHAR provides all the data sets in the same format. In addition, units, measurement range and labels are unified, as well as, body position IDs. Moreover, data sets with different sampling rates are unified using downsampling. What is more, data sets have been visually inspected to find visible errors, such as sensor in wrong orientation. OpenHAR improves re-usability of data sets by fixing these errors. Altogether OpenHAR contains over 65 million labeled data samples. This is equivalent to over 280 hours of data from 3D accelerometers. This includes data from 211 study subjects performing 17 daily human activities and wearing sensors in 14 different body positions.


Mummies from Italy, North Africa and the Canary Islands


title: Driver drowsiness detection using the in-ear EEG

abstract: Driver drowsiness monitoring is one of the most demanded technologies for active prevention of severe road accidents. Electroencephalogram (EEG) and several peripheral signals have been suggested for the drowsiness monitoring. However, each type of signal has partial limitations in terms of either convenience or accuracy. Recent emerged concept of in-ear EEG raises expectations due to reduced obtrusiveness. It is yet unclear whether the in-ear EEG is effective enough for drowsiness detection in comparison with on-scalp EEG or peripheral signals. In this work, we evaluated performance of the in-ear EEG in drivers' alertness-drowsiness classification for the first time. Simultaneously, we also tested three peripheral signals including electrocardiogram (ECG), photoplethysmogram (PPG), and galvanic skin response (GSR) which have advantage in convenience of measurement. The classification analysis using the in-ear EEG resulted in high classification accuracy comparable to that of the individual on-scalp EEG channels. The ECG, PPG and GSR showed competitive performance but only when used together in pairwise combinations. Our results suggest that the in-ear EEG would be viable alternative to the single channel EEG or the individual peripheral signals for the drowsiness monitoring.


title: Mcintosh as synecdoche: How teacher education's focus on white privilege undermines antiracism

abstract: In this article, members of the Midwest Critical Whiteness Collective argue that Peggy McIntosh's seminal “knapsack” article acts as a synecdoche, or as a stand-in, for all the antiracist work to be done in teacher education and that this limits our understanding and possibilities for action. The authors develop this argument by questioning the lack of critique of McIntosh's 1988 classic “invisible knapsack” article and sharing two narratives by members of their collective that illustrate problems with both the acceptance and the rejection of McIntosh's conception of white privilege. This discussion illuminates how white privilege pedagogy demands confession and how confession is a dead end for antiracist action. The authors also explore how McIntosh's ideas can lead to dangerous misreadings of student resistance. Acknowledging the initial fruitfulness of McIntosh's ideas, it is time for us to move to more complex treatments of working with white people on questions of race, white supremacy, and antiracism.


title: A Programmable Calculation Unit Employing Memcapacitor-based Neuromorphic Circuit

abstract: An efficient calculation unit is developed on the basis of programmable neuromorphic circuit for implementing arbitrary mathematical functions. To retrieve any specific function approximately, the regression algorithm through neural network (NN) is realized by a compact analog circuitry. The memcapacitor technology is associated to Neuron-MOS structure, which couples multiple memcapacitors on the floating gate of a MOS transistor to achieve multiply-accumulation (MAC) operation. In this manner, each synapse of NN is emulated by only one memcapacitor device and the weight is post-fabrication programmable due to the memcapacitive characteristics of memcapacitor. Two types of neurons including sigmoid and linear are implemented by differential pairs with Neuron-MOS transistors. For proof-of-concept, the memcapacitor-based approximate calculation unit (MC-ACU) for arbitrary two-operand computations is achieved with 461 devices. From the real circuit simulation results, all the example functions are retrieved with the maximum inaccuracy of 7.8%. The energy- and device-count of MC-ACU are only 36.7% and 15.7% of that of state-of-the-art works in approximate computing, respectively.


title: Coarse-to-Fine Stacked Fully Convolutional Nets for lymph node segmentation in ultrasound images

abstract: Ultrasound as a well-established imaging modality is widely used in imaging lymph nodes for clinical diagnosis and disease analysis. Quantitative analysis of lymph node features, morphology, and relations can provide valuable information for diagnosis and immune system studies. For such analysis, it is necessary to first accurately segment the lymph node areas in ultrasound images. In this paper, we develop a new deep learning method, called Coarse-to-Fine Stacked Fully Convolutional Nets (CFS-FCN), for automatically segmenting lymph nodes in ultrasound images. Our method consists of multiple stages of FCN modules. We train the CFS-FCN model to learn the segmentation knowledge from a coarse-to-fine, simple-to-complex manner. A data set of 80 ultrasound images containing both normal and diseased lymph nodes is used in our experiments, which show that our method considerably outperforms the state-of-the-art deep learning methods for lymph node segmentation.


title: Accelerating write by exploiting PCM asymmetries

abstract: To improve the write performance of PCM, this paper proposes a new write scheme, called two-stage-write, which leverages the speed and power difference between writing a zero bit and writing a one bit. Writing a one takes longer time but less electrical current than writing a zero. We propose to divide a write into stages: in the write-0 stage all zeros are written at an accelerated speed, and in the write-1 stage stage, all ones are written with increased parallelism, without violating power constraints. We also present a new coding scheme to improve the speed of the write-1 stage by further increasing the number of bits that can be written to PCM in parallel. Based on simulation experiments of a multi-core processor under various SPEC CPU 2006 workloads, our proposed techniques can reduce the memory latency of standard PCM by 68.3% and improve the system performance by 33.9% on average. In addition, the proposed two-stage-write shows 16.5% latency reduction and 9.2% performance improvement over Flip-N-Write.


title: Prediction of remission in obsessive compulsive disorder using a novel machine learning strategy

abstract: The study objective was to apply machine learning methodologies to identify predictors of remission in a longitudinal sample of 296 adults with a primary diagnosis of obsessive compulsive disorder (OCD). Random Forests is an ensemble machine learning algorithm that has been successfully applied to large-scale data analysis across vast biomedical disciplines, though rarely in psychiatric research or for application to longitudinal data. When provided with 795 raw and composite scores primarily from baseline measures, Random Forest regression prediction explained 50.8% (5000-run average, 95% bootstrap confidence interval [CI]: 50.3–51.3%) of the variance in proportion of time spent remitted. Machine performance improved when only the most predictive 24 items were used in a reduced analysis. Consistently high-ranked predictors of longitudinal remission included Yale–Brown Obsessive Compulsive Scale (Y-BOCS) items, NEO items and subscale scores, Y-BOCS symptom checklist cleaning/washing compulsion score, and several self-report items from social adjustment scales. Random Forest classification was able to distinguish participants according to binary remission outcomes with an error rate of 24.6% (95% bootstrap CI: 22.9–26.2%). Our results suggest that clinically-useful prediction of remission may not require an extensive battery of measures. Rather, a small set of assessment items may efficiently distinguish high- and lower-risk patients and inform clinical decision-making. Copyright © 2015 John Wiley & Sons, Ltd.


title: A robotic finger mechanism for robust industrial applications

abstract: Purpose – The purpose of this paper is to present the design and analysis of a robotic finger mechanism for robust industrial applications.Design/methodology/approach – The resultant design is a compact rigid link finger, which is adaptive to different shapes and sizes providing necessary grasping features. A number of such fingers can be assembled to function as a special purpose end effector.Findings – The mechanism removes a number of significant problems usually experienced with tendon‐based designs. The finger actuation mechanism forms a compact and positive drive unit within the end effector's body using solid mechanical linkages and integrated actuators.Practical implications – The paper discusses the design issues associated with a limited number of actuators to operate in a constrained environment and presents various considerations necessary to ensure safe and reliable operations.Originality/value – The design is original in existence and developed for special purpose handling applications that ...


title: MmWave Massive MIMO Based Wireless Backhaul for 5G Ultra-Dense Network

abstract: The ultra-dense network (UDN) has been considered as a promising candidate for future 5G networks to meet the explosive data demand. To realize UDN, a reliable, gigahertz bandwidth, and cost-effective backhaul connecting ultradense small-cell BSs and macrocell BS are prerequisite. Millimeter-wave can provide the potential gigabit-per-second traffic for wireless backhaul. Moreover, mmWave can easily be integrated with massive MIMO for improved link reliability. In this article, we discuss the feasibility of mmWave massive-MIMO-based wireless backhaul for 5G UDN, and the benefits and challenges are also addressed. In particular, we propose a digitally controlled phase shifter network (DPSN)-based hybrid precoding/combining scheme for mmWave massive MIMO, whereby the low-rank property of the mmWave massive MIMO channel matrix is leveraged to reduce the required cost and complexity of a transceiver with a negligible performance loss. One key feature of the proposed scheme is that the macrocell BS can simultaneously support multiple small-cell BSs with multiple streams for each small-cell BS, which is essentially different from conventional hybrid precoding/combining schemes, typically limited to single-user MIMO with multiple streams or multi-user MIMO with single stream for each user. Based on the proposed scheme, we further explore the fundamental issues of developing mmWave massive MIMO for wireless backhaul, and the associated challenges, insight, and prospects to enable mmWave massive-MIMO-based wireless backhaul for 5G UDN are discussed.


title: A Taxonomy and Survey of Cloud Computing Systems

abstract: The computational world is becoming very large and complex. Cloud Computing has emerged as a popular computing model to support processing large volumetric data using clusters of commodity computers. According to J.Dean and S. Ghemawat [1], Google currently processes over 20 terabytes of raw web data. It's some fascinating, large-scale processing of data that makes your head spin and appreciate the years of distributed computing fine-tuning applied to today's large problems. The evolution of cloud computing can handle such massive data as per on demand service. Nowadays the computational world is opting for pay-for-use models and Hype and discussion aside, there remains no concrete definition of cloud computing. In this paper, we first develop a comprehensive taxonomy for describing cloud computing architecture. Then we use this taxonomy to survey several existing cloud computing services developed by various projects world-wide such as Google, force.com, Amazon. We use the taxonomy and survey results not only to identify similarities and differences of the architectural approaches of cloud computing, but also to identify areas requiring further research.


title: A weighted k-nearest neighbor density estimate for geometric inference

abstract: Motivated by a broad range of potential applications in topological and geometric inference, we introduce a weighted version of the k-nearest neighbor density estimate. Various pointwise consistency results of this estimate are established. We present a general central limit theorem under the lightest possible conditions. In addition, a strong approximation result is obtained and the choice of the optimal set of weights is discussed. In particular, the classical k-nearest neighbor estimate is not optimal in a sense described in the manuscript. The proposed method has been implemented to recover level sets in both simulated and real-life data.


title: Success factors for Crowdfunding founders and funders

abstract: Crowdfunding has been used as one of the effective ways for entrepreneurs to raise funding especially in creative industries. Individuals as well as organizations are paying more attentions to the emergence of new crowdfunding platforms. In the Netherlands, the government is also trying to help artists access financial resources through crowdfunding platforms. This research aims at discovering the success factors for crowdfunding projects through crowdfunding platforms from both founders and funders perspective. We designed our own website for founders and funders to observe crowdfunding behaviors. Our research will contribute to crowdfunding success factors related to issues of trust and decision making and provide practical recommendations for practitioners and researchers.


title: The Body Surface as a Communication System: The State of the Art after 50 Years

abstract: The suggestion that the body surface might be used as an additional means of presenting information to human-machine operators has been around in the literature for nearly 50 years. Although recent technological advances have made the possibility of using the body as a receptive surface much more realistic, the fundamental limitations on the human information processing of tactile stimuli presented across the body surface are, however, still largely unknown. This literature review provides an overview of studies that have attempted to use vibrotactile interfaces to convey information to human operators. The importance of investigating any possible central cognitive limitations (i.e., rather than the peripheral limitations, such as related to sensory masking, that were typically addressed in earlier research) on tactile processing for the most effective design of body interfaces is highlighted. The applicability of the constraints emerging from studies of tactile processing under conditions of unisensory (i.e., purely tactile) stimulus presentation, to more ecologically valid conditions of multisensory stimulation, is also discussed. Finally, the results obtained from recent studies of tactile information processing under conditions of multisensory stimulation are described, and their implications for haptic/tactile interface design elucidated.


title: Research Article Optimized Projection Matrix for Compressive Sensing

abstract: Compressive sensing (CS) is mainly concerned with low-coherence pairs, since the number of samples needed to recover the signal is proportional to the mutual coherence between projection matrix and sparsifying matrix. Until now, papers on CS always assume the projection matrix to be a random matrix. In this paper, aiming at minimizing the mutual coherence, a method is proposed to optimize the projection matrix. This method is based on equiangular tight frame (ETF) design because an ETF has minimum coherence. It is impossible to solve the problem exactly because of the complexity. Therefore, an alternating minimization type method is used to find a feasible solution. The optimally designed projection matrix can further reduce the necessary number of samples for recovery or improve the recovery accuracy. The proposed method demonstrates better performance than conventional optimization methods, which brings benefits to both basis pursuit and orthogonal matching pursuit.


title: Generalized Discriminant Analysis Using a Kernel Approach

abstract: We present a new method that we call generalized discriminant analysis (GDA) to deal with nonlinear discriminant analysis using kernel function operator. The underlying theory is close to the support vector machines (SVM) insofar as the GDA method provides a mapping of the input vectors into high-dimensional feature space. In the transformed space, linear properties make it easy to extend and generalize the classical linear discriminant analysis (LDA) to nonlinear discriminant analysis. The formulation is expressed as an eigenvalue problem resolution. Using a different kernel, one can cover a wide class of nonlinearities. For both simulated data and alternate kernels, we give classification results, as well as the shape of the decision function. The results are confirmed using real data to perform seed classification.


title: Drone forensic framework: Sensor and data identification and verification

abstract: Ease of availability and affordability of unmanned aerial vehicles (UAV) have led to an increase in its popularity amongst the public. The proliferation of UAVs has also augmented several security issues. These devices are used for illegal activities such as drug smuggling and privacy invasion. The purpose of this research paper is to analyze the basic architecture of a drone, and to propose a generic drone forensic model that would improve the digital investigation process. This paper also provides recommendations on how one should perform forensics on the various components of a drone such as camera and Wi-Fi.


title: Telco User Activity Level Prediction with Massive Mobile Broadband Data

abstract: Telecommunication (telco) operators aim to provide users with optimized services and bandwidth in a timely manner. The goal is to increase user experience while retaining profit. To do this, knowing the changing behavior patterns of users through their activity levels in advance can be a great help for operators to adjust their management strategies and reduce operational risk. To achieve this goal, the operators can make use of knowledge discovered from telco’s historical mobile broadband (MBB) records to predict mobile access activity level at an early stage. In this article, we report our research in a real-world telco setting involving more than one million telco users. Our novel contribution includes representing users as documents containing a collection of changing spatiotemporal “words” that express user behavior. By extracting users’ space-time access records in MBB data, we use latent Dirichlet allocation (LDA) to learn user-specific compact topic features for user activity level prediction. We propose a scalable online expectation-maximization (OEM) algorithm that can scale LDA to massive MBB data, which is significantly faster than several state-of-the-art online LDA algorithms. Using these real-world MBB data, we confirm high performance in user activity level prediction. In addition, we show that the inferred topics indicate that future activity level anomalies correlate highly with early skewed bandwidth supply and demand relations. Thus, our prediction system can also guide the telco operators to balance the telecommunication network in terms of supply-demand relations, saving deployment costs and energy of cell towers in the future.


title: Dynamic Social Community Detection and Its Applications

abstract: Community structure is one of the most commonly observed features of Online Social Networks (OSNs) in reality. The knowledge of this feature is of great advantage: it not only provides helpful insights into developing more efficient social-aware solutions but also promises a wide range of applications enabled by social and mobile networking, such as routing strategies in Mobile Ad Hoc Networks (MANETs) and worm containment in OSNs. Unfortunately, understanding this structure is very challenging, especially in dynamic social networks where social interactions are evolving rapidly. Our work focuses on the following questions: How can we efficiently identify communities in dynamic social networks? How can we adaptively update the network community structure based on its history instead of recomputing from scratch? To this end, we present Quick Community Adaptation (QCA), an adaptive modularity-based framework for not only discovering but also tracing the evolution of network communities in dynamic OSNs. QCA is very fast and efficient in the sense that it adaptively updates and discovers the new community structure based on its history together with the network changes only. This flexible approach makes QCA an ideal framework applicable for analyzing large-scale dynamic social networks due to its lightweight computing-resource requirement. To illustrate the effectiveness of our framework, we extensively test QCA on both synthesized and real-world social networks including Enron, arXiv e-print citation, and Facebook networks. Finally, we demonstrate the applicability of QCA in real applications: (1) A social-aware message forwarding strategy in MANETs, and (2) worm propagation containment in OSNs. Competitive results in comparison with other methods reveal that social-based techniques employing QCA as a community detection core outperform current available methods.


title: A survey on educational process mining

abstract: Educational process mining (EPM) is an emerging field in educational data mining (EDM) aiming to make unexpressed knowledge explicit and to facilitate better understanding of the educational process. EPM uses log data gathered specifically from educational environments in order to discover, analyze, and provide a visual representation of the complete educational process. This paper introduces EPM and elaborates on some of the potential of this technology in the educational domain. It also describes some other relevant, related areas such as intentional mining, sequential pattern mining and graph mining. It highlights the components of an EPM framework and it describes the different challenges when handling event logs and other generic issues. It describes the data, tools, techniques and models used in EPM. In addition, the main work in this area is described and grouped by educational application domains.


title: Enhancing pre-defined workflows with ad hoc analytics using Galaxy, Docker and Jupyter

abstract: What does it take to convert a heap of sequencing data into a publishable result? First, common tools are employed to reduce primary data (sequencing reads) to a form suitable for further analyses (i.e., list of variable sites). The subsequent exploratory stage is much more ad hoc and requires development of custom scripts making it problematic for biomedical researchers. Here we describe a hybrid platform combining common analysis pathways with exploratory environments. It aims at fully encompassing and simplifying the “raw data-to-publication” pathway and making it reproducible.


title: Confidence Estimation For Information Extraction

abstract: Information extraction techniques automatically create structured databases from unstructured data sources, such as the Web or newswire documents. Despite the successes of these systems, accuracy will always be imperfect. For many reasons, it is highly desirable to accurately estimate the confidence the system has in the correctness of each extracted field. The information extraction system we evaluate is based on a linear-chain conditional random field (CRF), a probabilistic model which has performed well on information extraction tasks because of its ability to capture arbitrary, overlapping features of the input in a Markov model. We implement several techniques to estimate the confidence of both extracted fields and entire multi-field records, obtaining an average precision of 98% for retrieving correct fields and 87% for multi-field records.


title: Curie: A method for protecting SVM Classifier from Poisoning Attack

abstract: Machine learning is used in a number of security related applications such as biometric user authentication, speaker identification etc. A type of causative integrity attack against machine learning called Poisoning attack works by injecting specially crafted data points in the training data so as to increase the false positive rate of the classifier. In the context of the biometric authentication, this means that more intruders will be classified as valid user, and in case of speaker identification system, user A will be classified user B. In this paper, we examine poisoning attack against SVM and introduce - Curie - a method to protect the SVM classifier from the poisoning attack. The basic idea of our method is to identify the poisoned data points injected by the adversary and filter them out. Our method is light weight and can be easily integrated into existing systems. Experimental results show that it works very well in filtering out the poisoned data.


title: Covert channels using mobile device's magnetic field sensors

abstract: This paper presents a new covert channel using smartphone magnetic sensors. We show that modern smartphones are capable to detect the magnetic field changes induced by different computer components during I/O operations. In particular, we are able to create a covert channel between a laptop and a mobile device without any additional equipment, firmware modifications or privileged access on either of the devices. We present two encoding schemes for the covert channel communication and evaluate their effectiveness.


Inferring Developer Activities by Analyzing Successive Versions of Source Code


title: Incremental extreme learning machine with fully complex hidden nodes

abstract: Huang et al. [Universal approximation using incremental constructive feedforward networks with random hidden nodes, IEEE Trans. Neural Networks 17(4) (2006) 879-892] has recently proposed an incremental extreme learning machine (I-ELM), which randomly adds hidden nodes incrementally and analytically determines the output weights. Although hidden nodes are generated randomly, the network constructed by I-ELM remains as a universal approximator. This paper extends I-ELM from the real domain to the complex domain. We show that, as long as the hidden layer activation function is complex continuous discriminatory or complex bounded nonlinear piecewise continuous, I-ELM can still approximate any target functions in the complex domain. The universal capability of the I-ELM in the complex domain is further verified by two function approximations and one channel equalization problems.


title: A Review on Urdu Language Parsing

abstract: Natural Language Processing is the multidisciplinary area of Artificial Intelligence, Machine Learning and Computational Linguistic for processing human language automatically. It involves understanding and processing of human language. The way through which we share our contents or feelings have always great importance in understanding and processing of language. Parsing is the most suited approach in identifying and scanning what the available sentences expressed? Parsing is the process in which syntactic structure of sentence is identified using grammatical tags. The syntactically correct sentence structure is achieved by assigning grammatical labels to its constituents using lexicon and syntactic rules. Phrase and Dependency are two main structure formalisms for parsing natural language sentences. The growing use of web 2.0 has produced novel research challenges as people from different geographical areas are using this channel and sharing contents in their native languages. Urdu is one of such free word order native language which is widely shared over social media sites but identification and summarization of Urdu sentences is challenging task. In this review paper we present an overview to recent work in parsing of fixed order (i.e. English) and free word order languages (i.e Urdu) in order to reveal the most suited method for Urdu Language Parsing. This survey explored that dependency parsing is more appropriate for Urdu and other free word order languages and parsers of English language are not useful in parsing Urdu sentence due to its morphological, syntactical and grammatical differences.


title: The Triple Negative Paradox: Primary Tumor Chemosensitivity of Breast Cancer Subtypes

abstract: PURPOSE ::: Gene expression analysis identifies several breast cancer subtypes. We examined the relationship of neoadjuvant chemotherapy response to outcome among these breast cancer subtypes. ::: ::: ::: EXPERIMENTAL DESIGN ::: We used immunohistochemical profiles [human epidermal growth factor receptor 2-positive (HER2+)/hormone receptor-negative for HER2+/estrogen receptor-negative (ER-), hormone receptor and HER2- for basal-like, hormone receptor-positive for luminal] to subtype a prospectively maintained data set of patients with breast cancer treated with neoadjuvant anthracycline-based (doxorubicin plus cyclophosphamide, AC) chemotherapy. We analyzed each subtype for clinical and pathologic response to neoadjuvant chemotherapy and examined the relationship of response to distant disease-free survival and overall survival. ::: ::: ::: RESULTS ::: Of the 107 patients tested, 34 (32%) were basal-like, 11 (10%) were HER2+/ER-, and 62 (58%) were luminal. After neoadjuvant AC, 75% received subsequent chemotherapy and all received endocrine therapy if hormone receptor-positive. The chemotherapy regimen and pretreatment stage did not differ by subtype. Clinical response to AC was higher among the HER2+/ER- (70%) and basal-like (85%) than the luminal subtypes (47%; P < 0.0001). Pathologic complete response occurred in 36% of HER2+/ER-, 27% of basal-like, and 7% of luminal subtypes (P = 0.01). Despite initial chemosensitivity, patients with the basal-like and HER2+/ER- subtypes had worse distant disease-free survival (P = 0.04) and overall survival (P = 0.02) than those with the luminal subtypes. Regardless of subtype, only 2 of 17 patients with pathologic complete response relapsed. The worse outcome among basal-like and HER+/ER- subtypes was due to higher relapse among those with residual disease (P = 0.003). ::: ::: ::: CONCLUSIONS ::: Basal-like and HER2+/ER- subtypes are more sensitive to anthracycline-based neoadjuvant chemotherapy than luminal breast cancers. Patients that had pathologic complete response to chemotherapy had a good prognosis regardless of subtype. The poorer prognosis of basal-like and HER2+/ER- breast cancers could be explained by a higher likelihood of relapse in those patients in whom pathologic complete response was not achieved.


title: Toward a model of eye movement control in reading

abstract: The authors present several versions of a general model, titled the E-Z Reader model, of eye movement control in reading. The major goal of the modeling is to relate cognitive processing (specifically aspects of lexical access) to eye movements in reading. The earliest and simplest versions of the model (E-Z Readers 1 and 2) merely attempt to explain the total time spent on a word before moving forward (the gaze duration) and the probability of fixating a word; later versions (E-Z Readers 3-5) also attempt to explain the durations of individual fixations on individual words and the number of fixations on individual words. The final version (E-Z Reader 5) appears to be psychologically plausible and gives a good account of many phenomena in reading. It is also a good tool for analyzing eye movement data in reading. Limitations of the model and directions for future research are also discussed.


title: Roaring Bitmaps: Implementation of an Optimized Software Library

abstract: Compressed bitmap indexes are used in systems such as Git or Oracle to accelerate queries. They represent sets and often support operations such as unions, intersections, differences, and symmetric differences. Several important systems such as Elasticsearch, Apache Spark, Netflix's Atlas, LinkedIn's Pivot, Metamarkets' Druid, Pilosa, Apache Hive, Apache Tez, Microsoft Visual Studio Team Services and Apache Kylin rely on a specific type of compressed bitmap index called Roaring. We present an optimized software library written in C implementing Roaring bitmaps: CRoaring. It benefits from several algorithms designed for the single-instruction-multiple-data (SIMD) instructions available on commodity processors. In particular, we present vectorized algorithms to compute the intersection, union, difference and symmetric difference between arrays. We benchmark the library against a wide range of competitive alternatives, identifying weaknesses and strengths in our software. Our work is available under a liberal open-source license.


title: Exploring text datasets by visualizing relevant words

abstract: When working with a new dataset, it is important to first explore and familiarize oneself with it, before applying any advanced machine learning algorithms. However, to the best of our knowledge, no tools exist that quickly and reliably give insight into the contents of a selection of documents with respect to what distinguishes them from other documents belonging to different categories. In this paper we propose to extract `relevant words' from a collection of texts, which summarize the contents of documents belonging to a certain class (or discovered cluster in the case of unlabeled datasets), and visualize them in word clouds to allow for a survey of salient features at a glance. We compare three methods for extracting relevant words and demonstrate the usefulness of the resulting word clouds by providing an overview of the classes contained in a dataset of scientific publications as well as by discovering trending topics from recent New York Times article snippets.


title: Openhsm: An open key life cycle protocol for public key infrastructure’s hardware security modules

abstract: The private keys used in a PKI are its most important asset. Protect these keys from unauthorised use or disclosure is essential to secure a PKI. Relying parties need assurances that the private key used to sign their certificates is controlled and managed following pre-defined statement policy. Hardware Security Modules (HSM) offer physical and logical protection and should be considered for any PKI deployment. The software that manages keys inside an HSM should control all life cycle of a private key. Normally this kind of equipment implements a embedded key management protocol and this protocols are not available to public scrutiny due to industrial interests. Other important issue is that HSMs are targeted in their development to the Bank industry and not to PKI, making some important PKI issues, like, strict key usage control and a secure auditing trail, play a secondary role. This paper presents an open protocol to securely manage private keys inside HSMs. The protocol is described, analysed and discussed.


title: MPEG Digital Video Coding Standards

abstract: The efficient digital representation of image and video signals has been the subject of considerable research over the past 20 years. Digital video-coding technology has developed into a mature field and products have been developed that are targeted for a wide range of emerging applications, such as video on demand, digital TV/HDTV broadcasting, and multimedia image/video database services. With the increased commercial interest in video communications, the need for international image- and video-compression standards arose. To meet this need, the Moving Picture Experts Group (MPEG) was formed to develop coding standards. MPEG-1 and MPEG-2 video-coding standards have attracted much attention worldwide, with an increasing number of very large scale integration (VLSI) and software implementations of these standards becoming commercially available. MPEG-4, the most recent MPEG standard that is still under development, is targeted for future content-based multimedia applications. We provide an overview of the MPEG video-coding algorithms and standards and their role in video communications. We review the basic concepts and techniques that are relevant in the context of the MPEG video-compression standards and outline MPEG-1 and MPEG-2 video-coding algorithms. The specific properties of the standards related to their applications are presented, and the basic elements of the forthcoming MPEG-4 standard are also described. We also discuss the performance of the standards and their success in the market place.


title: Hybrid Tele-Manipulation System Using a Sensorized 3-D-Printed Soft Robotic Gripper and a Soft Fabric-Based Haptic Glove

abstract: This paper presents a hybrid tele-manipulation system, comprising of a sensorized 3-D-printed soft robotic gripper and a soft fabric-based haptic glove that aim at improving grasping manipulation and providing sensing feedback to the operators. The flexible 3-D-printed soft robotic gripper broadens what a robotic gripper can do, especially for grasping tasks where delicate objects, such as glassware, are involved. It consists of four pneumatic finger actuators, casings with through hole for housing the actuators, and adjustable base. The grasping length and width can be configured easily to suit a variety of objects. The soft haptic glove is equipped with flex sensors and soft pneumatic haptic actuator, which enables the users to control the grasping, to determine whether the grasp is successful, and to identify the grasped object shape. The fabric-based soft pneumatic haptic actuator can simulate haptic perception by producing force feedback to the users. Both the soft pneumatic finger actuator and haptic actuator involve simple fabrication technique, namely 3-D-printed approach and fabric-based approach, respectively, which reduce fabrication complexity as compared to the steps involved in a traditional silicone-based approach. The sensorized soft robotic gripper is capable of picking up and holding a wide variety of objects in this study, ranging from lightweight delicate object weighing less than 50 g to objects weighing 1100 g. The soft haptic actuator can produce forces of up to 2.1 N, which is more than the minimum force of 1.5 N needed to stimulate haptic perception. The subjects are able to differentiate the two objects with significant shape differences in the pilot test. Compared to the existing soft grippers, this is the first soft sensorized 3-D-printed gripper, coupled with a soft fabric-based haptic glove that has the potential to improve the robotic grasping manipulation by introducing haptic feedback to the users.


title: Mutual impedance of parallel and perpendicular coplanar surface monopoles

abstract: One dimensional integral formulas are derived for mutual impedance of arbitrary size, coplanar, parallel, and perpendicular surface monopoles. The integrals in formulas are expressed as exponential integrals where possible. The mutual impedance expression for parallel monopoles is a summation of exponential integrals and one-dimensional integrals. For perpendicular monopoles, the mutual impedance is in closed form, containing exponential integrals only. The final expressions are in a form suitable for numerical computation. Since the expressions contain at most one-dimensional integrals, they can be utilized to reduce the matrix filling time in the moment method formulations, especially when inhomogeneous sectioning is preferred. Additionally, they can be used in rectangular surface patch modeling of conducting surfaces with edges which are at an angle to the surface patches, providing the angle is small. To this end, the expressions were utilized in the moment method analysis of linearly tapered slot antennas. Very good accuracy was obtained with a reduction in computer time. >


title: Generative Temporal Models with Memory

abstract: We consider the general problem of modeling temporal data with long-range dependencies, wherein new observations are fully or partially predictable based on temporally-distant, past observations. A sufficiently powerful temporal model should separate predictable elements of the sequence from unpredictable elements, express uncertainty about those unpredictable elements, and rapidly identify novel elements that may help to predict the future. To create such models, we introduce Generative Temporal Models augmented with external memory systems. They are developed within the variational inference framework, which provides both a practical training methodology and methods to gain insight into the models' operation. We show, on a range of problems with sparse, long-term temporal dependencies, that these models store information from early in a sequence, and reuse this stored information efficiently. This allows them to perform substantially better than existing models based on well-known recurrent neural networks, like LSTMs.


title: Cutting the last wires for mobile communications by microwave power transfer

abstract: The advancements in microwave power transfer (MPT) over recent decades have enabled wireless power transfer over long distances. The latest breakthroughs in wireless communication - massive MIMO, small cells, and millimeterwave communication - make wireless networks suitable platforms for implementing MPT. This can lead to the elimination of the “last wires” connecting mobile devices to the grid for recharging, thereby tackling a huge long-standing ICT challenge. Furthermore, the seamless integration between MPT and wireless communication opens up a new area called wirelessly powered communications (WPC) where many new research directions arise, such as simultaneous information and power transfer, WPC network architectures, and techniques for safe and efficient WPC. This article provides an introduction to WPC by describing the key features of WPC, shedding light on a set of frequently asked questions, and identifying the key design issues and discussing possible solutions.


title: Clustering of XML documents based on structure and aggregated content

abstract: The main objective of the work is to improve the clustering efficiency and performance when we deal with very big datasets. This paper aims to improve the quality of XML data clustering by exploiting more features extracted from source schemas. In particular, it proposes clustering approach that gathers both content and structure of XML documents to determine similarity between them. The content and structure information are concluded using two different similarity methods that are then grouped via weight factor to compute the overall document similarity. The structural similarity of XML data are derived from edge summaries while content features similarity are derived from aggregate of set of similarity measures; Jaccard, Cosine measure and Jensen-Shannon divergence in one algorithm. However, we also experimented using Jaccard distance as content measure with edge summaries to prove that using an aggregation of content similarity measures can further improve the results. The experiments prove that clustering of XML documents based on structure only information produce worse solution in homogenous environment, while in heterogeneous environment clustering of XML document produce better result when the structure and the content are combined. Results have shown that performance and quality of the proposed approach is better in comparison of both XEdge and XCLSC approaches.


title: Interaction of the bacteriophage P1 recombinase Cre with the recombining site loxP.

abstract: The interaction between the P1 recombinase protein Cre and the DNA site at which it acts, loxP, has been studied by using nuclease protection techniques. The region of DNA protected by Cre against nuclease attack by DNase I or neocarzinostatin is a 34-base-pair (bp) region containing two 13-bp inverted repeats separated by an 8-bp spacer region. These protected sequences have previously been shown to be required for efficient Cre-mediated recombination at loxP. The results of the above protection experiments suggest (i) that no more than 34 bp may be required for loxP recombination and (ii) that the asymmetry of loxP recombination is due to the 8-bp spacer sequence. With neocarzinostatin, a specific nucleotide within the 8-bp spacer region is not protected. This nucleotide is located in a 2-bp sequence shown to be involved in a loxP crossover event, suggesting that this region remains exposed after Cre binding. Protection experiments have also been done with loxP sites that have either the left or right inverted repeat deleted. The nuclease protection pattern of these sites reveals that each loxP site consists of two binding domains for Cre, each being composed of one 13-bp inverted repeat and the contiguous 4 bp of the 8-bp spacer region.


title: A Crowd of Your Own: Crowdsourcing for On-Demand Personalization

abstract: Personalization is a way for computers to support people’s diverse interests and needs by providing content tailored to the individual. While strides have been made in algorithmic approaches to personalization, most require access to a significant amount of data. However, even when data is limited online crowds can be used to infer an individual’s personal preferences. Aided by the diversity of tastes among online crowds and their ability to understand others, we show that crowdsourcing is an effective on-demand tool for personalization. Unlike typical crowdsourcing approaches that seek a ground truth, we present and evaluate two crowdsourcing approaches designed to capture personal preferences. The first, taste-matching , identifies workers with similar taste to the requester and uses their taste to infer the requester’s taste. The second, taste-grokking , asks workers to explicitly predict the requester’s taste based on training examples. These techniques are evaluated on two subjective tasks, personalized image recommendation and tailored textual summaries. Taste-matching and taste-grokking both show improvement over the use of generic workers, and have different benefits and drawbacks depending on the complexity of the task and the variability of the taste space.


title: Enhancing Intrusion Detection System (IDS) by Using Honeybee Concepts and Framework

abstract: Intrusion Detection System has been studied for more than ten years. Though Artificial Intelligence (AI) techniques have been integrated to improve IDS but the success is still far from satisfaction. Thus, we believe a new strategy to improve IDS is badly needed. One of the solutions is by imitating the honeybee colony that can successfully protect their colony. In fact, the honeybee colony system and problems are quite similar with network system, and the way the bees protecting their colony can also be considered similar with the IDS in the network system. In this paper, we investigated the honeybee colony system as well as their detection system to get improvement methods for IDS engine in order to enhance IDS system for a better network defense. The adaptation of the honeybee protection and defense system itself is a new knowledge that can help other systems such as antivirus, antimalware, or even defense system to imitate the AI techniques in performing their functions. We train the proposed system with different types of attacks data and model different types of attack signatures. The performance of the proposed IDS is evaluated using NSL-KDD data set. The experiments show that the performance of the proposed model can detect novel intrusions and reduce false alarms.


title: EPPI: An E-cent-based privacy-preserving incentive mechanism for participatory sensing systems

abstract: The proliferation of mobile devices has facilitated the prevalence of participatory sensing applications in which participants collect and share information in their environments. The design of a participatory sensing application confronts two challenges: “privacy” and “incentive” which are two conflicting objectives and deserve deeper attention. Inspired by physical currency circulation system, this paper firstly proposes E-cent, a unit bearer currency. It is exchangeable, and participants can utilize it to participate in tasks anonymously. By employing E-cent, we further propose an E-cent-based privacy-preserving incentive mechanism, called EPPI, which exploits a pledge-based participating protocol to encourage participants to participate without revealing privacy and prohibit participants from sending false data. EPPI also takes advantage of a dynamic reward allocation scheme to maximize the value of the services under a budget constraint. To the best of our knowledge, EPPI is the first attempt to build an incentive mechanism while maintaining the desired privacy-preserving in participatory sensing systems. Extensive simulation and analysis results show that EPPI can achieve high anonymity level and remarkable incentive effects.


Improved prediction of outcome in Parkinson's disease using radiomics analysis of longitudinal DAT SPECT images


title: Early Developments of a Parallelly Actuated Humanoid, SAFFiR

abstract: This paper presents the design of our new 33 degree of freedom full size humanoid robot, SAFFiR (Shipboard Autonomous Fire Fighting Robot). The goal of this research project is to realize a high performance mixed force and position controlled robot with parallel actuation. The robot has two 6 DOF legs and arms, a waist, neck, and 3 DOF hands/fingers. The design is characterized by a central lightweight skeleton actuated with modular ballscrew driven force controllable linear actuators arranged in a parallel fashion around the joints. Sensory feedback on board the robot includes an inertial measurement unit, force and position output of each actuator, as well as 6 axis force/torque measurements from the feet. The lower body of the robot has been fabricated and a rudimentary walking algorithm implemented while the upper body fabrication is completed. Preliminary walking experiments show that parallel actuation successfully minimizes the loads through individual actuators.Copyright © 2013 by ASME


title: Fracture Detection in Traumatic Pelvic CT Images

abstract: Fracture detection in pelvic bones is vital for patient diagnostic decisions and treatment planning in traumatic pelvic injuries. Manual detection of bone fracture from computed tomography (CT) images is very challenging due to low resolution of the images and the complex pelvic structures. Automated fracture detection from segmented bones can significantly help physicians analyze pelvic CT images and detect the severity of injuries in a very short period. This paper presents an automated hierarchical algorithm for bone fracture detection in pelvic CT scans using adaptive windowing, boundary tracing, and wavelet transform while incorporating anatomical information. Fracture detection is performed on the basis of the results of prior pelvic bone segmentation via our registered active shape model (RASM). The results are promising and show that the method is capable of detecting fractures accurately.


title: Decision making, the P3, and the locus coeruleus-norepinephrine system

abstract: Psychologists and neuroscientists have had a long-standing interest in the P3, a prominent component of the event-related brain potential. This review aims to integrate knowledge regarding the neural basis of the P3 and to elucidate its functional role in information processing. The authors review evidence suggesting that the P3 reflects phasic activity of the neuromodulatory locus coeruleus-norepinephrine (LC-NE) system. They discuss the P3 literature in the light of empirical findings and a recent theory regarding the information-processing function of the LC-NE phasic response. The theoretical framework emerging from this research synthesis suggests that the P3 reflects the response of the LC-NE system to the outcome of internal decision-making processes and the consequent effects of noradrenergic potentiation of information processing.


title: Text-based LSTM networks for Automatic Music Composition

abstract: In this paper, we introduce new methods and discuss results of text-based LSTM (Long Short-Term Memory) networks for automatic music composition. The proposed network is designed to learn relationships within text documents that represent chord progressions and drum tracks in two case studies. In the experiments, word-RNNs (Recurrent Neural Networks) show good results for both cases, while character-based RNNs (char-RNNs) only succeed to learn chord progressions. The proposed system can be used for fully automatic composition or as semi-automatic systems that help humans to compose music by controlling a diversity parameter of the model.


title: DBpedia-A Large-scale , Multilingual Knowledge Base Extracted from Wikipedia

abstract: The DBpedia community project extracts structured, multilingual knowledge from Wikipedia and makes it freely available on the Web using Semantic Web and Linked Data technologies. The project extracts knowledge from 111 different language editions of Wikipedia. The largest DBpedia knowledge base which is extracted from the English edition of Wikipedia consists of over 400 million facts that describe 3.7 million things. The DBpedia knowledge bases that are extracted from the other 110 Wikipedia editions together consist of 1.46 billion facts and describe 10 million additional things. The DBpedia project maps Wikipedia infoboxes from 27 different language editions to a single shared ontology consisting of 320 classes and 1,650 properties. The mappings are created via a world-wide crowd-sourcing effort and enable knowledge from the different Wikipedia editions to be combined. The project publishes releases of all DBpedia knowledge bases for download and provides SPARQL query access to 14 out of the 111 language editions via a global network of local DBpedia chapters. In addition to the regular releases, the project maintains a live knowledge base which is updated whenever a page in Wikipedia changes. DBpedia sets 27 million RDF links pointing into over 30 external data sources and thus enables data from these sources to be used together with DBpedia data. Several hundred data sets on the Web publish RDF links pointing to DBpedia themselves and make DBpedia one of the central interlinking hubs in the Linked Open Data (LOD) cloud. In this system report, we give an overview of the DBpedia community project, including its architecture, technical implementation, maintenance, internationalisation, usage statistics and applications.


title: Instruction precomputation with memoization for fault detection

abstract: Fault tolerance (FT) has become a major concern in computing systems. Instruction duplication has been proposed to verify application execution at run time. Two techniques, instruction memoization and precomputation, have been shown to improve the performance and fault coverage of duplication. This work shows that the combination of these two techniques is much more powerful than either one in isolation. In addition to performance, it improves the long-lasting transient and permanent fault coverage upon the memoization scheme. Compared to the precomputation scheme, it reduces the long-lasting transient and permanent fault coverage of 10.6% of the instructions, but covers 2.6 times as many instructions against shorter transient faults. On a system with 2 integer ALUs, the combined scheme reduces the performance degradation due to duplication by on average 27.3% and 22.2% compared to the precomputation and memoization-based techniques, respectively, with similar hardware requirements.


title: S-OpenSGX: A system-level platform for exploring SGX enclave-based computing

abstract: Abstract Intel recently introduced Software Guard Extensions (SGX) to enable applications to protect their selected code and data from privileged platform software. As it draws wider attention from the security research community, an open-source emulator for SGX (OpenSGX) has been developed to allow researchers to explore the SGX environment. To the best of our knowledge, OpenSGX is currently the only publicly available SGX emulation platform; however, its system-level support is largely limited owing to its user-mode emulation. In particular, experiments that require system functionalities or device emulation cannot be conducted with the current OpenSGX. To solve this problem, we propose System-OpenSGX (S-OpenSGX), which leverages QEMU's system emulation to provide researchers with full system-level support for exploring SGX enclave-based programming. In this paper, we show the design of S-OpenSGX, including system functionalities such as scheduling, multithreading, page table handling, and SGX paging. Non-trivial issues derived from the difference between user-mode and system emulation, and our approaches to addressing them are also described. Lastly, we utilize S-OpenSGX to experiment with a new mitigation method against the data leaking attack on enclave threads and an APIC (Advanced Programmable Interrupt Controller) device modification to reduce SGX's mode switch overhead.


title: Machine learning based malware classification for Android applications using multimodal image representations

abstract: The popularity of smartphones usage especially Android mobile platform has increased to 80% of share in global smartphone operating systems market, as a result of which it is on the top in the attacker's target list. The fact of having more private data and low security assurance letting the attacker to write several malware programs in different ways for smartphone, and the possibility of obfuscating the malware detection applications through different coding techniques is giving more energy to attacker. Several approaches have been proposed to detect malwares through code analysis which are now severely facing the problem of code obfuscation and high computation requirement. We propose a machine learning based method to detect android malware by analyzing the visual representation of binary formatted apk file into Grayscale, RGB, CMYK and HSL. GIST feature from malware and benign image dataset were extracted and used to train machine learning algorithms. Initial experimental results are encouraging and computationally effective. Among machine learning algorithms Random Forest have achieved highest accuracy of 91% for grayscale image, which can be further improved by tuning the various parameters.


title: The magnetic resonance appearance of surfers' knots: a case report

abstract: Athletes are at increased risk of developing soft-tissue lesions of the lower limbs. Although the majority of these will be benign, the differential diagnosis is broad and increasingly, doctors are turning to magnetic resonance imaging (MRI) as a first-line investigation when presented with these sorts of lesions, both to narrow the differential diagnosis and exclude malignancy. We report the case of a 28-year-old Caucasian man who presented with 2 soft-tissue lesions of the right foot. History and examination of the nodules fitted with a diagnosis of surfers' knots, an unusual form of acquired, benign, connective tissue nodule that may appear over the tibial tuberosities, dorsum of the feet, and occasionally on the chest of surfers in association with repetitive microtrauma during surfing. MRI findings were consistent with this diagnosis with both lesions exhibiting T1 hypointensity and speckled T2 hypointensity with no significant blooming artifact on gradient echo imaging. When imaged with gadolinium, they demonstrated only mild contrast enhancement. MRI is a valuable tool when investigating athletes with soft-tissue lesions over the lower limbs where the possibility of malignancy must be addressed. In selected cases, MRI may be sufficient to permit a conservative approach to the management of these patients.


title: Talk, think, read (if absolutely necessary): The impact of social, personal, and documentary knowledge on task performance

abstract: The organizational memory literature has compared social sources to documentary archives as potential sources of knowledge that decision makers can use when they seek information needed to carry out business activities, but has not considered how an individual's personal stock of relevant knowledge will shape the benefits of such organizational sources of knowledge. This study considers how social interaction, access to documents, and personal knowledge directly and jointly contribute to individual task performance. A multi-stage laboratory experiment finds that task performance increases most when decision makers can talk with colleagues and then from drawing on their own personal knowledge, while books offer benefits only if people lack colleagues and/or personal knowledge. In turn, social interaction and personal knowledge become more important as tasks became more complex. When available in combination, meanwhile, multiple knowledge sources generate only diminishing marginal returns.


title: Large-Scale Aerial Image Categorization by Multi-Task Discriminative Topologies Discovery

abstract: Fast and accurately categorizing the millions of aerial images on Google Maps is a useful technique in multimedia applications. Existing methods cannot handle this task effectively due to two reasons. 1) It is challenging to build a realtime image categorization system, as some geo-aware Apps update over 20 aerial images per second. 2) The aerial images' topologies are the key to distinguish their categories, but they cannot be encoded by the generic visual descriptors. To solve these two problems, we propose an efficient aerial image categorization system, aiming at mining discriminative topologies of aerial images under a multi-task learning framework. Particularly, we first construct a region adjacency graph (RAG) that describes the topology of each aerial image. Thereby, aerial image categorization can be formulated as RAG-to-RAG matching. Based on graph theory, RAG-to-RAG matching is conducted by comparing all their respective graphlets (i.e., small subgraphs). Because the number of graphlets is huge, a multi-task feature selection algorithm is derived to discover topologies jointly discriminative to multiple categories. The discovered topologies are used to extract the discriminative graphlets. Finally, these graphlets are integrated into an AdaBoost model for predicting aerial image categories. Experiments show that our approach is competitive several existing recognition models. Further, over 24 aerial images are categorized per second, reflecting that our system is ready for real-world applications.


Ice-Bound: Combining richly-realized story with expressive gameplay


title: Research Article Geo-parsing Messages from Microtext

abstract: Widespread use of social media during crises has become commonplace, as shown by the volume of messages during the Haiti earthquake of 2010 and Japan tsunami of 2011. Location mentions are particularly important in disaster messages as they can show emergency responders where problems have occurred. This article explores the sorts of locations that occur in disaster-related social messages, how well off-the-shelf software identifies those locations, and what is needed to improve automated location identification, called geo-parsing. To do this, we have sampled Twitter messages from the February 2011 earthquake in Christchurch, Canterbury, New Zealand. We annotated locations in messages manually to make a gold standard by which to measure locations identified by a Named Entity Recognition software. The Stanford NER software found some locations that were proper nouns, but did not identify locations that were not capitalized, local streets and buildings, or non-standard place abbreviations and mis-spellings that are plentiful in microtext. We review how these problems might be solved in software research, and model a readable crisis map that shows crisis location clusters via enlarged place labels.


title: Tightly-coupled monocular visual-inertial fusion for autonomous flight of rotorcraft MAVs

abstract: There have been increasing interests in the robotics community in building smaller and more agile autonomous micro aerial vehicles (MAVs). In particular, the monocular visual-inertial system (VINS) that consists of only a camera and an inertial measurement unit (IMU) forms a great minimum sensor suite due to its superior size, weight, and power (SWaP) characteristics. In this paper, we present a tightly-coupled nonlinear optimization-based monocular VINS estimator for autonomous rotorcraft MAVs. Our estimator allows the MAV to execute trajectories at 2 m/s with roll and pitch angles up to 30 degrees. We present extensive statistical analysis to verify the performance of our approach in different environments with varying flight speeds.


title: Development of Single-Transistor-Control LDO Based on Flipped Voltage Follower for SoC

abstract: The design issues of a single-transistor-control (STC) low-drop-out (LDO) based on flipped voltage follower is discussed in this paper, in particular the feedback stability at different conditions of output capacitors, equivalent series resistances (ESRs) and load current. Based on the analysis, an STC LDO was implemented in a standard 0.35-mum MOS technology. It is proven experimentally that the LDO provides stable voltage regulation at a variety of output-capacitor/ESR conditions and is also stable in no output capacitor condition. The preset output voltage, minimum unregulated input voltage, maximum output current at a dropout voltage of 200 mV, ground current and active chip area are 1 V, 1.2 V, 50 mA, 95 muA, and 140 mum times 320 mum, respectively. The full-load transient response in the no output capacitor case is faster than a micro second and is about 300 ns.


title: Digital satellite communications

abstract: Elements of Satellite Communication. Communication Satellites & Orbital Mechanics. Earth Stations. Satellite Link. Time Division Multiple Access. Efficient Techniques. Satellite Packet Comm.. Digital Modulation. Carrier & Symbol Timing Synchronization. Satellite Spread Spectrum Comm.. Very Small Aperture Terminal Networks. Mobile Satellite Comm.


Diabetes and coronary artery disease


title: Embroidered Multiband Body-Worn Antenna for GSM/PCS/WLAN Communications

abstract: A novel textile-based body-worn antenna covering the Global System for Mobile Communications/personal communications services/wireless local-area network frequency bands is presented. This antenna was made of densely embroidered metal-coated polymer fibers (e-fibers). These e-fibers are 15 $\mu$ m thick and consist of high strength, flexible polymer cores with conductive silver coatings, providing mechanical flexibility and low loss at radio frequencies. When measured in free space, the textile antenna showed comparable performance to its copper counterpart, having ${\sim}2$ dBi realized gain at all three bands. This textile antenna was simulated and measured on a full body phantom to determine the body's influence on antenna performance, including frequency detuning and pattern shadowing. The measured radiation pattern of the body-worn antenna matched well with simulation at various on-body locations for the three bands. Field measurements were also carried out by mounting the antenna onto the shoulder of a jacket, and using it to replace the one of a cell phone. We found that the communication quality using the body-worn textile antenna was equivalent to the best location of the original cell-phone antenna. Therefore, this textile-based antenna provided for a more reliable body-worn communication when mounted on the body's shoulder.


title: Highly-linear voltage-to-time converter (VTC) circuit for time-based analog-to-digital converters (T-ADCs)

abstract: Time-based ADC is an essential block in designing software radio receivers because it exhibits higher speed and lower power compared to the conventional ADC, especially, at scaled CMOS technologies. In time-based ADCs, the input voltage is first converted to a pulse delay time by using a Voltage-to-Time Converter (VTC) circuit, and then the pulse delay time is converted to a digital word by using a Time-to-Digital Converter (TDC) circuit. In this paper, a novel VTC circuit is proposed which achieves high linearity and large dynamic analog input range. This new VTC circuit can be used in a 5 bit time-based ADC with no sample and hold circuit for analog input frequencies up to 4 GHz.


title: Deep Illumination: Approximating Dynamic Global Illumination with Generative Adversarial Network

abstract: We present Deep Illumination, a novel machine learning technique for approximating global illumination (GI) in real-time applications using a Conditional Generative Adversarial Network. Our primary focus is on generating indirect illumination and soft shadows with offline rendering quality at interactive rates. Inspired from recent advancement in image-to-image translation problems using deep generative convolutional networks, we introduce a variant of this network that learns a mapping from Gbuffers (depth map, normal map, and diffuse map) and direct illumination to any global illumination solution. Our primary contribution is showing that a generative model can be used to learn a density estimation from screen space buffers to an advanced illumination model for a 3D environment. Once trained, our network can approximate global illumination for scene configurations it has never encountered before within the environment it was trained on. We evaluate Deep Illumination through a comparison with both a state of the art real-time GI technique (VXGI) and an offline rendering GI technique (path tracing). We show that our method produces effective GI approximations and is also computationally cheaper than existing GI techniques. Our technique has the potential to replace existing precomputed and screen-space techniques for producing global illumination effects in dynamic scenes with physically-based rendering quality.


title: Therapeutic Effects of Horseback Riding Interventions: A Systematic Review and Meta-analysis

abstract: Equine-assisted therapies, such as therapeutic riding and hippotherapy, are believed to have positive physical and emotional effects in individuals with neuromotor, developmental, and physical disabilities. The purpose of this review was to determine whether therapeutic riding and hippotherapy improve balance, motor function, gait, muscle symmetry, pelvic movement, psychosocial parameters, and the patients' overall quality of life. ::: In this study, a literature search was conducted on MEDLINE, CINAHL, MBASE, SportDiscus, Cochrane Database of Systematic Reviews, Cochrane Controlled Trials Register, PEDro, DARE, Google Scholar, and Dissertation Abstracts. Only studies with a control/comparison group or self-controlled studies performing preintervention and postintervention assessment were included. Excluded were (1) studies not providing data on baseline score or end-point outcome, (2) single-subject studies, (3) studies providing only qualitative data, and (4) studies that used a mechanical horse. Sixteen trials were included. The methodologic quality of each study was evaluated using Downs and Black quality assessment tool. ::: Most of the studies showed a trend toward a beneficial effect of therapeutic riding and hippotherapy on balance and gross motor function. The meta-analysis showed improvement in both the Berg Balance Scale and the Gross Motor Function Measure in therapeutic riding and hippotherapy programs. ::: Programs such as therapeutic riding and hippotherapy are a viable intervention option for patients with balance, gait, and psychomotor disorders.


title: Teleoperation of On-Road Vehicles via Immersive Telepresence Using Off-the-shelf Components

abstract: The quality of visual information and response time are crucial aspects of any modern teleoperation system. This is especially true for operation of on-road vehicles, which must function in highly dynamic, unforgiving environments. In this work we demonstrate that a suitable teleoperation system can be exclusively composed of low-cost off-the-shelf components yet still meet the high performance demands of remotely driving a car on the road. The user is given immersive situational awareness through an on-board head-mounted display linked to an actuated stereoscopic camera, thereby maintaining depth perception and intuitive camera control. Communication speeds are evaluated over various wireless connection types, and a usability study shows that the system allows for advanced driving maneuvers while remotely controlled. 3G and 4G data networks are demonstrated to provide adequate bandwidth for the task given proper data compression, thus expanding the potential range for teleoperation. Applications for such a system are further discussed, extending to fleet management and autonomous vehicle safety measures.


title: An Analysis of Chinese Search Engine Filtering

abstract: The imposition of government mandates upon Internet search engine operation is a growing area of interest for both computer science and public policy. Users of these search engines often observe evidence of censorship, but the government policies that impose this censorship are not generally public. To better understand these policies, we conducted a set of experiments on major search engines employed by Internet users in China, issuing queries against a variety of different words: some neutral, some with names of important people, some political, and some pornographic. We conducted these queries, in Chinese, against Baidu, Google (including google.cn, before it was terminated), Yahoo!, and Bing. We found remarkably aggressive filtering of pornographic terms, in some cases causing non-pornographic terms which use common characters to also be filtered. We also found that names of prominent activists and organizers as well as top political and military leaders, were also filtered in whole or in part. In some cases, we found search terms which we believe to be "blacklisted". In these cases, the only results that appeared, for any of them, came from a short "whitelist" of sites owned or controlled directly by the Chinese government. By repeating observations over a long observation period, we also found that the keyword blocking policies of the Great Firewall of China vary over time. While our results don't offer any fundamental insight into how to defeat or work around Chinese internet censorship, they are still helpful to understand the structure of how censorship duties are shared between the Great Firewall and Chinese search engines.


title: Spacetime expression cloning for blendshapes

abstract: The goal of a practical facial animation retargeting system is to reproduce the character of a source animation on a target face while providing room for additional creative control by the animator. This article presents a novel spacetime facial animation retargeting method for blendshape face models. Our approach starts from the basic principle that the source and target movements should be similar. By interpreting movement as the derivative of position with time, and adding suitable boundary conditions, we formulate the retargeting problem as a Poisson equation. Specified (e.g., neutral) expressions at the beginning and end of the animation as well as any user-specified constraints in the middle of the animation serve as boundary conditions. In addition, a model-specific prior is constructed to represent the plausible expression space of the target face during retargeting. A Bayesian formulation is then employed to produce target animation that is consistent with the source movements while satisfying the prior constraints. Since the preservation of temporal derivatives is the primary goal of the optimization, the retargeted motion preserves the rhythm and character of the source movement and is free of temporal jitter. More importantly, our approach provides spacetime editing for the popular blendshape representation of facial models, exhibiting smooth and controlled propagation of user edits across surrounding frames.


title: Assessing ERP post-implementation success at the individual level: Revisiting the role of service quality

abstract: Whereas previous studies have devoted great attention to the success of Enterprise Resource Planning (ERP) system implementation, this study aims to investigate how the different qualities of an ERP system affect its post-implementation success from the user's perspective. We refined DeLone and McLean's IS success model to examine the relative importance of ERP system quality, information quality, and service quality to post-implementation success, with users' satisfaction, users' individual benefits, and a very critical yet seldom investigated variable, users' extended use of ERP systems, as the outcome variables. Our research model was empirically examined with data from 151 ERP users. We found that service quality, in conjunction with system quality and information quality, significantly affects ERP post-implementation success in terms of user satisfaction. More importantly, service quality was found to significantly interact with information quality and system quality to promote an ERP system's post-implementation success by increasing employees' extended use.


title: Multiwave: Doppler Effect Based Gesture Recognition in Multiple Dimensions

abstract: We constructed an acoustic, gesture-based recognition system called Multiwave, which leverages the Doppler Effect to translate multidimensional movements into user interface commands. Our system only requires the use of two speakers and a microphone to be operational. Since these components are already built in to most end user systems, our design makes gesture-based input more accessible to a wider range of end users. By generating a known high frequency tone from multiple speakers and detecting movement using changes in the sound waves, we are able to calculate a Euclidean representation of hand velocity that is then used for more natural gesture recognition and thus, more meaningful interaction mappings. We present the results of a user study of Multiwave to evaluate recognition rates for different gestures and report accuracy rates comparable to or better than the current state of the art. We also report subjective user feedback and some lessons learned from our system that provide additional insight for future applications of multidimensional gesture recognition.


ENHANCING VIEWER ’ S EMOTIONAL CONNECTIONS TO THE TRADITIONAL ART CREATIVE PROCESS VIA AN AI INTERACTIVE SYSTEM


Feature-Rich Memory-Based Classification for Shallow NLP and Information Extraction


title: Learning sentiment-inherent word embedding for word-level and sentence-level sentiment analysis

abstract: Vector-based word representations have made great progress on many Natural Language Processing tasks. However, due to the lack of sentiment information, the traditional word vectors are insufficient to settle sentiment analysis tasks. In order to capture the sentiment information, we extended Continuous Skip-gram model (Skip-gram) and presented two sentiment word embedding models by integrating sentiment information into semantic word representations. Experimental results showed that the sentiment word embeddings learned by two models indeed capture sentiment and semantic information as well. Moreover, the proposed sentiment word embedding models outperform traditional word vectors on both Chinese and English corpora.


title: Chemical properties of biocrude oil from the hydrothermal liquefaction of Spirulina algae, swine manure, and digested anaerobic sludge.

abstract: This study explores the influence of wastewater feedstock composition on hydrothermal liquefaction (HTL) biocrude oil properties and physico-chemical characteristics. Spirulina algae, swine manure, and digested sludge were converted under HTL conditions (300°C, 10-12 MPa, and 30 min reaction time). Biocrude yields ranged from 9.4% (digested sludge) to 32.6% (Spirulina). Although similar higher heating values (32.0-34.7 MJ/kg) were estimated for all product oils, more detailed characterization revealed significant differences in biocrude chemistry. Feedstock composition influenced the individual compounds identified as well as the biocrude functional group chemistry. Molecular weights tracked with obdurate carbohydrate content and followed the order of Spirulina<swine manure<digested sludge. A similar trend was observed in boiling point distributions and the long branched aliphatic contents. These findings show the importance of HTL feedstock composition and highlight the need for better understanding of biocrude chemistries when considering bio-oil uses and upgrading requirements.


title: Dynamically Scaling Apache Storm for the Analysis of Streaming Data

abstract: Stream processing platforms allow applications to analyse incoming data continuously. Several use cases exist that make use of these capabilities, ranging from monitoring of physical infrastructures to pre selecting video surveillance feeds for human inspection. It is difficult to predict how much computing resources are needed for these stream processing platforms, because the volume and velocity of input data may vary over time. The open source Apache Storm software provides a framework for developers to build processing applications that use the computing resources of all machines within an established cluster. Because of the varying processing needs of such applications, the platform should be able to automatically grow and shrink as needed. Unfortunately, the current Storm platform does not provide this capability. In this paper we describe the design and implementation of a tool that monitors several aspects of the Storm platform, the applications running on top of it, and external systems such as queues and databases. Based on this information, the tool decides whether extra servers are needed or machines may be decommissioned from the cluster, and then acts upon it by actually creating new virtual machines or shutting them down.


title: Padoc: Enabling social networking in proximity

abstract: Apps supporting social networking in proximity are gaining momentum as they enable both augmenting face-to-face interaction with a digital channel (e.g. classroom interaction systems) and augmenting digital interaction by providing a local real life feeling to it (e.g. nearby friends app in Facebook). Such apps effectively provide a cyber-physical space interweaving digital and face-to-face interaction. Currently such applications are mainly relying on Internet connection to the cloud, which makes them inaccessible in parts of the world with scarce Internet connection. Since many of their interactions happen locally, they could theoretically rely on Mobile Networking in Proximity (MNP), where data could be exchanged among devices without the need to rely on the availability of an Internet connection. Unfortunately, there is a lack of off-the-shelf programing support for MNP. This paper addresses this issue and presents Padoc, a middleware for social networking in proximity that provides multi-hop MNP support when cloud connection is unavailable. Furthermore the paper evaluates three MNP message diffusion strategies and presents Heya, a novel classroom interaction app running on iOS devices as a proof-of-concept built on top of Padoc.


title: Hairpin bandpass filter with tunable center frequency and tunable bandwidth based on screen printed ferroelectric varactors

abstract: This paper presents a twofold tunable planar hairpin filter to simultaneously control center frequency and bandwidth. Tunability is achieved by using functional thick film layers of the ferroelectric material Barium-Strontium-Titanate (BST). The center frequency of the filter is adjusted by varactors which are loading the hairpin resonators. Coupling varactors between the hairpin resonators enable the control of the bandwidth. The proposed filter structure is designed for a center frequency range from 650 MHz to 920 MHz and a bandwidth between 25 MHz and 85 MHz. This covers the specifications of the lower GSM bands. The functionality of the design is experimentally validated and confirmed by simulation results.


title: An iterative approach for business process template synthesis from compliance rules

abstract: Companies have to adhere to compliance requirements. Typically, both, business experts and compliance experts, are involved in compliance analysis of business operations. Hence, these experts need a common understanding of the business processes for effective compliance management. In this paper, we argue that process templates generated out of compliance requirements can be used as a basis for negotiation among business and compliance experts. We introduce a semi automated approach to synthesize process templates out of compliance requirements expressed in Linear Temporal Logic (LTL). As part of that, we show how general constraints related to business process execution are incorporated. Building upon existing work on process mining algorithms, our approach to synthesize process templates considers not only control-flow, but also data-flow dependencies. Finally, we elaborate on the application of the derived process templates and present an implementation of our approach.


title: Continuous Noninvasive Glucose Monitoring Technology Based on “Occlusion Spectroscopy”

abstract: Background: A truly noninvasive glucose-sensing device could revolutionalize diabetes treatment by leading to improved compliance with recommended glucose levels, thus reducing the long-term complications and cost of diabetes. Herein, we present the technology and evaluate the efficacy of a truly noninvasive device for continuous blood glucose monitoring, the NBM (OrSense Ltd.). Methods: In vitro analysis was used to validate the technology and algorithms. A clinical study was performed to quantify the in vivo performance of the NBM device. A total of 23 patients with type 1 (n = 12) and type 2 (n = 11) diabetes were enrolled in the clinical study and participated in 111 sessions. Accuracy was assessed by comparing NBM data with paired self-monitoring of blood glucose meter readings. Results: In vitro experiments showed a strong correlation between calculated and actual glucose concentrations. The clinical trial produced a total of 1690 paired glucose values (NBM vs reference). In the paired data set, the reference glucose range was 40–496 mg/dl. No systematic bias was found at any of the glucose levels examined (70, 100, 150, and 200 mg/dl). The mean relative absolute difference was 17.2%, and a Clarke error grid analysis showed that 95.5% of the measurements fall within the clinically acceptable AB and zone B, 25.7%). Conclusions: This study indicates the potential use of OrSense’s NBM device as a noninvasive sensor for continuous blood glucose evaluation. The device was safe and well tolerated.


title: Emotional Storytelling in the Classroom: Individual versus Group Interaction between Children and Robots

abstract: Robot assistive technology is becoming increasingly prevalent. Despite the growing body of research in this area, the role of type of interaction (i.e., small groups versus individual interactions) on effectiveness of interventions is still unclear. In this paper, we explore a new direction for socially assistive robotics, where multiple robotic characters interact with children in an interactive storytelling scenario. We conducted a between-subjects repeated interaction study where a single child or a group of three children interacted with the robots in an interactive narrative scenario. Results show that although the individual condition increased participant’s story recall abilities compared to the group condition, the emotional interpretation of the story content seemed more dependent on the difficulty level rather than the study condition. Our findings suggest that, despite the type of interaction, interactive narratives with multiple robots are a promising approach to foster children’s development of social-related skills.