title: Predicting Rat and Human Pregnane X Receptor Activators Using Bayesian Classification Models.

abstract: The pregnane X receptor (PXR) is a ligand-activated transcription factor that acts as a master regulator of metabolizing enzymes and transporters. To avoid adverse drug-drug interactions and diseases such as steatosis and cancers associated with PXR activation, identifying drugs and chemicals that activate PXR is of crucial importance. In this work, we developed ligand-based predictive computational models for both rat and human PXR activation, which allowed us to identify potentially harmful chemicals and evaluate species-specific effects of a given compound. We utilized a large publicly available data set of nearly 2000 compounds screened in cell-based reporter gene assays to develop Bayesian quantitative structure-activity relationship models using physicochemical properties and structural descriptors. Our analysis showed that PXR activators tend to be hydrophobic and significantly different from nonactivators in terms of their physicochemical properties such as molecular weight, logP, number of rings, and solubility. Our Bayesian models, evaluated by using 5-fold cross-validation, displayed a sensitivity of 75% (76%), specificity of 76% (75%), and accuracy of 89% (89%) for human (rat) PXR activation. We identified structural features shared by rat and human PXR activators as well as those unique to each species. We compared rat in vitro PXR activation data to in vivo data by using DrugMatrix, a large toxicogenomics database with gene expression data obtained from rats after exposure to diverse chemicals. Although in vivo gene expression data pointed to cross-talk between nuclear receptor activators that is captured only by in vivo assays, overall we found broad agreement between in vitro and in vivo PXR activation. Thus, the models developed here serve primarily as efficient initial high-throughput in silico screens of in vitro activity.


title: Further delineation of Loeys-Dietz syndrome type 4 in a family with mild vascular involvement and a TGFB2 splicing mutation

abstract: BackgroundThe Loeys-Dietz syndrome (LDS) is a rare autosomal dominant disorder characterized by thoracic aortic aneurysm and dissection and widespread systemic connective tissue involvement. LDS type 1 to 4 are caused by mutations in genes of the TGF-β signaling pathway: TGFBR1 and TGFBR2 encoding the TGF-β receptor (LDS1 and LDS2), SMAD3 encoding the TGF-β receptor cytoplasmic effector (LDS3), and TGFB2 encoding the TGF-β2 ligand (LDS4). LDS4 represents the mildest end of the LDS spectrum, since aneurysms are usually observed in fourth decade and the progression of the disease is slower than in the other forms.Case presentationWe report the clinical and molecular findings of an LDS4 Italian family. Genetic testing included TGFBR1, TGFBR2, SMAD3, and TGFB2 analysis by Sanger sequencing. In order to verify the effect of the identified splice mutation, RT-PCR analysis was performed.The proband, a 57-year-old woman, showed high palate, hypoplasic uvula, easy bruising, joint hypermobility, chronic pain, scoliosis, multiple relapsing hernias, dural ectasia, and mitral valve prolapse. Magnetic resonance angiography revealed tortuosity and ectasia of carotid, vertebral, cerebral, and segmental pulmonary arteries. Arterial aneurysm and dissection never occurred. Her 39- and 34-year-old daughters presented with a variable degree of musculoskeletal involvement. Molecular analysis disclosed the novel c.839-1G>A splice site mutation in the TGFB2 gene. This mutation activates a cryptic splice acceptor site in exon 6 leading to frameshift, premature termination codon and haploinsufficiency (p.Gly280Aspfs*41).ConclusionsOur data confirm that loss-of-function mutations in TGFB2 gene do not always lead to aggressive vascular phenotypes and that articular and skeletal signs are prevalent, therefore suggesting that LDS4 must be considered in patients with sparse signs of LDS and related disorders also in the absence of vascular events.


title: Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets

abstract: This paper presents a model based on Deep Learning algorithms of LSTM and GRU for facilitating an anomaly detection in Large Hadron Collider superconducting magnets. We used high resolution data available in Post Mortem database to train a set of models and chose the best possible set of their hyper-parameters. Using Deep Learning approach allowed to examine a vast body of data and extract the fragments which require further experts examination and are regarded as anomalies. The presented method does not require tedious manual threshold setting and operator attention at the stage of the system setup. Instead, the automatic approach is proposed, which achieves according to our experiments accuracy of 99%. This is reached for the largest dataset of 302 MB and the following architecture of the network: single layer LSTM, 128 cells, 20 epochs of training, look_back=16, look_ahead=128, grid=100 and optimizer Adam. All the experiments were run on GPU Nvidia Tesla K80


title: Bagged Clustering and its application to tourism market segmentation

abstract: Aim of the paper is to propose a segmentation technique based on the Bagged Clustering (BC) method. In the partitioning step of the BC method, B bootstrap samples with replacement are generated by drawing from the original sample. The fuzzy C-medoids Clustering (FCMdC) method is run on each bootstrap sample, obtaining (BxC) medoids and the membership degrees of each unit to the different clusters. The second step consists in running a hierarchical clustering algorithm on the (BxC) medoids. The best partition of the medoids is obtained investigating properly the dendrogram. Then each unit is assigned to each cluster based on the membership degrees observed in the partitioning step. The effectiveness of the suggested procedure has been shown analyzing a suggestive tourism segmentation problem. We analyze two sample of tourists, each one attending a different cultural attraction, enlightening differences among clusters in socio-economic characteristics and in the motivational reasons behind visit behavior.


title: Trading end-to-end latency for composability

abstract: The periodic resource model for hierarchical, compositional scheduling abstracts task groups by resource requirements. We study this model in the presence of dataflow constraints between the tasks within a group (intragroup dependencies), and between tasks in different groups (inter-group dependencies). We consider two natural semantics for dataflow constraints, namely, RTW (real-time workshop) semantics and LET (logical execution time) semantics. We show that while RTW semantics offers better end-to-end latency on the task group level, LET semantics allows tighter resource bounds in the abstraction hierarchy and therefore provides better composability properties. This result holds both for intragroup and intergroup dependencies, as well as for shared and for distributed resources


title: Developmental Benefits of Extracurricular Involvement: Do Peer Characteristics Mediate the Link Between Activities and Youth Outcomes?

abstract: In this article, we test: (a) the relation between school-based extracurricular participation and indicators of positive and negative development across a range of activity contexts, and (b) a mediation model linking activity participation, prosocial peers, and development. Extensive survey information was collected from a predominately White sample of middle class adolescents in 9th, 10th, and 12th grades. Extracurricular participation was related to more favorable academic, psychological, and behavioral adjustment; the pattern of findings differed by activity and outcome. In addition, we documented some support for the hypothesis that the link between extracurricular participation and positive adjustment is partly a function of associating with a prosocial peer group. Implications of these findings and suggestions for future research are presented.


title: Wide-area scene mapping for mobile visual tracking

abstract: We propose a system for easily preparing arbitrary wide-area environments for subsequent real-time tracking with a handheld device. Our system evaluation shows that minimal user effort is required to initialize a camera tracking session in an unprepared environment. We combine panoramas captured using a handheld omnidirectional camera from several viewpoints to create a point cloud model. After the offline modeling step, live camera pose tracking is initialized by feature point matching, and continuously updated by aligning the point cloud model to the camera image. Given a reconstruction made with less than five minutes of video, we achieve below 25 cm translational error and 0.5 degrees rotational error for over 80% of images tested. In contrast to camera-based simultaneous localization and mapping (SLAM) systems, our methods are suitable for handheld use in large outdoor spaces.


title: Imperative streams—a monadic combinator library for synchronous programming

abstract: The paper presents a generalization of Haskell's IO monad suitable for synchronous concurrent programming. The new monad integrates the deterministic concurrency paradigm of synchronous programming with the powerful abstraction features of functional languages and with full support for imperative programming. For event-driven applications, it offers an alternative to the use of existing, thread-based concurrency extensions of functional languages. The concepts presented have been applied in practice in a framework for programming interactive graphics.


title: Dance with Melody: An LSTM-autoencoder Approach to Music-oriented Dance Synthesis

abstract: Dance is greatly influenced by music. Studies on how to synthesize music-oriented dance choreography can promote research in many fields, such as dance teaching and human behavior research. Although considerable effort has been directed toward investigating the relationship between music and dance, the synthesis of appropriate dance choreography based on music remains an open problem. There are two main challenges: 1) how to choose appropriate dance figures, i.e., groups of steps that are named and specified in technical dance manuals, in accordance with music and 2) how to artistically enhance choreography in accordance with music. To solve these problems, in this paper, we propose a music-oriented dance choreography synthesis method using a long short-term memory (LSTM)-autoencoder model to extract a mapping between acoustic and motion features. Moreover, we improve our model with temporal indexes and a masking method to achieve better performance. Because of the lack of data available for model training, we constructed a music-dance dataset containing choreographies for four types of dance, totaling 907,200 frames of 3D dance motions and accompanying music, and extracted multidimensional features for model training. We employed this dataset to train and optimize the proposed models and conducted several qualitative and quantitative experiments to select the best-fitted model. Finally, our model proved to be effective and efficient in synthesizing valid choreographies that are also capable of musical expression.


title: Unsupervised Learning of 3D Model Reconstruction from Hand-Drawn Sketches

abstract: 3D objects modeling has gained considerable attention in the visual computing community. We propose a low-cost unsupervised learning model for 3D objects reconstruction from hand-drawn sketches. Recent advancements in deep learning opened new opportunities to learn high-quality 3D objects from 2D sketches via supervised networks. However, the limited availability of labeled 2D hand-drawn sketches data (i.e. sketches and its corresponding 3D ground truth models) hinders the training process of supervised methods. In this paper, driven by a novel design of combination of retrieval and reconstruction process, we developed a learning paradigm to reconstruct 3D objects from hand-drawn sketches, without the use of well-labeled hand-drawn sketch data during the entire training process. Specifically, the paradigm begins with the training of an adaption network via autoencoder with adversarial loss, embedding the unpaired 2D rendered image domain with the hand-drawn sketch domain to a shared latent vector space. Then from the embedding latent space, for each testing sketch image, we retrieve a few (e.g. five) nearest neighbors from the training 3D data set as prior knowledge for a 3D Generative Adversarial Network. Our experiments verify our network's robust and superior performance in handling 3D volumetric object generation from single hand-drawn sketch without requiring any 3D ground truth labels.


title: A comparison of the performance of latent Dirichlet allocation and the Dirichlet multinomial mixture model on short text

abstract: The expansion of the World Wide Web and the increasing popularity of microblogging websites such as Twitter and Facebook has created massive stores of textual data that is short in length. Although traditional topic models have proven to work successfully on collections of long texts such as books and news articles, they tend to produce results that are less coherent when applied to short text, such as status messages and product reviews. Over the last few decades it has become of greater relevance to analyse short texts due to the realisation that such bodies of text could potentially hold useful information. Latent Dirichlet allocation (LDA) is one of the most popular topic models and it makes the generative assumption that each document contains multiple topics in varying proportions, which is a sensible assumption about long text. On the other hand, the Dirichlet multinomial mixture model (GSDMM), a seemingly less popular topic model, assumes that a document can only belong to a single topic, which seems to be a more appropriate assumption for short text. The objective of this paper is to investigate the hypothesis that GSDMM will outperform LDA on short text, using topic coherence and stability as performance measures.


title: Neural network modelling of GaAs pHEMTs suitable for millimeter-wave mixer design

abstract: In this paper we present an approach for nonlinear modeling of GAs pHEMTs suitable for mixer design. We use artificial neural networks to model the DC drain current as well as the intrinsic capacitive core versus the intrinsic gate and drain voltages. The model is extracted from the measured DC current and S-parameters. The approach is validated by the comparison of the model simulations with the corresponding nonlinear measurements.


title: Overtaking & receding vehicle detection for driver assistance and naturalistic driving studies

abstract: Although on-road vehicle detection is a well-researched area, overtaking and receding vehicle detection with respect to (w.r.t) the ego-vehicle is less addressed. In this paper, the authors present a novel appearance-based method for detecting both overtaking and receding vehicles w.r.t the ego-vehicle. The proposed method is based on Haar-like features that are classified using Adaboost-cascaded classifiers, which result in detection windows that are tracked in two directions temporally to detect overtaking and receding vehicles. A detailed and novel evaluation method is presented with 51 overtaking and receding events occurring in 27000 video frames. Additionally, an analysis of the detected events is presented, specifically for naturalistic driving studies (NDS) to characterize the overtaking and receding events during a drive. To the best knowledge of the authors, this automated analysis of overtaking/receding events for NDS is a first of its kind in literature.


title: Obstacle Avoidance through Deep Networks based Intermediate Perception

abstract: Obstacle avoidance from monocular images is a challenging problem for robots. Though multi-view structure-from-motion could build 3D maps, it is not robust in textureless environments. Some learning based methods exploit human demonstration to predict a steering command directly from a single image. However, this method is usually biased towards certain tasks or demonstration scenarios and also biased by human understanding. In this paper, we propose a new method to predict a trajectory from images. We train our system on more diverse NYUv2 dataset. The ground truth trajectory is computed from the designed cost functions automatically. The Convolutional Neural Network perception is divided into two stages: first, predict depth map and surface normal from RGB images, which are two important geometric properties related to 3D obstacle representation. Second, predict the trajectory from the depth and normal. Results show that our intermediate perception increases the accuracy by 20% than the direct prediction. Our model generalizes well to other public indoor datasets and is also demonstrated for robot flights in simulation and experiments.


title: A small planar inverted-F antenna for wearable applications

abstract: Small printed antennas will replace the commonly used normal-mode helical antennas of mobile handsets and systems in the future. This paper presents a novel small planar inverted-F antenna (PIFA) which is a common PIFA in which a U-shaped slot is etched to form a dual band operation for wearable and ubiquitous computing equipment. Health issues are considered in selecting suitable antenna topology and the placement of the antenna. Various applications are presented while the paper mainly discusses about the GSM applications.


title: Merging Markov and DCT features for multi-class JPEG steganalysis

abstract: Blind steganalysis based on classifying feature vectors derived from images is becoming increasingly more powerful. ::: For steganalysis of JPEG images, features derived directly in the embedding domain from DCT coefficients ::: appear to achieve the best performance (e.g., the DCT features10 and Markov features21). The goal of this paper ::: is to construct a new multi-class JPEG steganalyzer with markedly improved performance. We do so first by extending ::: the 23 DCT feature set,10 then applying calibration to the Markov features described in21 and reducing ::: their dimension. The resulting feature sets are merged, producing a 274-dimensional feature vector. The new feature ::: set is then used to construct a Support Vector Machine multi-classifier capable of assigning stego images to ::: six popular steganographic algorithms-F5,22 OutGuess,18 Model Based Steganography without ,19 and with20 ::: deblocking, JP Hide&Seek,1 and Steghide.14 Comparing to our previous work on multi-classification,11, 12 the ::: new feature set provides significantly more reliable results.


title: Risk-RRT∗: A robot motion planning algorithm for the human robot coexisting environment

abstract: In the human robot coexisting environment, to reach the goal efficiently and safely is very meaningful for the mobile service robot. In this paper, a Risk based Rapidly-exploring Random Tree for optimal motion planning (Risk-RRT∗) algorithm is proposed by combining the comfort and collision risk (CCR) map with the RRT∗ algorithm, which provides a variant of the RRT∗ algorithm in the dynamic human robot coexisting environment. In the experiments, the time cost in the navigation process and the length of the trajectory are utilized for the evaluation of the proposed algorithm. A comparison with the Risk-RRT algorithm is carried out and experimental results reveal that our proposed algorithm can achieve a better performance than that of the Risk-RRT in both static and dynamic environments.


title: A study of big data processing constraints on a low-power Hadoop cluster

abstract: Big Data processing with Hadoop has been emerging recently, both on the computing cloud and enterprise deployment. However, wide-spread security exploits may hurt the reputation of public clouds. If Hadoop on the cloud is not an option, an organization has to build its own Hadoop clusters. But having a data center is not worth for a small organization both in terms of building and operating costs. Another viable solution is to build a cluster with low-cost ARM system-on-chip boards. This paper presents a study of a Hadoop cluster for processing Big Data built atop 22 ARM boards. The Hadoop's MapReduce was replaced by Spark and experiments on three different hardware configurations were conducted to understand limitations and constraints of the cluster. From the experimental results, it can be concluded that processing Big Data on an ARM cluster is highly feasible. The cluster could process a 34 GB Wikipedia article file in acceptable time, while generally consumed the power 0.061–0.322 kWh for all benchmarks. It has been found that I/O of the hardware is fast enough, but the power of CPUs is inadequate because they are largely spent for the Hadoop's I/O.


title: An efficient brain tumor detection methodology using K-means clustering algoriftnn

abstract: Segmentation of images holds an important position in the area of image processing. It becomes more important while typically dealing with medical images where pre-surgery and post surgery decisions are required for the purpose of initiating and speeding up the recovery process. Computer aided detection of abnormal growth of tissues is primarily motivated by the necessity of achieving maximum possible accuracy. Manual segmentation of these abnormal tissues cannot be compared with modern day's high speed computing machines which enable us to visually observe the volume and location of unwanted tissues. A well known segmentation problem within MRI is the task of labeling the tissue type which include White Matter (WM), Grey Matter (GM), Cerebrospinal Fluid (CSF) and sometimes pathological tissues like tumor etc. This paper describes an efficient method for automatic brain tumor segmentation for the extraction of tumor tissues from MR images. In this method segmentation is carried out using K-means clustering algorithm for better performance. This enhances the tumor boundaries more and is very fast when compared to many other clustering algorithms. The proposed technique produce appreciative results.


title: Factors affecting the long-term results of endodontic treatment

abstract: The influence of various factors that may affect the outcome of root canal therapy was evaluated in 356 patients 8 to 10 yr after the treatment. The results of treatment were directly dependent on the preoperative status of the pulp and periapical tissues. The rate of success for cases with vital or nonvital pulps but having no periapical radiolucency exceeded 96%, whereas only 86% of the cases with pulp necrosis and periapical radiolucency showed apical healing. The possibility of instrumenting the root canal to its full length and the level of root filling significantly affected the outcome of treatment. Of all of the periapical lesions present on previously root-filled teeth, only 62% healed after retreatment. The predictability from clinical and radiographic signs of the treatment-outcome in individual cases with preoperative periapical lesions cases was found to be low. Thus, factors which were not measured or identified may be critical to the outcome of endodontic treatment.


title: CLOUD DATA PATTERNS FOR CONFIDENTIALITY

abstract: Cloud computing enables cost-effective, self-service, and elastic hosting of applications in the Cloud. Applications may be partially or completely moved to the Cloud. When hosting or moving the database layer to the Cloud, challenges such as avoidance of disclosure of critical data have to be faced. The main challenges are handling different levels of confidentiality and satisfying security and privacy requirements. We provide reusable solutions in the form of patterns.


title: Improving Decision Analytics with Deep Learning: The Case of Financial Disclosures

abstract: Decision analytics commonly focuses on the text mining of financial news sources in order to provide managerial decision support and to predict stock market movements. Existing predictive frameworks almost exclusively apply traditional machine learning methods, whereas recent research indicates that traditional machine learning methods are not sufficiently capable of extracting suitable features and capturing the non-linear nature of complex tasks. As a remedy, novel deep learning models aim to overcome this issue by extending traditional neural network models with additional hidden layers. Indeed, deep learning has been shown to outperform traditional methods in terms of predictive performance. In this paper, we adapt the novel deep learning technique to financial decision support. In this instance, we aim to predict the direction of stock movements following financial disclosures. As a result, we show how deep learning can outperform the accuracy of random forests as a benchmark for machine learning by 5.66%.


title: Kinect Range Sensing: Structured-Light versus Time-of-Flight Kinect

abstract: This work compares Kinect Structured-Light with Kinect Time-of-Flight cameras.The results offer descriptions under which condition one is superior to the other.Solid insight of the devices is given to make decisions on their application.We propose a set of nine tests for comparing both Kinects, five of which are novel. Recently, the new Kinect One has been issued by Microsoft, providing the next generation of real-time range sensing devices based on the Time-of-Flight (ToF) principle. As the first Kinect version was using a structured light approach, one would expect various differences in the characteristics of the range data delivered by both devices.This paper presents a detailed and in-depth comparison between both devices. In order to conduct the comparison, we propose a framework of seven different experimental setups, which is a generic basis for evaluating range cameras such as Kinect. The experiments have been designed with the goal to capture individual effects of the Kinect devices as isolatedly as possible and in a way, that they can also be adopted, in order to apply them to any other range sensing device. The overall goal of this paper is to provide a solid insight into the pros and cons of either device. Thus, scientists who are interested in using Kinect range sensing cameras in their specific application scenario can directly assess the expected, specific benefits and potential problem of either device.


CacheP4: A Behavior-level Caching Mechanism for P4


title: The rademacher complexity of linear transformation classes

abstract: Bounds are given for the empirical and expected Rademacher complexity of classes of linear transformations from a Hilbert space H to a finite dimensional space. The results imply generalization guarantees for graph regularization and multi-task subspace learning.


title: Before the Repository: Defining the Preservation Threats to Research Data in the Lab

abstract: This paper describes the results of a large survey designed to quantify the risks and threats to the preservation of the research data in the lab and to determine the mitigating actions of researchers. A total of 724 National Science Foundation awardees completed this survey. Identifying risks and threats to digital preservation has been a significant research stream. Much of this work has been within the context of a preservation technology infrastructure such as data archives for a digital repository. This study looks at the risks and threats to research data prior to its inclusion in a preservation technology infrastructure. The greatest threat to preservation is human error, followed by equipment malfunction, obsolete software, and data corruption. Lost and mislabeled media are not components in the threat taxonomies developed for repositories; however, they do represent an important threat to research data in the lab. Researchers have recognized the need to mitigate the risks inherent in maintaining digital data by implementing data management in their lab environments and have taken their responsibility as data managers seriously; however, they would still prefer to have professional data management support.


title: Lightweight Internet protocols for web enablement of sensors using constrained gateway devices

abstract: Lightweight Internet protocols are increasingly being used in ubiquitous environment in order to optimize the resource usage of a constrained device like a smart mobile gateway. This paper presents a study on the various such protocols to optimize the usage of energy, and network resources, computation cost of a constrained gateway device. Comprehensive analysis and feature wise categorization of existing dominant protocols, namely MQTT (message queue telemetry transport), CoAP (constrained application protocol) are provided here to achieve improved understanding of the existing issues and gaps in this domain. The present work further identifies the best suited application areas for each protocol based on the results corresponding to typical resource requirements and performance attributes. Finally our vision on the research scope is presented here mainly for optimization of energy usage.


title: A data-driven approach to cleaning large face datasets

abstract: Large face datasets are important for advancing face recognition research, but they are tedious to build, because a lot of work has to go into cleaning the huge amount of raw data. To facilitate this task, we describe an approach to building face datasets that starts with detecting faces in images returned from searches for public figures on the Internet, followed by discarding those not belonging to each queried person. We formulate the problem of identifying the faces to be removed as a quadratic programming problem, which exploits the observations that faces of the same person should look similar, have the same gender, and normally appear at most once per image. Our results show that this method can reliably clean a large dataset, leading to a considerable reduction in the work needed to build it. Finally, we are releasing the FaceScrub dataset that was created using this approach. It consists of 141,130 faces of 695 public figures and can be obtained from http://vintage.winklerbros.net/facescrub.html.


title: Adversarial Feedback Loop

abstract: Thanks to their remarkable generative capabilities, GANs have gained great popularity, and are used abundantly in state-of-the-art methods and applications. In a GAN based model, a discriminator is trained to learn the real data distribution. To date, it has been used only for training purposes, where it's utilized to train the generator to provide real-looking outputs. In this paper we propose a novel method that makes an explicit use of the discriminator in test-time, in a feedback manner in order to improve the generator results. To the best of our knowledge it is the first time a discriminator is involved in test-time. We claim that the discriminator holds significant information on the real data distribution, that could be useful for test-time as well, a potential that has not been explored before. The approach we propose does not alter the conventional training stage. At test-time, however, it transfers the output from the generator into the discriminator, and uses feedback modules (convolutional blocks) to translate the features of the discriminator layers into corrections to the features of the generator layers, which are used eventually to get a better generator result. Our method can contribute to both conditional and unconditional GANs. As demonstrated by our experiments, it can improve the results of state-of-the-art networks for super-resolution, and image generation.


title: MIMO surface wave radar using time staggered FMCW chirp signals

abstract: In order to distinguish between different signal paths, full orthogonal waveforms for each transmit antenna element is desirable for the concept of MIMO radar. We investigate time staggered Frequency Modulated Continuous Wave (FMCW) chirp signals with an individual successive temporal delay larger than the maximum round-trip time of the radar signal. This concept is superior compared to those using time division multiplexing or multiple carrier frequencies in terms of unambiguous Doppler and bandwidth requirement, respectively. This is in particular important for long range marine surveillance using high-frequency surface wave radar where the frequency band is very congested and fast boats or helicopters have to be detected.


title: Mining sandboxes: Are we there yet?

abstract: The popularity of Android platform on mobile devices has attracted much attention from many developers and researchers, as well as malware writers. Recently, Jamrozik et al. proposed a technique to secure Android applications referred to as mining sandboxes. They used an automated test case generation technique to explore the behavior of the app under test and then extracted a set of sensitive APIs that were called. Based on the extracted sensitive APIs, they built a sandbox that can block access to APIs not used during testing. However, they only evaluated the proposed technique with benign apps but not investigated whether it was effective in detecting malicious behavior of malware that infects benign apps. Furthermore, they only investigated one test case generation tool (i.e., Droidmate) to build the sandbox, while many others have been proposed in the literature. In this work, we complement Jamrozik et al.'s work in two ways: (1) we evaluate the effectiveness of mining sandboxes on detecting malicious behaviors; (2) we investigate the effectiveness of multiple automated test case generation tools to mine sandboxes. To investigate effectiveness of mining sandboxes in detecting malicious behaviors, we make use of pairs of malware and benign app it infects. We build a sandbox based on sensitive APIs called by the benign app and check if it can identify malicious behaviors in the corresponding malware. To generate inputs to apps, we investigate five popular test case generation tools: Monkey, Droidmate, Droidbot, GUIRipper, and PUMA. We conduct two experiments to evaluate the effectiveness and efficiency of these test case generation tools on detecting malicious behavior. In the first experiment, we select 10 apps and allow test case generation tools to run for one hour; while in the second experiment, we select 102 pairs of apps and allow the test case generation tools to run for one minute. Our experiments highlight that 75.5%-77.2% of malware in our dataset can be uncovered by mining sandboxes — showing its power to protect Android apps. We also find that Droidbot performs best in generating test cases for mining sandboxes, and its effectiveness can be further boosted when coupled with other test case generation tools.


title: Securing Android: A Survey, Taxonomy, and Challenges

abstract: Recent years have seen a global adoption of smart mobile devices, particularly those based on Android. However, Android’s widespread adoption is marred with increasingly rampant malware threats. This article gives a survey and taxonomy of existing works that secure Android devices. Based on Android app deployment stages, the taxonomy enables us to analyze schemes that share similar objective and approach and to inspect their key differences. Additionally, this article highlights the limitations of existing works and current challenges. It thus distills the state of the art in Android security research and identifies potential research directions for safeguarding billions (and keep counting) of Android-run devices.


title: Patterns and trends in Building Information Modeling (BIM) research: A Latent Semantic Analysis

abstract: Abstract Building Information Modeling (BIM) has emerged as one of the key streams in construction and civil engineering research within the last decade. Given this interest in BIM and the rapidly increasing volume of BIM literature, it is important to understand and discern the core themes and trends emerging in BIM research, and its implications for broader research. The previously reported studies to identify the core of BIM research are typically subjective and qualitative, and hence, prone to bias and interpretation of a limited number of reviewed papers. There is a lack of comprehensive, quantified and systematic classification of the BIM literature. This research brings some clarity by synthesizing and labeling a large corpus of BIM research studies published from 2004 through 2014. Latent Semantic Analysis (LSA), a natural language processing technique was applied to the abstracts of 975 academic papers. This objective analysis reveals twelve principal research areas. Various specific research themes associated with each principal area have been identified. These principal research areas and research themes indicate the patterns and trends in BIM research.


General Formal Ontology (GFO) - A Foundational Ontology Integrating Objects and Processes [Version 1.0]


Dynamic Online Pricing with Incomplete Information


BaitBuster : A Clickbait Identification Framework Md Main


title: Planning with Hierarchical Task Networks in Video Games

abstract: Artificial intelligence (AI) technology can have a dramatic impact on the quality of video games. AI planning techniques are useful in a wide range of game components, including modules that control the behavior of fully autonomous units. However, planning is computationally expensive, and the CPU and memory resources available to game AI modules at runtime are scarce. Offline planning can be a good strategy to avoid runtime performance bottlenecks. ::: ::: In this work, we apply hierarchical task network (HTN) planning to video games. We describe a system that computes plans offline and then represents them as game scripts. This can be seen as a form of generating game scripts automatically, replacing the traditional approach of composing them by hand. We apply our ideas to the commercial game THE ELDER SCROLLS IV: OBLIVION, with encouraging results. Our system generates scripts automatically at a level of complexity that would require a great human effort to achieve.


title: Feature extraction approaches from natural language requirements for reuse in software product lines

abstract: Hybrid NLP approaches were more common for extracting textual requirements.There is a mixture of automated and semi-automated approaches from IR and data mining.Support tools were not made available to the public.Not all studies use software metrics in conjunction with experiments and case studies.Reconfirm practitioners guidelines' absence from selected studies (Alves et al., 2010). Requirements for implemented system can be extracted and reused for a production of a new similar system. Extraction of common and variable features from requirements leverages the benefits of the software product lines engineering (SPLE). Although various approaches have been proposed in feature extractions from natural language (NL) requirements, no related literature review has been published to date for this topic. This paper provides a systematic literature review (SLR) of the state-of-the-art approaches in feature extractions from NL requirements for reuse in SPLE. We have included 13 studies in our synthesis of evidence and the results showed that hybrid natural language processing approaches were found to be in common for overall feature extraction process. A mixture of automated and semi-automated feature clustering approaches from data mining and information retrieval were also used to group common features, with only some approaches coming with support tools. However, most of the support tools proposed in the selected studies were not made available publicly and thus making it hard for practitioners' adoption. As for the evaluation, this SLR reveals that not all studies employed software metrics as ways to validate experiments and case studies. Finally, the quality assessment conducted confirms that practitioners' guidelines were absent in the selected studies.


title: Current and lifetime comorbidity of the DSM-IV anxiety and mood disorders in a large clinical sample.

abstract: The comorbidity of current and lifetime DSM-IV anxiety and mood disorders was examined in 1,127 outpatients who were assessed with the Anxiety Disorders Interview Schedule for DSM-IV: Lifetime version (ADIS-IV-L). The current and lifetime prevalence of additional Axis I disorders in principal anxiety and mood disorders was found to be 57% and 81%, respectively. The principal diagnostic categories associated with the highest comorbidity rates were mood disorders, posttraumatic stress disorder (PTSD), and generalized anxiety disorder (GAD). A high rate of lifetime comorbidity was found between the anxiety and mood disorders; the lifetime association with mood disorders was particularly strong for PTSD, GAD, obsessive-compulsive disorder, and social phobia. The findings are discussed in regard to their implications for the classification of emotional disorders.


title: MCloudDB: A Mobile Cloud Database Service Framework

abstract: The recent advance in mobile network technologies (3G/4G) provides an efficient mobile network infrastructure supporting mobile users to access large volumes of mobile data. Now, many mobile applications are developed based on mobile databases on devices and conventional databases. Due to the limitations of mobile devices, using mobile databases on mobile devices encounters certain scalability issues in mobile data accesses and storage. One alternative approach is to use cloud-based databases to support mobile users and applications on mobile devices. This also runs into the several challenges, such as mobility support, localization, energy consumption, and performance concerns. This paper presents a mobile enabled cloud database solution, called MCloudDB, to address the related issues and needs. MCloudDB provides a mobile enabled cloud database infrastructure with a framework, which can be used as a bridge to connect and integrate mobile applications with back-end cloud databases to support mobile cloud data management and access services. The paper presents the design of this framework and the related key solutions for essential features in mobile cloud databases, such as multi-tenancy, elasticity, seamless connectivity and disaster recovery. A simple application example and related experiment results are reported.


Space syntax based agent simulation


title: On Relaxing Page Program Disturbance over 3D MLC Flash Memory

abstract: With the rapidly-increasing capacity demand over flash memory, 3D NAND flash memory has drawn tremendous attention as a promising solution to further reduce the bit cost and to increase the bit density. However, such advanced 3D devices will suffer more intensive program disturbance, compared to 2D NAND flash memory. Especially when multi-level-cell (MLC) technology is adopted, the deteriorated disturbance due to the program operations of intra and inter pages will become even more critical for reliability. In contrast to the past efforts that try to resolve the reliability issue with error correction codes or hardware designs, this work seeks for the redesign of the program operation. A disturb-aware programming scheme is proposed to not only relax the disturbance induced by slow cells as much as possible but also reduce the possibility in requiring a high voltage to program the slow cells. A series of experiments was conducted based on real 3D MLC flash chips, and the results demonstrate that the proposed scheme is extremely effective on reducing the disturbance as well as the bit error rate.


title: Managing Technical Debt in Enterprise Software Packages

abstract: We develop an evolutionary model and theory of software technical debt accumulation to facilitate a rigorous and balanced analysis of its benefits and costs in the context of a large commercial enterprise software package. Our theory focuses on the optimization problem involved in managing technical debt, and illustrates the different tradeoff patterns between software quality and customer satisfaction under early and late adopter scenarios at different lifecycle stages of the software package. We empirically verify our theory utilizing a ten year longitudinal data set drawn from 69 customer installations of the software package. We then utilize the empirical results to develop actionable policies for managing technical debt in enterprise software product adoption.


title: Temporal Answer Set Programming on Finite Traces

abstract: In this paper, we introduce an alternative approach to Temporal Answer Set Programming that relies on a variation of Temporal Equilibrium Logic (TEL) for finite traces. This approach allows us to even out the expressiveness of TEL over infinite traces with the computational capacity of (incremental) Answer Set Programming (ASP). Also, we argue that finite traces are more natural when reasoning about action and change. As a result, our approach is readily implementable via multi-shot ASP systems and benefits from an extension of ASP's full-fledged input language with temporal operators. This includes future as well as past operators whose combination offers a rich temporal modeling language. For computation, we identify the class of temporal logic programs and prove that it constitutes a normal form for our approach. Finally, we outline two implementations, a generic one and an extension of clingo.


title: Research Article An FPGA Implementation of a Parallelized MT19937 Uniform Random Number Generator

abstract: Recent times have witnessed an increase in use of high-performance reconfigurable computing for accelerating large-scale simulations. A characteristic of such simulations, like infrared (IR) scene simulation, is the use of large quantities of uncorrelated random numbers. It is therefore of interest to have a fast uniform random number generator implemented in reconfigurable hardware. While there have been previous attempts to accelerate the MT19937 pseudouniform random number generator using FPGAs we believe that we can substantially improve the previous implementations to develop a higher throughput and more areatime efficient design. Due to the potential for parallel implementation of random numbers generators, designs that have both a small area footprint and high throughput are to be preferred to ones that have the high throughput but with significant extra area requirements. In this paper, we first present a single port design and then present an enhanced 624 port hardware implementation of the MT19937 algorithm. The 624 port hardware implementation when implemented on a Xilinx XC2VP70-6 FPGA chip has a throughput of 119.6 × 109 32 bit random numbers per second which is more than 17x that of the previously best published uniform random number generator. Furthermore it has the lowest area time metric of all the currently published FPGA-based pseudouniform random number generators.


title: Individual 3D Face Generation Based on Candide-3 for Face Recognition

abstract: In this paper, an individual 3D face generation method has been proposed. This method is based on candide-3 and aims for pose normalization for face recognition system. By using ASM and AAM for facial feature point tracking and model parameters optimizing, this method decreases the computation time of 3D face generation and improves the level of the 3D model fitting with the input image. Through applying the method for pose normalization, the recognition rate can be improved greatly. Experiment results on CMU-PIE database show validity of this method.


title: Ethical aspects and future of artificial intelligence

abstract: Artificial Intelligence is the intelligence shown by machines or software. Artificial intelligence includes reasoning, natural processing language and even various algorithms are used to put the intelligence in the system. In this paper we investigate motivations and expectations for the development of Machine Intelligence. This paper also presents the role of ethics in developing artificial intelligence. In this paper we have compared the new emerging AI scope with the old technologies in various fields and its advantages to the society.


title: The Fuzzy ART algorithm: A categorization method for supplier evaluation and selection

abstract: For most of managers purchasing is a strategic issue. Thus, to select the suitable suppliers has strategic importance for every company. The objective of supplier selection is to reduce purchasing risk, maximize overall value to the purchaser and build a long term, reliable relationship between buyers and suppliers. Many methods have been proposed and used for supplier evaluation and selection; most of them try to rank the suppliers from the best to the worst and to choose the appropriate supplier(s). Supplier evaluation and selection is a complex and typical multi criteria decision-making problem. Because of human judgment needs in many area of supplier selection such as preferences on alternatives or on the attributes of suppliers or the class number and borders supplier selection becomes more difficult and risky. In this study, a new tool for supplier selection is proposed. In this paper, we applied Fuzzy Adaptive Resonance Theory (ART)'s classification ability to the supplier evaluation and selection area. The proposed selection method, using Fuzzy ART not only selects the most appropriate supplier(s) and also clusters all of the vendors according to chosen criteria. To explain the Fuzzy ART method a real-life supplier selection problem is solved and suppliers are categorized according to their similarities. The obtained results show that the proposed method is well suited as a decision-making tool for supplier evaluation and selection problem.


title: Age, Socioeconomic Status, and Health

abstract: the past century than in all prior human history, yet the maximal human life span has not increased commensurately, if at all (Fries 1980; Preston 1977). This suggests both utopian and distopian scenarios for the future. The utopian scenario, articulated by Fries (1980), has two premises. First, the human life span is not only finite, but also relatively fixed (at about 85 years on average). Second, we will increasingly postpone the onset of morbidity and disability, thus "compressing" their duration into the last years of the life span and improving the quality of life while reducing the need for medical care. Evidence that the human life span may not be fixed, and that life expectancy may be increasing as fast or faster than the rate of postponement of the onset of morbidity and mortality, has suggested to others a more distopian scenario: gains in life expectancy merely add years to life during which people are chronically ill or disabled, and thus higher consumers of health care (Gruenberg 1977; Manton 1982; Schneider and Brody 1983; Verbrugge 1984). Despite continuing disagreement as to whether "compression of morbidity" is now, or ever will be, a reality, proponents of both the


title: Experimental results based on macro-cell channel measurement

abstract: The next generation mobile communication systems tend to wideband. We present the experimental results based on the polarimetric MIMO (Multiple Input Multiple Output) channel measurement campaign at 2.53GHz with a bandwidth of 100MHz, performed in Shanghai. Our special interest focuses on the propagation characteristics and channel parameters in terms of path loss, channel impulse response (CIR) and Root-Mean-Square (RMS) delay in the urban rich-scattering environment. The comparison result of the measured path loss at 3.5GHz with the existing models such as SUI (Stanford University Interim), COST-231 and WINNER (Wireless World Initiative New Radio) is also given. Since the city, Shanghai, as one of the metropolitans in the world, has different building height/density and infrastructure construction from the most cities in European countries, the results will provide the first overview to the extraordinary propagation characteristics of 100MHz bandwidth channel in the particular environment like Shanghai.


title: Testing the technology acceptance model for evaluating healthcare professionals' intention to use an adverse event reporting system

abstract: Background. Many healthcare organizations have implemented adverse event reporting systems in the hope of learning from experience to prevent adverse events and medical errors. However, a number of these applications have failed or not been implemented as predicted. Objective. This study presents an extended technology acceptance model that integrates variables connoting trust and management support into the model to investigate what determines acceptance of adverse event reporting systems by healthcare professionals. Method. The proposed model was empirically tested using data collected from a survey in the hospital environment. A confirmatory factor analysis was performed to examine the reliability and validity of the measurement model, and a structural equation modeling technique was used to evaluate the causal model. Results. The results indicated that perceived usefulness, perceived ease of use, subjective norm, and trust had a significant effect on a professional’s intention to use an adverse event reporting system. Among them, subjective norm had the most contribution (total effect). Perceived ease of use and subjective norm also had a direct effect on perceived usefulness and trust, respectively. Management support had a direct effect on perceived usefulness, perceived ease of use, and subjective norm. Conclusion. The proposed model provides a means to understand what factors determine the behavioral intention of healthcare professionals to use an adverse event reporting system and how this may affect future use. In addition, understanding the factors contributing to behavioral intent may potentially be used in advance of system development to predict reporting systems acceptance.


title: A mammalian expression system for rapid production and purification of active MAP kinase phosphatases.

abstract: Expression of enzymatically active mammalian proteins in Escherichia coli can proven to be a challenging task due to poor solubility, improper folding, and lack of adequate posttranslational modification. Expression of mammalian proteins using baculovirus or yeast systems is time-consuming and may also be subject to inadequate modification. In order to overcome these technical difficulties, we have developed a mammalian expression system for the convenient subcloning of cDNA fragments, high-level expression, and one-step purification of enzymatically active proteins. The mammalian expression vector pEBG that expresses glutathione S-transferase fusion proteins was modified to create an SrfI restriction site in the multiple cloning site. The protein coding sequences of MAP kinase phosphatase-1 (MKP-1), MAP kinase phosphatase-2 (MKP-2), and the tumor suppressor PTEN were PCR-amplified using Pfu DNA polymerase and cloned into the SrfI site through SrfI digestion-coupled ligation. The resulting plasmids were transiently transfected into 293T cells using FuGENE 6 transfection reagent. Forty eight hours after transfection, cells were harvested and bioactive recombinant proteins were purified by glutathione-Sepharose beads. Protein yield, which ranged from 200 to 700 microg, was more than adequate for biochemical studies. The usefulness of this versatile system for studying protein function and its potential application for proteomics research are discussed.


title: Brain imaging of language plasticity in adopted adults : can a second language replace the first ?

abstract: This fish securing device has a relatively small diameter cylindrical axial assembly to which are hinged a number of fish securing arms. A spring loading mechanism contained within the axial assembly urges the hinged arms towards a closed fish securing position by means of an actuating linkage arm associated with each securing arm. A cocking and trip mechanism is actuatable upon tugging on a fish lure connected to one end of the axial assembly for releasing the securing arms from an open cocked position towards their closed position to secure a fish tugging on the lure. The device features an improved, compact, streamlined configuration whereby the spring loading mechanism is contained within the central tubular assembly and wherein the cocking and trippng mechanism and securing arm actuating linkages are disposed in a relatively protected position between the securing arms and the central axial assembly to minimize the possibility of damage or of entanglement with underwater growth, rocks or debris.


title: Dynamics of Retrieval Strategies for Remote Memories

abstract: Prevailing theory suggests that long-term memories are encoded via a two-phase process requiring early involvement of the hippocampus followed by the neocortex. Contextual fear memories in rodents rely on the hippocampus immediately following training but are unaffected by hippocampal lesions or pharmacological inhibition weeks later. With fast optogenetic methods, we examine the real-time contribution of hippocampal CA1 excitatory neurons to remote memory and find that contextual fear memory recall, even weeks after training, can be reversibly abolished by temporally precise optogenetic inhibition of CA1. When this inhibition is extended to match the typical time course of pharmacological inhibition, remote hippocampus dependence converts to hippocampus independence, suggesting that long-term memory retrieval normally depends on the hippocampus but can adaptively shift to alternate structures. Further revealing the plasticity of mechanisms required for memory recall, we confirm the remote-timescale importance of the anterior cingulate cortex (ACC) and implicate CA1 in ACC recruitment for remote recall.


title: Abusive Language Detection in Online User Content

abstract: Detection of abusive language in user generated online content has become an issue of increasing importance in recent years. Most current commercial methods make use of blacklists and regular expressions, however these measures fall short when contending with more subtle, less ham-fisted examples of hate speech. In this work, we develop a machine learning based method to detect hate speech on online user comments from two domains which outperforms a state-of-the-art deep learning approach. We also develop a corpus of user comments annotated for abusive language, the first of its kind. Finally, we use our detection tool to analyze abusive language over time and in different settings to further enhance our knowledge of this behavior.


title: Garp: a MIPS processor with a reconfigurable coprocessor

abstract: Typical reconfigurable machines exhibit shortcomings that make them less than ideal for general-purpose computing. The Garp Architecture combines reconfigurable hardware with a standard MIPS processor on the same die to retain the better features of both. Novel aspects of the architecture are presented, as well as a prototype software environment and preliminary performance results. Compared to an UltraSPARC, a Garp of similar technology could achieve speedups ranging from a factor of 2 to as high as a factor of 24 for some useful applications.


title: Unpacking cognitive reappraisal: goals, tactics, and outcomes.

abstract: Studies of emotion regulation typically contrast two or more strategies (e.g., reappraisal vs. suppression) and ignore variation within each strategy. To address such variation, we focused on cognitive reappraisal and considered the effects of goals (i.e., what people are trying to achieve) and tactics (i.e., what people actually do) on outcomes (i.e., how affective responses change). To examine goals, we randomly assigned participants to either increase positive emotion or decrease negative emotion to a negative stimulus. To examine tactics, we categorized participants’ reports of how they reappraised. To examine reappraisal outcomes, we measured experience and electrodermal responding. Findings indicated that (a) the goal of increasing positive emotion led to greater increases in positive affect and smaller decreases in skin conductance than the goal of decreasing negative emotion, and (b) use of the reality challenge tactic was associated with smaller increases in positive affect during reappraisal. These findings suggest that reappraisal can be implemented in the service of different emotion goals, using different tactics. Such differences are associated with different outcomes, and they should be considered in future research and applied attempts to maximize reappraisal success.


title: Apps, Trackers, Privacy, and Regulators: A Global Study of the Mobile Tracking Ecosystem

abstract: Third-party services form an integral part of the mobile ecosystem: they ease application development and enable features such as analytics, social network integration, and app monetization through ads. However, aided by the general opacity of mobile systems, such services are also largely invisible to users. This has negative consequences for user privacy as third-party services can potentially track users without their consent, even across multiple applications. Using real-world mobile traffic data gathered by the Lumen Privacy Monitor (Lumen), a privacy enhancing app with the ability to analyze network traffic on mobile devices in user space, we present insights into the mobile advertising and tracking ecosystem and its stakeholders. In this study, we develop automated methods to detect third-party advertising and tracking services at the traffic level. Using this technique we identify 2,121 such services, of which 233 were previously unknown to other popular advertising and tracking blacklists. We then uncover the business relationships between the providers of these services and characterize them by their prevalence in the mobile and Web ecosystem. Our analysis of the privacy policies of the largest advertising and tracking service providers shows that sharing harvested data with subsidiaries and third-party affiliates is the norm. Finally, we seek to identify the services likely to be most impacted by privacy regulations such as the European General Data Protection Regulation (GDPR) and ePrivacy directives.


title: Robot Button Pressing in Human Environments

abstract: In order to conduct many desirable functions, service robots will need to actuate buttons and switches that are designed for humans. This paper presents the design of a robot named SwitchIt that is small, relatively inexpensive, easily mounted on a mobile robot, and actuates buttons reliably. Its operating characteristics were developed after conducting a systematic study of buttons and switches in human environments. From this study, we develop a categorization of buttons based on a set of physical properties relevant for robots to operate them. After a human calibrates and annotates buttons in the robot's environment using a hand-held tablet, the system automatically recognizes, pushes, and detects the state of a variety of buttons. Empirical tests demonstrate that the system succeeds in operating 95.7% of 234 total buttons/switches in an office building and a household environment.


title: Bandwidth Optimal All-reduce Algorithms for Clusters of Workstations

abstract: We consider an efficient realization of the all-reduce operation with large data sizes in cluster environments, under the assumption that the reduce operator is associative and commutative. We derive a tight lower bound of the amount of data that must be communicated in order to complete this operation and propose a ring-based algorithm that only requires tree connectivity to achieve bandwidth optimality. Unlike the widely used butterfly-like all-reduce algorithm that incurs network contention in SMP/multi-core clusters, the proposed algorithm can achieve contention-free communication in almost all contemporary clusters, including SMP/multi-core clusters and Ethernet switched clusters with multiple switches. We demonstrate that the proposed algorithm is more efficient than other algorithms on clusters with different nodal architectures and networking technologies when the data size is sufficiently large.


title: Newsmap: a knowledge map for online news

abstract: Information technology has made possible the capture and accessing of a large number of data and knowledge bases, which in turn has brought about the problem of information overload. Text mining to turn textual information into knowledge has become a very active research area, but much of the research remains restricted to the English language. Due to the differences in linguistic characteristics and methods of natural language processing, many existing text analysis approaches have yet to be shown to be useful for the Chinese language. This research focuses on the automatic generation of a hierarchical knowledge map NewsMap, based on online Chinese news, particularly the finance and health sections. Whether in print or online, news still represents one important knowledge source that people produce and consume on a daily basis. The hierarchical knowledge map can be used as a tool for browsing business intelligence and medical knowledge hidden in news articles. In order to assess the quality of the map, an empirical study was conducted which shows that the categories of the hierarchical knowledge map generated by NewsMap are better than those generated by regular news readers, both in terms of recall and precision, on the sub-level categories but not on the top-level categories. NewsMap employs an improved interface combining a ID alphabetical hierarchical list and a 2D Self-Organizing Map (SOM) island display. Another empirical study compared the two visualization displays and found that users' performances can be improved by taking advantage of the visual cues of the 2D SOM display.


title: A Brief Study of In-Domain Transfer and Learning from Fewer Samples using A Few Simple Priors

abstract: Domain knowledge can often be encoded in the structure of a network, such as convolutional layers for vision, which has been shown to increase generalization and decrease sample complexity, or the number of samples required for successful learning. In this study, we ask whether sample complexity can be reduced for systems where the structure of the domain is unknown beforehand, and the structure and parameters must both be learned from the data. We show that sample complexity reduction through learning structure is possible for at least two simple cases. In studying these cases, we also gain insight into how this might be done for more complex domains.


title: The Lovelace 2.0 Test of Artificial Creativity and Intelligence

abstract: Observing that the creation of certain types of artistic artifacts necessitate intelligence, we present the Lovelace 2.0 Test of creativity as an alternative to the Turing Test as a means of determining whether an agent is intelligent. The Lovelace 2.0 Test builds off prior tests of creativity and additionally provides a means of directly comparing the relative intelligence of different agents.


title: Learning by Teaching a Robot: The Case of Handwriting

abstract: Thomas (all children's names have been changed) is five and a half years old and has been diagnosed with visuoconstructive deficits. He is under the care of an occupational therapist and tries to work around his inability to draw letters in a consistent manner. Vincent is six and struggles at school with his poor handwriting and even poorer self-confidence. Whereas Thomas is lively and always quick at shifting his attention from one activity to another, Vincent is shy and poised. Two very different children, each one facing the same difficulty to write in a legible manner. Additionally, hidden beyond these impaired skills, psychosocial difficulties arise: they underperform at school. Thomas has to go for follow-up visits every week, and they both live under the label of "special care." This is a source of anxiety for the children and for their parents alike.


Unsupervised Learning in Human Activity Recognition : A First Foray into Clustering Data Gathered from Wearable Sensors


title: Conflict-free Real-time AGV Routing

abstract: We present an algorithm for the problem of routing Automated Guided Vehicles (AGVs) in an automated logistic system. The algorithm avoids collisions, deadlocks and livelocks already at the time of route computation (conflict-free routing). After a preprocessing step the real-time computation for each request consists of the determination of a shortest path with time-windows and a following readjustment of these time-windows. Both is done in polynomial-time. Using goal-oriented search we get computation times which are appropriate for real-time routing. Additionally, in comparison to a static routing approach, used in Container Terminal Altenwerder (CTA) at Hamburg Harbour, our algorithm had an explicit advantage.


title: The peroneus brevis tendon at its insertion site on fifth metatarsal bone.

abstract: BACKGROUND ::: The differences at the attachment site of peroneus brevis (PB) to the fifth metatarsal bone is important in terms of the forces exerted on the bone and hence the mechanism of fractures involving this structure. In this study, we investigated the anatomical properties of PB at the insertion site to the base of fifth metatarsal bone, its possible intertendinous connections with peroneus tertius (PT) and their possible effects on the fracture occurrence at the bony attachment site. ::: ::: ::: METHODS ::: The length and the width of PB tendons at their mid- and end-points were measured and classified according to the insertion types. Besides, the length and the width of the base of fifth metatarsal bone were assessed. The slips extending from the PB tendons and their relationship with PT were also evaluated. The data was compared statistically with each other and between the right and left sides. ::: ::: ::: RESULTS ::: The length of PB tendon was measured 79.57±15.40mm on the right side; 81.48±14.31mm on the left. The width of PB tendon at the mid-point was 4.46±0.80mm on the right side; 4.42±0.94mm on the left. The width of the tendon at its insertion point was measured 14.85±3.40mm and 15.16±3.42mm on the right and left sides respectively. PB was divided into three types according to its attachment to base of fifth metatarsal bone (5thMB). Type I, Type II and Type III were observed at the rates of 59.5%, 28.6% and 11.5% respectively. It was observed that the slips to the bone were extending more commonly from PB than from PT and that the large majority of them were single having their insertions on the base of the proximal phalanx of the fifth toe. ::: ::: ::: CONCLUSIONS ::: Knowing the width and insertional types of PB aids in understanding the mechanism of fractures at the site of bony attachment. The existence of slips may help the surgeon in the procedures involving PB or the lateral side of the forefoot.


title: Macrodystrophia lipomatosa with associated fibrolipomatous hamartoma of the median nerve

abstract: Macrodystrophia lipomatosa (MDL) is a rare disease typically causing localized gigantism and is often associated with a fibrolipomatous hamartoma (FH) of the median or plantar nerve. A previously unreported case of MDL with associated FH of the median nerve is presented.


title: Automatic test case generation for unit software testing using genetic algorithm and mutation analysis

abstract: Software Engineers waste a lot of their time in the process of software testing. It also has been seen in the industries that a lot of money is depleting on the software process. In software testing process we apply test cases as input and check for final output. So our first concern is to choose the appropriate test cases for the software testing process. To give the correct output, it is very difficult to choose test cases. So generation of the test cases is a NP problem. To generate automatic test cases many nature inspired optimization algorithms have been used. These nature inspired optimization algorithms help to generate appropriate test cases. In this paper for generating automatic test cases genetic algorithm and mutation analysis had been used.


Casual Creators


Work Together , Play Smart : Collective Intelligence in League of Legends Teams


title: VectorH: Taking SQL-on-Hadoop to the Next Level

abstract: Actian Vector in Hadoop (VectorH for short) is a new SQL-on-Hadoop system built on top of the fast Vectorwise analytical database system. VectorH achieves fault tolerance and storage scalability by relying on HDFS, and extends the state-of-the-art in SQL-on-Hadoop systems by instrumenting the HDFS replication policy to optimize read locality. VectorH integrates with YARN for workload management, achieving a high degree of elasticity. Even though HDFS is an append-only filesystem, and VectorH supports (update-averse) ordered tables, trickle updates are possible thanks to Positional Delta Trees (PDTs), a differential update structure that can be queried efficiently. We describe the changes made to single-server Vectorwise to turn it into a Hadoop-based MPP system, encompassing workload management, parallel query optimization and execution, HDFS storage, transaction processing and Spark integration. We evaluate VectorH against HAWQ, Impala, SparkSQL and Hive, showing orders of magnitude better performance.


title: Characterizing and predicting which bugs get fixed: an empirical study of Microsoft Windows

abstract: We performed an empirical study to characterize factors that affect which bugs get fixed in Windows Vista and Windows 7, focusing on factors related to bug report edits and relationships between people involved in handling the bug. We found that bugs reported by people with better reputations were more likely to get fixed, as were bugs handled by people on the same team and working in geographical proximity. We reinforce these quantitative results with survey feedback from 358 Microsoft employees who were involved in Windows bugs. Survey respondents also mentioned additional qualitative influences on bug fixing, such as the importance of seniority and interpersonal skills of the bug reporter. Informed by these findings, we built a statistical model to predict the probability that a new bug will be fixed (the first known one, to the best of our knowledge). We trained it on Windows Vista bugs and got a precision of 68% and recall of 64% when predicting Windows 7 bug fixes. Engineers could use such a model to prioritize bugs during triage, to estimate developer workloads, and to decide which bugs should be closed or migrated to future product versions.


title: Valid Items for Screening Dysphagia Risk in Patients With Stroke: A Systematic Review

abstract: Background and Purpose—Screening for dysphagia is essential to the implementation of preventive therapies for patients with stroke. A systematic review was undertaken to determine the evidence-based validity of dysphagia screening items using instrumental evaluation as the reference standard. Methods—Four databases from 1985 through March 2011 were searched using the terms cerebrovascular disease, stroke deglutition disorders, and dysphagia. Eligibility criteria were: homogeneous stroke population, comparison to instrumental examination, clinical examination without equipment, outcome measures of dysphagia or aspiration, and validity of screening items reported or able to be calculated. Articles meeting inclusion criteria were evaluated for methodological rigor. Sensitivity, specificity, and predictive capabilities were calculated for each item. Results—Total source documents numbered 832; 86 were reviewed in full and 16 met inclusion criteria. Study quality was variable. Testing swallowing, generally wit...


title: NLP-based faceted search: Experience in the development of a science and technology search engine

abstract: An appropriate promotion, distribution and dissemination of scientific, artistic and technology developments can foster the collaboration between a country's productive and academic sectors. The purpose of this paper is to present a novel search engine aiming at helping people to access science and technology advances, researchers and institutions working in specific areas of research. Our search engine first collects information disseminated on the Web in academic institution sites and in researchers personal homepages. Then, after intensive text processing, it summarizes the information in an enriched and user-friendly presentation oriented to non-expert users. Stable performance and an acceptable level of effectiveness for automatic named entities recognition indicate the potential of our approach for bridging the gap between the heterogeneous and unstructured information available on the Web about the research and development advances in a country and the innovation required by the productive sectors.


title: Environmental factors, life events, and trauma in the course of bipolar disorder

abstract: The etiology and clinical course of bipolar disorder are considered to be determined by genetic and environmental factors. Although the kindling hypothesis emphasizes the impact of environmental factors on initial onset, their connection to the outcome and clinical course have been poorly established. Hence, there have been numerous research efforts to investigate the impact of environmental factors on the clinical course of illness. Our aim is to outline recent research on the impact of environmental determinants on the clinical course of bipolar disorder. We carried out a computer-aided search to find publications on an association between environmental factors, life events, and the clinical course of bipolar disorder. Publications in the reference lists of suitable papers have also been taken into consideration. We performed a narrative overview on all eligible publications. The available body of data supports an association between environmental factors and the clinical course of bipolar disorder. These factors comprise prenatal, early-life, and entire lifespan aspects. Given varying sample sizes and several methodological limitations, the reported quality and extent of the association between environmental factors and the clinical course of bipolar disorder should be interpreted with utmost caution. Systematic longitudinal long-term follow-up trials are needed to obtain a clearer and more robust picture.


title: Deep learning via semi-supervised embedding

abstract: We show how nonlinear embedding algorithms popular for use with shallow semi-supervised learning techniques such as kernel methods can be applied to deep multilayer architectures, either as a regularizer at the output layer, or on each layer of the architecture. This provides a simple alternative to existing approaches to deep learning whilst yielding competitive error rates compared to those methods, and existing shallow semi-supervised techniques.


title: The draft genome of the transgenic tropical fruit tree papaya (Carica papaya Linnaeus)

abstract: In the early 1990s an outbreak of papaya ringspot virus (PRSV) in the papaya groves in the Puna district of Hawaii caused severe damage to an important crop. Since then, the planting of two transgenic cultivars resistant to the virus — called 'SunUp' and 'Rainbow' — has helped to maintain yields. SunUp is a transgenic red-fleshed fruit that expresses the coat protein gene of a mild mutant of PRSV, conferring resistance via post-transcriptional gene silencing. Rainbow is a yellow-fleshed (and therefore more popular) F1 hybrid bred from SunUp. Now the draft genome sequence of the SunUp strain of papaya has been determined — a first for a commercial virus-resistant transgenic fruit tree. Comparison of this plant genome to those of Arabidopsis and others sheds light on the evolution of qualities such as biosynthesis, starch deposition, control of photosynthesis and pathways for creating the volatile compounds that contribute to the characteristic flavour of papaya. On the cover, the disease-free transgenic Rainbow and the severely infected, stunted and dying non-transgenic Sunrise grow in adjoining plots. Researchers from Hawaii and an international consortium have produced a draft genome assembly for 'SunUp', the first commercial virus-resistant transgenic fruit tree. Comparison of this plant genome to those of Arabidopsis and others sheds light on evolution of characteristics such as biosynthesis, starch deposition, control of photosynthesis and pathways for creating volatile compounds. Papaya, a fruit crop cultivated in tropical and subtropical regions, is known for its nutritional benefits and medicinal applications. Here we report a 3× draft genome sequence of ‘SunUp’ papaya, the first commercial virus-resistant transgenic fruit tree1 to be sequenced. The papaya genome is three times the size of the Arabidopsis genome, but contains fewer genes, including significantly fewer disease-resistance gene analogues. Comparison of the five sequenced genomes suggests a minimal angiosperm gene set of 13,311. A lack of recent genome duplication, atypical of other angiosperm genomes sequenced so far2,3,4,5, may account for the smaller papaya gene number in most functional groups. Nonetheless, striking amplifications in gene number within particular functional groups suggest roles in the evolution of tree-like habit, deposition and remobilization of starch reserves, attraction of seed dispersal agents, and adaptation to tropical daylengths. Transgenesis at three locations is closely associated with chloroplast insertions into the nuclear genome, and with topoisomerase I recognition sites. Papaya offers numerous advantages as a system for fruit-tree functional genomics, and this draft genome sequence provides the foundation for revealing the basis of Carica’s distinguishing morpho-physiological, medicinal and nutritional properties.


title: Spanish Multicenter Normative Studies (NEURONORMA Project): Norms for Verbal Fluency Tests

abstract: Lexical fluency tests are frequently used in clinical practice to assess language and executive function. As part of the Spanish multicenter normative studies (NEURONORMA project), we provide age- and education-adjusted norms for three semantic fluency tasks (animals, fruit and vegetables, and kitchen tools), three formal lexical tasks (words beginning with P, M, and R), and three excluded letter fluency tasks (excluded A, E, and S). The sample consists of 346 participants who are cognitively normal, community dwelling, and ranging in age from 50 to 94 years. Tables are provided to convert raw scores to age-adjusted scaled scores. These were further converted into education-adjusted scaled scores by applying regression-based adjustments. The current norms should provide clinically useful data for evaluating elderly Spanish people. These data may also be of considerable use for comparisons with other international normative studies. Finally, these norms should help improve the interpretation of verbal fluency tasks and allow for greater diagnostic accuracy.


title: Protecting Visual Secrets Using Adversarial Nets

abstract: Protecting visual secrets is an important problem due to the prevalence of cameras that continuously monitor our surroundings. Any viable solution to this problem should also minimize the impact on the utility of applications that use images. In this work, we build on the existing work of adversarial learning to design a perturbation mechanism that jointly optimizes privacy and utility objectives. We provide a feasibility study of the proposed mechanism and present ideas on developing a privacy framework based on the adversarial perturbation mechanism.


title: Task scheduling for context minimization in dynamically reconfigurable platforms

abstract: Dynamically reconfigurable hardware provides useful means to reduce the time-to-prototype and even the time-to-market in product designs. It also offers a good alternative in reconfiguring hardware logics to optimize the system performance. This paper targets an essential issue in reconfigurable computing, i.e., the minimization of configuration contexts. We explore different constraints on the CONTEXT MINIMIZATION problem. When the resulting subproblems are polynomial-time solvable, optimal algorithms are presented.


title: adaQN: An Adaptive Quasi-Newton Algorithm for Training RNNs

abstract: Recurrent Neural Networks (RNNs) are powerful models that achieve exceptional performance on several pattern recognition problems. However, the training of RNNs is a computationally difficult task owing to the well-known "vanishing/exploding" gradient problem. Algorithms proposed for training RNNs either exploit no (or limited) curvature information and have cheap per-iteration complexity, or attempt to gain significant curvature information at the cost of increased per-iteration cost. The former set includes diagonally-scaled first-order methods such as ADAGRAD and ADAM, while the latter consists of second-order algorithms like Hessian-Free Newton and K-FAC. In this paper, we present adaQN, a stochastic quasi-Newton algorithm for training RNNs. Our approach retains a low per-iteration cost while allowing for non-diagonal scaling through a stochastic L-BFGS updating scheme. The method uses a novel L-BFGS scaling initialization scheme and is judicious in storing and retaining L-BFGS curvature pairs. We present numerical experiments on two language modeling tasks and show that adaQN is competitive with popular RNN training algorithms.


title: Prioritizing test cases for regression testing

abstract: Test case prioritization techniques schedule test cases in an order that increases their effectiveness in meeting some performance goal. One performance goal, rate of fault detection , is a measure of how quickly faults are detected within the testing process; an improved rate of fault detection can provide faster feedback on the system under test, and let software engineers begin locating and correcting faults earlier than might otherwise be possible. In previous work, we reported the results of studies that showed that prioritization techniques can significantly improve rate of fault detection. Those studies, however, raised several additional questions: (1) can prioritization techniques be effective when aimed at specific modified versions; (2) what tradeoffs exist between fine granularity and coarse granularity prioritization techniques; (3) can the incorporation of measures of fault proneness into prioritization techniques improve their effectiveness? This paper reports the results of new experiments addressing these questions.


title: Magnetic sensors and their applications

abstract: Magnetic sensors can be classified according to whether they measure the total magnetic field or the vector components of the magnetic field. The techniques used to produce both types of magnetic sensors encompass many aspects of physics and electronics. Here, we describe and compare most of the common technologies used for magnetic field sensing. These include search coil, fluxgate, optically pumped, nuclear precession, SQUID, Hall-effect, anisotropic magnetoresistance, giant magnetoresistance, magnetic tunnel junctions, giant magnetoimpedance, magnetostrictive/piezoelectric composites, magnetodiode, magnetotransistor, fiber optic, magnetooptic, and microelectromechanical systems-based magnetic sensors. The usage of these sensors in relation to working with or around Earth's magnetic field is also presented


title: Computational trust and reputation models for open multi-agent systems: a review

abstract: In open environments, agents depend on reputation and trust mechanisms to evaluate the behavior of potential partners. The scientific research in this field has considerably increased, and in fact, reputation and trust mechanisms have been already considered a key elements in the design of multi-agent systems. In this paper we provide a survey that, far from being exhaustive, intends to show the most representative models that currently exist in the literature. For this enterprise we consider several dimensions of analysis that appeared in three existing surveys, and provide new dimensions that can be complementary to the existing ones and that have not been treated directly. Moreover, besides showing the original classification that each one of the surveys provide, we also classify models that where not taken into account by the original surveys. The paper illustrates the proliferation in the past few years of models that follow a more cognitive approach, in which trust and reputation representation as mental attitudes is as important as the final values of trust and reputation. Furthermore, we provide an objective definition of trust, based on Castelfranchi's idea that trust implies a decision to rely on someone.


title: Cellular Senescence: A Translational Perspective

abstract: Cellular senescence entails essentially irreversible replicative arrest, apoptosis resistance, and frequently acquisition of a pro-inflammatory, tissue-destructive senescence-associated secretory phenotype (SASP). Senescent cells accumulate in various tissues with aging and at sites of pathogenesis in many chronic diseases and conditions. The SASP can contribute to senescence-related inflammation, metabolic dysregulation, stem cell dysfunction, aging phenotypes, chronic diseases, geriatric syndromes, and loss of resilience. Delaying senescent cell accumulation or reducing senescent cell burden is associated with delay, prevention, or alleviation of multiple senescence-associated conditions. We used a hypothesis-driven approach to discover pro-survival Senescent Cell Anti-apoptotic Pathways (SCAPs) and, based on these SCAPs, the first senolytic agents, drugs that cause senescent cells to become susceptible to their own pro-apoptotic microenvironment. Several senolytic agents, which appear to alleviate multiple senescence-related phenotypes in pre-clinical models, are beginning the process of being translated into clinical interventions that could be transformative.


The Benefit of Low Cost Accelerometers for GNSS Anti-Spoofing


title: The Economic Value of Celebrity Endorsements

abstract: ABSTRACT What is the pay-off to enlisting celebrity endorsers? Although effects on stock returns are relatively well documented, little is known about any impact on sales—arguably a metric of more direct importance to advertising practitioners. This study of athlete endorsements finds there is a positive pay-off to a firm9s decision to sign an endorser, and that endorsements are associated with increasing sales in an absolute sense and relative to competing brands. Furthermore, sales and stock returns jump noticeably with each major achievement by the athlete. However, whereas stock-return effects are relatively constant, sales effects exhibit decreasing returns over time. Implications for practitioners are outlined.


title: The kernel recursive least-squares algorithm

abstract: We present a nonlinear version of the recursive least squares (RLS) algorithm. Our algorithm performs linear regression in a high-dimensional feature space induced by a Mercer kernel and can therefore be used to recursively construct minimum mean-squared-error solutions to nonlinear least-squares problems that are frequently encountered in signal processing applications. In order to regularize solutions and keep the complexity of the algorithm bounded, we use a sequential sparsification process that admits into the kernel representation a new input sample only if its feature space image cannot be sufficiently well approximated by combining the images of previously admitted samples. This sparsification procedure allows the algorithm to operate online, often in real time. We analyze the behavior of the algorithm, compare its scaling properties to those of support vector machines, and demonstrate its utility in solving two signal processing problems-time-series prediction and channel equalization.


title: The Morphometrics of “Masculinity” in Human Faces

abstract: In studies of social inference and human mate preference, a wide but inconsistent array of tools for computing facial masculinity has been devised. Several of these approaches implicitly assumed that the individual expression of sexually dimorphic shape features, which we refer to as maleness, resembles facial shape features perceived as masculine. We outline a morphometric strategy for estimating separately the face shape patterns that underlie perceived masculinity and maleness, and for computing individual scores for these shape patterns. We further show how faces with different degrees of masculinity or maleness can be constructed in a geometric morphometric framework. In an application of these methods to a set of human facial photographs, we found that shape features typically perceived as masculine are wide faces with a wide inter-orbital distance, a wide nose, thin lips, and a large and massive lower face. The individual expressions of this combination of shape features—the masculinity shape scores—were the best predictor of rated masculinity among the compared methods (r = 0.5). The shape features perceived as masculine only partly resembled the average face shape difference between males and females (sexual dimorphism). Discriminant functions and Procrustes distances to the female mean shape were poor predictors of perceived masculinity.


title: BigDL: A Distributed Deep Learning Framework for Big Data

abstract: In this paper, we present BigDL, a distributed deep learning framework for Big Data platforms and workflows. It is implemented on top of Apache Spark, and allows users to write their deep learning applications as standard Spark programs (running directly on large-scale big data clusters in a distributed fashion). It provides an expressive, "data-analytics integrated" deep learning programming model, so that users can easily build the end-to-end analytics + AI pipelines under a unified programming paradigm; by implementing an AllReduce like operation using existing primitives in Spark (e.g., shuffle, broadcast, and in-memory data persistence), it also provides a highly efficient "parameter server" style architecture, so as to achieve highly scalable, data-parallel distributed training. Since its initial open source release, BigDL users have built many analytics and deep learning applications (e.g., object detection, sequence-to-sequence generation, visual similarity, neural recommendations, fraud detection, etc.) on Spark.


title: Traffic management: a holistic approach to memory placement on NUMA systems

abstract: NUMA systems are characterized by Non-Uniform Memory Access times, where accessing data in a remote node takes longer than a local access. NUMA hardware has been built since the late 80's, and the operating systems designed for it were optimized for access locality. They co-located memory pages with the threads that accessed them, so as to avoid the cost of remote accesses. Contrary to older systems, modern NUMA hardware has much smaller remote wire delays, and so remote access costs per se are not the main concern for performance, as we discovered in this work. Instead, congestion on memory controllers and interconnects, caused by memory traffic from data-intensive applications, hurts performance a lot more. Because of that, memory placement algorithms must be redesigned to target traffic congestion. This requires an arsenal of techniques that go beyond optimizing locality. In this paper we describe Carrefour, an algorithm that addresses this goal. We implemented Carrefour in Linux and obtained performance improvements of up to 3.6 relative to the default kernel, as well as significant improvements compared to NUMA-aware patchsets available for Linux. Carrefour never hurts performance by more than 4% when memory placement cannot be improved. We present the design of Carrefour, the challenges of implementing it on modern hardware, and draw insights about hardware support that would help optimize system software on future NUMA systems.


title: Mitral Valve Leaflets Segmentation in Echocardiography using Convolutional Neural Networks*

abstract: Rheumatic heart disease remains a major burden in the developing countries. The World Heart Federation proposed guidelines for the echocardiographic detection of the disease, in which the mitral leaflets’ morphology assessment is a key indicator. The drawback is that these guidelines are dependent on the clinician experience. To overcome this limitation, we propose an automatic segmentation of the mitral leaflets using a new method based on convolutional neural network, specifically the UNet architecture. The results indicate a median DICE coefficient of 0.74 in PLAX and 0.79 in A4C for the anterior mitral leaflet segmentation, while median DICE of 0.60 in PLAX and 0.69 A4C are met for the posterior leaflet. A visual evaluation of this segmentation approach by two cardiologists is in line with the numerical results. The false detection due to overestimation and artifacts remains an issue to be addressed in the future.


title: Individual Constituents from Essential Oils Inhibit Biofilm Mass Production by Multi-Drug Resistant Staphylococcus aureus

abstract: Biofilm formation by Staphylococcus aureus represents a problem in both the medical field and the food industry, because the biofilm structure provides protection to embedded cells and it strongly attaches to surfaces. This circumstance is leading to many research programs seeking new alternatives to control biofilm formation by this pathogen. In this study we show that a potent inhibition of biofilm mass production can be achieved in community-associated methicillin-resistant S. aureus (CA-MRSA) and methicillin-sensitive strains using plant compounds, such as individual constituents (ICs) of essential oils (carvacrol, citral, and (+)-limonene). The Crystal Violet staining technique was used to evaluate biofilm mass formation during 40 h of incubation. Carvacrol is the most effective IC, abrogating biofilm formation in all strains tested, while CA-MRSA was the most sensitive phenotype to any of the ICs tested. Inhibition of planktonic cells by ICs during initial growth stages could partially explain the inhibition of biofilm formation. Overall, our results show the potential of EOs to prevent biofilm formation, especially in strains that exhibit resistance to other antimicrobials. As these compounds are food additives generally recognized as safe, their anti-biofilm properties may lead to important new applications, such as sanitizers, in the food industry or in clinical settings.