title: The marketing of menthol cigarettes in the United States: populations, messages, and channels.

abstract: This commentary looks at the marketing menthol cigarettes to various targeted populations--women, middle school youth and Asian/Pacific Islander immigrants as well as African Americans. The authors take the position that "ethnic awareness" as evidenced in the advertising of menthol cigarette brands to African Americans is just one of four distinct messages that tobacco marketers have used for what they have termed the "coolness" category. The other messages are: healthy/medicinal; fresh/refreshing/cool/clean/crisp; and youthfulness/silliness and fun. The commentary poses three questions: (a) Are new population segments being steered toward menthol cigarettes using marketing approaches that are similar to what has occurred with African Americans and women? (b) What exactly is the relationship between the marketing of menthol cigarettes and subsequent use of menthol tobacco products by specific population subgroups? (c) Are there lessons to be learned from the marketing of menthol cigarettes that can be used to improve the public health and medical communities' smoking cessation and tobacco use prevention communications efforts?


title: Spoken Language Understanding for a Nutrition Dialogue System

abstract: Food logging is recommended by dieticians for prevention and treatment of obesity, but currently available mobile applications for diet tracking are often too difficult and time-consuming for patients to use regularly. For this reason, we propose a novel approach to food journaling that uses speech and language understanding technology in order to enable efficient self-assessment of energy and nutrient consumption. This paper presents ongoing language understanding experiments conducted as part of a larger effort to create a nutrition dialogue system that automatically extracts food concepts from a user's spoken meal description. We first summarize the data collection and annotation of food descriptions performed via Amazon Mechanical Turk (AMT), for both a written corpus and spoken data from an in-domain speech recognizer. We show that the addition of word vector features improves conditional random field (CRF) performance for semantic tagging of food concepts, achieving an average F1 test score of 92.4 on written data; we also demonstrate that a convolutional neural network (CNN) with no hand-crafted features outperforms the best CRF on spoken data, achieving an F1 test score of 91.3. We illustrate two methods for associating foods with properties: segmenting meal descriptions with a CRF, and a complementary method that directly predicts associations with a feed-forward neural network. Finally, we conduct an end-to-end system evaluation through an AMT user study with worker ratings of 83% semantic tagging accuracy.


title: Development of a 5 DOF prosthetic arm for above elbow amputees

abstract: Recent progress in biomechatronics technology brings a lot of benefit to increase the mobility of above elbow (AE) amputees in their daily life activities. A prosthetic arm is used to compensate for the lost functions of the AE amputees absent arm. A number of commercial prosthetic arm have been developed since last few decades. However, many amputees have not used them due to the discrepancy between their expectations and the reality. One of the main factors that cause the loss of interest in current commercial prosthetic arm is the lack of desired DOF (degree of freedom) that results unnatural arm motion. In this paper, a 5 DOF externally powered prosthetic arm for AE amputees is proposed to increase their mobility in daily life activities. The proposed prosthesis is designed to generate natural human like arm motion while performing a daily life task. This paper summarizes the design and controllability of the proposed prosthesis.


title: Predicting Individual Physiologically Acceptable States for Discharge from a Pediatric Intensive Care Unit

abstract: Objective ::: Quantify physiologically acceptable PICU-discharge vital signs and develop machine learning models to predict these values for individual patients throughout their PICU episode. ::: ::: ::: Methods ::: EMR data from 7256 survivor PICU episodes (5632 patients) collected between 2009 and 2017 at Children's Hospital Los Angeles was analyzed. Each episode contained 375 variables representing physiology, labs, interventions, and drugs. Between medical and physical discharge, when clinicians determined the patient was ready for ICU discharge, they were assumed to be in a physiologically acceptable state space (PASS) for discharge. Each patient's heart rate, systolic blood pressure, diastolic blood pressure in the PASS window were measured and compared to age-normal values, regression-quantified PASS predictions, and recurrent neural network (RNN) PASS predictions made 12 hours after PICU admission. ::: ::: ::: Results ::: Mean absolute errors (MAEs) between individual PASS values and age-normal values (HR: 21.0 bpm; SBP: 10.8 mm Hg; DBP: 10.6 mm Hg) were greater (p < .05) than regression prediction MAEs (HR: 15.4 bpm; SBP: 9.9 mm Hg; DBP: 8.6 mm Hg). The RNN models best approximated individual PASS values (HR: 12.3 bpm; SBP: 7.6 mm Hg; DBP: 7.0 mm Hg). ::: ::: ::: Conclusions ::: The RNN model predictions better approximate patient-specific PASS values than regression and age-normal values.


title: A Cortical Circuit for Gain Control by Behavioral State

abstract: The brain's response to sensory input is strikingly modulated by behavioral state. Notably, the visual response of mouse primary visual cortex (V1) is enhanced by locomotion, a tractable and accessible example of a time-locked change in cortical state. The neural circuits that transmit behavioral state to sensory cortex to produce this modulation are unknown. In vivo calcium imaging of behaving animals revealed that locomotion activates vasoactive intestinal peptide (VIP)-positive neurons in mouse V1 independent of visual stimulation and largely through nicotinic inputs from basal forebrain. Optogenetic activation of VIP neurons increased V1 visual responses in stationary awake mice, artificially mimicking the effect of locomotion, and photolytic damage of VIP neurons abolished the enhancement of V1 responses by locomotion. These findings establish a cortical circuit for the enhancement of visual response by locomotion and provide a potential common circuit for the modulation of sensory processing by behavioral state.


title: Learning Sparse Representation With Variational Auto-Encoder for Anomaly Detection

abstract: Anomaly detection has a wide range of applications in security area such as network monitoring and smart city/campus construction. It has become an active research issue of great concern in recent years. However, most algorithms of the existing studies are powerless for large-scale and high-dimensional data, and the intermediate data extracted by some methods that can handle high-dimensional data will consume lots of storage space. In this paper, we propose a novel sparse representation framework that learns dictionaries based on the latent space of variational auto-encoder. For large-scale data sets, it can play the role of dimensionality reduction to obtain hidden information, and extract more high-level features than hand-crafted features. At the same time, for the storage of normal information, the space cost can be greatly reduced. To verify the versatility and performance of the proposed learning algorithm, we have experimented on different types of anomaly detection tasks, including KDD-CUP data set for network intrusion detection, Mnist data set for image anomaly detection, and UCSD pedestrian’s data set for abnormal event detection in surveillance videos. The experimental results demonstrate that the proposed algorithm outperforms competing algorithms in all kinds of anomaly detection tasks.


title: From ISA 88/95 meta-models to an OPC UA-based development tool for CPPS under IEC 61499

abstract: In the era of Industry 4.0, fast-growing requirements for an agile and effective vertical integration are challenging the smart manufacturing. Cyber-Physical Production System (CPPS) technology plays a key role in the Industry 4.0 factory. One of the reasons for this is that shop floor data collection, monitoring and processing are enabled by CPPS devices together with IEC 61499 standard. To this end modeling and developing software and hardware components focused on distributed control systems are provided. This improvement in data monitoring and processing results not only in an optimization of equipment predictive maintenance but also in the ability to integrate new control techniques. This approach provides the Oil&Gas industry with the foundation to transform their traditional production fields into Intelligent Oil Field (IOF) environments. The aim of this paper is to build bridges between the state of technology and the design of industrial control applications. In particular, a CPPS platform developed through a software engineering tool giving support to the design of applications is proposed. It is based on the IEC 61499 and on a configurable OPC-UA server that maps field device data into an ISA95/88-based information model. Thus, this proposal will not only save Oil&Gas engineer time, it also will save efforts of adopting this promising technology.


title: Denoising Auto-Encoder with Recurrent Skip Connections and Residual Regression for Music Source Separation

abstract: Convolutional neural networks with skip connections have shown good performance in music source separation. In this work, we propose a denoising Auto-encoder with Recurrent skip Connections (ARC). We use 1D convolution along the temporal axis of the time-frequency feature map in all layers of the fully-convolutional network. The use of 1D convolution makes it possible to apply recurrent layers to the intermediate outputs of the convolution layers. In addition, we also propose an enhancement network and a residual regression method to further improve the separation result. The recurrent skip connections, the enhancement module, and the residual regression all improve the separation quality. The ARC model with residual regression achieves 5.74 siganl-to-distoration ratio (SDR) in vocals with MUSDB in SiSEC 2018. We also evaluate the ARC model alone on the older dataset DSD100 (used in SiSEC 2016) and it achieves 5.91 SDR in vocals.


title: Social exclusion and xenophobia: Intolerant attitudes toward ethnic and religious minorities

abstract: The present research investigates the effects of social exclusion on attitudes toward ethnic and religious minorities. Native-born German participants who were socially excluded rather than included reported greater approval for stricter legislation regarding the naturalization of immigrants (Study 1), reported greater prejudice against openly observant Muslims (Studies 2 and 3), and stronger agreement with the view that immigrants are financial burdens to the state (Study 4). Social exclusion threatens the sense of personal control, which in turn leads to stronger rejection of stigmatized outgroups (Study 3). When perceived control was experimentally enhanced, the social exclusion effect disappeared (Study 4). The theoretical and practical implications of these findings are discussed.


title: Issues in Complex Event Processing Systems

abstract: Many Big Data technologies were built to enable the processing of human generated data, setting aside the enormous amount of data generated from Machine-to-Machine (M2M) interactions. M2M interactions create real-time data streams that are much more structured, often in the form of series of event occurrences. In this paper, we provide an overview on the main research issues confronted by existing Complex Event Processing (CEP) techniques, as a starting point for Big Data applications that enable the monitoring of complex event occurrences in M2M interactions.


title: Squeeze, rock, and roll; can tangible interaction with affective products support stress reduction?

abstract: Affective computing focuses on the interpretation of users emotions via physiological and behavioral inputs. Irrelevant gestures with a pen were found to increase when users were given a mentally demanding task. Accordingly, an embedded tangible interface was developed which afforded and measured a rolling behavior, and guided the user towards reaching a balanced state of movement. During informal evaluations users acknowledged how the device could contribute to stress reduction. Conclusion, tangible interfaces appear to offer a non-obtrusive means towards interpreting and reducing stress in the office work context.


title: Secure Cyber-Physical Systems: Current trends, tools and open research problems

abstract: To understand and identify the attack surfaces of a Cyber-Physical System (CPS) is an essential step towards ensuring its security. The growing complexity of the cybernetics and the interaction of independent domains such as avionics, robotics and automotive is a major hindrance against a holistic view CPS. Furthermore, proliferation of communication networks have extended the reach of CPS from a user-centric single platform to a widely distributed network, often connecting to critical infrastructure, e.g., through smart energy initiative. In this manuscript, we reflect on this perspective and provide a review of current security trends and tools for secure CPS. We emphasize on both the design and execution flows and particularly highlight the necessity of efficient attack surface detection. We provide a detailed characterization of attacks reported on different cyber-physical systems, grouped according to their application domains, attack complexity, attack source and impact. Finally, we review the current tools, point out their inadequacies and present a roadmap of future research.


title: Evolution of the structure and magneto-optical properties of ion beam synthesized iron nanoclusters

abstract: Changes in the structural, magnetic, and optical properties are observed during the synthesis of metallic nanoclusters fabricated on the surface of a thin silica layer by ion beam implantation of iron atoms. Iron atoms were implanted to the fluence of 1016 cm−2. The ion implantation depth in 400-nm thick SiO2 film on a Si (100) substrate was 25 nm. The implanted samples were subsequently annealed for periods of seconds to hours at 1000 °C with Electron Beam Annealing. Ellipsometry and Rutherford Backscattering Spectrometry spectra were fitted with appropriate models to retrieve the optical characteristics, composition, and structure of the samples. Magneto Optical Kerr Effect measurements and SQUID magnetometry were performed to investigate the effect of the structural changes identified by TEM measurements on the superparamagnetic and magneto-optical properties of the samples during the annealing process. The changes in the Fe crystalline(core)/amorphous oxide(shell) structure and the position of the nanoclusters relative to the surface observed for small annealing durations are shown to enhance the Kerr effect resulting into high coercive field and high amplitude in the Kerr rotation and ellipticity.


title: 180-Degree Commutation System of Permanent Magnet Brushless DC Motor Drive Based on Speed and Current Control

abstract: Abstract—To drive Brushless dc motor, each coil of conventional 120-degree commutation rotates only 120-degree with current to generate torque, and wait for next excitation in the other 60 degree without torque generated. However, each coil of 180-degree commutation works for the entire electrical period, which is expected to deliver more power. Analysis, modeling and simulation of 180-degree commutation have not been addressed in previous publications. In this paper, 180-degree commutation theory is analyzed. A 180-degree square-waveform inverter method is designed. A novel 180-degree commutation system is proposed with initial condition, speed and current control. Simulation results prove that the proposed 180-degree system works properly with greater maximum torque than conventional 120-degree system.


title: Time-dependent event hierarchy construction

abstract: In this paper, an algorithm called Time Driven Documents-partition (TDD) is proposed to construct an event hierarchy in a text corpus based on a given query. Specifically, assume that a query contains only one feature - Election. Election is directly related to the events such as 2006 US Midterm Elections Campaign, 2004 US Presidential Election Campaign and 2004 Taiwan Presidential Election Campaign, where these events may further be divided into several smaller events (e.g. the 2006 US Midterm Elections Campaign can be broken down into events such as campaign for vote, election results and the resignation of Donald H. Rumsfeld). As such, an event hierarchy is resulted. Our proposed algorithm, TDD, tackles the problem by three major steps: (1)Identify the features that are related to the query according to both the timestamps and the contents of the documents. The features identified are regarded as bursty features; (2) Extract the documents that are highly related to the bursty features based on time; (3) Partition the extracted documents to form events and organize them in a hierarchicalstructure. To the best of our knowledge, there is little works targeting for constructing a feature-based event hierarchy for a text corpus. Practically, event hierarchies can assist us to efficiently locate our target information in a text corpus easily. Again, assume that Election is used for a query. Without an event hierarchy, it is very difficult to identify what are the major events related to it, when do these events happened, as well as the features and the news articles that are related to each of these events. We have archived two-year news articles to evaluate the feasibility of TDD. The encouraging results indicated that TDD is practically sound and highly effective.


title: Explain to whom? Putting the User in the Center of Explainable AI

abstract: The ability to explain actions and decisions is often regarded as a basic ingredient of cognitive systems. But when researchers propose methods for making AI systems understandable, users are usually not involved or even mentioned. However, the purpose is to make people willing to accept the decision of a machine or to be better able to interact with it. Therefore, I argue that the evaluation of explanations must involve some form of user testing.


on energy pattern and policy for transportation sector in Malaysia


title: Diversity and natural functions of antibiotics produced by beneficial and plant pathogenic bacteria.

abstract: Soil- and plant-associated environments harbor numerous bacteria that produce antibiotic metabolites with specific or broad-spectrum activities against coexisting microorganisms. The function and ecological importance of antibiotics have long been assumed to yield a survival advantage to the producing bacteria in the highly competitive but resource-limited soil environments through direct suppression. Although specific antibiotics may enhance producer persistence when challenged by competitors or predators in soil habitats, at subinhibitory concentrations antibiotics exhibit a diversity of other roles in the life history of the producing bacteria. Many processes modulated by antibiotics may be inherently critical to the producing bacterium, such as the acquisition of substrates or initiation of developmental changes that will ensure survival under stressful conditions. Antibiotics may also have roles in more complex interactions, including in virulence on host plants or in shaping the outcomes of multitrophic interactions. The innate functions of antibiotics to producing bacteria in their native ecosystem are just beginning to emerge, but current knowledge already reveals a breadth of activities well beyond the historical perspective of antibiotics as weaponry in microbial conflicts.


title: Connected fermat spirals for layered fabrication

abstract: We develop a new kind of "space-filling" curves, connected Fermat spirals, and show their compelling properties as a tool path fill pattern for layered fabrication. Unlike classical space-filling curves such as the Peano or Hilbert curves, which constantly wind and bind to preserve locality, connected Fermat spirals are formed mostly by long, low-curvature paths. This geometric property, along with continuity, influences the quality and efficiency of layered fabrication. Given a connected 2D region, we first decompose it into a set of sub-regions, each of which can be filled with a single continuous Fermat spiral. We show that it is always possible to start and end a Fermat spiral fill at approximately the same location on the outer boundary of the filled region. This special property allows the Fermat spiral fills to be joined systematically along a graph traversal of the decomposed sub-regions. The result is a globally continuous curve. We demonstrate that printing 2D layers following tool paths as connected Fermat spirals leads to efficient and quality fabrication, compared to conventional fill patterns.


Psychometric Properties of the Serbian Version of the Maslach Burnout Inventory-Human Services Survey: A Validation Study among Anesthesiologists from Belgrade Teaching Hospitals


Future Coastal Population Growth and Exposure to Sea-Level Rise and Coastal Flooding - A Global Assessment


title: Building block of a programmable neuromorphic substrate: A digital neurosynaptic core

abstract: The grand challenge of neuromorphic computation is to develop a flexible brain-inspired architecture capable of a wide array of real-time applications, while striving towards the ultra-low power consumption and compact size of biological neural systems. Toward this end, we fabricated a building block of a modular neuromorphic architecture, a neurosynaptic core. Our implementation consists of 256 integrate-and-fire neurons and a 1,024×256 SRAM crossbar memory for synapses that fits in 4.2mm2 using a 45nm SOI process and consumes just 45pJ per spike. The core is fully configurable in terms of neuron parameters, axon types, and synapse states and its fully digital implementation achieves one-to-one correspondence with software simulation models. One-to-one correspondence allows us to introduce an abstract neural programming model for our chip, a contract guaranteeing that any application developed in software functions identically in hardware. This contract allows us to rapidly test and map applications from control, machine vision, and classification. To demonstrate, we present four test cases (i) a robot driving in a virtual environment, (ii) the classic game of pong, (iii) visual digit recognition and (iv) an autoassociative memory.


title: Teaching Deep Convolutional Neural Networks to Play Go

abstract: Mastering the game of Go has remained a longstanding challenge to the field of AI. Modern computer Go programs rely on processing millions of possible future positions to play well, but intuitively a stronger and more 'humanlike' way to play the game would be to rely on pattern recognition rather than brute force computation. Following this sentiment, we train deep convolutional neural networks to play Go by training them to predict the moves made by expert Go players. To solve this problem we introduce a number of novel techniques, including a method of tying weights in the network to 'hard code' symmetries that are expected to exist in the target function, and demonstrate in an ablation study they considerably improve performance. Our final networks are able to achieve move prediction accuracies of 41.1% and 44.4% on two different Go datasets, surpassing previous state of the art on this task by significant margins. Additionally, while previous move prediction systems have not yielded strong Go playing programs, we show that the networks trained in this work acquired high levels of skill. Our convolutional neural networks can consistently defeat the well known Go program GNU Go and win some games against state of the art Go playing program Fuego while using a fraction of the play time.


Taurus : A Parallel Transaction Recovery Method Based on Fine-Granularity Dependency Tracking


Protected-Designation-of-Origin Cheese in Grana Padano Antibodies for Detection of SLayer Protein Lactobacillus helveticus MIMLh 5-Specific


title: Learning to Rank and Discover for E-Commerce Search

abstract: E-Commerce (E-Com) search is an emerging problem with multiple new challenges. One of the primary challenges constitutes optimizing it for relevance and revenue and simultaneously maintaining a discovery strategy. The problem requires designing novel strategies to systematically “discover” promising items from the inventory, that have not received sufficient exposure in search results while minimizing the loss of relevance and revenue because of that. To this end, we develop a formal framework for optimizing E-Com search and propose a novel epsilon-explore Learning to Rank (eLTR) paradigm that can be integrated with the traditional learning to rank (LTR) framework to explore new or less exposed items. The key idea is to decompose the ranking function into (1) a function of content-based features, (2) a function of behavioral features, and introduce a parameter epsilon to regulate their relative contributions. We further propose novel algorithms based on eLTR to improve the traditional LTR used in the current E-Com search engines by “forcing” exploration of a fixed number of items while limiting the relevance drop. We also show that eLTR can be considered to be monotonic sub-modular and thus we can design a greedy approximation algorithm with a theoretical guarantee. We conduct experiments with synthetic data and compare eLTR with a baseline random selection and an upper confidence bound (UCB) based exploration strategies. We show that eLTR is an efficient algorithm for such exploration. We expect that the formalization presented in this paper will lead to new research in the area of ranking problems for E-com marketplaces.


title: Adherence to the DASH Diet Is Inversely Associated With Incidence of Type 2 Diabetes: The Insulin Resistance Atherosclerosis Study

abstract: OBJECTIVE The Dietary Approaches to Stop Hypertension (DASH) diet has been widely promoted; however, little is known about its impact on type 2 diabetes. RESEARCH DESIGN AND METHODS We evaluated the association of the DASH diet with incidence of type 2 diabetes among 862 participants of the Insulin Resistance Atherosclerosis Study (IRAS) who completed a 1-year food frequency questionnaire at baseline. Type 2 diabetes odds ratios (ORs) were estimated at tertiles of the DASH score. RESULTS An inverse association was observed in whites (tertile 2 vs. tertile 1, OR 0.66 [95% CI 0.29-1.48]) that became significant for the most extreme contrast (tertile 3 vs. tertile 1, 0.31 [0.13-0.75]), with adjustment for covariates. No association was observed in blacks or Hispanics (tertile 2 vs. tertile 1, 1.16 [0.61-2.18 ]; tertile 3 vs. tertile 1, 1.34 [0.70-2.58 ]). CONCLUSIONS Adherence to the DASH dietary pattern, which is rich in vegetables, fruit, and low-fat dairy products, may have the potential to prevent type 2 diabetes.


title: Twitter-Network Topic Model: A Full Bayesian Treatment for Social Network and Text Modeling

abstract: Twitter data is extremely noisy -- each tweet is short, unstructured and with informal language, a challenge for current topic modeling. On the other hand, tweets are accompanied by extra information such as authorship, hashtags and the user-follower network. Exploiting this additional information, we propose the Twitter-Network (TN) topic model to jointly model the text and the social network in a full Bayesian nonparametric way. The TN topic model employs the hierarchical Poisson-Dirichlet processes (PDP) for text modeling and a Gaussian process random function model for social network modeling. We show that the TN topic model significantly outperforms several existing nonparametric models due to its flexibility. Moreover, the TN topic model enables additional informative inference such as authors' interests, hashtag analysis, as well as leading to further applications such as author recommendation, automatic topic labeling and hashtag suggestion. Note our general inference framework can readily be applied to other topic models with embedded PDP nodes.


title: Pyramid Person Matching Network for Person Re-identification

abstract: In this work, we present a deep convolutional pyramid person matching network (PPMN) with specially designed Pyramid Matching Module to address the problem of person re-identification. The architecture takes a pair of RGB images as input, and outputs a similiarity value indicating whether the two input images represent the same person or not. Based on deep convolutional neural networks, our approach first learns the discriminative semantic representation with the semantic-component-aware features for persons and then employs the Pyramid Matching Module to match the common semantic-components of persons, which is robust to the variation of spatial scales and misalignment of locations posed by viewpoint changes. The above two processes are jointly optimized via a unified end-to-end deep learning scheme. Extensive experiments on several benchmark datasets demonstrate the effectiveness of our approach against the state-of-the-art approaches, especially on the rank-1 recognition rate.


title: [Treatment of vulvar cancer].

abstract: 125 patients with carcinoma of the vulva were evaluated with respect to corrected 5-year survival rates. In our study we try to answer two questions in the therapeutic management of vulval carcinoma: (1) Is organ-preserving surgery in stage I tumors adequate? (2) How important is the radiation therapy in the general treatment concept in more advanced stages of the disease? Our study shows that organ preservation seems to be possible in stage I disease, if resection of the tumor is complete with a margin of normal tissue. If inguinal lymph nodes are involved or the primary tumor is bulky, the best results are obtained with a combination of radical surgery and locoregional radiation therapy. Radiation therapy alone has a good effect during the early stage of the vulval cancer.


title: Avalanche instability in oxide charge balanced power MOSFETs

abstract: Power MOSFET designs have been moving to higher performance particularly in the medium voltage area. (60V to 300V) New designs require lower specific on-resistance (R SP ) thus forcing designers to push the envelope of increasing the electric field stress on the shielding oxide, reducing the cell pitch, and increasing the epitaxial (epi) drift doping to reduce on resistance. In doing so, time dependant avalanche instabilities have become a concern for oxide charge balanced power MOSFETs. Avalanche instabilities can initiate in the active cell and/or the termination structures. These instabilities cause the avalanche breakdown to increase and/or decrease with increasing time in avalanche. They become a reliability risk when the drain to source breakdown voltage (BV dss ) degrades below the operating voltage of the application circuit. This paper will explain a mechanism for these avalanche instabilities and propose an optimum design for the charge balance region. TCAD simulation was employed to give insight to the mechanism. Finally, measured data will be presented to substantiate the theory.


Factors Affecting the Adoption of Internet Banking in Jordan : An Extended TAM Model


title: AirSense: Opportunistic crowd-sensing based air quality monitoring system for smart city

abstract: Citizens are exposed to air pollutants both indoors and outdoors due to their activities, which may result in a variety of health effects. In this paper, we present AirSense, an opportunistic crowd-sensing based air quality monitoring system, aimed at collecting and aggregating sensor data to monitor air pollution in the vicinity and the city. We introduce a light weight, low power and low cost air quality monitoring device (AQMD) and demonstrate how AQMD and smart phone devices in a crowd collaboratively (through offloading) gather and share data of interest to the cloud. In cloud, collected data will be analyzed and an aggregated view will be generated for providing an air pollution heat map of the city. End user can view both the neighborhood (local) air quality and AQImap (air quality index map) of the city on his/her smartphone.


title: Art in Transit

abstract: This article examines the use of art in transit systems, which the Federal Transit Administration encourages. There is a wide variety of ways to incorporate art in transit systems. The article includes an interview with sculptor Brad Oldham who explains the special requirements of transit art, including its “touchability.” The article also describes how different agencies have incorporated art into their systems, including poetry contests, work with community organizations such as Boys and Girls Club, Big Brothers, Big Sisters, or the local botanical garden, and the use of various grant programs such as the National Endowment for the Arts and local versions. An accompanying article describes Oldham’s $1.4 million sculptural series called the Traveling Man.


title: Global illumination with radiance regression functions

abstract: We present radiance regression functions for fast rendering of global illumination in scenes with dynamic local light sources. A radiance regression function (RRF) represents a non-linear mapping from local and contextual attributes of surface points, such as position, viewing direction, and lighting condition, to their indirect illumination values. The RRF is obtained from precomputed shading samples through regression analysis, which determines a function that best fits the shading data. For a given scene, the shading samples are precomputed by an offline renderer. The key idea behind our approach is to exploit the nonlinear coherence of the indirect illumination data to make the RRF both compact and fast to evaluate. We model the RRF as a multilayer acyclic feed-forward neural network, which provides a close functional approximation of the indirect illumination and can be efficiently evaluated at run time. To effectively model scenes with spatially variant material properties, we utilize an augmented set of attributes as input to the neural network RRF to reduce the amount of inference that the network needs to perform. To handle scenes with greater geometric complexity, we partition the input space of the RRF model and represent the subspaces with separate, smaller RRFs that can be evaluated more rapidly. As a result, the RRF model scales well to increasingly complex scene geometry and material variation. Because of its compactness and ease of evaluation, the RRF model enables real-time rendering with full global illumination effects, including changing caustics and multiple-bounce high-frequency glossy interreflections.


title: Two-level text summarization from online news sources with sentiment analysis

abstract: People tend to read multiple news articles on a topic since a single article may not contain all important information. A summary of all the articles related to topic will save the time and energy. Text Summarization is a way of minimizing a textual document to a meaningful summary. In this research, an extractive-based approach is used to generate a two-level summary from online news articles. News topics covered include politics, sports health, science and movie reviews from Fox News from USA, NZ Herald from New Zealand, Hindustan Times from India, BBC from UK, etc. The first-level summary generates the summary of each article on all these topics. Sentiment Analysis is performed on the first-level summary to understand the variation in related news articles from different news agencies. The second-level summary generates the summary of the combined first-level summaries of two/three related articles on a topic. The ROUGE metric is used to evaluate the performance of summarization.


title: Top-down control of visual attention in object detection

abstract: Current computational models of visual attention focus on bottom-up information and ignore scene context. However, studies in visual cognition show that humans use context to facilitate object detection in natural scenes by directing their attention or eyes to diagnostic regions. Here we propose a model of attention guidance based on global scene configuration. We show that the statistics of low-level features across the scene image determine where a specific object (e.g. a person) should be located. Human eye movements show that regions chosen by the top-down model agree with regions scrutinized by human observers performing a visual search task for people. The results validate the proposition that top-down information from visual context modulates the saliency of image regions during the task of object detection. Contextual information provides a shortcut for efficient object detection systems.


Future trends of electrical propulsion and implications to ship design


title: The race to new mathematics of brains and consciousness — A tribute to John G. Taylor

abstract: This contribution presents a review of mathematical approaches to modeling brains and higher cognitive activity, including consciousness. We dedicate this paper to John Gerard Taylor on the somber occasion of remembering his lifelong contribution to science, with a focus on his pioneering work on neural networks and brain studies.


title: Audio-visual spontaneous emotion recognition

abstract: Automatic multimodal recognition of spontaneous emotional expressions is a largely unexplored and challenging problem. In this paper, we explore audio-visual emotion recognition in a realistic human conversation setting--the Adult Attachment Interview (AAI). Based on the assumption that facial expression and vocal expression are at the same coarse affective states, positive and negative emotion sequences are labeled according to Facial Action Coding System. Facial texture in visual channel and prosody in audio channel are integrated in the framework of Adaboost multi-stream hidden Markov model (AdaMHMM) in which the Adaboost learning scheme is used to build component HMM fusion. Our approach is evaluated in AAI spontaneous emotion recognition experiments.


DeSVi : An Architecture for Detecting SLA Violations in Cloud Computing Infrastructures


title: A randomized clinical trial of acceptance and commitment therapy versus progressive relaxation training for obsessive-compulsive disorder.

abstract: Objective: Effective treatments for obsessive-compulsive disorder (OCD) exist, but additional treatment options are needed. The effectiveness of 8 sessions of acceptance and commitment therapy (ACT) for adult OCD was compared with progressive relaxation training (PRT). Method: Seventy-nine adults (61% female) diagnosed with OCD (mean age 37 years; 89% Caucasian) participated in a randomized clinical trial of 8 sessions of ACT or PRT with no in-session exposure. The following assessments were completed at pretreatment, posttreatment, and 3-month follow-up by an assessor who was unaware of treatment conditions: Yale–Brown Obsessive Compulsive Scale (Y-BOCS), Beck Depression Inventory–II, Quality of Life Scale, Acceptance and Action Questionnaire, Thought Action Fusion Scale, and Thought Control Questionnaire. Treatment Evaluation Inventory was completed at posttreatment. Results: ACT produced greater changes at posttreatment and follow-up over PRT on OCD severity (Y-BOCS: ACT pretreatment 24.22, posttreatment 12.76, follow-up 11.79; PRT pretreatment 25.4, posttreatment 18.67, follow-up 16.23) and produced greater change on depression among those reporting at least mild depression before treatment. Clinically significant change in OCD severity occurred more in the ACT condition than PRT (clinical response rates: ACT posttreatment 46%–56%, follow-up 46%–66%; PRT posttreatment 13%–18%, follow-up 16%–18%). Quality of life improved in both conditions but was marginally in favor of ACT at posttreatment. Treatment refusal (2.4% ACT, 7.8% PRT) and dropout (9.8% ACT, 13.2% PRT) were low in both conditions. Conclusions: ACT is worth exploring as a treatment for OCD.


title: Credit Card Customer Segmentation and Target Marketing Based on Data Mining

abstract: Based on the real data of a Chinese commercial bank’s credit card, in this paper, we classify the credit card customers into four classifications by K-means. Then we built forecasting models separately based on four data mining methods such as C5.0, neural network, chi-squared automatic interaction detector, and classification and regression tree according to the background information of the credit cards holders. Conclusively, we obtain some useful information of decision tree regulation by the best model among the four. The information is not only helpful for the bank to understand related characteristics of different customers, but also marketing representatives to find potential customers and to implement target marketing.


title: Style Transfer Through Back-Translation

abstract: Style transfer is the task of rephrasing the text to contain specific stylistic properties without changing the intent or affect within the context. This paper introduces a new method for automatic style transfer. We first learn a latent representation of the input sentence which is grounded in a language translation model in order to better preserve the meaning of the sentence while reducing stylistic properties. Then adversarial generation techniques are used to make the output match the desired style. We evaluate this technique on three different style transformations: sentiment, gender and political slant. Compared to two state-of-the-art style transfer modeling techniques we show improvements both in automatic evaluation of style transfer and in manual evaluation of meaning preservation and fluency.


title: Scalable Daily Human Behavioral Pattern Mining from Multivariate Temporal Data

abstract: This work introduces a set of scalable algorithms to identify patterns of human daily behaviors. These patterns are extracted from multivariate temporal data that have been collected from smartphones. We have exploited sensors that are available on these devices, and have identified frequent behavioral patterns with a temporal granularity, which has been inspired by the way individuals segment time into events. These patterns are helpful to both end-users and third parties who provide services based on this information. We have demonstrated our approach on two real-world datasets and showed that our pattern identification algorithms are scalable. This scalability makes analysis on resource constrained and small devices such as smartwatches feasible. Traditional data analysis systems are usually operated in a remote system outside the device. This is largely due to the lack of scalability originating from software and hardware restrictions of mobile/wearable devices. By analyzing the data on the device, the user has the control over the data, i.e., privacy, and the network costs will also be removed.


title: From Red Wine to Red Tomato: Composition with Context

abstract: Compositionality and contextuality are key building blocks of intelligence. They allow us to compose known concepts to generate new and complex ones. However, traditional learning methods do not model both these properties and require copious amounts of labeled data to learn new concepts. A large fraction of existing techniques, e.g., using late fusion, compose concepts but fail to model contextuality. For example, red in red wine is different from red in red tomatoes. In this paper, we present a simple method that respects contextuality in order to compose classifiers of known visual concepts. Our method builds upon the intuition that classifiers lie in a smooth space where compositional transforms can be modeled. We show how it can generalize to unseen combinations of concepts. Our results on composing attributes, objects as well as composing subject, predicate, and objects demonstrate its strong generalization performance compared to baselines. Finally, we present detailed analysis of our method and highlight its properties.


title: Building Decision Trees for the Multi-class Imbalance Problem

abstract: Learning in imbalanced datasets is a pervasive problem prevalent in a wide variety of real-world applications. In imbalanced datasets, the class of interest is generally a small fraction of the total instances, but misclassification of such instances is often expensive. While there is a significant body of research on the class imbalance problem for binary class datasets, multi-class datasets have received considerably less attention. This is partially due to the fact that the multi-class imbalance problem is often much harder than its related binary class problem, as the relative frequency and cost of each of the classes can vary widely from dataset to dataset. In this paper we study the multi-class imbalance problem as it relates to decision trees (specifically C4.4 and HDDT), and develop a new multi-class splitting criterion. From our experiments we show that multi-class Hellinger distance decision trees, when combined with decomposition techniques, outperform C4.4.


title: qCon: QoS-Aware Network Resource Management for Fog Computing

abstract: Fog computing is a new computing paradigm that employs computation and network resources at the edge of a network to build small clouds, which perform as small data centers. In fog computing, lightweight virtualization (e.g., containers) has been widely used to achieve low overhead for performance-limited fog devices such as WiFi access points (APs) and set-top boxes. Unfortunately, containers have a weakness in the control of network bandwidth for outbound traffic, which poses a challenge to fog computing. Existing solutions for containers fail to achieve desirable network bandwidth control, which causes bandwidth-sensitive applications to suffer unacceptable network performance. In this paper, we propose qCon, which is a QoS-aware network resource management framework for containers to limit the rate of outbound traffic in fog computing. qCon aims to provide both proportional share scheduling and bandwidth shaping to satisfy various performance demands from containers while implementing a lightweight framework. For this purpose, qCon supports the following three scheduling policies that can be applied to containers simultaneously: proportional share scheduling, minimum bandwidth reservation, and maximum bandwidth limitation. For a lightweight implementation, qCon develops its own scheduling framework on the Linux bridge by interposing qCon's scheduling interface on the frame processing function of the bridge. To show qCon's effectiveness in a real fog computing environment, we implement qCon in a Docker container infrastructure on a performance-limited fog device-a Raspberry Pi 3 Model B board.


title: A Comprehensive Survey of Recent Trends in Cloud Robotics Architectures and Applications

abstract: Cloud robotics has recently emerged as a collaborative technology between cloud computing and service robotics enabled through progress in wireless networking, large scale storage and communication technologies, and the ubiquitous presence of Internet resources over recent years. Cloud computing empowers robots by offering them faster and more powerful computational capabilities through massively parallel computation and higher data storage facilities. It also offers access to open-source, big datasets and software, cooperative learning capabilities through knowledge sharing, and human knowledge through crowdsourcing. The recent progress in cloud robotics has led to active research in this area spanning from the development of cloud robotics architectures to its varied applications in different domains. In this survey paper, we review the recent works in the area of cloud robotics technologies as well as its applications. We draw insights about the current trends in cloud robotics and discuss the challenges and limitations in the current literature, open research questions and future research directions.


Effects of Nutrient-Fortified Milk-Based Formula on the Nutritional Status and Psychomotor Skills of Preschool Children


title: A MULTISCALE IMAGE REPRESENTATION USING HIERARCHICAL (BV, L²) DECOMPOSITIONS

abstract: We propose a new multiscale image decomposition which offers a hierarchical, adaptive representation for the different features in general images. The starting point is a vari- ational decomposition of an image, f = u0 + v0, where (u0 ,v 0) is the minimizer of a J-functional, J(f, λ0; X, Y ) = inf u+v=fuX + λ0� vp . Such minimizers are standard tools for image ma- nipulations (e.g., denoising, deblurring, compression); see, for example, (M. Mumford and J. Shah, Proceedings of the IEEE Computer Vision Pattern Recognition Conference, San Francisco, CA, 1985) and (L. Rudin, S. Osher, and E. Fatemi, Phys. D, 60 (1992), pp. 259-268). Here, u0 should capture "essential features" of f which are to be separated from the spurious components absorbed by v0, and λ0 is a fixed threshold which dictates separation of scales. To proceed, we iterate the refinement step (uj+1 ,v j+1) = arginf J(vj ,λ 02j ), leading to the hierarchical decomposition, f = k=0 uj + vk. We focus our attention on the particular case of (X, Y )=( BV, L 2 ) decomposition. The resulting hierarchical decomposition, f ∼ j uj , is essentially nonlinear. The questions of convergence, energy decomposition, localization, and adaptivity are discussed. The decomposition is constructed by nu- merical solution of successive Euler-Lagrange equations. Numerical results illustrate applications of the new decomposition to synthetic and real images. Both greyscale and color images are considered.


ALISTA: Analytic Weights Are As Good As Learned Weights in LISTA


title: Cohn topology-based 1:8 power divider for S-band array antenna feeding network

abstract: In this paper, a design of 1:8 power divider is proposed for S-band array antenna feeding network. The proposed power divider which is designed based on Cohn topology is implemented on an RO4003C dielectric substrate which has the relative permittivity and the thickness of 3.55 and 1.524mm, respectively. Seven pairs of 1:2 power divider are used to construct the design of 1:8 power divider. Some parameters such as insertion, isolation and voltage standing wave ratio (VSWR) are used to analyze the performance of power divider in the frequency range of 2–4GHz. The result shows that the 1: 2 power divider has the values of insertion loss, isolation and VSWR of greater than 3.08dB, greater than 24dB, and less than 1.1, respectively. Meanwhile, the 1:8 power divider has the insertion value of greater than 9.26dB, isolation value of greater than 23dB, and VSWR value of less than 1.5. The characterization result has shown an adequate performance for S-band array antenna feeding network of radar system.


title: Applying feature selection methods on fMRI data

abstract: In neuroscience, the ability to correlate and classify certain activity patterns of the brain to different physical and mental states of the subject is of high importance. Analysis of fMRI data is one of the venues in which this objective is being pursued. However data produced using fMRI technology is highly complex. To this end, machine learning becomes relevant. Prominent hurdles facing fMRI data analysis are their high-dimensionality (thousands of features per instance), low signal-to-noise ratio and interdependency. This motivates the use of feature selection methods in order to consolidate relevant information and discard noise. Many feature selection methods exist but only a few have been applied in the fMRI domain. In this paper we identify positive characteristics of feature selection algorithms that are beneficial when dealing with fMRI datasets. To do this, we evaluate representatives from each of the three main feature selection classes: Filters, wrappers and embedded methods. We have found probabilistic embedded methods to be the most suitable for fMRI data. We would therefore recommend using these (or similar) methods to process data with fMRI-like characteristics. Student report.


title: Recent Advances in Plasmonic Sensors

abstract: Plasmonic sensing has been an important multidisciplinary research field and has been extensively used in detection of trace molecules in chemistry and biology. The sensing techniques are typically based on surface-enhanced spectroscopies and surface plasmon resonances (SPRs). This review article deals with some recent advances in surface-enhanced Raman scattering (SERS) sensors and SPR sensors using either localized surface plasmon resonances (LSPRs) or propagating surface plasmon polaritons (SPPs). The advances discussed herein present some improvements in SERS and SPR sensing, as well as a new type of nanowire-based SPP sensor.


title: VulSeeker-pro: enhanced semantic learning based binary vulnerability seeker with emulation

abstract: Learning-based clone detection is widely exploited for binary vulnerability search. Although they solve the problem of high time overhead of traditional dynamic and static search approaches to some extent, their accuracy is limited, and need to manually identify the true positive cases among the top-M search results during the industrial practice. This paper presents VulSeeker-Pro, an enhanced binary vulnerability seeker that integrates function semantic emulation at the back end of semantic learning, to release the engineers from the manual identification work. It first uses the semantic learning based predictor to quickly predict the top-M candidate functions which are the most similar to the vulnerability from the target binary. Then the top-M candidates are fed to the emulation engine to resort, and more accurate top-N candidate functions are obtained. With fast filtering of semantic learning and dynamic trace generation of function semantic emulation, VulSeeker-Pro can achieve higher search accuracy with little time overhead. The experimental results on 15 known CVE vulnerabilities involving 6 industry widely used programs show that VulSeeker-Pro significantly outperforms the state-of-the-art approaches in terms of accuracy. In a total of 45 searches, VulSeeker-Pro finds 40 and 43 real vulnerabilities in the top-1 and top-5 candidate functions, which are 12.33× and 2.58× more than the most recent and related work Gemini. In terms of efficiency, it takes 0.22 seconds on average to determine whether the target binary function contains a known vulnerability or not.


title: Compact Personalized Models for Neural Machine Translation

abstract: We propose and compare methods for gradient-based domain adaptation of self-attentive neural machine translation models. We demonstrate that a large proportion of model parameters can be frozen during adaptation with minimal or no reduction in translation quality by encouraging structured sparsity in the set of offset tensors during learning via group lasso regularization. We evaluate this technique for both batch and incremental adaptation across multiple data sets and language pairs. Our system architecture - combining a state-of-the-art self-attentive model with compact domain adaptation - provides high quality personalized machine translation that is both space and time efficient.


title: Full-Bridge Three-Port Converters With Wide Input Voltage Range for Renewable Power Systems

abstract: A systematic method for deriving three-port converters (TPCs) from the full-bridge converter (FBC) is proposed in this paper. The proposed method splits the two switching legs of the FBC into two switching cells with different sources and allows a dc bias current in the transformer. By using this systematic method, a novel full-bridge TPC (FB-TPC) is developed for renewable power system applications which features simple topologies and control, a reduced number of devices, and single-stage power conversion between any two of the three ports. The proposed FB-TPC consists of two bidirectional ports and an isolated output port. The primary circuit of the converter functions as a buck-boost converter and provides a power flow path between the ports on the primary side. The FB-TPC can adapt to a wide source voltage range, and tight control over two of the three ports can be achieved while the third port provides the power balance in the system. Furthermore, the energy stored in the leakage inductance of the transformer is utilized to achieve zero-voltage switching for all the primary-side switches. The FB-TPC is analyzed in detail with operational principles, design considerations, and a pulsewidth modulation scheme (PWM), which aims to decrease the dc bias of the transformer. Experimental results verify the feasibility and effectiveness of the developed FB-TPC. The topology generation concept is further extended, and some novel TPCs, dual-input, and multiport converters are presented.


title: An omnidirectional vehicle on a basketball

abstract: This paper is about development of an omni directional vehicle on a spherical tire. The vehicle moves by rolling a spherical tire and balancing on it. We selected a basketball as a spherical tire, because basketball is made of rubber. In general, rubber tire has three features, which are durability, adaptabilities to the ground surface and force transmissivity. We think that using a basketball as a spherical tire is low-cost and advantageous. Next, we describe how to calculate torque and angular velocity, which motors generate to roll a basketball. They are needed for driving motors and dynamic simulations. Finally, we propose an interface to operate the vehicle. One, who rides on the vehicle, can move to all directions by shifting one's center of mass. A six-axis force-torque sensor senses weight shift


Named Entity Recognition in the Medical Domain with Constrained CRF Models


title: A Simplified Agile Methodology for Ontology Development

abstract: In this paper we introduce SAMOD, a.k.a. Simplified Agile Methodology for Ontology Development, a novel agile methodology for the development of ontologies by means of small steps of an iterative workflow that focuses on creating well-developed and documented models starting from exemplar domain descriptions. In addition, we discuss the results of an experiment where we asked nine people (with no or limited expertise in Semantic Web technologies and Ontology Engineering) to use SAMOD for developing a small ontology.


Deep Neural Solver for Math Word Problems


title: The Anatomy of a Large-Scale Hypertextual Web Search Engine

abstract: In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24million pages is available at http://google.stanford.edu/ To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of web pages involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the web, very little academic research has been done on them. Furthermore, due to rapid advance in technology and web proliferation, creating a web search engine today is very different from 3years ago. This paper provides an in-depth description of our large-scale web search engine - the first such detailed public description we know of to date. Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext to produce better search results. This paper addresses this question of how to build a practical large-scale system which can exploit the additional information present in hypertext. Also we look at the problem of how to effectively deal with uncontrolled hypertext collections, where anyone can publish anything they want.


title: A model of non-elemental olfactory learning in Drosophila

abstract: The pathways for olfactory learning in the fruitfly Drosophila have been extensively investigated, with mounting evidence that that the mushroom body is the site of the olfactory associative memory trace (Heisenberg, Nature 4:266---275, 2003; Gerber et al., Curr Opin Neurobiol 14:737---744, 2004). Heisenberg's description of the mushroom body as an associative learning device is a testable hypothesis that relates the mushroom body's function to its neural structure and input and output pathways. Here, we formalise a relatively complete computational model of the network interactions in the neural circuitry of the insect antennal lobe and mushroom body, to investigate their role in olfactory learning, and specifically, how this might support learning of complex (non-elemental; Giurfa, Curr Opin Neuroethol 13:726---735, 2003) discriminations involving compound stimuli. We find that the circuit is able to learn all tested non-elemental paradigms. This does not crucially depend on the number of Kenyon cells but rather on the connection strength of projection neurons to Kenyon cells, such that the Kenyon cells require a certain number of coincident inputs to fire. As a consequence, the encoding in the mushroom body resembles a unique cue or configural representation of compound stimuli (Pearce, Psychol Rev 101:587---607, 1994). Learning of some conditions, particularly negative patterning, is strongly affected by the assumption of normalisation effects occurring at the level of the antennal lobe. Surprisingly, the learning capacity of this circuit, which is a simplification of the actual circuitry in the fly, seems to be greater than the capacity expressed by the fly in shock-odour association experiments (Young et al. 2010).


title: WaterCooler: exploring an organization through enterprise social media

abstract: As organizations scale up, their collective knowledge increases, and the potential for serendipitous collaboration between members grows dramatically. However, finding people with the right expertise or interests becomes much more difficult. Semi-structured social media, such as blogs, forums, and bookmarking, present a viable platform for collaboration-if enough people participate, and if shared content is easily findable. Within the trusted confines of an organization, users can trade anonymity for a rich identity that carries information about their role, location, and position in its hierarchy. This paper describes WaterCooler, a tool that aggregates shared internal social media and cross-references it with an organization's directory. We deployed WaterCooler in a large global enterprise and present the results of a preliminary user study. Despite the lack of complete social networking affordances, we find that WaterCooler changed users' perceptions of their workplace, made them feel more connected to each other and the company, and redistributed users' attention outside their own business groups.


title: Retrieval and classification of food images

abstract: Automatic food understanding from images is an interesting challenge with applications in different domains. In particular, food intake monitoring is becoming more and more important because of the key role that it plays in health and market economies. In this paper, we address the study of food image processing from the perspective of Computer Vision. As first contribution we present a survey of the studies in the context of food image processing from the early attempts to the current state-of-the-art methods. Since retrieval and classification engines able to work on food images are required to build automatic systems for diet monitoring (e.g., to be embedded in wearable cameras), we focus our attention on the aspect of the representation of the food images because it plays a fundamental role in the understanding engines. The food retrieval and classification is a challenging task since the food presents high variableness and an intrinsic deformability. To properly study the peculiarities of different image representations we propose the UNICT-FD1200 dataset. It was composed of 4754 food images of 1200 distinct dishes acquired during real meals. Each food plate is acquired multiple times and the overall dataset presents both geometric and photometric variabilities. The images of the dataset have been manually labeled considering 8 categories: Appetizer, Main Course, Second Course, Single Course, Side Dish, Dessert, Breakfast, Fruit. We have performed tests employing different representations of the state-of-the-art to assess the related performances on the UNICT-FD1200 dataset. Finally, we propose a new representation based on the perceptual concept of Anti-Textons which is able to encode spatial information between Textons outperforming other representations in the context of food retrieval and Classification. HighlightsA comprehensive survey about food understanding research is provided.A new food dataset for classification and retrieval is introduced.A new image representation based on the concept of Anti-Textons is proposed.


title: A Survey of Adaptive Carrier Sensing Mechanisms for IEEE 802.11 Wireless Networks

abstract: This survey provides a comprehensive review of existing physical carrier sensing enhancements for IEEE 802.11 wireless networks. The original physical carrier sensing mechanism, used by wireless stations to gain access to the medium, is limited. Consequently, IEEE 802.11 networks are vulnerable to the presence of hidden and exposed nodes. Such nodes can significantly decrease system performance by increasing the collision rate and decreasing the channel spatial reuse. The value of the physical carrier sensing threshold is a key factor influencing the presence of hidden and exposed nodes in a wireless network. Several enhancements have been proposed in the literature, which attempt to mitigate the loss in performance caused by the limited carrier sensing. Firstly, the notion of an optimum carrier sensing threshold has been studied, and results indicate that it can be tuned to an optimum value. Building on the positive early results, further work was performed to develop mechanisms that dynamically adjust the threshold according to varying network conditions. This article presents an in-depth survey of the existing literature in the area, detailing the various approaches and their efficacy in addressing the problem of hidden and exposed nodes (and consequently increasing performance). It offers a comparison of the techniques, by evaluating the models, limitations, assumptions, and performance gains.


title: Acute In Vivo Evaluation of the Pittsburgh Pediatric Ambulatory Lung

abstract: Respiratory failure is a significant problem within the pediatric population. A means of respiratory support that readily allows ambulation could improve treatment. The Pittsburgh Pediatric Ambulatory Lung (P-PAL) is being developed as a wearable pediatric pump-lung for long-term respiratory support and has previously demonstrated positive benchtop results. This study aimed to evaluate acute (4-6 hours) in vivo P-PAL performance, as well as develop an optimal implant strategy for future long-term studies. The P-PAL was connected to healthy sheep (n = 6, 23-32 kg) via cannulation of the right atrium and pulmonary artery. Plasma-free hemoglobin (PfHb) and animal hemodynamics were measured throughout the study. Oxygen transfer rates were measured at blood flows of 1-2.5 L/min. All animals survived the complete study duration with no device exchanges. Flow limitation because of venous cannula occlusion occurred in trial 2 and was remedied via an altered cannulation approach. Blood exiting the P-PAL had 100% oxygen saturation with the exception of trial 4 during which inadequate device priming led to intrabundle clot formation. Plasma-free hemoglobin remained low (<20 mg/dl) for all trials. In conclusion, this study demonstrated successful performance of the P-PAL in an acute setting and established the necessary methods for future long-term evaluation.


title: Chip and PIN is Broken

abstract: EMV is the dominant protocol used for smart card payments worldwide, with over 730 million cards in circulation. Known to bank customers as “Chip and PIN”, it is used in Europe; it is being introduced in Canada; and there is pressure from banks to introduce it in the USA too. EMV secures credit and debit card transactions by authenticating both the card and the customer presenting it through a combination of cryptographic authentication codes, digital signatures, and the entry of a PIN. In this paper we describe and demonstrate a protocol flaw which allows criminals to use a genuine card to make a payment without knowing the card’s PIN, and to remain undetected even when the merchant has an online connection to the banking network. The fraudster performs a man-in-the-middle attack to trick the terminal into believing the PIN verified correctly, while telling the card that no PIN was entered at all. The paper considers how the flaws arose, why they remained unknown despite EMV’s wide deployment for the best part of a decade, and how they might be fixed. Because we have found and validated a practical attack against the core functionality of EMV, we conclude that the protocol is broken. This failure is significant in the field of protocol design, and also has important public policy implications, in light of growing reports of fraud on stolen EMV cards. Frequently, banks deny such fraud victims a refund, asserting that a card cannot be used without the correct PIN, and concluding that the customer must be grossly negligent or lying. Our attack can explain a number of these cases, and exposes the need for further research to bridge the gap between the theoretical and practical security of bank payment systems. It also demonstrates the need for the next version of EMV to be engineered properly.


title: Implicit theories and ability emotional intelligence

abstract: Previous research has shown that people differ in their implicit theories about the essential characteristics of intelligence and emotions. Some people believe these characteristics to be predetermined and immutable (entity theorists), whereas others believe that these characteristics can be changed through learning and behavior training (incremental theorists). The present study provides evidence that in healthy adults (N = 688), implicit beliefs about emotions and emotional intelligence (EI) may influence performance on the ability-based Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). Adults in our sample with incremental theories about emotions and EI scored higher on the MSCEIT than entity theorists, with implicit theories about EI showing a stronger relationship to scores than theories about emotions. Although our participants perceived both emotion and EI as malleable, they viewed emotions as more malleable than EI. Women and young adults in general were more likely to be incremental theorists than men and older adults. Furthermore, we found that emotion and EI theories mediated the relationship of gender and age with ability EI. Our findings suggest that people’s implicit theories about EI may influence their emotional abilities, which may have important consequences for personal and professional EI training.


title: Towards Viewpoint Invariant 3D Human Pose Estimation

abstract: We propose a viewpoint invariant model for 3D human pose estimation from a single depth image. To achieve this, our discriminative model embeds local regions into a learned viewpoint invariant feature space. Formulated as a multi-task learning problem, our model is able to selectively predict partial poses in the presence of noise and occlusion. Our approach leverages a convolutional and recurrent network architecture with a top-down error feedback mechanism to self-correct previous pose estimates in an end-to-end manner. We evaluate our model on a previously published depth dataset and a newly collected human pose dataset containing 100 K annotated depth images from extreme viewpoints. Experiments show that our model achieves competitive performance on frontal views while achieving state-of-the-art performance on alternate viewpoints.


title: Semantic recognition: unified framework for joint object detection and semantic segmentation

abstract: Understanding contents of an image, or scene labeling, is an important yet very challenging problem in artificial intelligence and computer vision to improve road safety. Semantic labeling and object detection in road scenes are strongly correlated tasks. Motivated by the complementary effect of the two tasks, we presented a novel framework to address the scene understanding problem. In this paper we propose a new framework for semantic labeling and object detection problem which is able to combine ideas from deep Convolutional Neural Network (CNN) for object detection and fully-connected Conditional Random Field (CRF) for segmenting and labeling. Specifically, we develop a new framework uses global image features to predict detection which drastically reduces its errors from background detections and a pairwise CRF is used as a post-processing step to enforce spatial consistency in the structured prediction. By combining the consistency between final detection results of CNN and CRF based graphical models, our unified framework can effectively leverage the advantages of leading techniques i.e., CRF and CNN for these two tasks. Extensive experiments on the PASCAL VOC 2007/2012 data sets demonstrate the effectiveness of our framework for Scene Understanding tasks.


title: Building a Large-Scale Knowledge Base for Machine Translation

abstract: Knowledge-based machine translation (KBMT) systems have achieved excellent results in constrained domains, but have not yet scaled up to newspaper text. The reason is that knowledge resources (lexicons, grammar rules, world models) must be painstakingly handcrafted from scratch. One of the hypotheses being tested in the PAN-GLOSS machine translation project is whether or not these resources can be semi-automatically acquired on a very large scale. ::: ::: This paper focuses on the construction of a large ontology (or knowledge base, or world model) for supporting KBMT. It contains representations for some 70,000 commonly encountered objects, processes, qualities, and relations. The ontology was constructed by merging various online dictionaries, semantic networks, and bilingual resources, through semi-automatic methods. Some of these methods (e.g., conceptual matching of semantic taxonomies) are broadly applicable to problems of importing/exporting knowledge from one KB to another. Other methods (e.g., bilingual matching) allow a knowledge engineer to build up an index to a KB in a second language, such as Spanish or Japanese.


title: Comparing images using the hausdorff distance

abstract: The Hausdorff distance measures the extent to which each point of a model set lies near some point of an image set and vice versa. Thus, this distance can be used to determine the degree of resemblance between two objects that are superimposed on one another. Efficient algorithms for computing the Hausdorff distance between all possible relative positions of a binary image and a model are presented. The focus is primarily on the case in which the model is only allowed to translate with respect to the image. The techniques are extended to rigid motion. The Hausdorff distance computation differs from many other shape comparison methods in that no correspondence between the model and the image is derived. The method is quite tolerant of small position errors such as those that occur with edge detectors and other feature extraction methods. It is shown that the method extends naturally to the problem of comparing a portion of a model against an image. >


title: Toward Provable Security Against Differential and Linear Cryptanalysis for Camellia and Related Ciphers

abstract: We present a new algorithm that evaluates provable security against differential and linear cryptanalysis for Feistel ciphers with invertible substitution-diffusion (SD)-based round functions. This algorithm computes an upper bound on the maximum expected differential or linear probability (MEDP or MELP) based on the number of rounds. We then apply our algorithm to Camellia (minus FL/FL 1 ). Previously, the best upper bounds for Camellia were 2 12 (both MEDP and MELP) for 3+ rounds. Our algorithm improves these bounds to 1.065 × 2 28 (MEDP) and 1.161 × 2 27 (MELP) for 6+ rounds. This


title: Digital Correlation Demodulator Design for RFID Reader Receiver

abstract: A high performance digital demodulator for RFID reader receiver is proposed. After carefully analyzing the characteristics of the received data, correlation operation is introduced into the demodulator to estimate the frequency of the received data and decode them. Simulation result indicates that this demodulator can work well under the 6dB input signal-to-noise ratio (SNR) with +/- 22% frequency deviation tolerance. FPGA implementation result shows that the communication distance between reader and tag can be extended to 5m above by the use of this demodulator.


title: Indonesians' Song Lyrics Topic Modelling Using Latent Dirichlet Allocation

abstract: Lyrics in songs have important roles to give identity and storyline of songs. Lyrics are also considered as one of influence factor for popularity of the songs. However, it is difficult to manually assess the topic from numerous songs especially Indonesian's songs which have high poetic level in the lyrics. Knowing the intrinsic value of lyrics from numerous songs becomes a challenge, especially the lyrics written in complicated and complex language like Bahasa. This paper aims to know and interpret the topics from Indonesian's song lyrics. Indonesian's songs were obtained from daily TOP 200 Spotify in January 2017 – January 2018 with 193 different songs using Bahasa in the lyrics. Latent Dirichlet Allocation (LDA) for the topic modeling was used in this paper. Using 10 topics based on perplexity results, LDA has proper way to interpret the topics in numerous songs by giving information about top words in every topic and topic probabilities for each document or song.


title: Unsupervised Segmentation of Chinese Corpus Using Accessor Variety (Extended Abstract)

abstract: The lack of word delimiters such as spaces in Chinese texts makes word segmentation a special issue in Chinese text processing. As the volume of Chinese texts grows rapidly on the Internet, the number of unknown words increases accordingly. However, word segmentation approaches relying solely on existing dictionaries are helpless in handling unknown words. In this paper, we propose a novel unsupervised method to segment large Chinese corpora using contextual information. In particular, the number of characters preceding and following a string, known as the accessors of the string, is used to measure the independence of the string. The greater the independence, the more likely it is that the string is a word. The segmentation problem is then considered an optimization problem to maximize the target function of this number over all word candidates in an utterance. Our purpose here is to explore the best function in terms of segmentation performance. The performance is evaluated with the word token recall measure in addition to word type precision and word type recall. Among the three types of target functions that we have explored, polynomial functions turn out to outperform others. This simple method is effective in unsupervised segmentation of Chinese texts and its performance is highly comparable to other recently reported unsupervised segmentation methods.


title: Advanced Eccentricity Fault Recognition in Permanent Magnet Synchronous Motors Using Stator Current Signature Analysis

abstract: In this paper, a novel index is introduced for static and dynamic eccentricity fault diagnosis in permanent magnet synchronous motors (PMSMs). The proposed index is a linear combination of the energy, shape factor, peak, head angle of the peak, area below the peak, gradient of the peak of the detail signals in wavelet decomposition, and coefficients of the autoregressive model, which are extracted from the stator current signature analysis. Principal component analysis is applied to the features as the linear transform for dimension reduction and elimination of linear dependence between the features. In order to demonstrate the capability of these indexes to estimate eccentricity type and degree, the fuzzy support vector machine is employed as a classifier. Classification of the results indicates that the nominated index can be utilized to detect eccentricity occurrence, recognize its type, and determine its degree precisely. Since extraction of efficient indexes closely depends on precise computation of necessary signals, the time-stepping finite element method is utilized to model the PMSM under eccentricity fault and calculate the stator currents as a proper signal for processing. Simulation results are verified by the experimental results.


title: A Novel Metal-Plate Monopole Antenna for DTV Application

abstract: In this paper, a novel metal-plate monopole antenna is presented for indoor digital television (DTV) signal coverage in the 468-880MHz band. The proposed antenna consists of two orthorhombic metal-plates with symmetrical bevels, a top-uploaded metal-plate and a ground plane. Simulation results show that the antenna achieves a bandwidth ranging from 468MHz to 880MHz which covers the DTV band (470-860MHZ) and shows stable radiation patterns in three coordinate planes.


SLITRK1 mutations in trichotillomania


title: A Survey of Predictive Modelling under Imbalanced Distributions

abstract: Many real world data mining applications involve obtaining predictive models using data sets with strongly imbalanced distributions of the target variable. Frequently, the least common values of this target variable are associated with events that are highly relevant for end users (e.g. fraud detection, unusual returns on stock markets, anticipation of catastrophes, etc.). Moreover, the events may have dierent costs and benets, which when associated with the rarity of some of them on the available training data creates serious problems to predictive modelling techniques. This paper presents a survey of existing techniques for handling these important applications of predictive analytics. Although most of the existing work addresses classication tasks (nominal target variables), we also describe methods designed to handle similar problems within regression tasks (numeric target variables). In this survey we discuss the main challenges raised by imbalanced distributions, describe the main approaches to these problems, propose a taxonomy of these methods and refer to some related problems within predictive modelling.


title: Augmenting feature model through customer preference mining by hybrid sentiment analysis

abstract: We use sentiment analysis of online product reviewers to extract customer preference information.The proposed sentiment analysis method is a hybrid combination of various affective lexicons.We adopt the commented features from product users to enhance the basic feature.We incorporate the customer preference information as attribute into the model.We demonstrate the feasibility and potential of the proposed method via an application case. A feature model is an essential tool to identify variability and commonality within a product line of an enterprise, assisting stakeholders to configure product lines and to discover opportunities for reuse. However, the number of product variants needed to satisfy individual customer needs is still an open question, as feature models do not incorporate any direct customer preference information. In this paper, we propose to incorporate customer preference information into feature models using sentiment analysis of user-generated online product reviews. The proposed sentiment analysis method is a hybrid combination of affective lexicons and a rough-set technique. It is able to predict sentence sentiments for individual product features with acceptable accuracy, and thus augment a feature model by integrating positive and negative opinions of the customers. Such opinionated customer preference information is regarded as one attribute of the features, which helps to decide the number of variants needed within a product line. Finally, we demonstrate the feasibility and potential of the proposed method via an application case of Kindle Fire HD tablets.


title: Sports Participation and Juvenile Delinquency: The Role of the Peer Context among Adolescent Boys and Girls with Varied Histories of Problem Behavior.

abstract: In a study of 1,344 urban adolescents, the authors examined the relation between participation in organized sports and juvenile delinquency. They compared youth who participated in sports to those who only participated in nonathletic activities and to those who did not participate in any organized activities. They also examined the indirect relations between sports and delinquency via 2 peer-related constructs-deviant peer affiliations and unstructured socializing. Finally, they examined the extent to which gender and prior externalizing problems moderated the direct and indirect relations between sports participation and delinquency. The authors found that the odds of nonviolent delinquency were higher among boys who participated in sports when compared to boys who participated only in nonathletic activities but not when compared to boys who did not participate in any organized activities. Deviant peer affiliations and unstructured socializing mediated the relation between sports participation and boys' nonviolent delinquency. Moreover, prior externalizing problems moderated the mediated path through peer deviance. The authors did not, however, find direct, mediated, or moderated relations between sports and boys' violent delinquency nor between sports and girls' violent or nonviolent delinquency. Language: en


title: Wasserstein GAN

abstract: We introduce a new algorithm named WGAN, an alternative to traditional GAN training. In this new model, we show that we can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches. Furthermore, we show that the corresponding optimization problem is sound, and provide extensive theoretical work highlighting the deep connections to other distances between distributions.


title: A 0.4 V Bulk-Driven Amplifier for Low-Power Data Converter Applications

abstract: This paper presents the design of an ultra low-voltage (ULV) pseudo operational transconductance amplifier (P-OTA) that is able to operate with a single supply voltage as low as 0.4 V. The proposed circuit is based on the bulk-driven technique and use of cross-coupled self-cascode pairs that boosts the differential DC gain. The stability condition of this structure for the DC gain is considered by definition of two coefficients to cancel out a controllable percentage of the denominator. This expression for stability condition yield optimized value for the DC gain. Also, as the principle of operation of the proposed technique relies on matching conditions, Monte Carlo analyzes are considered to study of the behavior of the proposed circuit against mismatches. The designed P-OTA have a DC gain of 64 dB, 212 KHz unity gain bandwidth, 57phase margin that is loaded by 10 pF differential capacitive loads, while consume only 16 μW. Eventually, from the proposed P-OTA, a low-power Sample and Hold (S/H) circuit with sampling frequency of 10 KS/s has been designed and simulated. The correct functionality for this configuration is verified from –30℃ to 70℃. The simulated data presented is obtained using the HSPICE Environment and is valid for the 90 nm triple-well CMOS process.


title: Visual Interaction Networks: Learning a Physics Simulator from Video

abstract: From just a glance, humans can make rich predictions about the future of a wide range of physical systems. On the other hand, modern approaches from engineering, robotics, and graphics are often restricted to narrow domains or require information about the underlying state. We introduce the Visual Interaction Network, a general-purpose model for learning the dynamics of a physical system from raw visual observations. Our model consists of a perceptual front-end based on convolutional neural networks and a dynamics predictor based on interaction networks. Through joint training, the perceptual front-end learns to parse a dynamic visual scene into a set of factored latent object representations. The dynamics predictor learns to roll these states forward in time by computing their interactions, producing a predicted physical trajectory of arbitrary length. We found that from just six input video frames the Visual Interaction Network can generate accurate future trajectories of hundreds of time steps on a wide range of physical systems. Our model can also be applied to scenes with invisible objects, inferring their future states from their effects on the visible objects, and can implicitly infer the unknown mass of objects. This work opens new opportunities for model-based decision-making and planning from raw sensory observations in complex physical environments.


title: Dual-mode dielectric filter

abstract: This paper presents a novel dual-mode dielectric filter, utilizing two degenerate modes, TM 120 and TM 210 . The structure consists of a cuboid dielectric resonator short-circuited on top and bottom surface by a rectangular metallic cavity. The frequencies of resonant modes are tunable by metallic screw while modes coupling coefficient controllable by metallic ring. On this basis, we design single-cavity, and double-cavity dual-mode dielectric filter for LTE band.


title: Transformational leadership and innovative work behavior

abstract: Purpose – The purpose of this paper is to explore the mediating role of psychological empowerment and the moderating role of self-construal (independent and interdependent) on the relationship between transformational leadership and employees’ innovative work behavior (IWB). Design/methodology/approach – A total of 639 followers and 87 leaders filled out questionnaires from cross-industry sample of five most innovative companies of China. Structural equation modeling was used to analyze the relations. Findings – Results revealed that psychological empowerment mediated the relationship between transformational leadership and IWB. The research established that transformational leadership positively influences IWB which includes idea generation as well as idea implementation. The results also showed that the relationship between transformational leadership and IWB was stronger among employees with a higher interdependent self-construal and a lower independent self-construal. Originality/value – This study ad...


title: Inspection of defects in fabrics using Gabor wavelets and principle component analysis

abstract: In this paper, a new method for inspection of textile defects in fabrics is presented. The method is based upon the extraction of fabric features by Gabor wavelets. The Gabor wavelets transform provides an effective way to analyze images and extract features of textures. Principal component analysis using singular value decomposition is used to reduce the dimension of feature vectors. Performance of the method has been tested with defective fabric images taken from TILDA textile texture database. Experiments show that these defects are detected accurately.


title: A linguistic ontology of space for natural language processing

abstract: We present a detailed semantics for linguistic spatial expressions supportive of computational processing that draws substantially on the principles and tools of ontological engineering and formal ontology. We cover language concerned with space, actions in space and spatial relationships and develop an ontological organization that relates such expressions to general classes of fixed semantic import. The result is given as an extension of a linguistic ontology, the Generalized Upper Model, an organization which has been used for over a decade in natural language processing applications. We describe the general nature and features of this ontology and show how we have extended it for working particularly with space. Treaitng the semantics of natural language expressions concerning space in this way offers a substantial simplification of the general problem of relating natural spatial language to its contextualized interpretation. Example specifications based on natural language examples are presented, as well as an evaluation of the ontology's coverage, consistency, predictive power, and applicability.


title: CASIA-OLHWDB1: A Database of Online Handwritten Chinese Characters

abstract: This paper describes a publicly available database, CASIA-OLHWDB1, for research on online handwritten Chinese character recognition. This database is the first of our series of online/offline handwritten characters and texts, collected using Anoto pen on paper. It contains unconstrained handwritten characters of 4,037 categories (3,866 Chinese characters and 171 symbols) produced by 420 persons, and 1,694,741 samples in total. It can be used for design and evaluation of character recognition algorithms and classifier design for handwritten text recognition systems. We have partitioned the samples into three grades and into training and test sets. Preliminary experiments on the database using a state-of-the-art recognizer justify the challenge of recognition.


A Software Defined Radio for Mesh Networks


title: Full STEAM ahead: Exactly sparse gaussian process regression for batch continuous-time trajectory estimation on SE(3)

abstract: This paper shows how to carry out batch continuous-time trajectory estimation for bodies translating and rotating in three-dimensional (3D) space, using a very efficient form of Gaussian-process (GP) regression. The method is fast, singularity-free, uses a physically motivated prior (the mean is constant body-centric velocity), and permits trajectory queries at arbitrary times through GP interpolation. Landmark estimation can be folded in to allow for simultaneous trajectory estimation and mapping (STEAM), a variant of SLAM.


title: Text Flow: A Unified Text Detection System in Natural Scene Images

abstract: The prevalent scene text detection approach follows four sequential steps comprising character candidate detection, false character candidate removal, text line extraction, and text line verification. However, errors occur and accumulate throughout each of these sequential steps which often lead to low detection performance. To address these issues, we propose a unified scene text detection system, namely Text Flow, by utilizing the minimum cost (min-cost) flow network model. With character candidates detected by cascade boosting, the min-cost flow network model integrates the last three sequential steps into a single process which solves the error accumulation problem at both character level and text line level effectively. The proposed technique has been tested on three public datasets, i.e, ICDAR2011 dataset, ICDAR2013 dataset and a multilingual dataset and it outperforms the state-of-the-art methods on all three datasets with much higher recall and F-score. The good performance on the multilingual dataset shows that the proposed technique can be used for the detection of texts in different languages.


title: The GRASP Multiple Micro UAV Testbed

abstract: In the last five years, advances in materials, electronics, sensors, and batteries have fueled a growth in the development of microunmanned aerial vehicles (MAVs) that are between 0.1 and 0.5 m in length and 0.1-0.5 kg in mass [1]. A few groups have built and analyzed MAVs in the 10-cm range [2], [3]. One of the smallest MAV is the Picoftyer with a 60-mmpropellor diameter and a mass of 3.3 g [4]. Platforms in the 50-cm range are more prevalent with several groups having built and flown systems of this size [5]-[7]. In fact, there are severalcommercially available radiocontrolled (PvC) helicopters and research-grade helicopters in this size range [8].


abstract: This paper tackles the problem of semi-supervised video object segmentation, that is, segmenting an object in a sequence given its mask in the first frame. One of the main challenges in this scenario is the change of appearance of the objects of interest. Their semantics, on the other hand, do not vary. This paper investigates how to take advantage of such invariance via the introduction of a semantic prior that guides the appearance model. Specifically, given the segmentation mask of the first frame of a sequence, we estimate the semantics of the object of interest, and propagate that knowledge throughout the sequence to improve the results based on an appearance model. We present Semantically-Guided Video Object Segmentation (SGV), which improves results over previous state of the art on two different datasets using a variety of evaluation metrics, while running in half a second per frame.


title: The Emergence of Canalization and Evolvability in an Open-Ended, Interactive Evolutionary System

abstract: Many believe that an essential component for the discovery of the tremendous diversity in natural organisms was the evolution of evolvability, whereby evolution speeds up its ability to innovate by...