title: Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets

abstract: Visual question answering (Visual QA) has attracted a lot of attention lately, seen essentially as a form of (visual) Turing test that artificial intelligence should strive to achieve. In this paper, we study a crucial component of this task: how can we design good datasets for the task? We focus on the design of multiple-choice based datasets where the learner has to select the right answer from a set of candidate ones including the target (\ie the correct one) and the decoys (\ie the incorrect ones). Through careful analysis of the results attained by state-of-the-art learning models and human annotators on existing datasets, we show that the design of the decoy answers has a significant impact on how and what the learning models learn from the datasets. In particular, the resulting learner can ignore the visual information, the question, or both while still doing well on the task. Inspired by this, we propose automatic procedures to remedy such design deficiencies. We apply the procedures to re-construct decoy answers for two popular Visual QA datasets as well as to create a new Visual QA dataset from the Visual Genome project, resulting in the largest dataset for this task. Extensive empirical studies show that the design deficiencies have been alleviated in the remedied datasets and the performance on them is likely a more faithful indicator of the difference among learning models. The datasets are released and publicly available via this http URL.


title: Simulation of mirages

abstract: A mirage is seen when atmospheric refraction distorts or displaces an image. We describe a mirage simulator that uses digital imaging equipment to generate mirage images from normal photographs. The simulation program relocates horizonal image lies into positions that they appear to occupy, according to rays traced from observer to object. Image-brightness adjustments are not required; we show that, while the atmosphere can change the size or shape of an object, it does not change its apparent brightness. The realistic quality of the computed images makes this simulator a useful tool in mirage analysis.


title: Mining Successful Answers in Stack Overflow

abstract: Recent research has shown that drivers of success in online question answering encompass presentation quality as well as temporal and social aspects. Yet, we argue that also the emotional style of a technical contribution influences its perceived quality. In this paper, we investigate how Stack Overflow users can increase the chance of getting their answer accepted. We focus on actionable factors that can be acted upon by users when writing an answer and making comments. We found evidence that factors related to information presentation, time and affect all have an impact on the success of answers.


title: Review on Powder-Based Electron Beam Additive Manufacturing Technology

abstract: This paper presents a thorough literature review of the powder-based electron beam additive manufactur- ing (EBAM) technology. EBAM, a relatively new additive manufacturing (AM) process, can produce full-density metallic parts directly from the electronic data of the designed part geometry. EBAM has gained broad attentions from different industries such as aerospace and biomedical, with great potential in a variety of applications. The paper first introduces the general aspects of EBAM. The unique characteristics, advantages and challenges of EBAM are then presented. Moreover, the hub of this paper includes extensive discussions of microstructures, mechanical properties, geometric attributes, which impact the application ranges of EBAM parts, with focus on commonly used titanium al- loys (in particular, Ti-6Al-4V). In the end, modeling efforts and process metrology of the EBAM process are discussed as well.


title: Holographic localization of passive UHF RFID transponders

abstract: In this paper a method for holographic localization of passive UHF-RFID transponders is presented. It is shown how persons or devices that are equipped with a RFID reader and that are moving along a trajectory can be enabled to locate tagged objects reliably. The localization method is based on phase values sampled from a synthetic aperture by a RFID reader. The calculated holographic image is a spatial probability density function that reveals the actual RFID tag position. Experimental results are presented which show that the holographically measured positions are in good agreement with the real position of the tag. Additional simulations have been carried out to investigate the positioning accuracy of the proposed method depending on different distortion parameters and measuring conditions. The effect of antenna phase center displacement is briefly discussed and measurements are shown that quantify the influence on the phase measurement.


title: Optimised Segmental Rotor Switched Reluctance Machines with a greater number of rotor segments than stator slots

abstract: Segmental Rotor Switched Reluctance Machines have been demonstrated to produce significantly more torque than Conventional Switched Reluctance Machines because each coil is able to link more magnetic flux. In previous work the range of topologies studied has been relatively restricted; there has been no consideration of the range of stator tooth and rotor segment number options and the designs have not been formally optimised in any way. This paper seeks to address this: using Finite Element toolsets for optimisation it describes the resulting machines - uniquely featuring a greater number of rotor segments than stator teeth. The paper assesses the credible tooth and segment options, with each option optimised to maximise torque capability. From this assessment conclusions are drawn which contrast each machine's performance based on criteria including torque production and loss. In order to validate this optimisation process, a prototype machine is built and its performance compared with these predictions.


title: The role of emotions for moral judgments depends on the type of emotion and moral scenario

abstract: Emotions seem to play a critical role in moral judgment. However, the way in which emotions exert their influence on moral judgments is still poorly understood. This study proposes a novel theoretical approach suggesting that emotions influence moral judgments based on their motivational dimension. We tested the effects of two types of induced emotions with equal valence but with different motivational implications (anger and disgust), and four types of moral scenarios (disgust-related, impersonal, personal, and beliefs) on moral judgments. We hypothesized and found that approach motivation associated with anger would make moral judgments more permissible, while disgust, associated with withdrawal motivation, would make them less permissible. Moreover, these effects varied as a function of the type of scenario: the induced emotions only affected moral judgments concerning impersonal and personal scenarios, while we observed no effects for the other scenarios. These findings suggest that emotions can play an important role in moral judgment, but that their specific effects depend upon the type of emotion induced. Furthermore, induced emotion effects were more prevalent for moral decisions in personal and impersonal scenarios, possibly because these require the performance of an action rather than making an abstract judgment. We conclude that the effects of induced emotions on moral judgments can be predicted by taking their motivational dimension into account. This finding has important implications for moral psychology, as it points toward a previously overlooked mechanism linking emotions to moral judgments.


title: Next-Generation DNA Sequencing Methods

abstract: Recent scientific discoveries that resulted from the application of nextgeneration DNA sequencing technologies highlight the striking impact of these massively parallel platforms on genetics. These new methods have expanded previously focused readouts from a variety of DNA preparation protocols to a genome-wide scale and have fine-tuned their resolution to single base precision. The sequencing of RNA also has transitioned and now includes full-length cDNA analyses, serial analysis of gene expression (SAGE)-based methods, and noncoding RNA discovery. Next-generation sequencing has also enabled novel applications such as the sequencing of ancient DNA samples, and has substantially widened the scope of metagenomic analysis of environmentally derived samples. Taken together, an astounding potential exists for these technologies to bring enormous change in genetic and biological research and to enhance our fundamental biological knowledge.


title: The Psychophysics Toolbox

abstract: The Psychophysics Toolbox is a software package that supports visual psychophysics. Its routines provide an interface between a high-level interpreted language (MATLAB on the Macintosh) and the video display hardware. A set of example programs is included with the Toolbox distribution.


title: Modeling and Segmentation of Surgical Workflow from Laparoscopic Video

abstract: Modeling and analyzing surgeries based on signals that are obtained automatically from the operating room (OR) is a field of recent interest. It can be valuable for analyzing and understanding surgical workflow, for skills evaluation and developing context-aware ORs. In minimally invasive surgery, laparoscopic video is easy to record but it is challenging to extract meaningful information from it. We propose a method that uses additional information about tool usage to perform a dimensionality reduction on image features. Using Canonical Correlation Analysis (CCA) a projection of a high-dimensional image feature space to a low dimensional space is obtained such that semantic information is extracted from the video. To model a surgery based on the signals in the reduced feature space two different statistical models are compared. The capability of segmenting a new surgery into phases only based on the video is evaluated. Dynamic Time Warping which strongly depends on the temporal order in combination with CCA shows the best results.


EMOTION AND REASON IN MORAL JUDGMENT 1 Beyond dual-processes : The interplay of reason and emotion in moral judgment


title: A Fast Vehicle Detection Method for Road Monitoring

abstract: To address the issue that current traffic monitoring system needs to automatically detect the objects on the road, a fast vehicle detection method was proposed. Applied the YOLO framework, this paper considered the vehicle detection as a regression problem of vehicle location prediction and classification prediction. Under the precondition of guaranteeing accuracy, the structure of convolution neural network (CNN) is optimized to speed up the detection. The vehicle model CarNet was trained on a dataset containing a large number of road vehicle sample images. The experimental results show that with this method vehicle in video can be detected quickly even on a computer without GPU. The method also shows good robustness and high precision.


title: Kinematic and dynamic modeling of an infrastructure hybrid climbing robot

abstract: Metallic bridges, nuclear plant ducts, telecom and electric power masts, truss shaped structures planted on gyms and showrooms ceilings, astronomy and military facilities are egregious infrastructures in modern age for which locomotion on them is inevitable in order to perform specific tasks including maintenance, constructions (welding and riveting) and periodic inspections (scavenging, searching for impairments) and etc. Therefore, climbing robots are designed to cover the mentioned duties. Since stability and precision of a climbing robot is inevitable, parallel mechanism is proposed. Considering the fact that maneuvering ability is an important factor for climbing robot in a complicated environment (truss shaped environment, scaffolds), the mobility of the robot is increased by a serial linkage added to the parallel portion of the robot to overtake obstacles and cross branches. Hence, a stiff and precise hybrid (parallel / serial) robot is proposed here by composing serial and parallel modules for climbing scaffolds. In this paper design and modeling of the mentioned hybrid climbing robot, consisting kinematics and kinetics is studied. The robot is a 3limbed gripping mechanism with one base (main body) to which the limbs are attached to. A closed kinematic chain is made by 2 limbs, grasping the support terrain firmly. The mechanism is a non-fully parallel mechanism which needs special calculations for modeling. Robot's movement is classified into two phases: 1-manipulation, 2-locomotion. In this manipulation phase is discussed as the primary design and all of the modeling is verified by conducting some comparative and analytic simulation in MATLAB. It is shown that the designed robot can successfully perform operational tasks after its climbing through the trusses.


title: Transformer-less bidirectional PWM converter integrating cell voltage equalizer using voltage multiplier for series connected energy storage cells

abstract: Traditional energy storage systems consisting of lithium-ion battery cells or electric double-layer capacitors (EDLCs) connected in series require a cell voltage equalizer in addition to a bidirectional PWM converter. The system is prone to complexity as two converters (i.e., the equalizer and bidirectional converter) are separately necessary. To simplify the systems, this paper proposes the transformer-less bidirectional PWM converter integrating a cell voltage equalizer using a voltage multiplier. An inductor in a conventional bidirectional PWM converter is replaced with series-connected inductors in the proposed converter in order to produce square wave voltage with an arbitrary amplitude, by which the voltage multiplier is driven. The voltage multiplier in the proposed integrated converter is essentially a switchless circuit, and therefore, the bidirectional converter and voltage equalizer can be integrated with no additional switch, reducing both the system and circuit complexities. The charge-discharge cycling test for nice EDLCs connected in series was performed using the proposed integrated converter from a voltage-imbalanced condition. The voltage imbalance was gradually eliminated while charge-discharge cycling, demonstrating the integrated functions.


title: Wounds measured from digital photographs using photodigital planimetry software: validation and rater reliability.

abstract: UNLABELLED :::  Traditional wound tracing technique consists of tracing the perimeter of the wound on clear acetate with a fine-tip marker, then placing the tracing on graph paper and counting the grids to calculate the surface area. Standard wound measurement technique for calcu- lating wound surface area (wound tracing) was compared to a new wound measurement method using digital photo-planimetry software ([DPPS], PictZar® Digital Planimetry). ::: ::: ::: METHODS ::: Two hundred wounds of varying etiologies were measured and traced by experienced exam- iners (raters). Simultaneously, digital photographs were also taken of each wound. The digital photographs were downloaded onto a PC, and using DPPS software, the wounds were measured and traced by the same examiners. Accuracy, intra- and interrater reliability of wound measurements obtained from tracings and from DPPS were studied and compared. Both accuracy and rater variability were directly related to wound size when wounds were measured and traced in the tradi- tional manner. ::: ::: ::: RESULTS ::: In small (< 4 cm2), regularly shaped (round or oval) wounds, both accuracy and rater reliability was 98% and 95%, respectively. However, in larger, irregularly shaped wounds or wounds with epithelial islands, DPPS was more accurate than traditional mea- suring (3.9% vs. 16.2% [average error]). The mean inter-rater reliabil- ity score was 94% for DPPS and 84% for traditional measuring. The mean intrarater reliability score was 98.3% for DPPS and 89.3% for traditional measuring. In contrast to traditional measurements, DPPS may provide a more objective assessment since it can be done by a technician who is blinded to the treatment plan. Planimetry of digital photographs allows for a closer examination (zoom) of the wound and better visibility of advancing epithelium. ::: ::: ::: CONCLUSION ::: Measurements of wounds performed on digital photographs using planimetry software were simple and convenient. It was more accurate, more objective, and resulted in better correlation within and between examiners. .


title: SecMR – a secure multipath routing protocol for ad hoc networks

abstract: Abstract Multipath routing in ad hoc networks increases the resiliency against security attacks of collaborating malicious nodes, by maximizing the number of nodes that an adversary must compromise in order to take control of the communication. In this paper, we identify several attacks that render multipath routing protocols vulnerable to collaborating malicious nodes. We propose an on-demand multipath routing protocol, the secure multipath routing protocol (SecMR), and we analyze its security properties. Finally, through simulations, we evaluate the performance of the SecMR protocol in comparison with existing secure multipath routing protocols.


title: Neuron tracing in perspective

abstract: The study of the structure and function of neuronal cells and networks is of crucial importance in the endeavor to understand how the brain works. A key component in this process is the extraction of neuronal morphology from microscopic imaging data. In the past four decades, many computational methods and tools have been developed for digital reconstruction of neurons from images, with limited success. As witnessed by the growing body of literature on the subject, as well as the organization of challenging competitions in the field, the quest for a robust and fully automated system of more general applicability still continues. The aim of this work, is to contribute by surveying recent developments in the field for anyone interested in taking up the challenge. Relevant aspects discussed in the article include proposed image segmentation methods, quantitative measures of neuronal morphology, currently available software tools for various related purposes, and morphology databases. (c) 2010 International Society for Advancement of Cytometry.


title: Universal accumulators with efficient nonmembership proofs

abstract: Based on the notion of accumulators, we propose a new cryptographic scheme called universal accumulators. This scheme enables one to commit to a set of values using a short accumulator and to efficiently compute a membership witness of any value that has been accumulated. Unlike traditional accumulators, this scheme also enables one to efficiently compute a nonmembership witness of any value that has not been accumulated. We give a construction for universal accumulators and prove its security based on the strong RSA assumption. We further present a construction for dynamic universal accumulators; this construction allows one to dynamically add and delete inputs with constant computational cost. Our construction directly builds upon Camenisch and Lysyanskaya's dynamic accumulator scheme. Universal accumulators can be seen as an extension to dynamic accumulators with support of nonmembership witness. We also give an efficient zero-knowledge proof protocol for proving that a committed value is not in the accumulator. Our dynamic universal accumulator construction enables efficient membership revocation in an anonymous fashion.


title: Deep Learning on Mobile Systems

abstract: Training of deep learning methods which are widely utilized in intelligent systems requires powerful computers, however, they are also used on mobile systems. Hence, application programming interfaces providing execution of pre-trained models on mobile systems have been introduced. In this study, two interfaces running on Android systems are tested on 40 objects for both object detection and labelling. Morever, ease of installations, resource usages were also compared. According to the test results, labelling performance of the model provided by Tensorflow interface is higher than the model of MLKit.


title: Implementation and stability analysis of prioritized whole-body compliant controllers on a wheeled humanoid robot in uneven terrains

abstract: In this work, we implement the floating base prioritized whole-body compliant control framework described in Sentis et al. (IEEE Transactions on Robotics 26(3):483---501, 2010) on a wheeled humanoid robot maneuvering in sloped terrains. We then test it for a variety of compliant whole-body behaviors including balance and kinesthetic mobility on irregular terrain, and Cartesian hand position tracking using the co-actuated (i.e. two joints are simultaneously actuated with one motor) robot's upper body. The implementation serves as a hardware proof for a variety of whole-body control concepts that had previously been developed and tested in simulation. First, behaviors of two and three priority tasks are implemented and successfully executed on the humanoid hardware. In particular, first and second priority tasks are linearized in the task space through model feedback and then controlled through task accelerations. Postures, on the other hand, are shown to be asymptotically stable when using prioritized whole-body control structures and then successfully tested in the real hardware. To cope with irregular terrains, the base is modeled as a six degree of freedom floating system and the wheels are characterized through contact and rolling constraints. Finally, center of mass balance capabilities using whole-body compliant control and kinesthetic mobility are implemented and tested in the humanoid hardware to climb terrains with various slopes.


title: Search Heuristics, Case-based Reasoning And Software Project Effort Prediction

abstract: This paper reports on the use of search techniques to help optimise a case-based reasoning (CBR) system for predicting software project effort. A major problem, common to machine learning (ML) techniques in general, has been dealing with large numbers of case features, some of which can hinder the prediction process. Unfortunately searching for the optimal feature subset is a combinatorial problem and therefore NP-hard. This paper examines the use of random searching, hill climbing and forward sequential selection (FSS) to tackle this problem. Results from examining a set of real software project data show that even random searching was better than using all available features (average error 35.6% rather than 50.8%). Hill climbing and FSS both produced results substantially better than the random search (15.3 and 13.1% respectively), but FSS was computationally more efficient. Providing a description of the fitness landscape of a problem along with search results is a step towards the classification of search problems and their assignment to optimum search techniques. This paper attempts to describe the fitness landscape of this problem by combining the results from random searches and hill climbing, as well as using multi-dimensional scaling to aid visualisation. Amongst other findings, the visualisation results suggest that some form of heuristic-based initialisation might prove useful for this problem.


n the use of statistical models to predict crop yield responses to climate change


title: A framework for mesencephalic dopamine systems based on predictive Hebbian learning

abstract: We develop a theoretical framework that shows how mesencephalic dopamine systems could distribute to their targets a signal that represents information about future expectations. In particular, we show how activity in the cerebral cortex can make predictions about future receipt of reward and how fluctuations in the activity levels of neurons in diffuse dopamine systems above and below baseline levels would represent errors in these predictions that are delivered to cortical and subcortical targets. We present a model for how such errors could be constructed in a real brain that is consistent with physiological results for a subset of dopaminergic neurons located in the ventral tegmental area and surrounding dopaminergic neurons. The theory also makes testable predictions about human choice behavior on a simple decision-making task. Furthermore, we show that, through a simple influence on synaptic plasticity, fluctuations in dopamine release can act to change the predictions in an appropriate manner.


title: Differential patterns of antimicrobial resistance in population subsets of Escherichia coli isolated from waste- and surface waters

abstract: article i nfo The species Escherichia coli comprises different subgroups with distinct phylogeny, physiology and ecology and, thus, presumably, with different roles in antimicrobial resistance dissemination. E. coli strains isolated from raw and treated municipal wastewater and from urban water streams were characterized in terms of phylogenetic groups, antimicrobial resistance patterns and the presence of class 1 and class 2 integrons. Our main objective was to investigate the contribution of the different phylo-groups in antimicrobial resistance dissemination in urban waters. Groups A and B1 were predominant in all types of water, evidencing, respectively, the lowest and the highest resistance prevalence. Municipal wastewater treatment was accompanied by significant increases of ciprofloxacin and streptomycin resistance (pb0.01). Antimicrobial resistance prevalence differed significantly between the different phylo-groups and within the same group, mainly in group A. Such differences contributed to explain the higher ciprofloxacin and streptomycin resistance rates observed in treated effluent in comparison with the raw wastewater. We conclude that the dynamics of the bacterial populations has a major role on the dissemination of antimicrobial resistance in the environment.


Performance Analysis of Naive Bayes and J 48 Classification Algorithm for Data Classification


title: Synthesis of fully substituted naphthyridines: a novel domino four-component reaction in a deep eutectic solvent system based on choline chloride/urea

abstract: Abstract The diversity-oriented synthesis of a naphthyridine scaffold has been demonstrated via a novel domino four-component reaction. The syntheses were achieved by reacting a diamine, 1,1-bis(methylthio)-2-nitroethylene, 2-aminoprop-1-ene-1,1,3-tricarbonitrile, and various carbonyl compounds using a deep eutectic solvent (DES), based on choline chloride/urea, thus providing a new class of poly-functionalized fused naphthyridine derivatives with the concomitant formation of three new rings and six σ bonds. The reaction conditions were mild and did not require additional base catalysts. Given the inexpensive, nontoxic, and recyclable nature of the DES, these reaction conditions are simple and highly environmentally friendly.


title: MacroBase: Prioritizing Attention in Fast Data

abstract: As data volumes continue to rise, manual inspection is becoming increasingly untenable. In response, we present MacroBase, a data analytics engine that prioritizes end-user attention in high-volume fast data streams. MacroBase enables efficient, accurate, and modular analyses that highlight and aggregate important and unusual behavior, acting as a search engine for fast data. MacroBase is able to deliver order-of-magnitude speedups over alternatives by optimizing the combination of explanation and classification tasks and by leveraging a new reservoir sampler and heavy-hitters sketch specialized for fast data streams. As a result, MacroBase delivers accurate results at speeds of up to 2M events per second per query on a single core. The system has delivered meaningful results in production, including at a telematics company monitoring hundreds of thousands of vehicles.


title: Urban scene understanding from aerial and ground LIDAR data

abstract: We present a framework to segment cultural and natural features, given 3D aerial scans of a large urban area, and (optionally) registered ground level scans of the same area. This system provides a primary step to achieve the ultimate goal of detecting every object from a large number of varied categories, from antenna to power plants. Our framework first identifies local patches of the ground surface and roofs of buildings. This is accomplished by tensor voting that infers surface orientation from neighboring regions as well as local 3D points. We then group adjacent planar surfaces with consistent pose to find surface segments and classify them as either the terrain or roofs of buildings. The same approach is also applied to delineate vertical faces of buildings, as well as free-standing vertical structures such as fences. The inferred large structures are then used as geometric context to segment linear structures, such as power lines, and structures attached to walls and roofs from remaining unclassified 3D points in the scene. We demonstrate our system on real LIDAR datasets acquired from typical urban regions with areas of a few square kilometers each, and provide a quantitative analysis of performance using externally provided ground truth.


title: Preattentive grouping and Attentive selection for early visual computation

abstract: The segmentation of objects in a real world scene is a prerequisite for any higher level recognition or interpretation process. Biological visual systems exploit efficient mechanisms for object extraction which seem to be mostly data driven. We propose a network for perceptual grouping inspired from neurophysiological and psychophysical findings, incorporating a phase diffusion process which labels the whole image into its constituent objects and the background, followed by a selective attention stage which sequentially extracts objects in the scene. The image is processed by four successive stages, copying the design of visual cortical mechanisms. Direction specific edge responses are used as starting points for a competitive and cooperative phase process. The resulting phase image is processed by an attention mechanism, extracting homogeneous regions using both spatial and phase information, followed by the generation of a saccadic signal.


title: A Secure Environment for Untrusted Helper Applications (Confining the Wily Hacker)

abstract: Many popular programs, such as Netscape, use untrusted helper applications to process data from the network. Unfortunately, the unauthenticated network data they interpret could well have been created by an adversary, and the helper applications are usually too complex to be bug-free. This raises significant security concerns. Therefore, it is desirable to create a secure environment to contain untrusted helper applications. We propose to reduce the risk of a security breach by restricting the program's access to the operating system. In particular, we intercept and filter dangerous system calls via the Solaris process tracing facility. This enabled us to build a simple, clean, user-mode implementation of a secure environment for untrusted helper applications. Our implementation has negligible performance impact, and can protect pre-existing applications.


title: Helper-less physically unclonable functions and chip authentication

abstract: Physically Unclonable Functions (PUFs) have been recently proposed as a way to include, in chips, functions that can act as “fingerprints” of the chip, to be used in applications like chip authentication (strong PUF) or private ID generation (weak PUF). Most of the schemes proposed for weak PUF exploit helper data to make the PUF more reliable. The use of an helper, however, has some drawbacks such as complexity and the introduction of a possible attack point. In this manuscript we propose the use of a new type of weak PUF called helper-less PUFs (H-PUF) that does not require the use of helper data. We analyze theoretically the problem of designing an H-PUF and use the result to show how an H-PUF can be built. We also show that the proposed PUF, albeit being of weak type, can also be used for chip authentication.


title: Predictors for Neoplastic Progression in Patients With Barrett's Esophagus: A Prospective Cohort Study

abstract: OBJECTIVES ::: Patients with Barrett's esophagus (BE) have an increased risk of developing esophageal adenocarcinoma (EAC). As the absolute risk remains low, there is a need for predictors of neoplastic progression to tailor more individualized surveillance programs. The aim of this study was to identify such predictors of progression to high-grade dysplasia (HGD) and EAC in patients with BE after 4 years of surveillance and to develop a prediction model based on these factors. ::: ::: ::: METHODS ::: We included 713 patients with BE (≥ 2 cm) with no dysplasia (ND) or low-grade dysplasia (LGD) in a multicenter, prospective cohort study. Data on age, gender, body mass index (BMI), reflux symptoms, tobacco and alcohol use, medication use, upper gastrointestinal (GI) endoscopy findings, and histology were prospectively collected. As part of this study, patients with ND underwent surveillance every 2 years, whereas those with LGD were followed on a yearly basis. Log linear regression analysis was performed to identify risk factors associated with the development of HGD or EAC during surveillance. ::: ::: ::: RESULTS ::: After 4 years of follow-up, 26/713 (3.4%) patients developed HGD or EAC, with the remaining 687 patients remaining stable with ND or LGD. Multivariable analysis showed that a known duration of BE of ≥ 10 years (risk ratio (RR) 3.2; 95% confidence interval (CI) 1.3-7.8), length of BE (RR 1.11 per cm increase in length; 95% CI 1.01-1.2), esophagitis (RR 3.5; 95% CI 1.3-9.5), and LGD (RR 9.7; 95% CI 4.4-21.5) were significant predictors of progression to HGD or EAC. In a prediction model, we found that the annual risk of developing HGD or EAC in BE varied between 0.3% and up to 40%. Patients with ND and no other risk factors had the lowest risk of developing HGD or EAC (<1%), whereas those with LGD and at least one other risk factor had the highest risk of neoplastic progression (18-40%). ::: ::: ::: CONCLUSIONS ::: In patients with BE, the risk of developing HGD or EAC is predominantly determined by the presence of LGD, a known duration of BE of ≥10 years, longer length of BE, and presence of esophagitis. One or combinations of these risk factors are able to identify patients with a low or high risk of neoplastic progression and could therefore be used to individualize surveillance intervals in BE.


title: Global Indices for Kinematic and Force Transmission Performance in Parallel Robots

abstract: This paper defines, develops, and demonstrates new indices for quantifying kinematic and force transmission performance of parallel robotic mechanisms. Conventional indices are first analyzed and shown to be insufficient for characterizing common design specifications based on worst-case performance. New indices are then developed, analyzed, and extended to simply and intuitively quantify global worst-case kinematic and force performance. These new tools are then successfully applied to the design, optimization, and analysis of a linear delta robot for medical applications.


title: Parameter sharing between dependency parsers for related languages

abstract: Previous work has suggested that parameter sharing between transition-based neural dependency parsers for related languages can lead to better performance, but there is no consensus on what parameters to share. We present an evaluation of 27 different parameter sharing strategies across 10 languages, representing five pairs of related languages, each pair from a different language family. We find that sharing transition classifier parameters always helps, whereas the usefulness of sharing word and/or character LSTM parameters varies. Based on this result, we propose an architecture where the transition classifier is shared, and the sharing of word and character parameters is controlled by a parameter that can be tuned on validation data. This model is linguistically motivated and obtains significant improvements over a monolingually trained baseline. We also find that sharing transition classifier parameters helps when training a parser on unrelated language pairs, but we find that, in the case of unrelated languages, sharing too many parameters does not help.


title: Forecasting stock market crisis events using deep and statistical machine learning techniques

abstract: Abstract This work contributes to this ongoing debate on the nature and the characteristics of propagation channels of crash events in international stock markets. Specifically, we investigate transmission mechanisms across stock markets along with effects from bond and currency markets. Our approach comprises a solid forecasting mechanism of the probability of a stock market crash event in various time frames. The developed approach combines different machine learning algorithms which are presented with daily stock, bond and currency data from 39 countries that cover a large spectrum of economies. Specifically, we leverage the merits of a series of techniques including Classification Trees, Support Vector Machines, Random Forests, Neural Networks, Extreme Gradient Boosting, and Deep Neural Networks. To the best of our knowledge, this is the first time that Deep Learning and Boosting approaches are considered in the literature as a means of predicting stock market crisis episodes. The independent variables included in our data contain information regarding both the two fundamental linkage channels through which financial contagion can be initiated: returns and volatility. We apply a suite of machine learning algorithms for selecting the most relevant variables out of a large set of proposed ones. Finally, we employ bootstrap sampling for adjusting the imbalanced nature of the available fitting dataset. Our experimental results provide strong evidence that stock market crises tend to exhibit persistence. We also find significant evidence of interdependence and cross-contagion effects among stock, bond and currency markets. Finally, we show that the use of Deep Neural Networks significantly increases the classification accuracy, while offering a robust way to create a global systemic early warning tool that is more efficient and risk-sensitive than the currently established ones. Thus, central banks may use these tools to early adjust their monetary policy, so as to ensure financial stability.


title: Yawning detection for monitoring driver fatigue based on two cameras

abstract: Driver fatigue is one of the main reasons causing traffic accidents. Yawning is an important character of driver fatigue. Mouth geometric character represents state of driver. So driver's mouth detection and information extraction is especially important. This paper proposes to locate and track driver's mouth using two CCD cameras to detect driver's fatigue in real-time. One of them is with cloud platform control. Camera A, which is fixed, is mainly used to supply driver's head position. Camera B, which is controllable, is to locate driver's mouth and extract mouth information. First, collecting monitoring video of camera A (low resolution ratio) and locate driver's face using fast image processing algorithm. Second, we send commands to camera B (high resolution ratio) through serial port to make it shoot driver's head all the time according to face position in Camera A. Then we use haar-like features to detect driver's mouth in camera B and track it according to historical position. At last, yawning is detected by the ratio of mouth height and width. Through this method the resolution ratio of driver's mouth is higher than one camera and the feature information is more accuracy. It provides a better basis for driver fatigue judging.


title: A Comparison of Some Structural Testing Strategies

abstract: Several structural testing strategies are compared in terms of their relative coverage of the program's structure and also in terms of the number of test cases needed to satisfy each strategy. Some of the deficiencies of such comparisons are discussed. >


title: Individual differences in facial configuration in large-billed crows

abstract: The recognition of individuals is a basic cognitive ability of social animals. A prerequisite for individual recognition is distinct characteristics that can be used to distinguish between other conspecific individuals. Studies of birds have shown that visual information, such as colour patterning, is used in individual recognition. However, in the case of monochromatic birds, colour patterning cannot be used to identify individuals. Therefore, we expected that the configuration of facial features, such as the shape of the bills or eyes, may have enough individuality to permit individual recognition in such species. In this study, we aimed to clarify visible individual differences in the facial configuration of large-billed crows (Corvus macrorhynchos). Specifically, we analysed the profile pictures of 16 crows. We measured 26 variables in 20 pictures of each bird and then performed principal component analysis and discriminant function analysis. The results showed that the configuration of the facial profiles was individually distinct, but re-classification by discriminant functions implied that it did not clearly differ between sexes. These results suggest that crows may be able to recognise individuals on the basis of the individuality of facial configuration, even in the absence of any conspicuous colour patterning.


title: Soft Computing Techniques for Dependable Cyber-Physical Systems

abstract: Cyber-physical systems (CPSs) were envisaged as a way to manipulate the objects in the physical world through computer intelligence. This is usually done by providing a communication bridge between actuation and computing elements. This sought after control is hampered not only by the unavoidable certainty found in the physical world but also by the limitations of contemporary communication networks. These limitations hamper fine-grained control of elements that may be separated by large-scale distances. In this regard, soft computing is an emerging paradigm that can help to manage the unreliability of CPS by using techniques, including fuzzy systems, neural networks, evolutionary computation, probabilistic reasoning, and rough sets. We present a comprehensive contemporary review of soft computing techniques for CPS dependability modeling, analysis, and improvement. This paper provides an overview of CPS applications, explores the foundations of dependability engineering, and highlights the potential role of soft computing techniques for CPS dependability with various case studies while also identifying common pitfalls and future directions. In addition, this paper provides a comprehensive survey of the use of various soft computing techniques for making CPS dependable. This paper is timely due to the increasingly central role that CPSs are beginning to play in modern societies and the need to leverage all the relevant methodologies and tools (such as those provided by soft computing) for the development of highly dependable CPS.


A Data Layout Transformation ( DLT ) Accelerator : Architectural support for data movement optimization in accelerated systems


title: Smart cameras with onboard signcryption for securing IoT applications

abstract: Cameras are expected to become key sensor devices for various internet of things (IoT) applications. Since cameras often capture highly sensitive information, security is a major concern. Our approach towards data security for smart cameras is rooted on protecting the captured images by signcryption based on elliptic curve cryptography (ECC). Signcryption achieves resource-efficiency by performing data signing and encryption in a single step. By running the signcryption on the sensing unit, we can relax some security assumptions for the camera host unit which typically runs a complex software stack. We introduce our system architecture motivated by a typical case study for camera-based IoT applications, evaluate security properties and present performance results of an ARM-based implementation.


title: "Why Should I Trust Interactive Learners?"Explaining Interactive Queries of Classifiers to Users

abstract: Although interactive learning puts the user into the loop, the learner remains mostly a black box for the user. Understanding the reasons behind queries and predictions is important when assessing how the learner works and, in turn, trust. Consequently, we propose the novel framework of explanatory interactive learning: in each step, the learner explains its interactive query to the user, and she queries of any active classifier for visualizing explanations of the corresponding predictions. We demonstrate that this can boost the predictive and explanatory powers of and the trust into the learned model, using text (e.g. SVMs) and image classification (e.g. neural networks) experiments as well as a user study.


title: Ensemble Learning in Fixed Expansion Layer Networks for Mitigating Catastrophic Forgetting

abstract: Catastrophic forgetting is a well-studied attribute of most parameterized supervised learning systems. A variation of this phenomenon, in the context of feedforward neural networks, arises when nonstationary inputs lead to loss of previously learned mappings. The majority of the schemes proposed in the literature for mitigating catastrophic forgetting were not data driven and did not scale well. We introduce the fixed expansion layer (FEL) feedforward neural network, which embeds a sparsely encoding hidden layer to help mitigate forgetting of prior learned representations. In addition, we investigate a novel framework for training ensembles of FEL networks, based on exploiting an information-theoretic measure of diversity between FEL learners, to further control undesired plasticity. The proposed methodology is demonstrated on a basic classification task, clearly emphasizing its advantages over existing techniques. The architecture proposed can be enhanced to address a range of computational intelligence tasks, such as regression problems and system control.


title: End-to-end Video-level Representation Learning for Action Recognition

abstract: From the frame/clip-level feature learning to the video-level representation building, deep learning methods in action recognition have developed rapidly in recent years. However, current methods suffer from the confusion caused by partial observation training, or without end-to-end learning, or restricted to single temporal scale modeling and so on. In this paper, we build upon two-stream ConvNets and propose Deep networks with Temporal Pyramid Pooling (DTPP), an end-to-end video-level representation learning approach, to address these problems. Specifically, at first, RGB images and optical flow stacks are sparsely sampled across the whole video. Then a temporal pyramid pooling layer is used to aggregate the frame-level features which consist of spatial and temporal cues. Lastly, the trained model has compact video-level representation with multiple temporal scales, which is both global and sequence-aware. Experimental results show that DTPP achieves the state-of-the-art performance on two challenging video action datasets: UCF101 and HMDB51, either by ImageNet pre-training or Kinetics pre-training.


Towards a Methodology for Building Ontologies


title: Decoding fast-paced error-related potentials in monitoring protocols

abstract: Error-related EEG potentials (ErrP) can be used for brain-machine interfacing (BMI). Decoding of these signals, indicating subject's perception of erroneous system decisions or actions can be used to correct these actions or to improve the overall interfacing system. Multiple studies have shown the feasibility of decoding these potentials in single-trial using different types of experimental protocols and feedback modalities. However, previously reported approaches are limited by the use of long inter-stimulus intervals (ISI>2s). In this work we assess if it is possible to overcome this limitation. Our results show that it is possible to decode error-related potentials elicited by stimuli presented with ISIs lower than 1s without decrease in performance. Furthermore, the increase in the presentation rate did not increase the subject workload. This suggests that the presentation rate for ErrP-based BMI protocols using serial monitoring paradigms can be substantially increased with respect to previous works.


title: Opinion Spam Detection in Web Forum: A Real Case Study

abstract: Opinion spamming refers to the illegal marketing practice which involves delivering commercially advantageous opinions as regular users. In this paper, we conduct a real case study based on a set of internal records of opinion spams leaked from a shady marketing campaign. We explore the characteristics of opinion spams and spammers in a web forum to obtain some insights, including subtlety property of opinion spams, spam post ratio, spammer accounts, first post and replies, submission time of posts, activeness of threads, and collusion among spammers. Then we present features that could be potentially helpful in detecting spam opinions in threads. The results of spam detection on first posts show: (1) spam first posts put more focus on certain topics such as the user experiences' on the promoted items, (2) spam first posts generally use more words and pictures to showcase the promoted items in an attempt to impress people, (3) spam first posts tend to be submitted during work time, and (4) the threads that spam first posts initiate are more active to be placed at striking positions. The spam detection on replies is more challenging. Besides lower spam ratio and less content, replies even do not mention the promoted items. Their major intention is to keep the discussion in a thread alive to attract more attention on it. Submission time of replies, thread activeness, position of replies, and spamicity of first post are more useful than content-based features in spam detection on replies.


title: Beating Atari with Natural Language Guided Reinforcement Learning

abstract: We introduce the first deep reinforcement learning agent that learns to beat Atari games with the aid of natural language instructions. The agent uses a multimodal embedding between environment observations and natural language to self-monitor progress through a list of English instructions, granting itself reward for completing instructions in addition to increasing the game score. Our agent significantly outperforms Deep Q-Networks (DQNs), Asynchronous Advantage Actor-Critic (A3C) agents, and the best agents posted to OpenAI Gym on what is often considered the hardest Atari 2600 environment: Montezuma's Revenge.


title: Learning to Poke by Poking: Experiential Learning of Intuitive Physics

abstract: We investigate an experiential learning paradigm for acquiring an internal model of intuitive physics. Our model is evaluated on a real-world robotic manipulation task that requires displacing objects to target locations by poking. The robot gathered over 400 hours of experience by executing more than 100K pokes on different objects. We propose a novel approach based on deep neural networks for modeling the dynamics of robot's interactions directly from images, by jointly estimating forward and inverse models of dynamics. The inverse model objective provides supervision to construct informative visual features, which the forward model can then predict and in turn regularize the feature space for the inverse model. The interplay between these two objectives creates useful, accurate models that can then be used for multi-step decision making. This formulation has the additional benefit that it is possible to learn forward models in an abstract feature space and thus alleviate the need of predicting pixels. Our experiments show that this joint modeling approach outperforms alternative methods.


title: Answer Garden: a tool for growing organizational memory

abstract: Answer Garden allows organizations to develop databases of commonly asked questions that grow “organically” as new questions arise and are answered. It is designed to help in situations (such as field service organizations and customer “hot lines”) where there is a continuing stream of questions, many of which occur over and over, but some of which the organization has never seen before. The system includes a branching network of diagnostic questions that helps users find the answers they want. If the answer is not present, the system automatically sends the question to the appropriate expert, and the answer is returned to the user as well as inserted into the branching network. Experts can also modify this network in response to users' problems. ::: Our initial Answer Garden database contains questions and answers about how to use the X Window System.


title: Probabilistic Student Models: Bayesian Belief Networks and Knowledge Space Theory

abstract: The applicability of Knowledge Space Theory (Falmagne and Doignon) and Bayesian Belief Networks (Pearl) as probabilistic student models imbedded in an Intelligent Tutoring System is examined. Student modeling issues such as knowledge representation, adaptive assessment, curriculum advancement, and student feedback are addressed. Several factors contribute to uncertainty in student modeling such as careless errors and lucky guesses, learning and forgetting, and unanticipated student response patterns. However, a probabilistic student model can represent uncertainty regarding the estimate of the student's knowledge and can be tested using empirical student data and established statistical techniques.


title: Modeling the closed-current loop of PWM boost DC-DC converters operating in CCM with peak current-mode control

abstract: This paper derives the transfer function from error voltage to duty cycle, which captures the quasi-digital behavior of the closed-current loop for pulsewidth modulated (PWM) dc-dc converters operating in continuous-conduction mode (CCM) using peak current-mode (PCM) control, the current-loop gain, the transfer function from control voltage to duty cycle (closed-current loop transfer function), and presents experimental verification. The sample-and-hold effect, or quasi-digital (discrete) behavior in the current loop with constant-frequency PCM in PWM dc-dc converters is described in a manner consistent with the physical behavior of the circuit. Using control theory, a transfer function from the error voltage to the duty cycle that captures the quasi-digital behavior is derived. This transfer function has a pole that can be in either the left-half plane or right-half plane, and captures the sample-and-hold effect accurately, enabling the characterization of the current-loop gain and closed-current loop for PWM dc-dc converters with PCM. The theoretical and experimental response results were in excellent agreement, confirming the validity of the transfer functions derived. The closed-current loop characterization can be used for the design of a controller for the outer voltage loop.


title: On the forward kinematics of cable-driven parallel robots

abstract: Most cable-driven parallel robots are kinematically over-constrained mechanisms. This results in a non-trivial computation of the forward kinematic transformation. It is well known that the forward kinematics of parallel robots may have multiple solutions and in general the convergence of numerical methods is unknown. In recent works, it was proposed to formulate the forward kinematics as optimization problem that models the cables as linear springs in order to compute the platform pose which has minimal potential energy in the cables. In this paper, we analyzed this objective function. Using the Hessian matrix, we show that under certain conditions the problem at hand is convex and we can expect a unique and stable minimum. The computations are exemplified for point-shaped platforms as well as for the planar case. For the spatial case, we present an encouraging numerical study. An ordinary least squares method is then applied to find a position approximation and an improvement to previous methods is demonstrated.


title: A three phase grid connected SPV power generating system using EPLL based control technique

abstract: The integration of Solar Photovoltaic (SPV) power generation system with the grid by using Enhanced Phase Locked Loop (EPLL) scheme based indirect control. The developed control scheme for synchronizing the SPV system with utility grid improves the power factor of the source and voltage profile at the point of common coupling (PCC). The proposed controller maintain the voltage profile at PCC through reactive power compensation. The proposed system includes SPV array, a DC/DC boost converter, maximum power point tracking (MPPT) controller, voltage source converter (VSC), filter, various loads and three phase utility grid. The proposed grid synchronization technique provides better active and reactive power control, reduced harmonic and load balancing. Testing for the proposed grid integrated SPV system has been performed for two type of operations i.e. unity power factor (UPF) mode operation of the system and voltage regulation with balancing of loads with linear load and non-linear loads.


title: Reactivity and Deliberation: A Survey on Multi-Robot Systems

abstract: Multi Robot Systems are, nowadays, an important research area within Robotics and Artificial Intelligence. Although Multi Robot Systems can be regarded as a particular case of Multi Agent Systems, it seems appropriate to study the Multi Robot Systems from a specific viewpoint, because of the issues which arise from the embodiment of agents operating in real environments. In this paper, we present an analysis of Multi Robot Systems by looking at their cooperative aspects. In particular, we propose a taxonomy of Multi Robot Systems and a characterization of reactive and social deliberative behaviors of the Multi Robot System as a whole. Finally, we address some Multi Robot Systems, which we consider representative of the various nodes in our taxonomy.


title: MAT: A Multimodal Attentive Translator for Image Captioning

abstract: In this work we formulate the problem of image captioning as a multimodal translation task. Analogous to machine translation, we present a sequence-to-sequence recurrent neural networks (RNN) model for image caption generation. Different from most existing work where the whole image is represented by convolutional neural network (CNN) feature, we propose to represent the input image as a sequence of detected objects which feeds as the source sequence of the RNN model. In this way, the sequential representation of an image can be naturally translated to a sequence of words, as the target sequence of the RNN model. To represent the image in a sequential way, we extract the objects features in the image and arrange them in a order using convolutional neural networks. To further leverage the visual information from the encoded objects, a sequential attention layer is introduced to selectively attend to the objects that are related to generate corresponding words in the sentences. Extensive experiments are conducted to validate the proposed approach on popular benchmark dataset, i.e., MS COCO, and the proposed model surpasses the state-of-the-art methods in all metrics following the dataset splits of previous work. The proposed approach is also evaluated by the evaluation server of MS COCO captioning challenge, and achieves very competitive results, e.g., a CIDEr of 1.029 (c5) and 1.064 (c40).


title: Machine Learning and Data Mining Methods in Diabetes Research

abstract: Abstract The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.


title: A pilot randomised control trial of a parent training intervention for pre-school children with autism

abstract: Few attempts have been made to conduct randomised control trials (RCTs) of interventions for pre-school children with autism. We report findings of a pilot RCT for a parent training intervention with a focus on the development of joint attention skills and joint action routines. Twenty-four children meeting ICD-10 criteria for childhood autism (mean age = 23 months) were identified using the CHAT screen and randomised to the parent training group or to local services only. A follow-up was conducted 12 months later (mean age = 35 months). There was some evidence that the parent training group made more progress in language development than the local services group. However, the present pilot study was compromised by several factors: a reliance on parental report to measure language, non-matching of the groups on initial IQ, and a lack of systematic checking regarding the implementation of the parent training intervention. Furthermore, three parents in the local services group commenced intensive, home-based behavioural intervention during the course of the study. The difficulties encountered in the conduct of RCTs for pre-school children with autism are discussed. Methodological challenges and strategies for future well-designed RCTs for autism interventions are highlighted.


title: How AUDI AG Established Big Data Analytics in Its Digital Transformation

abstract: Digital transformation, which often includes establishing big data analytics capabilities, poses considerable challenges for traditional manufacturing organizations, such as car companies. Successfully introducing big data analytics requires substantial organizational transformation and new organizational structures and business processes. Based on the three-stage evolution of big data analytics capabilities at AUDI, we provide recommendations for how traditional manufacturing organizations can successfully introduce big data analytics and master the related organizational transformations.


title: An Unsupervised Approach to Anomaly Detection in Music Datasets

abstract: This paper presents an unsupervised method for systematically identifying anomalies in music datasets. The model integrates categorical regression and robust estimation techniques to infer anomalous scores in music clips. When applied to a music genre recognition dataset, the new method is able to detect corrupted, distorted, or mislabeled audio samples based on commonly used features in music information retrieval. The evaluation results show that the algorithm outperforms other anomaly detection methods and is capable of finding problematic samples identified by human experts. The proposed method introduces a preliminary framework for anomaly detection in music data that can serve as a useful tool to improve data integrity in the future.


title: EmoCog: Computational Integration of Emotion and Cognitive Architecture

abstract: Since the reinvigoration of emotions research, many computationalmodels of emotion have been developed. None ofthese models, however, fully address the integration of emotiongeneration and emotional effect in the context of cognitiveprocesses. This paper seeks to unify various modelsof computational emotions while fully integrating with workdone in cognitive architectures. We propose a perspective onhow this integration would occur and EmoCog, a cognitivearchitecture with mechanisms for emotion generation and effects.


title: Teamwork in Health Care: Maximizing Collective Intelligence via Inclusive Collaboration and Open Communication

abstract: Research shows that health care teams are more effective when team members are encouraged to speak up and team members’ input influences the team’s work.


title: Learning Parameterized Skills

abstract: We introduce a method for constructing skills capable of solving tasks drawn from a distribution of parameterized reinforcement learning problems. The method draws example tasks from a distribution of interest and uses the corresponding learned policies to estimate the topology of the lower-dimensional piecewise-smooth manifold on which the skill policies lie. This manifold models how policy parameters change as task parameters vary. The method identifies the number of charts that compose the manifold and then applies non-linear regression in each chart to construct a parameterized skill by predicting policy parameters from task parameters. We evaluate our method on an underactuated simulated robotic arm tasked with learning to accurately throw darts at a parameterized target location.


title: Seeking safety on the internet: Relationship between social anxiety and problematic internet use

abstract: As internet use becomes increasingly integral to modern life, the hazards of excessive use are also becoming apparent. Prior research suggests that socially anxious individuals are particularly susceptible to problematic internet use. This vulnerability may relate to the perception of online communication as a safer means of interacting, due to greater control over self-presentation, decreased risk of negative evaluation, and improved relationship quality. To investigate these hypotheses, a general sample of 338 completed an online survey. Social anxiety was confirmed as a significant predictor of problematic internet use when controlling for depression and general anxiety. Social anxiety was associated with perceptions of greater control and decreased risk of negative evaluation when communicating online, however perceived relationship quality did not differ. Negative expectations during face-to-face interactions partially accounted for the relationship between social anxiety and problematic internet use. There was also preliminary evidence that preference for online communication exacerbates face-to-face avoidance.


title: A Survey of Network Virtualization

abstract: Due to the existence of multiple stakeholders with conflicting goals and policies, alterations to the existing Internet architecture are now limited to simple incremental updates; deployment of any new, radically different technology is next to impossible. To fend off this ossification, network virtualization has been propounded as a diversifying attribute of the future inter-networking paradigm. By introducing a plurality of heterogeneous network architectures cohabiting on a shared physical substrate, network virtualization promotes innovations and diversified applications. In this paper, we survey the existing technologies and a wide array of past and state-of-the-art projects on network virtualization followed by a discussion of major challenges in this area.


title: A Shape-based Approach for Salient Object Detection Using Deep Learning

abstract: Salient object detection is a key step in many image analysis tasks as it not only identifies relevant parts of a visual scene but may also reduce computational complexity by filtering out irrelevant segments of the scene. In this paper, we propose a novel salient object detection method that combines a shape prediction driven by a convolutional neural network with the mid and low-region preserving image information. Our model learns a shape of a salient object using a CNN model for a target region and estimates the full but coarse saliency map of the target image. The map is then refined using image specific low-to-mid level information. Experimental results show that the proposed method outperforms previous state-of-the-arts methods in salient object detection.


Modeling the Dunning-Kruger Effect: A Rational Account of Inaccurate Self-Assessment.


title: Visual Question Answering With a Hybrid Convolution Recurrent Model

abstract: Visual Question Answering (VQA) is a relatively new task, which tries to infer answer sentences for an input image coupled with a corresponding question. Instead of dynamically generating answers, they are usually inferred by finding the most probable answer from a fixed set of possible answers. Previous work did not address the problem of finding all possible answers, but only modeled the answering part of VQA as a classification task. To tackle this problem, we infer answer sentences by using a Long Short-Term Memory (LSTM) network that allows us to dynamically generate answers for (image, question) pairs. In a series of experiments, we discover an end-to-end Deep Neural Network structure, which allows us to dynamically answer questions referring to a given input image by using an LSTM decoder network. With this approach, we are able to generate both less common answers, which are not considered by classification models, and more complex answers with the appearance of datasets containing answers that consist of more than three words.


title: Desh: deep learning for system health prediction of lead times to failure in HPC

abstract: Today's large-scale supercomputers encounter faults on a daily basis. Exascale systems are likely to experience even higher fault rates due to increased component count and density. Triggering resilience-mitigating techniques remains a challenge due to the absence of well defined failure indicators. System logs consist of unstructured text that obscures essential system health information contained within. In this context, efficient failure prediction via log mining can enable proactive recovery mechanisms to increase reliability. This work aims to predict node failures that occur in supercomputing systems via long short-term memory (LSTM) networks that exploit recurrent neural networks (RNNs). Our framework, Desh1 ( De ep Learning for S ystem H ealth), diagnoses and predicts failures with short lead times. Desh identifies failure indicators with enhanced training and classification for generic applicability to logs from operating systems and software components without the need to modify any of them. Desh uses a novel three-phase deep learning approach to (1) train to recognize chains of log events leading to a failure, (2) re-train chain recognition of events augmented with expected lead times to failure, and (3) predict lead times during testing/inference deployment to predict which specific node fails in how many minutes. Desh obtains as high as 3 minutes average lead time with no less than 85% recall and 83% accuracy to take proactive actions on the failing nodes, which could be used to migrate computation to healthy nodes.


Predicting Disease Progression with a Model for Multivariate Longitudinal Clinical Data


title: A Joint Model of Text and Aspect Ratings for Sentiment Summarization

abstract: Online reviews are often accompanied with numerical ratings provided by users for a set of service or product aspects. We propose a statistical model which is able to discover corresponding topics in text and extract textual evidence from reviews supporting each of these aspect ratings ‐ a fundamental problem in aspect-based sentiment summarization (Hu and Liu, 2004a). Our model achieves high accuracy, without any explicitly labeled data except the user provided opinion ratings. The proposed approach is general and can be used for segmentation in other applications where sequential data is accompanied with correlated signals.


title: Automatic Ingestion Monitor: A Novel Wearable Device for Monitoring of Ingestive Behavior

abstract: Objective monitoring of food intake and ingestive behavior in a free-living environment remains an open problem that has significant implications in study and treatment of obesity and eating disorders. In this paper, a novel wearable sensor system (automatic ingestion monitor, AIM) is presented for objective monitoring of ingestive behavior in free living. The proposed device integrates three sensor modalities that wirelessly interface to a smartphone: a jaw motion sensor, a hand gesture sensor, and an accelerometer. A novel sensor fusion and pattern recognition method was developed for subject-independent food intake recognition. The device and the methodology were validated with data collected from 12 subjects wearing AIM during the course of 24 h in which both the daily activities and the food intake of the subjects were not restricted in any way. Results showed that the system was able to detect food intake with an average accuracy of 89.8%, which suggests that AIM can potentially be used as an instrument to monitor ingestive behavior in free-living individuals.


title: Automatic Extraction of Semantic Relations from Wikipedia

abstract: We introduce a novel approach to extract semantic relations (e.g., is-a and part-of relations) from Wikipedia articles. These relations are used to build up a large and up-to-date thesaurus providing background knowledge for tasks such as determining semantic ontology mappings. Our automatic approach uses a comprehensive set of semantic patterns, finite state machines and NLP techniques to extract millions of relations between concepts. An evaluation for different domains shows the high quality and effectiveness of the proposed approach. We also illustrate the value of the newly found relations for improving existing ontology mappings.


title: Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks

abstract: Indoor scene understanding is central to applications such as robot navigation and human companion assistance. Over the last years, data-driven deep neural networks have outperformed many traditional approaches thanks to their representation learning capabilities. One of the bottlenecks in training for better representations is the amount of available per-pixel ground truth data that is required for core scene understanding tasks such as semantic segmentation, normal prediction, and object edge detection. To address this problem, a number of works proposed using synthetic data. However, a systematic study of how such synthetic data is generated is missing. In this work, we introduce a large-scale synthetic dataset with 400K physically-based rendered images from 45K realistic 3D indoor scenes. We study the effects of rendering methods and scene lighting on training for three computer vision tasks: surface normal prediction, semantic segmentation, and object boundary detection. This study provides insights into the best practices for training with synthetic data (more realistic rendering is worth it) and shows that pretraining with our new synthetic dataset can improve results beyond the current state of the art on all three tasks.


title: GlobalPhone: A multilingual text & speech database in 20 languages

abstract: This paper describes the advances in the multilingual text and speech database GlobalPhone, a multilingual database of high-quality read speech with corresponding transcriptions and pronunciation dictionaries in 20 languages. GlobalPhone was designed to be uniform across languages with respect to the amount of data, speech quality, the collection scenario, the transcription and phone set conventions. With more than 400 hours of transcribed audio data from more than 2000 native speakers GlobalPhone supplies an excellent basis for research in the areas of multilingual speech recognition, rapid deployment of speech processing systems to yet unsupported languages, language identification tasks, speaker recognition in multiple languages, multilingual speech synthesis, as well as monolingual speech recognition in a large variety of languages.


title: The Social Media Ecology: User Perceptions, Strategies and Challenges

abstract: Many existing studies of social media focus on only one platform, but the reality of users' lived experiences is that most users incorporate multiple platforms into their communication practices in order to access the people and networks they desire to influence. In order to better understand how people make sharing decisions across multiple sites, we asked our participants (N=29) to categorize all modes of communication they used, with the goal of surfacing their mental models about managing sharing across platforms. Our interview data suggest that people simultaneously consider "audience" and "content" when sharing and these needs sometimes compete with one another; that they have the strong desire to both maintain boundaries between platforms as well as allowing content and audience to permeate across these boundaries; and that they strive to stabilize their own communication ecosystem yet need to respond to changes necessitated by the emergence of new tools, practices, and contacts. We unpack the implications of these tensions and suggest future design possibilities.


title: Strategic Information Systems Alignment: Alignment of IS/IT with Business Strategy

abstract: Using information systems effectively requires an understanding of the organisation, management, and the technology shaping the systems. All information systems can be described as organisational and management solutions to challenges posed by the environment. The advances in information systems have affect on our day-to day lives . As the technology is evolving immensely so are the opportunities in a healthy way to prepare the organisation in the competitive advantage environment In order to manage the IS/IT based systems, it is important to have an appropriate strategy that defines the systems and provide means to manage them. Strategic Information Systems Alignment (SISA) is an effective way of developing and maintaining the IS/IT systems that support the business operations. Alignment of the IS/IT plans and the business plans is essential for improved business performance, this research looks at the key features of SISA in the changing business circumstances in Saudi Arabia.


title: Dictionary Learning for Sparse Approximations With the Majorization Method

abstract: In order to find sparse approximations of signals, an appropriate generative model for the signal class has to be known. If the model is unknown, it can be adapted using a set of training samples. This paper presents a novel method for dictionary learning and extends the learning problem by introducing different constraints on the dictionary. The convergence of the proposed method to a fixed point is guaranteed, unless the accumulation points form a continuum. This holds for different sparsity measures. The majorization method is an optimization method that substitutes the original objective function with a surrogate function that is updated in each optimization step. This method has been used successfully in sparse approximation and statistical estimation [ e.g., expectation-maximization (EM)] problems. This paper shows that the majorization method can be used for the dictionary learning problem too. The proposed method is compared with other methods on both synthetic and real data and different constraints on the dictionary are compared. Simulations show the advantages of the proposed method over other currently available dictionary learning methods not only in terms of average performance but also in terms of computation time.


title: Inkjet printing of microsensors

abstract: In the past few years, inkjet printing of microelectronics has been emerging as a cost effective and an environment friendly microfabrication technique. In this paper, inkjet printing of microsensors is investigated, using both resistive and capacitive strain-sensing operating devices. An inexpensive conductive polymer, poly(3,4-ethylenedioxythiophene) oxidized with poly(styrenesulfonate) (PEDOT:PSS) is used as base material for inkjet printing. The experimental characterization shows that the PEDOT:PSS preserves its piezoresistive properties after being printed in thin film patterns, leading to large gauge factors. Stress sensitive interdigitated capacitors fabricated through inkjet printing have as well a high sensitivity and are reported, to our knowledge, for the first time.


title: Adiponectin and Atherosclerosis in Rheumatoid Arthritis

abstract: In the present study, we examined the potential impact of adiponectin on carotid ultrasound determined atherosclerosis in 210 (119 black and 91 white) RA patients in mixed regression models. Total adiponectin concentrations were smaller in patients with compared to those without the metabolic syndrome (MetS) defined waist criterion (median (range) = 6.47 (1.23–34.54) versus 8.38 (0.82–85.30) ng/mL, , resp.); both total and high molecular weight (HMW) adiponectin concentrations were larger in patients with compared to those without joint deformities (7.97 (0.82–85.30) and 3.51 (0.01–35.40) versus 5.36 (1.29–19.49) and 2.34 (0.01–19.49) ng/mL, and 0.02, resp.). Total and HMW adiponectin concentrations were associated with carotid artery plaque in patients with MetS waist (odds ratio (95% CI) = 0.87 (0.76–0.99) and 0.92 (0.85–0.99) per 1-standard deviation increment, for both) and those without joint deformities (odds ratio (95% CI) = 0.94 (0.88–0.99) and 0.94 (0.89–0.99), for both). Plaque prevalence was lower in patients without compared to those with joint deformities (23.4% versus 42.6, in multivariable analysis). In RA patients with abdominal obesity or no clinically evident joint damage, adiponectin concentrations are reduced but nevertheless associated with decreased carotid atherosclerosis.


title: Indigenous multimedia content development for next generation smart schools: a cognitive instructional design approach

abstract: There is a need to look at indigenous digital multimedia educational content development approaches that would meet the needs of indigenous community. This paper hopes to highlight the design of multimedia digital content based on the cognitive instructional design architecture for indigenous content thought suitable to create future knowledge workers (k-Insan) in Malaysia based on ME2 and MEL packages.


title: Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks

abstract: The purpose of this study was to develop a method of classifying cancers to specific diagnostic categories based on their gene expression signatures using artificial neural networks (ANNs). We trained the ANNs using the small, round blue-cell tumors (SRBCTs) as a model. These cancers belong to four distinct diagnostic categories and often present diagnostic dilemmas in clinical practice. The ANNs correctly classified all samples and identified the genes most relevant to the classification. Expression of several of these genes has been reported in SRBCTs, but most have not been associated with these cancers. To test the ability of the trained ANN models to recog- nize SRBCTs, we analyzed additional blinded samples that were not previously used for the train- ing procedure, and correctly classified them in all cases. This study demonstrates the potential applications of these methods for tumor diagnosis and the identification of candidate targets for therapy. © 2001 Nature Publishing Group http://medicine.nature.com


title: Integrating RFIDs and Smart Objects into a UnifiedInternet of Things Architecture

abstract: The term Internet of Things refers to the networked interconnection of objects of diverse nature, such as electronic devices, sensors, but also physical objects and beings as well as virtual data and environments. Although the basic concept of the Internet of Things sounds simple, its application is difficult and, so far, the respective existing architectural models are rather monolithic and are dominated by several limitations. The paper introduces a generic Internet of Things architecture trying to resolve the existing restrictions of current architectural models by integrating both RFID and smart object-based infrastructures, while also exploring a third parameter, i.e. the social potentialities of the Internet of Things building blocks towards shaping the “Social Internet of Things”. The proposed architecture is based on a layered lightweight and open middle-ware solution following the paradigm of Service Oriented Architecture and the Semantic Model Driven Ap-proach, which is realized at both design-time and deployment–time covering the whole service lifecycle for the corresponding services and applications provided.


title: Distilling Knowledge for Search-based Structured Prediction

abstract: Many natural language processing tasks can be modeled into structured prediction and solved as a search problem. In this paper, we distill an ensemble of multiple models trained with different initialization into a single model. In addition to learning to match the ensemble's probability output on the reference states, we also use the ensemble to explore the search space and learn from the encountered states in the exploration. Experimental results on two typical search-based structured prediction tasks -- transition-based dependency parsing and neural machine translation show that distillation can effectively improve the single model's performance and the final model achieves improvements of 1.32 in LAS and 2.65 in BLEU score on these two tasks respectively over strong baselines and it outperforms the greedy structured prediction models in previous literatures.


title: Body Piercing Medical Concerns with Cutting-Edge Fashion

abstract: Body piercing is becoming increasingly common in the United States.1–3 The practice of piercing a site other than the earlobe for jewelry display is becoming so accepted that a navel ring was worn by one of the contestants in the 1997 Miss America Pageant (Morgan D. Teen introduces body-piercing to Miss America Pageant. Reuters Limited. Sept. 10, 1997. Available from www.pathfinder.com/iW4LUg…inment/latest/RB/1997Sep10/91.html). Along with the growing popularity of body piercing come risks of morbidity. Furthermore, most body piercing in the United States is being performed by unlicensed, unregulated individuals.1–3 ::: ::: The piercing of various body sites has been practiced for thousands of years. Reasons for piercing have included adornment, rites of passage, religious purposes, and sexual practices.1–6 In Western society, piercing of the earlobe for adornment has become common. Piercing other sites of the body has been considered nontraditional until recent years.1,2,5 ::: ::: Health care professionals are faced with treating complications related to body piercing. This review examines the various body piercing complications and their treatment and discusses psychological, ethical, and legislative issues related to body piercing.


title: Identifying emerging research fields: a longitudinal latent semantic keyword analysis

abstract: This study aims to gain insights into emerging research fields in the area of marketing and tourism. It provides support for the use of quantitative techniques to facilitate content analysis. The authors present a longitudinal latent semantic analysis of keywords. The proposed method is illustrated by two different examples: a scholarly journal (International Marketing Review) and conference proceedings (ENTER eTourism Conference). The methodology reveals an understanding of the current state of the art of marketing research and e-tourism by identifying neglected, popular or upcoming thematic research foci. The outcomes are compared with former results generated by traditional content analysis techniques. Findings confirm that the proposed methodology has the potential to complement qualitative content analysis, as the semantic analysis produces similar outcomes to qualitative content analysis to some extent. This paper reviews a journal's content over a period of nearly three decades. The authors argue that the suggested methodology facilitates the analysis dramatically and can thus be simply applied on a regular basis in order to monitor topic development within a specific research domain.


title: Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data

abstract: The most commonly used statistical models of civil war onset fail to correctly predict most occurrences of this rare event in out-of-sample data. Statistical methods for the analysis of binary data, such as logistic regression, even in their rare event and regularized forms, perform poorly at prediction. We compare the performance of Random Forests with three versions of logistic regression (classic logistic regression, Firth rare events logistic regression, and L1-regularized logistic regression), and find that the algorithmic approach provides significantly more accurate predictions of civil war onset in out-of-sample data than any of the logistic regression models. The article discusses these results and the ways in which algorithmic statistical methods like Random Forests can be useful to more accurately predict rare events in conflict data.


title: Understanding words in sentence contexts: The time course of ambiguity resolution

abstract: Spoken language comprehension requires rapid integration of information from multiple linguistic sources. In the present study we addressed the temporal aspects of this integration process by focusing on the time course of the selection of the appropriate meaning of lexical ambiguities (‘‘bank’’) in sentence contexts. Successful selection of the contextually appropriate meaning of the ambiguous word is dependent upon the rapid binding of the contextual information in the sentence to the appropriate meaning of the ambiguity. We used the N400 to identify the time course of this binding process. The N400 was measured to target words that followed three types of context sentences. In the concordant context, the sentence biased the meaning of the sentence-final ambiguous word so that it was related to the target. In the discordant context, the sentence context biased the meaning so that it was not related to the target. In the unrelated control condition, the sentences ended in an unambiguous noun that was unrelated to the target. Half of the concordant sentences biased the dominant meaning, and the other half biased the subordinate meaning of the sentence-final ambiguous words. The ISI between onset of the target word and offset of the sentence-final word of the context sentence was 100 ms in one version of the experiment, and 1250 ms in the second version. We found that (i) the lexically dominant meaning is always partly activated, independent of context, (ii) initially both dominant and subordinate meaning are (partly) activated, which suggests that contextual and lexical factors both contribute to sentence interpretation without context completely overriding lexical information, and (iii) strong lexical influences remain present for a relatively long period of time. 2003 Elsevier Science (USA). All rights reserved.


title: Identification and Characterization of Hydrophobic Escherichia coli Virulence Proteins by Liquid Chromatography–Electrospray Ionization Mass Spectrometry

abstract: Virulence of enterotoxicogenic Escherichia coli is mediated by rodlike, rigid, highly hydrophobic proteins designated fimbriae or colonization factors (CFs). More than 20 different colonization factors have been described so far using predominantly immunological and genetic methods. To characterize these hydrophobic proteins by liquid chromatography–mass spectrometry (LC–MS), different methodologies were explored. A novel LC–MS method was developed using hexafluoroisopropanol to maintain the hydrophobic proteins in solution. In addition, these proteins were digested with cyanogen bromide and peptide mapping by LC–MS was established. This technique was particularly useful in identification of closely related CFs. Both LC–MS and peptide mapping methodologies were found to be useful in characterizing highly hydrophobic CFs of E. coli. To search for molecular weights of mature proteins in the National Center for Biotechnology Information (NCBI) database, a new feature was developed and its applicability tested. The identification of a class of pathogenic virulence proteins, either intact or digested, is possible with molecular weight database searching.


title: Transformation of the IT function at British Petroleum

abstract: In 1989, the IT function of the exploration and production division of British Petroleum Company set out to transform itself in response to a severe economic environment and poor internal perceptions of IT performance. This case study traces and analyzes the changes made over six years. The authors derive a model of the transformed IT organization comprising seven components that they suggest can guide IT departments in general as they seek to reform themselves in the late 1990s.This model is seen to fit well with recent thinking on general management in that the seven components of change can be reclassified into the Bartlett and Ghoshal (1994) framework of purpose, process and people. Some suggestions are made on how to apply the model in other organizations.


title: Generalization of active filters for EMI reduction and harmonics compensation

abstract: This paper provides generalized analysis of active filters used as electromagnetic interference (EMI) filters and active-power filters. Insertion loss and impedance increase of various types of active-filter topologies are described with applicable requirements and limitations as well as the rationale for selecting active-filter topology according to different applications.


title: Extrofitting: Enriching Word Representation and its Vector Space with Semantic Lexicons

abstract: We propose post-processing method for enriching not only word representation but also its vector space using semantic lexicons, which we call extrofitting. The method consists of 3 steps as follows: (i) Expanding 1 or more dimension(s) on all the word vectors, filling with their representative value. (ii) Transferring semantic knowledge by averaging each representative values of synonyms and filling them in the expanded dimension(s). These two steps make representations of the synonyms close together. (iii) Projecting the vector space using Linear Discriminant Analysis, which eliminates the expanded dimension(s) with semantic knowledge. When experimenting with GloVe, we find that our method outperforms Faruqui's retrofitting on some of word similarity task. We also report further analysis on our method in respect to word vector dimensions, vocabulary size as well as other well-known pretrained word vectors (e.g., Word2Vec, Fasttext).


title: Linear discriminant analysis for the small sample size problem: an overview

abstract: Dimensionality reduction is an important aspect in the pattern classification literature, and linear discriminant analysis (LDA) is one of the most widely studied dimensionality reduction technique. The application of variants of LDA technique for solving small sample size (SSS) problem can be found in many research areas e.g. face recognition, bioinformatics, text recognition, etc. The improvement of the performance of variants of LDA technique has great potential in various fields of research. In this paper, we present an overview of these methods. We covered the type, characteristics and taxonomy of these methods which can overcome SSS problem. We have also highlighted some important datasets and software/packages.


title: UniPass: design and evaluation of a smart device-based password manager for visually impaired users

abstract: Visually impaired users face various challenges in web authentication. We designed UniPass, an accessible password manager for visually impaired users based on a smart device. To evaluate UniPass, we tested and compared UniPass with two commercial password managers: LastPass, a popular password manager and StrongPass, a smart device-based password manager. Our study results of ten users, six blind and four with low vision, suggest that password managers are a promising authentication approach for visually impaired users. Participants using UniPass had the highest task completion rate and took the shortest time to complete an authentication related task. Furthermore, the majority (seven out of ten) of our participants preferred UniPass over LastPass and StrongPass.


title: Lateral Road-mark Reconstruction Using Neural Network for Safe Autonomous Driving in Snow-wet Environments

abstract: This paper highlights the importance of utilizing artificial intelligence to safely localize autonomous vehicles in hard weather and environmental conditions. An edge profile is created to encode the lateral road-marks as a series of peaks. A neural network is utilized to explore different relationships between the peaks among a set of map edge profiles. Based on the generalization capability of the trained network, the LIDAR profiles are reconstructed during the autonomous driving to regularly reform the peaks in terms of distribution and amplitude patterns. A reconstructed profile is then matched to the corresponding map profile to estimate the lateral vehicle position accurately. The proposed framework has been tested in very hard situations of snow and grass accumulation at the roadside, snow lines inside lanes and, on wet and old road surfaces. The experimental results have verified the reliability and safety of reconstructing LIDAR profiles to achieve precise lateral localization against the changes of road, weather and environmental conditions.