title: Putting the learning curve in context

abstract: Abstract In implementation of change, people learn new ways of doing things, develop new skills, and adopt new organizational routines. This paper applies learning curve theory to implementation by developing a system dynamics model that includes two extensions to classic learning curve theory. First, the model includes a required output level for the individual. Second, the model includes a budget constraint on time that forces a choice between an old and a new way to achieve the output. Doing work the new way builds experience, increasing productivity and thus favoring continued use of the new skill, but model analysis demonstrates that this reinforcing process works to favor the new skill only at relatively high levels of productivity. Otherwise, the same process is a vicious cycle, driving out the new skill. The model exhibits a mode of behavior in which learning begins and then stalls and another mode in which the new skill becomes the preferred one. The paper identifies the tipping point between these two modes and characterizes the transition problem: Learning by doing is a dynamic process, a transition from use of an old way to a new way that requires accumulating experience beyond a threshold.


title: Media, Identity, and Gender: Tracking Feminist Media and Journalism Theories and Methodologies

abstract: This article attempts to illustrate some general theoretical and methodological approaches to journalism and media coming from feminist perspective. The research questions raised by media feminists scholars relate not only to the key issue of representation, but also to wider examination of the place of women in the media industries, to women as producers and as audiences, and women’s reflections of their own situation in relationship to the media spaces. At the heart of this article lies a case-study. It asks whether there are any gender differences in a general perception held in a newsroom of a small Australian newspaper – that female journalists work differently to male journalists. On the basis of qualitative methodologies (participant observation, and indepth interviews) the study explores the question whether there are observable differences in the way female journalists and male journalists work in that particular newsroom. Put it simple, we are interested here in whether Australian female journalists in the studied newspaper newsroom conform to a traditional, patriarchal style of journalism?


title: RansomTracer: Exploiting Cyber Deception for Ransomware Tracing

abstract: Ransomware is a type of malware that encrypts data or locks a device to extort a ransom. Recently, a variety of high-profile ransomware attacks have been reported, and many ransomware defense systems have been proposed. However, none specializes in resisting untargeted attacks such as those by remote desktop protocol (RDP) attack ransomware. To resolve this problem, this paper proposes a way to combat RDP ransomware attacks by trapping and tracing. It discovers and ensnares the attacker through a network deception environment and uses an auxiliary tracing technology to find the attacker, finally achieving the goal of deterring the ransomware attacker and countering the RDP attack ransomware. Based on cyber deception, an auxiliary ransomware traceable system called RansomTracer is introduced in this paper. RansomTracer collects clues about the attacker by deploying monitors in the deception environment. Then, it automatically extracts and analyzes the traceable clues. Experiments and evaluations show that RansomTracer ensnares the adversary in the deception environment and improves the efficiency of clue analysis significantly. In addition, it is able to recognize the clues that identify the attacker and the screening rate reaches 98.34%.


title: End-to-End Information Extraction without Token-Level Supervision

abstract: Most state-of-the-art information extraction approaches rely on token-level labels to find the areas of interest in text. Unfortunately, these labels are time-consuming and costly to create, and consequently, not available for many real-life IE tasks. To make matters worse, token-level labels are usually not the desired output, but just an intermediary step. End-to-end (E2E) models, which take raw text as input and produce the desired output directly, need not depend on token-level labels. We propose an E2E model based on pointer networks, which can be trained directly on pairs of raw input and output text. We evaluate our model on the ATIS data set, MIT restaurant corpus and the MIT movie corpus and compare to neural baselines that do use token-level labels. We achieve competitive results, within a few percentage points of the baselines, showing the feasibility of E2E information extraction without the need for token-level labels. This opens up new possibilities, as for many tasks currently addressed by human extractors, raw input and output data are available, but not token-level labels.


title: Trends in Eye Tracking Scanpaths: Segmentation Effect?

abstract: Eye tracking has been widely used to investigate user interactions with the Web to improve user experience. In our previous work, we developed an algorithm called Scanpath Trend Analysis (STA) that analyses eye movement sequences (i.e., scanpaths) of multiple users on a web page and identifies their most commonly followed path in terms of the visual elements of the page. These visual elements are mainly the segments of a page generated by automated segmentation approaches. In our previous work, we also showed that the STA algorithm performs better than other existing algorithms in terms of providing the most representative scanpath of users. However, we did not know whether the validity of the algorithm is limited to a particular segmentation approach. In this paper, we investigate the effect of two different segmentation approaches on the STA algorithm. The results suggest that the validity of the algorithm is not affected by the segmentation approach used. Specifically, the resulting scanpath of the STA algorithm is the most representative scanpath of users in comparison with the resulting scanpaths of other existing algorithms regardless of the segmentation approach used.


title: The distribution of target registration error in rigid-body point-based registration

abstract: Guidance systems designed for neurosurgery, hip surgery, spine surgery and for approaches to other anatomy that is relatively rigid can use rigid-body transformations to accomplish image registration. These systems often rely on point-based registration to determine the transformation and many such systems use attached fiducial markers to establish accurate fiducial points for the registration, the points being established by some fiducial localization process. Accuracy is important to these systems, as is knowledge of the level of that accuracy. An advantage of marker-based systems, particularly those in which the markers are bone-implanted, is that registration error depends only on the fiducial localization and is, thus, to a large extent independent of the particular object being registered. Thus, it should be possible to predict the clinical accuracy of marker-based systems on the basis of experimental measurements made with phantoms or previous patients. For most registration tasks, the most important error measure is target registration error (TRE), which is the distance after registration between corresponding points not used in calculating the registration transform. Here, the authors derive an approximation to the distribution of TRE; this is an extension of previous work that gave the expected squared value of TRE. They show the distribution of the squared magnitude of TRE and that of the component of TRE in an arbitrary direction. Using numerical simulations, the authors show that their theoretical results are a close match to the simulated ones.


title: Simple Black-Box Adversarial Attacks on Deep Neural Networks

abstract: Deep neural networks are powerful and popular learning models that achieve state-of-the-art pattern recognition performance on many computer vision, speech, and language processing tasks. However, these networks have also been shown susceptible to crafted adversarial perturbations which force misclassification of the inputs. Adversarial examples enable adversaries to subvert the expected system behavior leading to undesired consequences and could pose a security risk when these systems are deployed in the real world.,,,,,, In this work, we focus on deep convolutional neural networks and demonstrate that adversaries can easily craft adversarial examples even without any internal knowledge of the target network. Our attacks treat the network as an oracle (black-box) and only assume that the output of the network can be observed on the probed inputs. Our attacks utilize a novel local-search based technique to construct numerical approximation to the network gradient, which is then carefully used to construct a small set of pixels in an image to perturb. We demonstrate how this underlying idea can be adapted to achieve several strong notions of misclassification. The simplicity and effectiveness of our proposed schemes mean that they could serve as a litmus test for designing robust networks.


Automatic text processing: the transformation, analysis, and retrieval of information by computer


title: ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features

abstract: A successful method for removing artifacts from electroencephalogram (EEG) recordings is Independent Component Analysis (ICA), but its implementation remains largely user-dependent. Here, we propose a completely automatic algorithm (ADJUST) that identifies artifacted independent components by combining stereotyped artifact-specific spatial and temporal features. Features were optimized to capture blinks, eye movements, and generic discontinuities on a feature selection dataset. Validation on a totally different EEG dataset shows that (1) ADJUST’s classification of independent components largely matches a manual one by experts (agreement on 95.2% of the data variance), and (2) Removal of the artifacted components detected by ADJUST leads to neat reconstruction of visual and auditory eventrelated potentials from heavily artifacted data. These results demonstrate that ADJUST provides a fast, efficient, and automatic way to use ICA for artifact removal. Descriptors: Electroencephalography, Independent component analysis, EEG artifacts, EEG artefacts, Event-related potentials, Ongoing brain activity, Automatic classification, Thresholding


title: Off-Line Signature Recognition and Verification Using Neural Network

abstract: In this paper, we present an off-line signature recognition and verification system using global and grid features of the signatures. An artificial neural network based on back propagation algorithm is used for recognition and verification. Performance measures like the learning rate FAR and FRR are analyzed. The system was tested with 400 test signature samples, which include genuine and forgery signatures of twenty individuals. With this system, a false rejection ratio of less than 0.1 and a false acceptance ratio of less than 0.2 are achieved.


title: Honey Encryption: Encryption beyond the Brute-Force Barrier

abstract: Honey encryption (HE) addresses the challenge of encrypting messages using keys that are vulnerable to guessing attacks, such as the passwords selected by ordinary users. HE creates a ciphertext that, when decrypted with an incorrect key or password, yields a valid-looking but bogus message. So, attackers can't tell when decryption has been successful. Counterintuitively, HE enables the encryption of a message using a weak password such that even a strong attacker--one with unlimited computing power--can't decrypt the message with certainty. You can use HE to encrypt the list of passwords in a password manager, credentials used in SSH (Secure Shell), and so on. HE fuses the creative use of honey objects and decoys in system security with the rigor and principled application imparted by cryptography.


title: Autonomous driving in a multi-level parking structure

abstract: Recently, the problem of autonomous navigation of automobiles has gained substantial interest in the robotics community. Especially during the two recent DARPA grand challenges, autonomous cars have been shown to robustly navigate over extended periods of time through complex desert courses or through dynamic urban traffic environments. In these tasks, the robots typically relied on GPS traces to follow pre-defined trajectories so that only local planners were required. In this paper, we present an approach for autonomous navigation of cars in indoor structures such as parking garages. Our approach utilizes multi-level surface maps of the corresponding environments to calculate the path of the vehicle and to localize it based on laser data in the absence of sufficiently accurate GPS information. It furthermore utilizes a local path planner for controlling the vehicle. In a practical experiment carried out with an autonomous car in a real parking garage we demonstrate that our approach allows the car to autonomously park itself in a large-scale multi-level structure.


title: Secure Integration of Asymmetric and Symmetric Encryption Schemes

abstract: This paper shows a generic and simple conversion from weak asymmetric and symmetric encryption schemes into an asymmetric encryption scheme which is secure in a very strong sense- indistinguishability against adaptive chosen-ciphertext attacks in the random oracle model. In particular, this conversion can be applied efficiently to an asymmetric encryption scheme that provides a large enough coin space and, for every message, many enough variants of the encryption, like the ElGamal encryption scheme.


title: A DAG Model of Synchronous Stochastic Gradient Descent in Distributed Deep Learning

abstract: With huge amounts of training data, deep learning has made great breakthroughs in many artificial intelligence (AI) applications. However, such large-scale data sets present computational challenges, requiring training to be distributed on a cluster equipped with accelerators like GPUs. With the fast increase of GPU computing power, the data communications among GPUs have become a potential bottleneck on the overall training performance. In this paper, we first propose a general directed acyclic graph (DAG) model to describe the distributed synchronous stochastic gradient descent (S-SGD) algorithm, which has been widely used in distributed deep learning frameworks. To understand the practical impact of data communications on training performance, we conduct extensive empirical studies on four state-of-the-art distributed deep learning frameworks (i.e., Caffe-MPI, CNTK, MXNet and TensorFlow) over multi-GPU and multi-node environments with different data communication techniques, including PCIe, NVLink, 10GbE, and InfiniBand. Through both analytical and experimental studies, we identify the potential bottlenecks and overheads that could be further optimized. At last, we make the data set of our experimental traces publicly available, which could be used to support simulation-based studies.


title: Transformational leadership and employee psychological well-being: The mediating role of employee trust in leadership

abstract: The relationship between employees’ perceptions of their managers’ transformational leadership style and employees’ psychological well-being was examined in two studies. In Study 1, trust in the leader fully mediated the positive relationship between perceptions of managers’ transformational leadership and employee psychological well-being in a cross-sectional sample (n=436). Study 2 (n=269) (1) replicated the mediated effect found in Study 1; (2) extended the model by showing that active management-by-exception and laissez-faire behaviours negatively affected employee psychological well-being by reducing trust in the manager; and (3) excluded the possibility that these results were accounted for by individual differences or liking of the manager. Theoretical and practical applications, as well as directions for future research are discussed.


title: On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models

abstract: Recent work has shown that it is possible to train deep neural networks that are verifiably robust to norm-bounded adversarial perturbations. Most of these methods are based on minimizing an upper bound on the worst-case loss over all possible adversarial perturbations. While these techniques show promise, they remain hard to scale to larger networks. Through a comprehensive analysis, we show how a careful implementation of a simple bounding technique, interval bound propagation (IBP), can be exploited to train verifiably robust neural networks that beat the state-of-the-art in verified accuracy. While the upper bound computed by IBP can be quite weak for general networks, we demonstrate that an appropriate loss and choice of hyper-parameters allows the network to adapt such that the IBP bound is tight. This results in a fast and stable learning algorithm that outperforms more sophisticated methods and achieves state-of-the-art results on MNIST, CIFAR-10 and SVHN. It also allows us to obtain the first verifiably robust model on a downscaled version of ImageNet.


title: Towards Bayesian Deep Learning: A Survey

abstract: While perception tasks such as visual object recognition and text understanding play an important role in human intelligence, the subsequent tasks that involve inference, reasoning and planning require an even higher level of intelligence. The past few years have seen major advances in many perception tasks using deep learning models. For higher-level inference, however, probabilistic graphical models with their Bayesian nature are still more powerful and flexible. To achieve integrated intelligence that involves both perception and inference, it is naturally desirable to tightly integrate deep learning and Bayesian models within a principled probabilistic framework, which we call Bayesian deep learning. In this unified framework, the perception of text or images using deep learning can boost the performance of higher-level inference and in return, the feedback from the inference process is able to enhance the perception of text or images. This survey provides a general introduction to Bayesian deep learning and reviews its recent applications on recommender systems, topic models, and control. In this survey, we also discuss the relationship and differences between Bayesian deep learning and other related topics like Bayesian treatment of neural networks.


title: A Warm Heart and a Clear Head The Contingent Effects of Weather on Mood and Cognition

abstract: Prior studies on the association between weather and psychological changes have produced mixed results. In part, this inconsistency may be because weather's psychological effects are moderated by two important factors: the season and time spent outside. In two correlational studies and an experiment manipulating participants' time outdoors (total N = 605), pleasant weather (higher temperature or barometric pressure) was related to higher mood, better memory, and “broadened” cognitive style during the spring as time spent outside increased. The same relationships between mood and weather were not observed during other times of year, and indeed hotter weather was associated with lower mood in the summer. These results are consistent with findings on seasonal affective disorder, and suggest that pleasant weather improves mood and broadens cognition in the spring because people have been deprived of such weather during the winter.


Gated Recurrent Units for Airline Sentiment Analysis of Twitter Data


title: The effects of an action video game on visual and affective information processing

abstract: Abstract Playing action video games can have beneficial effects on visuospatial cognition and negative effects on social information processing. However, these two effects have not been demonstrated in the same individuals in a single study. The current study used event-related brain potentials (ERPs) to examine the effects of playing an action or non-action video game on the processing of emotion in facial expression. The data revealed that 10 h of playing an action or non-action video game had differential effects on the ERPs relative to a no-contact control group. Playing an action game resulted in two effects: one that reflected an increase in the amplitude of the ERPs following training over the right frontal and posterior regions that was similar for angry, happy, and neutral faces; and one that reflected a reduction in the allocation of attention to happy faces. In contrast, playing a non-action game resulted in changes in slow wave activity over the central–parietal and frontal regions that were greater for targets (i.e., angry and happy faces) than for non-targets (i.e., neutral faces). These data demonstrate that the contrasting effects of action video games on visuospatial and emotion processing occur in the same individuals following the same level of gaming experience. This observation leads to the suggestion that caution should be exercised when using action video games to modify visual processing, as this experience could also have unintended effects on emotion processing.


title: Experimental study in Ku-band of the propagation inside Empty Substrate Integrated Waveguides

abstract: In the last years a great number of substrate-integrated circuits has been developed. These circuits are a compromise between the advantages of waveguides: high-quality factor and low losses, and the advantages of planar circuits: low-cost and easy compact integration. Recently, a new methodology for manufacturing empty waveguides integrated in a planar substrate has been proposed. To manufacture this Empty Substrate-Integrated Waveguide, a rectangular hole is emptied in the substrate and plated. Finally, the structure is closed with two metallic covers that are soldered above and below the substrate. Hence, the Empty Substrate-Integrated Waveguide could be modeled with an ideal rectangular waveguide. However, the degree of error that this assumption implies has not been evaluated yet. This evaluation is imperative, since this new manufacturing process strongly differs from that of a traditional single-piece rectangular waveguide. Then, in this work, for the first time, the propagation constant of an Empty Substrate-Integrated Waveguide has been measured, and compared to the propagation constant of an equivalent ideal rectangular waveguide, in order to determine how accurately an Empty Substrate-Integrated Waveguide can be modeled with a rectangular waveguide, and if the design methods for rectangular waveguide devices can be directly transferred to this novel substrate-integrated waveguide.


title: Trust calibration within a human-robot team: Comparing automatically generated explanations

abstract: Trust is a critical factor for achieving the full potential of human-robot teams. Researchers have theorized that people will more accurately trust an autonomous system, such as a robot, if they have a more accurate understanding of its decision-making process. Studies have shown that hand-crafted explanations can help maintain trust when the system is less than 100% reliable. In this work, we leverage existing agent algorithms to provide a domain-independent mechanism for robots to automatically generate such explanations. To measure the explanation mechanism's impact on trust, we collected self-reported survey data and behavioral data in an agent-based online testbed that simulates a human-robot team task. The results demonstrate that the added explanation capability led to improvement in transparency, trust, and team performance. Furthermore, by observing the different outcomes due to variations in the robot's explanation content, we gain valuable insight that can help lead to refinement of explanation algorithms to further improve human-robot trust calibration.


Study and Analysis of Data mining Algorithms for Healthcare Decision Support System


title: Paraphrase Alignment for Synonym Evidence Discovery

abstract: We describe a new unsupervised approach for synonymy discovery by aligning paraphrases in monolingual domain corpora. For that purpose, we identify phrasal terms that convey most of the concepts within domains and adapt a methodology for the automatic extraction and alignment of paraphrases to identify paraphrase casts from which valid synonyms are discovered. Results performed on two different domain corpora show that general synonyms as well as synonymic expressions can be identified with a 67.27% precision.


title: Three-Dimensional, Kinematic, Human Behavioral Pattern-Based Features for Multimodal Emotion Recognition

abstract: This paper presents a multimodal emotion recognition method that uses a feature-level combination of three-dimensional (3D) geometric features (coordinates, distance and angle of joints), kinematic features such as velocity and displacement of joints, and features extracted from daily behavioral patterns such as frequency of head nod, hand wave, and body gestures that represent specific emotions. Head, face, hand, body, and speech data were captured from 15 participants using an infrared sensor (Microsoft Kinect). The 3D geometric and kinematic features were developed using raw feature data from the visual channel. Human emotional behavior-based features were developed using inter-annotator agreement and commonly observed expressions, movements and postures associated to specific emotions. The features from each modality and the behavioral pattern-based features (head shake, arm retraction, body forward movement depicting anger) were combined to train the multimodal classifier for the emotion recognition system. The classifier was trained using 10-fold cross validation and support vector machine (SVM) to predict six basic emotions. The results showed improvement in emotion recognition accuracy (The precision increased by 3.28% and the recall rate by 3.17%) when the 3D geometric, kinematic, and human behavioral pattern-based features were combined for multimodal emotion recognition using supervised classification.


DETERMINANTS OF VIOLENCE IN THE FAMILY : TOWARD A THEORETICAL INTEGRATION


title: Bug fix-time prediction model using naïve Bayes classifier

abstract: Predicting bug fix-time is an important issue in order to assess the software quality or to estimate the time and effort needed during the bug triaging. Previous work has proposed several bug fix-time prediction models that had taken into consideration various bug report attributes (e.g. severity, number of developers, dependencies) in order to know which bug to fix first and how long it will take to fix it. Our aim is to distinguish the very fast and the very slow bugs in order to prioritize which bugs to start with and which to exclude at the mean time respectively. We used the data of four systems taken from three large open source projects Mozilla, Eclipse, Gnome. We used naive Bayes classifier to compute our prediction model.


title: Constant-gm low-power rail-to-rail operational amplifier

abstract: A low-power rail-to-rail operational amplifier (Op-amp) was realized in SMIC 0.18 μm CMOS process. Under a 1.8V supply voltage, a constant transconductance (within 5.4% of maximum variation) is ensured for the whole common-mode input range. The class AB output stage also has a full voltage swing. The circuit provides a gain bandwidth of 17.3-MHz and a DC gain of 99.6 dB. The input transistors operate in weak inversion, which have big g m /I d value, so the power consumption is reduced to only 89.02μW (C load =5pf).


title: Scene recognition with CNNs: objects, scales and dataset bias

abstract: Since scenes are composed in part of objects, accurate recognition of scenes requires knowledge about both scenes and objects. In this paper we address two related problems: 1) scale induced dataset bias in multi-scale convolutional neural network (CNN) architectures, and 2) how to combine effectively scene-centric and object-centric knowledge (i.e. Places and ImageNet) in CNNs. An earlier attempt, Hybrid-CNN[23], showed that incorporating ImageNet did not help much. Here we propose an alternative method taking the scale into account, resulting in significant recognition gains. By analyzing the response of ImageNet-CNNs and Places-CNNs at different scales we find that both operate in different scale ranges, so using the same network for all the scales induces dataset bias resulting in limited performance. Thus, adapting the feature extractor to each particular scale (i.e. scale-specific CNNs) is crucial to improve recognition, since the objects in the scenes have their specific range of scales. Experimental results show that the recognition accuracy highly depends on the scale, and that simple yet carefully chosen multi-scale combinations of ImageNet-CNNs and Places-CNNs, can push the stateof-the-art recognition accuracy in SUN397 up to 66.26% (and even 70.17% with deeper architectures, comparable to human performance).


Linear Slip Control for Improved Antilock Braking System


title: Cavity-backed monopole antenna for UAV communication applications

abstract: In this paper, a directive, robust, and light weight antenna for Unmanned Arial Vehicles (UAV) communication applications is presented. The proposed antenna exhibits dualband operation from 4.53 GHz to 5.16 GHz with a bandwidth of 13.26% and from 5.3 GHz to 5.9 GHz with a bandwidth of 10.7%. Furthermore, it is characterized by good and stable radiation characteristics with overall realized gain of 7.07 dB at 4.7 GHz and 7.9 dB at 5.68 GHz. The proposed design shows flexible ability in terms of tuning the resonance frequencies by scaling some design parameters. The antenna is simulated and optimized using both CSTMWS and HFSS. The two different simulators shows very good agreement in terms of the operating bands and the radiation patterns.


title: Threat evaluation of enemy air fighters via neural network-based Markov chain modeling

abstract: Threat evaluation (TE) is a process used to assess the threat values (TVs) of air-breathing threats (ABTs), such as air fighters, that are approaching defended assets (DAs). This study proposes an automatic method for conducting TE using radar information when ABTs infiltrate into territory where DAs are located. The method consists of target asset (TA) prediction and TE. We divide a friendly territory into discrete cells based on the effective range of anti-aircraft missiles. The TA prediction identifies the TA of each ABT by predicting the ABT's movement through cells in the territory via a Markov chain, and the cell transition is modeled by neural networks. We calculate the TVs of the ABTs based on the TA prediction results. A simulation-based experiment revealed that the proposed method outperformed TE based on the closest point of approach or the radial speed vector methods.


title: Sparse Representation based Multi-sensor Image Fusion: A Review

abstract: As a result of several successful applications in computer vision and image processing, sparse representation (SR) has attracted significant attention in multi-sensor image fusion. Unlike the traditional multiscale transforms (MSTs) that presume the basis functions, SR learns an over-complete dictionary from a set of training images for image fusion, and it achieves more stable and meaningful representations of the source images. By doing so, the SR-based fusion methods generally outperform the traditional MST-based image fusion methods in both subjective and objective tests. In addition, they are less susceptible to mis-registration among the source images, thus facilitating the practical applications. This survey paper proposes a systematic review of the SR-based multi-sensor image fusion literature, highlighting the pros and cons of each category of approaches. Specifically, we start by performing a theoretical investigation of the entire system from three key algorithmic aspects, (1) sparse representation models; (2) dictionary learning methods; and (3) activity levels and fusion rules. Subsequently, we show how the existing works address these scientific problems and design the appropriate fusion rules for each application, such as multi-focus image fusion and multi-modality (e.g., infrared and visible) image fusion. At last, we carry out some experiments to evaluate the impact of these three algorithmic components on the fusion performance when dealing with different applications. This article is expected to serve as a tutorial and source of reference for researchers preparing to enter the field or who desire to employ the sparse representation theory in other fields.


title: Single-Fed Microstrip Patch Antenna With Switchable Polarization

abstract: A single-fed reconfigurable microstrip antenna that can provide various polarization diversities is presented in this paper. The antenna is excited by a microstrip feed line through aperture coupling. When two PIN diodes are used to respectively reconfigure the coupling slot and the open stub of the feed line, the polarization of the microstrip antenna can be switched between vertical and horizontal polarizations. For the reconfigurable antenna with the linear polarization diversity, it can be converted to a circularly-polarized antenna with switchable polarization sense by introducing a perturbation segment. Moreover, an antenna with the quadri-polarization diversity, including dual orthogonal linear polarizations and two circular polarizations, is also developed from the structure of the switchable circular polarization microstrip antenna, and only three diodes are required. Details of the antenna design are shown, and the measured results for the constructed prototype are also exhibited and discussed.


title: Return of Frustratingly Easy Domain Adaptation

abstract: Unlike human learning, machine learning often fails to handle changes between training (source) and test (target) input distributions. Such domain shifts, common in practical scenarios, severely damage the performance of conventional machine learning methods. Supervised domain adaptation methods have been proposed for the case when the target data have labels, including some that perform very well despite being "frustratingly easy" to implement. However, in practice, the target domain is often unlabeled, requiring unsupervised adaptation. We propose a simple, effective, and efficient method for unsupervised domain adaptation called CORrelation ALignment (CORAL). CORAL minimizes domain shift by aligning the second-order statistics of source and target distributions, without requiring any target labels. Even though it is extraordinarily simple--it can be implemented in four lines of Matlab code--CORAL performs remarkably well in extensive evaluations on standard benchmark datasets.


title: See, Hear, and Read: Deep Aligned Representations

abstract: We capitalize on large amounts of readily-available, synchronous data to learn a deep discriminative representations shared across three major natural modalities: vision, sound and language. By leveraging over a year of sound from video and millions of sentences paired with images, we jointly train a deep convolutional network for aligned representation learning. Our experiments suggest that this representation is useful for several tasks, such as cross-modal retrieval or transferring classifiers between modalities. Moreover, although our network is only trained with image+text and image+sound pairs, it can transfer between text and sound as well, a transfer the network never observed during training. Visualizations of our representation reveal many hidden units which automatically emerge to detect concepts, independent of the modality.


title: Learning author-topic models from text corpora

abstract: We propose an unsupervised learning technique for extracting information about authors and topics from large text collections. We model documents as if they were generated by a two-stage stochastic process. An author is represented by a probability distribution over topics, and each topic is represented as a probability distribution over words. The probability distribution over topics in a multi-author paper is a mixture of the distributions associated with the authors. The topic-word and author-topic distributions are learned from data in an unsupervised manner using a Markov chain Monte Carlo algorithm. We apply the methodology to three large text corpora: 150,000 abstracts from the CiteSeer digital library, 1740 papers from the Neural Information Processing Systems (NIPS) Conferences, and 121,000 emails from the Enron corporation. We discuss in detail the interpretation of the results discovered by the system including specific topic and author models, ranking of authors by topic and topics by author, parsing of abstracts by topics and authors, and detection of unusual papers by specific authors. Experiments based on perplexity scores for test documents and precision-recall for document retrieval are used to illustrate systematic differences between the proposed author-topic model and a number of alternatives. Extensions to the model, allowing for example, generalizations of the notion of an author, are also briefly discussed.


title: Softworms: the design and control of non-pneumatic, 3D-printed, deformable robots

abstract: Robots that can easily interact with humans and move through natural environments are becoming increasingly essential as assistive devices in the home, office and hospital. These machines need to be safe, effective, and easy to control. One strategy towards accomplishing these goals is to build the robots using soft and flexible materials to make them much more approachable and less likely to damage their environment. A major challenge is that comparatively little is known about how best to design, fabricate and control deformable machines. Here we describe the design, fabrication and control of a novel soft robotic platform (Softworms) as a modular device for research, education and public outreach. These robots are inspired by recent neuromechanical studies of crawling and climbing by larval moths and butterflies (Lepidoptera, caterpillars). Unlike most soft robots currently under development, the Softworms do not rely on pneumatic or fluidic actuators but are electrically powered and actuated using either shape-memory alloy microcoils or motor tendons, and they can be modified to accept other muscle-like actuators such as electroactive polymers. The technology is extremely versatile, and different designs can be quickly and cheaply fabricated by casting elastomeric polymers or by direct 3D printing. Softworms can crawl, inch or roll, and they are steerable and even climb steep inclines. Softworms can be made in any shape but here we describe modular and monolithic designs requiring little assembly. These modules can be combined to make multi-limbed devices. We also describe two approaches for controlling such highly deformable structures using either model-free state transition-reward matrices or distributed, mechanically coupled oscillators. In addition to their value as a research platform, these robots can be developed for use in environmental, medical and space applications where cheap, lightweight and shape-changing deformable robots will provide new performance capabilities.


title: Stereo Acoustic Echo Cancellation Employing Frequency-Domain Preprocessing and Adaptive Filter

abstract: This paper proposes a windowing frequency domain adaptive filter and an upsampling block transform preprocessing to solve the stereo acoustic echo cancellation problem. The proposed adaptive filter uses windowing functions with smooth cutoff property to reduce the spectral leakage during filter updating, so the utilization of the independent noise introduced by preprocessing in stereo acoustic echo cancellation can be increased. The proposed preprocessing is operated in short blocks with low processing delay, and it uses frequency-domain upsampling to meet the minimal block length requirement given by the band limit of simultaneous masking. Therefore, the simultaneous masking can be well utilized to improve the audio quality. The acoustic echo cancellation simulations and the audio quality evaluation show that, the proposed windowing frequency domain adaptive filter performs better than the conventional frequency domain adaptive filter in both mono and stereo cases, and the upsampling block transform preprocessing provides better audio quality and stereo acoustic echo cancellation performance than the half-wave preprocessing at the same noise level.


title: Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning

abstract: Knowledge bases (KB), both automatically and manually constructed, are often incomplete --- many valid facts can be inferred from the KB by synthesizing existing information. A popular approach to KB completion is to infer new relations by combinatory reasoning over the information found along other paths connecting a pair of entities. Given the enormous size of KBs and the exponential number of paths, previous path-based models have considered only the problem of predicting a missing relation given two entities or evaluating the truth of a proposed triple. Additionally, these methods have traditionally used random paths between fixed entity pairs or more recently learned to pick paths between them. We propose a new algorithm MINERVA, which addresses the much more difficult and practical task of answering questions where the relation is known, but only one entity. Since random walks are impractical in a setting with combinatorially many destinations from a start node, we present a neural reinforcement learning approach which learns how to navigate the graph conditioned on the input query to find predictive paths. Empirically, this approach obtains state-of-the-art results on several datasets, significantly outperforming prior methods.


title: Design and experimental attitude control of an unmanned Tilt-Rotor aerial vehicle

abstract: The design and experimental control of an unmanned Tilt-Rotor aerial vehicle operating in Bi-Rotor mode is presented in this article. Tilt-Rotor UAVs exhibit important flight characteristics due to their ability to fly both like a rotorcraft (in Bi-Rotor Mode) and like a fixed-wing aerial system. The ability of having a dual flight envelope makes ideal for complex missions where both the speed and flight endurance of a fixed-wing design and the hovering, aggressive maneuvering and vertical take-off/landing capabilities of a helicopter are needed. In the work presented, the main design principles, the Bi-Rotor mode nonlinear/linearized dynamics and the corresponding attitude tracking controller design are exhibited. Experimental studies in attitude stabilization indicate the overall system and controller's efficiency in hovering operation.


title: Disease Prediction by Machine Learning Over Big Data From Healthcare Communities

abstract: With big data growth in biomedical and healthcare communities, accurate analysis of medical data benefits early disease detection, patient care, and community services. However, the analysis accuracy is reduced when the quality of medical data is incomplete. Moreover, different regions exhibit unique characteristics of certain regional diseases, which may weaken the prediction of disease outbreaks. In this paper, we streamline machine learning algorithms for effective prediction of chronic disease outbreak in disease-frequent communities. We experiment the modified prediction models over real-life hospital data collected from central China in 2013–2015. To overcome the difficulty of incomplete data, we use a latent factor model to reconstruct the missing data. We experiment on a regional chronic disease of cerebral infarction. We propose a new convolutional neural network (CNN)-based multimodal disease risk prediction algorithm using structured and unstructured data from hospital. To the best of our knowledge, none of the existing work focused on both data types in the area of medical big data analytics. Compared with several typical prediction algorithms, the prediction accuracy of our proposed algorithm reaches 94.8% with a convergence speed, which is faster than that of the CNN-based unimodal disease risk prediction algorithm.


title: Security analysis on consumer and industrial IoT devices

abstract: The fast development of Internet of Things (IoT) and cyber-physical systems (CPS) has triggered a large demand of smart devices which are loaded with sensors collecting information from their surroundings, processing it and relaying it to remote locations for further analysis. The wide deployment of IoT devices and the pressure of time to market of device development have raised security and privacy concerns. In order to help better understand the security vulnerabilities of existing IoT devices and promote the development of low-cost IoT security methods, in this paper, we use both commercial and industrial IoT devices as examples from which the security of hardware, software, and networks are analyzed and backdoors are identified. A detailed security analysis procedure will be elaborated on a home automation system and a smart meter proving that security vulnerabilities are a common problem for most devices. Security solutions and mitigation methods will also be discussed to help IoT manufacturers secure their products.


title: Real-time implementation of self-tuning fuzzy PID controller for FOPDT system base on microcontroller STM32

abstract: In case of a digital communication between instruments and controller from A/D converter or of variation a model of plant. Performance of controller is degraded because of the fixed controller parameters. To solve this problem, a self-tuning fuzzy PID controller is proposed. The first order plus dead time (FOPDT) is a high dynamic system. Therefore, the controller must be intelligent. This paper presents the development and implementation of the self-tuning fuzzy PID controller for controlling the FOPDT system. The result was compared with the PID controller design using root locus technique. The water level plant used represented FOPDT system and the mathematical model of the system was approximated by using the System Identification Toolbox in MATLAB software. The control programming and the self-tuning fuzzy PID controller algorithm are used MATLAB/ Simulink. Experimental results show that, the FOPDT system has improved significantly using self-tuning fuzzy PID controller compared to the conventional PID controller.


An Authentication Protocol Using Ultrasonic Ranging


title: Door-detection using computer vision and fuzzy logic

abstract: One of the existing methods to perform the navigation in indoor environments consists in using a topological map in combination with appropriate behaviours. Door is a very common element in this kind of environments and its detection can be very useful either to know the environment structure, to aid an exploration task and to perform an efficient navigation. In this work we use the information provided by the camera of a robot to assign a belief degree on the existence of a door in it. The segments of the images captured are analyzed in order to detect those segments that could belong to the frame of a door. Several fuzzy concepts are defined to lead the search process and find different cases in which doors can be seen. Features of the segments like size, direction or the distance between them are measured and analyzed using fuzzy logic in order to establish a membership degree of the segments on the defined fuzzy concepts. The proposed method has proved to successfully detect typical doors of indoor environments under strong perspective deformations using grey-level images. Furthermore, according to our experimentation it is suitable for real-time applications in mobile robots.


title: Semantics-Driven Implicit Aspect Detection in Consumer Reviews

abstract: With consumer reviews becoming a mainstream part of e-commerce, a good method of detecting the product or service aspects that are discussed is desirable. This work focuses on detecting aspects that are not literally mentioned in the text, or implicit aspects. To this end, a co-occurrence matrix of synsets from WordNet and implicit aspects is constructed. The semantic relations that exist between synsets in WordNet are exploited to enrich the co-occurrence matrix with more contextual information. Comparing this method with a similar method which is not semantics-driven clearly shows the benefit of the proposed method. Especially corpora of limited size seem to benefit from the added semantic context.


title: Tall fescue performance and protein alteration during drought stress

abstract: Cool-season turfgrasses are commonly under drought stress during the summer in the United States and often require supplemental irrigation to maintain acceptable turfgrass quality. A study was conducted at Oklahoma State University to evaluate the performance and protein alteration of tall fescue during drought stress. Mature tall fescue plants of three diverse cultivars (‘Kentucky 31’, ‘2 nd Millennium’ and ‘Rebel Exeda’) were subjected to two treatments: well-water and drought condition. Volumetric soil water content (VSWC), leaf relative water content (RWC), turf quality (TQ) and electrolyte leakage (EL) were measured at 0, 7, and 11 d following treatments. In wellwatered plants, root characteristics were measured at the end of the study period. Protein analysis was conducted to detect for changes of dehydrin protein expression. The VSWC was maintained at the same level for each treatment for all three cultivars throughout the study. There were no differences in RWC among the three cultivars under well-watered conditions throughout the study. Tall fescue RWC of all three cultivars declined after initiation of the drought treatment. Under drought stress, TQ decreased for all three cultivars as drought stress increased but ‘Kentucky 31’ had higher TQ compared to ‘2 nd


title: Induction of Word and Phrase Alignments for Automatic Document Summarization

abstract: Current research in automatic single-document summarization is dominated by two effective, yet naive approaches: summarization by sentence extraction and headline generation via bag-of-words models. While successful in some tasks, neither of these models is able to adequately capture the large set of linguistic devices utilized by humans when they produce summaries. One possible explanation for the widespread use of these models is that good techniques have been developed to extract appropriate training data for them from existing document/abstract and document/headline corpora. We believe that future progress in automatic summarization will be driven both by the development of more sophisticated, linguistically informed models, as well as a more effective leveraging of document/abstract corpora. In order to open the doors to simultaneously achieving both of these goals, we have developed techniques for automatically producing word-to-word and phrase-to-phrase alignments between documents and their human-written abstracts. These alignments make explicit the correspondences that exist in such document/abstract pairs and create a potentially rich data source from which complex summarization algorithms may learn. This paper describes experiments we have carried out to analyze the ability of humans to perform such alignments, and based on these analyses, we describe experiments for creating them automatically. Our model for the alignment task is based on an extension of the standard hidden Markov model and learns to create alignments in a completely unsupervised fashion. We describe our model in detail and present experimental results that show that our model is able to learn to reliably identify word- and phrase-level alignments in a corpus of document, abstract pairs.


title: FlowSampler: Visual Analysis of Urban Flows in Geolocated Social Media Data

abstract: Analysis of flows such as human movement can help spatial planners better understand territorial patterns in urban environments. In this paper, we describe FlowSampler, an interactive visual interface designed for spatial planners to gather, extract and analyse human flows in geolocated social media data. Our system adopts a graph-based approach to infer movement pathways from spatial point type data and expresses the resulting information through multiple linked multiple visualisations to support data exploration. We describe two use cases to demonstrate the functionality of our system and characterise how spatial planners utilise it to address analytical task.


title: Sexual Economics: Sex as Female Resource for Social Exchange

abstract: A heterosexual community can be analyzed as a marketplace in which men seek to acquire sex from women by offering other resources in exchange. Societies will therefore define gender roles as if women are sellers and men buyers of sex. Societies will endow female sexuality, but not male sexuality, with value (as in virginity, fidelity, chastity). The sexual activities of different couples are loosely interrelated by a marketplace, instead of being fully separate or private, and each couple’s decisions may be influenced by market conditions. Economic principles suggest that the price of sex will depend on supply and demand, competition among sellers, variations in product, collusion among sellers, and other factors. Research findings show gender asymmetries (reflecting the complementary economic roles) in prostitution, courtship, infidelity and divorce, female competition, the sexual revolution and changing norms, unequal status between partners, cultural suppression of female sexuality, abusive relationships, rape, and sexual attitudes. Sexual activity is often regarded as among the most private of activities, negotiated by two individuals on the basis of their own individual desires and values. Idealistic treatments describe the two individuals as potentially equal and interchangeable. In this article, we place sexual negotiations in the context of a cultural


title: Scalable attack graph for risk assessment

abstract: The growth in the size of networks and the number of vulnerabilities is increasingly challenging to manage network security. Especially, difficult to manage are multi-step attacks which are attacks using one or more vulnerabilities as stepping stones. Attack graphs are widely used for analyzing multi-step attacks. However, since these graphs had large sizes, it was too expensive to work with. In this paper, we propose a mechanism to manage attack graphs using a divide and conquer approach. To enhance efficiency of risk analyzer working with attack graphs, we converted a large graph to multiple sub-graphs named risk units and provide the light-weighted graphs to the analyzers. As a result, when k order of time complexity algorithms work with an attack graph with n vertices, a division having c of overhead vertices reduces the workloads from nk to r(n + c)k. And the coefficient r becomes smaller geometrically from 2−kdepended on their division rounds. By this workload reduction, risk assessment processes which work with large size attack graphs become more scalable and resource practical.


title: Marine Litter Distribution and Density in European Seas, from the Shelves to Deep Basins

abstract: Anthropogenic litter is present in all marine habitats, from beaches to the most remote points in the oceans. On the seafloor, marine litter, particularly plastic, can accumulate in high densities with deleterious consequences for its inhabitants. Yet, because of the high cost involved with sampling the seafloor, no large-scale assessment of distribution patterns was available to date. Here, we present data on litter distribution and density collected during 588 video and trawl surveys across 32 sites in European waters. We found litter to be present in the deepest areas and at locations as remote from land as the Charlie-Gibbs Fracture Zone across the Mid-Atlantic Ridge. The highest litter density occurs in submarine canyons, whilst the lowest density can be found on continental shelves and on ocean ridges. Plastic was the most prevalent litter item found on the seafloor. Litter from fishing activities (derelict fishing lines and nets) was particularly common on seamounts, banks, mounds and ocean ridges. Our results highlight the extent of the problem and the need for action to prevent increasing accumulation of litter in marine environments.


title: Lara: A Key-Value Algebra underlying Arrays and Relations

abstract: Data processing systems roughly group into families such as relational, array, graph, and key-value. Many data processing tasks exceed the capabilities of any one family, require data stored across families, or run faster when partitioned onto multiple families. Discovering ways to execute computation among multiple available systems, let alone discovering an optimal execution plan, is challenging given semantic differences between disparate families of systems. In this paper we introduce a new algebra, Lara, which underlies and unifies algebras representing the families above in order to facilitate translation between systems. We describe the operations and objects of Lara---union, join, and ext on associative tables---and show her properties and equivalences to other algebras. Multi-system optimization has a bright future, in which we proffer Lara for the role of universal connector.


title: An Analysis of the Mongeau-Sankoff Algorithm for Music Information Retrieval

abstract: An essential problem in music information retrieval is to determinethesimilaritybetweentwogivenmelodies; there are several melodic similarity measures that have been proposed, among others, the Mongeau-Sankoff measure. In this work we implemented a modified version of the Mongeau-Sankoff measure. We conducted an experimental study to compare the implemented measure with other similarity measures; this evaluation was done in the context of the 2005 edition of the MIREX symbolic melodic similarity competition. The most relevant result of our work is an implementation of the Mongeau-Sankoff measure that presents greater effectiveness when compared to other current melodic similarity measures.


title: Learning-based seat belt detection in image using salient gradient

abstract: Seat belt detection is a critical challenge in traffic video surveillance. To tackle this challenge, an automatic seat belt detection approach is proposed in this paper. The proposed approach yields three significant steps. First, an edge detection method is used to extract edge features of the input image. Second, a salient gradient map is constructed by considering salient gradient features in the image. Finally, the obtained salient gradient map is incorporated into a machine learning approach to perform binary decision on whether the input image yields seat belt or not. Experimental results are provided to demonstrate the superior performance of the proposed approach.


title: Steganalysis of least significant bit matching using multi-order differences

abstract: This paper presents a learning-based steganalysis/detection method to attack spatial domain least significant bit LSB matching steganography in grayscale images, which is the antetype of many sophisticated steganographic methods. We model the message embedded by LSB matching as the independent noise to the image, and theoretically prove that LSB matching smoothes the histogram of multi-order differences. Because of the dependency among neighboring pixels, histogram of low order differences can be approximated by Laplace distribution. The smoothness caused by LSB matching is especially apparent at the peak of the histogram. Consequently, the low order differences of image pixels are calculated. The co-occurrence matrix is utilized to model the differences with the small absolute value in order to extract features. Finally, support vector machine classifiers are trained with the features so as to identify a test image either an original or a stego image. The proposed method is evaluated by LSB matching and its improved version "Hugo". In addition, the proposed method is compared with state-of-the-art steganalytic methods. The experimental results demonstrate the reliability of the new detector. Copyright © 2013 John Wiley & Sons, Ltd.


title: A model of force and impedance in human arm movements

abstract: This paper describes a simple computational model of joint torque and impedance in human arm movements that can be used to simulate three-dimensional movements of the (redundant) arm or leg and to design the control of robots and human-machine interfaces. This model, based on recent physiological findings, assumes that (1) the central nervous system learns the force and impedance to perform a task successfully in a given stable or unstable dynamic environment and (2) stiffness is linearly related to the magnitude of the joint torque and increased to compensate for environment instability. Comparison with existing data shows that this simple model is able to predict impedance geometry well.


title: Behavior pattern extraction by trajectory analysis

abstract: Trajectory clustering and behavior pattern extraction are the foundations of research into activity perception of objects in motion. In this paper, a new framework is proposed to extract behavior patterns through trajectory analysis. Firstly, we introduce directional trimmed mean distance (DTMD), a novel method used to measure similarity between trajectories. DTMD has the attributes of anti-noise, self-adaptation and the capability to determine the direction for each trajectory. Secondly, we use a hierarchical clustering algorithm to cluster trajectories. We design a length-weighted linkage rule to enhance the accuracy of trajectory clustering and reduce problems associated with incomplete trajectories. Thirdly, the motion model parameters are estimated for each trajectory's classification, and behavior patterns for trajectories are extracted. Finally, the difference between normal and abnormal behaviors can be distinguished.


title: Microreboot -- A Technique for Cheap Recovery

abstract: A significant fraction of software failures in large-scale Internet systems are cured by rebooting, even when the exact failure causes are unknown. However, rebooting can be expensive, causing nontrivial service disruption or downtime even when clusters and failover are employed. In this work we separate process recovery from data recovery to enable microrebooting -- a fine-grain technique for surgically recovering faulty application components, without disturbing the rest of the application. We evaluate microrebooting in an Internet auction system running on an application server. Microreboots recover most of the same failures as full reboots, but do so an order of magnitude faster and result in an order of magnitude savings in lost work. This cheap form of recovery engenders a new approach to high availability: microreboots can be employed at the slightest hint of failure, prior to node failover in multi-node clusters, even when mistakes in failure detection are likely; failure and recovery can be masked from end users through transparent call-level retries; and systems can be rejuvenated by parts, without ever being shut down.


title: Introspection: Accelerating Neural Network Training By Learning Weight Evolution

abstract: Neural Networks are function approximators that have achieved state-of-the-art accuracy in numerous machine learning tasks. In spite of their great success in terms of accuracy, their large training time makes it difficult to use them for various tasks. In this paper, we explore the idea of learning weight evolution pattern from a simple network for accelerating training of novel neural networks. ::: ::: We use a neural network to learn the training pattern from MNIST classification and utilize it to accelerate training of neural networks used for CIFAR-10 and ImageNet classification. Our method has a low memory footprint and is computationally efficient. This method can also be used with other optimizers to give faster convergence. The results indicate a general trend in the weight evolution during training of neural networks.


title: Analysis of the Split Ring Resonator (SRR) Antenna Applied to Passive UHF-RFID Tag Design

abstract: An electrically small planar passive UHF-RFID tag based on an edge-coupled split ring resonator (EC-SRR) antenna is presented in this work. In order to explore the potentiality and limitations of the SRR antenna and to aid the tag design, an analytical study of the SRR radiation properties at its fundamental resonance is presented for the first time. Radiation resistance, efficiency, polarization, bandwidth, and impedance matching with the radio-frequency identification (RFID) application-specific integrated circuit (ASIC) are treated in the study. Based on such analysis, the tag design process is presented, and a tag prototype of size $30\;\text{mm}\times30\;\text{mm}\ (\lambda_{0}/11\times\lambda_{0}/11)$ is designed to operate in the North-American UHF-RFID band (902–928 MHz) and manufactured. The measured read range is in good agreement with the simulation and reaches 9.3 m at 911 MHz. The tag also features a mitigation of the blind spots, providing a minimum measured read range of 4.2 m.


title: Cache-conscious structure layout

abstract: Hardware trends have produced an increasing disparity between processor speeds and memory access times. While a variety of techniques for tolerating or reducing memory latency have been proposed, these are rarely successful for pointer-manipulating programs.This paper explores a complementary approach that attacks the source (poor reference locality) of the problem rather than its manifestation (memory latency). It demonstrates that careful data organization and layout provides an essential mechanism to improve the cache locality of pointer-manipulating programs and consequently, their performance. It explores two placement techniques---clustering and coloring---that improve cache performance by increasing a pointer structure's spatial and temporal locality, and by reducing cache-conflicts.To reduce the cost of applying these techniques, this paper discusses two strategies---cache-conscious reorganization and cache-conscious allocation---and describes two semi-automatic tools---ccmorph and ccmalloc---that use these strategies to produce cache-conscious pointer structure layouts. ccmorph is a transparent tree reorganizer that utilizes topology information to cluster and color the structure. ccmalloc is a cache-conscious heap allocator that attempts to co-locate contemporaneously accessed data elements in the same physical cache block. Our evaluations, with microbenchmarks, several small benchmarks, and a couple of large real-world applications, demonstrate that the cache-conscious structure layouts produced by ccmorph and ccmalloc offer large performance benefits---in most cases, significantly outperforming state-of-the-art prefetching.


title: A MIXED-INTEGER PROGRAMMING APPROACH TO THE CLUSTERING PROBLEM WITH AN APPLICATION IN CUSTOMER SEGMENTATION

abstract: Abstract This paper presents a mathematical programming based clustering approach that is applied to a digital platform company’s customer segmentation problem involving demographic and transactional attributes related to the customers. The clustering problem is formulated as a mixed-integer programming problem with the objective of minimizing the maximum cluster diameter among all clusters. In order to overcome issues related to computational complexity of the problem, we developed a heuristic approach that improves computational times dramatically without compromising from optimality in most of the cases that we tested. The performance of this approach is tested on a real problem. The analysis of our results indicates that our approach is computationally efficient and creates meaningful segmentation of data.


title: On the placement of Web server replicas

abstract: There has been an increasing deployment of content distribution networks (CDNs) that offer hosting services to Web content providers. CDNs deploy a set of servers distributed throughout the Internet and replicate provider content across these servers for better performance and availability than centralized provider servers. Existing work on CDNs has primarily focused on techniques for efficiently redirecting user requests to appropriate CDN servers to reduce request latency and balance load. However, little attention has been given to the development of placement strategies for Web server replicas to further improve CDN performance. We explore the problem of Web server replica placement in detail. We develop several placement algorithms that use workload information, such as client latency and request rates, to make informed placement decisions. We then evaluate the placement algorithms using both synthetic and real network topologies, as well as Web server traces, and show that the placement of Web replicas is crucial to CDN performance. We also address a number of practical issues when using these algorithms, such as their sensitivity to imperfect knowledge about client workload and network topology, the stability of the input data, and methods for obtaining the input.


title: Cognitive models of discourse comprehension for narrative generation

abstract: Recent work in the area of narrative generation has sought to develop systems that automatically produce experiences for a user that are understood as stories. Much of this prior work, however, has focused on the structural aspects of narrative rather than the process of narrative comprehension undertaken by readers. Cognitive theories of narrative discourse comprehension define explicit models of a reader’s mental state during reading. These cognitive models are created to test hypotheses and explain empirical results about the comprehension processes of readers. They do not often contain sufficient precision for implementation on a computer, and thus, they are not yet suitable for computational generation purposes. This dissertation employs cognitive models of narrative discourse comprehension to define an explicit computational model of a reader’s comprehension process during reading, predicting aspects of narrative focus and inferencing with precision. This computational model is employed in a narrative discourse generation system to select content from an event log, creating discourses that satisfy comprehension criteria. The results of three experiments are presented and discussed, exhibiting empirical support for the computational reader model and the results of generation. This dissertation makes a number of contributions that advance the state-of-the-art in narrative discourse generation: a formal model of narrative focus, a formal model of online inferencing in narrative, a method of selecting narrative discourse content to satisfy comprehension criteria, and implementation and evaluation of these models.


title: Dual-band printed slot antenna for the 5G wireless communication network

abstract: In this paper, a dual-band printed slot antenna for 5G wireless communication application is presented The proposed antenna is of size of 10 × 10 mm2 and is printed on a 0.762 mm-thick Neltec NH9320 substrate with dielectric constant of e r = 3.2 and loss tangent tanδ=0.0024. The design consists of an elliptical slot with a semicircular and a triangular sector radiating patch. The simulated result implies that the designed antenna has dual-band at 30.5 and 41.5 GHz which has a bandwidth 1.5 GHz each. The simulated radiation patterns show that the antenna is directional.


title: A Miniature Vibrotactile Sensory Substitution Device for Multifingered Hand Prosthetics

abstract: A multisite, vibrotactile sensory substitution system, that could be used in conjunction with artificial touch sensors in multifingered prostheses, to deliver sensory feedback to upper limb amputees is presented. The system is based on a low cost/power/size smart architecture of off-the-shelf miniaturized vibration motors; the main novelty is that it is able to generate stimuli where both vibration amplitude and frequency as well as beat interference can be modulated. This paper is aimed at evaluating this system by investigating the capability of healthy volunteers to perceive-on their forearms-vibrations with different amplitudes and/or frequencies. In addition, the ability of subjects in spatially discriminating stimulations on three forearm sites and recognizing six different combinations of stimulations was also addressed. Results demonstrate that subjects were able to discriminate different force amplitudes exerted by the device (accuracies greater than 75%); when both amplitude and frequency were simultaneously varied, the pure discrimination of amplitude/frequency variation was affected by the variation of the other. Subjects were also able to discriminate with an accuracy of 93% three different sites and with an accuracy of 78% six different stimulation patterns.


title: Evolution of Fish Let-7 MicroRNAs and Their Expression Correlated to Growth Development in Blunt Snout Bream

abstract: The lethal-7 (let-7) miRNA, known as one of the first founding miRNAs, is present in multiple copies in a genome and has diverse functions in animals. In this study, comparative genomic analysis of let-7 miRNAs members in fish species indicated that let-7 miRNA is a sequence conserved family in fish, while different species have the variable gene copy numbers. Among the ten members including let-7a/b/c/d/e/f/g/h/i/j, the let-7a precursor sequence was more similar to ancestral sequences, whereas other let-7 miRNA members were separate from the late differentiation of let-7a. The mostly predicted target genes of let-7 miRNAs are involved in biological process, especially developmental process and growth through Gene Ontology (GO) enrichment analysis. In order to identify the possible different functions of these ten miRNAs in fish growth development, their expression levels were quantified in adult males and females of Megalobrama amblycephala, as well as in 3-, 6-, and 12-months-old individuals with relatively slow- and fast-growth rates. These ten miRNAs had similar tissue expression patterns between males and females, with higher expression levels in the brain and pituitary than that in other tissues (p < 0.05). Among these miRNAs, the relative expression level of let-7a was the highest among almost all the tested tissues, followed by let-7b, let-7d and let-7c/e/f/g/h/i/j. As to the groups with different growth rates, the expression levels of let-7 miRNAs in pituitary and brain from the slow-growth group were always significantly higher than that in the fast-growth group (p < 0.05). These results suggest that let-7 miRNA members could play an important role in the regulation of growth development in M. amblycephala through negatively regulating expression of their target genes.


title: Solid-state circuit breaker based on active thyristor topologies

abstract: Solid-state circuit breakers (SCBs), based on modern high-power semiconductors, offer considerable advantages when compared to mechanical circuit breakers with respect to speed and life. For instance, voltage quality of a power grid can be improved during a short circuit because the fault current is reduced. The interval over which voltage distortions occur, caused by a three-phase short circuit, can be limited to a few 100/spl mu/s. In contrast to this, present circuit breaker technology requires at least 100ms to clear a fault. Major drawbacks of all SCB solutions presented so far are material costs and on-state losses. Primarily because of these disadvantages, SCB systems have not been widely used yet. Consequently, new solutions should be developed which are more cost effective and offer reduced on-state losses. This paper presents a conceptual approach based on active thyristor circuits. It is proven that these topologies can lead to a wider integration of SCBs in existing grids due to the economical advantages of silicon-controlled rectifiers compared to turn-off semiconductors. After a brief introduction and a presentation of the fundamentals, different topologies are described and assessed in this paper.


title: Journal of Informetrics 1 (2007) 8–15 Finding scientific gems with Google’s PageRank algorithm

abstract: We apply the Google PageRank algorithm to assess the relative importance of all publications in the Physical Review family of journals from 1893 to 2003. While the Google number and the number of citations for each publication are positively correlated, outliers from this linear relation identify some exceptional papers or “gems” that are universally familiar to physicists.


Count-Based Exploration with Neural Density Models


title: Contextual enrichment of lexical units in utterance interpretation: Evidence from Chinese and English

abstract: Abstract The goal of this study is twofold. First, it will focus on the contextual enrichment of lexical units in utterance interpretation and discuss how the semantic multiplicity or underdeteminacy of lexical units is specified in context. It is argued here that relevance theory (Sperber & Wilson 1986/1995; 1998) serves as the most suitable theoretical framework to explain the interplay of lexical units and context. Narrowing and broadening, the two main processes of contextual enrichment, are demonstrated through numerous examples from English and Chinese. Second, the study investigates the role of language and/or culture-specificity in contextual enrichment. Kecskes (2003) has suggested that language and/or culture-specificity is involved as a constraint both in salience and contextual processes. I will argue that cultural knowledge constrains various forms of contextual enrichment, and use Chinese examples to support this hypothesis.


title: Sex beyond the genitalia: The human brain mosaic

abstract: Whereas a categorical difference in the genitals has always been acknowledged, the question of how far these categories extend into human biology is still not resolved. Documented sex/gender differences in the brain are often taken as support of a sexually dimorphic view of human brains (“female brain” or “male brain”). However, such a distinction would be possible only if sex/gender differences in brain features were highly dimorphic (i.e., little overlap between the forms of these features in males and females) and internally consistent (i.e., a brain has only “male” or only “female” features). Here, analysis of MRIs of more than 1,400 human brains from four datasets reveals extensive overlap between the distributions of females and males for all gray matter, white matter, and connections assessed. Moreover, analyses of internal consistency reveal that brains with features that are consistently at one end of the “maleness-femaleness” continuum are rare. Rather, most brains are comprised of unique “mosaics” of features, some more common in females compared with males, some more common in males compared with females, and some common in both females and males. Our findings are robust across sample, age, type of MRI, and method of analysis. These findings are corroborated by a similar analysis of personality traits, attitudes, interests, and behaviors of more than 5,500 individuals, which reveals that internal consistency is extremely rare. Our study demonstrates that, although there are sex/gender differences in the brain, human brains do not belong to one of two distinct categories: male brain/female brain.


title: Cortical Signals for Rewarded Actions and Strategic Exploration

abstract: Summary In stable environments, decision makers can exploit their previously learned strategies for optimal outcomes, while exploration might lead to better options in unstable environments. Here, to investigate the cortical contributions to exploratory behavior, we analyzed single-neuron activity recorded from four different cortical areas of monkeys performing a matching-pennies task and a visual search task, which encouraged and discouraged exploration, respectively. We found that neurons in multiple regions in the frontal and parietal cortex tended to encode signals related to previously rewarded actions more reliably than unrewarded actions. In addition, signals for rewarded choices in the supplementary eye field were attenuated during the visual search task and were correlated with the tendency to switch choices during the matching-pennies task. These results suggest that the supplementary eye field might play a unique role in encouraging animals to explore alternative decision-making strategies.


title: Survey on Prediction Algorithms in Smart Homes

abstract: The world has entered into a “smart” era. One area becoming smart is the place where we live—homes. Smart homes are expected to be equipped with numerous sensors to continually monitor, sense, and actuate the space. The data from these sensors can be used to provide various types of services by automating common tasks while causing minimal disruption to daily life. In order to provide these services, a system must have sufficient intelligence to predict future events based on its observations. This paper first examines the requirements for smart home predictions. It then comprehensively reviews prediction algorithms and variations that have been proposed and investigated in smart environments, such as smart homes. It is these prediction algorithms that provide the intelligence required by a smart home. Comparisons are also made upon these prediction algorithms on their features and models.


title: A clinical study on the influence of suturing material on oral wound healing

abstract: Background/Aim. Suture materials play an important role in healing, enabling reconstruction and reassembly of tissue separated by the surgical procedure or trauma, and at the same time facilitating and promoting healing and hemostasis. Suture materials are used daily in oral surgery, and are consid- ered to be substances most commonly implanted in human body. The aim of this clinical study was to examine the speed of wound healing and complications incidence, after the use of three different absorbable synthetic suture materials in oral surgery (catgut, Dexon and Vicryl rapide), and to ascertain which one is the most suitable for oral surgery. Methods. The study was conducted on 96 patients undergoing root re- section or surgical extraction of third molars. Each of the su- ture materials (catgut, Dexon and Vicryl rapide) was used for 8 root resections and 8 surgical third molar extractions in the maxilla, as well as in the mandible (a total of 32 surgical inter- ventions for each suture material). Results. The faster wound healing was obtained with Vicryl rapide compared to other two suturing material tested. There was no significant differ- ence regarding the presence of local reaction in all the three groups of patients on the 21st postoperative day. Conclu- sion. The results of our clinical study point out that Vycril- rapid contributes more than catgut or Dexon to faster healing of human wounds, with fewer incidences of wound dehis- cence and milder local reactions.


Systems Analysis Design Methods


title: DSS-PSP - A Decision Support Software for Evaluating Students’ Performance

abstract: Prediction, utilizing machine learning and data mining techniques is a significant tool, offering a first step and a helping hand for educators to early recognize those students who are likely to exhibit poor performance. In this work, we introduce a new decision support software for predicting the students’ performance at the final examinations. The proposed software is based on a novel 2-level classification technique which achieves better performance than any examined single learning algorithm. Furthermore, significant advantages of the presented tool are its simple and user-friendly interface and that it can be deployed in any platform under any operating system.


title: Face recognition using Histogram of co-occurrence Gabor phase patterns

abstract: The fusion of Local Binary Patterns (LBP) and Gabor magnitude features has been demonstrated to be one of the most successful descriptors for face recognition. Recently, several Gabor phase based features like Histogram of Gabor Phase Patterns (HGPP) and Local Gabor XOR Patterns (LGXP) also show competitive results and complementary attributes to Gabor magnitude based features. However, in these two typical Gabor phase based approaches only the binary relationship between neighboring Gabor phases is used, which may lose some discriminative information. To investigate the potential of Gabor phase features for robust face recognition, this paper proposes a novel local descriptor, named Histogram of Co-occurrence Gabor Phase Patterns (HCGPP). In HCGPP, Gabor Phase features are first extracted and quantized into different ranges. Second we estimate the histograms of cooccurrence Gabor phase patterns in each face region. Finally, a nearest-neighbor classifier with the dissimilarity measure χ2 is used for classification. Extensive experimental results on FERET and AR databases show the significant advantages of the proposed method over the state-of-the art ones in terms of recognition rate.


title: Real-time monitoring of GPS-tracking tractor based on ZigBee multi-hop mesh network

abstract: This research is a part of smart farm system in the framework of precision agriculture. The system was installed and tested over a year. The tractor tracking system employs the Global Positioning System (GPS) and ZigBee wireless network based on mesh topology to make the system communicate covering a large area. Router nodes are used for re-transmission of data in the network. A software was developed for acquiring data from tractor, storing data and displaying in real time on a web site.


title: Image Super-Resolution with Fast Approximate Convolutional Sparse Coding

abstract: We present a computationally efficient architecture for image super-resolution that achieves state-of-the-art results on images with large spatial extend. Apart from utilizing Convolutional Neural Networks, our approach leverages recent advances in fast approximate inference for sparse coding. We empirically show that upsampling methods work much better on latent representations than in the original spatial domain. Our experiments indicate that the proposed architecture can serve as a basis for additional future improvements in image super-resolution.


title: We are Humor Beings: Understanding and Predicting Visual Humor

abstract: Humor is an integral part of human lives. Despite being tremendously impactful, it is perhaps surprising that we do not have a detailed understanding of humor yet. As interactions between humans and AI systems increase, it is imperative that these systems are taught to understand subtleties of human expressions such as humor. In this work, we are interested in the question - what content in a scene causes it to be funny? As a first step towards understanding visual humor, we analyze the humor manifested in abstract scenes and design computational models for them. We collect two datasets of abstract scenes that facilitate the study of humor at both the scene-level and the object-level. We analyze the funny scenes and explore the different types of humor depicted in them via human studies. We model two tasks that we believe demonstrate an understanding of some aspects of visual humor. The tasks involve predicting the funniness of a scene and altering the funniness of a scene. We show that our models perform well quantitatively, and qualitatively through human studies. Our datasets are publicly available.


title: Integration of GMR Sensors with Different Technologies

abstract: Less than thirty years after the giant magnetoresistance (GMR) effect was described, GMR sensors are the preferred choice in many applications demanding the measurement of low magnetic fields in small volumes. This rapid deployment from theoretical basis to market and state-of-the-art applications can be explained by the combination of excellent inherent properties with the feasibility of fabrication, allowing the real integration with many other standard technologies. In this paper, we present a review focusing on how this capability of integration has allowed the improvement of the inherent capabilities and, therefore, the range of application of GMR sensors. After briefly describing the phenomenological basis, we deal on the benefits of low temperature deposition techniques regarding the integration of GMR sensors with flexible (plastic) substrates and pre-processed CMOS chips. In this way, the limit of detection can be improved by means of bettering the sensitivity or reducing the noise. We also report on novel fields of application of GMR sensors by the recapitulation of a number of cases of success of their integration with different heterogeneous complementary elements. We finally describe three fully functional systems, two of them in the bio-technology world, as the proof of how the integrability has been instrumental in the meteoric development of GMR sensors and their applications.


title: An Empirical Evaluation of In-Memory Multi-Version Concurrency Control

abstract: Multi-version concurrency control (MVCC) is currently the most popular transaction management scheme in modern database management systems (DBMSs). Although MVCC was discovered in the late 1970s, it is used in almost every major relational DBMS released in the last decade. Maintaining multiple versions of data potentially increases parallelism without sacrificing serializability when processing transactions. But scaling MVCC in a multi-core and in-memory setting is non-trivial: when there are a large number of threads running in parallel, the synchronization overhead can outweigh the benefits of multi-versioning. ::: ::: To understand how MVCC perform when processing transactions in modern hardware settings, we conduct an extensive study of the scheme's four key design decisions: concurrency control protocol, version storage, garbage collection, and index management. We implemented state-of-the-art variants of all of these in an in-memory DBMS and evaluated them using OLTP workloads. Our analysis identifies the fundamental bottlenecks of each design choice.


title: Text Understanding with the Attention Sum Reader Network

abstract: Several large cloze-style context-question-answer datasets have been introduced recently: the CNN and Daily Mail news data and the Children's Book Test. Thanks to the size of these datasets, the associated text comprehension task is well suited for deep-learning techniques that currently seem to outperform all alternative approaches. We present a new, simple model that uses attention to directly pick the answer from the context as opposed to computing the answer using a blended representation of words in the document as is usual in similar models. This makes the model particularly suitable for question-answering problems where the answer is a single word from the document. Ensemble of our models sets new state of the art on all evaluated datasets.


title: High bioavailability curcumin: an anti-inflammatory and neurosupportive bioactive nutrient for neurodegenerative diseases characterized by chronic neuroinflammation

abstract: Neuroinflammation is a pathophysiological process present in a number of neurodegenerative disorders, such as Alzheimer's disease, Huntington's disease, Parkinson's disease, stroke, traumatic brain injury including chronic traumatic encephalopathy and other age-related CNS disorders. Although there is still much debate about the initial trigger for some of these neurodegenerative disorders, during the progression of disease, broad range anti-inflammatory drugs including cytokine suppressive anti-inflammatory drugs (CSAIDs) might be promising therapeutic options to limit neuroinflammation and improve the clinical outcome. One of the most promising CSAIDs is curcumin, which modulates the activity of several transcription factors (e.g., STAT, NF-κB, AP-1) and their pro-inflammatory molecular signaling pathways. However, normal curcumin preparations demonstrate low bioavailability in vivo. To increase bioavailability, preparations of high bioavailability curcumin have been introduced to achieve therapeutically relevant concentrations in target tissues. This literature review aims to summarize the pharmacokinetic and toxicity profile of different curcumin formulations.


title: What drives consumers to spread electronic word of mouth in online consumer-opinion platforms

abstract: The advance of the Internet facilitates consumers to share and exchange consumption-related advice through online consumer reviews. This relatively new form of word-of-mouth communication, electronic word-of-mouth (eWOM) communication, has only recently received significant managerial and academic attention. Many academic studies have looked at the effectiveness of positive eWOM communication, examining the process by which eWOM influences consumer purchasing decisions. eWOM behavior is primarily explained from the individual rational perspective that emphasizes a cost and benefit analysis. However, we felt there was a need for an extensive study that examines consumers' motives for eWOM. In this paper, we focus on the factors that drive consumers to spread positive eWOM in online consumer-opinion platforms. Building on the social psychology literature, we identified a number of key motives of consumers' eWOM intention and developed an associated model. We empirically tested the research model with a sample of 203 members of a consumer review community, OpenRice.com. The model explains 69% of the variance, with reputation, sense of belonging and enjoyment of helping other consumers significantly related to consumers' eWOM intention. The results of this study provide important implications for research and practice. Highlights? In this study, we developed a model examining motives of consumers' eWOM intention. ? We tested the model with a sample of 203 members of a consumer review community. ? The model explains 69 percent of the variance. ? Reputation, sense of belonging, and enjoyment of helping are significant factors.


title: A Comparison of String Metrics for Matching Names and Records

abstract: We describe an open-source Java toolkit of methods for matching names and records. We summarize results obtained from using various string distance metrics on the task of matching entity names. These metrics include distance functions proposed by several different communities, such as edit-distance metrics, fast heuristic string comparators, token-based distance metrics, and hybrid methods. We then describe an extension to the toolkit which allows records to be compared. We discuss some issues involved in performing a similar comparision for record-matching techniques, and finally present results for some baseline record-matching algorithms that aggregate string comparisons between fields.


title: Fast and robust multi-atlas segmentation of brain magnetic resonance images

abstract: Abstract We introduce an optimised pipeline for multi-atlas brain MRI segmentation. Both accuracy and speed of segmentation are considered. We study different similarity measures used in non-rigid registration. We show that intensity differences for intensity normalised images can be used instead of standard normalised mutual information in registration without compromising the accuracy but leading to threefold decrease in the computation time. We study and validate also different methods for atlas selection. Finally, we propose two new approaches for combining multi-atlas segmentation and intensity modelling based on segmentation using expectation maximisation (EM) and optimisation via graph cuts. The segmentation pipeline is evaluated with two data cohorts: IBSR data ( N = 18, six subcortial structures: thalamus, caudate, putamen, pallidum, hippocampus, amygdala) and ADNI data ( N = 60, hippocampus). The average similarity index between automatically and manually generated volumes was 0.849 (IBSR, six subcortical structures) and 0.880 (ADNI, hippocampus). The correlation coefficient for hippocampal volumes was 0.95 with the ADNI data. The computation time using a standard multicore PC computer was about 3–4 min. Our results compare favourably with other recently published results.


title: Looking for good recommendations: a comparative evaluation of recommender systems

abstract: A number of researches in the Recommender Systems (RSs) domain suggest that the recommendations that are "best" according to objective metrics are sometimes not the ones that are most satisfactory or useful to the users. The paper investigates the quality of RSs from a user-centric perspective. We discuss an empirical study that involved 210 users and considered seven RSs on the same dataset that use different baseline and state-of-the-art recommendation algorithms. We measured the user's perceived quality of each of them, focusing on accuracy and novelty of recommended items, and on overall users' satisfaction. We ranked the considered recommenders with respect to these attributes, and compared these results against measures of statistical quality of the considered algorithms as they have been assessed by past studies in the field using information retrieval and machine learning algorithms.


title: Blind hero: enabling guitar hero for the visually impaired

abstract: Very few video games have been designed or adapted to allow people with vision impairment to play. Music/rhythm games however are particularly suitable for such people as they are perfectly capable of perceiving audio signals. Guitar Hero is a popular rhythm game yet it is not accessible to the visually impaired as it relies on visual stimuli. This paper explores replacing visual stimuli with haptic stimuli as a viable strategy to make games accessible. We developed a glove that transforms visual information into haptic feedback using small pager motors attached to the tip of each finger. This allows a blind player to play Guitar Hero. Several tests have been conducted and despite minor changes to the gameplay, visually impaired players are able to play the game successfully and enjoy the challenge the game provides. The results of the study also give valuable insights on how to make mainstream games blind-accessible.