title: SiC MOSFET gate drive design considerations

abstract: The purpose of this paper is to provide guidance on how to design gate driver circuits for Silicon Carbide (SiC) MOSFETs. There are new commercially available SiC MOSFETs available in discrete and module packages which are much faster and more efficient than their traditional IGBT counterparts. To take full advantage of these benefits we need to understand the requirements for a new breed of gate drivers that are tailored to meet the unique drive and protection characteristics of SiC MOSFETs. Traditional IGBT based fault protection schemes such as desaturation (desat) detection can be implemented with some modifications to protect SiC MOSFETs. However, due to the higher switching speed of the new SiC devices, it is worth another look at all the design and implementation aspects of a good SiC MOSFET gate driver.


Ultrawideband combined antenna with improved matching


title: Graph Classification via Deep Learning with Virtual Nodes

abstract: Learning representation for graph classification turns a variable-size graph into a fixed-size vector (or matrix). Such a representation works nicely with algebraic manipulations. Here we introduce a simple method to augment an attributed graph with a virtual node that is bidirectionally connected to all existing nodes. The virtual node represents the latent aspects of the graph, which are not immediately available from the attributes and local connectivity structures. The expanded graph is then put through any node representation method. The representation of the virtual node is then the representation of the entire graph. In this paper, we use the recently introduced Column Network for the expanded graph, resulting in a new end-to-end graph classification model dubbed Virtual Column Network (VCN). The model is validated on two tasks: (i) predicting bio-activity of chemical compounds, and (ii) finding software vulnerability from source code. Results demonstrate that VCN is competitive against well-established rivals.


Learning to Create Jazz Melodies Using a Product of Experts


title: NFC-enabled, tattoo-like stretchable biosensor manufactured by “cut-and-paste” method

abstract: The wearables industry is lacking in devices that have the ability to provide valuable biometrics data in a soft, wireless and disposable system. Such a system should be high performance, multifunctional, but battery-free and low cost. Near field communication (NFC) is a wireless communication protocol built in many smartphones nowadays that can read data from battery-free passive tags. As a result, NFC-enabled wearable biosensors have been reported, but they are either unstretchable or have to be manufactured by labor- and time-intensive photolithography and transfer-printing processes. Using a dry and freeform “cut-and-paste” method, we have built a wireless and low-cost stretchable biosensor that integrates temperature sensor, light source/sensor, NFC chip, and antenna. It is battery-free and can be laminated on any part of human skin like a temporary transfer tattoo. The sensor can fully follow the stretching and compression of skin without mechanical failure or delamination. Thus, it is imperceptible to wear and can perform high-fidelity sensing. Potential applications include, but are not limited to, skin thermography and photometry.


title: Hand Data Glove: A Wearable Real-Time Device for Human- Computer Interaction

abstract: In this paper, a real-time Human-Computer Interaction (HCI) based on the hand data glove and K-NN classifier for gesture recognition is proposed. HCI is moving more and more natural and intuitive way to be used. One of the important parts of our body is our hand which is most frequently used for the Interaction in Digital Environment and thus complexity and flexibility of motion of hands are the research topics. To recognize these hand gestures more accurately and successfully data glove is used. Here, gloves are used to capture current position of the hand and the angles between the joints and then these features are used to classify the gestures using K-NN classifier. The gestures classified are categorized as clicking, rotating, dragging, pointing and ideal position. Recognizing these gestures relevant actions are taken, such as air writing and 3D sketching by tracking the path helpful in virtual augmented reality (VAR). The results show that glove used for interaction is better than normal static keyboard and mouse as the interaction process is more accurate and natural in dynamic environment with no distance limitations. Also it enhances the user’s interaction and immersion feeling.


title: CP and MIP Methods for Ship Scheduling with Time-Varying Draft

abstract: Existing ship scheduling approaches either ignore constraints on ship draft (distance between the waterline and the keel), or model these in very simple ways, such as a constant draft limit that does not change with time. However, in most ports the draft restriction changes over time due to variation in environmental conditions. More accurate consideration of draft constraints would allow more cargo to be scheduled for transport on the same set of ships. ::: ::: We present constraint programming (CP) and mixed integer programming (MIP) models for the problem of scheduling ships at a port with time-varying draft constraints so as to optimise cargo throughput at the port. We also investigate the effect of several variations to the CP model, including a model containing sequence variables, and a model with ordered inputs. Our model allows us to solve realistic instances of the problem to optimality in a very short time, and produces better schedules than both scheduling with constant draft, and manual scheduling approaches used in practice at ports.


title: State Assignment for Finite State Machine Synthesis

abstract: This paper proposes simulated annealing based algorithm for the synthesis of a finite state machine to determine the optimal state assignment with less area and power dissipation. The algorithm has two annealing stages. In the first rough annealing stage it tries to search in global scope by the proposed rough search method. In the second focusing annealing stage it tries to search in local scope by using proposed focusing search methods intending changing solution slightly. In both stages, the experience of past solution is utilised by combing the best solution in the past and the current solution. The experiments performed on a large suite of benchmarks have established the fact that the proposed method outperforms the published GA-based algorithms. The results have shown the effectiveness of the proposed method in achieving optimal state assignment for finite state machine.


title: Inertia Compensation Control of a One-Degree-of-Freedom Exoskeleton for Lower-Limb Assistance: Initial Experiments

abstract: A new method of lower-limb exoskeleton control aimed at improving the agility of leg-swing motion is presented. In the absence of control, an exoskeleton's mechanism usually hinders agility by adding mechanical impedance to the legs. The uncompensated inertia of the exoskeleton will reduce the natural frequency of leg swing, probably leading to lower step frequency during walking as well as increased metabolic energy consumption. The proposed controller emulates inertia compensation by adding a feedback loop consisting of low-pass filtered angular acceleration multiplied by a negative gain. This gain simulates negative inertia in the low-frequency range. The resulting controller combines two assistive effects: increasing the natural frequency of the lower limbs and performing net work per swing cycle. The controller was tested on a statically mounted exoskeleton that assists knee flexion and extension. Subjects performed movement sequences, first unassisted and then using the exoskeleton, in the context of a computer-based task resembling a race. In the exoskeleton's baseline state, the frequency of leg swing and the mean angular velocity were consistently reduced. The addition of inertia compensation enabled subjects to recover their normal frequency and increase their selected angular velocity. The work performed by the exoskeleton was evidenced by catch trials in the protocol.


title: Rationale-Augmented Convolutional Neural Networks for Text Classification

abstract: We present a new Convolutional Neural Network (CNN) model for text classification that jointly exploits labels on documents and their component sentences. Specifically, we consider scenarios in which annotators explicitly mark sentences (or snippets) that support their overall document categorization, i.e., they provide rationales. Our model exploits such supervision via a hierarchical approach in which each document is represented by a linear combination of the vector representations of its component sentences. We propose a sentence-level convolutional model that estimates the probability that a given sentence is a rationale, and we then scale the contribution of each sentence to the aggregate document representation in proportion to these estimates. Experiments on five classification datasets that have document labels and associated rationales demonstrate that our approach consistently outperforms strong baselines. Moreover, our model naturally provides explanations for its predictions.


title: Radar Signal Processing Radar Signal Processing

abstract: ■ This article recounts the development of radar signal processing at Lincoln Laboratory. The Laboratory’s significant efforts in this field were initially driven by the need to provide detected and processed signals for air and ballistic missile defense systems. The first processing work was on the Semi-Automatic Ground Environment (SAGE) air-defense system, which led to algorithms and techniques for detection of aircraft in the presence of clutter. This work was quickly followed by processing efforts in ballistic missile defense, first in surface-acoustic-wave technology, in concurrence with the initiation of radar measurements at the Kwajalein Missile Range, and then by exploitation of the newly evolving technology of digital signal processing, which led to important contributions for ballistic missile defense and Federal Aviation Administration applications. More recently, the Laboratory has pursued the computationally challenging application of adaptive processing for the suppression of jamming and clutter signals. This article discusses several important programs in these areas.


title: Compiler-Directed Lightweight Checkpointing for Fine-Grained Guaranteed Soft Error Recovery

abstract: This paper presents Bolt, a compiler-directed soft error recovery scheme, that provides fine-grained and guaranteed recovery without excessive performance and hardware overhead. To get rid of expensive hardware support, the compiler protects the architectural inputs during their entire liveness period by safely checkpointing the last updated value in idempotent regions. To minimize the performance overhead, Bolt leverages a novel compiler analysis that eliminates those checkpoints whose value can be reconstructed by other checkpointed values without compromising the recovery guarantee. As a result, Bolt incurs only 4.7% performance overhead on average which is 57% reduction compared to the state-of-the-art scheme that requires expensive hardware support for the same recovery guarantee as Bolt.


title: Influence of social desirability on age differences in self-reports of mood and personality

abstract: Increased age has been found to be associated with differences in affect and personality that have been interpreted in terms of better emotional regulation and increased maturity. However, these findings have largely been based on self-report data, and the primary goal of the current research was to investigate the hypothesis that age-related differences in affect and in certain desirable personality traits might, at least partially, reflect age differences in social desirability. As expected, increased age was associated with lower levels of negative affect and Neuroticism and higher levels of positive affect, life satisfaction, Agreeableness, and Conscientiousness, and scores on the social desirability scale were positively related to age and to desirable self-report characteristics but negatively related to undesirable self-report characteristics. Importantly, controlling for the variance in the social desirability measure resulted in less positive age trends in both types of self-report measures.


title: Repetitive head impacts do not affect postural control following a competitive athletic season

abstract: Abstract Evidence suggests that Repetitive Head Impacts (RHI) directly influence the brain over the course of a single contact collision season yet do not significantly impact a player's performance on the standard clinical concussion assessment battery. The purpose of this study was to investigate changes in static postural control after a season of RHI in Division I football athletes using more sensitive measures of postural control as compared to a non-head contact sports. Fourteen Division I football players (CON) (age = 20.4 ± 1.12 years) and fourteen non-contact athletes (NON) (2 male, 11 female; age = 19.85 ± 1.21 years) completed a single trial of two minutes of eyes open quiet upright stance on a force platform (1000 Hz) prior to athletic participation (PRE) and at the end of the athletic season (POST). All CON athletes wore helmets outfitted with Head Impact Telemetry (HIT) sensors and total number of RHI and linear accelerations forces of each RHI were recorded. Center of pressure root mean square (RMS), peak excursion velocity (PEV), and sample entropy (SampEn) in the anteroposterior (AP) and mediolateral (ML) directions were calculated. CON group experienced 649.5 ± 496.8 mean number of impacts, 27.1 ± 3.0 mean linear accelerations, with ≈ 1% of total player impacts exceeded 98 g over the course of the season. There were no significant interactions for group x time RMS in the AP ( p = 0.434) and ML ( p = 0.114) directions, PEV in the AP ( p = 0.262) and ML ( p = 0.977) directions, and SampEn in the AP ( p = 0.499) and ML ( p = 0.984) directions. In addition, no significant interactions for group were observed for RMS in the AP ( p = 0.105) and ML ( p = 0.272) directions, PEV in the AP ( p = 0.081) and ML ( p = 0.143) directions, and SampEn in the AP ( p = 0.583) and ML ( p = 0.129) directions. These results suggest that over the course of a single competitive season, RHI do not negatively impact postural control even when measured with sensitive non-linear metrics.


title: Directly modeling voiced and unvoiced components in speech waveforms by neural networks

abstract: This paper proposes a novel acoustic model based on neural networks for statistical parametric speech synthesis. The neural network outputs parameters of a non-zero mean Gaussian process, which defines a probability density function of a speech waveform given linguistic features. The mean and covariance functions of the Gaussian process represent deterministic (voiced) and stochastic (unvoiced) components of a speech waveform, whereas the previous approach considered the unvoiced component only. Experimental results show that the proposed approach can generate speech waveforms approximating natural speech waveforms.


title: Learning multiscale sparse representations for image and video restoration

abstract: Abstract : A framework for learning multiscale sparse representations of color images and video with over complete dictionaries is presented in this paper. Following the single-scale grayscale K-SVD algorithm introduced in [1], which formulates the sparse dictionary learning and image representation as an optimization problem efficiently solved via orthogonal matching pursuit and SVD, this proposed multiscale learned representation is obtained based on an efficient quadtree decomposition of the learned dictionary and overlapping image patches. The proposed framework provides an alternative to pre-defined dictionaries such as wavelets, and leads to state-of-the-art results in a number of image and video enhancement and restoration applications. The presentation of the framework here proposed is accompanied by numerous examples demonstrating its practical power.


title: An algorithm of pornographic image detection

abstract: Real-time detection of pornographic images can effectively prevent pornographic images from spreading on the Internet. The methods of detection mainly focus on the identification of skin region. To overcome the disadvantages of traditional algorithms, in this paper, we propose a skin model based on the combination of YIQ, YUV, and HSV. In the step of the pre-dealing, we use white balance algorithm to achieve better skin area. Then, texture model based on Gray Level Co-Matrix (GLCM) and geometric structure of human beings are used to decrease the disruptions of the background region similar with the skin area. The features which extracted from the last images dealt by color and texture model are input into Support Vector Machines (SVM), through which the pornographic images and assorted control images are classified successfully. An experiment using 400 pornographic images and 400 assorted control images is carried out. Experimental results show that the algorithm of skin texture- based and feather-based detection has perfect performances, and the detection rate can reach 88.89%.


title: Multi-channel ECG classification using forests of randomized shapelet trees

abstract: Data series of multiple channels occur at high rates and in massive quantities in several application domains, such as healthcare. In this paper, we study the problem of multi-channel ECG classification. We map this problem to multivariate data series classification and propose five methods for solving it, using a split-and-combine approach. The proposed framework is evaluated using three base-classifiers on real-world data for detecting Myocardial Infarction. Extensive experiments are performed on real ECG data extracted from the Physiobank data repository. Our findings emphasize the importance of selecting an appropriate base-classifier for multivariate data series classification, while demonstrating the superiority of the Random Shapelet Forest (0.825 accuracy) against competitor methods (0.664 accuracy for 1-NN under cDTW).


title: Field-Testing of High-Level Decentralized Controllers for a Multi-Function Drone Swarm

abstract: This paper describes the results of initial field testing of a high-level decentralized controller for a swarm of multi-rotor drones. This controller allows the intuitive implementation of a wide variety of swarm behaviors. The parameters of this controller were mapped to two possible missions. The proposed controller structure is parameterized to enable the evolution of swarm behaviors in future work. In this paper, a set of hand coded controllers, previously evaluated in simulator were field tested. The simulator used considers each UAV as a simple point-mass model of each agent. As such, the ability to transfer the controller structure from simulated UAVs to real UAVs is investigated. The experiments use a fleet of enhanced networking-capable commercial-of-the-shelf drones that were especially developed to support and demonstrate the proposed high-level controller in the decentralized swarm environment. The paper describes hardware and controller implementation and then proves feasibility and great potential of the proposed approach in a series of flight tests conducted by a swarm of four identical drones.


title: Design and Implementation of a Web-Based Virtual Classroom System

abstract: Despite the increasing growth in popularity of virtual classrooms and online-learning, some factors which include limited bandwidth for audio and video, poor transmission quality, poor collaborative work and limited feedback during virtual classroom sessions still need to be addressed. This paper presents the design and implementation of a virtual classroom system that focuses on collaborative learning between students and tutors at remote locations. The different components of the system allows students to engage in group activities and collaborate with instructors in a commonly shared windows on which text, audio or video objects are added and shared online. The system was developed and hosted on the web using Moodle, Elluminate, WAMP Server, Java Script, MySQL, PHP and Dreamweaver. A practical demonstration was carried out using an Undergraduate Course of the Department of Computer Science in the School of Sciences, Federal University of Technology, Akure, Nigeria. The results obtained from the demonstration exhibit the suitability and adequacy of the virtual classroom system. The evaluation of the system based on selected indices also shows its level of performance and ratings.


title: Massive Access Management for QoS Guarantees in 3GPP Machine-to-Machine Communications

abstract: Realizing machine-to-machine (M2M) communications requires to construct and to manage scrupulous connections (logically and physically) from devices controllers to an enormous number of devices. By leveraging existing cellular infrastructures providing higher layers connections, the most challenging task is to efficiently manage massive accesses on the air interface. Consequently, in this letter, a massive access management (MAM) is proposed, which provides the most critical guarantees of quality-of-service (QoS), for devices. By deriving sufficient conditions of QoS guarantees, we show that the proposed MAM can effectively satisfy diverse QoS requirements, thus enabling the M2M communications over 3GPP scenarios.


title: A linear CMOS SOI SP14T antenna switch for cellular applications

abstract: A high-power low-loss antenna switch is designed and fabricated in a 180nm SOI CMOS process. The RF switch is intended for mobile front ends which cover Quad band GSM and multiple WCDMA and LTE bands.


title: Space Vector Modulation for Multiphase Inverters Based on a Space Partitioning Algorithm

abstract: Since the late 1990s, multiphase drives have become a serious alternative to three-phase drives in some particular applications such as electric ship propulsion, locomotive traction, electric vehicles, and high-power industrial applications. Nowadays, the research activity is focused on the development of control strategies that can exploit the degrees of freedom that exist in multiphase machines. As known, a multiphase motor cannot be analyzed using the space vector representation in a single d -q plane, but it is necessary to introduce multiple d-q planes. In this paper, the problem of the space vector modulation (SVM) of multiphase inverters is solved extending the theory of SVM used for traditional three-phase voltage source inverters and introducing the concept of reciprocal vector. The proposed approach, confirmed by experimental tests, allows the full exploitation of the dc input voltage and the simultaneous modulation of voltage space vectors in different d-q planes.


title: Pose-Based Two-Stream Relational Networks for Action Recognition in Videos

abstract: Recently, pose-based action recognition has gained more and more attention due to the better performance compared with traditional appearance-based methods. However, there still exist two problems to be further solved. First, existing pose-based methods generally recognize human actions with captured 3D human poses which are very difficult to obtain in real scenarios. Second, few pose-based methods model the action-related objects in recognizing human-object interaction actions in which objects play an important role. To solve the problems above, we propose a pose-based two-stream relational network (PSRN) for action recognition. In PSRN, one stream models the temporal dynamics of the targeted 2D human pose sequences which are directly extracted from raw videos, and the other stream models the action-related objects from a randomly sampled video frame. Most importantly, instead of fusing two-streams in the class score layer as before, we propose a pose-object relational network to model the relationship between human poses and action-related objects. We evaluate the proposed PSRN on two challenging benchmarks, i.e., Sub-JHMDB and PennAction. Experimental results show that our PSRN obtains the state-the-of-art performance on Sub-JHMDB (80.2%) and PennAction (98.1%). Our work opens a new door to action recognition by combining 2D human pose extracted from raw video and image appearance.


title: Abusing public third-party services for EDoS attacks

abstract: Cloud computing has been widely adopted nowadays as it provides economical, elastic and scalable services to customers. The cloud resources are offered in an on-demand manner and the consumers are charged based on their resource usage, known as "pay-as-you-go". Such a cloud utility scheme opens the door to Economic Denial of Sustainability (EDoS) attacks in which the cloud consumers would suffer from financial losses. In this paper, we uncover the severity of EDoS attacks through demonstrating that EDoS attacks can be easily conducted at very low costs. In specific, we show that attackers can launch amplification attacks against the cloud consumers by abusing the free public third-party services provided by the Internet giants such as Google, Microsoft, Facebook and LinkedIn. Through studying the characteristics of 10 main public third-party services, we reveal that all of them can be abused to launch EDoS attacks and the amplification factor can reach up to 135K. To combat against the uncovered attacks, we propose several mitigation strategies for the third-party service providers as well as the cloud consumers.


title: A Prediction and Planning Framework for Road Safety Analysis, Obstacle Avoidance and Driver Information

abstract: This paper presents a prediction and planning framework for analysing the safety and interaction of moving objects in complex road scenes. Rather than detecting specific, known, dangerous configurations, we simulate all the possible motion and interaction of objects. This simulation is used to detect dangerous situations, and to select the best path. The best path can be chosen according to a number of different criterion, such as: smoothest motion, largest avoiding distance, or quickest path. This framework can be applied, either as a driver warning system (open loop), or as an action recommendation system (human in the loop), or as an intelligent cruise control system (closed loop). This framework is evaluated using synthetic data, using simple and complex road scenes.


title: Revisiting 802.11 power consumption modeling in smartphones

abstract: WiFi activity is a major source of power consumption in today's smartphones. Consequently, accurately WiFi power consumption models are extremely useful for researchers and app developers. Among a large number of models proposed recently, a model introduced by Serrano et al. was the first to add a new component - a per-frame energy toll incurred as a frame traverses the protocol stack - to the power consumption of the wireless NIC. The authors called this new component cross-factor and validated the accuracy of the model on a large number of devices, mostly 802.11g wireless routers and APs. This paper examines the validity of the model introduced by Serrano et al. on today's smartphones. We try to answer two questions: (i) Can the model accurately estimate the power consumption due to WiFi activity in today's smartphones given the complexity of modern smartphone architectures? (ii) Does the model remain valid in the case of 802.11n/ac interfaces, and if yes, can it reflect the impact of the new MAC features (e.g., MIMO, channel bonding) on the WiFi power consumption? Additionally, we study the impact of the power saving mode (PSM) which was ignored in the original model and show that ignoring PSM results in significant overestimation of the total power consumption at low frame generation rates. Accordingly, we propose a new model that works across the full range of frame generation rates and verify its accuracy for a wide range of parameters and devices.


title: Interim follow-up of a randomized controlled trial comparing Chinese style mind body (Tai Chi) and stretching exercises on cognitive function in subjects at risk of progressive cognitive decline

abstract: Objectives ::: We reported the interim findings of a randomized controlled trial (RCT) to examine the effects of a mind body physical exercise (Tai Chi) on cognitive function in Chinese subjects at risk of cognitive decline. ::: ::: Subjects ::: 389 Chinese older persons with either a Clinical Dementia Rating (CDR 0.5) or amnestic-MCI participated in an exercise program. The exercise intervention lasted for 1 year; 171 subjects were trained with 24 forms simplified Tai Chi (Intervention, I) and 218 were trained with stretching and toning exercise (Control, C). The exercise comprised of advised exercise sessions of at least three times per week. ::: ::: Results ::: At 5th months (2 months after completion of training), both I and C subjects showed an improvement in global cognitive function, delayed recall and subjective cognitive complaints (paired t-tests, p < 0.05). Improvements in visual spans and CDR sum of boxes scores were observed in I group (paired t-tests, p < 0.001). Three (2.2%) and 21(10.8%) subjects from the I and C groups progressed to dementia (Pearson chi square = 8.71, OR = 5.34, 95% CI 1.56–18.29). Logistic regression analysis controlled for baseline group differences in education and cognitive function suggested I group was associated with stable CDR (OR = 0.14, 95%CI = 0.03–0.71, p = 0.02). ::: ::: Conclusions ::: Our interim findings showed that Chinese style mind body (Tai Chi) exercise may offer specific benefits to cognition, potential clinical interests should be further explored with longer observation period. Copyright © 2010 John Wiley & Sons, Ltd.


title: Convolutional capsule network for classification of breast cancer histology images

abstract: Automatization of the diagnosis of any kind of disease is of great importance and it's gaining speed as more and more deep learning solutions are applied to different problems. One of such computer aided systems could be a decision support too able to accurately differentiate between different types of breast cancer histological images - normal tissue or carcinoma. In this paper authors present a deep learning solution, based on convolutional capsule network for classification of four types of images of breast tissue biopsy when hematoxylin and eusin staining is applied. The cross-validation accuracy was achieved to be 0.87 with equaly high sensitivity.


title: Medley: A Library of Embeddables to Explore Rich Material Properties for 3D Printed Objects

abstract: In our everyday life, we interact with and benefit from objects with a wide range of material properties. In contrast, personal fabrication machines (e.g., desktop 3D printers) currently only support a much smaller set of materials. Our goal is to close the gap between current limitations and the future of multi-material printing by enabling people to explore the reuse of material from everyday objects into their custom designs. To achieve this, we develop a library of embeddables--everyday objects that can be cut, worked and embedded into 3D printable designs. We describe a design space that characterizes the geometric and material properties of embeddables. We then develop Medley---a design tool whereby users can import a 3D model, search for embeddables with desired material properties, and interactively edit and integrate their geometry to fit into the original design. Medley also supports the final fabrication and embedding process, including instructions for carving or cutting the objects, and generating optimal paths for inserting embeddables. To validate the expressiveness of our library, we showcase numerous examples augmented by embeddables that go beyond the objects' original printed materials.


title: Mixing Context Granularities for Improved Entity Linking on Question Answering Data across Entity Categories

abstract: The first stage of every knowledge base question answering approach is to link entities in the input question. We investigate entity linking in the context of a question answering task and present a jointly optimized neural architecture for entity mention detection and entity disambiguation that models the surrounding context on different levels of granularity. We use the Wikidata knowledge base and available question answering datasets to create benchmarks for entity linking on question answering data. Our approach outperforms the previous state-of-the-art system on this data, resulting in an average 8% improvement of the final score. We further demonstrate that our model delivers a strong performance across different entity categories.


title: Neuroscience of aesthetics.

abstract: Aesthetic evaluations are appraisals that influence choices in important domains of human activity, including mate selection, consumer behavior, art appreciation, and possibly even moral judgment. The nascent field of neuroaesthetics is advancing our understanding of the role of aesthetic evaluations by examining their biological bases. Here, we conduct a selective review of the literature on neuroaesthetics to demonstrate that aesthetic experiences likely emerge from the interaction between emotion–valuation, sensory–motor, and meaning–knowledge neural systems. This tripartite model can in turn be evoked to explain phenomena central to aesthetics, such as context effects on preferences. Indeed, context-dependent appraisals that focus on objects rather than on outcomes could be an important factor distinguishing aesthetic experiences from other kinds of evaluations.


title: Forecasting the presence and intensity of hostility on Instagram using linguistic and social features

abstract: Online antisocial behavior, such as cyberbullying, harassment, and trolling, is a widespread problem that threatens free discussion and has negative physical and mental health consequences for victims and communities. While prior work has proposed automated methods to identify hostile comments in online discussions, these methods work retrospectively on comments that have already been posted, making it difficult to intervene before an interaction escalates. In this paper we instead consider the problem of forecasting future hostilities in online discussions, which we decompose into two tasks: (1) given an initial sequence of non-hostile comments in a discussion, predict whether some future comment will contain hostility; and (2) given the first hostile comment in a discussion, predict whether this will lead to an escalation of hostility in subsequent comments. Thus, we aim to forecast both the presence and intensity of hostile comments based on linguistic and social features from earlier comments. To evaluate our approach, we introduce a corpus of over 30K annotated Instagram comments from over 1,100 posts. Our approach is able to predict the appearance of a hostile comment on an Instagram post ten or more hours in the future with an AUC of .82 (task 1), and can furthermore distinguish between high and low levels of future hostility with an AUC of .91 (task 2).


title: INTER-DOCUMENT SIMILARITIES, LANGUAGE MODELS, AND AD HOC INFORMATION RETRIEVAL

abstract: Search engines have become a crucial tool for finding information in repositories containing large amounts of textual data in unstructured form (e.g., the Web). However, the task of ad hoc information retrieval, that is, finding documents within a corpus that are relevant to an information need specified using a query, remains a hard challenge. ::: The language modeling approach to information retrieval provides an effective framework for approaching various problems and has yielded impressive empirical performance. However, most previous work on language models for information retrieval focuses on document-specific characteristics to estimate documents' language models, and therefore does not take into account the structure of the surrounding corpus, a potentially rich source of additional information. ::: We present a novel perspective for approaching the task of ad hoc retrieval: information provided by document-based language models can be enhanced by the incorporation of information drawn from clusters of similar documents that are created offline. We present several retrieval algorithms that are natural instantiations of this idea and that post performance that is substantially better than that of the standard language modeling approach. We also show that the best performing of these algorithms posts state-of-the-art performance for structural re-ranking, wherein an initially retrieved subset of the documents is re-ranked to obtain high precision specifically among the first few documents, using inter-document similarities within the list as an extra information source. ::: As further exploration of the re-ranking approach just described, and inspired by the PageRank and HITS (hubs and authorities) algorithms for Web search, we propose a graph-based framework that applies to document collections lacking hyperlink information. Specifically, centrality induced over graphs wherein links represent asymmetric language-model-based inter-document similarities constitutes the basis of effective re-ranking algorithms. Combining our two paradigms for similarity representation---i.e., clusters of documents and links representing language-model-based inter-item similarities---helps to improve the effectiveness of centrality-based approaches. For example, document "authoritativeness" as induced by the HITS algorithm over cluster-document graphs is a highly effective re-ranking criterion. Furthermore, "authoritative" clusters are shown to contain a high percentage of relevant documents.


Design of Low Voltage Low Power CMOS OP-AMP


title: A reactive controller framework for quadrupedal locomotion on challenging terrain

abstract: We propose a reactive controller framework for robust quadrupedal locomotion, designed to cope with terrain irregularities, trajectory tracking errors and poor state estimation. The framework comprises two main modules: One related to the generation of elliptic trajectories for the feet and the other for control of the stability of the whole robot. We propose a task space CPG-based trajectory generation that can be modulated according to terrain irregularities and the posture of the robot trunk. To improve the robot's stability, we implemented a null space based attitude control for the trunk and a push recovery algorithm based on the concept of capture points. Simulations and experimental results on the hydraulically actuated quadruped robot HyQ will be presented to demonstrate the effectiveness of our framework.


title: Polarity detection of sarcastic political tweets

abstract: Sarcasm is an activity of saying or writing in such a way that the textual meaning of what is said is opposite of what is meant. Generally, sarcastic sentences are used to express negative feelings. Thus in political polarity detection, it can be used to know the dissatisfaction of people against a particular party, candidate or government and hence considering this other aspects can determine the poll results. This paper presents two algorithms, one to identify a sarcastic tweet and other to perform polarity detection on political sarcastic tweets. It also includes the current approach we are using to identify the sarcastic political tweets and future aspects. It also emphasizes on using the social networking sites and mainly Twitter as a source for predicting political results.


title: Social and emotional support as a protective factor against current depression among individuals with adverse childhood experiences

abstract: Depression is one of the most prevalent mental health disorders among adults with adverse childhood experiences (ACE). Several studies have well documented the protective role of social support against depression in other populations. However, the impact of perceived social and emotional support (PSES) on current depression in a large community sample of adults with ACE has not been studied yet. This study tests the hypothesis that PSES is a protective factor against current depression among adults with ACE. ::: ::: Data from the 2010 Behavioral Risk Factor Surveillance System (BRFSS) involving adults with at least one ACE were used for the purpose of this study (n = 12.487). PSES had three categories: Always, Usually/Sometimes, and Rarely/Never. Current depression, defined based on the responses to the eight-item Patient Health Questionnaire (PHQ-8) depression scale, was treated as a binary outcome of interest: Present or absent. Logistic regression models were used for the analysis adjusting for all potential confounders. ::: ::: When compared to individuals who reported that they rarely/never received social and emotional support, individuals who reported that they always received were 87% less likely to report current depression (AOR: 0.13 [95% CI: 0.08–0.21]); and those who reported that they usually/sometimes received social and emotional support were 69% less likely to report current depression (AOR: 0.31 [95% CI: 0.20–0.46]). ::: ::: The results of this study highlight the importance of social and emotional support as a protective factor against depression in individuals with ACE. Health care providers should routinely screen for ACE to be able to facilitate the necessary social and emotional support.


title: Efficient attack plan recognition using automated planning

abstract: Network attacks are becoming ever more sophisticated and are able to hide more easily in the increasing amount of traffic being generated by everyday activity. Administrators are placed in the unfortunate position of distinguishing between the two. The attack graph has been in use for some time because it provides a concise knowledge representation, and has had successful security metrics developed from it. Previous methods of attack plan recognition have relied on statistical inference to capture network attacks, however they are computationally expensive and can fail to capture obvious cause and effect relationships. In this paper, we use automated planning to capture new properties of attack graphs and use it for plan recognition. Experimental results demonstrate the efficacy of our approach.


title: Smart Cities Data Streams Integration: experimenting with Internet of Things and social data flows

abstract: Smart cities are nowadays expanding and flourishing worldwide with Internet of Things (IoT), i.e. smart things like sensors and actuators, and mobile devices applications and installations which change the citizens' and authorities' everyday life. Smart cities produce daily huge streams of sensors data while citizens interact with Web and/or mobile devices utilizing social networks. In such a smart city context, new approaches to integrate big data streams from both sensors and social networks are needed to exploit big data production and circulation towards offering innovative solutions and applications. The SmartSantander infrastructure (EU FP7 project) has offered the ground for the SEN2SOC experiment which has integrated sensor and social data streams. This presentation outlines its research and industrial perspective and potential impact.


title: Estimating Origin-Destination Flows Using Mobile Phone Location Data

abstract: Using an algorithm to analyze opportunistically collected mobile phone location data, the authors estimate weekday and weekend travel patterns of a large metropolitan area with high accuracy.


title: Topic-Relevance Map: Visualization for Improving Search Result Comprehension

abstract: We introduce topic-relevance map, an interactive search result visualization that assists rapid information comprehension across a large ranked set of results. The topic-relevance map visualizes a topical overview of the search result space as keywords with respect to two essential information retrieval measures: relevance and topical similarity. Non-linear dimensionality reduction is used to embed high-dimensional keyword representations of search result data into angles on a radial layout. Relevance of keywords is estimated by a ranking method and visualized as radiuses on the radial layout. As a result, similar keywords are modeled by nearby points, dissimilar keywords are modeled by distant points, more relevant keywords are closer to the center of the radial display, and less relevant keywords are distant from the center of the radial display. We evaluated the effect of the topic-relevance map in a search result comprehension task where 24 participants were summarizing search results and produced a conceptualization of the result space. The results show that topic-relevance map significantly improves participants' comprehension capability compared to a conventional ranked list presentation.


title: Business ecosystem evolution and strategic considerations: A literature review

abstract: Business ecosystems describe the network of firms, which collectively produce a holistic, integrated technological system that creates value for customers. Understanding the mode of ecosystem change can provide valuable information for organizations that are positioned in these networked environments. In this paper, we endeavor to provide this understanding by synthesizing the work of scholars in the field business ecosystems through a review of the literature. Our findings underline the roles of ecosystem members, the endogenous and exogenous factors of ecosystem evolution, the dynamics of ecosystem change, and the strategic consideration of firms that participate in business ecosystems.


title: Roughness perception of textures on a haptic matrix display

abstract: Physical tactile diagrams have traditionally been available to make visual 2-D graphics accessible to individuals who are blind and visually impaired. However, the methods used have drawbacks for which dynamic haptic matrix display devices have been developed to overcome. As texture is a key component of many tactile diagrams, this study examined the psychophysical response of perceived roughness when square wave gratings were explored with a haptic matrix display by blind and sighted participants. Ridge width, groove width, and contact area were all found to be significant factors contributing to the sensation of roughness. When ridge width was held constant, perceived roughness was a decreasing and approximately linear function of groove width. With groove width held constant, however, perceived roughness was found to be an inverted U-shaped quadratic function of ridge width. The magnitude of the sensation of roughness was found to increase when contact area was increased. Finally, no difference was found between the performance of blind and sighted participants. The resulting information has implications for the effective creation of distinct textures using haptic matrix displays for virtual environments and tactile diagrams.


title: CFTL: a convertible flash translation layer adaptive to data access patterns

abstract: The flash translation layer (FTL) is a software/hardware in terface inside NAND flash memory. Since FTL has a critical impact on the performance of NAND flash-based devices, a variety of FTL schemes have been proposed to improve their performance. In this paper, we propose a novel hybrid FTL scheme named Convertible Flash Translation Layer (CFTL). Unlike other existing FTLs using static address mapping schemes, CFTL is adaptive to data access patterns so that it can dynamically switch its mapping scheme to either a read-optimized or a write-optimized mapping scheme. In addition to this convertible scheme, we propose an efficient caching strategy to further improve the CFTL performance with only a simple hint. Consequently, both the convertible feature and the caching strategy empower CFTL to achieve good read performance as well as good write performance.


Tracing the dynamic changes in perceived tonal organization in a spatial representation of musical keys.


title: Silent myocardial ischemia is associated with autonomic neuropathy and other cardiovascular risk factors in type 1 and type 2 diabetic subjects, especially in those with microalbuminuria

abstract: The prevalence of silent myocardial ischemia (SMI) seems to be above average in diabetic subjects. As routine screening is costly, identifying high-risk populations is mandatory. This study aimed to estimate the prevalence of SMI in diabetic subjects and in controls and to define the diabetic population at risk. We studied 353 asymptomatic caucasian subjects (217 with diabetes and 136 controls matched by age, sex, and cardiovascular risk factors) with normal resting ECG. The diabetic group included 39 type 1 and 178 type 2 diabetics (age 57 +/- 11 yr, 162 males/55 females). Subjects performed the Treadmill Test (TT) and, when abnormal, underwent single-photon emission computed tomography (SPECT) with exercise testing or dipyridamole injection. Coronary angiography was performed if the SPECT was suggestive of ischemia. TT was positive in 16 (8.5%) diabetics: 3 with type 1 and 13 with type 2. No controls had positive TT. SPECT was performed in 13 subjects and was positive in 10; angiography was performed in 7 and identified significant lesions in all cases. Patients with SMI were older and had a higher prevalence of autonomic neuropathy, hypertension, and dyslipidemia than those without. Microalbuminuria was also higher in the SMI group (613 +/- 211 vs 72 +/- 245 mg/d; p < 0.05). We conclude that diabetic patients aged over 60 with autonomic neuropathy and other cardiovascular risk factors should be screened for the presence of SMI especially if they have increased microalbuminuria.


title: Spectrogram: A Mixture-of-Markov-Chains Model for Anomaly Detection in Web Traffic

abstract: We present Spectrogram, a machine learning based statistical anomaly detection (AD) sensor for defense against web-layer code-injection attacks. These attacks include PHP file inclusion, SQL-injection and cross-sitescripting; memory-layer exploits such as buffer overflows are addressed as well. Statistical AD sensors offer the advantage of being driven by the data that is being protected and not by malcode samples captured in the wild. While models using higher order statistics can often improve accuracy, trade-offs with false-positive rates and model efficiency remain a limiting usability factor. This paper presents a newmodel and sensor framework that offers a favorable balance under this constraint and demonstrates improvement over some existing approaches. Spectrogram is a network situated sensor that dynamically assembles packets to reconstruct content flows and learns to recognize legitimate web-layer script input. We describe an efficient model for this task in the form of a mixture of Markovchains and derive the corresponding training algorithm. Our evaluations show significant detection results on an array of real world web layer attacks, comparing favorably against other AD approaches.


title: Quadrocopter Trajectory Generation and Control

abstract: Abstract An algorithm is presented that allows the calculation of flight trajectories for quadrocopters. Trajectory feasibility constraints regarding the vehicle dynamics and input constraints are derived. They are then used in the planning algorithm to guarantee the feasibility of generated trajectories. The translational degrees of freedom of the quadrotor are decoupled, and time-optimal trajectories are found for each degree of freedom separately. The trajectory generation is fast enough to be performed online. Control inputs are calculated from the generated trajectory, and used to achieve closed-loop control similar to model predictive control. The trajectory generation and tracking performance is demonstrated in the ETH Zurich Flying Machine Arena testbed. Experimental results show good performance, with unmodeled aerodynamic effects causing trajectory deviations when decelerating from high speeds. Development potential for the future is highlighted, focusing on improving the performance and correcting for aerodynamic effects.


title: Program Interference in MLC NAND Flash Memory: Characterization, Modeling, and Mitigation

abstract: As NAND flash memory continues to scale down to smaller process technology nodes, its reliability and endurance are degrading. One important source of reduced reliability is the phenomenon of program interference: when a flash cell is programmed to a value, the programming operation affects the threshold voltage of not only that cell, but also the other cells surrounding it. This interference potentially causes a surrounding cell to move to a logical state (i.e., a threshold voltage range) that is different from its original state, leading to an error when the cell is read. Understanding, characterizing, and modeling of program interference, i.e., how much the threshold voltage of a cell shifts when another cell is programmed, can enable the design of mechanisms that can effectively and efficiently predict and/or tolerate such errors. In this paper, we provide the first experimental characterization of and a realistic model for program interference in modern MLC NAND flash memory. To this end, we utilize the read-retry mechanism present in some state-of-the-art 2Y-nm (i.e., 20-24nm) flash chips to measure the changes in threshold voltage distributions of cells when a particular cell is programmed. Our results show that the amount of program interference received by a cell depends on 1) the location of the programmed cells, 2) the order in which cells are programmed, and 3) the data values of the cell that is being programmed as well as the cells surrounding it. Based on our experimental characterization, we develop a new model that predicts the amount of program interference as a function of threshold voltage values and changes in neighboring cells. We devise and evaluate one application of this model that adjusts the read reference voltage to the predicted threshold voltage distribution with the goal of minimizing erroneous reads. Our analysis shows that this new technique can reduce the raw flash bit error rate by 64% and thereby improve flash lifetime by 30%. We hope that the understanding and models developed in this paper lead to other error tolerance mechanisms for future flash memories.


title: Bridgeless High-Power-Factor Buck Converter

abstract: A bridgeless buck power factor correction rectifier that substantially improves efficiency at low line of the universal-line range is introduced. By eliminating input bridge diodes, the proposed rectifier's efficiency is further improved. Moreover, the rectifier doubles its output voltage, which extends useable energy of the bulk capacitor after a dropout of the line voltage. The operation and performance of the proposed circuit was verified on a 700-W, universal-line experimental prototype operating at 65 kHz. The measured efficiencies at 50% load from 115 and 230 V line are both close to 96.4%. The efficiency difference between low line and high line is less than 0.5% at full load. A second-stage half-bridge converter was also included to show that the combined power stages easily meet Climate Saver Computing Initiative Gold Standard.


title: Know What You Don't Know: Unanswerable Questions for SQuAD

abstract: Extractive reading comprehension systems can often locate the correct answer to a question in a context document, but they also tend to make unreliable guesses on questions for which the correct answer is not stated in the context. Existing datasets either focus exclusively on answerable questions, or use automatically generated unanswerable questions that are easy to identify. To address these weaknesses, we present SQuAD 2.0, the latest version of the Stanford Question Answering Dataset (SQuAD). SQuAD 2.0 combines existing SQuAD data with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD 2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering. SQuAD 2.0 is a challenging natural language understanding task for existing models: a strong neural system that gets 86% F1 on SQuAD 1.1 achieves only 66% F1 on SQuAD 2.0.


Plate Recognition using Python and OpenCV


title: "Everybody knows what you're doing": a critical design approach to personal informatics

abstract: We present an alternative approach to the design of personal informatics systems: instead of motivating people to examine their own behaviors, this approach promotes awareness of and reflection on the infrastructures behind personal informatics and the modes of engagement that they promote. Specifically, this paper presents an interface that displays personal web browsing data. The interface aims to reveal underlying infrastructure using several methods: drawing attention to the scope of mined data by displaying deliberately selected sensitive data, using purposeful malfunction as a way to encourage reverse engineering, and challenging normative expectations around data mining by displaying information in unconventional ways. Qualitative results from a two-week deployment show that these strategies can raise people's awareness about data mining, promote efficacy and control over personal data, and inspire reflection on the goals and assumptions embedded in infrastructures for personal data analytics.


Behavioural Finance


title: Adoption of Implicit eWOM in Facebook: An Affect-as-Information Theory Perspective.

abstract: Electronic word of mouth (eWOM) has gained inescapable attention among both practitioners and academia. Its importance lies in its simplicity yet profound impact on customers’ attitude toward specific brands or goods, and consequently affecting customer loyalty and intention to purchase. Although Social Network Services have emerged as platforms to spread eWOM, less attention has been paid towards implicit eWOM which is displayed by liking pages of products or through check-in feature of Facebook. Using the theoretical lens of affect-as-information theory, this study shows affective attitude of users toward implicit eWOM influences acceptance of eWOM. We also study how tie strength, image building, and involvement with Facebook collectively determine affective attitude. The implications for theory and practice are discussed as well as limitations and future research directions.


title: Evaluation of extracts produced by some tropical fungi as potential cholinesterase inhibitors

abstract: Extracts of culture of fungal taxa belonging to Chaetomiun sp., Guignardia mangiferae, Pestalotiopsis guepinii, Phomopsis sp., Physalospora sp., and sterile mycelium isolated as endophytes from leaves of members of the plant fafmilies Anacardiaceae, Leguminosae and Palmae, and seven different species of Penicillium isolated from insects were tested for their in vitro acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) inhibitory activity using Wllman´s colorimetric method adapted for thin layer chromotherapy. The results showed hig rate of cholinesterase inhibitory activity, with the best result being obtained with Penicillium extracts, indicating that they might represent a valuable source for pharmaceutical applications and are worthy of further study.


Substance abuse in children and adolescents.


title: Involvement of the human frontal eye field and multiple parietal areas in covert visual selection during conjunction search

abstract: Searching for a target object in a cluttered visual scene requires active visual attention if the target differs from distractors not by elementary visual features but rather by a feature conjunction. We used functional magnetic resonance imaging (fMRI) in human subjects to investigate the functional neuroanatomy of attentional mechanisms employed during conjunction search. In the experimental condition, subjects searched for a target defined by a conjunction of colour and orientation. In the baseline condition, subjects searched for a uniquely coloured target, regardless of its orientation. Eye movement recordings outside the scanner verified subjects' ability to maintain fixation during search. Reaction times indicated that the experimental condition was attentionally more demanding than the baseline condition. Differential activations between conditions were therefore ascribed to top-down modulation of neural activity. The frontal eye field, the ventral precentral sulcus and the following posterior parietal regions were consistently activated: (i) the postcentral sulcus; (ii) the posterior; and (iii) the anterior part of the intraparietal sulcus; and (iv) the junction of the intraparietal with the transverse occipital sulcus. Parietal regions were spatially distinct and displayed differential amplitudes of signal increase with a maximal amplitude in the posterior intraparietal sulcus. Less consistent activation was found in the lateral fusiform gyrus. These results suggest an involvement of the human frontal eye field in covert visual selection of potential targets during search. These results also provide evidence for a subdivision of posterior parietal cortex in multiple areas participating in covert visual selection, with a major contribution of the posterior intraparietal sulcus.


title: Differences on social acceptance of humanoid robots between Japan and the UK

abstract: To validate a questionnaire for measuring people’s acceptance of humanoid robots in cross-cultural research (the Frankenstein Syndrome Questionnaire: FSQ), an online survey was conducted in both the UK and Japan including items on perceptions of the relation to the family and commitment to religions, and negative attitudes toward robots (the NARS). The results suggested that 1) the correlations between the FSQ subscale scores and NARS were sufficient, 2) the UK people felt more negative toward humanoid robots than did the Japanese people, 3) young UK people had more expectation for humanoid robots, 4) relationships between social acceptance of humanoid robots and negative attitudes toward robots in general were different between the nations and generations, and 5) there were no correlations between the FSQ subscale scores, and perception of the relation to the family and commitment to religions.


title: Utilizing marginal net utility for recommendation in e-commerce

abstract: Traditional recommendation algorithms often select products with the highest predicted ratings to recommend. However, earlier research in economics and marketing indicates that a consumer usually makes purchase decision(s) based on the product's marginal net utility (i.e., the marginal utility minus the product price). Utility is defined as the satisfaction or pleasure user u gets when purchasing the corresponding product. A rational consumer chooses the product to purchase in order to maximize the total net utility. In contrast to the predicted rating, the marginal utility of a product depends on the user's purchase history and changes over time. According to the Law of Diminishing Marginal Utility, many products have the decreasing marginal utility with the increase of purchase count, such as cell phones, computers, and so on. Users are not likely to purchase the same or similar product again in a short time if they already purchased it before. On the other hand, some products, such as pet food, baby diapers, would be purchased again and again. To better match users' purchase decisions in the real world, this paper explores how to recommend products with the highest marginal net utility in e-commerce sites. Inspired by the Cobb-Douglas utility function in consumer behavior theory, we propose a novel utility-based recommendation framework. The framework can be utilized to revamp a family of existing recommendation algorithms. To demonstrate the idea, we use Singular Value Decomposition (SVD) as an example and revamp it with the framework. We evaluate the proposed algorithm on an e-commerce (shop.com) data set. The new algorithm significantly improves the base algorithm, largely due to its ability to recommend both products that are new to the user and products that the user is likely to re-purchase.


title: Using the balanced scorecard in assessing the performance of e-SCM diffusion: A multi-stage perspective

abstract: Electronic supply chain management (e-SCM), a specific form of inter-organizational systems, has generally regarded as one of the major strategies to create competitive advantage. The diffusion of e-SCM among trading partners is critical for its final successful use and accordingly, performance impact. However, the diffusion process is complex and dynamic in nature and involves an evolutionary property across time. Innovation diffusion theory (IDT) is defined for effectively exploring diffusion process with multiple stages. Moreover, prior studies have found inconclusive results of IT-enabled performance due to inadequate measures. The balanced scorecard (BSC) with the extension to SCM, incorporating four performance perspectives, is appropriate for overcoming this problem. Grounding on the IDT and BSC, this study proposes a novel framework for exploring the relationships between a stage-based structure and the BSC. Data are collected from a questionnaire survey. The results indicate that there are significant differences between external diffusion and the two earlier stages, adoption and internal diffusion, on the four BSC perspectives. Furthermore, all of the four perspectives are well realized at external diffusion stage. Implications for managers and scholars are discussed.


title: An overview of textual semantic similarity measures based on web intelligence

abstract: Computing the semantic similarity between terms (or short text expressions) that have the same meaning but which are not lexicographically similar is a key challenge in many computer related fields. The problem is that traditional approaches to semantic similarity measurement are not suitable for all situations, for example, many of them often fail to deal with terms not covered by synonym dictionaries or are not able to cope with acronyms, abbreviations, buzzwords, brand names, proper nouns, and so on. In this paper, we present and evaluate a collection of emerging techniques developed to avoid this problem. These techniques use some kinds of web intelligence to determine the degree of similarity between text expressions. These techniques implement a variety of paradigms including the study of co-occurrence, text snippet comparison, frequent pattern finding, or search log analysis. The goal is to substitute the traditional techniques where necessary.


The milling landscape of Northwest Portugal


title: The challenge for multichannel services: Cross-channel free-riding behavior

abstract: In multichannel environments, consumers can move easily among different channels. They engage in cross-channel free-riding when they use one retailer's channel to obtain information or evaluate products and then switch to another retailer's channel to complete the purchase. Cross-channel free-riding erodes profits and is one of the most important issues that firms face in the multichannel era. The current study focuses on the most popular type of cross-channel free-riding: searching for product information in an online store and then purchasing in another brick-and-mortar store. It explores antecedents that may contribute to consumer switching behaviors through a questionnaire focused on cross-channel free-riding behavior. The empirical results reveal that when consumers perceive more multichannel self-efficacy, they engage in more cross-channel free-riding behavior. Perceived service quality of competitors' offline store and the reduced risk in the brick-and-mortar channel influence the attractiveness of this behavior and increase cross-channel free-riding intentions. By increasing within-firm lock-in levels, firms can reduce consumers' cross-channel free-riding intentions.


Elementary Number Theory: A Computer Approach.


title: A Miniaturized Antipodal Vivaldi Antenna With Improved Radiation Characteristics

abstract: In this letter, a modified antipodal Vivaldi antenna is presented. A novel tapered slot edge (TSE) structure is employed in this design. The proposed TSE has the capacity to extend the low-end bandwidth limitation and improve the radiation characteristics in the lower frequencies. A prototype of the modified antenna is fabricated and experimentally studied as well. The measured results show reasonable agreement with the simulated ones that validate the design procedure and confirm the benefits of the modification.


title: Automatic segmentation of mandibular canal in cone beam CT images using conditional statistical shape model and fast marching

abstract: Purpose ::: Accurate segmentation of the mandibular canal in cone beam CT data is a prerequisite for implant surgical planning. In this article, a new segmentation method based on the combination of anatomical and statistical information is presented to segment mandibular canal in CBCT scans.


title: Large-Scale Image Mining with Flickr Groups

abstract: The availability of large annotated visual resources, such as ImageNet, recently led to important advances in image mining tasks. However, the manual annotation of such resources is cumbersome. Exploiting Web datasets as a substitute or complement is an interesting but challenging alternative. The main problems to solve are the choice of the initial dataset and the noisy character of Web text-image associations. This article presents an approach which first leverages Flickr groups to automatically build a comprehensive visual resource and then exploits it for image retrieval. Flickr groups are an interesting candidate dataset because they cover a wide range of user interests. To reduce initial noise, we introduce innovative and scalable image reranking methods. Then, we learn individual visual models for 38,500 groups using a low-level image representation. We exploit off-the-shelf linear models to ensure scalability of the learning and prediction steps. Finally, Semfeat image descriptions are obtained by concatenating prediction scores of individual models and by retaining only the most salient responses. To provide a comparison with a manually created resource, a similar pipeline is applied to ImageNet. Experimental validation is conducted on the ImageCLEF Wikipedia Retrieval 2010 benchmark, showing competitive results that demonstrate the validity of our approach.


Social media marketing, functional branding strategy and intentional branding


title: Background Subtraction via generalized fused lasso foreground modeling

abstract: Background Subtraction (BS) is one of the key steps in video analysis. Many background models have been proposed and achieved promising performance on public data sets. However, due to challenges such as illumination change, dynamic background etc. the resulted foreground segmentation often consists of holes as well as background noise. In this regard, we consider generalized fused lasso regularization to quest for intact structured foregrounds. Together with certain assumptions about the background, such as the low-rank assumption or the sparse-composition assumption (depending on whether pure background frames are provided), we formulate BS as a matrix decomposition problem using regularization terms for both the foreground and background matrices. Moreover, under the proposed formulation, the two generally distinctive background assumptions can be solved in a unified manner. The optimization was carried out via applying the augmented Lagrange multiplier (ALM) method in such a way that a fast parametric-flow algorithm is used for updating the foreground matrix. Experimental results on several popular BS data sets demonstrate the advantage of the proposed model compared to state-of-the-arts.


title: The Diagnosis of and Treatment Recommendations for Anxiety Disorders

abstract: Methods: This guideline is based on controlled trials of psychotherapy and pharmacotherapy, retrieved by a systematic search for original articles that were published up to 1 July 2013. Experts from 20 specialty societies and other organizations evaluated the evidence for each treatment option from all available randomized clinical trials and from a synthesis of the recommendations of already existing international and German guidelines. Results: 403 randomized controlled trials were evaluated. It was concluded that anxiety disorders should be treated with psychotherapy, psychopharmacological drugs, or both. Response rates to initial treatment vary from 45% to 65%. Cognitive behavioral therapy is supported by higher-level evidence than any other psychotherapeutic technique. Psychodynamic therapy is recommended as a second-line treatment. Among anxiolytic drugs, the agents of first choice are selective serotonin reuptake inhibitors and serotoninnorepinephrine reuptake inhibitors. The patient’s preference should be considered in the choice of treatment. Drug treatment should be continued for 6 to 12 months after remission. If psychotherapy or drug treatment is not adequately effective, then the treatment should be switched to the other form, or to a combination of both. Conclusion: The large amount of data now available from randomized controlled trials permits the formulation of robust evidence-based recommendations for the treatment of anxiety disorders. Future work should more closely address the necessary duration of psychotherapy and the efficacy of combined psychotherapy and drug treatment. ►Cite this as:


title: Learning to Rank Using User Clicks and Visual Features for Image Retrieval

abstract: The inconsistency between textual features and visual contents can cause poor image search results. To solve this problem, click features, which are more reliable than textual information in justifying the relevance between a query and clicked images, are adopted in image ranking model. However, the existing ranking model cannot integrate visual features, which are efficient in refining the click-based search results. In this paper, we propose a novel ranking model based on the learning to rank framework. Visual features and click features are simultaneously utilized to obtain the ranking model. Specifically, the proposed approach is based on large margin structured output learning and the visual consistency is integrated with the click features through a hypergraph regularizer term. In accordance with the fast alternating linearization method, we design a novel algorithm to optimize the objective function. This algorithm alternately minimizes two different approximations of the original objective function by keeping one function unchanged and linearizing the other. We conduct experiments on a large-scale dataset collected from the Microsoft Bing image search engine, and the results demonstrate that the proposed learning to rank models based on visual features and user clicks outperforms state-of-the-art algorithms.


title: Study and Analysis of the Effects of SiGe Source and Pocket-Doped Channel on Sensing Performance of Dielectrically Modulated Tunnel FET-Based Biosensors

abstract: Dielectrically modulated tunnel FET (DMTFET)-based biosensors show higher sensitivity but lower subthreshold current compared with their dielectrically modulated FET counterpart. In this context, the effect of use of silicon–germanium (SiGe) source and n+-pocket-doped channel is investigated with the help of extensive device-level simulations. This paper explores the underlying physics of germanium composition variation in the source region, and doping concentration variation in n+-pocket region, from the perspective of biomolecule conjugation. The effects of source bandgap and tunneling length over the band-to-band tunneling component have been analyzed, and, subsequently, the sensing performance of DMTFETs has been estimated. The results show that SiGe-source DMTFET has significant superiority over n+-pocket DMTFET for attaining higher subthreshold current level while retaining acceptable sensitivity. Such sensitivity-current optimization has been studied for different gate and drain biases, and the suitable biasing range of operation has been indicated. In addition, the relative efficiency of SiGe source and n+-pocket-doped channel has been studied under different biomolecule sample specifications. Finally, the influence of trap-assisted tunneling on DMTFET sensing performance has been analyzed, and the comparative role of SiGe source and n+ pocket has also been indicated in this context.


title: Minimum Cross Entropy Thresholding for SAR Images

abstract: Vision plays the most important role in human perception, which is limited to only the visual band of the electromagnetic spectrum. Therefore, the need for Radar imaging systems, to recover some sources that are not within human visual band, is raised. This paper presents a new algorithm for Synthetic Aperture Radar (SAR) images segmentation based on thresholding technique. Generally, segmentation of a SAR image falls into two categories; one based on grey levels and the other based on texture. The present paper deals with SAR images segmentation based on grey levels. We developed a new formula using Minimum Cross Entropy Thresholding (MCET) method for estimating optimal threshold value based on Gamma distribution to analyzing data on images; that means histogram of SAR images is assumed to be a mixture of Gamma distributions. The proposed method is iterative which decreases the number of operation to converge tends to the optimal solution. It is applied on bi-modal and multimodal scenarios. The results obtained are promising.


title: Miniature IMU/INS with optimally fused low drift MEMS gyro and accelerometers for applications in GPS-denied environments

abstract: TAI's multi-sensor fusion technology is accelerating the development of accurate MEMS sensor-based inertial navigation in situations where GPS does not operate reliably (GPS-denied environments). TAI has demonstrated that one inertial device per axis is not sufficient to produce low drift errors for long term accuracy needed for GPS-denied applications. TAI's technology uses arrays of off-the-shelf MEMS inertial sensors to create an inertial measurement unit (IMU) suitable for inertial navigation systems (INS) that require only occasional GPS updates. Compared to fiber optics gyros, properly combined MEMS gyro arrays are lower cost, fit into smaller volume, use less power and have equal or better performance. The patents TAI holds address this development for both gyro and accelerometer arrays. Existing inertial measurement units based on such array combinations, the backbone of TAI's inertial navigation system (INS) design, have demonstrated approximately 100 times lower sensor drift error to support very accurate angular rates, very accurate position measurements, and very low angle error for long durations. TAI's newest, fourth generation, product occupies small volume, has low weight, and consumes little power. The complete assembly can be potted in a protective sheath to form a rugged standalone product. An external exoskeleton case protects the electronic assembly for munitions and UAV applications. TAI's IMU/INS will provide the user with accurate real-time navigation information in difficult situations where GPS is not reliable. The key to such accurate performance is to achieve low sensor drift errors. The INS responds to quick movements without introducing delays while sharply reducing sensor drift errors that result in significant navigation errors. Discussed in the paper are physical characteristics of the IMU, an overview of the system design, TAI's systematic approach to drift reduction and some early results of applying a sigma point Kalman filter to sustain low gyro drift.


title: An LTCC-based 35-GHz substrate-integrated-waveguide bandpass filter

abstract: Low-temperature co-fired ceramic (LTCC) is used for a novel bandpass filter implementation. The filter is based on a substrate-integrated waveguide (SIW) having its pass band around 35GHz. The implementation of this filter illustrates the feasibility of LTCC at these frequencies. Using a microstrip-to-SIW transition for coupling the first resonator additionally reduces the filter's size and loss. The filter is designed such that active components (e.g. MMIC amplifiers) can be placed into a cavity right next to the filter. Filter syntheses has been done by extracting the length of the resonators and the insertion loss of the irises. This method reduces optimization time significantly.


Hierarchical Dirichlet processes


title: Named entity similarity computation: The case of social event entities

abstract: We consider events as complex named entities, recursively consisting of simple named entities (people, places, organizations, dates, etc.) and/or other complex named entities. We have developed a processing chain dedicated to extracting, indexing and searching for social events in Web pages. In this context, we are proposing a generic similarity computation function that targets any type of complex named entity. In this paper, we implement this function according to three approaches which we shall describe and then experiment on a set of social events.


title: Learning to Learn Second-Order Back-Propagation for CNNs Using LSTMs

abstract: Convolutional neural networks (CNNs) typically suffer from slow convergence rates in training, which limits their wider application. This paper presents a new CNN learning approach, based on second-order methods, aimed at improving: a) Convergence rates of existing gradient-based methods, and b) Robustness to the choice of learning hyper-parameters (e.g., learning rate). We derive an efficient back-propagation algorithm for simultaneously computing both gradients and second derivatives of the CNN's learning objective. These are then input to a Long Short Term Memory (LSTM) to predict optimal updates of CNN parameters in each learning iteration. Both meta-learning of the LSTM and learning of the CNN are conducted jointly. Evaluation on image classification demonstrates that our second-order backpropagation has faster convergences rates than standard gradient-based learning for the same CNN, and that it converges to better optima leading to better performance under a budgeted time for learning. We also show that an LSTM learned to learn a small CNN network can be readily used for learning a larger network.


title: Automatic Stance Detection Using End-to-End Memory Networks

abstract: We present a novel end-to-end memory network for stance detection, which jointly (i) predicts whether a document agrees, disagrees, discusses or is unrelated with respect to a given target claim, and also (ii) extracts snippets of evidence for that prediction. The network operates at the paragraph level and integrates convolutional and recurrent neural networks, as well as a similarity matrix as part of the overall architecture. The experimental evaluation on the Fake News Challenge dataset shows state-of-the-art performance.


title: Vulvovaginal reconstruction following radical surgery.

abstract: An important aspect of the care of women with a gynaecological malignancy is not only improved survival, but complete rehabilitation. There are a number of reconstructive techniques available which can be used at the time of radical surgery, or at some later date, to correct the untoward effects of therapy. Whenever possible, the least morbid, yet most reliable reconstructive procedure should be performed at the initial surgery to decrease postoperative morbidity and wound infection and improve rehabilitation and body image. For many situations there is no single ideal procedure, therefore one should be familiar with several techniques in order to select or adapt the procedure best suited to the circumstances. The split thickness skin graft (STSG) is used primarily to cover skin defects where there has been little or no loss of subcutaneous tissue, such as after skinning vulvectomy for carcinoma in situ. It is also the procedure of choice for vaginal reconstruction after simple vaginectomy for extensive in situ carcinoma and for congenital absence of the vagina. It may also be useful in the management of vaginal distortion, secondary to previous surgery or radiation therapy. In gynaecology, full thickness skin flaps are used when there has been major loss of skin and subcutaneous tissue of the vulva, groin or vagina. Defined arterial and fasciocutaneous flaps are more reliable than random cutaneous flaps, but they are not mutually exclusive in their application. Thus, one or the other may be used for the same defect in differing patients, depending on the situation. Circumstances that dictate which flap is preferable include size, contour, depth of the deformity, proximity of the deformity to the potential donor site, presence of necrosis and infection, and the requirement for new blood supply, as in an irradiated wound. In appropriately selected patients the myocutaneous flap will provide the most reliable source of a new blood supply. If the requirement for a new blood supply is of paramount importance, and the myocutaneous flap is too thick, the skin and subcutaneous tissue may be sacrificed to reduce the size of the flap. A STSG can then be applied at a later time to achieve the desired result. There are many other situations when several reconstructive procedures used simultaneously, or serially, may be required to achieve a balance between anatomy and function. However, ultimate success will depend largely on patient selection, familiarity with the procedures, and exacting surgical technique.


title: Insatiate boosted forest: Towards data exploitation in object detection

abstract: Boosted forest (BF) is a commonly used method for object detection. With the help of cascade strategy, it can efficiently reject non-object windows and finally, combined with sliding window paradigm, give the locations of target objects in an image. In the literature, many aspects of cascaded boosted forest (CBF) have been well studied, such as image representation, tree split and cascade structure. Although it has been extensively investigated, CBF is still not saturated. In this work, we demonstrate by a series of experiments that the performance of a CBF-based object detector can be significantly improved by careful data exploitation. Specifically, we use a simple yet efficient approach to collect more training samples with high quality. We show that the trained CBF-based detector can be included in the data collection loop to provide better training samples. Our experiments are conducted on two challenging pedestrian detection benchmarks: Caltech and Kitti. Taking Caltech for example, we get the best balance between performance and run-time: 17.2% miss rate (MR) while running at 11 frames per second (FPS) on a moderate CPU. Compared with state-of-the-art CBF-based detectors our method gives better or similar performance while running significantly faster.


title: Tagless Staged Interpreters for Simpler Typed Languages

abstract: We have built the first family of tagless interpretations for a higher-order typed object language in a typed metalanguage (Haskell or ML) that require no dependent types, generalized algebraic data types, or postprocessing to eliminate tags. The statically type-preserving interpretations include an evaluator, a compiler (or staged evaluator), a partial evaluator, and call-by-name and call-by-value CPS transformers. ::: ::: Our main idea is to encode HOAS using cogen functions rather than data constructors. In other words, we represent object terms not in an initial algebra but using the coalgebraic structure of the λ-calculus. Our representation also simulates inductive maps from types to types, which are required for typed partial evaluation and CPS transformations. ::: ::: Our encoding of an object term abstracts over the various ways to interpret it, yet statically assures that the interpreters never get stuck. To achieve self-interpretation and show Jones-optimality, we relate this exemplar of higher-rank and higher-kind polymorphism to plugging a term into a context of let-polymorphic bindings. ::: ::: It should also be possible to define languages with a highly refined syntactic type structure. Ideally, such a treatment should be metacircular, in the sense that the type structure used in the defined language should be adequate for the defining language.


title: Methods to detect, characterize, and remove motion artifact in resting state fMRI

abstract: Abstract Head motion systematically alters correlations in resting state functional connectivity fMRI (RSFC). In this report we examine impact of motion on signal intensity and RSFC correlations. We find that motion-induced signal changes (1) are often complex and variable waveforms, (2) are often shared across nearly all brain voxels, and (3) often persist more than 10 s after motion ceases. These signal changes, both during and after motion, increase observed RSFC correlations in a distance-dependent manner. Motion-related signal changes are not removed by a variety of motion-based regressors, but are effectively reduced by global signal regression. We link several measures of data quality to motion, changes in signal intensity, and changes in RSFC correlations. We demonstrate that improvements in data quality measures during processing may represent cosmetic improvements rather than true correction of the data. We demonstrate a within-subject, censoring-based artifact removal strategy based on volume censoring that reduces group differences due to motion to chance levels. We note conditions under which group-level regressions do and do not correct motion-related effects.


title: Development of a nature inspired algorithm based controller for DC servo motor

abstract: Proportional-Integral-Derivative (PID) controller is incorporated in Direct-Current (DC) servo motor control application due to its efficiency and ease of implementation. Nevertheless, the existing PID tuning methods are time consuming, and yet expertise and experience are required. Therefore, new approaches to tune PID controller is very important with the aim of obtaining better performances. The objective of this research is to investigate nature inspired algorithms used in PID controller tuning optimization. The simulation results show that both meta-heuristic algorithms Simulated Annealing (SA) and Differential Evolution (DE) based PID controller produce better performance compared with Ziegler Nichols (ZN) PID controller.


title: Urogenital tract disorders in children suspected of being sexually abused

abstract: Introduction ::: Child sexual abuse (CSA) is generally defined as child exploitation that leads to achievement of sexual satisfaction. According to data from European countries, sexual abuse of children affects 10–40% of girls and 5–20% of boys.


title: Building trusted path on untrusted device drivers for mobile devices

abstract: Mobile devices are frequently used as terminals to interact with many security-critical services such as mobile payment and online banking. However, the large client software stack and the continuous proliferation of malware expose such interaction under various threats, including passive attacks like phishing and active ones like direct code manipulation. This paper proposes TrustUI, a new trusted path design for mobile devices that enables secure interaction between end users and services based on ARM's TrustZone technology. TrustUI is built with a combination of key techniques including cooperative randomization of the trusted path and secure delegation of network interaction. With such techniques, TrustUI not only requires no trust of the commodity software stack, but also takes a step further by excluding drivers for user-interacting devices like touch screen from its trusted computing base (TCB). Hence, TrustUI has a much smaller TCB, requires no access to device driver code, and may easily adapt to many devices. A prototype of TrustUI has been implemented on a Samsung Exynos 4412 board and evaluation shows that TrustUI provides strong protection of users interaction.


title: A conceptual framework for integrated STEM education

abstract: The global urgency to improve STEM education may be driven by environmental and social impacts of the twenty-first century which in turn jeopardizes global security and economic stability. The complexity of these global factors reach beyond just helping students achieve high scores in math and science assessments. Friedman (The world is flat: A brief history of the twenty-first century, 2005) helped illustrate the complexity of a global society, and educators must help students prepare for this global shift. In response to these challenges, the USA experienced massive STEM educational reforms in the last two decades. In practice, STEM educators lack cohesive understanding of STEM education. Therefore, they could benefit from a STEM education conceptual framework. The process of integrating science, technology, engineering, and mathematics in authentic contexts can be as complex as the global challenges that demand a new generation of STEM experts. Educational researchers indicate that teachers struggle to make connections across the STEM disciplines. Consequently, students are often disinterested in science and math when they learn in an isolated and disjoined manner missing connections to crosscutting concepts and real-world applications. The following paper will operationalize STEM education key concepts and blend learning theories to build an integrated STEM education framework to assist in further researching integrated STEM education.


title: Automatic band selection for wavelet reconstruction in the application of defect detection

abstract: Abstract In this paper, we present a multiresolution approach for the inspection of local defects embedded in homogeneously textured surfaces. It is based on an efficient image restoration scheme using the wavelet transforms. By properly selecting the smooth subimage or the combination of detail subimages at different resolution levels for image reconstruction, the global repetitive texture pattern can be effectively removed and only local anomalies are preserved in the restored image. A wavelet band selection procedure is developed to automatically determine the best reconstruction parameters based on the energy distribution of wavelet coefficients. Experimental results show that the decomposed subimages and the number of resolution levels determined by the automatic band selection scheme are similar to the manual selection results, and the defects in a variety of real textures including machined surfaces, natural wood, sandpaper and textile fabrics are well detected.


title: 12.1 A 0.518mm2 quasi-current-mode hysteretic buck DC-DC converter with 3μs load transient response in 0.35μm BCDMOS

abstract: As more features have been added to mobile devices, it has become necessary to integrate more DC-DC converters into the power-management IC. Consequently, there is a growing need for an area-efficient and simple controller design for DC-DC converters. A simple hysteretic control without any additional component for compensation is a very attractive solution because it is not only cost-effective, but also immediately responds to load change. However, a conventional current-mode hysteretic controller with low-ESR output capacitor, shown in Fig. 12.1.1, has an inherent trade-off between transient response and RC network time constant for emulating inductor current [1]. For instance, at a switching frequency less than several MHz, which is widely used in industry, the RC network occupies a relatively large die area because the capacitance C SEN of the network can be up to 100pF at a switching frequency of 1 MHz. Another issue with the hysteretic converter is its variable switching frequency, which leads to difficulty in designing an EMI filter [2]. To overcome these limitations, several state-of-the-art hysteretic converters have been reported that provide fast transient response and fixed switching frequency [2-5]. However, they suffer from noise due to the differentiator for amplifying ripple voltage [2], or need a large external or internal capacitor [3, 4], or generate high switching loss in the converter [5]. In this paper, we present an area-efficient quasi-current-mode hysteretic buck converter with fixed switching frequency. By employing a quasi inductor current emulator (QICE) with reset operation, the controller only uses a total internal capacitance of 3pF and provides fast transient response.


title: Performance Evaluation of Passivated Silicon Carrier-Selective Contact Solar Cell

abstract: In this paper, a solar cell with amorphous silicon (a-Si) passivation that uses transition metal oxides (TMOs) for charge carrier collection has been modeled and analyzed for its performance. Charge carrier selection has been incorporated through selective band alignment provided by NiO and TiO2 TMOs for electrons and holes, respectively. The presence of a-Si interfacial layers for defect passivation prevents carrier loss due to high interface state densities at the Si/TMO interfaces. This structure is a potential candidate for high-efficiency solar cells (>25%) with a realistic approach towards the solution of high defect density. Optical analysis of the proposed structure has been done to analyze the amount of light entering the silicon and capable of generating electron–hole pairs. The amount of light lost has also been quantified into reflection loss, parasitic absorption, and back metal absorption. Impact of surface passivation quality and doping of NiO and TiO2 on its performance has been analyzed through the detailed device simulations. This provides a realistic pathway for engineering of passivation quality and doping of metal oxides for improved performance.