title: Hybrid supervised clustering based ensemble scheme for text classification

abstract: Purpose ::: ::: ::: ::: ::: The immense quantity of available unstructured text documents serve as one of the largest source of information. Text classification can be an essential task for many purposes in information retrieval, such as document organization, text filtering and sentiment analysis. Ensemble learning has been extensively studied to construct efficient text classification schemes with higher predictive performance and generalization ability. The purpose of this paper is to provide diversity among the classification algorithms of ensemble, which is a key issue in the ensemble design. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: An ensemble scheme based on hybrid supervised clustering is presented for text classification. In the presented scheme, supervised hybrid clustering, which is based on cuckoo search algorithm and k-means, is introduced to partition the data samples of each class into clusters so that training subsets with higher diversities can be provided. Each classifier is trained on the diversified training subsets and the predictions of individual classifiers are combined by the majority voting rule. The predictive performance of the proposed classifier ensemble is compared to conventional classification algorithms (such as Naive Bayes, logistic regression, support vector machines and C4.5 algorithm) and ensemble learning methods (such as AdaBoost, bagging and random subspace) using 11 text benchmarks. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The experimental results indicate that the presented classifier ensemble outperforms the conventional classification algorithms and ensemble learning methods for text classification. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: The presented ensemble scheme is the first to use supervised clustering to obtain diverse ensemble for text classification


title: Plenoptic depth estimation from multiple aliased views

abstract: With a sampled light field acquired from a plenoptic camera, several low-resolution views of the scene are available from which to infer depth. Unlike traditional multiview stereo, these views may be highly aliased due to the sparse sampling lattice in space, which can lead to reconstruction errors. We first analyse the conditions under which aliasing is a problem, and discuss the trade-offs for different parameter choices in plenoptic cameras. We then propose a method to compensate for the aliasing, whilst fusing the information from the multiple views to correctly recover depth maps. We show results on synthetic and real data, demonstrating the effectiveness of our method.


title: Calculation of detection and false alarm probabilities in spectrum pooling systems

abstract: The innovative new strategy of spectrum pooling enables public access to spectral ranges of already licensed yet rarely used frequency bands by overlaying a secondary mobile radio system (the rental system, RS) to an existing one (the licensed system, LS). Coexistence of both systems is realized by filling the idle time-frequency gaps of the LS. A key issue in spectrum pooling is the reliable and efficient detection of those spectral ranges that are currently accessed by the LS as those ranges have to be spared from the RS's transmission power. In this letter, formulas for the calculation of the detection and false alarm probability are derived for the general case of an arbitrary measurement covariance matrix, allowing for a maximum exploitation of the proposed distributed detection approach.


title: Impact of S-1 plus Cisplatin Neoadjuvant Chemotherapy on Scirrhous Gastric Cancer

abstract: Objective: This retrospective study aimed to address the therapeutic outcome for scirrhous gastric cancer patients by evaluating the effect of neoadjuvant chemoth


title: An accelerated proximal gradient algorithm for nuclear norm regularized least squares problems

abstract: The a‐ne rank minimization problem, which consists of flnding a matrix of minimum rank subject to linear equality constraints, has been proposed in many areas of engineering and science. A speciflc rank minimization problem is the matrix completion problem, in which we wish to recover a (low-rank) data matrix from incomplete samples of its entries. A recent convex relaxation of the rank minimization problem minimizes the nuclear norm instead of the rank of the matrix. Another possible model for the rank minimization problem is the nuclear norm regularized linear least squares problem. This regularized problem is a special case of an unconstrained nonsmooth convex optimization problem, in which the objective function is the sum of a convex smooth function with Lipschitz continuous gradient and a convex function on a set of matrices. In this paper, we propose an accelerated proximal gradient algorithm, which terminates in O(1= p †) iterations with an †-optimal solution, to solve this unconstrained nonsmooth convex optimization problem, and in particular, the nuclear norm regularized linear least squares problem. We report numerical results for solving large-scale randomly generated matrix completion problems. The numerical results suggest that our algorithm is e‐cient and robust in solving large-scale random matrix completion problems. In particular, we are able to solve random matrix completion problems with matrix dimensions up to 10 5 each in less than 10 minutes on a modest PC.


title: Recurrent Neural Networks with Top-k Gains for Session-based Recommendations

abstract: RNNs have been shown to be excellent models for sequential data and in particular for data that is generated by users in an session-based manner. The use of RNNs provides impressive performance benefits over classical methods in session-based recommendations. In this work we introduce novel ranking loss functions tailored to RNNs in the recommendation setting. The improved performance of these losses over alternatives, along with further tricks and refinements described in this work, allow for an overall improvement of up to 35% in terms of MRR and Recall@20 over previous session-based RNN solutions and up to 53% over classical collaborative filtering approaches. Unlike data augmentation-based improvements, our method does not increase training times significantly. We further demonstrate the performance gain of the RNN over baselines in an online A/B test.


title: Shadow Detection and Removal from Real Images: State of Art

abstract: Shadow detection and removal is used in various image processing applications like video surveillance, scene interpretation and object recognition. Ignoring the existence of shadows in images may cause serious problems like object merging, object lose, misinterpretation and alternation of object shape in various visual processing applications like segmentation, scene analysis and tracking. Many algorithms have been proposed in the literature, that deals with shadow detection and removal from images as well as videos. A comparative and empirical evaluation of the existing approaches in video has already been reported, but we lack a similar one in case of still images. This paper presents a comprehensive survey of existing shadow detection and removal algorithms reported in the case of still images. Evaluation metrics involved in shadow detection and removal techniques are discussed and the inefficiency of conventional metrics such as; per pixel accuracy, Precision, Recall, FScore etc in detection phase are also explored. Quantitative and qualitative evaluation of selected methods are also discussed. To the best of our knowledge this is the first article that exclusively discusses shadow detection and removal methodologies from real images.


title: Aerodynamic shape optimization of wind turbine blades using a Reynolds-averaged Navier–Stokes model and an adjoint method

abstract: Computational fluid dynamics (CFD) is increasingly used to analyze wind turbines, and the next logical step is to develop CFD-based optimization to enable further gains in performance and reduce model uncertainties. We present an aerodynamic shape optimization framework consisting of a Reynolds-averaged Navier Stokes solver coupled to a numerical optimization algorithm, a geometry modeler, and a mesh perturbation algorithm. To efficiently handle the large number of design variables, we use a gradient-based optimization technique together with an adjoint method for computing the gradients of the torque coefficient with respect to the design variables. To demonstrate the effectiveness of the proposed approach, we maximize the torque of the NREL VI wind turbine blade with respect to pitch, twist, and airfoil shape design variables while constraining the blade thickness. We present a series of optimization cases with increasing number of variables, both for a single wind speed and for multiple wind speeds. For the optimization at a single wind speed performed with respect to all the design variables (1 pitch, 11 twist, and 240 airfoil shape variables), the torque coefficient increased by 22.4% relative to the NREL VI design. For the multiple-speed optimization, the torque increased by an average of 22.1%. Depending on the CFD mesh size and number of design variables, the optimization time ranges from 2 to 24h when using 256 cores, which means that wind turbine designers can use this process routinely. Copyright © 2016 John Wiley & Sons, Ltd.


title: Cross-Modal Learning to Rank via Latent Joint Representation

abstract: Cross-modal ranking is a research topic that is imperative to many applications involving multimodal data. Discovering a joint representation for multimodal data and learning a ranking function are essential in order to boost the cross-media retrieval (i.e., image-query-text or text-query-image). In this paper, we propose an approach to discover the latent joint representation of pairs of multimodal data (e.g., pairs of an image query and a text document) via a conditional random field and structural learning in a listwise ranking manner. We call this approach cross-modal learning to rank via latent joint representation (CML²R). In CML²R, the correlations between multimodal data are captured in terms of their sharing hidden variables (e.g., topics), and a hidden-topic-driven discriminative ranking function is learned in a listwise ranking manner. The experiments show that the proposed approach achieves a good performance in cross-media retrieval and meanwhile has the capability to learn the discriminative representation of multimodal data.


title: Data security and threat modeling for smart city infrastructure

abstract: Smart city opens up data with a wealth of information that brings innovation and connects government, industry and citizens. Cyber insecurity, on the other hand has raised concerns among data privacy and threats to smart city systems. In this paper, we look into security issues in smart city infrastructure from both technical and business operation perspectives and propose an approach to analyze threats and to improve data security of smart city systems. The assessment process takes hundreds of features into account. Data collected during the assessment stage are then imported into an algorithm that calculates the threat factor. Mitigation strategies are provided to help reducing risks of smart city systems from being hacked into and to protect data from being misused, stolen or identifiable. Study shows that the threat factor can be reduced significantly by following this approach. Experiments show that this comprehensive approach can reduce the risks of cyber intrusions to smart city systems. It can also deal with privacy concerns in this big data arena.


title: Real-time hair mesh simulation

abstract: We present a robust real-time hair simulation method using hair meshes. Leveraging existing simulation models for sheet-based cloth, we introduce a volumetric force model for incorporating hair interactions inside the hair mesh volume. We also introduce a position correction method that minimizes the local deformation of the hair mesh due to collision handling. We demonstrate the robustness of our hair simulation method using large time steps with fast motion, and we show that our method can recover the initial hair shape even when the hair mesh goes through substantial deformation.


title: Omphalolith: a rare entity but important to recognize.

abstract: Omphalolith (umbolith) is an uncommon entity under normal circumstances. However, in a deeply retracted umbilicus in an obese individual, the accumulation of sebum and keratin may lead to the formation of a stone. This calculus may remain undiagnosed for many years until revealed by secondary infection or ulceration. We report an interesting case of an omphalolith in a young woman. In our literature search, very few cases of omphalolith have been reported.


title: A low phase noise wideband VCO with 8-shaped inductor

abstract: This paper presents a wideband voltage-controlled oscillator (VCO) with 8-shaped inductor for multi-band wireless communication system. The 8-shaped coils serve to reduce inductive crosstalk. The whole circuit is fully designed and simulated using 180nm CMOS technology process. The VCO provides the tuning range (TR) of 37.2% from 3.61 GHz to 5.26 GHz. The single-side phase noise at 1MHz offset are −129.5 dBc/Hz and −123.7 dBc/Hz at minimum and maximum frequencies, respectively. By adopting a 7-bit switched body-grounded NMOS varactor array with one reverse-biased diode varactor pair, the low VCO gain (less than 43 MHz/V) is achieved in whole tuning range. The layout occupies 0.32×0.46 mm2 and consumes 15 mA from the 1.8 V supply.


title: Fabric defect detection using linear filtering and morphological operations

abstract: An algorithm with linear filters and morphological operations has been proposed for automatic fabric defect detection. The algorithm is applied off-line and real-time to denim fabric samples for five types of defects. All defect types have been detected successfully and the defective regions are labeled. The defective fabric samples are then classified by using feed forward neural network method. Both defect detection and classification application performances are evaluated statistically. Defect detection performance of real time and off-line applications are obtained as 88% and 83% respectively. The defective images are classified with an average accuracy rate of 96.3%.


title: Consumer adoption of mobile technologies: a literature review

abstract: Although the total number of mobile subscriptions reached 6.8 billion worldwide ITU, 2013 confirming in the process that mobile technology is widespread, academic literature has so far focused only on explaining adoption of mobile technology and services. This literature review analyses 67 studies from the period 2005-2013 and finds the following: a most studies are quantitative in nature and come from Asia; b the Technology Acceptance Model is the most used theory; c future research should also utilise qualitative methods and examine the behavioural outcomes of mobile adoption instead of simply adoption in consumer markets.


title: A Hybrid Approach at Emotional State Detection: Merging Theoretical Models of Emotion with Data-Driven Statistical Classifiers

abstract: With the rising popularity of affective computing techniques, there have been several advances in the field of emotion recognition systems. However, despite the several advances in the field, these systems still face scenario adaptability and practical implementation issues. In light of these issues, we developed a nonspecific method for emotional state classification in interactive environments. The proposed method employs a two-layer classification process to detect Arousal and Valence (the emotion's hedonic component), based on four psycho physiological metrics: Skin Conductance, Heart Rate and Electromyography measured at the corrugator supercilii and zygomaticus major muscles. The first classification layer applies multiple regression models to correctly scale the aforementioned metrics across participants and experimental conditions, while also correlating them to the Arousal or Valence dimensions. The second layer then explores several machine learning techniques to merge the regression outputs into one final rating. The obtained results indicate we are able to classify Arousal and Valence independently from participant and experimental conditions with satisfactory accuracy (97% for Arousal and 91% for Valence).


title: Feature Reduction for Document Clustering and Classification

abstract: Often users receive search results which contain a wide range of documents, only some of which are relevant to their information needs. To address this problem, ever more systems not only locate information for users, but also organise that information on their behalf. We look at two main automatic approaches to information organisation: interactive clustering of search results and pre-categorising documents to provide hierarchical browsing structures. To be feasible in real world applications, both of these approaches require accurate yet efficient algorithms. Yet, both suffer from the curse of dimensionality — documents are typically represented by hundreds or thousands of words (features) which must be analysed and processed during clustering or classification. In this paper, we discuss feature reduction techniques and their application to document clustering and classification, showing that feature reduction improves efficiency as well as accuracy. We validate these algorithms using human relevance assignments and categorisation.


title: Emission of Carbonaceous Species from Biomass Burning in the Traditional Rural Cooking Stove in Bangladesh

abstract: Characterization of carbonaceous species from the particulate matters (PM) after combustion of seven commonly used biomass species, albizia tree (Albizia julibrissin), dry leaves (mahogany tree), jackfruit tree (Artocarpus heterophyllus), rain tree (Samanea saman), mahogany tree (Swietenia mahogany), cow dung and mango tree (Mangifer aindica) was done. PM samples were collected on quartz fiber filters emitted from biomass burning in a typical rural cooking stove. PM loaded filters were characterized with scanning electron microscope (SEM) for surface morphology, fourier transform infrared (FTIR) to determine the functional group of organic compounds. Black carbon (BC) and brown carbon (BrC) concentrations were determined with Aethalometer. A TOC analyzer was used to determine the total organic carbon (TOC) present in the biomass samples. The surface morphology was almost similar for all biomass burning PM samples. The average concentrations of BC and BrC were 5.85 ± 4.40 and 13.0 ± 8.80 μg·mǅ, respectively. The emission factors of BC and BrC were 1.08 ± 0.89 and 2.35 ± 1.67 mg·gǃ, respectively. Concentration of BC was the highest in dry leaves and the lowest in mango tree. The emission factors of the determined biomass followed the sequence-dry leaves of mahogany > albizia tree > jackfruit tree > rain tree > cow dung > mahogany tree > mango tree. PM from mango tree had lower emissions compared to the other biomass species used in this study.


title: Visual imagery and enactment of actions in memory

abstract: It was assumed that self-performing an action necessarily focuses information- processing on action-relevant information in order to guarantee smooth enactment. As a consequence, enacting an action should provide the subjects with excellent item-specific information and hinder the subjects from encoding contextual information that is not a part of the action proper. These hypotheses were tested in paired-associate learning experiments in which unrelated action-verb-pairs served as stimuli. Free recall (FR) of the action verbs was considered to indicate item- specific encoding, and cued recall (CR)—with one element of a pair serving as a cue for the other—to reflect context encoding. The verb-pairs were learned essentially under four types of instructions: under standard learning instructions (as a control), under enactment instructions, under self-imagery instructions, and under other- imagery instructions. The results demonstrated that enactment led to better FR than standard learning and the two imagery conditions, showing that enactment provides excellent item-specific information. CR was equally poor after overt enactment and self-imagined performance and worse after standard learning and after imagining somebody else performing an action, showing that motor encoding hinders pair integration—i.e. efficient context encoding.


Deep Learning: Fundamentals, Theory and Applications


title: Convolutional Neural Network-Based Robot Navigation Using Uncalibrated Spherical Images †

abstract: Vision-based mobile robot navigation is a vibrant area of research with numerous algorithms having been developed, the vast majority of which either belong to the scene-oriented simultaneous localization and mapping (SLAM) or fall into the category of robot-oriented lane-detection/trajectory tracking. These methods suffer from high computational cost and require stringent labelling and calibration efforts. To address these challenges, this paper proposes a lightweight robot navigation framework based purely on uncalibrated spherical images. To simplify the orientation estimation, path prediction and improve computational efficiency, the navigation problem is decomposed into a series of classification tasks. To mitigate the adverse effects of insufficient negative samples in the "navigation via classification" task, we introduce the spherical camera for scene capturing, which enables 360° fisheye panorama as training samples and generation of sufficient positive and negative heading directions. The classification is implemented as an end-to-end Convolutional Neural Network (CNN), trained on our proposed Spherical-Navi image dataset, whose category labels can be efficiently collected. This CNN is capable of predicting potential path directions with high confidence levels based on a single, uncalibrated spherical image. Experimental results demonstrate that the proposed framework outperforms competing ones in realistic applications.


title: Benefits of Expressing Gratitude Expressing Gratitude to a Partner Changes One’s View of the Relationship

abstract: This research was conducted to examine the hypothesis that expressing gratitude to a relationship partner enhances one’s perception of the relationship’s communal strength. In Study 1 (N = 137), a cross-sectional survey, expressing gratitude to a relationship partner was positively associated with the expresser’s perception of the communal strength of the relationship. In Study 2 (N = 218), expressing gratitude predicted increases in the expresser’s perceptions of the communal strength of the relationship across time. In Study 3 (N = 75), participants were randomly assigned to an experimental condition, in which they expressed gratitude to a friend, or to one of three control conditions, in which they thought grateful thoughts about a friend, thought about daily activities, or had positive interactions with a friend. At the end of the study, perceived communal strength was higher among participants in the expression-of-gratitude condition than among those in all three control conditions. We discuss the th...


GAN-based Data Augmentation for Improved Liver Lesion Classification


title: Interpolation error compensation method for look-up table based IPMSM drive

abstract: In this paper, interpolation error compensation method for look-up table(LUT) based IPMSM(Interior Permanent Magnet Synchronous Motor) drive is proposed. Generally, the LUT based IPMSM drive is widely used on automotive application because of its reliable current control. However, the LUT data is memorized discreetly, so the linear interpolation method is used for the absent data condition. Because this interpolation method does not follow the PMSM operation trace, the error is unavoidable between the capable operation point and the interpolated output. This paper proposes the compensation method for this interpolation error. Simulation and Experiment are performed for verify the reliability of the proposed control method.


title: Intent aware adaptive admittance control for physical Human-Robot Interaction

abstract: Effective physical Human-Robot Interaction (pHRI) needs to account for variable human dynamics and also predict human intent. Recently, there has been a lot of progress in adaptive impedance and admittance control for human-robot interaction. Not as many contributions have been reported on online adaptation schemes that can accommodate users with varying physical strength and skill level during interaction with a robot. The goal of this paper is to present and evaluate a novel adaptive admittance controller that can incorporate human intent, nominal task models, as well as variations in the robot dynamics. An outer-loop controller is developed using an ARMA model which is tuned using an adaptive inverse control technique. An inner-loop neuroadaptive controller linearizes the robot dynamics. Working in conjunction and online, this two-loop technique offers an elegant way to decouple the pHRI problem. Experimental results are presented comparing the performance of different types of admittance controllers. The results show that efficient online adaptation of the robot admittance model for different human subjects can be achieved. Specifically, the adaptive admittance controller reduces jerk which results in a smooth human-robot interaction.


title: Reduction of Power Electronic Components in Multilevel Converters Using New Switched Capacitor-Diode Structure

abstract: In this paper, a new structure for symmetric and cascade multilevel converter is presented. This structure requires the least power electronic components, gate driver circuits, power diode, and dc voltage source. The number of output levels is high. Reduction of the number of power electronic switches against voltage levels is an important factor in multilevel converters due to safety in switches, circuit size, cost, installation area, and complexity in control. To maximize the number of output level in the proposed optimized structure in order to attain different goals, such as reduction of stress on switches, the result of comparing the proposed structure and other structures present. The proposed method is robust against the switching faults in multilevel converter for dc source and the equipment. The simulation and experimental results presented in this paper show the performance of the proposed method.


Personalizing Mobile Fitness Apps using Reinforcement Learning


title: Nonverbal communication in socially assistive human-robot interaction

abstract: Socially assistive robots provide assistance to human users through interactions that are inherently social. This category includes robot tutors that provide students with personalized one-on-one lessons (Ramachandran, Litoiu, & Scassellati, 2016), robot therapy assistants that help mediate social interactions between children with ASD and adult therapists (Scassellati, Admoni, & Mataric, 2012), and robot coaches that motivate children to make healthy eating choices (Short et al., 2014).


title: Design of Analog Integrated Circuits by Using Genetic Algorithm

abstract: This paper applies the Artificial Intelligence technique called Genetic Algorithm (GA) to perform analog integrated circuits design, synthesis and optimization in order to reduce the development time and increase precision of this kind of circuits. In this work an accurate method to determine the device sizes in an analog integrated circuit on the basis of genetic algorithm is presented. To evaluate algorithm, an example in both time and frequency domains for a two-stage CMOS Operational Transconductance Amplifier (OTA) is presented in 0.18 µm process. The simulation results confirm the efficiency of GA in determining the device sizes in an analog circuit.


title: Dynamic 3D avatar creation from hand-held video input

abstract: We present a complete pipeline for creating fully rigged, personalized 3D facial avatars from hand-held video. Our system faithfully recovers facial expression dynamics of the user by adapting a blendshape template to an image sequence of recorded expressions using an optimization that integrates feature tracking, optical flow, and shape from shading. Fine-scale details such as wrinkles are captured separately in normal maps and ambient occlusion maps. From this user- and expression-specific data, we learn a regressor for on-the-fly detail synthesis during animation to enhance the perceptual realism of the avatars. Our system demonstrates that the use of appropriate reconstruction priors yields compelling face rigs even with a minimalistic acquisition system and limited user assistance. This facilitates a range of new applications in computer animation and consumer-level online communication based on personalized avatars. We present realtime application demos to validate our method.


title: Finding an emotional face in a crowd: Emotional and perceptual stimulus factors influence visual search efficiency

abstract: In this article, we examine how emotional and perceptual stimulus factors influence visual search efficiency. In an initial task, we run a visual search task, using a large number of target/distractor emotion combinations. In two subsequent tasks, we then assess measures of perceptual (rated and computational distances) and emotional (rated valence, arousal and potency) stimulus properties. In a series of regression analyses, we then explore the degree to which target salience (the size of target/distractor dissimilarities) on these emotional and perceptual measures predict the outcome on search efficiency measures (response times and accuracy) from the visual search task. The results show that both emotional and perceptual stimulus salience contribute to visual search efficiency. The results show that among the emotional measures, salience on arousal measures was more influential than valence salience. The importance of the arousal factor may be a contributing factor to contradictory history of results w...


title: A novel microstrip fed circular patch antenna with band notches for Bluetooth/UWB/X-band and Ku band applications

abstract: In this paper a novel microstrip fed circular patch antenna is analyzed in details for applications in Bluetooth, UWB, X-band and Ku band respectively. The basic antenna structure is a circular patch which is modified by adding complementary circular split ring resonators and T strip connected to the top of the patch. The antenna is fed by a 50 ohm microstrip feed line. A slot of dimension 2×3.6 mm2 is inserted in the ground plane which consists of a staircase structure having appropriate dimensions. The ground is later modified with meander shaped slots for Bluetooth, X band and Ku band applications. The characteristics of the proposed antenna is carried out in HFSS 2013.


title: Cancer preventive effects of the indigo plant, Polygonum tinctorium

abstract: From historic times, plant products have received much attention due to their various potentials for the treatment of diseases. We have long been interested in the study of medicinal plant products and we have recently focussed on trying to identify some active ingredients in the indigo plant, Polygonum tinctorium (P. tinctorium). This plant is renowned in China, the Korean peninsula and Japan as a medicinal plant having anti-inflammatory, anti-pyrexic, and detoxification effects. From our studies, it became obvious that tryptanthrin and indirubin, both compounds originating from indican in the leaves of P. tinctorium, are responsible for many of the biological activities of this plant. Tryptanthrin has a potent anti-inflammatory activity and shows growth inhibitory activity against cancer cell lines in vitro. Based on these activities, we tested the effect of this substance on azoxymethane-induced intestinal tumorigenesis in rats because carcinogenesis in the intestines is closely associated with inflammation. As a result, tryptanthrin inhibited the incidence of intestinal tumors. Furthermore, indirubin has been reported to possess an anti-leukemic activity and P. tinctorium also contains various anti-oxidative substances, such as gallic acid and caffeic acid, with potential anti-tumor activity. We consider it likely that P. tinctorium shows cancer preventive activity as a consequence of the integral effects of these substances.


title: Reconstructing constructivism: Causal models, Bayesian learning mechanisms, and the theory theory.

abstract: We propose a new version of the “theory theory” grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and non-technical way, and review an extensive but relatively recent body of empirical results that supports these ideas. These include new studies of the mechanisms of learning. Children infer causal structure from statistical information, through their own actions on the world and through observations of the actions of others. Studies demonstrate these learning mechanisms in children from 16 months to 4 years old and include research on causal statistical learning, informal experimentation through play, and imitation and informal pedagogy. They also include studies of the variability and progressive character of intuitive theory change, particularly theory of mind. These studies investigate both the physical and psychological and social domains. We conclude with suggestions for further collaborative projects between developmental and computational cognitive scientists.


title: Gratifications for using CMC technologies: A comparison among SNS, IM, and e-mail

abstract: Why and how people choose to use a particular computer-mediated communication (CMC) technology is a major concern. This study seeks to address the issues by applying the uses and gratifications theory, and attempts to explore the general and specific gratifications sought from the use of three CMC technologies. Three separate empirical surveys were conducted to investigate the gratifications sought from social networking sites, instant messaging, and e-mail. Then factor analysis was undertaken to extract the gratifications sought from each CMC technology. The extracted gratifications sought were then compared among the three technologies for concluding the general and specific gratifications. Four general gratifications were extracted among the three CMC technologies, including relationship maintenance, information seeking, amusement, and style. Two gratifications were specific: the sociability gratification sought from using instant messaging and social networking sites; and the gratification of kill time sought from using instant messaging. Moreover, the important levels of gratifications sought from the three CMC technologies were found to be different. Our findings provide evidence to explain why not all traditional CMC technologies are replaced by innovative and advanced ones. The results of this study may be applied to CMC technology design and provide implications for future research.


title: Set-Based Concurrent Engineering process within the LeanPPD environment

abstract: This paper presents a newly defined set-based concurrent engineering process, which the authors believe addresses some of the key challenges faced by engineering enterprises in the 21st century. The main principles of Set-Based Concurrent Engineering (SBCE) have been identified via an extensive literature review. Based on these principles the SBCE baseline model was developed. The baseline model defines the stages and activities which represent the product development process to be employed in the LeanPPD (lean product and process development) project. The LeanPPD project is addressing the needs of European manufacturing companies for a new model that extends beyond lean manufacturing, and incorporates lean thinking in the product design development process.


title: A Model for Stability Study of PFC Power Supplies

abstract: Typically a power-factor-correction power supply consists of two stages, one responsible for power factor correction and the other for voltage regulation. In this paper, we propose to use a constant power sink to represent the voltage regulating stage, resulting in an analytically tractable model that is able to predict the period-doubled oscillations at line frequency which are not detectable by other conventional models. Using a generalized averaging approach, we investigate the low- frequency dynamics and derive closed-form stability conditions which accurately locate the stability boundaries on selected parameter planes. The model can be conveniently used to evaluate the performance of power-factor-correction power supplies, such as harmonic distortion and power factor. Experimental results are presented to verify the model.


title: Handwritten signature attributes for its verification

abstract: Handwritten signature verification is a biometrie technology that uses the signature for identification purposes with the aim of establishing the authority for making automated transactions, obtaining access to computer terminals or physical access to the controlled area. The effectiveness of biometric verification is determined mainly by difference of attributes used for authentication. The paper examines authentication systems based on handwritten signature and the main informative parameters of signature such as size, shape, velocity, pressure, etc. We analyzed their statistical characteristics and considered methods to extract them using Wavelet transform, discrete Radon and Fourier transforms. Handwritten signature data acquisition methods were investigated in order to design an effective verification algorithm.


title: An Overview of Concept Drift Applications

abstract: In most challenging data analysis applications, data evolve over time and must be analyzed in near real time. Patterns and relations in such data often evolve over time, thus, models built for analyzing such data quickly become obsolete over time. In machine learning and data mining this phenomenon is referred to as concept drift. The objective is to deploy models that would diagnose themselves and adapt to changing data over time. This chapter provides an application oriented view towards concept drift research, with a focus on supervised learning tasks. First we overview and categorize application tasks for which the problem of concept drift is particularly relevant. Then we construct a reference framework for positioning application tasks within a spectrum of problems related to concept drift. Finally, we discuss some promising research directions from the application perspective, and present recommendations for application driven concept drift research and development.


title: A model for both the fast and slow spreadings of sudden events on social networks

abstract: Rumor spreading has been studied for decades, but its underlying mechanism is still under debate, especially for those ones based on internet. By focusing on the spreading data of six typical events on Sina Weibo, we surprisingly find that the spreading of modern news shows some new features, i.e. either explosively fast or slow, depending on the individual events. To understand its mechanism, we present a Susceptible-Accepted-Recovered (SAR) model with both information sensitivity and social reinforcement. By numerical simulations we show that the model can reproduce the main spreading patterns of the six typical events and the spreading speed can be promoted by increasing either the strength of information sensitivity or the social reinforcement. Moreover, we find that the dependence of final accepted size on some key parameters, such as the transmission probability and information sensitivity, can change from continuous to discontinuous when the strength of the social reinforcement is large. An edge-based theory analysis is presented to explain the explosive spreading by information sensitivity and social reinforcement.


title: Adversarial Uncertainty Quantification in Physics-Informed Neural Networks

abstract: Abstract We present a deep learning framework for quantifying and propagating uncertainty in systems governed by non-linear differential equations using physics-informed neural networks. Specifically, we employ latent variable models to construct probabilistic representations for the system states, and put forth an adversarial inference procedure for training them on data, while constraining their predictions to satisfy given physical laws expressed by partial differential equations. Such physics-informed constraints provide a regularization mechanism for effectively training deep generative models as surrogates of physical systems in which the cost of data acquisition is high, and training data-sets are typically small. This provides a flexible framework for characterizing uncertainty in the outputs of physical systems due to randomness in their inputs or noise in their observations that entirely bypasses the need for repeatedly sampling expensive experiments or numerical simulators. We demonstrate the effectiveness of our approach through a series of examples involving uncertainty propagation in non-linear conservation laws, and the discovery of constitutive laws for flow through porous media directly from noisy data.


title: Wireless Sensor Networks: Technology, Protocols, and Applications

abstract: Chapter 1. Introduction and Overview of Wireless Sensor Networks. Chapter 2. Commercial and Scientific Applications of Wireless Sensor Networks. Chapter 3. Basic Wireless Sensor Technology. Chapter 4. Wireless Sensors Networks Protocols: Physical Layer. Chapter 5. Medium Access Control Protocols for Wireless Sensor Networks. Chapter 6. Sensors Network Protocols: Routing Protocols. Chapter 7. Transport Control Protocols for Wireless Sensors Networks. Chapter 8. Middleware for Sensor Networks. Chapter 9. Network Management for Wireless Sensor Networks. Chapter 10. Operating Systems for Sensor Networks. Chapter 11. Performance and Traffic Management Issues. Appendix A: Analysis. Appendix B: Discussions. Index.


title: Self-recognition and the right hemisphere

abstract: Although monkeys can perceive complex stimuli such as faces1, only the higher apes are capable of recognizing their own face in a mirror2. Here we show that in humans the right hemisphere of the brain seems to be preferentially involved in self-face recognition. Our findings indicate that neural substrates of the right hemisphere may selectively participate in processes linked to self-awareness.


title: The Theory and Practice of Culturally Relevant Education: A Synthesis of Research Across Content Areas

abstract: Many teachers and educational researchers have claimed to adopt tenets of culturally relevant education (CRE). However, recent work describes how standardized curricula and testing have marginalized CRE in educational reform discourses. In this synthesis of research, we sought examples of research connecting CRE to positive student outcomes across content areas. It is our hope that this synthesis will be a reference useful to educational researchers, parents, teachers, and education leaders wanting to reframe public debates in education away from neoliberal individualism, whether in a specific content classroom or in a broader educational community.


Attitudes toward Vegans and Vegetarians: The Role of Anticipated Moral Reproach and Dissonance


title: GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking

abstract: Model compression is essential for serving large deep neural nets on devices with limited resources or applications that require real-time responses. As a case study, a state-of-the-art neural language model usually consists of one or more recurrent layers sandwiched between an embedding layer used for representing input tokens and a softmax layer for generating output tokens. For problems with a very large vocabulary size, the embedding and the softmax matrices can account for more than half of the model size. For instance, the bigLSTM model achieves state-of- the-art performance on the One-Billion-Word (OBW) dataset with around 800k vocabulary, and its word embedding and softmax matrices use more than 6GBytes space, and are responsible for over 90% of the model parameters. In this paper, we propose GroupReduce, a novel compression method for neural language models, based on vocabulary-partition (block) based low-rank matrix approximation and the inherent frequency distribution of tokens (the power-law distribution of words). The experimental results show our method can significantly outperform traditional compression methods such as low-rank approximation and pruning. On the OBW dataset, our method achieved 6.6 times compression rate for the embedding and softmax matrices, and when combined with quantization, our method can achieve 26 times compression rate, which translates to a factor of 12.8 times compression for the entire model with very little degradation in perplexity.


title: DFuseNet: Deep Fusion of RGB and Sparse Depth Information for Image Guided Dense Depth Completion

abstract: In this paper we propose a convolutional neural network that is designed to upsample a series of sparse range measurements based on the contextual cues gleaned from a high resolution intensity image. Our approach draws inspiration from related work on super-resolution and in-painting. We propose a novel architecture that seeks to pull contextual cues separately from the intensity image and the depth features and then fuse them later in the network. We argue that this approach effectively exploits the relationship between the two modalities and produces accurate results while respecting salient image structures. We present experimental results to demonstrate that our approach is comparable with state of the art methods and generalizes well across multiple datasets.


title: Functional system architectures towards fully automated driving

abstract: The functional system architecture of an automated vehicle plays a crucial role in the performance of the vehicle. When considered as a backbone, it does not only transmit information between distinct layers, but rather serves as a feedback mechanism coordinating the degradation between them and thereby regulates the behavior of the system against failures. Hence, the design of robust functional architectures is essential to cope with the uncertainties of the world. This paper summarizes existing system architectures and investigates them regarding their robustness against measurement inaccuracies, failures, and unexpected evolution of traffic situations. After illustrating their strengths and deficiencies, we derive the requirements and propose a structure for future, robust system architectures.


title: Authorship categorization of public domain literature

abstract: We defined a set of quantifiable features for authorship categorization. We performed our experiments on public domain literature — all books analyzed were obtained in plain text format through Project Gutenberg's online repository of classic books. We tested three machine learning algorithms: Artificial Neural Network, Naive Bayes Classifier, and Support Vector Machine with our features. We found that certain features, such as punctuation and various suffixes result in a higher accuracy. In addition, the Support Vector Machine classifier produces repeatedly higher accuracies than other classifiers and seems to be a far superior method of classification in terms of authorship categorization.


title: Towards a Better Match in Siamese Network Based Visual Object Tracker

abstract: Recently, Siamese network based trackers have received tremendous interest for their fast tracking speed and high performance. Despite the great success, this tracking framework still suffers from several limitations. First, it cannot properly handle large object rotation. Second, tracking gets easily distracted when the background contains salient objects. In this paper, we propose two simple yet effective mechanisms, namely angle estimation and spatial masking, to address these issues. The objective is to extract more representative features so that a better match can be obtained between the same object from different frames. The resulting tracker, named Siam-BM, not only significantly improves the tracking performance, but more importantly maintains the realtime capability. Evaluations on the VOT2017 dataset show that Siam-BM achieves an EAO of 0.335, which makes it the best-performing realtime tracker to date.


title: HealthDep: An Efficient and Secure Deduplication Scheme for Cloud-Assisted eHealth Systems

abstract: In this paper, we analyze the inherent characteristic of electronic medical records (EMRs) from actual electronic health (eHealth) systems, where we found that first, multiple patients would generate large amounts of duplicate EMRs and second, cross-patient duplicate EMRs would be generated numerously only in the case that the patients consult doctors in the same department. We then propose the first efficient and secure encrypted EMRs deduplication scheme for cloud-assisted eHealth systems (HealthDep). With the integration of our analysis results, HealthDep allows the cloud server to efficiently perform the EMRs deduplication, and enables the cloud server to reduce storage costs by more than 65% while ensuring the confidentiality of EMRs. Security analysis shows that HealthDep provides a stronger security guarantee than Marforio et al. 's scheme (NDSS 2014) and Bellare et al. 's scheme (USENIX Security 2013). Algorithm implementation and performance analysis demonstrate the feasibility and high efficiency of HealthDep.


title: In Pursuit of Architectural Agility: Experimenting with Microservices

abstract: Modern enterprises need their IT infrastructure to be agile, for them to adapt smoothly and swiftly to how the business organization evolves in pursuit of the enterprise goals. Addressing that demand entails pursuing "business and architecture isomorphism". The modest offering of a good fraction of enterprise IT installations, however, suggests that such a tenet is easier said than done. At the same time, there is growing consensus that the microservices architecture style has propelling potential to seize that goal, especially now that state-of-the-art technology begins to match its demands proficiently. We have experienced the latter claim to hold true in a Proof-of-Concept implementation and quantitative evaluation performed for a medium-size enterprise seeking guidance on the architecture concept to adopt for their new IT infrastructure. Our project included a fair amount of technology scouting and evaluation, whose principal outcomes, all most encouraging, we report here. The conclusion we arrived at, which we believe applies to numerous other enterprises, is that embracing the microservices architecture style meets with solid technology support, assures architectural agility, and yields satisfactory returns for run-time performance.


title: ERP Investments and the Market Value of Firms: Toward an Understanding of Influential ERP Project Variables

abstract: This study contributes to the growing body of literature on the value of enterprise resource planning (ERP) investments at the firm level. Using an organization integration lens that takes into account investments in complementary resources as well as an options thinking logic about the value of an ERP platform, we argue that not all ERP purchases have the same potential impact at the firm level due to ERP project decisions made at the time of purchase. Based on a sample of 116 investment announcements in United Statesbased firms between 1997 and 2001, we find support for our hypotheses that ERP projects with greater functional scope (two or more value-chain modules) or greater physical scope (multiple sites) result in positive, higher shareholder returns. Furthermore, the highest increases in returns (3.29) are found for ERP purchases with greater functional scope and greater physical scope; negative returns are found for projects with lesser functional scope and lesser physical scope. These findings provide empirical support for prior theory about the organizational integration benefits of ERP systems, the contribution of complementary resource investments to the business value of IT investments, and the growth options associated with IT platform investments. The article concludes with implications of our firm-level findings for this first wave of enterprise systems.


title: A natural gesture interface for operating robotic systems

abstract: A gesture-based interaction framework is presented for controlling mobile robots. This natural interaction paradigm has few physical requirements, and thus can be deployed in many restrictive and challenging environments. We present an implementation of this scheme in the control of an underwater robot by an on-site human operator. The operator performs discrete gestures using engineered visual targets, which are interpreted by the robot as parametrized actionable commands. By combining the symbolic alphabets resulting from several visual cues, a large vocabulary of statements can be produced. An iterative closest point algorithm is used to detect these observed motions, by comparing them with an established database of gestures. Finally, we present quantitative data collected from human participants indicating accuracy and performance of our proposed scheme.


title: An experimental study on hydrothermal treatment of sweet sorghum bagasse for the extraction of hemicellulose

abstract: Hydrothermal treatment can be used for the on-farm processing of lignocellulosic materials. This study investigated the hydrothermal treatment of sweet sorghum bagasse (SSB) powder for the extraction of hemicellulose from it. Changes in chemical composition of SSB and the formation of sugars and hydrolytic products were studied. The optimum conditions of 12.54 % (g/g) substrate concentration and 90 min of isothermal treatment residence time at 394 K were conducive to the extraction of 72.61–72.77 % hemicellulose and producing a hydrolysate containing 56.06–63.54 g/L reducing sugars and 5.52–6.80 g/L furfurals. The treated SSB residue contained about 56.30–56.42 % (g/g) cellulose and 31.40–31.43 % (g/g) lignin in it. The substrate concentration and isothermal treatment residence time were significant in the responses observed.


title: Dielectric waveguide with planar multi-mode excitation for high data-rate chip-to-chip interconnects

abstract: An all-electrical, low-cost, wideband chip-to-chip link on a multi-mode dielectric waveguide is proposed. The signal is coupled from the silicon chip to the fundamental and polarization-orthogonal degenerate Ex11 and Ey11 waveguide modes using planar electric and slot dipole antennas, respectively. This approach doubles the capacity of a single line without sacrificing robustness or adding implementation cost and complexity. Two independent ultra-wideband 30GHz channels, each from 90 GHz to 120 GHz, are demonstrated. The large available bandwidth will be channelized in frequency for optimal overall efficiency with a CMOS transceiver. Various design aspects of the structure are examined and discussed. The proposed waveguide offers a solution for Terabit-per-second (Tbps) electrical wireline links.


title: A Soft Switching Three-phase Current-fed Bidirectional DC-DC Converter With High Efficiency Over a Wide Input Voltage Range

abstract: In this paper, a three-phase current-fed dual-active-bridge (DAB) bidirectional DC-DC converter is presented. Compared to the voltage source DAB converter, the proposed converter allows low RMS current and maintains zero voltage switching (ZVS) in the whole operating range by keeping the ratio between primary side and secondary side DC-link voltage constant, leading to high efficient energy conversion over a wide input voltage range. In addition, the ZVS conditions can be maintained using small DC inductors and the input current ripple still remains small because of high DC inductor current ripples being alleviated by three-phase interleaving structure. Furthermore, the proposed topology with Y-Y connected transformers is proven to have better current sharing capability compared with other three-phase topologies with different transformer connections. The operation mode analysis, soft switching conditions, and hardware design guidelines are derived in this paper. A 6-kW hardware prototype with input voltage range of 24~48 V and rated 288 V output voltage is developed and tested in the laboratory. The experimental results verified that the proposed converter could maintain high efficiency over a wide input voltage and power range.


title: Challenging Images For Minds and Machines

abstract: There is no denying the tremendous leap in the performance of machine learning methods in the past half-decade. Some might even say that specific sub-fields in pattern recognition, such as machine-vision, are as good as solved, reaching human and super-human levels. Arguably, lack of training data and computation power are all that stand between us and solving the remaining ones. In this position paper we underline cases in vision which are challenging to machines and even to human observers. This is to show limitations of contemporary models that are hard to ameliorate by following the current trend to increase training data, network capacity or computational power. Moreover, we claim that attempting to do so is in principle a suboptimal approach. We provide a taster of such examples in hope to encourage and challenge the machine learning community to develop new directions to solve the said difficulties.


A Sustainable Manufacturing Strategy Decision Framework in the Context of Multi-Criteria Decision-Making


title: Emotion Recognition based on EEG using LSTM Recurrent Neural Network

abstract: Emotion is the most important component in daily interaction between people. Nowadays, it is important to make the computers understand user’s emotion who interacts with it in human-computer interaction (HCI) systems. Electroencephalogram (EEG) signals are the main source of emotion in our body. Recently, emotion recognition based on EEG signals have attracted many researchers and many methods were reported. Different types of features were extracted from EEG signals then different types of classifiers were applied to these features. In this paper, a deep learning method is proposed to recognize emotion from raw EEG signals. Long-Short Term Memory (LSTM) is used to learn features from EEG signals then the dense layer classifies these features into low/high arousal, valence, and liking. DEAP dataset is used to verify this method which gives an average accuracy of 85.65%, 85.45%, and 87.99% with arousal, valence, and liking classes, respectively. The proposed method introduced high average accuracy in comparison with the traditional techniques.


title: Switching control strategy to extend the ZVS operating range of a Dual Active Bridge AC/DC converter

abstract: A switching control strategy to extend the zero-voltage-switching (ZVS) operating range of a Dual Active Bridge (DAB) AC/DC converter to the entire input-voltage interval and the full power range is proposed. The converter topology consists of a DAB DC/DC converter, receiving a rectified AC line voltage via a synchronous rectifier. The DAB comprises a primary side half bridge and secondary side full bridge, linked by a high-frequency isolation transformer and inductor. Using conventional control strategies, the soft-switching boundary conditions are exceeded at the higher voltage conversion ratios of the AC input interval. A novel pulse-width-modulation strategy to fully eliminate these boundaries and its analysis are presented in this paper, allowing increased performance (in terms of efficiency and stresses). Additionally, by using a half bridge / full bridge configuration, the number of active components is reduced. A prototype converter was constructed and experimental results are given to validate the theoretical analyses and practical feasibility of the proposed strategy.


title: A Preliminary Review of Enterprise Serverless Cloud Computing (Function-as-a-Service) Platforms

abstract: In line with cloud computing emergence as the dominant enterprise computing paradigm, our conceptualization of the cloud computing reference architecture and service construction has also evolved. For example, to address the need for cost reduction and rapid provisioning, virtualization has moved beyond hardware to containers. More recently, serverless computing or Function-as-a-Service has been presented as a means to introduce further cost-efficiencies, reduce configuration and management overheads, and rapidly increase an application's ability to speed up, scale up and scale down in the cloud. The potential of this new computation model is reflected in the introduction of serverless computing platforms by the main hyperscale cloud service providers. This paper provides an overview and multi-level feature analysis of seven enterprise serverless computing platforms. It reviews extant research on these platforms and identifies the emergence of AWS Lambda as a de facto base platform for research on enterprise serverless cloud computing. The paper concludes with a summary of avenues for further research.


title: Determinantal Point Processes for Mini-Batch Diversification

abstract: We study a mini-batch diversification scheme for stochastic gradient descent (SGD). While classical SGD relies on uniformly sampling data points to form a mini-batch, we propose a non-uniform sampling scheme based on the Determinantal Point Process (DPP). The DPP relies on a similarity measure between data points and gives low probabilities to mini-batches which contain redundant data, and higher probabilities to mini-batches with more diverse data. This simultaneously balances the data and leads to stochastic gradients with lower variance. We term this approach Diversified Mini-Batch SGD (DM-SGD). We show that regular SGD and a biased version of stratified sampling emerge as special cases. Furthermore, DM-SGD generalizes stratified sampling to cases where no discrete features exist to bin the data into groups. We show experimentally that our method results more interpretable and diverse features in unsupervised setups, and in better classification accuracies in supervised setups.


title: Vision-based autonomous load handling for automated guided vehicles

abstract: The paper presents a method for automatically detecting pallets and estimating their position and orientation. For detection we use a sliding window approach with efficient candidate generation, fast integral features and a boosted classifier. Specific information regarding the detection task such as region of interest, pallet dimensions and pallet structure can be used to speed up and validate the detection process. Stereo reconstruction is employed for depth estimation by applying Semi-Global Matching aggregation with Census descriptors. Offline test results show that successful detection is possible under 0.5 seconds.


title: OS-MAC: An Efficient MAC Protocol for Spectrum-Agile Wireless Networks

abstract: Wireless networks and devices have been rapidly gaining popularity over their wired counterparts. This popularity, in turn, has been generating an explosive and ever-increasing demand for, and hence creating a shortage of, the radio spectrum. Existing studies indicate that this foreseen spectrum shortage is not so much due to the scarcity of the radio spectrum, but due to the inefficiency of current spectrum access methods, thus leaving spectrum opportunities along both the time and the frequency dimensions that wireless devices can exploit. Fortunately, recent technological advances have made it possible to build software-defined radios (SDRs) which, unlike traditional radios, can switch from one frequency band to another at little or no cost. We propose a MAC protocol, called Opportunistic Spectrum MAC (OS-MAC), for wireless networks equipped with cognitive radios like SDRs. OS-MAC (1) adaptively and dynamically seeks and exploits opportunities in both licensed and unlicensed spectra and along both the time and the frequency dimensions; (2) accesses and shares spectrum among different unlicensed and licensed users; and (3) coordinates with other unlicensed users for better spectrum utilization. Using extensive simulation, OS-MAC is shown to be far more effective than current access protocols from both the network's and the user's perspectives. By comparing its performance with an Ideal-MAC protocol, OS-MAC is also shown to not only outperform current access protocols, but also achieve performance very close to that obtainable under the Ideal-MAC protocol.


title: Quo vadis TAM

abstract: The Technology Acceptance model (TAM) is one of the most influential theories in Information Systems. However, despite the model's significant contributions, the intense focus on TAM has diverted researchers’ attention away from other important research issues and has created an illusion of progress in knowledge accumulation. Furthermore, the independent attempts by several researchers to expand TAM in order to adapt it to the constantly changing IT environments has lead to a state of theoretical chaos and confusion in which it is not clear which version of the many iterations of TAM is the commonly accepted one. The present commentary discusses these concerns, speculates on the possible contributions to the current state of affairs, and makes several suggestions to alleviate the problems associated with TAM and to advance IT adoption research to the next stage.


A water dispenser modified to promote water use by piglets in the first days after birth


title: Serum Zinc Correlates with Parent- and Teacher- Rated Inattention in Children with Attention-Deficit/Hyperactivity Disorder

abstract: Objective: The aim of this study was to explore the relationship of zinc nutrition to the severity of attention-deficit/hyperactivity disorder (ADHD) symptoms in a middle-class American sample with well-diagnosed ADHD. Previous reports of zinc in ADHD, including two positive clinical trials of supplementation, have come mainly from countries and cultures with different diets and/or socioeconomic realities. Method: Children 5–10 years of age with DISC- and clinician-diagnosed ADHD had serum zinc determinations and parent and teacher ratings of ADHD symptoms. Zinc levels were correlated (Pearson's and multiple regression) with ADHD symptom ratings. Results: Forty-eight children (37 boys, 11 girls; 33 combined type, 15 inattentive) had serum zinc levels with a median/mode at the lowest 30% of the laboratory reference range; 44 children also had parent/teacher ratings. Serum magnesium levels were normal. Nutritional intake by a parent-answered food frequency questionnaire was unremarkable. Serum zinc correlat...


title: Wired and wireless intrusion detection system: Classifications, good characteristics and state-of-the-art

abstract: Abstract In computer and network security, standard approaches to intrusion detection and response attempt to detect and prevent individual attacks. Intrusion Detection System (IDS) and intrusion prevention systems (IPS) are real-time software for risk assessment by monitoring for suspicious activity at the network and system layer. Software scanner allows network administrator to audit the network for vulnerabilities and thus securing potential holes before attackers take advantage of them. In this paper we try to define the intruder, types of intruders, detection behaviors, detection approaches and detection techniques. This paper presents a structural approach to the IDS by introducing a classification of IDS. It presents important features, advantages and disadvantages of each detection approach and the corresponding detection techniques. Furthermore, this paper introduces the wireless intrusion protection systems. The goal of this paper is to place some characteristics of good IDS and examine the positioning of intrusion prevention as part of an overall layered security strategy and a review of evaluation criteria for identifying and selecting IDS and IPS. With this, we hope to introduce a good characteristic in order to improve the capabilities for early detection of distributed attacks in the preliminary phases against infrastructure and take a full spectrum of manual and automatic response actions against the source of attacks.


title: A Demonstration of the OtterTune Automatic Database Management System Tuning Service

abstract: Database management systems (DBMSs) have a plethora of tunable knobs that control almost everything in the system. The performance of a DBMS is highly dependent on these configuration knobs, however, getting this tuning right is hard. Many organizations resort to hiring experts to configure these knobs, but this is prohibitively expensive. As databases grow in both size and complexity, optimizing a DBMS has surpassed the abilities of even the best human experts. We recently introduced OtterTune, a tuning service that is able to automatically find good settings for a DBMS's configuration knobs. OtterTune leverages data collected from previous tuning efforts to train machine learning models, and recommends new configurations that are as good as or better than ones generated by existing tools or a human expert. In this demonstration, we showcase OtterTune's ability to automatically select a configuration that improves a DBMS's performance.


A review and experimental evaluation of recent advances in time series classification


title: Optimal Energy Management for a Li-Ion Battery/Supercapacitor Hybrid Energy Storage System Based on a Particle Swarm Optimization Incorporating Nelder–Mead Simplex Approach

abstract: Combining a high-power source like a supercapacitor with a lithium-ion battery for electric vehicle applications results in performance improvements, high efficiency, long lifetime, lightweight design, and relatively modest cost of the overall source. A hybrid energy storage system controlled by a smart energy management strategy can play a key role in the design and development of multisource electric vehicles. In this study, an optimal energy management strategy based on particle swarm optimization incorporating the Nelder–Mead simplex method is proposed. The goal of the proposed strategy is to minimize the battery power stress and improve its lifetime. This is achieved by coupling a rule-based method based on the knowledge of the battery and supercapacitor efficiency operating with a hybrid Particle Swarm–Nelder–Mead optimization algorithm. This latter approach is proposed to optimize the control parameters of the rule-based energy management strategy. Once the offline optimization algorithm is completed, the control method can be implemented online. The developed strategy is tested in a simulation environment and in an experimental platform using a power emulator test bench of a lithium-ion battery/supercapacitor hybrid energy storage system. The results, in terms of battery power stress and lifetime, are compared with a conventional rule-based method and a monosource with a single high-power lithium-ion battery. Obtained results show the effectiveness of the proposed strategy allowing the satisfaction of the requested performance with better battery usage. The evaluation results also demonstrate significant lifetime enhancements for the Li-ion battery, an increase of up to 20% as compared to the monosource based on a regular single battery.


Validation of the EORTC QLQ-GINET21 questionnaire for assessing quality of life of patients with gastrointestinal neuroendocrine tumours


title: Generalizing matrix factorization through flexible regression priors

abstract: Predicting user "ratings" on items is a crucial task in recommender systems. Matrix factorization methods that computes a low-rank approximation of the incomplete user-item rating matrix provide state-of-the-art performance, especially for users and items with several past ratings (warm starts). However, it is a challenge to generalize such methods to users and items with few or no past ratings (cold starts). Prior work [4][32] have generalized matrix factorization to include both user and item features for performing better regularization of factors as well as provide a model for smooth transition from cold starts to warm starts. However, the features were incorporated via linear regression on factor estimates. In this paper, we generalize this process to allow for arbitrary regression models like decision trees, boosting, LASSO, etc. The key advantage of our approach is the ease of computing --- any new regression procedure can be incorporated by "plugging" in a standard regression routine into a few intermediate steps of our model fitting procedure. With this flexibility, one can leverage a large body of work on regression modeling, variable selection, and model interpretation. We demonstrate the usefulness of this generalization using the MovieLens and Yahoo! Buzz datasets.


title: Stock Prediction Using Event-Based Sentiment Analysis

abstract: We propose a novel approach to label social media text using significant stock market events (big losses or gains). Since stock events are easily quantifiable using returns from indices or individual stocks, they provide meaningful and automated labels. We extract significant stock movements and collect appropriate pre, post and contemporaneous text from social media sources (for example, tweets from twitter). Subsequently, we assign the respective label (positive or negative) for each tweet. We train a model on this collected set and make predictions for labels of future tweets. We aggregate the net sentiment per each day (amongst other metrics) and show that it holds significant predictive power for subsequent stock market movement. We create successful trading strategies based on this system and find significant returns over other baseline methods.


title: Online social networks: Why do students use facebook?

abstract: The growth and popularity of online social networks has created a new world of collaboration and communication. More than a billion individuals around the world are connected and networked together to create, collaborate, and contribute their knowledge and wisdom. Despite the importance of online social networks, there is relatively little theory-driven empirical research available to address this new type of communication and interaction phenomena. In this paper, we explored the factors that drive students to use online social networks (e.g., Facebook). Specifically, we conceptualized the use of online social networks as intentional social action and we examined the relative impact of social influence, social presence, and the five key values from the uses and gratification paradigm on We-Intention to use online social networks. An empirical study of Facebook users (n=182) revealed that We-Intention to use online social networks is strongly determined by social presence. Among the five values, social related factors had the most significant impact on the intention to use. Implications for research and practice are discussed.


title: Convolutional neural networks with multi-valued neurons

abstract: Convolutional neural network (CNN) has been successfully used in many fields including image recognition. CNN is composed of input, convolution, pooling, hidden and output layers, and the weights and biases between layers except the ones between convolution and pooling layers are acquired by learning. In comparison to the conventional neural networks, the learning cost of CNN is higher, and the learning time is longer especially when hidden layer(s) are added. Recently, complex- and quaternion-valued neural networks have drawn much attention. In complex-valued neural networks, inputs, weights, biases and outputs are complex numbers, and in quaternion-valued neural networks, these parameters are quaternions. It has been shown that both methods exhibit excellent accuracy in various applications such as classification and function approximation problems with less computational burden. In this study, we propose CNNs with complex- and quaternion-valued neurons where complex and quaternion numbers are used between pooling, hidden and output layers. We here show that CNNs with complex- and quaternion-valued neurons have higher learning ability in handwritten digit image classification with the MNIST data than the real-valued counterpart.


title: S-Sense: A Sentiment Analysis Framework for Social Media Sensing

abstract: Due to the explosive growth of social media usage in Thailand, many businesses and organizations including market research agencies are seeking for tools which could perform real-time sentiment analysis on the large contents. In this paper, we propose S-Sense, a framework for analyzing sentiment on Thai social media. The proposed framework consists of analysis modules and language resources. Two main analysis modules, intention and sentiment, are based on classification algorithm to automatically assign appropriate intention and sentiment class labels for a given text. To train classification models, language resources, i.e., corpus and lexicon, are needed. Corpus consists of a collection of texts manually labeled with appropriate intention and sentiment classes. Lexicon consists of both general terms from dictionary and clue terms which help identifying the intention and sentiment. To evaluate performance and robustness of the analysis modules, we prepare a data set from Twitter posts and Pantip web board in mobile service domain. The experiments are set up to compare the performance between two different lexicon sets, i.e., general and clue terms. The results show that incorporating clue terms into feature vectors for constructing the classification models yield significant improvement in terms of accuracy.


title: Variational End-to-End Navigation and Localization

abstract: Deep learning has revolutionized the ability to learn “end-to-end” autonomous vehicle control directly from raw sensory data. While there have been recent extensions to handle forms of navigation instruction, these works are unable to capture the full distribution of possible actions that could be taken and to reason about localization of the robot within the environment. In this paper, we extend end-to-end driving networks with the ability to perform point-to-point navigation as well as probabilistic localization using only noisy GPS data. We define a novel variational network capable of learning from raw camera data of the environment as well as higher level roadmaps to predict (1) a full probability distribution over the possible control commands; and (2) a deterministic control command capable of navigating on the route specified within the map. Additionally, we formulate how our model can be used to localize the robot according to correspondences between the map and the observed visual road topology, inspired by the rough localization that human drivers can perform. We test our algorithms on real-world driving data that the vehicle has never driven through before, and integrate our point-topoint navigation algorithms onboard a full-scale autonomous vehicle for real-time performance. Our localization algorithm is also evaluated over a new set of roads and intersections to demonstrates rough pose localization even in situations without any GPS prior.


Multi-lingual Entity Discovery and Linking


title: Challenge benchmarks for verification of real-time programs

abstract: Real-time systems, and in particular safety-critical systems, are a rich source of challenges for the program verification community as software errors can have catastrophic consequences. Unfortunately, it is nearly impossible to find representative safety-critical programs in the public domain. This has been significant impediment to research in the field, as it is very difficult to validate new ideas or techniques experimentally. This paper presents open challenges for verification of real-time systems in the context of the Real-time Specification for Java. But, our main contribution is a family of programs, called CDx, which we present as an open source benchmark for the verification community.


title: Suicidal ligature strangulation with an elastic band.

abstract: An unusual case of self-strangulation with an elastic band is described. The victim was a young Hispanic male with a complicated psychiatric history, including suicide attempts. Mechanisms of strangulation and mechanical asphyxial death are discussed briefly.


title: Social Media and the Arab Spring Politics Comes First

abstract: The goal of this article is to place the role that social media plays in collective action within a more general theoretical structure, using the events of the Arab Spring as a case study. The article presents two broad theoretical principles. The first is that one cannot understand the role of social media in collective action without first taking into account the political environment in which they operate. The second principle states that a significant increase in the use of the new media is much more likely to follow a significant amount of protest activity than to precede it. The study examines these two principles using political, media, and protest data from twenty Arab countries and the Palestinian Authority. The findings provide strong support for the validity of the claims.


title: Assessing the Accuracy of Georeferenced Point Clouds Produced via Multi-View Stereopsis from Unmanned Aerial Vehicle (UAV) Imagery

abstract: Sensor miniaturisation, improved battery technology and the availability of low-cost yet advanced Unmanned Aerial Vehicles (UAV) have provided new opportunities for environmental remote sensing. The UAV provides a platform for close-range aerial photography. Detailed imagery captured from micro-UAV can produce dense point clouds using multi-view stereopsis (MVS) techniques combining photogrammetry and computer vision. This study applies MVS techniques to imagery acquired from a multi-rotor micro-UAV of a natural coastal site in southeastern Tasmania, Australia. A very dense point cloud ( < 1–3 cm point spacing) is produced in an arbitrary coordinate system using full resolution imagery, whereas other studies usually downsample the original imagery. The point cloud is sparse in areas of complex vegetation and where surfaces have a homogeneous texture. Ground control points collected with Differential Global Positioning System (DGPS) are identified and used for georeferencing via a Helmert transformation. This study compared georeferenced point clouds to a Total Station survey in order to assess and quantify their geometric accuracy. The results indicate that a georeferenced point cloud accurate to 25–40 mm can be obtained from imagery acquired from ~50 m. UAV-based image capture provides the spatial and temporal resolution required to map and monitor natural landscapes. This paper assesses the accuracy of the generated point clouds based on field survey points. Based on our key findings we conclude that sub-decimetre terrain change (in this case coastal erosion) can be monitored.


title: Haskell session types with (almost) no class

abstract: We describe an implementation of session types in Haskell. Session types statically enforce that client-server communication proceeds according to protocols. They have been added to several concurrent calculi, but few implementations of session types are available. Our embedding takes advantage of Haskell where appropriate, but we rely on no exotic features. Thus our approach translates with minimal modification to other polymorphic, typed languages such as ML and Java. Our implementation works with existing Haskell concurrency mechanisms, handles multiple communication channels and recursive session types, and infers protocols automatically. While our implementation uses unsafe operations in Haskell, it does not violate Haskell's safety guarantees. We formalize this claim in a concurrent calculus with unsafe communication primitives over which we layer our implementation of session types, and we prove that the session types layer is safe. In particular, it enforces that channel-based communication follows consistent protocols.


title: The spread of emotion via facebook

abstract: In this paper we study large-scale emotional contagion through an examination of Facebook status updates. After a user makes a status update with emotional content, their friends are significantly more likely to make a valence-consistent post. This effect is significant even three days later, and even after controlling for prior emotion expressions by both users and their friends. This indicates not only that emotional contagion is possible via text-only communication and that emotions flow through social networks, but also that emotion spreads via indirect communications media.


title: Robust Disambiguation of Named Entities in Text

abstract: Disambiguating named entities in natural-language text maps mentions of ambiguous names onto canonical entities like people or places, registered in a knowledge base such as DBpedia or YAGO. This paper presents a robust method for collective disambiguation, by harnessing context from knowledge bases and using a new form of coherence graph. It unifies prior approaches into a comprehensive framework that combines three measures: the prior probability of an entity being mentioned, the similarity between the contexts of a mention and a candidate entity, as well as the coherence among candidate entities for all mentions together. The method builds a weighted graph of mentions and candidate entities, and computes a dense subgraph that approximates the best joint mention-entity mapping. Experiments show that the new method significantly outperforms prior methods in terms of accuracy, with robust behavior across a variety of inputs.


title: Pentoxifylline and intermittent claudication: review of clinical trials and cost-effectiveness analyses.

abstract: Intermittent claudication (IC) is common in the elderly ; the prevalence is approximately 6% in 50- to 60-year-old patients and 10-20% in those over the age of 70. Several risk factors, especially smoking, are associated with increased prevalence. Disease progression results in increasingly debilitating and costly surgical intervention for about 20% of patients. This report reviews findings from some of the clinical studies that demonstrated the efficacy of pentoxifylline, the only U.S.-approved medical therapy for IC. Findings from a recently published cost-effectiveness analysis are presented. IC is difficult to study clinically because pain is both variable and subjective. In two multicenter, randomized, placebo-controlled studies, carefully monitored treadmill testing showed that pentoxifylline-treated patients had significantly improved walking distances even in the presence of a placebo effect. The pentoxifylline effect was pronounced in patients from a clinical target population defined by low baseline resting pressure ratios (≤0.8) and long disease duration (>1 year). To understand the social implications of these findings, treadmill distances were converted to comparable distances on flat ground. Improvements on pentoxifylline therapy translate to walking distances that enable greater daily function. This improvement has significant practical benefit to the quality of life of IC patients. Using Medicare expenditure data, it was found that pentoxifylline therapy reduced average hospital costs per patients by $1,173. Direct medical cost savings of $69 to $3,090 were suggested by sensitivity analyses. In analyses of practical aspects of walking distance as well as cost-effectiveness analyses, pentoxifylline appears to be a highly useful treatment for IC.


title: In VINI veritas: realistic and controlled network experimentation

abstract: This paper describes VINI, a virtual network infrastructure that allows network researchers to evaluate their protocols and services in a realistic environment that also provides a high degree of control over network conditions. VINI allows researchers to deploy and evaluate their ideas with real routing software, traffic loads, and network events. To provide researchers flexibility in designing their experiments, VINI supports simultaneous experiments with arbitrary network topologies on a shared physical infrastructure. This paper tackles the following important design question: What set of concepts and techniques facilitate flexible, realistic, and controlled experimentation (e.g., multiple topologies and the ability to tweak routing algorithms) on a fixed physical infrastructure? We first present VINI's high-level design and the challenges of virtualizing a single network. We then present PL-VINI, an implementation of VINI on PlanetLab, running the "Internet In a Slice". Our evaluation of PL-VINI shows that it provides a realistic and controlled environment for evaluating new protocols and services.


title: Compositional Distributional Semantics with Compact Closed Categories and Frobenius Algebras

abstract: The provision of compositionality in distributional models of meaning, where a word is represented as a vector of co-occurrence counts with every other word in the vocabulary, offers a solution to the fact that no text corpus, regardless of its size, is capable of providing reliable co-occurrence statistics for anything but very short text constituents. The purpose of a compositional distributional model is to provide a function that composes the vectors for the words within a sentence, in order to create a vectorial representation that re ects its meaning. Using the abstract mathematical framework of category theory, Coecke, Sadrzadeh and Clark showed that this function can directly depend on the grammatical structure of the sentence, providing an elegant mathematical counterpart of the formal semantics view. The framework is general and compositional but stays abstract to a large extent. This thesis contributes to ongoing research related to the above categorical model in three ways: Firstly, I propose a concrete instantiation of the abstract framework based on Frobenius algebras (joint work with Sadrzadeh). The theory improves shortcomings of previous proposals, extends the coverage of the language, and is supported by experimental work that improves existing results. The proposed framework describes a new class of compositional models thatfind intuitive interpretations for a number of linguistic phenomena. Secondly, I propose and evaluate in practice a new compositional methodology which explicitly deals with the different levels of lexical ambiguity (joint work with Pulman). A concrete algorithm is presented, based on the separation of vector disambiguation from composition in an explicit prior step. Extensive experimental work shows that the proposed methodology indeed results in more accurate composite representations for the framework of Coecke et al. in particular and every other class of compositional models in general. As a last contribution, I formalize the explicit treatment of lexical ambiguity in the context of the categorical framework by resorting to categorical quantum mechanics (joint work with Coecke). In the proposed extension, the concept of a distributional vector is replaced with that of a density matrix, which compactly represents a probability distribution over the potential different meanings of the specific word. Composition takes the form of quantum measurements, leading to interesting analogies between quantum physics and linguistics.


title: A biometric approach to aesthetic crown lengthening: part I--midfacial considerations.

abstract: Although human dental anatomy is taught in university curricula, clinicians often witness restorations that are not proportional to one another. Dental restorations should also be proportional to periodontal supporting tissues as an essential aspect of dental anatomy. Measurements can be performed directly on a patient's teeth with aesthetic gauges used to confirm the correct position of the supporting osseous topography. This article demonstrates a technique using these gauges to objectively determine the correct position of the underlying hard tissues and render predictable, aesthetic treatment.


title: Increasing network throughput by integrating protocol layers

abstract: Integrating protocol data manipulations is a strategy for increasing the throughput of network protocols. The idea is to combine a series of protocol layers into a pipeline so as to access message data more efficiently. This paper introduces a widely-applicable technique for integrating protocols. This technique not only improves performance, but also preserves the modularity of protocol layers by automatically integrating independently expressed protocols. The paper also describes a prototype integration tool, and studies the performance limits and scalability of protocol integration. >


title: 3D face texture modeling from uncalibrated frontal and profile images

abstract: 3D face modeling from 2D face images is of significant importance for face analysis, animation and recognition. Previous research on this topic mainly focused on 3D face modeling from a single 2D face image; however, a single face image can only provide a limited description of a 3D face. In many applications, for example, law enforcement, multi-view face images are usually captured for a subject during enrollment, which makes it desirable to build a 3D face texture model, given a pair of frontal and profile face images. We first determine the correspondence between un-calibrated frontal and profile face images through facial landmark alignment. An initial 3D face shape is then reconstructed from the frontal face image, followed by shape refinement utilizing the depth information provided by the profile image. Finally, face texture is extracted by mapping the frontal face image on the recovered 3D face shape. The proposed method is utilized for 2D face recognition in two scenarios: (i) normalization of probe image, and (ii) enhancing the representation capability of gallery set. Experimental results comparing the proposed method with a state-of-the-art commercial face matcher and densely sampled LBP on a subset of the FERET database show the effectiveness of the proposed 3D face texture model.


title: Does the G-spot exist? A review of the current literature

abstract: In 1950, Gräfenberg described a distinct erotogenic zone on the anterior wall of the vagina, which was referred to as the Gräfenberg spot (G-spot) by Addiego, Whipple (a nurse) et al. in 1981. As a result, the G-spot has become a central topic of popular speculation and a basis of a huge business surrounding it. In our opinion, these sexologists have made a hotchpotch of Gräfenberg's thoughts and ideas that were set forth and expounded in his 1950 article: the intraurethral glands are not the corpus spongiosum of the female urethra, and Gräfenberg did not report an orgasm of the intraurethral glands. G-spot amplification is a cosmetic surgery procedure for temporarily increasing the size and sensitivity of the G-spot in which a dermal filler or a collagen-like material is injected into the bladder-vaginal septum. All published scientific data point to the fact that the G-spot does not exist, and the supposed G-spot should not be identified with Gräfenberg's name. Moreover, G-spot amplification is not medically indicated and is an unnecessary and inefficacious medical procedure.


title: Digital forensics research: The next 10 years

abstract: Today's Golden Age of computer forensics is quickly coming to an end. Without a clear strategy for enabling research efforts that build upon one another, forensic research will fall behind the market, tools will become increasingly obsolete, and law enforcement, military and other users of computer forensics products will be unable to rely on the results of forensic analysis. This article summarizes current forensic research directions and argues that to move forward the community needs to adopt standardized, modular approaches for data representation and forensic processing.


title: Inception Single Shot MultiBox Detector for object detection

abstract: In the current object detection field, one of the fastest algorithms is the Single Shot Multi-Box Detector (SSD), which uses a single convolutional neural network to detect the object in an image. Although SSD is fast, there is a big gap compared with the state-of-the-art on mAP. In this paper, we propose a method to improve SSD algorithm to increase its classification accuracy without affecting its speed. We adopt the Inception block to replace the extra layers in SSD, and call this method Inception SSD (I-SSD). The proposed network can catch more information without increasing the complexity. In addition, we use the batch-normalization (BN) and the residual structure in our I-SSD network architecture. Besides, we propose an improved non-maximum suppression method to overcome its deficiency on the expression ability of the model. The proposed I-SSD algorithm achieves 78.6% mAP on the Pascal VOC2007 test, which outperforms SSD algorithm while maintaining its time performance. We also construct an Outdoor Object Detection (OOD) dataset to testify the effectiveness of the proposed I-SSD on the platform of unmanned vehicles.