title: Control strategies applied to kinesthetic haptic devices

abstract: In this paper, the state of the art in force feedback haptic devices is presented. The importance of the current searching for better immersion characteristics has motivated an interesting research in advanced control strategies. The passive, optimal, adaptive and robust control strategies applied in the last two years to haptic devices have improved the stability and response of contact with rigid and soft virtual objects. All this has shown that control can improve the human sensation of reality in virtual worlds.


title: Max-pooling convolutional neural networks for vision-based hand gesture recognition

abstract: Automatic recognition of gestures using computer vision is important for many real-world applications such as sign language recognition and human-robot interaction (HRI). Our goal is a real-time hand gesture-based HRI interface for mobile robots. We use a state-of-the-art big and deep neural network (NN) combining convolution and max-pooling (MPCNN) for supervised feature learning and classification of hand gestures given by humans to mobile robots using colored gloves. The hand contour is retrieved by color segmentation, then smoothened by morphological image processing which eliminates noisy edges. Our big and deep MPCNN classifies 6 gesture classes with 96% accuracy, nearly three times better than the nearest competitor. Experiments with mobile robots using an ARM 11 533MHz processor achieve real-time gesture recognition performance.


title: A Secure and Efficient Uniqueness-and-Anonymity-Preserving Remote User Authentication Scheme for Connected Health Care

abstract: Connected health care has several applications including telecare medicine information system, personally controlled health records system, and patient monitoring. In such applications, user authentication can ensure the legality of patients. In user authentication for such applications, only the legal user/patient himself/herself is allowed to access the remote server, and no one can trace him/her according to transmitted data. Chang et al. proposed a uniqueness-and-anonymity-preserving remote user authentication scheme for connected health care (Chang et al., J Med Syst 37:9902, 2013). Their scheme uses the user’s personal biometrics along with his/her password with the help of the smart card. The user’s biometrics is verified using BioHashing. Their scheme is efficient due to usage of one-way hash function and exclusive-or (XOR) operations. In this paper, we show that though their scheme is very efficient, their scheme has several security weaknesses such as (1) it has design flaws in login and authentication phases, (2) it has design flaws in password change phase, (3) it fails to protect privileged insider attack, (4) it fails to protect the man-in-the middle attack, and (5) it fails to provide proper authentication. In order to remedy these security weaknesses in Chang et al.’s scheme, we propose an improvement of their scheme while retaining the original merit of their scheme. We show that our scheme is efficient as compared to Chang et al.’s scheme. Through the security analysis, we show that our scheme is secure against possible attacks. Further, we simulate our scheme for the formal security verification using the widely-accepted AVISPA (Automated Validation of Internet Security Protocols and Applications) tool to ensure that our scheme is secure against passive and active attacks. In addition, after successful authentication between the user and the server, they establish a secret session key shared between them for future secure communication.


title: State-dependent computations: spatiotemporal processing in cortical networks

abstract: A conspicuous ability of the brain is to seamlessly assimilate and process spatial and temporal features of sensory stimuli. This ability is indispensable for the recognition of natural stimuli. Yet, a general computational framework for processing spatiotemporal stimuli remains elusive. Recent theoretical and experimental work suggests that spatiotemporal processing emerges from the interaction between incoming stimuli and the internal dynamic state of neural networks, including not only their ongoing spiking activity but also their 'hidden' neuronal states, such as short-term synaptic plasticity.


title: Geomagnetism for Smartphone-Based Indoor Localization: Challenges, Advances, and Comparisons

abstract: Geomagnetism has recently attracted considerable attention for indoor localization due to its pervasiveness and independence from extra infrastructure. Its location signature has been observed to be temporally stable and spatially discernible for localization purposes. This survey examines and analyzes the recent challenges and advances in geomagnetism-based indoor localization using smartphones. We first study smartphone-based geomagnetism measurements. We then review recent efforts in database construction and computation reduction, followed by state-of-the-art schemes in localizing the target. For each category, we identify practical deployment challenges and compare related studies. Finally, we summarize future directions and provide a guideline for new researchers in this field.


title: Boosting Algorithms: Regularization, Prediction and Model Fitting

abstract: We present a statistical perspective on boosting. Special emphasis is given to estimating potentially complex parametric or nonparametric models, including generalized linear and additive models as well as regression models for survival analysis. Concepts of degrees of freedom and corresponding Akaike or Bayesian information criteria, particularly useful for regularization and variable selection in high-dimensional covariate spaces, are discussed as well. The practical aspects of boosting procedures for fitting statistical models are illustrated by means of the dedicated open-source software package mboost. This package implements functions which can be used for model fitting, prediction and variable selection. It is flexible, allowing for the implementation of new boosting algorithms optimizing user-specified loss functions.


title: Adaptive Observers Applied to Pan Temperature Control of Induction Hobs

abstract: In this paper, a pan temperature control of induction hobs is described. The aim is that the user may select the cooking temperature instead of the power for the cooker. Pan temperature has to be estimated from a thermal sensor placed under the ceramic glass. Heat transmission from the induction coil to the pan and from the pan to the thermal sensor is modeled using a state-space model. As induction heating is highly dependent on thermal and magnetic pan properties, two different adaptive schemes are proposed and compared. Both guarantee a proper closed loop behavior. The contribution of this paper is related with temperature adaptive observer for different pans. The resultant system is simple, user friendly, robust, and safe, and is used for automatic cooking.


title: Learning the Kernel Matrix with Semi-Definite Programming

abstract: Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive definite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space -- classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semi-definite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -- using the labelled part of the data one can learn an embedding also for the unlabelled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method to learn the 2-norm soft margin parameter in support vector machines, solving another important open problem. Finally, the novel approach presented in the paper is supported by positive empirical results.


title: Statistical Section Segmentation in Free-Text Clinical Records

abstract: Automatically segmenting and classifying clinical free text into sections is an important first step to automatic information retrieval, information extraction and data mining tasks, as it helps to ground the significance of the text within. In this work we describe our approach to automatic section segmentation of clinical records such as hospital discharge summaries and radiology reports, along with section classification into pre-defined section categories. We apply machine learning to the problems of section segmentation and section classification, comparing a joint (one-step) and a pipeline (two-step) approach. We demonstrate that our systems perform well when tested on three data sets, two for hospital discharge summaries and one for radiology reports. We then show the usefulness of section information by incorporating it in the task of extracting comorbidities from discharge summaries.


Selecting a Software Elicitation Technique According to Layers of Knowledge and Preciseness: A Case Study.


title: Acne scarring treatment using skin needling

abstract: BACKGROUND ::: Acne is a common condition seen in up to 80% of people between 11 and 30 years of age and in up to 5% of older adults. In some patients, it can result in permanent scars that are surprisingly difficult to treat. A relatively new treatment, termed skin needling (needle dermabrasion), seems to be appropriate for the treatment of rolling scars in acne. ::: ::: ::: AIM ::: To confirm the usefulness of skin needling in acne scarring treatment. ::: ::: ::: METHODS ::: The present study was conducted from September 2007 to March 2008 at the Department of Systemic Pathology, University of Naples Federico II and the UOC Dermatology Unit, University of Rome La Sapienza. In total, 32 patients (20 female, 12 male patients; age range 17-45) with acne rolling scars were enrolled. Each patient was treated with a specific tool in two sessions. Using digital cameras, photos of all patients were taken to evaluate scar depth and, in five patients, silicone rubber was used to make a microrelief impression of the scars. The photographic data were analysed by using the sign test statistic (alpha < 0.05) and the data from the cutaneous casts were analysed by fast Fourier transformation (FFT). ::: ::: ::: RESULTS ::: Analysis of the patient photographs, supported by the sign test and of the degree of irregularity of the surface microrelief, supported by FFT, showed that, after only two sessions, the severity grade of rolling scars in all patients was greatly reduced and there was an overall aesthetic improvement. No patient showed any visible signs of the procedure or hyperpigmentation. ::: ::: ::: CONCLUSION ::: The present study confirms that skin needling has an immediate effect in improving acne rolling scars and has advantages over other procedures.


Cognitive Walkthrough for HCI evaluation: basic concepts, evolutions and variants, research issues


title: An offset algorithm for polyline curves

abstract: Polyline curves which are composed of line segments and arcs are widely used in engineering applications. In this paper, a novel offset algorithm for polyline curves is proposed. The offset algorithm comprises three steps. Firstly, the offsets of all the segments of polyline curves are calculated. Then all the offsets are trimmed or joined to build polyline curves that are called untrimmed offset curves. Finally, a clipping algorithm is applied to the untrimmed offset curves to yield the final results. The offset algorithm can deal with polyline curves that are self-intersection, overlapping or containing small arcs. The new algorithm has been implemented in a commercial system TiOpenCAD 8.0 and its reliability is verified by a great number of examples.


title: Development of Head-Mounted Projection Displays for Distributed, Collaborative, Augmented Reality Applications

abstract: Distributed systems technologies supporting 3D visualization and social collaboration will be increasing in frequency and type over time. An emerging type of head-mounted display referred to as the head-mounted projection display (HMPD) was recently developed that only requires ultralight optics (i.e., less than 8 g per eye) that enables immersive multiuser, mobile augmented reality 3D visualization, as well as remote 3D collaborations. In this paper a review of the development of lightweight HMPD technology is provided, together with insight into what makes this technology timely and so unique. Two novel emerging HMPD-based technologies are then described: a teleportal HMPD (T-HMPD) enabling face-to-face communication and visualization of shared 3D virtual objects, and a mobile HMPD (M-HMIPD) designed for outdoor wearable visualization and communication. Finally, the use of HMPD in medical visualization and training, as well as in infospaces, two applications developed in the ODA and MIIND labs respectively, are discussed.


title: Research on design of fuzzy controller of smart car steering system

abstract: A fuzzy control method with parameter self-tuning is presented,which optimizes control of smart car steering system.A method of fuzzy controller's design and implementation is introduced,and rules of fuzzy parameter self-tuning are obtained through experiments.Problems such as inaccuracy and overshoot when smart car swerving are improved.With this method,the steering control system of smart car has good adaptability and robustness.


title: Catering service robot

abstract: Catering service robot working in restaurants is integrated with autonomous mobile, multi-sensor data fusion, and multimodal human computer interaction techniques. Catering service robot can replace or partially take place of the restaurant staffs; it has good marketing effectiveness and potential profitable advantages. In this paper the design of a catering service robot is introduced. Beautiful appearance is used on the robot's upper body; the wheeled mobile platform is installed in the bottom of the robot. The robot can be used for customers ordering dishes and offers delivering services in the restaurant with a friendly interactions and self-mobility. The paper analyzes the catering service robot system and describes the framework of the hardware platform and mechanical structure. The software system of the robot includes the motor unit of software module, the realization of autonomous mobile and ordering software module. Finally, the simulation and experiments are carried out.


title: Symmetric solutions for an elliptic partial differential equation that arises in stochastic production planning with production constraints

abstract: Abstract In this article we consider the question of the existence of positive symmetric solutions to the problems of the following type Δ u = a ( | x | ) h ( u ) + b ( | x | ) g ( u ) for x ∈ R N , which are called entire large solutions. Here N ≥ 3, and we assume that a and b are nonnegative continuous spherically symmetric functions on R N . We extend results previously obtained for special cases of h and g and we will describe a real-world model in which such problems may arise.


title: Digital Technologies and the Changing Business Models in Creative Industries

abstract: This paper develops a holistic framework to examine the changing business models in creative industries in the digital economy, using evidence gathered from two strands of empirical work. The research found that digital technologies have facilitated pervasive changes in business models in all sectors of the creative industries, but with only a few exceptions, the resultant new business models are often neither novel nor unprecedented. In many cases, digital technologies simply allow organisations to deploy a wider range of business models than previously available to them, often by adapting and applying traditional business models to different ranges or stages of products in the online environment, or simultaneously adopting multiple business models as a portfolio. The paper identifies a series of emerging trends, and highlights three areas for future research.


title: Form perception with a 49-point electrotactile stimulus array on the tongue: a technical note.

abstract: Form perception with the tongue was studied with a 49-point electrotactile array. Five sighted adult human subjects (3M/2F) each received 4 blocks of 12 tactile patterns, approximations of circles, squares, and vertex-up equilateral triangles, sized to 4×4, 5×5, 6×6, and 7×7 electrode arrays. Perception with electrical stimulation of the tongue is better than with fingertip electrotactile stimulation, and the tongue requires 3% (5--15 V) of the voltage. The mean current for tongue subjects was 1.612 mA. Tongue shape recognition performance across all sizes was 79.8%. The approximate dimensions of the electrotactile array and the dimensions of compartments built into dental retainers have been determined. The goal is to develop a practical, cosmetically acceptable, wireless system for blind persons, with a miniature TV camera, microelectronics, and FM transmitter built into a pair of glasses, and the electrotactile array in a dental orthodontic retainer.


title: Information technology capability and firm performance: contradictory findings and their possible causes

abstract: Several studies support the positive link between information technology capability and firm performance, including Bharadwaj (2000) and Santhanam and Hartono (2003), which appeared in MIS Quarterly. We conducted a study to see if this link is still statistically significant. It is now over a decade since the first study was published, during which several significant developments in the IT industry have taken place. Unlike the 1990s, when proprietary information systems prevailed, the 2000s are characterized by more standardized and homogeneous information systems and with the rapid adoption of ERP and web technologies. Thus, we attempted to reexamine the link between IT capability and firm performance with data from the 2000s. Surprisingly, the results of our current analysis showed no significant link between IT capability and firm performance. Contrary to earlier studies, IT leader firms in our study didn't show better financial performance than control firms. We discuss several possible causes for the change in findings and present an in-depth comparison in business performance between the two groups--IT leader and control--over a period extending from 1991 to 2007.


title: Online Self-Presentation on Facebook and Self Development During the College Transition

abstract: Self-presentation, a central element of young people's identity development, now extends from face-to-face contexts to social networking sites. Online self-presentation may change when youth transition to college, faced with the need to reclaim or redefine themselves in the new environment. Drawing on theories of self-presentation and self development, this study explores changes in youth's online self-presentation during their transition to a residential college. It also examines associations between online self-presentation and students' self-esteem and self-concept clarity. We surveyed 218 college freshmen (M age = 18.07; 64 % female, 79 % White) at the beginning and again at the end of their first semester. Freshmen's Facebook self-presentation became less restricted later in the semester. Broad, deep, positive, and authentic Facebook self-presentation was positively associated with perceived support from the audience, which contributed to higher self-esteem contemporaneously, though not longitudinally. Intentional Facebook self-presentation engaged students in self-reflection, which was related to lower self-concept clarity concurrently but higher self-esteem longitudinally. FINDINGS clarified the paths from multifaceted online self-presentation to self development via interpersonal and intrapersonal processes during college transition. Language: en


title: MRI Image-to-Image Translation for Cross-Modality Image Registration and Segmentation.

abstract: We develop a novel cross-modality generation framework that learns to generate predicted modalities from given modalities in MR images without real acquisition. Our proposed method performs image-to-image translation by means of a deep learning model that leverages conditional generative adversarial networks (cGANs). Our framework jointly exploits the low-level features (pixel-wise information) and high-level representations (e.g. brain tumors, brain structure like gray matter, etc.) between cross modalities which are important for resolving the challenging complexity in brain structures. Based on our proposed framework, we first propose a method for cross-modality registration by fusing the deformation fields to adopt the cross-modality information from predicted modalities. Second, we propose an approach for MRI segmentation, translated multichannel segmentation (TMS), where given modalities, along with predicted modalities, are segmented by fully convolutional networks (FCN) in a multi-channel manner. Both these two methods successfully adopt the cross-modality information to improve the performance without adding any extra data. Experiments demonstrate that our proposed framework advances the state-of-the-art on five MRI datasets. We also observe encouraging results in cross-modality registration and segmentation on some widely adopted datasets. Overall, our work can serve as an auxiliary method in clinical diagnosis and be applied to various tasks in medical fields. ::: Keywords: Image-to-image, cross-modality, registration, segmentation, MRI


title: Dynamic Stability of a Rocker Bogie Vehicle: Longitudinal Motion

abstract: This paper describes a unified measure of stability of a Rocker Bogie vehicle that accounts for the tendency to slide, tipover, or lose contact with the ground considering both static equilibrium and dynamic effects. The measure of stability is computed by solving for the range of acceptable velocities and accelerations that satisfy a set of dynamic constraints. The maximum acceptable velocity serves as a dynamic stability measure, whereas the maximum acceptable acceleration at zero velocity serves as a static stability measure. The utility of the static and dynamic stability margins are demonstrated for both two dimensional and longitudinal quasi-3D motion in several examples.


title: MindNavigator: Exploring the Stress and Self-Interventions for Mental Wellness

abstract: Mental wellness is a desirable health outcome for students. However, current personal informatics systems do not adequately support students in creating concrete mental health-related goals and turning them into actionable plans. In this paper, we introduce MindNavigator - a workshop in which groups of college students were invited to generate behavioral change goals to manage daily life stress and practice personalized interventions for two weeks. We describe the manner in which participants identified both stressors and pleasures to create actionable, engaging, and open-ended behavioral plans that aided in stress relief. We found that the social nature of the workshop helped participants understand themselves and execute self-intervention in new ways. Through this practice, we build on prior studies to propose an analytical framework of personal informatics for mental wellness.


title: PicToon: a personalized image-based cartoon system

abstract: In this paper, we present PicToon, a cartoon system which can generate a personalized cartoon face from an input Picture. PicToon is easy to use and requires little user interaction. Our system consists of three major components: an image-based Cartoon Generator, an interactive Cartoon Editor for exaggeration, and a speech-driven Cartoon Animator. First, to capture an artistic style, the cartoon generation is decoupled into two processes: sketch generation and stroke rendering. An example-based approach is taken to automatically generate sketch lines which depict the facial structure. An inhomogeneous non-parametric sampling plus a flexible facial template is employed to extract the vector-based facial sketch. Various styles of strokes can then be applied. Second, with the pre-designed templates in Cartoon Editor, the user can easily make the cartoon exaggerated or more expressive. Third, a real-time lip-syncing algorithm is also developed that recovers a statistical audio-visual mapping between the character's voice and the corresponding lip configuration. Experimental results demonstrate the effectiveness of our system.


title: Facebook's emotional consequences: Why Facebook causes a decrease in mood and why people still use it

abstract: Abstract Facebook is the world’s most popular online social network and used by more than one billion people. In three studies, we explored the hypothesis that Facebook activity negatively affects people’s emotional state. A first study shows that the longer people are active on Facebook, the more negative is their mood afterwards. The second study provides causal evidence for this effect by showing that Facebook activity leads to a deterioration of mood compared to two different control conditions. Furthermore, it was demonstrated that this effect is mediated by a feeling of not having done anything meaningful. With such negative outcomes for its users, the question arises as to why so many people continue to use Facebook on a daily basis. A third study suggests that this may be because people commit an affective forecasting error in that they expect to feel better after using Facebook, whereas, in fact, they feel worse.


title: Mixed-Initiative Design of Game Levels: Integrating Mission and Space into Level Generation

abstract: A level designer typically creates the levels of a game to cater for a certain set of objectives, or mission. But in procedural content generation, it is common to treat the creation of missions and the generation of levels as two separate concerns. This often leads to generic levels that allow for various missions. However, this also creates a generic impression for the player, because the potential for synergy between the objectives and the level is not utilised. Following up on the mission-space generation concept, as described by Dormans, we explore the possibilities of procedurally generating a level from a designer-made mission. We use a generative grammar to transform a mission into a level in a mixed-initiative design setting. We provide two case studies, dungeon levels for a rogue-like game, and platformer levels for a metroidvania game. The generators differ in the way they use the mission to generate the space, but are created with the same tool for content generation based on model transformations. We discuss the differences between the two generation processes and compare it with a parameterized approach.


title: A Colour Palette for Automatic Detection of Blue-White Veil.

abstract: Colour assessment of pigmented skin lesions are essential for the diagnosis of malignant melanoma. However, visual interpretation of colour is subjective and prone to error. Computer programs can provide support to clinicians to overcome this subjectivity. So far, methods for colour analysis of this nature have utilised statistical classification models. This paper puts forward an alternative framework: an effort to reproduce the experience of human observer. The proposed method introduces a perceptually intuitive and semantically meaningful approach for colour and colour-related feature detection. As a case study, the task of automatic detection and segmentation of blue-white veil feature in dermoscopy images is examined. Our proposed method, as shown in our experiments, outperforms the prior art for this task, while it attempts to mimic the human perception of skin lesion colours.


title: Authentication of FPGA bitstreams: why and how

abstract: Encryption of volatile FPGA bitstreams provides confidentiality to the design but does not ensure its authenticity. This paper motivates the need for adding authentication to the configuration process by providing application examples where this functionality is useful. An examination of possible solutions is followed by suggesting a practical one in consideration of the FPGA's configuration environment constraints. The solution presented here involves two symmetric-key encryption cores running in parallel to provide both authentication and confidentiality while sharing resources for efficient implementation.


title: Optimized invariant representation of network traffic for detecting unseen malware variants

abstract: New and unseen polymorphic malware, zero-day attacks, or other types of advanced persistent threats are usually not detected by signature-based security devices, firewalls, or anti-viruses. This represents a challenge to the network security industry as the amount and variability of incidents has been increasing. Consequently, this complicates the design of learning-based detection systems relying on features extracted from network data. The problem is caused by different joint distribution of observation (features) and labels in the training and testing data sets. This paper proposes a classification system designed to detect both known as well as previously-unseen security threats. The classifiers use statistical feature representation computed from the network traffic and learn to recognize malicious behavior. The representation is designed and optimized to be invariant to the most common changes of malware behaviors. This is achieved in part by a feature histogram constructed for each group of HTTP flows (proxy log records) of a user visiting a particular hostname and in part by a feature self-similarity matrix computed for each group. The parameters of the representation (histogram bins) are optimized and learned based on the training samples along with the classifiers. The proposed classification system was deployed on large corporate networks, where it detected 2,090 new and unseen variants of malware samples with 90% precision (9 of 10 alerts were malicious), which is a considerable improvement when compared to the current flow-based approaches or existing signature-based web security devices.


title: Design of a Ternary Memory Cell Using CNTFETs

abstract: This paper presents a novel design of a ternary memory cell using carbon nanotube field-effect transistors (CNTFETs). Ternary logic is a promising alternative to conventional binary logic because it allows simplicity and energy efficiency in modern digital design due to the reduced circuit overhead in interconnects and chip area. In this paper, a novel design of a ternary memory cell based on CNTFETs is proposed; this cell uses a transmission gate for the write operation and a buffer for the read operation to make them separate. Chirality of the CNTFETs is utilized for threshold voltage control, thus avoiding the use of additional power supplies. Extensive simulation results using SPICE are reported to show that the two memory operations of the proposed ternary cell perform correctly at 0.9 V power supply. The static noise margin and read/write delay of the proposed ternary memory cell are also very good; by utilizing the latest CNTFET layout design tools, it is shown that the proposed ternary memory cell achieves a significant saving in area (41.6%) compared with its CMOS ternary counterpart at 32 nm.


title: STARIMA-based traffic prediction with time-varying lags

abstract: Based on the observation that the correlation between observed traffic at two measurement points or traffic stations may be time-varying, attributable to the time-varying speed which subsequently causes variations in the time required to travel between the two points, in this paper, we develop a modified Space-Time Autoregressive Integrated Moving Average (STARIMA) model with time-varying lags for short-term traffic flow prediction. Particularly, the temporal lags in the modified STARIMA change with the time-varying speed at different time of the day or equivalently change with the (time-varying) time required to travel between two measurement points. Firstly, a technique is developed to evaluate the temporal lag in the STARIMA model, where the temporal lag is formulated as a function of the spatial lag (spatial distance) and the average speed. Secondly, an unsupervised classification algorithm based on ISODATA algorithm is designed to classify different time periods of the day according to the variation of the speed. The classification helps to determine the appropriate time lag to use in the STARIMA model. Finally, a STARIMA-based model with time-varying lags is developed for short-term traffic prediction. Experimental results using real traffic data show that the developed STARIMA-based model with time-varying lags has superior accuracy compared with its counterpart developed using the traditional cross-correlation function and without employing time-varying lags.


title: Coping as a mediator of emotion.

abstract: There is widespread conviction among health care professionals that coping affects emotion. Yet theory and research have traditionally emphasized the effects of emotion on coping. The present research addresses this imbalance by evaluating the extent to which coping mediated emotions during stressful encounters in two Caucasian, community-residing samples. Subjects' recently experienced stressful encounters, the ways they coped with the demands of those encounters, and the emotions they experienced during two stages of those encounters were assessed repeatedly. The extent to which eight forms of coping mediated each of four sets of emotions was evaluated with a series of hierarchical regression analyses (of residuals). Coping was associated with changes in all four sets of emotions, with some forms of coping associated with increases in positive emotions and other forms associated with increases in negative emotions.


title: A survey on human activity recognition from videos

abstract: Understanding the activities of human from videos is demanding task in Computer Vision. Identifying the actions being accomplished by the human in the video sequence automatically and tagging their actions is the prime functionality of intelligent video systems. The goal of activity recognition is to identify the actions and objectives of one or more objects from a series of examination on the action of object and their environmental condition. The major applications of Human Activity Recognition varies from Content-based Video Analytics, Robotics, Human-Computer Interaction, Human fall detection, Ambient Intelligence, Visual Surveillance, Video Indexing etc. This paper collectively summarizes and deciphers the various methodologies, challenges and issues of Human Activity Recognition systems. Variants of Human Activity Recognition systems such as Human Object Interactions and Human-Human Interactions are also explored. Various benchmarking datasets and their properties are being explored. The Experimental Evaluation of various papers are analyzed efficiently with the various performance metrics like Precision, Recall, and Accuracy.


title: A survey on communication infrastructure for micro-grids

abstract: A micro-grid is a small scale power supply network that is designed to provide electricity to a small community with its own renewable energy sources. Due to distributed generation variability, security and load sharing issues, an efficient communication infrastructure is necessary between its agents (load, generation and storage units). Numerous research efforts are being developed to come up with such communication techniques that can overcome the barriers to implement the concept of micro-grids. This paper covers the features, characteristics and challenges of micro-grids and their associated communication techniques.


title: A multilinear singular value decomposition

abstract: We discuss a multilinear generalization of the singular value decomposition. There is a strong analogy between several properties of the matrix and the higher-order tensor decomposition; uniqueness, link with the matrix eigenvalue decomposition, first-order perturbation effects, etc., are analyzed. We investigate how tensor symmetries affect the decomposition and propose a multilinear generalization of the symmetric eigenvalue decomposition for pair-wise symmetric tensors.


title: A Mediating Schema Approach to the Data Transfer between Heterogeneous Health Care Systems

abstract: One of the main challenges of exchanging patient care records between heterogeneous interoperable systems is the difficulty in overcoming semantic differences between systems. These challenges are exacerbated by the lack of standardization between health care information systems and the costs associated with software upgrades and data restructuring. As a solution to this problem, this paper proposes a context specific mediating schema data translation mechanism that allows patient care records to transfer automatically between health care systems. This approach builds upon existing technologies and is specific to the health care industry. The proposed approach has been validated by implementing a concept demonstration prototype system that involves mediation in the transfer of emergency healthcare information from a pre-hospital system to a hospital system.


title: Ushare: user controlled social media based on blockchain

abstract: This paper presents the potential for blockchain based solutions to disrupt the world of social networking. We offer Ushare, a user centric blockchain supported social media network that enables users to control, trace and claim ownership of every piece of content they share. Harnessing peer-to-peer capabilities of the blockchain technology allows a truly decentralized, secure, anonymous and traceable content distribution network. Ushare consists of four key components: the blockchain, a hash table with encrypted content shared by a user, a Turing complete relationship system to control the the maximum number of shares performed by user's circle members and a local personal certificate authority that manages the user's circles and encrypts data to be shared before it is broadcasted to the network.


title: Optical negative-index metamaterials

abstract: Artificially engineered metamaterials are now demonstrating unprecedented electromagnetic properties that cannot be obtained with naturally occurring materials. In particular, they provide a route to creating materials that possess a negative refractive index and offer exciting new prospects for manipulating light. This review describes the recent progress made in creating nanostructured metamaterials with a negative index at optical wavelengths, and discusses some of the devices that could result from these new materials.


title: Design and simulation of high sensitive capacitive pressure sensor with slotted diaphragm

abstract: This paper presents a high sensitive MEMS capacitive pressure sensor for biomedical applications. Two sensor designs incorporating clamped and slotted diaphragm are implemented and compared to realize the pressure-sensitive components. The pressure sensor has been designed to measure pressures in the range of 0 to 60 mmhg that is in the range of intraocular pressure sensors. Intraocular pressure sensors are important in detection and treatment of an incurable disease called glaucoma. The capacitive pressure sensor is formed using p++ silicon diaphragm as a biocompatible material. The novelty of this method relies on p++si diaphragm includes some slots to reduce the effect of residual stress and stiffness of diaphragm. The slotted diaphragm makes capacitive pressure sensor more sensitive. That is more suitable for measuring intraocular pressure sensor. The results yield a sensitivity of 1.811×10−5 1/Pa for the clamped and 5.96×10−5 1/Pa for the slotted pressure sensor with a 0.55 × 0.55 mm2 diaphragm. Furthermore, the pull-in voltage for the clamped pressure sensor is 51v, while the pull-in voltage of the slotted one is 38v.


title: When Unsupervised Domain Adaptation Meets Tensor Representations

abstract: Domain adaption (DA) allows machine learning methods trained on data sampled from one distribution to be applied to data sampled from another. It is thus of great practical importance to the application of such methods. Despite the fact that tensor representations are widely used in Computer Vision to capture multi-linear relationships that affect the data, most existing DA methods are applicable to vectors only. This renders them incapable of reflecting and preserving important structure in many problems. We thus propose here a learning-based method to adapt the source and target tensor representations directly, without vectorization. In particular, a set of alignment matrices is introduced to align the tensor representations from both domains into the invariant tensor subspace. These alignment matrices and the tensor subspace are modeled as a joint optimization problem and can be learned adaptively from the data using the proposed alternative minimization scheme. Extensive experiments show that our approach is capable of preserving the discriminative power of the source domain, of resisting the effects of label noise, and works effectively for small sample sizes, and even one-shot DA. We show that our method outperforms the state-of-the-art on the task of cross-domain visual recognition in both efficacy and efficiency, and particularly that it outperforms all comparators when applied to DA of the convolutional activations of deep convolutional networks.


title: Towards Moral Autonomous Systems

abstract: Both the ethics of autonomous systems and the problems of their technical implementation have by now been studied in some detail. Less attention has been given to the areas in which these two separate concerns meet. This paper, written by both philosophers and engineers of autonomous systems, addresses a number of issues in machine ethics that are located at precisely the intersection between ethics and engineering. We first discuss different approaches towards the conceptual design of autonomous systems and their implications on the ethics implementation in such systems. Then we examine problematic areas regarding the specification and verification of ethical behavior in autonomous systems, particularly with a view towards the requirements of future legislation. We discuss transparency and accountability issues that will be crucial for any future wide deployment of autonomous systems in society. Finally we consider the, often overlooked, possibility of intentional misuse of AI systems and the possible dangers arising out of deliberately unethical design, implementation, and use of autonomous robots.


The Discipline and Practice of Qualitative Research


title: MATLAB Based Back-Propagation NeuralNetwork for Automatic Speech Recognition

abstract: Speech interface to computer is the next big step that the technology needs to take for general users. Automatic speech recognition (ASR) will play an important role in taking technology to the people. There are numerous applications of speech recognition such as direct voice input in aircraft, data entry, speech-to-text processing, voice user interfaces such as voice dialling. ASR system can be divided into two different parts, namely feature extraction and feature recognition. In this paper we present MATLAB based feature recognition using backpropagation neural network for ASR. The objective of this research is to explore how neural networks can be employed to recognize isolated-word speech as an alternative to the traditional methodologies. The general techniques developed here can be further extended to other applications such as sonar target recognition, missile tracking and classification of underwater acoustic signals. Back-propagation neural network algorithm uses input training samples and their respective desired output values to learn to recognize specific patterns, by modifying the activation values of its nodes and weights of the links connecting its nodes. Such a trained network is later used for feature recognition in ASR systems.


title: PWM waveform generation using rotary encoder on Spartan-3E starter kit

abstract: A PWM waveform generator with variable duty cycle has been designed and implemented in this paper. The design has been simulated for different frequencies. The waveform is generated on Spartan 3E FPGA starter kit which has on board rotary encoder. The on board clock signal and rotary encoder generates the required PWM signal. The duty cycle of PWM is varied by rotating the encoder in clockwise and anticlockwise directions. Various frequencies of PWM signal is generated by pressing it up and down. The design has been proposed where duty cycle can be varied from 0 to 100%. The frequency of the on board clock signal i.e., 50 MHz and this has been varied to generate different frequencies. The output can be observed on DSO and CRO. The PWM is used in controlling inverters, electrical motors and many communication and control applications. The usage of FPGA provides the reconfigurable architecture and also provides flexibility as well as economically viable.


title: A time-controllable Allan variance method for MEMS IMU

abstract: Purpose – The purpose of this paper is to reduce the calculation burden and speed up the estimation process of Allan variance method while ensuring the exactness of the analysis results.Design/methodology/approach – A series of six‐hour static tests have been implemented at room temperature, and the static measurements have been collected from MEMS IMU. In order to characterize the various types of random noise terms for the IMU, the basic definition and main procedure of the Allan variance method are investigated. Unlike the normal Allan variance method, which has the shortcomings of processing large data sets and requiring long computation time, a modified Allan variance method is proposed based on the features of data distribution in the log‐log plot of the Allan standard deviation versus the averaging time.Findings – Experiment results demonstrate that the modified Allan variance method can effectively estimate the noise coefficients for MEMS IMU, with controllable computation time and acceptable esti...


Implementing ML Algorithms with


Neuronal 5-HT Receptors and SERT


title: Impact of an animal-assisted therapy programme on physiological and psychosocial variables of paediatric oncology patients

abstract: The objective of this study was to propose an intervention and safety protocol for performing animal-assisted therapy (AAT) and evaluating its efficacy in children under outpatient oncological treatment based on psychological, physiological, and quality of life indicators for the children and caregivers. The sample consisted of 24 children diagnosed with leukaemia and solid tumours (58% girls with a mean age of 8.0 years) who underwent an AAT programme consisting of three 30-min sessions in an open group. Two dogs (one Labrador retriever and one golden retriever) were used, and activities such as sensory stimulation, gait training, and socialization were conducted. The exclusion criteria were severe mental problems, inability to answer the questions included in the instruments used, allergy to animals, unavailability/lack of interest, isolation precaution, surgical wound, use of invasive devices, ostomy, no current blood count for evaluation, neutropaenia, infection, fever, diarrhoea, vomiting, respiratory symptoms at the beginning of the intervention or 1 week before the intervention, hospitalization or scheduled surgery, and non-completion of the AAT programme. The variables analysed using validated self or other evaluations were stress, pain, mood, anxiety, depression, quality of life, heart rate, and blood pressure. A quasi-experimental study design was used. We observed a decrease in pain (p = 0.046, d = –0.894), irritation (p = 0.041, d = –0.917), and stress (p = 0.005; d = –1.404) and a tendency towards improvement of depressive symptoms (p = 0.069; d = –0.801). Among the caregivers, an improvement was observed in anxiety (p = 0.007, d = –1.312), mental confusion (p = 0.006, d = –1.350), and tension (p = 0.006, d = –1.361). Therefore, the selection criteria and care protocols used for the AAT programme in the oncological context were adequate, and the programme was effective.


title: Debiasing Decisions Improved Decision Making With a Single Training Intervention

abstract: From failures of intelligence analysis to misguided beliefs about vaccinations, biased judgment and decision making contributes to problems in policy, business, medicine, law, education, and private life. Early attempts to reduce decision biases with training met with little success, leading scientists and policy makers to focus on debiasing by using incentives and changes in the presentation and elicitation of decisions. We report the results of two longitudinal experiments that found medium to large effects of one-shot debiasing training interventions. Participants received a single training intervention, played a computer game or watched an instructional video, which addressed biases critical to intelligence analysis (in Experiment 1: bias blind spot, confirmation bias, and fundamental attribution error; in Experiment 2: anchoring, representativeness, and social projection). Both kinds of interventions produced medium to large debiasing effects immediately (games ≥ −31.94% and videos ≥ −18.60%) that persisted at least 2 months later (games ≥ −23.57% and videos ≥ −19.20%). Games that provided personalized feedback and practice produced larger effects than did videos. Debiasing effects were domain general: bias reduction occurred across problems in different contexts, and problem formats that were taught and not taught in the interventions. The results suggest that a single training intervention can improve decision making. We suggest its use alongside improved incentives, information presentation, and nudges to reduce costly errors associated with biased judgments and decisions.


title: Exploring causes of frustration for software developers

abstract: When learning to program, frustrating experiences contribute to negative learning outcomes and poor retention in the field. Defining a common framework that explains why these experiences occur can lead to better interventions and learning mechanisms. To begin constructing such a framework, we asked 45 software developers about the severity of their frustration and to recall their most recent frustrating programming experience. As a result, 67% considered their frustration to be severe. Further, we distilled the reported experiences into 11 categories, which include issues with mapping behaviors to code and broken programming tools. Finally, we discuss future directions for defining our framework and designing future interventions.


title: A Filtering Microstrip Antenna Array

abstract: A new filtering microstrip antenna array is presented. The antenna elements, together with the very compact feeding network, function as a third-order bandpass filter. The feeding network, which consists of one power divider and two baluns, provides the first two stages, and the microstrip antenna elements provide the last stage in the filter design. The equivalent lumped circuit model is analyzed, and the detail synthesis procedure is presented. A third-order filtering 2 × 2 microstrip antenna array is designed at a center frequency of 5 GHz with 3% fractional bandwidth and Chebyshev 0.3-dB equal-ripple broadside antenna gain response. The results from circuit model, full-wave simulation, and measurements agree well. Compared to the conventional patch antenna array, the proposed filtering microstrip antenna array successfully suppresses the unwanted signals in out-of-band, preserves good selectivity at band edges, and retains the flatness of the passband broadside antenna gain response.


title: Multi-View Data Generation Without View Supervision

abstract: The development of high-dimensional generative models has recently gained a great surge of interest with the introduction of variational auto-encoders and generative adversarial neural networks. Different variants have been proposed where the underlying latent space is structured, for example, based on attributes describing the data to generate. We focus on a particular problem where one aims at generating samples corresponding to a number of objects under various views. We assume that the distribution of the data is driven by two independent latent factors: the content, which represents the intrinsic features of an object, and the view, which stands for the settings of a particular observation of that object. Therefore, we propose a generative model and a conditional variant built on such a disentangled latent space. This approach allows us to generate realistic samples corresponding to various objects in a high variety of views. Unlike many multi-view approaches, our model doesn't need any supervision on the views but only on the content. Compared to other conditional generation approaches that are mostly based on binary or categorical attributes, we make no such assumption about the factors of variations. Our model can be used on problems with a huge, potentially infinite, number of categories. We experiment it on four image datasets on which we demonstrate the effectiveness of the model and its ability to generalize.


title: Search, Mining, and Their Applications on Mobile Devices: Introduction to the Special Issue

abstract: In recent years, mobile devices have become the most popular interface for users to retrieve and access information: recent reports show that users spend significantly more time and issue more search queries on mobile devices than on desktops in the United States.1 The accelerated growth of mobile usage brings unique opportunities to the information retrieval and data mining research communities. Mobile devices capture rich contextual and personal signals that can be leveraged to accurately predict users’ intent for serving more relevant content and can even proactively provide novel zero-query recommendations. Apple Siri, Google Now, and Microsoft Cortana are recent examples of such emerging systems. Furthermore, mobile devices constantly generate a huge amount of sensor footprints (e.g., GPS, motion sensors) and user activity data (e.g., used apps) that are often missing from their desktop counterparts. These new sources of implicit and explicit user feedback are valuable for discovering actionable knowledge, and designing better systems that serve each individual the right content at the right time and location. In addition, by aggregating mobile interactions across individuals, one can infer interesting conclusions beyond search and recommendation. Generating real-time traffic estimates is one example of such applications. This special issue focuses on research problems of search, mining, and their applications in mobile devices. Topics of interest in this special issue include but are not limited to mobile data mining and management, mobile search, personalization and recommendation, mobile user interfaces and human-computer interaction, and new applications in the mobile environment. The aim of this special issue is to bring together top experts across multiple disciplines, including information retrieval, data mining, mobile computing, and cyberphysical systems, such that academic and industrial researchers can exchange ideas and share the latest developments on the state of the art and practice of mobile search and mobile data mining.


title: Estimating local optimums in EM algorithm over Gaussian mixture model

abstract: EM algorithm is a very popular iteration-based method to estimate the parameters of Gaussian Mixture Model from a large observation set. However, in most cases, EM algorithm is not guaranteed to converge to the global optimum. Instead, it stops at some local optimums, which can be much worse than the global optimum. Therefore, it is usually required to run multiple procedures of EM algorithm with different initial configurations and return the best solution. To improve the efficiency of this scheme, we propose a new method which can estimate an upper bound on the logarithm likelihood of the local optimum, based on the current configuration after the latest EM iteration. This is accomplished by first deriving some region bounding the possible locations of local optimum, followed by some upper bound estimation on the maximum likelihood. With this estimation, we can terminate an EM algorithm procedure if the estimated local optimum is definitely worse than the best solution seen so far. Extensive experiments show that our method can effectively and efficiently accelerate conventional multiple restart EM algorithm.


title: Depth-of-field-based alpha-matte extraction

abstract: In compositing applications, objects depicted in images frequently have to be separated from their background, so that they can be placed in a new environment. Alpha mattes are important tools aiding the selection of objects, but cannot normally be created in a fully automatic way. We present an algorithm that requires as input two images---one where the object is in focus, and one where the background is in focus---and then automatically produces an alpha matte indicating which pixels belong to the object. This algorithm is inspired by human visual processing and involves nonlinear response compression, center-surround mechanisms as well as a filling-in stage. The output can then be refined with standard computer vision techniques.


title: Attribute Relationship Evaluation Methodology for Big Data Security

abstract: There has been an increasing interest in big data and big data security with the development of network technology and cloud computing. However, big data is not an entirely new technology but an extension of data mining. In this paper, we describe the background of big data, data mining and big data features, and propose attribute selection methodology for protecting the value of big data. Extracting valuable information is the main goal of analyzing big data which need to be protected. Therefore, relevance between attributes of a dataset is a very important element for big data analysis. We focus on two things. Firstly, attribute relevance in big data is a key element for extracting information. In this perspective, we studied on how to secure a big data through protecting valuable information inside. Secondly, it is impossible to protect all big data and its attributes. We consider big data as a single object which has its own attributes. We assume that a attribute which have a higher relevance is more important than other attributes.


title: Combining Information Extraction Systems Using Voting and Stacked Generalization

abstract: This article investigates the effectiveness of voting and stacked generalization -also known as stacking- in the context of information extraction (IE). A new stacking framework is proposed that accommodates well-known approaches for IE. The key idea is to perform cross-validation on the base-level data set, which consists of text documents annotated with relevant information, in order to create a meta-level data set that consists of feature vectors. A classifier is then trained using the new vectors. Therefore, base-level IE systems are combined with a common classifier at the meta-level. Various voting schemes are presented for comparing against stacking in various IE domains. Well known IE systems are employed at the base-level, together with a variety of classifiers at the meta-level. Results show that both voting and stacking work better when relying on probabilistic estimates by the base-level systems. Voting proved to be effective in most domains in the experiments. Stacking, on the other hand, proved to be consistently effective over all domains, doing comparably or better than voting and always better than the best base-level systems. Particular emphasis is also given to explaining the results obtained by voting and stacking at the meta-level, with respect to the varying degree of similarity in the output of the base-level systems.


title: SDN Based Architecture for IoT and Improvement of the Security

abstract: With the exponential growth of devices connected to the Internet, security networks as one of the hardest challenge for network managers. Maintaining and securing such large scale and heterogeneous network is a challenging task. In this context, the new networking paradigm, the Software Defined Networking (SDN), introduces many opportunities and provides the potential to overcome those challenges. In this article, we first present a new SDN based architecture for networking with or without infrastructure, that we call an SDN domain. A single domain includes wired network, wireless network and Ad-Hoc networks. Next, we propose a second architecture to include sensor networks in an SDN-based network and in a domain. Third, we interconnect multiple domains and we describe how we can enhanced the security of each domain and how to distribute the security rules in order not to compromise the security of one domain. Finally, we propose a new secure and distributed architecture for IoT (Internet of Things).


title: Thermal management of an IGBT module using two-phase cooling

abstract: A lot of effort has been put to develop the next generation cooling technologies for Insulated Gate Bipolar Transistor module of hybrid vehicles. Two phase cooling has been identified as a potential solution for cooling of such modules. This paper explores the viability and implementation of a two phase cooling scheme and its comparison with single phase cooling. For this inverter module of Toyota Prius consisting of 12 pairs of IGBT devices and diodes is used. Thermal simulation technique is used to study the effect of convective heat transfer coefficient, cold plate dimensions, total power on IGBT, thermal interface material and its thickness on the thermal performance of the module.


title: Extracorporeal Shockwave Therapy Versus Kinesiology Taping in the Management of Plantar Fasciitis: A Randomized Clinical Trial

abstract: Objectives ::: This study aims to compare the efficacy of extracorporeal shockwave therapy (ESWT) and kinesiology taping in the treatment of plantar fasciitis. ::: ::: ::: Patients and methods ::: The study included 80 patients diagnosed with plantar fasciitis. The patients were randomized into two groups as ESWT (9 males, 28 females; mean age 47.8±12.4 years; range 40 to 55 years) and kinesiology taping (KT, 7 males, 26 females; mean age 47.7±9.8 years; range 40 to 55 years) groups. Groups were similar regarding age, sex, and body mass index (all p>0.05). Three patients in ESWT group and seven patients in KT group were lost to follow-up. ESWT was applied once a week for five weeks, while KT was applied every five days for five weeks. Patients' pain and functional status were evaluated with visual analog scale, heel tenderness index, and foot and ankle outcome score before and after treatment. ::: ::: ::: Results ::: At the study onset, there were no statistically significant differences between the two groups in their visual analog scale, heel tenderness index, and foot and ankle outcome scores. Five weeks later, both groups showed significant improvement in all parameters (p<0.05), but no significant differences were observed between the groups in the visual analog scale, heel tenderness index, and foot and ankle outcome score scores. ::: ::: ::: Conclusion ::: Both ESWT and KT treatments improved pain levels and function and quality of life in individuals with plantar fasciitis. Neither method was superior in treating plantar fasciitis.


title: An adaptive FEC to protect RoHC and UDP-Lite H.264 video critical data

abstract: This paper describes how to use an Adaptive Forward Error Correction (AFEC) algorithm to efficiently protect the critical data areas of compressed headers and UDP-Lite packets for data transport in a radio link layer of a wireless connection. Augmented with RoHC and UDP-Lite, for H.264 video transmissions over wireless channels in a heterogeneous wired-wireless environment, the erroneous packet payloads can be useful and better able to cope with lost packets (native UDP case), by adopting some of the erasure and error resilient modes in H.264. The context transfer during inter/intra handover is also discussed. Simulations demonstrated that the proposed scenario significantly improves the PSNR performance and video quality.


title: A machine learning approach for instance matching based on similarity metrics

abstract: The Linking Open Data (LOD) project is an ongoing effort to construct a global data space, i.e. the Web of Data. One important part of this project is to establish owl:sameAs links among structured data sources. Such links indicate equivalent instances that refer to the same real-world object. The problem of discovering owl:sameAs links between pairwise data sources is called instance matching. Most of the existing approaches addressing this problem rely on the quality of prior schema matching, which is not always good enough in the LOD scenario. In this paper, we propose a schema-independent instance-pair similarity metric based on several general descriptive features. We transform the instance matching problem to the binary classification problem and solve it by machine learning algorithms. Furthermore, we employ some transfer learning methods to utilize the existing owl:sameAs links in LOD to reduce the demand for labeled data. We carry out experiments on some datasets of OAEI2010. The results show that our method performs well on real-world LOD data and outperforms the participants of OAEI2010.


title: Compressive Sensing MRI with Wavelet Tree Sparsity

abstract: In Compressive Sensing Magnetic Resonance Imaging (CS-MRI), one can reconstruct a MR image with good quality from only a small number of measurements. This can significantly reduce MR scanning time. According to structured sparsity theory, the measurements can be further reduced to O(K + log n) for tree-sparse data instead of O(K + K log n) for standard K-sparse data with length n. However, few of existing algorithms have utilized this for CS-MRI, while most of them model the problem with total variation and wavelet sparse regularization. On the other side, some algorithms have been proposed for tree sparse regularization, but few of them have validated the benefit of wavelet tree structure in CS-MRI. In this paper, we propose a fast convex optimization algorithm to improve CS-MRI. Wavelet sparsity, gradient sparsity and tree sparsity are all considered in our model for real MR images. The original complex problem is decomposed into three simpler subproblems then each of the subproblems can be efficiently solved with an iterative scheme. Numerous experiments have been conducted and show that the proposed algorithm outperforms the state-of-the-art CS-MRI algorithms, and gain better reconstructions results on real MR images than general tree based solvers or algorithms.


title: Missing information (applicable and inapplicable) in relational databases

abstract: There has been some technical and justified criticism of the treatment of missing information in the data sublanguage SQL and in IBM's Database 2 system (a relational database management system). Some of this criticism has been directed (by mistake) at the relational model. The purpose of this paper is to clarify and extend the treatment of missing information by the relational model. The clarification places heavy emphasis on the semantic aspects of missing information. The extension, which is relatively minor, provides a systematic approach (independent of data type) to dealing with the inapplicability of certain properties to some objects. This extension does not invalidate any part of the present version of the relational model.


title: Unsupervised disaggregation of appliances using aggregated consumption data

abstract: Non-Intrusive Load Monitoring (NILM) is a technique that determines the electrical load composition of a household through a single point of measurement at the main power feed. In contrast with the majority of the existing approaches to solve this problem which require training, here we explore an unsupervised approach to determine the number of appliances in the household, their power consumption and state, at any given moment. We attempt to achieve this without using any a priori information on the number and type of appliances. Our approach is to first create clusters of steady-state changes and then employ a matching pursuit algorithm to reconstruct the original power signals using the clusters that were found as the sources in a linear blind source separation strategy. Changes in steady-state, sometimes referred to as events, are characterized by their change in real and reactive power (P and Q). Ultimately, the results may be applied to other features in an attempt to improve the separation between clusters. The preliminary results point toward a mixed scenario: large appliances (roughly above 400W) were easily identified, but the small appliances typically clustered together and were difficult to separate. We conclude that the errors occur during clustering which indicates that, in order to increase the purity of the clusters, perhaps other features could be used.


title: The Long-Term Health Consequences of Child Physical Abuse, Emotional Abuse, and Neglect: A Systematic Review and Meta-Analysis

abstract: Background ::: ::: Child sexual abuse is considered a modifiable risk factor for mental disorders across the life course. However the long-term consequences of other forms of child maltreatment have not yet been systematically examined. The aim of this study was to summarise the evidence relating to the possible relationship between child physical abuse, emotional abuse, and neglect, and subsequent mental and physical health outcomes. ::: ::: Methods and Findings ::: ::: A systematic review was conducted using the Medline, EMBASE, and PsycINFO electronic databases up to 26 June 2012. Published cohort, cross-sectional, and case-control studies that examined non-sexual child maltreatment as a risk factor for loss of health were included. All meta-analyses were based on quality-effects models. Out of 285 articles assessed for eligibility, 124 studies satisfied the pre-determined inclusion criteria for meta-analysis. Statistically significant associations were observed between physical abuse, emotional abuse, and neglect and depressive disorders (physical abuse [odds ratio (OR) = 1.54; 95% CI 1.16–2.04], emotional abuse [OR = 3.06; 95% CI 2.43–3.85], and neglect [OR = 2.11; 95% CI 1.61–2.77]); drug use (physical abuse [OR = 1.92; 95% CI 1.67–2.20], emotional abuse [OR = 1.41; 95% CI 1.11–1.79], and neglect [OR = 1.36; 95% CI 1.21–1.54]); suicide attempts (physical abuse [OR = 3.40; 95% CI 2.17–5.32], emotional abuse [OR = 3.37; 95% CI 2.44–4.67], and neglect [OR = 1.95; 95% CI 1.13–3.37]); and sexually transmitted infections and risky sexual behaviour (physical abuse [OR = 1.78; 95% CI 1.50–2.10], emotional abuse [OR = 1.75; 95% CI 1.49–2.04], and neglect [OR = 1.57; 95% CI 1.39–1.78]). Evidence for causality was assessed using Bradford Hill criteria. While suggestive evidence exists for a relationship between maltreatment and chronic diseases and lifestyle risk factors, more research is required to confirm these relationships. ::: ::: Conclusions ::: ::: This overview of the evidence suggests a causal relationship between non-sexual child maltreatment and a range of mental disorders, drug use, suicide attempts, sexually transmitted infections, and risky sexual behaviour. All forms of child maltreatment should be considered important risks to health with a sizeable impact on major contributors to the burden of disease in all parts of the world. The awareness of the serious long-term consequences of child maltreatment should encourage better identification of those at risk and the development of effective interventions to protect children from violence.


title: Real-time eye tracking using a smart camera

abstract: Real-time eye and iris tracking is important for handsoff gaze-based password entry, instrument control by paraplegic patients, Internet user studies, as well as homeland security applications. In this project, a smart camera, LabVIEW and vision software tools are utilized to generate eye detection and tracking algorithms. The algorithms are uploaded to the smart camera for on-board image processing. Eye detection refers to finding eye features in a single frame. Eye tracking is achieved by detecting the same eye features across multiple image frames and correlating them to a particular eye. The algorithms are tested for eye detection and tracking under different conditions including different angles of the face, head motion speed, and eye occlusions to determine their usability for the proposed applications. This paper presents the implemented algorithms and performance results of these algorithms on the smart camera.


title: Machine Learning on Linked Data, a Position Paper

abstract: The combination of linked data and machine learning is emerging as an interesting area of research. However, while both fields have seen an exponential growth in popularity in the past decade, their union has received relatively little attention. We suggest that the field is currently too complex and divergent to allow collaboration and to attract new researchers. What is needed is a simple perspective, based on unifying principles. Focusing solely on RDF, with all other semantic web technology as optional additions is an important first step. We hope that this view will provide a low-complexity outline of the field to entice new contributions, and to unify existing ones.


title: How Presentation Flaws Affect Perceived Site Quality, Trust, and Intention to Purchase from an Online Store

abstract: Although there has been a great deal of research on impression formation, little application of that research has been made to electronic commerce. A research model was constructed that hypothesized errors, poor style, and incompleteness to be inversely related to the users' level of perceived quality of an online store. Further, this perceived quality of the online store's Web site would be directly related to users' trust in the store and, ultimately, to users' intentions to purchase from the store. An experimental study with 272 undergraduate and graduate student volunteers supported all the hypotheses. In addition, it was found that the relationship between the factors and perceived quality was mediated by the perception of the flaws. The perception of flaws rather than the actual flaws influenced users' perception of quality. Supplemental analysis also seemed to indicate a pattern of diminishing effects with each subsequent flaw.


title: Knowledge-based processing of complex stock market events

abstract: Usage of background knowledge about events and their relations to other concepts in the application domain, can improve the quality of event processing. In this paper, we describe a system for knowledge-based event detection of complex stock market events based on available background knowledge about stock market companies. Our system profits from data fusion of live event stream and background knowledge about companies which is stored in a knowledge base. Users of our system can express their queries in a rule language which provides functionalities to specify semantic queries about companies in the SPARQL query language for querying the external knowledge base and combine it with event data stream. Background makes it possible to detect stock market events based on companies attributes and not only based on syntactic processing of stock price and volume.


title: HDLTex: Hierarchical Deep Learning for Text Classification

abstract: Increasingly large document collections require improved information processing methods for searching, retrieving, and organizing text. Central to these information processing methods is document classification, which has become an important application for supervised learning. Recently the performance of traditional supervised classifiers has degraded as the number of documents has increased. This is because along with growth in the number of documents has come an increase in the number of categories. This paper approaches this problem differently from current document classification methods that view the problem as multi-class classification. Instead we perform hierarchical classification using an approach we call Hierarchical Deep Learning for Text classification (HDLTex). HDLTex employs stacks of deep learning architectures to provide specialized understanding at each level of the document hierarchy.


title: State of practice in requirements engineering: contemporary data

abstract: Little contemporary data exists that documents software requirements elicitation, requirements specification, document development, and specification validation practices. An exploratory survey of more than 3,000 software professionals was conducted and nearly 250 responses were obtained. Survey data obtained includes characteristics of projects, practices, organizations, and practitioners related to requirements engineering. Selected results are presented along with interpretations of this data.


title: Applying virtual reality for trust-building e-commerce environments

abstract: The application of virtual reality in e-commerce has enormous potential for transforming online shopping into a real-world equivalent. However, the growing research interest focuses on virtual reality technology adoption for the development of e-commerce environments without addressing social and behavioral facets of online shopping such as trust. At the same time, trust is a critical success factor for e-commerce and remains an open issue as to how it can be accomplished within an online store. This paper shows that the use of virtual reality for online shopping environments offers an advanced customer experience compared to conventional web stores and enables the formation of customer trust. The paper presents a prototype virtual shopping mall environment, designed on principles derived by an empirically tested model for building trust in e-commerce. The environment is evaluated with an empirical study providing evidence and explaining that a virtual reality shopping environment would be preferred by customers over a conventional web store and would facilitate the assessment of the e-vendor’s trustworthiness.


title: Inferring extended finite state machine models from software executions

abstract: The ability to reverse-engineer models of software behaviour is valuable for a wide range of software maintenance, validation and verification tasks. Current reverse-engineering techniques focus either on control-specific behaviour (e.g., in the form of Finite State Machines), or data-specific behaviour (e.g., as pre / post-conditions or invariants). However, typical software behaviour is usually a product of the two; models must combine both aspects to fully represent the software's operation. Extended Finite State Machines (EFSMs) provide such a model. Although attempts have been made to infer EFSMs, these have been problematic. The models inferred by these techniques can be non-deterministic, the inference algorithms can be inflexible, and only applicable to traces with specific characteristics. This paper presents a novel EFSM inference technique that addresses the problems of inflexibility and non-determinism. It also adapts an experimental technique from the field of Machine Learning to evaluate EFSM inference techniques, and applies it to three diverse software systems.


title: Theory of mind and its relationship with executive functions and emotion recognition in borderline personality disorder

abstract: Impaired social cognition has been claimed to be a mechanism underlying the development and maintenance of borderline personality disorder (BPD). One important aspect of social cognition is the theory of mind (ToM), a complex skill that seems to be influenced by more basic processes, such as executive functions (EF) and emotion recognition. Previous ToM studies in BPD have yielded inconsistent results. This study assessed the performance of BPD adults on ToM, emotion recognition, and EF tasks. We also examined whether EF and emotion recognition could predict the performance on ToM tasks. We evaluated 15 adults with BPD and 15 matched healthy controls using different tasks of EF, emotion recognition, and ToM. The results showed that BPD adults exhibited deficits in the three domains, which seem to be task-dependent. Furthermore, we found that EF and emotion recognition predicted the performance on ToM. Our results suggest that tasks that involve real-life social scenarios and contextual cues are more sensitive to detect ToM and emotion recognition deficits in BPD individuals. Our findings also indicate that (a) ToM variability in BPD is partially explained by individual differences on EF and emotion recognition; and (b) ToM deficits of BPD patients are partially explained by the capacity to integrate cues from face, prosody, gesture, and social context to identify the emotions and others' beliefs.


title: RadarMAC: Mitigating Radar Interference in Self-Driving Cars

abstract: Self-driving cars typically rely on a set of radars for mapping the environment to avoid obstacles and operate safely. Currently, the radar parameters are uncoordinated and can interfere with each other. This results in inefficient use of the spectrum and, more importantly, in dangerous blinding of the radars especially in dense urban settings, posing a barrier to widespread deployment of self-driving cars. We present RadarMAC - the first system architecture and dynamic radar parameter assignment algorithms for radar interference mitigation in self-driving cars. We characterize the degrees of freedom for vehicular radar parameters, and model the channel parameter assignment problem as one of dynamically coloring the corresponding time-varying interference graph. RadarMAC guarantees interference-free radar operations within a defined capacity region. Using extensive simulations, we demonstrate that RadarMAC significantly outperforms the state-of-the-art random assignment scheme commonly used today with off-the-shelf radars in terms of safety and spectrum efficiency metrics in both dense and sparse settings. Finally, we identify a set of research challenges in medium access control for vehicular radars.


1 Hardware Implementation of a Fuzzy Logic 2 Controller for a Hybrid Wind-Solar System in an 3 Isolated Site 4


title: A BURIED MUSCLE CONE IMPLANT: I. Development of a Tunneled Hemispherical Type

abstract: A TUNNELED hemispherical muscle cone implant and the stages in its development are described. This implant is covered completely by conjunctival tissue, thereby overcoming difficulties encountered with partially covered types. It is so designed that movement is transmitted quickly and adequately to the prosthetic eye, although there is no direct contact between the implant and the prosthesis. DEVELOPMENT OF THE MUSCLE CONE IMPLANT Stage 1. —The first of the series of muscle cone implants 1 described here was used originally in April 1946. It was a variation of the basic integrated type devised by Cutler. 2 The differences were in the mechanical principles and the surgical technics involved. The implant was designed to distribute the work of support and rotation to larger areas of the rectus muscles than is possible with Cutler's implant. It (fig. 1 A ) was 17 mm. in lateral diameter and 13 mm. in its anteroposterior dimension. A


title: A dual band FMCW radar receiver with integrated active balun and baseband AGC loop

abstract: This work presents the design of a dual band frequency modulated continuous wave (FMCW) radar receiver (RX) at the 2.4 and 5.8 GHz industrial, scientific and medical (ISM) bands. The designed low noise amplifier (LNA) has a single ended 50 Ω input stage followed by an active balanced-to-unbalanced (balun) stage. The radio frequency (RF) signal is then down-converted to a low intermediate frequency (IF) by a multi-tanh Gilbert cell mixer whose local oscillator (LO) port is driven by an integrated fractional-N phase locked loop (Frac-N PLL). After down-conversion, the resulting IF signal is low pass filtered and amplified by a baseband variable gain amplifier (VGA). Fabricated on an IBM 0.18 μm BiCMOS process, the measured RX performance is in good agreement with simulations achieving a measured maximum conversion gain (CG) of 82.0 and 77.2 dB and a noise figure (NF) of 7.3 and 8.0 dB at the 2.4 and 5.8 GHz bands respectively. The receiver consumes 22.3 mA from a 3 V supply and occupies a chip area of 0.32 mm2. When used as a primary FMCW radar transceiver (TRX), the designed chip achieves a ranging precision of 0.30 and 0.31 mm at the 2.4 and 5.8 GHz bands respectively. To the best of the author's knowledge, this dual band FMCW radar TRX has the highest level of integration reported in the literature.


title: Combining multiple depth cameras and projectors for interactions on, above and between surfaces

abstract: Instrumented with multiple depth cameras and projectors, LightSpace is a small room installation designed to explore a variety of interactions and computational strategies related to interactive displays and the space that they inhabit. LightSpace cameras and projectors are calibrated to 3D real world coordinates, allowing for projection of graphics correctly onto any surface visible by both camera and projector. Selective projection of the depth camera data enables emulation of interactive displays on un-instrumented surfaces (such as a standard table or office desk), as well as facilitates mid-air interactions between and around these displays. For example, after performing multi-touch interactions on a virtual object on the tabletop, the user may transfer the object to another display by simultaneously touching the object and the destination display. Or the user may "pick up" the object by sweeping it into their hand, see it sitting in their hand as they walk over to an interactive wall display, and "drop" the object onto the wall by touching it with their other hand. We detail the interactions and algorithms unique to LightSpace, discuss some initial observations of use and suggest future directions.


title: Video-based heart rate measurement using head motion tracking and ICA

abstract: A noninvasive and easy-operation measurement of the heart rate has great potential in home healthcare. We present a simple and high running efficiency method for measuring heart rate from a video. By only tracking one feature point which is selected from a small ROI (Region of Interest) in the head area, we extract trajectories of this point in both X-axis and Y-axis. After a series of processes including signal filtering, interpolation, the Independent Component Analysis (ICA) is used to obtain a periodic signal, and then the heart rate can be calculated. We evaluated on 10 subjects and compared to a commercial heart rate measuring instrument (YUYUE YE680B) and achieved high degree of agreement. A running time comparison experiment to the previous proposed motion-based method is carried out and the result shows that the time cost is greatly reduced in our method.


title: Gait variability and basal ganglia disorders: stride-to-stride variations of gait cycle timing in Parkinson's disease and Huntington's disease

abstract: Summary: The basal ganglia are thought to play an important role in regulating motor programs involved in gait and in the fluidity and sequencing of movement. We postulated that the ability to maintain a steady gait, with low stride-to-stride variability of gait cycle timing and its subphases, would be diminished with both Parkinson's disease (PD) and Huntington's disease (HD). To test this hypothesis, we obtained quantitative measures of stride-to-stride variability of gait cycle timing in subjects with PD (n = 15), HD (n = 20), and disease-free controls (n = 16). All measures of gait variability were significantly increased in PD and HD. In subjects with PD and HD, gait variability measures were two and three times that observed in control subjects, respectively. The degree of gait variability correlated with disease severity. In contrast, gait speed was significantly lower in PD, but not in HD, and average gait cycle duration and the time spent in many subphases of the gait cycle were similar in control subjects, HD subjects, and PD subjects. These findings are consistent with a differential control of gait variability, speed, and average gait cycle timing that may have implications for understanding the role of the


title: Unilateral frontalis sling for the surgical correction of unilateral poor-function ptosis.

abstract: PURPOSE ::: To evaluate the functional and cosmetic results after frontalis sling repair for unilateral ptosis associated with either poor levator function or synkinesis. ::: ::: ::: METHODS ::: Preoperative and postoperative photographs and records of 127 patients who underwent unilateral frontalis sling ptosis repair were retrospectively reviewed. An eyelid crease incision was used in all cases, with suturing of the sling material directly to tarsus. ::: ::: ::: RESULTS ::: Preoperative diagnosis for all patients was either unilateral poor-function blepharoptosis or ptosis associated with levator synkinesis. Underlying causes included 75 congenital, 13 posttraumatic, 11 congenital "jaw-winking," 10 cranial nerve III palsies, 9 myasthenia gravis, 5 chronic progressive external ophthalmoplegia, and 4 congenital "double-elevator" palsies. There was a mean follow-up of 11.6 months. Twenty-eight eyelids required reoperation: 11 for undercorrection, 6 for overcorrection with keratopathy, 2 for upper eyelid crease revision, 7 for correction of poor contour, 1 for a broken sling, and 1 for removal of an infected exposed polytetraflouroethylene sling. Lagophthalmos of greater than 2 mm was noted in 18 patients, 5 of whom had persistent keratopathy requiring reoperation. No other complications were reported, except for 1 suture granuloma. Good to excellent final postoperative eyelid height was achieved in 121 patients (95%) after all surgeries and with conscious recruitment of the frontalis muscle. A large majority of patients and/or parents expressed satisfaction with the final cosmetic result and were not bothered by any asymmetric lagophthalmos in downgaze or lack of a synchronous blink. However, 19 of 25 amblyopic patients were less satisfied with passive eyelid height as they failed to recruit the ipsilateral frontalis muscle to activate the sling during binocular viewing. In 17 of these 19 patients, good to excellent eyelid height could be achieved with conscious active brow elevation. ::: ::: ::: CONCLUSIONS ::: Unilateral sling provides good to excellent functional and cosmetic results in unilateral poor-function ptosis. However, patients with amblyopia usually require conscious effort to activate the frontalis muscle to achieve satisfactory eyelid height.


title: Communication in Virtual Teams: The Role of Emotional Intelligence

abstract: Emotional intelligence (EI) has received increasing attention in recent years as a driver of team effectiveness. However, research has yet to address EI in virtual teams (VT). The purpose of our study was to examine EI as a predictor of VT effectiveness. Further, we investigated quality of communication as a mediator of the EI-team viability relationship. We employed a work simulation using 228 undergraduate students (57 teams). Multilevel modeling was used to test our hypotheses. Our results support that EI is a driver of team viability, and that quality of communication serves as one mechanism through which this influence exists.


Mathematics Anxiety among Male and Female Students


title: The Akamai network: a platform for high-performance internet applications

abstract: Comprising more than 61,000 servers located across nearly 1,000 networks in 70 countries worldwide, the Akamai platform delivers hundreds of billions of Internet interactions daily, helping thousands of enterprises boost the performance and reliability of their Internet applications. In this paper, we give an overview of the components and capabilities of this large-scale distributed computing platform, and offer some insight into its architecture, design principles, operation, and management.


title: Upset now?: emotion contagion in distributed groups

abstract: The importance of emotion to group outcomes in FtF highlights the need to understand emotion contagion in distributed groups. The present study examines the transfer of negative emotion in online groups. Negative emotion was induced in one of three group members completing a task in CMC. The data suggest that emotion contagion took place at the group level, with partners experiencing more negative emotion, more disagreement, higher verbosity, and use of more complex language in induced groups compared to control groups. Induced groups also performed better on the group task, raising questions about the effects of negative emotion contagion in online groups.


title: Experimental Demonstration and Analysis of a 1.35-kV 0.92-m $\Omega \cdot \text {cm}^{2}$ SiC Superjunction Schottky Diode

abstract: This paper presents the fabrication, experimental analysis and electrical characterization of the first functional SiC superjunction (SJ) device. A trench-etching-and-sidewall-implant method has been developed to implement the SJ principle on a SiC Schottky diode. Several key process steps, including deep trench etching, ion implantation, and high-temperature annealing, are found to have noticeable influences on the device performances. The corresponding influences are studied by both simulation-aided theoretical analysis and experimental measurements. The highest measured cell blocking voltage was 1350 V, which achieves 95% of the simulated blocking voltage for the perfectly charge-balanced SJ structure. The measured device specific on-resistance was 0.92 $\text{m}\Omega \cdot \text {cm}^{2}$ . The SJ drift region specific on-resistance as low as 0.32 $\text{m}\Omega \cdot \text {cm}^{2}$ was obtained after subtracting the substrate resistance. This result successfully breaks the SiC 1-D unipolar limit.


title: Player-adaptive Spelunky level generation

abstract: Procedural Content Generation (PCG) is nowadays widely applied to many different aspects of computer games. However, it can do more than to assist level designers during game creation. It can generate personalized levels according to the tastes and abilities of players online. This has already been demonstrated for (largely 1D) scrolling games and we show in this work how personalized, difficulty-adjusted levels can be generated for the more complex 2D platformer Spelunky. As direct and indirect player feedback is taken into account, the method may be filed under the Experience-Driven PCG approach. Our approach is based on a rather generic rule set that may also be transferred to similar games. We also present a user study showing that most users appreciate the online adaptation but are especially critical about making the game easier to play at any time.


title: Culture and conformity: A meta-analysis of studies using Asch’s

abstract: A meta-analysis of conformity studies using an Asch-type line judgment task (1952b, 1956) was conducted to investigate whether the level of conformity has changed over time and whether it is related crogs-culturally to individualism-collectivism. The fiterature search produced 133 studies drawn from 17 countries. An analysis of U.S. studies found that conformity has declined since the 1950s. Results from 3 surveys were used to assess a country's individualism-collectivism, and for each survey the measures were found to be significantly related to conformity. Collectivist countries tended to show higher levels of conformity than individualist countries. Conformity research must attend more to cultural variables and to their role in the processes involved in social influence. The view has long been held that conformity is to some extent a product of cultural conditions, and it is a stable feature of popular stereotypes that some national groups are conforming and submissive, whereas others are independent and self-assertive (e.g., Peabody, 1985 ). Likewise, the extent to which dissidence is tolerated in a society will vary at different points in its history, and several commentators have suggested that the relatively high levels of conformity found in experiments conducted in the early 1950s (notably Asch, 1952b, 1956) was in part a product of the McCarthy era (e.g., Larsen, 1974; Mann, 1980; Perrin & Spencer, 1981 ). Although Asch's ( 1952b, 1956) seminal research is often interpreted as demonstrating that conformity is fundamental to group processes (Friend, Rafferty, & Bramel, 1990), Asch was as much concerned with those factors that enabled individuals to resist group pressure, factors which he saw as rooted in a society's values and socialization practices. That we have found the tendency to conformity in our society so strong that reasonably intelligent and well-meaning young people are willing to call White Black is a matter of concern. It raises questions about our ways of education and about the values that guide our conduct. (Asch, 1955, p. 34)