title: Information hiding in medical images: a robust medical image watermarking system for E-healthcare

abstract: Electronic transmission of the medical images is one of the primary requirements in a typical Electronic-Healthcare (E-Healthcare) system. However this transmission could be liable to hackers who may modify the whole medical image or only a part of it during transit. To guarantee the integrity of a medical image, digital watermarking is being used. This paper presents two different watermarking algorithms for medical images in transform domain. In first technique, a digital watermark and Electronic Patients Record (EPR) have been embedded in both regions; Region of Interest (ROI) and Region of Non-Interest (RONI). In second technique, Region of Interest (ROI) is kept untouched for tele-diagnosis purpose and Region of Non-Interest (RONI) is used to hide the digital watermark and EPR. In either algorithm 8ź×ź8 block based Discrete Cosine Transform (DCT) has been used. In each 8ź×ź8 block two DCT coefficients are selected and their magnitudes are compared for embedding the watermark/EPR. The selected coefficients are modified by using a threshold for embedding bit a `0' or bit `1' of the watermark/EPR. The proposed techniques have been found robust not only to singular attacks but also to hybrid attacks. Comparison results viz-a - viz payload and robustness show that the proposed techniques perform better than some existing state of art techniques. As such the proposed algorithms could be useful for e-healthcare systems.


title: Towards Better Interpretability in Deep Q-Networks

abstract: Deep reinforcement learning techniques have demonstrated superior performance in a wide variety of environments. As improvements in training algorithms continue at a brisk pace, theoretical or empirical studies on understanding what these networks seem to learn, are far behind. In this paper we propose an interpretable neural network architecture for Q-learning which provides a global explanation of the model's behavior using key-value memories, attention and reconstructible embeddings. With a directed exploration strategy, our model can reach training rewards comparable to the state-of-the-art deep Q-learning models. However, results suggest that the features extracted by the neural network are extremely shallow and subsequent testing using out-of-sample examples shows that the agent can easily overfit to trajectories seen during training.


title: Exploring the Relationship between Worry and Impulsivity in Military Recruits: The Role of Mindfulness and Self‐compassion as Potential Mediators

abstract: When military recruits cannot tolerate the stressful environment in the military, they typically become more impulsive. Impulsivity serves to avoid the stress, or, in other words, to release the pressure and cope. Becoming more impulsive, however, is related to damaging behaviours and unsuccessful coping. This research explored the relationship between worry and impulsivity in armed forces, and the possibility of mindfulness and self-compassion mediating this relationship. Participants (n = 166), who were in the second week of their basic military training, completed questionnaires in mindfulness, self-compassion, worry and impulsivity. Results indicated that worry related positively to impulsivity. Further, the negative relationship of worry with mindfulness and self-compassion mediated the relationship between worry and impulsivity. Findings support the notion that lacking mindfulness and self-compassion, in excessive worriers, may lead to impulsivity. This research concluded that mindfulness and self-compassion may assist military personnel who are exposed to highly stressful environments. Alternative explanations and future directions are discussed. Copyright © 2014 John Wiley & Sons, Ltd.


title: Innovative Decision Reference Based Algorithm for Photovoltaic Maximum Power Point Tracking

abstract: A novel decision reference based method for the maximum power point tracking (MPPT) of PV arrays is presented in this paper. The proposed decision reference was derived from a simplified solar cell model. This method solves the problems of conventional MPPT algorithms, such as oscillation of the operating point at the steady state and confusion under rapidly changing insolation. It is shown by simulation and experimental results that the method properly tracks a rapidly changing insolation profile. The signal to noise ratio (SNR) of the new decision reference is also higher than those of conventional P&O and INC methods. An updating subroutine was included in the proposed MPPT algorithm to compensate for temperature and aging effects.


title: Tourist Trip Planning Functionalities: State–of–the–Art and Future

abstract: When tourists visit a city or region, they cannot visit every point of interest available, as they are constrained in time and budget. Tourist recommender applications help tourists by presenting a personal selection. Providing adequate tour scheduling support for these kinds of applications is a daunting task for the application developer. The objective of this paper is to demonstrate how existing models from the field of Operations Research (OR) fit this scheduling problem, and enable a wide range of tourist trip planning functionalities. Using the Orienteering Problem (OP) and its extensions to model the tourist trip planning problem, allows to deal with a vast number of practical planning problems.


Transfer Learning and Sentence Level Features for Named Entity Recognition on Tweets


title: A modified Mediterranean dietary intervention for adults with major depression: Dietary protocol and feasibility data from the SMILES trial

abstract: BACKGROUND ::: The SMILES trial was the first randomized controlled trial (RCT) explicitly designed to evaluate a dietary intervention, conducted by qualified dietitians, for reducing depressive symptomatology in adults with clinical depression. ::: ::: ::: OBJECTIVES ::: Here we detail the development of the prescribed diet (modified Mediterranean diet (ModiMedDiet)) for individuals with major depressive disorders (MDDs) that was designed specifically for the SMILES trial. We also present data demonstrating the extent to which this intervention achieved improvements in diet quality. ::: ::: ::: METHODS ::: The ModiMedDiet was designed using a combination of existing dietary guidelines and scientific evidence from the emerging field of nutritional psychiatric epidemiology. Sixty-seven community dwelling individuals (Melbourne, Australia) aged 18 years or over, with current poor quality diets, and MDDs were enrolled into the SMILES trial. A retention rate of 93.9 and 73.5% was observed for the dietary intervention and social support control group, respectively. The dietary intervention (ModiMedDiet) consisted of seven individual nutrition counselling sessions delivered by a qualified dietitian. The control condition comprised a social support protocol matched to the same visit schedule and length. ::: ::: ::: RESULTS ::: This manuscript details the first prescriptive individualized dietary intervention delivered by dietitians for adults with major depression. Significant improvements in dietary quality were observed among individuals randomized to the ModiMedDiet group. These dietary improvements were also found to be associated with changes in depressive symptoms. ::: ::: ::: DISCUSSION/CONCLUSION ::: The ModiMedDiet, a novel and individually tailored intervention designed specifically for adults with major depression, can be effectively implemented in clinical practice to manage this highly prevalent and debilitating condition. ::: ::: ::: TRIAL REGISTRATION ::: Australia and New Zealand Clinical Trials Register (ANZCTR): ACTRN12612000251820. Registered 29 February 2012.


title: A framework for calibration of electromagnetic surgical navigation system

abstract: In this paper, we present a framework of calibrating an electromagnetic tracker (Northern Digital's Aurora) using an accurate optical tracker (the Optotrak system, also from Northern Digital). First, registration methods for these two navigation systems are developed. Sub millimeter accuracy registration is achieved for both cases. We also address the latency between the two different trackers. The registration accuracy for dynamic acquired data is greatly improved after we compensate for the tracker latency. In our calibration approach, we sample the measurement field of the Aurora and compute the position and orientation error using the Optotrak measurements and previously computed registration results as "ground truth". Then we approximate the error field using Bernstein polynomials. Another comparative technique we use is to decompose the error space using KD tree, and then approximate each atomic cell with local interpolation. Experimental results show significant improvement in tracking accuracy for both position and orientation. Finally we discuss our future directions.


title: The Playtime Principle: Large-scale cross-games interest modeling

abstract: The collection and analysis of behavioral telemetry in digital games has in the past five years become an integral part of game development. One of the key challenges in game analytics is the development of methods for characterizing and predicting player behavior as it evolves over time. Characterizing behavior is necessary for monitoring player populations and gradually improve game design and the playing experience. Predicting behavior is necessary to describe player engagement and prevent future player churn. In this paper, methods and theory from kernel archetype analysis and random process models are utilized to evaluate the playtime behavior, i.e. time spent playing specific games as a function of time, of over 6 million players, across more than 3000 PC and console games from the Steam platform, covering a combined playtime of more than 5 billion hours. A number of conclusions can be derived from this large-scale analysis, notably that playtime as a function of time, across the thousands of games in the dataset, and irrespective of local differences in the playtime frequency distribution, can be modeled using the same model: the Weibull distribution. This suggests that there are fundamental properties governing player engagement as it evolves over time, which we here refer to as the Playtime Principle. Additionally, the analysis shows that there are distinct clusters, or archetypes, in the playtime frequency distributions of the investigated games. These archetypal groups correspond to specific playtime distributions. Finally, the analysis reveals information about player behavior across a very large dataset, showing for example that the vast majority of games are players for less than 10 hours, and very few players spend more than 30–35 hours on any specific game.


Forensic DHCP Information Extraction from Home Routers Memory Forensics on SOHO / Enduser Embedded Routing and Gateway Systems


title: Impacts of Disruption on Secondary Task Knowledge: Recovery Modes and Social Nuances

abstract: The working memory of the human brain has always had a strong influence on the design of Human-Computer Interaction. Yet, it is limited in capacity and loses its contents over time. Research regarding these constraints typically focused on single user tasks and systems. Collaborative settings and systems introduce the need for the secondary task of coordination which shares the same conditions and constraints as the knowledge of the primary task. Additionally, it is easily compromised by interruption and interference. Our approach seeks to understand the impacts of disruption on secondary task knowledge, but from a different angle than previous related work. Since it is hard to avoid disruption entirely, it aims to understand how users recover from disruptions in order to help them recover the best way possible from different types of interruptions using appropriate mechanisms and cues. This paper reports on the results of one of the first experiments along the way, observing the effects of four types of interruptions revealing different modes of recovery and social nuances that inform the design of adaptive coordination support systems.


title: Multilingual Models for Compositional Distributed Semantics

abstract: We present a novel technique for learning semantic representations, which extends the distributional hypothesis to multilingual data and joint-space embeddings. Our models leverage parallel data and learn to strongly align the embeddings of semantically equivalent sentences, while maintaining sufficient distance between those of dissimilar sentences. The models do not rely on word alignments or any syntactic information and are successfully applied to a number of diverse languages. We extend our approach to learn semantic representations at the document level, too. We evaluate these models on two cross-lingual document classification tasks, outperforming the prior state of the art. Through qualitative analysis and the study of pivoting effects we demonstrate that our representations are semantically plausible and can capture semantic relationships across languages without parallel data.


title: Neural stem cells constitutively secrete neurotrophic factors and promote extensive host axonal growth after spinal cord injury

abstract: Neural stem cells (NSCs) offer the potential to replace lost tissue after nervous system injury. This study investigated whether grafts of NSCs (mouse clone C17.2) could also specifically support host axonal regeneration after spinal cord injury and sought to identify mechanisms underlying such growth. In vitro, prior to grafting, C17.2 NSCs were found for the first time to naturally constitutively secrete significant quantities of several neurotrophic factors by specific ELISA, including nerve growth factor, brain-derived neurotrophic factor, and glial cell line-derived neurotrophic factor. When grafted to cystic dorsal column lesions in the cervical spinal cord of adult rats, C17.2 NSCs supported extensive growth of host axons of known sensitivity to these growth factors when examined 2 weeks later. Quantitative real-time RT-PCR confirmed that grafted stem cells expressed neurotrophic factor genes in vivo. In addition, NSCs were genetically modified to produce neurotrophin-3, which significantly expanded NSC effects on host axons. Notably, overexpression of one growth factor had a reciprocal effect on expression of another factor. Thus, stem cells can promote host neural repair in part by secreting growth factors, and their regeneration-promoting activities can be modified by gene delivery.


title: Clustering by fast search and find of density peaks

abstract: Cluster analysis is aimed at classifying elements into categories on the basis of their similarity. Its applications range from astronomy to bioinformatics, bibliometrics, and pattern recognition. We propose an approach based on the idea that cluster centers are characterized by a higher density than their neighbors and by a relatively large distance from points with higher densities. This idea forms the basis of a clustering procedure in which the number of clusters arises intuitively, outliers are automatically spotted and excluded from the analysis, and clusters are recognized regardless of their shape and of the dimensionality of the space in which they are embedded. We demonstrate the power of the algorithm on several test cases.


title: A review of data security and cryptographic techniques in IoT based devices

abstract: The idea of the Internet of Things (IoT) is to connect or give access to everything to the Internet. IoT environment not only provides the facility of Human to Machine connectivity, however, it also creates Machine to Machine connectivity. As everything is going to connect to the Internet and also generating the data. So, the data generating by these devices is growing up rapidly, that huge amount of data is called Big Data. This data has huge Volume, High Velocity, and different Variety. The security of this data is a risk. As we know that, the IoT devices have constraints like low power and less computational speed and the traditional encryption algorithms like DES, 3DES, and AES are more complex. Traditional encryption algorithm seems not feasible for IoT devices. So, we need to develop Lightweight encryption algorithm for IoT devices for secure communication and secure data transmission in IoT environment. Cryptography and Steganography techniques are used for securing the data over the Internet. Cryptography encrypts the data by using a key and make a ciphertext that cannot be readable by the normal user. Steganography hides the data by concealing it into another medium like data, image, audio, video, or mixed. This paper provides a review of important lightweight cryptographic techniques used for IoT devices.


A Theoretical and Practice Framework for Universal School-Based Prevention


title: The practical use of SiC devices in high power, high frequency inverters for industrial induction heating applications

abstract: Induction welding and high frequency hardening are the two most straightforward applications in induction heating for the power inverter. Practical industrial welders require at the same time power levels up to 1 MW while frequencies are in the range between 200–500 kHz, depending on the characteristics of the tube to be weld, while hardening power supplies require power levels up to 400 kW with frequencies in the range up to 200 kHz but with an extreme high number of power cycles. Si-IGBT as well as Si-MOS technologies are normally used in today's induction heating converters, but the new SiC devices offer important benefits, especially in the higher frequency ranges, making this new material very attractive for high power, high frequency induction heating inverters. The paper aims to give practical technical considerations, based on experimental results, for the realistic use of SiC devices in resonant inverters for this type of industrial material processing application.


title: Anxiety disorders

abstract: Anxiety disorders constitute the largest group of mental disorders in most western societies and are a leading cause of disability. The essential features of anxiety disorders are excessive and enduring fear, anxiety or avoidance of perceived threats, and can also include panic attacks. Although the neurobiology of individual anxiety disorders is largely unknown, some generalizations have been identified for most disorders, such as alterations in the limbic system, dysfunction of the hypothalamic-pituitary-adrenal axis and genetic factors. In addition, general risk factors for anxiety disorders include female sex and a family history of anxiety, although disorder-specific risk factors have also been identified. The diagnostic criteria for anxiety disorders varies for the individual disorders, but are generally similar across the two most common classification systems: the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) and the International Classification of Diseases, Tenth Edition (ICD-10). Despite their public health significance, the vast majority of anxiety disorders remain undetected and untreated by health care systems, even in economically advanced countries. If untreated, these disorders are usually chronic with waxing and waning symptoms. Impairments associated with anxiety disorders range from limitations in role functioning to severe disabilities, such as the patient being unable to leave their home.


Innovative Technology for CPU Based Attestation and Sealing


title: Ocular biometrics: A survey of modalities and fusion approaches

abstract: Display Omitted A literature review of ocular modalities such as iris and periocular is presented.Information fusion approaches that combine ocular modalities with other modalities are reviewed.Future research directions are presented on sensing technologies, algorithms, and fusion approaches. Biometrics, an integral component of Identity Science, is widely used in several large-scale-county-wide projects to provide a meaningful way of recognizing individuals. Among existing modalities, ocular biometric traits such as iris, periocular, retina, and eye movement have received significant attention in the recent past. Iris recognition is used in Unique Identification Authority of India's Aadhaar Program and the United Arab Emirate's border security programs, whereas the periocular recognition is used to augment the performance of face or iris when only ocular region is present in the image. This paper reviews the research progression in these modalities. The paper discusses existing algorithms and the limitations of each of the biometric traits and information fusion approaches which combine ocular modalities with other modalities. We also propose a path forward to advance the research on ocular recognition by (i) improving the sensing technology, (ii) heterogeneous recognition for addressing interoperability, (iii) utilizing advanced machine learning algorithms for better representation and classification, (iv) developing algorithms for ocular recognition at a distance, (v) using multimodal ocular biometrics for recognition, and (vi) encouraging benchmarking standards and open-source software development.


title: Propagation of adipogenic signals through an epigenomic transition state.

abstract: The transcriptional mechanisms by which temporary exposure to developmental signals instigates adipocyte differentiation are unknown. During early adipogenesis, we find transient enrichment of the glucocorticoid receptor (GR), CCAAT/enhancer-binding protein beta (CEBPbeta), p300, mediator subunit 1, and histone H3 acetylation near genes involved in cell proliferation, development, and differentiation, including the gene encoding the master regulator of adipocyte differentiation, peroxisome proliferator-activated receptor gamma2 (PPARgamma2). Occupancy and enhancer function are triggered by adipogenic signals, and diminish upon their removal. GR, which is important for adipogenesis but need not be active in the mature adipocyte, functions transiently with other enhancer proteins to propagate a new program of gene expression that includes induction of PPARgamma2, thereby providing a memory of the earlier adipogenic signal. Thus, the conversion of preadipocyte to adipocyte involves the formation of an epigenomic transition state that is not observed in cells at the beginning or end of the differentiation process.


title: Parallel Dual-Loop Antenna for WWAN/LTE Metal-Rimmed Smartphone

abstract: A simple internal antenna design for wireless wide-area network/long-term evolution (LTE) operations is investigated, and it can be incorporated into a mobile phone that has an unbroken metal rim of height 5 mm. The proposed antenna is mainly composed of two parallel slots (dual loop) embedded on the system ground, and by feeding them with an L-shaped coupled feedline with matching networks, good excitation of multiple loop resonant modes can be obtained, which is capable of covering the GSM850/900 (824–960 MHz), DCS/PCS/UMTS2100, and LTE2300/2500 (1710–2690 MHz) operating bands. In addition, the feasibility of realizing decoupled multiantennas ( $2 \times 2$ multi-in multi-out antenna system) is also demonstrated, and its performances are acceptable for smartphone applications.


title: Real-time Business Process Intelligence. Comparison of different architectural approaches using the example of the order-to-cash process.

abstract: Business operations are becoming more and more integrated with the real- time intelligence. Core business activities are being carried out through OLTP systems that provide limited monitoring capabilities of the running process instances. The arti- cle shows how to turn the gap between the classic transactional system and the process- centric approach into an organization that provides more accurate and faster decisions on the strategic and operational management levels. This study aims at determination of what kind of information can be retrieved during the process execution and it tries to identify the need for the real-time process intelligence on the example of the order-to- cash process. Furthermore, we compare two architectural approaches of the real-time process intelligence monitoring system. The proposed frameworks retrieve process data from the ERP system in order to record crucial performance indicators on a real-time basis with the use of the in-memory technology.


title: A high-stability, ultra-low-power quartz differential oscillator circuit for demanding radio applications

abstract: A crystal-based differential oscillator circuit offering simultaneously high stability and ultra-low power consumption is presented for timekeeping and demanding radio applications. The differential circuit structure -in contrast to that of the conventional 3-points- does not require any loading capacitance to be functional and the power consumption can thus be minimized. Although the loading capacitance is omitted a very precise absolute oscillation frequency can be obtained as well as an excellent insensitivity to temperature variations thanks to the reduced parasitics of a deep-submicron technology. The power consumption of a 12.8MHz quartz oscillator including an amplitude regulation mechanism is below 1 µA under a 1.8 to 0.6V supply voltage range.


title: Which of the following SPARQL Queries are Similar? Why?

abstract: Linked data on the Web can be accessed by SPARQL queries. Previously executed queries to SPARQL endpoints are valuable information sources about the underlying data structure and schema of data sources. These queries reveal how resources are related to each other and they re flect the user interests on the data. Therefore, methods for query logs analysis provides a basis for extracting relevant and qualitative data from the LOD cloud. These query logs analysis rely on similarity measures for SPARQL queries in order to determine whether a query is related to another query. However, there is no commonly agreed notion of similarity for SPARQL queries. Instead, there are several measures that not only vary computational complexity and output values, but also on their purpose. In this paper, we present a comparison and a discussion of the existing similarity measures for SPARQL queries in the literature. Our results are guidelines designed to provide guidance on the application of similarity measures for comparing SPARQL queries.


title: Twelve tips for giving feedback effectively in the clinical environment.

abstract: BACKGROUND ::: Feedback is an essential element of the educational process for clinical trainees. Performance-based feedback enables good habits to be reinforced and faulty ones to be corrected. Despite its importance, most trainees feel that they do not receive adequate feedback and if they do, the process is not effective. ::: ::: ::: AIMS AND METHODS ::: The authors reviewed the literature on feedback and present the following 12 tips for clinical teachers to provide effective feedback to undergraduate and graduate medical trainees. In most of the tips, the focus is the individual teacher in clinical settings, although some of the suggestions are best adopted at the institutional level. ::: ::: ::: RESULTS ::: Clinical educators will find the tips practical and easy to implement in their day-to-day interactions with learners. The techniques can be applied in settings whether the time for feedback is 5 minutes or 30 minutes. ::: ::: ::: CONCLUSIONS ::: Clinical teachers can improve their skills for giving feedback to learners by using the straightforward and practical tools described in the subsequent sections. Institutions should emphasise the importance of feedback to their clinical educators, provide staff development and implement a mechanism by which the quantity and quality of feedback is monitored.


title: Pathogenesis of ankylosing spondylitis

abstract: Ankylosing spondylitis (AS) is a potentially disabling form of seronegative spondyloarthritis. The main symptom of AS is inflammatory spinal pain; with time, some patients develop ankylosis and spinal immobility. The pathology mainly affects the entheses, where ligaments, tendons and capsules are attached to the bone. Three processes are observed at the entheses: inflammation, bone erosion and syndesmophyte (spur) formation. Tumor necrosis factor is an important mediator of the inflammatory processes, but this proinflammatory cytokine is not closely involved in bone erosion or syndesmophyte formation. The major causative factors of AS are genetic, with the gene encoding HLA-B27 being the most important genetic factor. Several other susceptibility genes have also been identified. An enormous number of papers have been published and many diverse hypotheses have been generated regarding the pathogenesis of AS. This Review outlines the key areas of current research in this field, describes several hypotheses regarding the pathogenesis of AS, which are under intense investigation, and concludes with a dissection of the processes involved in bone erosion and syndesmophyte formation.


title: CSIFT: A SIFT Descriptor with Color Invariant Characteristics

abstract: SIFT has been proven to be the most robust local invariant feature descriptor. SIFT is designed mainly for gray images. However, color provides valuable information in object description and matching tasks. Many objects can be misclassified if their color contents are ignored. This paper addresses this problem and proposes a novel colored local invariant feature descriptor. Instead of using the gray space to represent the input image, the proposed approach builds the SIFT descriptors in a color invariant space. The built Colored SIFT (CSIFT) is more robust than the conventional SIFT with respect to color and photometrical variations. The evaluation results support the potential of the proposed approach.


title: EFFICIENCY OF HIGHER ORDER FINITE ELEMENTS FOR THE ANALYSIS OF SEISMIC WAVE PROPAGATION

abstract: The analysis of wave propagation problems in linear damped media must take into account both propagation features and attenuation process. To perform accurate numerical investigations by the finite differences or finite element method, one must consider a specific problem known as the numerical dispersion of waves. Numerical dispersion may increase the numerical error during the propagation process as the wave velocity (phase and group) depends on the features of the numerical model. In this paper, the numerical modelling of wave propagation by the finite element method is thus analyzed and dis-cussed for linear constitutive laws. Numerical dispersion is analyzed herein through 1D computations investigating the accuracy of higher order 15-node finite elements towards numerical dispersion. Concerning the numerical analy-sis of wave attenuation, a rheological interpretation of the classical Rayleigh assumption has for instance been previously proposed in this journal.


title: Fabrication of a High-Efficiency Waveguide Antenna Array via Direct Metal Laser Sintering

abstract: The direct metal laser sintering (DMLS) technique, using aluminum alloy, is adopted to fabricate a relatively large $8 \times 8$ wideband multilayer waveguide array with an integrated corporate-fed network in the Ku-band. This DMLS technique offers the flexibility to fabricate waveguide structures with complicated embedded structures with good reliability and high precision. It is favorably compared to the time-consuming machining technique when the array must be built in separate pieces and secured together. Experimental results show there is $1.0 \sim 1.5~\hbox{dB}$ gain improvement compared to one that is fabricated by machining technique. Antenna aperture efficiency is higher than 80% over a wide operational bandwidth and even exceeding 90% at most frequencies. This work shows that the metal-direct-printing technique is a promising manufacturing method for the fabrication of complicated waveguide antenna arrays.


YZU-NLP at EmoInt-2017: Determining Emotion Intensity Using a Bi-directional LSTM-CNN Model


title: Hand gesture recognition based on convexity approach and background subtraction

abstract: This paper presents a method for hand gesture recognition using convexity defect and background subtraction. First, the background subtraction is used to eliminate the useless information. To find contour of segmented hand images we used images processing techniques. After that we calculate the convex hull and convexity defects for this contour. The feature extraction purposes to detect and extract features that can be used to determine the significance of a given hand gesture. The features must be able to characterize gesture only, and invariant under translation and rotation of hand gesture to ensure reliable recognition. We propose a method to extract a series of features based on convex defect detection, catching advantage of the close relationship of convex defect and fingertips. This method is mere, efficient and free from gesture direction and position. We have tested five hand gestures classes to show using one, two, three, four, and five fingers one by one.


title: Analysis, Design, and Experimental Results of a Novel Soft-Switching Snubberless Current-Fed Half-Bridge Front-End Converter-Based PV Inverter

abstract: This paper proposes a new novel snubberless current-fed half-bridge front-end isolated dc/dc converter-based inverter for photovoltaic applications. It is suitable for grid-tied (utility interface) as well as off-grid (standalone) application based on the mode of control. The proposed converter attains clamping of the device voltage by secondary modulation, thus eliminating the need of snubber or active-clamp. Zero-current switching or natural commutation of primary devices and zero-voltage switching of secondary devices is achieved. Soft-switching is inherent owing to the proposed secondary modulation and is maintained during wide variation in voltage and power transfer capacity and thus is suitable for photovoltaic (PV) applications. Primary device voltage is clamped at reflected output voltage, and secondary device voltage is clamped at output voltage. Steady-state operation and analysis, and design procedure are presented. Simulation results using PSIM 9.0 are given to verify the proposed analysis and design. An experimental converter prototype rated at 200 W has been designed, built, and tested in the laboratory to verify and demonstrate the converter performance over wide variations in input voltage and output power for PV applications. The proposed converter is a true isolated boost converter and has higher voltage conversion (boost) ratio compared to the conventional active-clamped converter.


Business process variability modeling


title: TransA: An Adaptive Approach for Knowledge Graph Embedding

abstract: Knowledge representation is a major topic in AI, and many studies attempt to represent entities and relations of knowledge base in a continuous vector space. Among these attempts, translation-based methods build entity and relation vectors by minimizing the translation loss from a head entity to a tail one. In spite of the success of these methods, translation-based methods also suffer from the oversimplified loss metric, and are not competitive enough to model various and complex entities/relations in knowledge bases. To address this issue, we propose \textbf{TransA}, an adaptive metric approach for embedding, utilizing the metric learning ideas to provide a more flexible embedding method. Experiments are conducted on the benchmark datasets and our proposed method makes significant and consistent improvements over the state-of-the-art baselines.


title: Dopaminergic basis of salience dysregulation in psychosis

abstract: Disrupted salience processing is proposed as central in linking dysregulated dopamine function with psychotic symptoms. Several strands of evidence are now converging in support of this model. Animal studies show that midbrain dopamine neurons are activated by unexpected salient events. In psychotic patients, neurochemical studies have confirmed subcortical striatal dysregulation of dopaminergic neurotransmission, whereas functional magnetic resonance imaging (fMRI) studies of salience tasks have located alterations in prefrontal and striatal dopaminergic projection fields. At the clinical level, this may account for the altered sense of meaning and significance that predates the onset of psychosis. This review draws these different strands of evidence together in support of an emerging understanding of how dopamine dysregulation may lead to aberrant salience and psychotic symptoms.


title: EEG-Based Driver Drowsiness Estimation Using Convolutional Neural Networks

abstract: Deep learning, including convolutional neural networks (CNNs), has started finding applications in brain-computer interfaces (BCIs). However, so far most such approaches focused on BCI classification problems. This paper extends EEGNet, a 3-layer CNN model for BCI classification, to BCI regression, and also utilizes a novel spectral meta-learner for regression (SMLR) approach to aggregate multiple EEGNets for improved performance. Our model uses the power spectral density (PSD) of EEG signals as the input. Compared with raw EEG inputs, the PSD inputs can reduce the computational cost significantly, yet achieve much better regression performance. Experiments on driver drowsiness estimation from EEG signals demonstrate the outstanding performance of our approach.


title: Human Motion Segmentation via Robust Kernel Sparse Subspace Clustering

abstract: Studies on human motion have attracted a lot of attentions. Human motion capture data, which much more precisely records human motion than videos do, has been widely used in many areas. Motion segmentation is an indispensable step for many related applications, but current segmentation methods for motion capture data do not effectively model some important characteristics of motion capture data, such as Riemannian manifold structure and containing non-Gaussian noise. In this paper, we convert the segmentation of motion capture data into a temporal subspace clustering problem. Under the framework of sparse subspace clustering, we propose to use the geodesic exponential kernel to model the Riemannian manifold structure, use correntropy to measure the reconstruction error, use the triangle constraint to guarantee temporal continuity in each cluster and use multi-view reconstruction to extract the relations between different joints. Therefore, exploiting some special characteristics of motion capture data, we propose a new segmentation method, which is robust to non-Gaussian noise, since correntropy is a localized similarity measure. We also develop an efficient optimization algorithm based on block coordinate descent method to solve the proposed model. Our optimization algorithm has a linear complexity while sparse subspace clustering is originally a quadratic problem. Extensive experiment results both on simulated noisy data set and real noisy data set demonstrate the advantage of the proposed method.Studies on human motion have attracted a lot of attentions. Human motion capture data, which much more precisely records human motion than videos do, has been widely used in many areas. Motion segmentation is an indispensable step for many related applications, but current segmentation methods for motion capture data do not effectively model some important characteristics of motion capture data, such as Riemannian manifold structure and containing non-Gaussian noise. In this paper, we convert the segmentation of motion capture data into a temporal subspace clustering problem. Under the framework of sparse subspace clustering, we propose to use the geodesic exponential kernel to model the Riemannian manifold structure, use correntropy to measure the reconstruction error, use the triangle constraint to guarantee temporal continuity in each cluster and use multi-view reconstruction to extract the relations between different joints. Therefore, exploiting some special characteristics of motion capture data, we propose a new segmentation method, which is robust to non-Gaussian noise, since correntropy is a localized similarity measure. We also develop an efficient optimization algorithm based on block coordinate descent method to solve the proposed model. Our optimization algorithm has a linear complexity while sparse subspace clustering is originally a quadratic problem. Extensive experiment results both on simulated noisy data set and real noisy data set demonstrate the advantage of the proposed method.


title: A Microservice Architecture for the Intranet of Things and Energy in Smart Buildings: Research Paper

abstract: This paper presents challenges and issues in smart buildings and the Internet of Things (IoT), which we identified in years of research in real buildings. To tackle these challenges, a decentralized service-oriented architecture based on a message-oriented middleware has been implemented for the domain of smart buildings. It uses a network-transparent IoT message bus and provides the means for composing applications from auxiliary services, which facilitate device abstraction, protocol adaption, modularity, and maintainability. We demonstrate the flexibility of our architecture by describing how three distinct applications---privacy-enhancing energy data visualization, automated building energy management, and a generic user interface---can be integrated and operated simultaneously in our real smart building laboratory. We compare the advantages of our architecture to conventional ones and provide a best-practice solution for the Intranet of Things and Energy in smart buildings.


title: A Truly Balanced Q-Band CMOS Frequency Doubler Based on Hybrid Quadrature Coupler

abstract: In this letter, a truly balanced CMOS Q-band frequency doubler with an ultra-compact differential hybrid quadrature coupler is demonstrated using 90-nm CMOS technology. The doubler incorporates two Gilbert-cells fed by I/Q signals to compensate the time delay from transconductance stage and avoid output DC offset, providing truly balanced differential outputs. The doubler achieves a 3-dB bandwidth from 40 to 49 GHz with a minimum conversion loss of 1.6 dB at 44 GHz, while the output amplitude imbalance is measured to be less than 0.7dB. The measured I/Q mismatch of the quadrature coupler is less than 1° within 10 GHz bandwidth. The doubler chip consumes 9.6 mA from a 1.2-V supply, and the chip dimension is $0.7\times 0.9$ mm2.


title: Local visual words coding for low bit rate mobile visual search

abstract: Mobile visual search has attracted extensive attention for its huge potential for numerous applications. Research on this topic has been focused on two schemes: sending query images, and sending compact descriptors extracted on mobile phones. The first scheme requires about 30-40KB data to transmit, while the second can reduce the bit rate by 10 times. In this paper, we propose a third scheme for extremely low bit rate mobile visual search, which sends compressed visual words consisting of vocabulary tree histogram and descriptor orientations rather than descriptors. This scheme can further reduce the bit rate with few extra computational costs on the client. Specifically, we store a vocabulary tree and extract visual descriptors on the mobile client. A light-weight pre-retrieval is performed to obtain the visited leaf nodes in the vocabulary tree. The orientation of each local descriptor and the tree histogram are then encoded to be transmitted to server. Our new scheme transmits less than 1KB data, which reduces the bit rate in the second scheme by 3 times, and obtains about 30% improvement in terms of search accuracy over the traditional Bag-of-Words baseline. The time cost is only 1.5 secs on the client and 240 msecs on the server.


title: Locally Adaptive Color Correction for Underwater Image Dehazing and Matching

abstract: Underwater images are known to be strongly deteriorated by a combination of wavelength-dependent light attenuation and scattering. This results in complex color casts that depend both on the scene depth map and on the light spectrum. Color transfer, which is a technique of choice to counterbalance color casts, assumes stationary casts, defined by global parameters, and is therefore not directly applicable to the locally variable color casts encountered in underwater scenarios. To fill this gap, this paper introduces an original fusion-based strategy to exploit color transfer while tuning the color correction locally, as a function of the light attenuation level estimated from the red channel. The Dark Channel Prior (DCP) is then used to restore the color compensated image, by inverting the simplified Koschmieder light transmission model, as for outdoor dehazing. Our technique enhances image contrast in a quite effective manner and also supports accurate transmission map estimation. Our extensive experiments also show that our color correction strongly improves the effectiveness of local keypoints matching.


title: Lighting estimation in indoor environments from low-quality images

abstract: Lighting conditions estimation is a crucial point in many applications. In this paper, we show that combining color images with corresponding depth maps (provided by modern depth sensors) allows to improve estimation of positions and colors of multiple lights in a scene. Since usually such devices provide low-quality images, for many steps of our framework we propose alternatives to classical algorithms that fail when the image quality is low. Our approach consists in decomposing an original image into specular shading, diffuse shading and albedo. The two shading images are used to render different versions of the original image by changing the light configuration. Then, using an optimization process, we find the lighting conditions allowing to minimize the difference between the original image and the rendered one.


title: Esports Analytics Through Encounter Detection

abstract: Esports is computer games played in a competitive environment, and analytics in this domain is focused on player and team behavior. Multiplayer Online Battle Arena (MOBA) games are among the most played digital games in the world. In these es, teams of players fight against each other in enclosed arena environs, with a complex gameplay focused on tactical combat. Here we present a technique for segmenting matches into spatio‐temporally defined components referred to as encounters, enabling performance analysis. We apply encounter‐based analysis to match data from the popular esport game DOTA, and present win probability predictions based on encounters. Finally,metrics for evaluating team performance during match runtime are proposed.


title: Miniaturized NFC Antenna Design for a Tablet PC With a Narrow Border and Metal Back-Cover

abstract: A novel structure of the near-field communication (NFC) antenna design for a tablet PC is proposed. This tablet PC has a narrow border and full metallic back-cover. A miniaturized loop antenna design is achieved by attaching ferrite sheets on both sides of the loop antenna. The ferrite sheets may reduce eddy currents induced on the adjacent metallic back-cover by the loop antenna to improve the communication range of the NFC. Only the edge of the tablet PC allows the antenna to radiate due to the full metallic back-cover. Thus, the NFC antenna needs to be narrow to be installed on the edge of the tablet PC. Therefore, we propose a miniaturized NFC antenna with the dimensions of $41.5\ ({L}) \times 7.5\ (W) \times 0.45\ (T)\ \hbox{mm}^3$ only. Simulated magnetic field distributions are consistent with measured voltage distributions. This design has a good communication range more than 6 cm in front of the touchscreen panel and reaches 2 cm over the other side above the metal back-cover.


title: Human contour extraction from RGBD camera for action recognition

abstract: Spatio-temporal cuboid pyramid (STCP) for action recognition using depth motion sequences [1] is influenced by depth camera error which leads the depth motion sequence (DMS) existing many kinds of noise, especially on the surface. It means that the dimension of DMS is awfully high and the feature for action recognition becomes less apparent. In this paper, we present an effective method to reduce noise, which is to segment foreground. We firstly segment and extract human contour in the color image using convolutional network model. Then, human contour is re-segmented utilizing depth information. Thirdly we project each frame of the segmented depth sequence onto three views. We finally extract features from cuboids and recognize human actions. The proposed approach is evaluated on three public benchmark datasets, i.e., UTKinect-Action Dataset, MSRActionPairs Dataset and 3D Online Action Dataset. Experimental results show that our method achieves state-of-the-art performance.


title: Stable, circulation-preserving, simplicial fluids

abstract: Visual quality, low computational cost, and numerical stability are foremost goals in computer animation. An important ingredient in achieving these goals is the conservation of fundamental motion invariants. For example, rigid and deformable body simulation benefits greatly from conservation of linear and angular momenta. In the case of fluids, however, none of the current techniques focuses on conserving invariants, and consequently, they often introduce a visually disturbing numerical diffusion of vorticity. Visually just as important is the resolution of complex simulation domains. Doing so with regular (even if adaptive) grid techniques can be computationally delicate. In this chapter, we propose a novel technique for the simulation of fluid flows. It is designed to respect the defining differential properties, i.e., the conservation of circulation along arbitrary loops as they are transported by the flow. Consequently, our method offers several new and desirable properties: (1) arbitrary simplicial meshes (triangles in 2D, tetrahedra in 3D) can be used to define the fluid domain; (2) the computations are efficient due to discrete operators with small support; (3) the method is stable for arbitrarily large time steps; (4) it preserves discrete circulation avoiding numerical diffusion of vorticity; and (5) its implementation is straightforward.


title: Sparse Eigenmotions derived from daily life kinematics implemented on a dextrous robotic hand

abstract: Our hands are considered one of the most complex to control actuated systems, thus, emulating the manipulative skills of real hands is still an open challenge even in anthropomorphic robotic hand. While the action of the 4 long fingers and simple grasp motions through opposable thumbs have been successfully implemented in robotic designs, complex in-hand manipulation of objects was difficult to achieve. We take an approach grounded in data-driven extraction of control primitives from natural human behaviour to develop novel ways to understand the dexterity of hands. We collected hand kinematics datasets from natural, unconstrained human behaviour of daily life in 8 healthy in a studio flat environment. We then applied our Sparse Motion Decomposition approach to extract spatio-temporally localised modes of hand motion that are both time-scale and amplitude-scale invariant. These Sparse EigenMotions (SEMs)[1] form a sparse symbolic code that encodes continuous hand motions. We mechanically implemented the common SEMs on our novel dexterous robotic hand [2] in open-loop control. We report that without processing any feedback during grasp control, several of the SEMs resulted in stable grasps of different daily life objects. The finding that SEMs extracted from daily life implement stable grasps in open-loop control of dexterous hands, lends further support for our hypothesis the brain controls the hand using sparse control strategies.


title: On Minimal Infrequent Itemset Mining

abstract: A new algorithm for minimal infrequent itemset mining is presented. Potential applications of finding infrequent itemsets include statistical disclosure risk assessment, bioinformatics, and fraud detection. This is the first algorithm designed specifically for finding these rare itemsets. Many itemset properties used implicitly in the algorithm are proved. The problem is shown to be NP-complete. Experimental results are then presented.


title: DeepIoT: Compressing Deep Neural Network Structures for Sensing Systems with a Compressor-Critic Framework

abstract: Recent advances in deep learning motivate the use of deep neutral networks in sensing applications, but their excessive resource needs on constrained embedded devices remain an important impediment. A recently explored solution space lies in compressing (approximating or simplifying) deep neural networks in some manner before use on the device. We propose a new compression solution, called DeepIoT, that makes two key contributions in that space. First, unlike current solutions geared for compressing specific types of neural networks, DeepIoT presents a unified approach that compresses all commonly used deep learning structures for sensing applications, including fully-connected, convolutional, and recurrent neural networks, as well as their combinations. Second, unlike solutions that either sparsify weight matrices or assume linear structure within weight matrices, DeepIoT compresses neural network structures into smaller dense matrices by finding the minimum number of non-redundant hidden elements, such as filters and dimensions required by each layer, while keeping the performance of sensing applications the same. Importantly, it does so using an approach that obtains a global view of parameter redundancies, which is shown to produce superior compression. We conduct experiments with five different sensing-related tasks on Intel Edison devices. DeepIoT outperforms all compared baseline algorithms with respect to execution time and energy consumption by a significant margin. It reduces the size of deep neural networks by 90% to 98.9%. It is thus able to shorten execution time by 71.4% to 94.5%, and decrease energy consumption by 72.2% to 95.7%. These improvements are achieved without loss of accuracy. The results underscore the potential of DeepIoT for advancing the exploitation of deep neural networks on resource-constrained embedded devices.


title: An Efficient SVD-Based Method for Image Denoising

abstract: Nonlocal self-similarity of images has attracted considerable interest in the field of image processing and has led to several state-of-the-art image denoising algorithms, such as block matching and 3-D, principal component analysis with local pixel grouping, patch-based locally optimal wiener, and spatially adaptive iterative singular-value thresholding. In this paper, we propose a computationally simple denoising algorithm using the nonlocal self-similarity and the low-rank approximation (LRA). The proposed method consists of three basic steps. First, our method classifies similar image patches by the block-matching technique to form the similar patch groups, which results in the similar patch groups to be low rank. Next, each group of similar patches is factorized by singular value decomposition (SVD) and estimated by taking only a few largest singular values and corresponding singular vectors. Finally, an initial denoised image is generated by aggregating all processed patches. For low-rank matrices, SVD can provide the optimal energy compaction in the least square sense. The proposed method exploits the optimal energy compaction property of SVD to lead an LRA of similar patch groups. Unlike other SVD-based methods, the LRA in SVD domain avoids learning the local basis for representing image patches, which usually is computationally expensive. The experimental results demonstrate that the proposed method can effectively reduce noise and be competitive with the current state-of-the-art denoising algorithms in terms of both quantitative metrics and subjective visual quality.


title: Web Application Tests with Selenium

abstract: Web applications tend to continuously evolve and thus need thorough, yet lean and automatic, regression testing. In this installment of Software Technology, Andreas Kornstadt and his colleagues describe automatic regression testing for Web applications that uses the Selenium testing framework. Selenium is portable open source software available for Windows, Linux, and Macintosh. Tests are written as HTML tables or in a number of programming languages and can run directly in most Web browsers. Andreas and his colleagues also provide many useful testing hints for practitioners. We look forward to hearing from both readers and prospective column authors about this column and the technologies we want to know more about.


title: Multi-view deep learning for consistent semantic mapping with RGB-D cameras

abstract: Visual scene understanding is an important capability that enables robots to purposefully act in their environment. In this paper, we propose a novel deep neural network approach to predict semantic segmentation from RGB-D sequences. The key innovation is to train our network to predict multi-view consistent semantics in a self-supervised way. At test time, its semantics predictions can be fused more consistently in semantic keyframe maps than predictions of a network trained on individual views. We base our network architecture on a recent single-view deep learning approach to RGB and depth fusion for semantic object-class segmentation and enhance it with multi-scale loss minimization. We obtain the camera trajectory using RGB-D SLAM and warp the predictions of RGB-D images into ground-truth annotated frames in order to enforce multi-view consistency during training. At test time, predictions from multiple views are fused into keyframes. We propose and analyze several methods for enforcing multi-view consistency during training and testing. We evaluate the benefit of multi-view consistency training and demonstrate that pooling of deep features and fusion over multiple views outperforms single-view baselines on the NYUDv2 benchmark for semantic segmentation. Our end-to-end trained network achieves state-of-the-art performance on the NYUDv2 dataset in single-view segmentation as well as multi-view semantic fusion.


title: Laser-Cutting Pneumatics

abstract: Pneumatic devices require tight tolerances to keep them leak-free. Specialized companies offer various off-the-shelf devices, while these work well for many applications, there are also situations where custom design and production of pneumatic parts are desired. Cost efficiency, design flexibility, rapid prototyping, and MRI compatibility requirements are reasons why we investigated a method to design and produce different pneumatic devices using a laser cutter from acrylic, acetal, and rubber-like materials. The properties of the developed valves, pneumatic cylinders, and stepper motors were investigated. At 4-bar working pressure, the 4/3-way valves are capable of 5-Hz switching frequency and provide at most 22-L/min airflow. The pneumatic cylinder delivers 48 N of force, the acrylic stepper motor 30 N. The maximum switching frequency over 6-m long transmission lines is 4.5 Hz, using 2-mm tubing. A MRI-compatible robotic biopsy system driven by the pneumatic stepper motors is also demonstrated. We have shown that it is possible to construct pneumatic devices using laser-cutting techniques. This way, plastic MRI-compatible cylinders, stepper motors, and valves can be developed. Provided that a laser-cutting machine is available, the described pneumatic devices can be fabricated within hours at relatively low cost, making it suitable for rapid prototyping applications.


title: Unsupervised Transcription of Historical Documents

abstract: We present a generative probabilistic model, inspired by historical printing processes, for transcribing images of documents from the printing press era. By jointly modeling the text of the document and the noisy (but regular) process of rendering glyphs, our unsupervised system is able to decipher font structure and more accurately transcribe images into text. Overall, our system substantially outperforms state-of-the-art solutions for this task, achieving a 31% relative reduction in word error rate over the leading commercial system for historical transcription, and a 47% relative reduction over Tesseract, Google’s open source OCR system.


title: Discovering Motion Primitives for Unsupervised Grouping and One-Shot Learning of Human Actions, Gestures, and Expressions

abstract: This paper proposes a novel representation of articulated human actions and gestures and facial expressions. The main goals of the proposed approach are: 1) to enable recognition using very few examples, i.e., one or k-shot learning, and 2) meaningful organization of unlabeled datasets by unsupervised clustering. Our proposed representation is obtained by automatically discovering high-level subactions or motion primitives, by hierarchical clustering of observed optical flow in four-dimensional, spatial, and motion flow space. The completely unsupervised proposed method, in contrast to state-of-the-art representations like bag of video words, provides a meaningful representation conducive to visual interpretation and textual labeling. Each primitive action depicts an atomic subaction, like directional motion of limb or torso, and is represented by a mixture of four-dimensional Gaussian distributions. For one--shot and k-shot learning, the sequence of primitive labels discovered in a test video are labeled using KL divergence, and can then be represented as a string and matched against similar strings of training videos. The same sequence can also be collapsed into a histogram of primitives or be used to learn a Hidden Markov model to represent classes. We have performed extensive experiments on recognition by one and k-shot learning as well as unsupervised action clustering on six human actions and gesture datasets, a composite dataset, and a database of facial expressions. These experiments confirm the validity and discriminative nature of the proposed representation.


title: Automatic phase prediction from low-level surgical activities

abstract: Purpose ::: Analyzing surgical activities has received a growing interest in recent years. Several methods have been proposed to identify surgical activities and surgical phases from data acquired in operating rooms. These context-aware systems have multiple applications including: supporting the surgical team during the intervention, improving the automatic monitoring, designing new teaching paradigms.


title: Research Methods in Computer Science: The Challenges and Issues

abstract: Research methods are essential parts in conducting any research project. Although they have been theorized and summarized based on best practices, every field of science requires an adaptation of the overall approaches to perform research activities. In addition, any specific research needs a particular adjustment to the generalized approach and specializing them to suit the project in hand. However, unlike most well-established science disciplines, computing research is not supported by well-defined, globally accepted methods. This is because of its infancy and ambiguity in its definition, on one hand, and its extensive coverage and overlap with other fields, on the other hand. This article discusses the research methods in science and engineering in general and in computing in particular. It shows that despite several special parameters that make research in computing rather unique, it still follows the same steps that any other scientific research would do. The article also shows the particularities that researchers need to consider when they conduct research in this field.


title: Convolution neural network-based lane change intention prediction of surrounding vehicles for ACC

abstract: Adaptive cruise control is one of the most widely used vehicle driver assistance systems. However, uncertainty about drivers' lane change maneuvers in surrounding vehicles, such as unexpected cut-in, remains a challenge. We propose a novel adaptive cruise control framework combining convolution neural network (CNN)-based lane-change-intention inference and a predictive controller. We transform real-world driving data, collected on public roads with only standard production sensors, to a simplified bird's-eye view. This enables a CNN-based inference approach with low computational cost and robustness to noisy input. The predicted inference of traffic participants' lane change intention is utilized to improve safety and ride comfort with model predictive control. Simulation results based on driving scene reconstruction demonstrate the superior performance of inference using the proposed CNN-based approach, as well as enhanced safety and ride comfort.


title: A Predictive Stock Market Technical Analysis Using Fuzzy Logic

abstract: Decision making process in stock trading is a complex one. There are numbers of technical indicators that are used by traders to study trends of the market and make buying and selling decisions based on their observations. This research seeks to deploy fuzzy inference to stock market, with four indicators used in technical analysis to aid in the decision making process in order to deal with probability. The four technical indicators are the Moving Average Convergence/Divergence (MACD), Relative Strength Index (RSI), Stochastic Oscillator (SO) and On-Balance Volume (OBV). The fuzzy rules are a combination of the trading rules for each of the indicators used as the input variables of the fuzzy system and for all the four technical indicators used, the membership functions were also defined. The result is a recommendation to buy, sell or hold. Data were collected for two Nigerian banks for testing and evaluation of the system. The technical indicators were then computed for each data and from the computed technical indicators; experiment was carried out for two months. The system generated satisfactory recommendation as when to buy, sell or hold, when the output is compared with actual data collected from the Nigerian Stock Exchange. The system can therefore act as an effective model for traders in the stock market when there is a combination of the recommendation with the individual’s trading skills.


title: Deep Feature Based Contextual Model for Object Detection

abstract: Object detection is one of the most active areas in computer vision, which has made significant improvement in recent years. Current state-of-the-art object detection methods mostly adhere to the framework of regions with convolutional neural network (R-CNN) and only use local appearance features inside object bounding boxes. Since these approaches ignore the contextual information around the object proposals, the outcome of these detectors may generate a semantically incoherent interpretation of the input image. In this paper, we propose an ensemble object detection system which incorporates the local appearance, the contextual information in term of relationships among objects and the global scene based contextual feature generated by a convolutional neural network. The system is formulated as a fully connected conditional random field (CRF) defined on object proposals and the contextual constraints among object proposals are modeled as edges naturally. Furthermore, a fast mean field approximation method is utilized to inference in this CRF model efficiently. The experimental results demonstrate that our approach achieves a higher mean average precision (mAP) on PASCAL VOC 2007 datasets compared to the baseline algorithm Faster R-CNN.


title: Who Killed My Battery? Analyzing Mobile Browser Energy Consumption

abstract: Despite the growing popularity of mobile web browsing, the energy consumed by a phone browser while surfing the web is poorly understood. We present an infrastructure for measuring the precise energy used by a mobile browser to render web pages. We then measure the energy needed to render financial, e-commerce, email, blogging, news and social networking sites. Our tools are sufficiently precise to measure the energy needed to render individual web elements, such as cascade style sheets (CSS), Javascript, images, and plug-in objects. Our results show that for popular sites, downloading and parsing cascade style sheets and Javascript consumes a significant fraction of the total energy needed to render the page. Using the data we collected we make concrete recommendations on how to design web pages so as to minimize the energy needed to render the page. As an example, by modifying scripts on the Wikipedia mobile site we reduced by 30% the energy needed to download and render Wikipedia pages with no change to the user experience. We conclude by estimating the point at which offloading browser computations to a remote proxy can save energy on the phone.


title: Precision tracking with sparse 3D and dense color 2D data

abstract: Precision tracking is important for predicting the behavior of other cars in autonomous driving. We present a novel method to combine laser and camera data to achieve accurate velocity estimates of moving vehicles. We combine sparse laser points with a high-resolution camera image to obtain a dense colored point cloud. We use a color-augmented search algorithm to align the dense color point clouds from successive time frames for a moving vehicle, thereby obtaining a precise estimate of the tracked vehicle's velocity. Using this alignment method, we obtain velocity estimates at a much higher accuracy than previous methods. Through pre-filtering, we are able to achieve near real time results. We also present an online method for real-time use with accuracies close to that of the full method. We present a novel approach to quantitatively evaluate our velocity estimates by tracking a parked car in a local reference frame in which it appears to be moving relative to the ego vehicle. We use this evaluation method to automatically quantitatively evaluate our tracking performance on 466 separate tracked vehicles. Our method obtains a mean absolute velocity error of 0.27 m/s and an RMS error of 0.47 m/s on this test set. We can also qualitatively evaluate our method by building color 3D car models from moving vehicles. We have thus demonstrated that our method can be used for precision car tracking with applications to autonomous driving and behavior modeling.


The psychopharmacology of chronic pain


title: Development of a MATLAB/Simulink Model of a Single-Phase Grid-Connected Photovoltaic System

abstract: Because of their deployment in dispersed locations on the lowest voltage portions of the grid, photovoltaic (PV) systems pose unique challenges to power system engineers. Computer models that accurately simulate the relevant behavior of PV systems would thus be of high value. However, most of today's models either do not accurately model the dynamics of the maximum power point trackers (MPPTs) or anti-islanding algorithms, or they involve excessive computational overhead for this application. To address this need, a Matlab/Simulink model of a single-phase grid-connected PV inverter has been developed and experimentally tested. The development of the PV array model, the integration of the MPPT with an averaged model of the power electronics, and the Simulink implementation are described. It is experimentally demonstrated that the model works well in predicting the general behaviors of single-phase grid-connected PV systems. This paper concludes with a discussion of the need for a full gradient-based MPPT model, as opposed to a commonly used simplified MPPT model.


title: A different manner to share a house: is a colonial species possible in Ceriantharia (Cnidaria; Anthozoa)?

abstract: Ceriantharians or tube-dwelling sea anemones are known for synthesizing soft and flexible tubes made of mucus, cnidae filaments (ptychocysts), and sediments found on the soft bottom. These tubes are used to house and protect them from danger, although many species of marine invertebrates use ceriantharian tubes as alternative substrates. Little is known about the organizational structure of ceriantharians in their own tubes. Although ceriantharians are always considered solitary animals, this study presents the first record of a ceriantharian colony of Botrucnidifer norvegicus Carlgren, 1912. Future studies regarding the population structure in ceriantharian aggregations may help to clarify this unusual habit for Ceriantharia.


title: Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks

abstract: The electrocardiogram (ECG) plays an imperative role in the medical field, as it records heart signal over time and is used to discover numerous cardiovascular diseases. If a documented ECG signal has a certain irregularity in its predefined features, this is called arrhythmia, the types of which include tachycardia, bradycardia, supraventricular arrhythmias, and ventricular, etc. This has encouraged us to do research that consists of distinguishing between several arrhythmias by using deep neural network algorithms such as multi-layer perceptron (MLP) and convolution neural network (CNN). The TensorFlow library that was established by Google for deep learning and machine learning is used in python to acquire the algorithms proposed here. The ECG databases accessible at PhysioBank.com and kaggle.com were used for training, testing, and validation of the MLP and CNN algorithms. The proposed algorithm consists of four hidden layers with weights, biases in MLP, and four-layer convolution neural networks which map ECG samples to the different classes of arrhythmia. The accuracy of the algorithm surpasses the performance of the current algorithms that have been developed by other cardiologists in both sensitivity and precision.


title: Genetic algorithm-based heuristic for feature selection in credit risk assessment

abstract: In this paper, an advanced novel heuristic algorithm is presented, the hybrid genetic algorithm with neural networks (HGA-NN), which is used to identify an optimum feature subset and to increase the classification accuracy and scalability in credit risk assessment. This algorithm is based on the following basic hypothesis: the high-dimensional input feature space can be preliminarily restricted to only the important features. In this preliminary restriction, fast algorithms for feature ranking and earlier experience are used. Additionally, enhancements are made in the creation of the initial population, as well as by introducing an incremental stage in the genetic algorithm. The performances of the proposed HGA-NN classifier are evaluated using a real-world credit dataset that is collected at a Croatian bank, and the findings are further validated on another real-world credit dataset that is selected in a UCI database. The classification accuracy is compared with that presented in the literature. Experimental results that were achieved using the proposed novel HGA-NN classifier are promising for feature selection and classification in retail credit risk assessment and indicate that the HGA-NN classifier is a promising addition to existing data mining techniques.


title: To appear in the 1 st International Conference on Informatics in Control, Automation and Robotics Iterative Linear Quadratic Regulator Design for Nonlinear Biological Movement Systems

abstract: This paper presents an Iterative Linear Quadratic Regulator (ILQR) method for locally-optimal feedback control of nonlinear dynamical systems. The method is applied to a musculo-skeletal arm model with 10 state dimensions and 6 controls, and is used to compute energy-optimal reaching movements. Numerical comparisons with three existing methods demonstrate that the new method converges substantially faster and finds slightly better solutions.


title: A Review of Sex Differences in Peer Relationship Processes: Potential Trade-offs for the Emotional and Behavioral Development of Girls and Boys

abstract: Theory and research on sex differences in adjustment focus largely on parental, societal, and biological influences. However, it also is important to consider how peers contribute to girls’ and boys’ development. This paper provides a critical review of sex differences in: several peer-relationship processes, including behavioral and social-cognitive styles, stress and coping, and relationship provisions. Based on this review, a speculative peer-socialization model is presented that considers the implications of these sex differences for girls’ and boys’ emotional and behavioral development. Central to this model is the idea that sex-linked relationship processes have costs and benefits for girls’ and boys’ adjustment. Finally, we present recent research testing certain model components and propose approaches for testing understudied aspects of the model.


Discourse Mode Identification in Essays


title: Automated question answering system using ontology and semantic role

abstract: Semantic similarity is an essential part for question answering, it is used various fields such as Artificial Intelligence, Natural Language Processing, information retrieval, Document Retrieval and Automatic evaluations. This paper mainly focuses on similarity measure based on the posted query, and finding the appropriate meaning between the words. Accessing an accurate answer from the web document is challenging task. The proposed approach is used to analyze and measuring the similarity between the words. It presents the Web And semantic knowledge-Driven automatic question answering system (WAD). It encompasses three phases to enhance the performance of QA system using the web as well as the semantic knowledge. Initially, the WAD approach determines the user query, query expansion technique and entity linking method. The ontology based information is used in WAD to rank the answers and experimental results provide the result with high accuracy than the baseline method.


title: High pressure MWPC for hard X-ray astronomy

abstract: Abstract The second generation of detectors for hard X-ray astronomy essentially requires large area detectors and efficient background discrimination. A conventional scheme is to use an array of NaI scintillator crystals with a large number of photomultipliers, a light collection system and individual guard counters for charged particle rejection. High pressure proportional counters can provide an alternative instrument with comparatively higher performance in terms of effective area and background discrimination at lower cost and weight. A 900 cm 2 detector, with a thin aluminium window (0.16 g/cm 2 ) and a 4 atm xenon filling has been developed and tested on a balloon flight.


title: Accelerometer Localization in the View of a Stationary Camera

abstract: This paper addresses the problem of localizing an accelerometer in the view of a stationary camera as a first step towards multi-model activity recognition. This problem is challenging as accelerometers are visually occluded, they measure proper acceleration including effects of gravity and their orientation is unknown and changes over time relative to camera viewpoint. Accelerometers are localized by matching acceleration estimated along visual point trajectories to accelerometer data. Trajectories are constructed from point feature tracking (KLT) and by grid sampling from a dense flow field. We also construct 3D trajectories with visual depth information. The similarity between accelerometer data and a trajectory is computed by counting the number of frames in which the norms of accelerations in both sequences exceed a threshold. For quantitative evaluation we collected a challenging dataset consisting of video and accelerometer data of a person preparing a mixed salad with accelerometer-equipped kitchen utensils. Trajectories from dense optical flow yielded a higher localization accuracy compared to point feature tracking.


title: Visual and Textual Sentiment Analysis of a Microblog Using Deep Convolutional Neural Networks

abstract: Sentiment analysis of online social media has attracted significant interest recently. Many studies have been performed, but most existing methods focus on either only textual content or only visual content. In this paper, we utilize deep learning models in a convolutional neural network (CNN) to analyze the sentiment in Chinese microblogs from both textual and visual content. We first train a CNN on top of pre-trained word vectors for textual sentiment analysis and employ a deep convolutional neural network (DNN) with generalized dropout for visual sentiment analysis. We then evaluate our sentiment prediction framework on a dataset collected from a famous Chinese social media network (Sina Weibo) that includes text and related images and demonstrate state-of-the-art results on this Chinese sentiment analysis benchmark.


title: Application of FPGA based PLL in a CSI fed induction heating prototype

abstract: Modern induction heating (IH) power converters operate marginally above resonant frequency to supply power to the targeted work-piece at near unity power factor (upf). The resonant frequency continuously increases as the work-piece gets heated up. A possible method of dynamically detecting the resonant frequency may be to calculate the phase-shift between current and voltage continuously during the process. The phase-shift between voltage and current is ideally zero at resonance. So resonant frequency may be identified by varying the frequency until the phase-shift is zero. For this some controllers use a phase-locked loop (PLL) strategy. In this paper, the dynamic tracking of resonant frequency, using a field-programmable gate array (FPGA) based digital-PLL, is presented. The scheme is first simulated off-line. Finally, the logic is implemented on controller hardware and practically tested in a laboratory made experimental set-up of 2kW at around 10kHz.


title: Vedic Divider: Novel Architecture (ASIC) for High Speed VLSI Applications

abstract: Vedic Mathematics is the ancient methodology of Indian mathematics which has a unique computational technique for calculations based on 16 Sutras (Formulae). Novel divider architecture for high speed VLSI application using such ancient methodology is presented in this paper. Propagation delay and dynamic power consumption of a divider circuitry were minimized significantly by removing unnecessary recursion through Vedic division methodology. The functionality of these circuits was checked and performance parameters like propagation delay and dynamic power consumption were calculated by spice spectre using 90nm CMOS technology. The propagation delay of the resulting 16-bit binary dividend by an 8-bit divisor circuitry was only ~10.5ns and consumed ~24ÂµW power for a layout area of ~10.25 mm2. By combining Boolean logic with ancient Vedic mathematics, substantial amount of iteration were eliminated that resulted in ~45% reduction in delay and ~30% reduction in power compared with the mostly used (Digit Recurrence, Convergence & Series Expansion) architectures.


title: Designing A Virtual Empowerment Mobile App for the Blinds

abstract: The Virtual Empowerment App for the Blinds is an empowerment mobile application for the blind. It is developed based on the modules of the existing Latihan Maya Kejayaan Orang Kurang Upaya app, which was intended to empower persons with physical disabilities. As the requirements of persons with physical disabilities greatly differ from that of vision disabilities, this study aims to develop an app that would help blind users to navigate the module of Latihan Maya Kejayaan OKU by themselves based on the requirements observed from the blind users. An interface design to develop a functional and practical app for the blinds is proposed. A comparison between different gesture platforms was also compared. Three tests were conducted, namely the preliminary testing, pilot testing and final testing to analyze the interaction of users when using the app. Results suggested that the extended app developed based on the design proposed in this study were easily navigated and adapted by persons with vision disabilities.


title: Ecological divergence among five co-occurring species of old-field goldenrods

abstract: Approximately 130 species of goldenrods are native to North America and many occur sympatrically. Such cooccurrence among closely related species raises the question of whether differences among the species in smallscale distribution and growth forms facilitate their co-occurrence. We investigated five goldenrods that frequently co-occur within their native ranges in Pennsylvania USA old fields. We measured goldenrod abundances, soil textures, nutrients, pH, and moisture within 30 old fields, and determined biomass allocation and flower and seed traits for each goldenrod species at a common site. Ordination revealed that Solidago altissima and S. gigantea were associated with fields having circum-neutral soils, whereas Euthamia graminifolia and S. rugosa achieved their highest abundances on acidic soils. Soil clay content and moisture may be associated with a further separation of species as the abundance of S. altissima tended to be higher on well-drained soils while S. gigantea had a tendency to attain its highest abundances on moist soils that had relatively stable moisture levels over time. Euthamia was more likely to be abundant on clay-rich soils while S. rugosa was often associated with soils containing little clay. Solidago juncea tended to associate with droughty soils that underwent marked soil-moisture changes over time. The latter goldenrod had the greatest absolute and relative root mass, the least absolute and relative leaf mass, highest seed-reproductive allocation, and heaviest achenes. In contrast, S. gigantea and Euthamia, which were often associated with more mesic and stable soil moisture conditions, allocated the least to roots and relatively high amounts of mass to leaves. Solidago gigantea, S. altissima, and Euthamia are invasive species across Europe. The species with the highest colonization rate across Europe, S. gigantea, allocated the most to reproduction in our study, while S. altissima, with the second highest colonization rate, was highly clonal producing the most rhizome mass. Life-history trait variation among goldenrods appears to be linked to differences in small-scale distributions and rates of colonization.


title: Audio Chord Recognition with a Hybrid Recurrent Neural Network

abstract: In this paper, we present a novel architecture for audio chord estimation using a hybrid recurrent neural network. The architecture replaces hidden Markov models (HMMs) with recurrent neural network (RNN) based language models for modelling temporal dependencies between chords. We demonstrate the ability of feed forward deep neural networks (DNNs) to learn discriminative features directly from a time-frequency representation of the acoustic signal, eliminating the need for a complex feature extraction stage. For the hybrid RNN architecture, inference over the output variables of interest is performed using beam search. In addition to the hybrid model, we propose a modification to beam search using a hash table which yields improved results while reducing memory requirements by an order of magnitude, thus making the proposed model suitable for real-time applications. We evaluate our model's performance on a dataset with publicly available annotations and demonstrate that the performance is comparable to existing state of the art approaches for chord recognition.


title: Towards a consistent terminology for software measurement

abstract: Although software measurement plays an increasingly important role in Software Engineering, there is no consensus yet on many of the concepts and terminology used in this field. Even worse, vocabulary conflicts and inconsistencies can be frequently found amongst the many sources and references commonly used by software measurement researchers and practitioners. This article presents an analysis of the current situation, and provides a comparison framework that can be used to identify and address the discrepancies, gaps, and terminology conflicts that current software measurement proposals present. A basic software measurement ontology is introduced, that aims at contributing to the harmonization of the different software measurement proposals and standards, by providing a coherent set of common concepts used in software measurement. The ontology is also aligned with the metrology vocabulary used in other more mature measurement engineering disciplines.


title: Predicting Phishing Websites Using Classification Mining Techniques with Experimental Case Studies

abstract: Classification Data Mining (DM) Techniques can be a very useful tool in detecting and identifying e-banking phishing websites. In this paper, we present a novel approach to overcome the difficulty and complexity in detecting and predicting e-banking phishing website. We proposed an intelligent resilient and effective model that is based on using association and classification Data Mining algorithms. These algorithms were used to characterize and identify all the factors and rules in order to classify the phishing website and the relationship that correlate them with each other. We implemented six different classification algorithm and techniques to extract the phishing training data sets criteria to classify their legitimacy. We also compared their performances, accuracy, number of rules generated and speed. A Phishing Case study was applied to illustrate the website phishing process. The rules generated from the associative classification model showed the relationship between some important characteristics like URL and Domain Identity, and Security and Encryption criteria in the final phishing detection rate. The experimental results demonstrated the feasibility of using Associative Classification techniques in real applications and its better performance as compared to other traditional classifications algorithms.


title: Synchronizable Test Sequences of Finite State Machines

abstract: The finite state machine (FSM) model is commonly used for specifying communicati on protocols and other types of distributed systems. With the use of multiple te sters for an FSM, the synchronization between inputs from different testers beco mes a problem. A synchronizable test sequence of an FSM is a test sequence for w hich the synchronization problem either does not exist or can be solved by commu nication between testers. In this paper, we consider two testing strategies for an FSM: port-based testing, which does not allow testers for the FSM to communic ate with each other, and group-based testing, which divides the ports of the FSM into groups and allows the testers for ports in the same group to communicate w ith each other. For each type of testing, we define a necessary and sufficient c ondition under which a test sequence of an FSM is synchronizable and show how to generate a set of testers according to a given test sequence. Also, we discuss the issues of test sequence generation and fault detection, and present the results of some empirical studies.


title: A robust RGB-D SLAM system for 3D environment with planar surfaces

abstract: With the increasing popularity of RGB-depth (RGB-D) sensors such as the Microsoft Kinect, there have been much research on capturing and reconstructing 3D environments using a movable RGB-D sensor. The key process behind these kinds of simultaneous location and mapping (SLAM) systems is the iterative closest point or ICP algorithm, which is an iterative algorithm that can estimate the rigid movement of the camera based on the captured 3D point clouds. While ICP is a well-studied algorithm, it is problematic when it is used in scanning large planar regions such as wall surfaces in a room. The lack of depth variations on planar surfaces makes the global alignment an ill-conditioned problem. In this paper, we present a novel approach for registering 3D point clouds by combining both color and depth information. Instead of directly searching for point correspondences among 3D data, the proposed method first extracts features from the RGB images, and then back-projects the features to the 3D space to identify more reliable correspondences. These color correspondences form the initial input to the ICP procedure which then proceeds to refine the alignment. Experimental results show that our proposed approach can achieve better accuracy than existing SLAMs in reconstructing indoor environments with large planar surfaces.


title: "Being Down" Challenging Violence In Urban Schools

abstract: This book looks at the lives of young people and the violence that is a daily part of their world in an urban high school. "Being Down" studies school violence comprehensively - linking school ethnography, policy analysis, and urban studies and encompassing city politics, racism, policing, and systemic and economic injustices in it's all-too-real portrait of youth violence in American society. Rich with dialogue, this timely volume considers school violence in its many forms, including gun and weapon usage, assault and harassment, bullying, fighting, suicide, and hate crimes. In addition to youth violence, the book examines federal policies and court cases that have shaped judicial and punitive responses to violence. Chapters cover: Zero tolerance policy; School discipline; Urban policing; Community and school relations; Segregation and school inequities; Alternative schools; Boot Camps


title: Diagnostic Criteria for Malingered Neurocognitive Dysfunction: Proposed Standards for Clinical Practice and Research

abstract: Over the past 10 years, widespread and concerted research efforts have led to increasingly sophisticated and efficient methods and instruments for detecting exaggeration or fabrication of cognitive dysfunction. Despite these psychometric advances, the process of diagnosing malingering remains difficult and largely idiosyncratic. This article presents a proposed set of diagnostic criteria that define psychometric, behavioral, and collateral data indicative of possible, probable, and definite malingering of cognitive dysfunction, for use in clinical practice and for defining populations for clinical research. Relevant literature is reviewed, and limitations and benefits of the proposed criteria are discussed.


title: A CCPP algorithm based on the standard map for the mobile robot

abstract: This paper introduces a new integrated algorithm to achieve the complete coverage path planning (CCPP) task for the mobile robot in a given obstacles-included terrain. The algorithm combines the cellular decomposition approach and the chaotic Standard map together to design the coverage procedure. The cellular decomposition approach decompose the target region into several rectangular feasible sub-regions. Then the chaotic Standard map in the full mapping state produces the complete coverage trajectories inside the feasible sub-regions, and the connection trajectories between two adjacent feasible sub-regions. Compared with the general cellular decomposition method, the proposed integrated algorithm needs no designated start point and goal point to link two adjacent sub-regions. The planned trajectories demonstrate a good distribution characteristics with regard to completeness and evenness. No obstacles-avoidance method and boundaries detection are needed in the coverage procedure.


title: CRTS: A type system for representing clinical recommendations

abstract: Background: Clinical guidelines and recommendations are the driving wheels of the evidence-based medicine (EBM) paradigm, but these are available primarily as unstructured text and are generally highly heterogeneous in nature. This significantly reduces the dissemination and automatic application of these recommendations at the point of care. A comprehensive structured representation of these recommendations is highly beneficial in this regard. Objective: The objective of this paper to present Clinical Recommendation Type System (CRTS), a common type system that can effectively represent a clinical recommendation in a structured form. Methods: CRTS is built by analyzing 125 recommendations and 195 research articles corresponding to 6 different diseases available from UpToDate, a publicly available clinical knowledge system, and from the National Guideline Clearinghouse, a public resource for evidence-based clinical practice guidelines. Results: We show that CRTS not only covers the recommendations but also is flexible to be extended to represent information from primary literature. We also describe how our developed type system can be applied for clinical decision support, medical knowledge summarization, and citation retrieval. Conclusion: We showed that our proposed type system is precise and comprehensive in representing a large sample of recommendations available for various disorders. CRTS can now be used to build interoperable information extraction systems that automatically extract clinical recommendations and related data elements from clinical evidence resources, guidelines, systematic reviews and primary publications. ::: Keywords: guidelines and recommendations, type system, clinical decision support, evidence-based medicine, information storage and retrieval


The NoiseFiltersR Package: Label Noise Preprocessing in R


title: UTD: Classifying Semantic Relations by Combining Lexical and Semantic Resources

abstract: This paper describes our system for SemEval-2010 Task 8 on multi-way classification of semantic relations between nominals. First, the type of semantic relation is classified. Then a relation type-specific classifier determines the relation direction. Classification is performed using SVM classifiers and a number of features that capture the context, semantic role affiliation, and possible pre-existing relations of the nominals. This approach achieved an F1 score of 82.19% and an accuracy of 77.92%.


title: 100-Mb/s NRZ Visible Light Communications Using a Postequalized White LED

abstract: This letter describes a high-speed visible light communications link that uses a white-light light-emitting diode (LED). Such devices have bandwidths of few megahertz, severely limiting the data rates of any communication system. Here, we demonstrate that by detecting only the blue component of the LED, and using a simple first-order analogue equalizer, a data rate of 100 Mb/s can be achieved using on-off keying nonreturn-to-zero modulation.