title: Diet Networks: Thin Parameters for Fat Genomics

abstract: Learning tasks such as those involving genomic data often poses a serious challenge: the number of input features can be orders of magnitude larger than the number of training examples, making it difficult to avoid overfitting, even when using the known regularization techniques. We focus here on tasks in which the input is a description of the genetic variation specific to a patient, the single nucleotide polymorphisms (SNPs), yielding millions of ternary inputs. Improving the ability of deep learning to handle such datasets could have an important impact in medical research, more specifically in precision medicine, where high-dimensional data regarding a particular patient is used to make predictions of interest. Even though the amount of data for such tasks is increasing, this mismatch between the number of examples and the number of inputs remains a concern. Naive implementations of classifier neural networks involve a huge number of free parameters in their first layer (number of input features times number of hidden units): each input feature is associated with as many parameters as there are hidden units. We propose a novel neural network parametrization which considerably reduces the number of free parameters. It is based on the idea that we can first learn or provide a distributed representation for each input feature (e.g. for each position in the genome where variations are observed in data), and then learn (with another neural network called the parameter prediction network) how to map a feature's distributed representation (based on the feature's identity not its value) to the vector of parameters specific to that feature in the classifier neural network (the weights which link the value of the feature to each of the hidden units). This approach views the problem of producing the parameters associated with each feature as a multi-task learning problem. We show experimentally on a population stratification task of interest to medical studies that the proposed approach can significantly reduce both the number of parameters and the error rate of the classifier.


title: Economic impact of childhood/adolescent ADHD in a European setting: the Netherlands as a reference case

abstract: Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent psychiatric disorder in children/adolescents. This study reviews available European-based studies of ADHD-related costs and applies the findings to the Netherlands to estimate annual national costs for children/adolescents from a societal perspective. A systematic literature search was conducted for primary studies in Europe, published January 1, 1990 through April 23, 2013. Per-person cost estimates were converted to 2012 Euros and used to estimate annual national ADHD-related costs based on the Dutch 2011 census, ADHD prevalence rates, family composition, and employment rates. Seven studies met the inclusion criteria. The average total ADHD-related costs ranged from €9,860 to €14,483 per patient and annual national costs were between €1,041 and €1,529 million (M). The largest cost category was education (€648 M), representing 62 and 42 % of the low- and high-value overall national estimates, respectively. By comparison, ADHD patient healthcare costs ranged between €84 M (8 %) and €377 M (25 %), and social services costs were €4.3 M (0.3–0.4 %). While the majority of the costs were incurred by ADHD patients themselves, €161 M (11–15 %) was healthcare costs to family members that were attributable to having an ADHD child/adolescent. In addition, productivity losses of family members were €143–€339 M (14–22 %). Despite uncertainties because of the small number of studies identified and the wide range in the national cost estimates, our results suggest that ADHD imposes a significant economic burden on multiple public sectors in Europe. The limited number of European-based studies examining the economic burden of ADHD highlights the need for more research in this area.


title: Opinion Mining and Sentiment Analysis

abstract: Opinions are ubiquitous in text, and readers of on-line text — from consumers to sports fans to news addicts to governments — can benefit from automatic methods that synthesise useful opinion-orientated information from the sea of data. In this chapter on opinion mining and sentiment analysis, we introduce an idealised, end-to-end opinion analysis system and describe its components, including constructing opinion lexica, performing sentiment analysis, and producing opinion summaries.


title: Peritoneal molecular environment, adhesion formation and clinical implication.

abstract: Whether induced by infection, inflammation, ischemia, and/or surgical injury, peritoneal adhesions are the leading cause of pelvic pain, bowel obstruction and infertility. It is clear that while postsurgical peritoneal wounds heal without adhesions in some patients, others develop severe scarring from seemingly equal procedures; in addition, in the same patient, adhesions can develop at one surgical site and not in another. The mechanisms underlying the predisposition to form adhesions as well as their site specificity are completely unknown. However, a large number of intraperitoneal surgical procedures are performed each day in the USA, and thus many patients are at risk of developing postoperative adhesions. Therefore, understanding of adhesion formation at the molecular level is essential and in the absence of such information, attempts to prevent patients from developing adhesions will remain an empirical process. The unprecedented advancement in molecular biology during the past decade has led to the identification of many biologically active molecules with the potential of regulating inflammatory and immune responses, angiogenesis and tissue remodeling, events that are central to normal peritoneal wound healing and adhesion formation. Although, the insight into their importance in the development of tissue fibrosis has substantially increased, their major roles in peritoneal biological functions and adhesion formation remain at best speculative. This article reviews the clinical implications of adhesions and attempts to highlight some of the key molecules i.e. growth factors, cytokines, chemokines, proteases and extracellular matrix, that are recognized to regulate inflammation, fibrinolysis, angiogenesis, and tissue remodeling, events that are central to peritoneal wound repair and adhesion formation. Finally, the article discusses the potential application and site specific delivery of several active compounds that are developed to alter the local inflammatory and immune response i.e., cytokine/chemokine network, targeted gene delivery and development of a new generation of biomaterials to prevent adhesion formation. Such understanding of peritoneal biology not only assist us to better manage patients with adhesion, but also those with endometriosis and malignant diseases that affect the peritoneal cavity.


title: Small terminal deletions of the long arm of chromosome 2: Two new cases

abstract: We report on 2 girls with small de novo terminal deletions of the long arm of chromosome 2 and breakpoints within q37. Four cases with similar or more extensive deletions have been previously reported in full. Hypotonia and psychomotor retardation were the only manifestations common to all 6 cases. The phenotype associated with small terminal 2q deletions is variable and clearly not always as mild as indicated in previous reports. The abnormality may also be more common than has been assumed. 12 refs., 3 figs., 1 tab.


title: Fast Estimation of Strains for Cross-Beams Six-Axis Force/Torque Sensors by Mechanical Modeling

abstract: Strain distributions are crucial criteria of cross-beams six-axis force/torque sensors. The conventional method for calculating the criteria is to utilize Finite Element Analysis (FEA) to get numerical solutions. This paper aims to obtain analytical solutions of strains under the effect of external force/torque in each dimension. Genetic mechanical models for cross-beams six-axis force/torque sensors are proposed, in which deformable cross elastic beams and compliant beams are modeled as quasi-static Timoshenko beam. A detailed description of model assumptions, model idealizations, application scope and model establishment is presented. The results are validated by both numerical FEA simulations and calibration experiments, and test results are found to be compatible with each other for a wide range of geometric properties. The proposed analytical solutions are demonstrated to be an accurate estimation algorithm with higher efficiency.


title: Deep learning based modeling for the lateral movement of a strip in hot finishing mill

abstract: In this paper, the problem of system identification for the lateral motion of a strip in hot finishing mill is investigated. The movement is affected by various asymmetric factors with respect to rolling force. Not only that, the tension between rolling mills determines the direction of strip's moving. Consequently, the movement of a strip is complex dynamics with rolling condition, tension and other phenomena. To identify a neural network type system model in the existence of both uncertain parameters and nonlinear signals, deep learning based modelling is employed.


title: MuEns: A Multimodal Human-Machine Music Ensemble for Live Concert Performance

abstract: Musical ensemble between human musicians and computers is a challenging task. We achieve this with a concert-quality synchronization using machine learning. Our system recognizes the position in a given song from the human performance using the microphone and camera inputs, and responds in real-time with audio and visual feedback as a music ensemble. We address three crucial requirements in a musical ensemble system. First, our system interacts with human players through both audio and visual cues, the conventional modes of coordination for musicians. Second, our system synchronizes with human performances while retaining its intended musical expression. Third, our system prevents failures during a concert due to bad tracking, by displaying an internal confidence measure and allowing a backstage human operator to "intervene" if the system is unconfident. We show the feasibility of the system with several experiments, including a professional concert.


title: Voltage Glitch Attacks on Mixed-Signal Systems

abstract: Spply voltage glitches are a well-known fault njection method used to attack electronic circuits. The aim of this paper is to identify the specific threats of mixed signal systems and to provide some solutions to ensure their security. Indeed, many Systems on Chip use both analog and digital circuits but, most of the time, the security of such application is considered only from an exclusively digital or sometimes nalog oint of view. However, in mixed-signals systems, analog and digital solutions coexist and must be considered as a unique system to ensure the security of the whole application. In this purpose, this paper gives an overview of voltage glitch attacks effects and countermeasures for analog and digital blocks as part of Mixed-Signal SoCs (AMS-SoCs). It also emphasizes the unique behavior of mixed-signal circuits during glitch attacks and suggest some guidelines to associate efficiently analog and digital solutions to secure a mixed-signal system.


title: Information elaboration and team performance: Examining the psychological origins and environmental contingencies

abstract: Information elaboration enables functionally diverse teams to transform their breadth of knowledge resources into actionable solutions to complex problems. The current study advances information elaboration theory and research in two ways. First, we identify how team ability and social motivation composition characteristics provide the psychological origins of complex information processing efforts. Second, we identify environmental turbulence as an important boundary condition, clarifying when information elaboration benefits team performance and when it does not. These ideas were tested in a sample of 4-person self-managed teams (N=68) which were functionally diverse and performed a cooperative strategic decision-making task. Results indicate that cognitive ability equips teams with the “can do” ability for complex elaboration efforts through emergent team mental models, whereas low preferences for self-reliance provide the “will do” motivation for in-depth information exchange through collective leadership. In turn, teams benefited from information elaboration in turbulent but not stable environments.


title: A reconfigurable camera add-on for high dynamic range, multispectral, polarization, and light-field imaging

abstract: We propose a non-permanent add-on that enables plenoptic imaging with standard cameras. Our design is based on a physical copying mechanism that multiplies a sensor image into a number of identical copies that still carry the plenoptic information of interest. Via different optical filters, we can then recover the desired information. A minor modification of the design also allows for aperture sub-sampling and, hence, light-field imaging. As the filters in our design are exchangeable, a reconfiguration for different imaging purposes is possible. We show in a prototype setup that high dynamic range, multispectral, polarization, and light-field imaging can be achieved with our design.


title: EasyAlign: an automatic phonetic alignment tool under Praat

abstract: We provide a user-friendly automatic phonetic alignment tool for continuous speech, named EasyAlign. It is developed as a plug-in of Praat, the popular speech analysis software, and it is freely available. Its main advantage is that one can easily align speech from an orthographic transcription. It requires a few minor manual steps and the result is a multi-level annotation within a TextGrid composed of phonetic, syllabic, lexical and utterance tiers. Evaluation showed that the performances of this HTK-based aligner compare to human alignment and to other existing alignment tools. It was originally fully available for French, English. Community’s interests for its extension to other languages helped to develop a straight-forward methodology to add languages. While Spanish and Taiwan Min were recently added, other languages are under development.


title: Fire detection using statistical color model in video sequences

abstract: In this paper, we propose a real-time fire-detector that combines foreground object information with color pixel statistics of fire. Simple adaptive background model of the scene is generated by using three Gaussian distributions, where each distribution corresponds to the pixel statistics in the respective color channel. The foreground information is extracted by using adaptive background subtraction algorithm, and then verified by the statistical fire color model to determine whether the detected foreground object is a fire candidate or not. A generic fire color model is constructed by statistical analysis of the sample images containing fire pixels. The first contribution of the paper is the application of real-time adaptive background subtraction method that aids the segmentation of the fire candidate pixels from the background. The second contribution is the use of a generic statistical model for refined fire-pixel classification. The two processes are combined to form the fire detection system and applied for the detection of fire in the consecutive frames of video sequences. The frame-processing rate of the detector is about 40 fps with image size of 176x144 pixels, and the algorithm's correct detection rate is 98.89%.


title: Recent technological updates and clinical applications of induced pluripotent stem cells

abstract: Induced pluripotent stem cells (iPSCs) were first described in 2006 and have since emerged as a promising cell source for clinical applications. The rapid progression in iPSC technology is still ongoing and directed toward increasing the efficacy of iPSC production and reducing the immunogenic and tumorigenic potential of these cells. Enormous efforts have been made to apply iPSC-based technology in the clinic, for drug screening approaches and cell replacement therapy. Moreover, disease modeling using patient-specific iPSCs continues to expand our knowledge regarding the pathophysiology and prospective treatment of rare disorders. Furthermore, autologous stem cell therapy with patient-specific iPSCs shows great propensity for the minimization of immune reactions and the provision of a limitless supply of cells for transplantation. In this review, we discuss the recent updates in iPSC technology and the use of iPSCs in disease modeling and regenerative medicine.


title: Geographic proximity to coal plants and U.S. public support for extending the Production Tax Credit

abstract: The Production Tax Credit (PTC) is an important policy instrument through which the federal government promotes renewable energy development in the United States. However, the efficacy of the PTC is hampered by repeated expirations and short-term extensions, and by the general uncertainty surrounding its future status. We examine the factors driving variation in public support for the extension of the PTC using a nationally representative, internet-based survey. Americans living near a coal-fired power plant are significantly more likely to support extending the PTC than are their peers who are more insulated from the externalities of burning coal. The evidence for this dynamic was strongest and most statistically significant among subjects experimentally primed to think about the adverse health effects of burning coal. Raising awareness of the public health ramifications of generating electricity from fossil fuels holds the potential to increase support for renewable energy policies among those living in proximity to coal plants, even in a highly politicized policy debate.


title: C-BPMN: A Context Aware BPMN for Modeling Complex Business Process

abstract: A complex business process demands adaptability as it has been highly influenced by the contextual information. The contextual information declares the underlying semantics on which the process logic depends. Thus one of the challenges of a business process modeling is to include the context sensitivity within the modeling itself. BPMN is the widely accepted tool in this field. All the process modeling languages like EPC, UML, BPMN are not able to express the context awareness as required. In this paper an attempt has been made to offer a means for modeling a complex business process with necessary contextual information. We have proposed a context model in terms of a graph, extended the existing BPMN by adding new construct and integrated the said components to achieve our goal. The methodology as stated certainly offers necessary understandability, maintainability and the adaptability as a whole. Moreover the model is validated using Colored Petri Net and is expected to behave properly in a real life environment.


title: DocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Tagging

abstract: Tagging news articles or blog posts with relevant tags from a collection of predefined ones is coined as document tagging in this work. Accurate tagging of articles can benefit several downstream applications such as recommendation and search. In this work, we propose a novel yet simple approach called DocTag2Vec to accomplish this task. We substantially extend Word2Vec and Doc2Vec---two popular models for learning distributed representation of words and documents. In DocTag2Vec, we simultaneously learn the representation of words, documents, and tags in a joint vector space during training, and employ the simple $k$-nearest neighbor search to predict tags for unseen documents. In contrast to previous multi-label learning methods, DocTag2Vec directly deals with raw text instead of provided feature vector, and in addition, enjoys advantages like the learning of tag representation, and the ability of handling newly created tags. To demonstrate the effectiveness of our approach, we conduct experiments on several datasets and show promising results against state-of-the-art methods.


title: Molecular docking studies for discovery of plant-derived α-glucosidase inhibitors

abstract: The identification of naturally occurring a glucosidase inhibitors has been actively pursued with the aim of developing therapeutics for the treatment of type 2 diabetes mellitus. To identify aglucosidase inhibitors, we screened 40 natural compounds, including flavonoids and phenolics, using the structure based molecular docking approach. The rank of each compound was determined on the basis of the binding free energy of the lowest energy cluster. Results showed that all the tested compounds exhibited a binding energy ranging from -8.2 kcal/mol to 3.6 kcal/mol, indicating the variation in antidiabetic potential of tested compounds. The top screened compounds were rutin, quercetin, and myricetin, which exhibited higher inhibitory activities (IC50 = 1.0 to 84.1 ug/mL) against a glucosidase than acarbose (IC50 = 140.5 ug/mL), a reference inhibitor. We also demonstrated that a variety of inhibitory actions (competitive and noncompetitive inhibition) exist among these compounds. Our results suggest that rutin, quercetin, and myricetin can be used to further develop potent a glucosidase inhibitors.


title: Multiscale Image Segmentation by Integrated Edge and Region Detection

abstract: This paper is concerned with the detection of low-level structure in images. It describes an algorithm for image segmentation at multiple scales. The detected regions are homogeneous and surrounded by closed edge contours. Previous approaches to multiscale segmentation represent an image at different scales using a scale-space. However, structure is only represented implicitly in this representation, structures at coarser scales are inherently smoothed, and the problem of structure extraction is unaddressed. This paper argues that the issues of scale selection and structure detection cannot be treated separately. A new concept of scale is presented that represents image structures at different scales, and not the image itself. This scale is integrated into a nonlinear transform which makes structure explicit in the transformed domain. Structures that are stable (locally invariant) to changes in scale are identified as being perceptually relevant. The transform can be viewed as collecting spatially distributed evidence for edges and regions, and making it available at contour locations, thereby facilitating integrated detection of edges and regions without restrictive models of geometry or homogeneity. In this sense, it performs Gestalt analysis. All scale parameters of the transform are automatically determined, and the structure of any arbitrary geometry can be identified without any smoothing, even at coarse scales.


title: Ambient energy harvesting nonvolatile processors: From circuit to system

abstract: Energy harvesting is gaining more and more attentions due to its characteristics of ultra-long operation time without maintenance. However, frequent unpredictable power failures from energy harvesters bring performance and reliability challenges to traditional processors. Nonvolatile processors are promising to solve such a problem due to their advantage of zero leakage and efficient backup and restore operations. To optimize the nonvolatile processor design, this paper proposes new metrics of nonvolatile processors to consider energy harvesting factors for the first time. Furthermore, we explore the nonvolatile processor design from circuit to system level. A prototype of energy harvesting nonvolatile processor is set up and experimental results show that the proposed performance metric meets the measured results by less than 6.27% average errors. Finally, the energy consumption of nonvolatile processor is analyzed under different benchmarks.


title: Ontologies and knowledge bases: Towards a terminological clarification

abstract: The word \ontology" has recently gained a good popularity within the knowledge engineering community. However, its meaning tends to remain a bit vague, as the term is used in very difierent ways. Limiting our attention to the various proposals made in the current debate in AI, we isolate a number of interpretations, which in our opinion deserve a suitable clariflcation. We elucidate the implications of such various interpretations, arguing for the need of clear terminological choices regarding the technical use of terms like \ontology", \conceptualization" and \ontological commitment". After some comments on the use \Ontology" (with the capital \o") as a term which denotes a philosophical discipline, we analyse the possible confusion between an ontology intended as a particular conceptual framework at the knowledge level and an ontology intended as a concrete artifact at the symbol level, to be used for a given purpose. A crucial point in this clariflcation efiort is the careful analysis of Gruber’ s deflnition of an ontology as a speciflcation of a conceptualization.


title: Automated construction of visual-linguistic knowledge via concept learning from cartoon videos

abstract: Learning mutually-grounded vision-language knowledge is a foundational task for cognitive systems and human-level artificial intelligence. Most of knowledge-learning techniques are focused on single modal representations in a static environment with a fixed set of data. Here, we explore an ecologically more-plausible setting by using a stream of cartoon videos to build vision-language concept hierarchies continuously. This approach is motivated by the literature on cognitive development in early childhood. We present the model of deep concept hierarchy (DCH) that enables the progressive abstraction of concept knowledge in multiple levels. We develop a stochastic method for graph construction, i.e. a graph Monte Carlo algorithm, to search efficiently the huge compositional space of the vision-language concepts. The concept hierarchies are built incrementally and can handle concept drift, allowing for being deployed in lifelong learning environments. Using a series of approximately 200 episodes of educational cartoon videos we demonstrate the emergence and evolution of the concept hierarchies as the video stories unfold. We also present the application of the deep concept hierarchies for context-dependent translation between vision and language, i.e. the transcription of a visual scene into text and the generation of visual imagery from text.


title: Demonstration of preferential binding of SYBR Green I to speci ® c DNA fragments in real-time multiplex PCR

abstract: SYBR Green I (SG) is widely used in real-time PCR applications as an intercalating dye and is included in many commercially available kits at undisclosed concentrations. Binding of SG to double-stranded DNA is non-specific and additional testing, such as DNA melting curve analysis, is required to confirm the generation of a specific amplicon. The use of melt curve analysis eliminates the necessity for agarose gel electrophoresis because the melting temperature (T(m)) of the specific amplicon is analogous to the detection of an electrophoretic band. When using SG for real-time PCR multiplex reactions, discrimination of amplicons should be possible, provided the T(m) values are sufficiently different. Real-time multiplex assays for Vibrio cholerae and Legionella pneumophila using commercially available kits and in-house SG mastermixes have highlighted variability in performance characteristics, in particular the detection of only a single product as assessed by T(m) analysis but multiple products as assessed by agarose gel electrophoresis. The detected T(m) corresponds to the amplicon with the higher G+C% and larger size, suggesting preferential binding of SG during PCR and resulting in the failure to detect multiple amplicons in multiplex reactions when the amount of SG present is limiting. This has implications for the design and routine application of diagnostic real-time PCR assays employing SG.


title: Analysis of probabilistic flooding in VANETs for optimal rebroadcast probabilities

abstract: A number of probabilistic flooding schemes have been recently considered in VANETs to address problems of information dissemination in safety applications. The design approach has so far been simulative, a method which does not guarantee that the derived protocols will work when the simulation settings are violated in practice. In this paper, motivated by the need to design information dissemination protocols with verifiable properties prior to implementation, we employ mathematical models of single and multiple lane roads to investigate probabilistic flooding in VANETs using mathematical analysis. We demonstrate that the system can be described by linear difference equations the solutions of which yield the probability of all vehicles receiving the critical message as a function of the rebroadcast probability, the number of neighbors of each vehicle and the number of vehicles. We utilize the obtained solutions to derive the desired rebroadcast probabilities as a function of the transmission range, the vehicle density and the dissemination distance. The obtained results are in line with results obtained using extensive simulations and can be used as a baseline to develop information dissemination protocols with verifiable properties.


title: Activity theory for designing mobile learning

abstract: Mobile computing offers potential opportunities for students' learning. It is important to have an operational understanding of the context in developing a user interface that is both useful and flexible. The author believes that the complexity of the relationships involved can be analysed using activity theory. Activity theory, as a social and cultural psychological theory, can be used to design a mobile learning environment. This paper presents the use of activity theory as a framework for describing the components of an activity system for the design of a context-aware mobile learning application.


Matrix Decomposition


title: Effects of an Integrated Neurofeedback System with Dry Electrodes: EEG Acquisition and Cognition Assessment

abstract: Electroencephalogram (EEG) neurofeedback improves cognitive capacity and behaviors by regulating brain activity, which can lead to cognitive enhancement in healthy people and better rehabilitation in patients. The increased use of EEG neurofeedback highlights the urgent need to reduce the discomfort and preparation time and increase the stability and simplicity of the system's operation. Based on brain-computer interface technology and a multithreading design, we describe a neurofeedback system with an integrated design that incorporates wearable, multichannel, dry electrode EEG acquisition equipment and cognitive function assessment. Then, we evaluated the effectiveness of the system in a single-blind control experiment in healthy people, who increased the alpha frequency band power in a neurofeedback protocol. We found that upregulation of the alpha power density improved working memory following short-term training (only five training sessions in a week), while the attention network regulation may be related to other frequency band activities, such as theta and beta. Our integrated system will be an effective neurofeedback training and cognitive function assessment system for personal and clinical use.


title: Options for independent bladder management in patients with spinal cord injury and hand function prohibiting intermittent catheterization

abstract: Aims ::: ::: Choosing the appropriate bladder management strategy for the spinal cord injury patient with neurogenic bladder and hand function prohibitive of self catheterization (urethral or stomal) catheterization is complex and based on limited literature. We have catalogued the available data in this review. ::: ::: ::: ::: Methods ::: ::: A literature review was conducted on external sphincterotomy, suprapubic tube, ileal conduit, and ileovesicostomy between 1994 and 2012. Articles on neurogenic bladder focused primarily on spinal cord injury were included. Important aspects of each technique, patient selection, urologic events, and quality of life are described. ::: ::: ::: ::: Conclusion ::: ::: The available literature consists primarily of level 3 data. Patient reported outcomes were rarely measured. External sphincterotomy is a good option for males who are candidates for an external catheter and who wish to avoid a complicated reconstruction—most will need re-operations for failure. Suprapubic tube is an option in both genders. Complaints usually involve urine leakage and urinary tract infection, which typically resolve with conservative measures. There is some evidence to support patient satisfaction. Ileal conduit is an option for all patients with quadriplegia, offering continuous drainage and absence of foreign material. Many providers and patients will choose more conservative options first. The ileovesicostomy is best applied to small bladders with severe overactivity. The “reversibility” of this procedure makes it attractive to those not interested in an ileal conduit and who have had complications from prolonged suprapubic tube placement. The authors conclude with recommendations for future research, most importantly more standard reporting of objective data. Neurourol. Urodynam. 34:167–176, 2015. © 2013 Wiley Periodicals, Inc.


title: EEG classification using sparse Bayesian extreme learning machine for brain–computer interface

abstract: Mu rhythm is a spontaneous neural response occurring during a motor imagery (MI) task and has been increasingly applied to the design of brain–computer interface (BCI). Accurate classification of MI is usually rather difficult to be achieved since mu rhythm is very weak and likely to be contaminated by other background noises. As an extension of the single layer feedforward network, extreme learning machine (ELM) has recently proven to be more efficient than support vector machine that is a benchmark for MI-related EEG classification. With probabilistic inference, this study introduces a sparse Bayesian ELM (SBELM)-based algorithm to improve the classification performance of MI. SBELM is able to automatically control the model complexity and exclude redundant hidden neurons by combining advantageous of both ELM and sparse Bayesian learning. The effectiveness of SBELM for MI-related EEG classification is validated on a public dataset from BCI Competition IV IIb in comparison with several other competing algorithms. Superior classification accuracy confirms that the proposed SBELM-based algorithm is a promising candidate for performance improvement of an MI BCI.


Men ' s and women ' s perspectives on using a powered mobility device : Benefits and societal challenges


title: Cell Wall Biosynthesis in Filamentous Fungi

abstract: The cell wall provides physical strength to cells and defines the morphology of fungi. During hyphal development of filamentous fungi, the apical region and the branching sites of the cell are remodeled to support hyphal extension and formation of a new hypha. The cell wall has contact with the environment and thus is the place of first contact with external stresses originating outside of the cells. The cell wall also acts as a matrix for various extracellular proteins such as enzymes and sensor proteins. Budding yeast (Saccharomyces cerevisiae) and filamentous fungi (Aspergillus species) are industrially and medically important fungi belonging to the Ascomycota. These fungi share similar composition in the cell wall although they are morphologically different. Fungal cell walls are usually composed of glucose, mannose, N-acetyl-d-glucosamine, proteins, and lipids. Some glycans composed of galactofuranose or N-acetyl-d-galactosamine are found characteristically in the cell wall of Aspergillus species. In this chapter, we present an overview of current knowledge on cell wall biogenesis and wall-stress sensing in fungi, particularly focusing on recent findings in filamentous fungi.


title: Neglect and Related Disorders

abstract: Neglect is a failure to report, respond, or orient to contralateral stimuli that is not caused by an elemental sensorimotor deficit. Subtypes of neglect are distinguished by input (attentional) or output (intentional) demands, the distribution (personal, spatial, and representational), and the means of eliciting the signs (unilateral or bilateral stimuli). In this article we discuss how to assess patients for neglect, the pathophysiology of neglect, and the treatment of neglect.


title: A Framework to Construct Knowledge Base for Cyber Security

abstract: There are some independent cyber security knowledge bases for different aspect now. In the internet, there is also much cyber security related content which exists in the form of text. Fusion of these cyber security related information can be a meaningful work. In this paper, we propose a framework to integrate existing cyber security knowledge base and extract cyber security related information from text. In the framework, we construct a vulnerability-centric ontology and train a Stanford named entity recognizer to extract cyber security related entity from text. Finally, through experiment, we verify the importance of one feature named useGazette to training a named entity recognizer in cyber security domain.


title: PROMISE: Predicting Query Behavior to Enable Predictive Caching Strategies for OLAP Systems

abstract: This paper discusses the PROMISE (Predicting User Behavior in Multidimensional Information System Environments) approach, that deploys information about characteristic patterns in the user's multidimensional data access in order to improve caching algorithms of OLAP systems. The paper motivates this approach by presenting results of an analysis of the user behavior in a real-world OLAP environment. Further contributions of this paper are a model for characteristic OLAP query patterns based on Markov Models and a corresponding OLAP query prediction algorithm.


title: Mediation Analysis in Social Psychology: Current Practices and New Recommendations

abstract: A key aim of social psychology is to understand the psychological processes through which independent variables affect dependent variables in the social domain. This objective has given rise to statistical methods for mediation analysis. In mediation analysis, the significance of the relationship between the independent and dependent variables has been integral in theory testing, being used as a basis to determine (1) whether to proceed with analyses of mediation and (2) whether one or several proposed mediator(s) fully or partially accounts for an effect. Synthesizing past research and offering new arguments, we suggest that the collective evidence raises considerable concern that the focus on the significance between the independent and dependent variables, both before and after mediation tests, is unjustified and can impair theory development and testing. To expand theory involving social psychological processes, we argue that attention in mediation analysis should be shifted towards assessing the magnitude and significance of indirect effects.


title: 3D dynamic walking on stepping stones with control barrier functions

abstract: 3D dynamical walking subject to precise footstep placements is crucial for navigating real world terrain with discrete footholds. We present a novel methodology that combines control Lyapunov functions—to achieve periodic walking—and control Barrier functions—to enforce strict constraints on step length and step width—unified in a single optimization-based controller. We numerically validate our proposed method by demonstrating dynamic 3D walking at 0.6 m/s on DURUS, a 23 degree-of-freedom underactuated humanoid robot.


title: Beobot 2.0: Autonomous mobile robot localization and navigation in outdoor pedestrian environment

abstract: We present Beobot 2.0 [1], an autonomous mobile robot designed to operate in unconstrained urban environments. The goal of the project is to create service robots that can be deployed for various tasks that require long range travel. Over the past two years, Beobot has successfully traversed various paths across the USC campus, demonstrating its robustness in recognizing and following different types of roads, avoiding obstacles such as pedestrians and service vehicles, and finding its way to the goal.


title: “...No one Can Hack My Mind”: Comparing Expert and Non-Expert Security Practices

abstract: The state of advice given to people today on how to stay safe online has plenty of room for improvement. Too many things are asked of them, which may be unrealistic, time consuming, or not really worth the effort. To improve the security advice, our community must find out what practices people use and what recommendations, if messaged well, are likely to bring the highest benefit while being realistic to ask of people. In this paper, we present the results of a study which aims to identify which practices people do that they consider most important at protecting their security online. We compare self-reported security practices of non-experts to those of security experts (i.e., participants who reported having five or more years of experience working in computer security). We report on the results of two online surveys—one with 231 security experts and one with 294 MTurk participants—on what the practices and attitudes of each group are. Our findings show a discrepancy between the security practices that experts and non-experts report taking. For instance, while experts most frequently report installing software updates, using two-factor authentication and using a password manager to stay safe online, non-experts report using antivirus software, visiting only known websites, and changing passwords frequently.


title: Understanding brand performance measures: using Dirichlet benchmarks

abstract: Abstract Sales of a brand are determined by measures such as how many customers buy the brand, how often, and how much they also buy other brands. Scanner panel operators routinely report these “brand performance measures” (BPMs) to their clients. In this position paper, we consider how to understand, interpret, and use these measures. The measures are shown to follow well-established patterns. One is that big and small brands differ greatly in how many buyers they have, but usually far less in how loyal these buyers are. The Dirichlet model predicts these patterns. It also provides a broader framework for thinking about all competitive repeat-purchase markets—from soup to gasoline, prescription drugs to aviation fuel, where there are large and small brands, and light and heavy buyers, in contexts as diverse as the United States, United Kingdom, Japan, Germany, and Australasia. Numerous practical uses of the framework are illustrated: auditing the performance of established brands, predicting and evaluating the performance of new brands, checking the nature of unfamiliar markets, of partitioned markets, and of dynamic market situations more generally (where the Dirichlet provides theoretical benchmarks for price promotions, advertising, etc.). In addition, many implications for our understanding of consumers, brands, and the marketing mix logically follow from the Dirichlet framework. In repeat-purchase markets, there is often a lack of segmentation between brands and the typical consumer exhibits polygamous buying behavior (though there might be strong segmentation at the category level). An understanding of these applications and implications leads to consumer insights, imposes constraints on marketing action, and provides norms for evaluating brands and for assessing marketing initiatives.


title: Ranking Practices and Distinction in League of Legends

abstract: Player ranking is a common feature of competitive online games, but little research work has closely examined the ways it mediates player practices within this game genre. In this paper, we present a qualitative study of player practices around ranking in League of Legends (LoL), published by Riot Games and currently one of the most popular eSports games. We found that ranking is a cornerstone of LoL's competitive gaming practices, shaping the ways players distinguished and narrated their game experiences, thus engendering a culture of collaboration and competition through distinction.


The Potential of Unmanned Aerial Vehicles for Mapping


title: The NERV methodology: A lightweight process for addressing non-functional requirements in agile software development

abstract: Agile software development has become very popular around the world in recent years, with methods such as Scrum and Extreme Programming (XP). Literature suggests that functionality is the primary focus in Agile processes while non-functional requirements (NFRs) are either ignored or ill-defined. However, for software to be of good quality both functional requirements (FRs) and NFRs need to be taken into consideration; lack of attention to NFRs has been documented to be the cause of failure for many software projects. Hence special attention needs to be focused on NFRs in Agile software development. Requirements elicitation, implementation in design, development, and validation are important activities of software development. Agile has good processes to elicit functional requirements that are captured as user stories. However, NFRs elicitation has not been given enough attention within Agile processes. Additionally, reasoning and validating of NFRs have been lacking. This research proposes the “NERV Methodology: Nonfunctional Requirements Elicitation, Reasoning, and Validation in Agile Processes.” Current results show the artifacts developed in this research can potentially help software development organizations address NFRs in early Agile processes.


title: Social paradoxes of majority rule voting and renormalization group

abstract: Real-space renormalization group ideas are used to study a voting problem in political science. A model to construct self-directed pyramidal structures from bottom up to the top is presented. Using majority rules, it is shown that a minority and even a majority can be systematically self-eliminated from top leadership, provided the hierarchy has a minimal number of levels. In some cases, 70% of the population is found to have zero representation after six hierarchical levels. Results are discussed with respect to the internal operation of political organizations.


title: TuckER: Tensor Factorization for Knowledge Graph Completion

abstract: Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is a task of inferring missing facts based on existing ones. We propose TuckER, a relatively straightforward but powerful linear model based on Tucker decomposition of the binary tensor representation of knowledge graph triples. TuckER outperforms previous state-of-the-art models across standard link prediction datasets, acting as a strong baseline for more elaborate models. We show that TuckER is a fully expressive model, derive sufficient bounds on its embedding dimensionalities and demonstrate that several previously introduced linear models can be viewed as special cases of TuckER.


title: Experiences with selecting search engines using metasearch

abstract: Search engines are among the most useful and high-profile resources on the Internet. The problem of finding information on the Internet has been replaced with the problem of knowing where search engines are, what they are designed to retrieve, and how to use them. This article describes and evaluates SavvySearch, a metasearch engine designed to intelligently select and interface with multiple remote search engines. The primary metasearch issue examined is the importance of carefully selecting and ranking remote search engines for user queries. We studied the efficacy of SavvySearch's incrementally acquired metaindex approach to selecting search engines by analyzing the effect of time and experience on performance. We also compared the metaindex approach to the simpler categorical approach and showed how much experience is required to surpass the simple scheme.


title: Scenarios for Multistage Stochastic Programs

abstract: A major issue in any application of multistage stochastic programming is the representation of the underlying random data process. We discuss the case when enough data paths can be generated according to an accepted parametric or nonparametric stochastic model. No assumptions on convexity with respect to the random parameters are required. We emphasize the notion of representative scenarios (or a representative scenario tree) relative to the problem being modeled.


title: Fast $\epsilon$-free Inference of Simulation Models with Bayesian Conditional Density Estimation

abstract: Many statistical models can be simulated forwards but have intractable likelihoods. Approximate Bayesian Computation (ABC) methods are used to infer properties of these models from data. Traditionally these methods approximate the posterior over parameters by conditioning on data being inside an $\epsilon$-ball around the observed data, which is only correct in the limit $\epsilon\!\rightarrow\!0$. Monte Carlo methods can then draw samples from the approximate posterior to approximate predictions or error bars on parameters. These algorithms critically slow down as $\epsilon\!\rightarrow\!0$, and in practice draw samples from a broader distribution than the posterior. We propose a new approach to likelihood-free inference based on Bayesian conditional density estimation. Preliminary inferences based on limited simulation data are used to guide later simulations. In some cases, learning an accurate parametric representation of the entire true posterior distribution requires fewer model simulations than Monte Carlo ABC methods need to produce a single sample from an approximate posterior.


title: Patient-Specific Epileptic Seizure Onset Detection Algorithm Based on Spectral Features and IPSONN Classifier

abstract: This paper proposes a patient-specific epileptic seizure onset detection algorithm. In this algorithm, spectral features in five frequency bands (Δ, α, s, θ and γ) is extracted from small frames of seizure and non-seizure EEG signals by applying Discrete Wavelet Transform (DWT) and Discrete Fourier Transform (DFT). These features can create the maximum distinction between two classes. Then a neural network (NN) classifier based on improved particle swarm optimization (IPSO) is used to determine an optimal nonlinear decision boundary. This classifier allows adjusting the parameter of the NN classifier, efficiently. Finally, the performance of algorithm is evaluated based on three measures, sensitivity, specificity and latency. The results indicate that the proposed algorithm obtain a higher sensitivity and smaller latency than other common algorithms. The proposed algorithm can be used as a seizure onset detector to initiate the just-in time therapy methods.


title: Experimental Evaluation of a Rare-Earth-Free PMASynRM With Ferrite Magnets for Automotive Applications

abstract: Permanent-magnet (PM) synchronous motor (PMSM) with rare-earth PMs is most popular for automotive applications because of its excellent performance such as high power density, high torque density, and high efficiency. However, the rare-earth PMs have problems such as high cost and limited supply of rare-earth material. Therefore, the electric motors with less or no rare-earth PMs are required in electric vehicle (EV) and hybrid electric vehicle (HEV) applications. This paper proposes and examines a PM-assisted synchronous reluctance motor (PMASynRM) with ferrite magnets that has competitive power density and efficiency of the rare-earth PMSM employed in HEV. The PMASynRM for automotive applications is designed taking into account the irreversible demagnetization of ferrite magnets and the mechanical strength. The prototype PMASynRM has been manufactured, and several performances such as torque, output power, losses, and efficiency are evaluated. Furthermore, the performances of the high-power PMASynRM are estimated based on the experimental results of the prototype PMASynRM, and the possibility of the application of the proposed PMASynRM to EV and HEV is discussed.


title: Spatiochromatic Context Modeling for Color Saliency Analysis

abstract: Visual saliency is one of the most noteworthy perceptual abilities of human vision. Recent progress in cognitive psychology suggests that: 1) visual saliency analysis is mainly completed by the bottom-up mechanism consisting of feedforward low-level processing in primary visual cortex (area V1 ) and 2) color interacts with spatial cues and is influenced by the neighborhood context, and thus it plays an important role in a visual saliency analysis. From a computational perspective, the most existing saliency modeling approaches exploit multiple independent visual cues, irrespective of their interactions (or are not computed explicitly), and ignore contextual influences induced by neighboring colors. In addition, the use of color is often underestimated in the visual saliency analysis. In this paper, we propose a simple yet effective color saliency model that considers color as the only visual cue and mimics the color processing in V1 . Our approach uses region-/boundary-defined color features with spatiochromatic filtering by considering local color-orientation interactions, therefore captures homogeneous color elements, subtle textures within the object and the overall salient object from the color image. To account for color contextual influences, we present a divisive normalization method for chromatic stimuli through the pooling of contrary/complementary color units. We further define a color perceptual metric over the entire scene to produce saliency maps for color regions and color boundaries individually. These maps are finally globally integrated into a one single saliency map. The final saliency map is produced by Gaussian blurring for robustness. We evaluate the proposed method on both synthetic stimuli and several benchmark saliency data sets from the visual saliency analysis to salient object detection. The experimental results demonstrate that the use of color as a unique visual cue achieves competitive results on par with or better than 12 state-of-the-art approaches.


title: Detecting different sub-types of acute myelogenous leukemia using dictionary learning and sparse representation

abstract: Leukemia (a cancer of leukocytes) basically develops in the bone marrow. Acute myelogenous leukemia (a type of leukemia) has eight sub-types according to French-American-British classification. These forms can be visually observed by pathologists using microscopic images of infected cells. However, identification task is tedious and usually difficult due to varying features. Automatic leukemia detection is an important topic in the domain of cancer diagnosis. This paper presents a novel method based on dictionary learning and sparse representation for detecting and classification of different sub-types of AML. For each class, two intensity and label dictionaries are designed for representation using image patches of training samples. New image is represented by all dictionaries and the one with minimum error determine the type of class. We considered M2, M3 and M5 sub-types for evaluation of the method. The initial implementing of the proposed method achieved 97.53% average accuracy for different sub-types of AML.


title: High-Performance Hardware Merge Sorter

abstract: State-of-the-art studies show that FPGA-based hardware merge sorters (HMSs) can achieve superior performance compared with optimized algorithms on CPUs and GPUs. The performance of any HMS is proportional to its operating frequency (F) and the number of records that can be output each cycle (E). However, all existing HMSs have a problem that F drops significantly with increasing E due to the increase of the number of levels of gates. In this paper, we propose novel architectures for HMSs where the number of levels of gates is constant when E is increased. We implement some HMSs adopting the proposed architectures on a Virtex-7 FPGA. The evaluation shows that an HMS of E = 32 operates at 311MHz and achieves 3.13x higher throughput than the state-of-the-art HMS.


title: Peripheral Stimulation and its Effect on Perceived Spatial Scale in Virtual Environments

abstract: The following series of experiments explore the effect of static peripheral stimulation on the perception of distance and spatial scale in a typical head-mounted virtual environment. It was found that applying constant white light in an observers far periphery enabled the observer to more accurately judge distances using blind walking. An effect of similar magnitude was also found when observers estimated the size of a virtual space using a visual scale task. The presence of the effect across multiple psychophysical tasks provided confidence that a perceptual change was, in fact, being invoked by the addition of the peripheral stimulation. These results were also compared to observer performance in a very large field of view virtual environment and in the real world. The subsequent findings raise the possibility that distance judgments in virtual environments might be considerably more similar to those in the real world than previous work has suggested.


title: Learning a unified control policy for safe falling

abstract: Being able to fall safely is a necessary motor skill for humanoids performing highly dynamic tasks, such as running and jumping. We propose a new method to learn a policy that minimizes the maximal impulse during the fall. The optimization solves for both a discrete contact planning problem and a continuous optimal control problem. Once trained, the policy can compute the optimal next contacting body part (e.g. left foot, right foot, or hands), contact location and timing, and the required joint actuation. We represent the policy as a mixture of actor-critic neural network, which consists of n control policies and the corresponding value functions. Each pair of actor-critic is associated with one of the n possible contacting body parts. During execution, the policy corresponding to the highest value function will be executed while the associated body part will be the next contact with the ground. With this mixture of actor-critic architecture, the discrete contact sequence planning is solved through the selection of the best critics while the continuous control problem is solved by the optimization of actors. We show that our policy can achieve comparable, sometimes even higher, rewards than a recursive search of the action space using dynamic programming, while enjoying 50 to 400 times of speed gain during online execution.


title: Regularized discriminant analysis

abstract: Abstract Linear and quadratic discriminant analysis are considered in the small-sample, high-dimensional setting. Alternatives to the usual maximum likelihood (plug-in) estimates for the covariance matrices are proposed. These alternatives are characterized by two parameters, the values of which are customized to individual situations by jointly minimizing a sample-based estimate of future misclassification risk. Computationally fast implementations are presented, and the efficacy of the approach is examined through simulation studies and application to data. These studies indicate that in many circumstances dramatic gains in classification accuracy can be achieved.


title: Lensless-camera based machine learning for image classification

abstract: Machine learning (ML) has been widely applied to image classification. Here, we extend this application to data generated by a camera comprised of only a standard CMOS image sensor with no lens. We first created a database of lensless images of handwritten digits. Then, we trained a ML algorithm on this dataset. Finally, we demonstrated that the trained ML algorithm is able to classify the digits with accuracy as high as 99% for 2 digits. Our approach clearly demonstrates the potential for non-human cameras in machine-based decision-making scenarios.


title: Measuring semantic similarity between words using web search engines

abstract: Semantic similarity measures play important roles in information retrieval and Natural Language Processing. Previous work in semantic web-related applications such as community mining, relation extraction, automatic meta data extraction have used various semantic similarity measures. Despite the usefulness of semantic similarity measures in these applications, robustly measuring semantic similarity between two words (or entities) remains a challenging task. We propose a robust semantic similarity measure that uses the information available on the Web to measure similarity between words or entities. The proposed method exploits page counts and text snippets returned by a Web search engine. We deflne various similarity scores for two given words P and Q, using the page counts for the queries P, Q and P AND Q. Moreover, we propose a novel approach to compute semantic similarity using automatically extracted lexico-syntactic patterns from text snippets. These difierent similarity scores are integrated using support vector machines, to leverage a robust semantic similarity measure. Experimental results on Miller-Charles benchmark dataset show that the proposed measure outperforms all the existing web-based semantic similarity measures by a wide margin, achieving a correlation coe‐cient of 0:834. Moreover, the proposed semantic similarity measure signiflcantly improves the accuracy (F-measure of 0:78) in a community mining task, and in an entity disambiguation task, thereby verifying the capability of the proposed measure to capture semantic similarity using web content.


title: Computer-based assessment: a versatile educational tool

abstract: Abstract There are many types of Computer-Based Assessment in use today in higher education, from formative self-tests to summative final exams. CBA is used in various ways as an integral part of many currently used Computer Aided Learning environments. This paper surveys some of the reasons for using computers for assessment and some of the types of test in use, including evidence that coverage can extend beyond first year assessments, where it seems to be stuck in some disciplines. One project and one type are examined in more detail: randomly generated open access tests. With this type of test students are allowed to practise in their own time before sitting the same test for a grade. Evidence from one particular test, in statistics, was taken from computer logs, questionnaires and individual interviews and is used to show that the test style motivated students to study, and, for some students, directed their revision even when they were away from the computer. The results show that random-based tests can have a number of major advantages over fixed assessments, including: increased lifespan, security and flexibility, improved student motivation for study; and use as a learning resource. They also indicate that CBA does not have to be viewed in isolation from the learning environment in which it is situated, but can have an impact upon a student’s study strategy, for example through increased revision. Taken together with previous studies and the survey of uses, there is now a body of evidence to suggest that CBA is an extremely versatile educational tool.


title: An Attention Mechanism for Neural Answer Selection Using a Combined Global and Local View

abstract: We propose a new attention mechanism for neural based question answering, which depends on varying granularities of the input. Previous work focused on augmenting recurrent neural networks for question answering systems with simple attention mechanisms which are a function of the similarity between a question embedding and an answer embeddings across time. We extend this by making the attention mechanism dependent on a global embedding of the answer attained using a separate network. We evaluate our system on InsuranceQA, a large question answering dataset. Our model outperforms current state-of-the-art results on InsuranceQA. Further, we examine which sections of text our attention mechanism focuses on, and explore its performance across different parameter settings.


title: TSAaaS: Time Series Analytics as a Service on IoT

abstract: In recent years, the evolving of IoT (Internet of Things) has resulted in the deployment of massive numbers of sensors in various fields, such as the Energy and Utility (EaU) industry. These sensors are continuously producing a huge amount of time series data, which creates a correspondingly huge demand for time series data analysis, such as pattern searching. However, analysis on time series data is currently implemented as custom applications, a strategy that suffers from low efficiency and high maintenance costs. Hence there is a need to provide a service for analysis on time series data that reduces maintenance costs and enhances query efficiency. Existing time series data management services lack the capability to perform pattern searches on the massive amount of data from sensors. This paper presents Time Series analytics as a Service (TSaaaS), a scalable analytic service for time series data in IoT scenarios. We designed pattern searching in TSaaaS that can support efficient and effective searching on truly massive amounts of time series data with very little overhead on the IoT system. To simplify access to the TSaaaS, we created a group of RESTful web interfaces. TSaaaS is implemented as an extension to the Time Series Database service in the IBM cloud platform offering (Codename: BlueMix), which is a new product to accelerate IoT application development. TSaaaS targets a future release of the Time Series Database service. We have conducted proofs of concept (PoC) of TSaaaS with real-world customers from power meter management and bridge monitoring in China. The pilot results and other experiments show that for a selection of pattern cases provided by customers, pattern searches via our service are 10-100 times faster than the existing techniques, while the additional storage cost for the service provider accounts for only about 0.4% of original time series data.


title: A dual band hat feed for reflector antennas in Q-V band

abstract: A dual band (Q-V) hat fed cylindrical reflector antenna is presented. The proposed antenna is fed with a dual band corrugated hat feed. The hat feed is designed using a dual corrugation optimized to obtain a similar radiation pattern and a good matching on the two bands. The feed is illuminated by a parallel plate waveguide fed with a rectangular horn which provides large bandwidth and good efficiency. Results show that the proposed design provides an efficient solution for dual band high gain antennas operating at mmw.


title: IoT based smart healthcare kit

abstract: The paper presents the design and implementation of an IOT-based health monitoring system for emergency medical services which can demonstrate collection, integration, and interoperation of IoT data flexibly which can provide support to emergency medical services like Intensive Care Units (ICU), using a INTEL GALILEO 2ND generation development board. The proposed model enables users to improve health related risks and reduce healthcare costs by collecting, recording, analyzing and sharing large data streams in real time and efficiently. The idea of this project came so to reduce the headache of patient to visit to doctor every time he need to check his blood pressure, heart beat rate, temperature etc. With the help of this proposal the time of both patients and doctors are saved and doctors can also help in emergency scenario as much as possible. The proposed outcome of the project is to give proper and efficient medical services to patients by connecting and collecting data information through health status monitors which would include patient's heart rate, blood pressure and ECG and sends an emergency alert to patient's doctor with his current status and full medical information.


title: A feasibility study on SSVEP-based interaction with motivating and immersive virtual and augmented reality

abstract: Non-invasive steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) systems offer high bandwidth compared to other BCI types and require only minimal calibration and training. Virtual reality (VR) has been already validated as effective, safe, affordable and motivating feedback modality for BCI experiments. Augmented reality (AR) enhances the physical world by superimposing informative, context sensitive, computer generated content. In the context of BCI, AR can be used as a friendlier and more intuitive real-world user interface, thereby facilitating a more seamless and goal directed interaction. This can improve practicality and usability of BCI systems and may help to compensate for their low bandwidth. In this feasibility study, three healthy participants had to finish a complex navigation task in immersive VR and AR conditions using an online SSVEP BCI. Two out of three subjects were successful in all conditions. To our knowledge, this is the first work to present an SSVEP BCI that operates using target stimuli integrated in immersive VR and AR (head-mounted display and camera). This research direction can benefit patients by introducing more intuitive and effective real-world interaction (e.g. smart home control). It may also be relevant for user groups that require or benefit from hands free operation (e.g. due to temporary situational disability).


title: Ubiquitous sensing for smart and aware environments

abstract: As computing technology continues to become increasingly pervasive and ubiquitous, we envision the development of environments that can sense what we are doing and support our daily activities. In this article, we outline our efforts toward building such environments and discuss the importance of a sensing and signal-understanding infrastructure that leads to awareness of what is happening in an environment and how it can best be supported. Such an infrastructure supports both high- and low-end data transmission and processing, while allowing for detailed interpretation, modeling and recognition from sensed information. We are currently prototyping several aware environments to aid in the development and study of such sensing and computation in real-world settings.


title: Being a DevOps Developer

abstract: In many IT sectors, DevOps is here to stay, helping deliver higher-quality services more efficiently. Thinking like a DevOps developer is an essential trait of an enlightened software professional. This article is part of a theme issue on DevOps.


title: Advanced Data Exploitation in Speech Analysis: An overview

abstract: With recent advances in machine-learning techniques for automatic speech analysis (ASA)-the computerized extraction of information from speech signals-there is a greater need for high-quality, diverse, and very large amounts of data. Such data could be game-changing in terms of ASA system accuracy and robustness, enabling the extraction of feature representations or the learning of model parameters immune to confounding factors, such as acoustic variations, unrelated to the task at hand. However, many current ASA data sets do not meet the desired properties. Instead, they are often recorded under less than ideal conditions, with the corresponding labels sparse or unreliable.


A Chatbot Based On AIML Rules Extracted From Twitter Dialogues


title: A one-time signature using run-length encoding

abstract: A one-time signature scheme using run-length encoding is presented, that in the random oracle model offers security against chosen-message attacks. For parameters of interest, the proposed scheme enables about 33% faster verification with a comparable signature size than a construction of Merkle and Winternitz. The public key size remains unchanged (1 hash value). The main price for the faster verification is an increase of the time for signing messages and for key generation.


title: Big Data Analysis with Signal Processing on Graphs: Representation and processing of massive data sets with irregular structure

abstract: Analysis and processing of very large data sets, or big data, poses a significant challenge. Massive data sets are collected and studied in numerous domains, from engineering sciences to social networks, biomolecular research, commerce, and security. Extracting valuable information from big data requires innovative approaches that efficiently process large amounts of data as well as handle and, moreover, utilize their structure. This article discusses a paradigm for large-scale data analysis based on the discrete signal processing (DSP) on graphs (DSPG). DSPG extends signal processing concepts and methodologies from the classical signal processing theory to data indexed by general graphs. Big data analysis presents several challenges to DSPG, in particular, in filtering and frequency analysis of very large data sets. We review fundamental concepts of DSPG, including graph signals and graph filters, graph Fourier transform, graph frequency, and spectrum ordering, and compare them with their counterparts from the classical signal processing theory. We then consider product graphs as a graph model that helps extend the application of DSPG methods to large data sets through efficient implementation based on parallelization and vectorization. We relate the presented framework to existing methods for large-scale data processing and illustrate it with an application to data compression.


Ontology Learning with Deep Learning: a Case Study on Patient Safety Using PubMed


title: Toward Predictable Performance in Software Packet-Processing Platforms

abstract: To become a credible alternative to specialized hardware, general-purpose networking needs to offer not only flexibility, but also predictable performance. Recent projects have demonstrated that general-purpose multicore hard-ware is capable of high-performance packet processing, but under a crucial simplifying assumption of uniformity: all processing cores see the same type/amount of traffic and run identical code, while all packets incur the same type of conventional processing (e.g., IP forwarding). Instead, we present a general-purpose packet-processing system that combines ease of programmability with predictable performance, while running a diverse set of applications and serving multiple clients with different needs. Offering predictability in this context is considered a hard problem because software processes contend for shared hardware resources--caches, memory controllers, buses--in unpredictable ways. Still, we show that, in our system, (a) the way in which resource contention affects performance is predictable and (b) the overall performance depends little on how different processes are scheduled on different cores. To the best of our knowledge, our results constitute the first evidence that, when designing software network equipment, flexibility and predictability are not mutually exclusive goals.


title: An efficient user authentication and key agreement scheme for heterogeneous wireless sensor network tailored for the Internet of Things environment

abstract: The concept of Internet of Things (IOT), which is already at our front doors, is that every object in the Internet infrastructure (II) is interconnected into a global dynamic expanding network. Sensors and smart objects are beside classical computing devices key parties of the IOT. We can already exploit the benefits of the IOT by using various weareables or smart phones which are full of diverse sensors and actuators and are connected to the II via GPRS or Wi-Fi. Since sensors are a key part of IOT, thus are wireless sensor networks (WSN). Researchers are already working on new techniques and efficient approaches on how to integrate WSN better into the IOT environment. One aspect of it is the security aspect of the integration. Recently, Turkanovic et?al.'s proposed a highly efficient and novel user authentication and key agreement scheme (UAKAS) for heterogeneous WSN (HWSN) which was adapted to the IOT notion. Their scheme presented a novel approach where a user from the IOT can authenticate with a specific sensor node from the HWSN without having to communicate with a gateway node. Moreover their scheme is highly efficient since it is based on a simple symmetric cryptosystem. Unfortunately we have found that Turkanovic et?al.'s scheme has some security shortcomings and is susceptible to some cryptographic attacks. This paper focuses on overcoming the security weaknesses of Turkanovic et?al.'s scheme, by proposing a new and improved UAKAS. The proposed scheme enables the same functionality but improves the security level and enables the HWSN to dynamically grow without influencing any party involved in the UAKAS. The results of security analysis by BAN-logic and AVISPA tools confirm the security properties of the proposed scheme.


title: PerpSearch: An integrated crime detection system

abstract: Information technologies such as data mining and social network analysis have been widely used in law enforcement to solve crimes. Recent research indicates that geographic profiling also plays an important role in facilitating the investigation of crimes. However, lack of integration makes those systems less helpful in practice. In this paper, we propose an integrated system called PerpSearch that will take a given description of a crime, including its location, type, and the physical description of suspects (personal characteristics or vehicles) as input. To detect suspects, the system will process these inputs through four integrated components: geographic profiling, social network analysis, crime patterns, and physical matching. Essentially, geographic profiling determines “where” the suspects are, while other components determine “who” the suspects are. We then process the results using a score engine to give investigators a ranked list of individuals. To date, we have implemented a prototype of the system based on current Alabama law enforcement data.


title: Mixed-ADC/DAC Multipair Massive MIMO Relaying Systems: Performance Analysis and Power Optimization

abstract: High power consumption and expensive hardware are two bottlenecks for practical massive multiple-input multiple-output (mMIMO) systems. One promising solution is to employ low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). In this paper, we consider a general multipair mMIMO relaying system with a mixed-ADC/DAC architecture, in which some antennas are connected to low-resolution ADCs/DACs, while the rest of the antennas are connected to high-resolution ADCs/DACs. Leveraging on the additive quantization noise model, both exact and approximate closed-form expressions for the achievable rate are derived. It is shown that the achievable rate can approach the unquantized one by using only 2–3 bits of resolutions. Moreover, a power scaling law is presented to reveal that the transmit power can be scaled down inversely proportional to the number of antennas at the relay. We further propose an efficient power allocation scheme by solving a complementary geometric programming problem. In addition, a tradeoff between the achievable rate and power consumption for different numbers of low-resolution ADCs/DACs is investigated by deriving the energy efficiency. Our results reveal that the large antenna array can be exploited to enable the mixed-ADC/DAC architecture, which significantly reduces the power consumption and hardware cost for practical mMIMO systems.


title: SLOWBot (chatbot) Lifestyle Assistant

abstract: SLOWbot is a research project conducted via a collaboration between iaso health and FBK (Fondazione Bruno Kessler). There are now thousands of available healthy aging apps, but most don't deliver on their promise to support a healthy aging process in people that need it the most. The neediest include the over-fifties age group, particularly those wanting to prevent the diseases of aging or whom already have a chronic disease. Even the motivated "quantified selfers" discard their health apps after only a few months. Our research aims to identify new ways to ensure adherence to a healthy lifestyle program tailored for an over fifties audience which is delivered by a chatbot. The research covers the participant onboarding process and examines barriers and issues with gathering predictive data that might inform future improved uptake and adherence as well as an increase in health literacy by the participants. The healthy lifestyle program will ultimately be delivered by our "SLOWbot" which guides the participant to make informed and enhanced health decision making, specifically around food choices (a "longevity eating plan").


title: Multi-stage classifier design

abstract: In many classification systems, sensing modalities have different acquisition costs. It is often unnecessary to use every modality to classify a majority of examples. We study a multi-stage system in a prediction time cost reduction setting, where the full data is available for training, but for a test example, measurements in a new modality can be acquired at each stage for an additional cost. We seek decision rules to reduce the average measurement acquisition cost. We formulate an empirical risk minimization problem (ERM) for a multi-stage reject classifier, wherein the stage k classifier either classifies a sample using only the measurements acquired so far or rejects it to the next stage where more attributes can be acquired for a cost. If we restrict ourselves to binary classification setting then, to solve the ERM problem, we show that the optimal reject classifier at each stage is a combination of two binary classifiers, one biased towards positive examples and the other biased towards negative examples. We use this parameterization to construct stage-by-stage global surrogate risk, develop an iterative algorithm in the boosting framework and present convergence and generalization results. We test our work on synthetic, medical and explosives detection datasets. Our results demonstrate that substantial cost reduction without a significant sacrifice in accuracy is achievable.


title: Sherlock: Micro-Environment Sensing for Smartphones

abstract: Context-awareness is getting increasingly important for a range of mobile and pervasive applications on nowadays smartphones. Whereas human-centric contexts (e.g., indoor/ outdoor, at home/in office, driving/walking) have been extensively researched, few attempts have studied from phones' perspective (e.g., on table/sofa, in pocket/bag/hand). We refer to such immediate surroundings as micro-environment, usually several to a dozen of centimeters, around a phone. In this study, we design and implement Sherlock, a micro-environment sensing platform that automatically records sensor hints and characterizes the micro-environment of smartphones. The platform runs as a daemon process on a smartphone and provides finer-grained environment information to upper layer applications via programming interfaces. Sherlock is a unified framework covering the major cases of phone usage, placement, attitude, and interaction in practical uses with complicated user habits. As a long-term running middleware, Sherlock considers both energy consumption and user friendship. We prototype Sherlock on Android OS and systematically evaluate its performance with data collected on fifteen scenarios during three weeks. The preliminary results show that Sherlock achieves low energy cost, rapid system deployment, and competitive sensing accuracy.


title: Contextual Dependencies In Unsupervised Word Segmentation

abstract: Developing better methods for segmenting continuous text into words is important for improving the processing of Asian languages, and may shed light on how humans learn to segment speech. We propose two new Bayesian word segmentation methods that assume unigram and bigram models of word dependencies respectively. The bigram model greatly outperforms the unigram model (and previous probabilistic models), demonstrating the importance of such dependencies for word segmentation. We also show that previous probabilistic models rely crucially on sub-optimal search procedures.


HAND TALK ASSISTIVE SYSTEM


Computational Linguistics


title: System-in-package integrated antennas in industrial and prospective packages for MMW applications

abstract: During past years, various research teams have been implied in the development of 60 GHz chipset solutions, using both BiCMOS and advanced CMOS technologies. But for the 60 GHz market to flourish not only low cost RFICs are required, low cost antennas and packages are also key points. In order to address these issues, we review in this paper achievable antenna performance discussing possible integration schemes at industrial level, describing industrial substrate solutions for MMW large volume consumer applications, antenna design optimization and 3D on wafer characterization. HR SOI CMOS integrated antennas and High Temperature Co-fired Ceramics (HTCC) substrate based antennas are described with measured gain values of 3.7 dBi and 5.8 dBi at 60 GHz, respectively.


title: NoSQL Databases

abstract: In this document, I present the main notions of NoSQL databases and compare four selected products (Riak, MongoDB, Cassandra, Neo4J) according to their capabilities with respect to consistency, availability, and partition tolerance, as well as performance. I also propose a few criteria for selecting the right tool for the right situation.


title: Offline handwritten Malayalam Character Recognition based on chain code histogram

abstract: Optical Character Recognition plays an important role in Digital Image Processing and Pattern Recognition. Even though ambient study had been performed on foreign languages like Chinese and Japanese, effort on Indian script is still immature. OCR in Malayalam language is more complex as it is enriched with largest number of characters among all Indian languages. The challenge of recognition of characters is even high in handwritten domain, due to the varying writing style of each individual. In this paper we propose a system for recognition of offline handwritten Malayalam vowels. The proposed method uses Chain code and Image Centroid for the purpose of extracting features and a two layer feed forward network with scaled conjugate gradient for classification.


title: Spontaneous production of a pyeloduodenal fistula

abstract: The intraoperative finding of a pyeloduodenal fistula, which has developed spontaneously, is reported. The etiopathogenesis of this syndrome is discussed briefly.


title: Modeling of human body movement on personal mobility interface using LIDAR

abstract: This paper proposes the contactless measurement scheme using LIDAR (Light Detection And Ranging) and the modeling human body movment for the personal mobility interface including twisting motion. We have already proposed the saddle type human body motion interface. This interface uses not only conventional translational human motions but also twisting motion, namely it makes full use of the human motion characteristics. The mechanism of the interface consists of the saddle and universal joint connecting the saddle and personal mobility, and tracing loins motion. Due to these features, the proposed interface shows a potential to realize intuitive operation in the basic experiment. However, the problems have also remained: The height of the saddle should be adjuested for the users' height before riding on the PMV (Personal Mobility Vehicle). And there are plays between the saddle and buttocks of the user, and backlash of the saddle mechanism. This problem prevents a small human motion from measurment. This paper, therefore, proposes the contactless measurement using LIDAR and discusses the fitting methods from measured data points to human body movement.


title: Autostereoscopic displays and computer graphics

abstract: Autostereoscopic displays present a three-dimensional image to a viewer without the need for glasses or other encumbering viewing aids. Three classes of autostereoscopic displays are described: reimaging displays, volumetric displays, and parallax displays. Reimaging displays reproject an existing three-dimensional object to a new location or depth. Volumetric displays illuminate points in a spatial volume. Parallax displays emit directionally-varying image information into the viewing zone. Parallax displays are the most common autostereoscopic displays and are most compatible with computer graphics. Different display technologies of the three types are described. Computer graphics techniques useful for three-dimensional image generation are outlined.


title: Photometric analysis of astronomical images by the wavelet transform

abstract: An application of the wavelet analysis to astronomical images is presented. It is shown that this new method is a powerful tool for the detection of features whose scales are known. The detection is easy and accurate because, in a wavelet analysis, the effect of the background is removed. If a model is fitted to the objects, photometric data can be deduced from the parameters of the wavelet transform. If Gaussian models fit to the objects and to the point-spread function, the process is analytical. To extend the method to other models, numerical simulations have to be performed


title: Evidence against a typology: a taxometric analysis of the sexuality of male-to-female transsexuals.

abstract: Previous theories and research have suggested there are two distinct types of male-to-female (MF) transsexuals and these types can be distinguished by their sexuality. Using the scales Attraction to Femininity in Males, Core Autogynephilia, Autogynephilic Interpersonal Fanasy, and Attraction to Transgender Fiction as indicator variables, taxometric analysis was applied to an online-recruited sample of 308 MF transsexuals to investigate whether such a distinction is justified. In accordance with previous research findings, MF transsexuals categorized as “nonandrophilic” scored significantly higher on Core Autogynephilia than did those categorized as “androphilic”; they also scored significantly higher on Attraction to Femininity in Males and Attraction to Transgender Fiction. Results of one of the taxometric procedures, L-Mode, gave slightly more support for a dimensional, rather than taxonic (two-type), latent structure. Results of the two other taxometric procedures, MAMBAC and MAXCOV, showed greater support for a dimensional latent structure. Although these results require replication with a more representative sample, they show little support for a taxonomy, which contradicts previous theory that has suggested MF transsexuals’ sexuality is typological.


title: 5G antenna in inverted microstrip gap waveguide technology including a transition to microstrip

abstract: A 28 GHz four-beam antenna based on the combination of a 4×4 Butler matrix and a Fabry-Perot superlayer is the final goal of this work. The matrix is designed in the low loss inverted microstrip gap waveguide technology, including the design of a transition to microstrip to enable measurements. Multibeam antennas with high efficiency are of interest for the future 5G communication systems to be used for instance in MIMO systems.


title: Dynamic Bayesian Networks for Vehicle Classification in Video

abstract: Vehicle classification has evolved into a significant subject of study due to its importance in autonomous navigation, traffic analysis, surveillance and security systems, and transportation management. While numerous approaches have been introduced for this purpose, no specific study has been conducted to provide a robust and complete video-based vehicle classification system based on the rear-side view where the camera's field of view is directly behind the vehicle. In this paper, we present a stochastic multiclass vehicle classification system which classifies a vehicle (given its direct rear-side view) into one of four classes: sedan, pickup truck, SUV/minivan, and unknown. A feature set of tail light and vehicle dimensions is extracted which feeds a feature selection algorithm to define a low-dimensional feature vector. The feature vector is then processed by a hybrid dynamic Bayesian network to classify each vehicle. Results are shown on a database of 169 videos for four classes.


title: A systematic review of quantitative studies on the enjoyment of digital entertainment games

abstract: Enjoyment has been identified as a central component of the player experience (PX), but various, overlapping concepts within PX make it difficult to develop valid measures and a common understanding of game enjoyment. We conducted a systematic review of 87 quantitative studies, analyzing different operationalizations and measures of game enjoyment, its determinants, and how these were related to other components of PX, such as flow, presence and immersion. Results suggest that game enjoyment describes the positive cognitive and affective appraisal of the game experience, and may in part be associated with the support of player needs and values. Further, we outline that enjoyment is distinct from flow in that it may occur independently of challenge and cognitive involvement, and argue that enjoyment may be understood as the valence of the player experience. We conclude with a discussion of methodological challenges and point out opportunities for future research on game enjoyment.


Calibrating Large-area Mask Projection Stereolithography for Its Accuracy and Resolution Improvements


AXR-CMP: Architecture Support in Accelerator-Rich CMPs


title: A demonstration of the MaxStream federated stream processing system

abstract: MaxStream is a federated stream processing system that seamlessly integrates multiple autonomous and heterogeneous Stream Processing Engines (SPEs) and databases. In this paper, we propose to demonstrate the key features of MaxStream using two application scenarios, namely the Sales Map & Spikes business monitoring scenario and the Linear Road Benchmark, each with a different set of requirements. More specifically, we will show how the MaxStream Federator can translate and forward the application queries to two different commercial SPEs (Coral8 and StreamBase), as well as how it does so under various persistency requirements.


title: AUTOSAR migration from existing automotive software

abstract: The AUTOSAR consortium was founded to manage the growing electronics complexity and improve cost-efficiency without any compromises with quality as well as reusability. It is expected that AUTOSAR, open and standardized automotive software architecture, is widespread in automotive industry worldwide. In general, automotive embedded software has been closely coupled with the hardware, and the boundary between application software and hardware related software is not clear. The AUTOSAR approach requires a more different approaching way to develop automotive embedded software than today. In AUTOSAR, the application software and infrastructural software are clearly separated through the concept of RTE (Run Time Environment), and the paradigm of design is shifted from coding to configuration. By introducing AUTOSAR, existing system need to be translated into AUTOSAR and, several migration concepts are necessary for the successful transition. ECU developments typically start with legacy software, and it is unavoidable to rearrange existing software according to the AUTOSAR concept. This paper shows how to construct AUTOSAR application software components and basic software modules for already developed ECU software.


title: Model-Based Count Series Clustering for Bike Sharing System Usage Mining: A Case Study with the Vélib’ System of Paris

abstract: Today, more and more bicycle sharing systems (BSSs) are being introduced in big cities. These transportation systems generate sizable transportation data, the mining of which can reveal the underlying urban phenomenon linked to city dynamics. This article presents a statistical model to automatically analyze the trip data of a bike sharing system. The proposed solution partitions (i.e., clusters) the stations according to their usage profiles. To do so, count series describing the stations’s usage through departure/arrival counts per hour throughout the day are built and analyzed. The model for processing these count series is based on Poisson mixtures and introduces a station scaling factor that handles the differences between the stations’s global usage. Differences between weekday and weekend usage are also taken into account. This model identifies the latent factors that shape the geography of trips, and the results may thus offer insights into the relationships between station neighborhood type (its amenities, its demographics, etc.) and the generated mobility patterns. In other words, the proposed method brings to light the different functions in different areas that induce specific patterns in BSS data. These potentials are demonstrated through an in-depth analysis of the results obtained on the Paris Vélib’ large-scale bike sharing system.