title: Knowledge Fusion via Embeddings from Text, Knowledge Graphs, and Images

abstract: We present a baseline approach for cross-modal knowledge fusion. Different basic fusion methods are evaluated on existing embedding approaches to show the potential of joining knowledge about certain concepts across modalities in a fused concept representation.


title: The Clavien-Dindo Classification of Surgical Complications

abstract: Background and Aims: The lack of consensus on how to define and grade adverse postoperative events has greatly hampered the evaluation of surgical procedures. A new classification of complications, initiated in 1992, was updated 5 years ago. It is based on the type of therapy needed to correct the complication. The principle of the classification was to be simple, reproducible, flexible, and applicable irrespective of the cultural background. The aim of the current study was to critically evaluate this classification from the perspective of its use in the literature, by assessing interobserver variability in grading complex complication scenarios and to correlate the classification grades with patients', nurses', and doctors' perception. Material and Methods: Reports from the literature using the classification system were systematically analyzed. Next, 11 scenarios illustrating difficult cases were prepared to develop a consensus on how to rank the various complications. Third, 7 centers from different continents, having routinely used the classification, independently assessed the 11 scenarios. An agreement analysis was performed to test the accuracy and reliability of the classification. Finally, the perception of the severity was tested in patients, nurses, and physicians by presenting 30 scenarios, each illustrating a specific grade of complication. Results: We noted a dramatic increase in the use of the classification in many fields of surgery. About half of the studies used the contracted form, whereas the rest used the full range of grading. Two-thirds of the publications avoided subjective terms such as minor or major complications. The study of 11 difficult cases among various centers revealed a high degree of agreement in identifying and ranking complications (89% agreement), and enabled a better definition of unclear situations. Each grade of complications significantly correlated with the perception by patients, nurses, and physicians (P < 0.05, Kruskal-Wallis test). Conclusions: This 5-year evaluation provides strong evidence that the classification is valid and applicable worldwide in many fields of surgery. No modification in the general principle of classification is warranted in view of the use in ongoing publications and trials. Subjective, inaccurate, or confusing terms such as "minor or major" should be removed from the surgical literature.


title: From functional programs to pipelined dataflow circuits

abstract: We present a translation from programs expressed in a functional IR into dataflow networks as an intermediate step within a Haskell-to-Hardware compiler. Our networks exploit pipeline parallelism, particularly across multiple tail-recursive calls, via non-strict function evaluation. To handle the long-latency memory operations common to our target applications, we employ a latency-insensitive methodology that ensures arbitrary delays do not change the functionality of the circuit. We present empirical results comparing our networks against their strict counterparts, showing that non-strictness can mitigate small increases in memory latency and improve overall performance by up to 2� .


title: Algorithms and applications for approximate nonnegative matrix factorization

abstract: The development and use of low-rank approximate nonnegative matrix factorization (NMF) algorithms for feature extraction and identification in the fields of text mining and spectral data analysis are presented. The evolution and convergence properties of hybrid methods based on both sparsity and smoothness constraints for the resulting nonnegative matrix factors are discussed. The interpretability of NMF outputs in specific contexts are provided along with opportunities for future work in the modification of NMF algorithms for large-scale and time-varying data sets.


title: E-PROCUREMENT: CURRENT ISSUES & FUTURE CHALLENGES

abstract: E-procurement has been identified as an area of B2B e-commerce where IS-enabled transformations of business processes and practices are likely to yield significant benefits. In this paper we present the findings from a recent survey of e-procurement in Australia. The survey is designed around the OECD model of e-business adoption and provides a picture of the readiness, intensity and impact of eprocurement in Australian organisations. The survey findings indicate that e-procurement has transitioned from a largely operational concern and now takes a more strategic position in organisations. E-procurement implementations have increased in both reach and scope, with more organisations implementing systems that span the whole enterprise, encompassing a greater range of procurement functions and activities. However, whilst progress has been made a number of challenges still remain. None of the respondents who had implemented e-procurement reported major or significant benefits. Moderate benefits were realised with respect to improved supply chain integration, reduced employee overhead and improved supplier sourcing. In this paper we present the findings of the 2006 Australian National e-Procurement Survey. We discuss the findings and their relation to previous surveys and studies and outline implications for future research on IS-enabled innovations in procurement.


title: A scalable distributed stream mining system for highway traffic data

abstract: To achieve the concept of smart roads, intelligent sensors are being placed on the roadways to collect real-time traffic streams. Traditional method is not a real-time response, and incurs high communication and storage costs. Existing distributed stream mining algorithms do not consider the resource limitation on the lightweight devices such as sensors. In this paper, we propose a distributed traffic stream mining system. The central server performs various data mining tasks only in the training and updating stage and sends the interesting patterns to the sensors. The sensors monitor and predict the coming traffic or raise alarms independently by comparing with the patterns observed in the historical streams. The sensors provide real-time response with less wireless communication and small resource requirement, and the computation burden on the central server is reduced. We evaluate our system on the real highway traffic streams in the GCM Transportation Corridor in Chicagoland.


Efficient Parallel Spatial Skyline Evaluation Using MapReduce.


title: An efficient error correction interface for speech recognition on mobile touchscreen devices

abstract: Correcting speech recognition errors on a mobile touchscreen device is an unavoidable but time-consuming task that requires a lot of user effort. To reduce this user effort, we previously proposed an error correction method using long context match with Web N-gram, which we combined with a simple gesture-based user interface. This method automatically replaces an error word with its corresponding correct word. However, it was evaluated only substitution errors in sentences, each of which involves only one error. In this paper, we extend this method to be used for more general cases when a sentence has more than one error. It recovers not only substitution errors but also deletion errors and insertion errors. For recovering deletion errors, it predicts a deleted word based on the phonemes and the part-of-speech tags of its surrounding words. Our experimental results show that the proposed method recovered the errors more accurately with less user effort than the conventional Word Confusion Network based error correction interface.


title: Anchoring 2D gesture annotations in augmented reality

abstract: Augmented reality enhanced collaboration systems often allow users to draw 2D gesture annotations onto video feeds to help collaborators to complete physical tasks. This works well for static cameras, but for movable cameras, perspective effects cause problems when trying to render 2D annotations from a new viewpoint in 3D. In this paper, we present a new approach towards solving this problem by using gesture enhanced annotations. By first classifying which type of gesture the user drew, we show that it is possible to render annotations in 3D in a way that conforms more to the original intention of the user than with traditional methods. We first determined a generic vocabulary of important 2D gestures for remote collaboration by running an Amazon Mechanical Turk study with 88 participants. Next, we designed a novel system to automatically handle the top two 2D gesture annotations — arrows and circles. Arrows are handled by identifying their anchor points and using surface normals for better perspective rendering. For circles, we designed a novel energy function to help infer the object of interest using both 2D image cues and 3D geometric cues. Results indicate that our approach outperforms previous methods in terms of better conveying the original drawing's meaning from different viewpoints.


title: Methodologies toward Highly Efficient Perovskite Solar Cells

abstract: A perovskite solar cell (PSC) employing an organic-inorganic lead halide perovskite light harvester, seeded in 2009 with power conversion efficiency (PCE) of 3.8% and grown in 2011 with PCE of 6.5% in dye-sensitized solar cell structure, has received great attention since the breakthrough reports ≈10% efficient solid-state PCSs demonstrating 500 h stability. Developments of device layout and high-quality perovskite film eventually lead to a PCE over 22%. As of October 31, 2017, the highest PCE of 22.7% is listed in an efficiency chart provided by NREL. In this Review, the methodologies to obtain highly efficient PSCs are described in detail. In order to achieve a PCE of over 20% reproducibly, key technologies are disclosed from the viewpoint of precursor solution chemistry, processing for defect-free perovskite films, and passivation of grain boundaries. Understanding chemical species in precursor solution, crystal growth kinetics, light-matter interaction, and controlling defects is expected to give important insights into not only reproducible production of high PCE over 20% but also further enhancement of the PCE of PCSs.


title: The Abused/Abuser Hypothesis of Child Sexual Abuse: A Critical Review of Theory and Research

abstract: A widespread belief among the general public and professionals alike is that “sexual abuse causes sexual abuse” (Finkelhor et al., 1986; Kempe and Kempe, 1984; Lanyon, 1986). That is, sexually abused children and adolescents who have engaged in sexual behavior with an adult (or a significantly older adolescent) are commonly thought to be at risk in later years of themselves becoming sexually involved with children and adolescents. This belief is referred to here as the “abused/abuser hypothesis of child and adolescent sexual abuse.”


title: A 2D + 3D Rich Data Approach to Scene Understanding

abstract: On your one-minute walk from the coffee machine to your desk each morning, you pass by dozens of scenes - a kitchen, an elevator, your office - and you effortlessly recognize them and perceive their 3D structure. But this one-minute scene-understanding problem has been an open challenge in computer vision since the field was first established 50 years ago. In this dissertation, we aim to rethink the path researchers took over these years, challenge the standard practices and implicit assumptions in the current research, and redefine several basic principles in computational scene understanding. ::: The key idea of this dissertation is that learning from rich data under natural setting is crucial for finding the right representation for scene understanding. First of all, to overcome the limitations of object-centric datasets, we built the Scene Understanding (SUN) Database, a large collection of real-world images that exhaustively spans all scene categories. This scene-centric dataset provides a more natural sample of human visual world, and establishes a realistic benchmark for standard 2D recognition tasks. However, while an image is a 2D array, the world is 3D and our eyes see it from a viewpoint, but this is not traditionally modeled. To obtain a 3D understanding at high-level, we reintroduce geometric figures using modern machinery. To model scene viewpoint, we propose a panoramic place representation to go beyond aperture computer vision and use data that is close to natural input for human visual system. This paradigm shift toward rich representation also opens up new challenges that require a new kind of big data - data with extra descriptions, namely rich data. Specifically, we focus on a highly valuable kind of rich data - multiple viewpoints in 3D - and we build the SUN3D data base to obtain an integrated place-centric representation of scenes. We argue for the great importance of modeling the computer's role as an agent in a 3D scene, and demonstrate the power of place-centric scene representation. (Copies available exclusively from MIT Libraries, libraries.mit.edu/docs - [email protected])


title: Using data mining to improve assessment of credit worthiness via credit scoring models

abstract: Credit scoring model have been developed by banks and researchers to improve the process of assessing credit worthiness during the credit evaluation process. The objective of credit scoring models is to assign credit risk to either a ''good risk'' group that is likely to repay financial obligation or a ''bad risk'' group who has high possibility of defaulting on the financial obligation. Construction of credit scoring models requires data mining techniques. Using historical data on payments, demographic characteristics and statistical techniques, credit scoring models can help identify the important demographic characteristics related to credit risk and provide a score for each customer. This paper illustrates using data mining to improve assessment of credit worthiness using credit scoring models. Due to privacy concerns and unavailability of real financial data from banks this study applies the credit scoring techniques using data of payment history of members from a recreational club. The club has been facing a problem of rising number in defaulters in their monthly club subscription payments. The management would like to have a model which they can deploy to identify potential defaulters. The classification performance of credit scorecard model, logistic regression model and decision tree model were compared. The classification error rates for credit scorecard model, logistic regression and decision tree were 27.9%, 28.8% and 28.1%, respectively. Although no model outperforms the other, scorecards are relatively much easier to deploy in practical applications.


title: Microglia Function in Alzheimer’s Disease

abstract: Contrary to early views, we now know that systemic inflammatory/immune responses transmit to the brain. The microglia, the resident “macrophages” of the brain’s innate immune system, are most responsive, and increasing evidence suggests that they enter a hyper-reactive state in neurodegenerative conditions and aging. As sustained over-production of microglial pro-inflammatory mediators is neurotoxic, this raises great concern that systemic inflammation (that also escalates with aging) exacerbates or possibly triggers, neurological diseases (Alzheimer’s, prion, motoneuron disease). It is known that inflammation has an essential role in the progression of Alzheimer’s disease (AD), since amyloid-β (Aβ) is able to activate microglia, initiating an inflammatory response, which could have different consequences for neuronal survival. On one hand, microglia may delay the progression of AD by contributing to the clearance of Aβ, since they phagocyte Aβ and release enzymes responsible for Aβ degradation. Microglia also secrete growth factors and anti-inflammatory cytokines, which are neuroprotective. In addition, microglia removal of damaged cells is a very important step in the restoration of the normal brain environment, as if left such cells can become potent inflammatory stimuli, resulting in yet further tissue damage. On the other hand, as we age microglia become steadily less efficient at these processes, tending to become over-activated in response to stimulation and instigating too potent a reaction, which may cause neuronal damage in its own right. Therefore, it is critical to understand the state of activation of microglia in different AD stages to be able to determine the effect of potential anti-inflammatory therapies. We discuss here recent evidence supporting both the beneficial or detrimental performance of microglia in AD, and the attempt to find molecules/biomarkers for early diagnosis or therapeutic interventions.


title: Language Is Not Just for Talking -- Redundant Labels Facilitate Learning of Novel Categories

abstract: In addition to having communicative functions, verbal labels may play a role in shaping concepts. Two experiments assessed whether the presence of labels affected category formation. Subjects learned to categorize “aliens” as those to be approached or those to be avoided. After accuracy feedback on each response was provided, a nonsense label was either presented or not. Providing nonsense category labels facilitated category learning even though the labels were redundant and all subjects had equivalent experience with supervised categorization of the stimuli. A follow-up study investigated differences between learning verbal and nonverbal associations and showed that learning a nonverbal association did not facilitate categorization. The findings show that labels make category distinctions more concrete and bear directly on the language-and-thought debate.


title: Join driving: A smart phone-based driving behavior evaluation system

abstract: In this paper, we develop a smart phone-based driving behavior evaluation system, named Join Driving, which helps drivers notice how aggressive their driving behaviors are and be aware of the riding comfort level of passengers. The proposed evaluation system is made of two parts: driving events detection and evaluation part and riding comfort level evaluation part. In driving events detection and evaluation part, the proposed system, Join Driving, first presents a model to detect drivers' driving events, based on the data collected from the acceleration, orientation and GPS sensors in smart phones. Then, based on the detected drivers' driving events, Join Driving implements a novel scoring mechanism to quantitatively evaluate how aggressive these driving events are. In riding comfort level evaluation part, the proposed system gives the specific scores to rate passengers' riding comfort level based on ISO 2631. Finally, several practical experiments are conducted to evaluate the effectiveness of the proposed scoring system.


title: Multilinear Sparse Principal Component Analysis

abstract: In this brief, multilinear sparse principal component analysis (MSPCA) is proposed for feature extraction from the tensor data. MSPCA can be viewed as a further extension of the classical principal component analysis (PCA), sparse PCA (SPCA) and the recently proposed multilinear PCA (MPCA). The key operation of MSPCA is to rewrite the MPCA into multilinear regression forms and relax it for sparse regression. Differing from the recently proposed MPCA, MSPCA inherits the sparsity from the SPCA and iteratively learns a series of sparse projections that capture most of the variation of the tensor data. Each nonzero element in the sparse projections is selected from the most important variables/factors using the elastic net. Extensive experiments on Yale, Face Recognition Technology face databases, and COIL-20 object database encoded the object images as second-order tensors, and Weizmann action database as third-order tensors demonstrate that the proposed MSPCA algorithm has the potential to outperform the existing PCA-based subspace learning algorithms.


title: Concomitant loss of dynorphin, NARP, and orexin in narcolepsy

abstract: Background:Narcolepsy with cataplexy is associated with a loss of orexin/hypocretin. It is speculated that an autoimmune process kills the orexin-producing neurons, but these cells may survive yet fail to produce orexin. Objective:To examine whether other markers of the orexin neurons are lost in narcolepsy with cataplexy. Methods:We used immunohistochemistry and in situ hybridization to examine the expression of orexin, neuronal activity-regulated pentraxin (NARP), and prodynorphin in hypothalami from five control and two narcoleptic individuals. Results:In the control hypothalami, at least 80% of the orexin-producing neurons also contained prodynorphin mRNA and NARP. In the patients with narcolepsy, the number of cells producing these markers was reduced to about 5 to 10% of normal. Conclusions:Narcolepsy with cataplexy is likely caused by a loss of the orexin-producing neurons. In addition, loss of dynorphin and neuronal activity-regulated pentraxin may contribute to the symptoms of narcolepsy.


title: RGCNN: Regularized Graph CNN for Point Cloud Segmentation

abstract: Point cloud, an efficient 3D object representation, has become popular with the development of depth sensing and 3D laser scanning techniques. It has attracted attention in various applications such as 3D tele-presence, navigation for unmanned vehicles and heritage reconstruction. The understanding of point clouds, such as point cloud segmentation, is crucial in exploiting the informative value of point clouds for such applications. Due to the irregularity of the data format, previous deep learning works often convert point clouds to regular 3D voxel grids or collections of images before feeding them into neural networks, which leads to voluminous data and quantization artifacts. In this paper, we instead propose a regularized graph convolutional neural network (RGCNN) that directly consumes point clouds. Leveraging on spectral graph theory, we treat features of points in a point cloud as signals on graph, and define the convolution over graph by Chebyshev polynomial approximation. In particular, we update the graph Laplacian matrix that describes the connectivity of features in each layer according to the corresponding learned features, which adaptively captures the structure of dynamic graphs. Further, we deploy a graph-signal smoothness prior in the loss function, thus regularizing the learning process. Experimental results on the ShapeNet part dataset show that the proposed approach significantly reduces the computational complexity while achieving competitive performance with the state of the art. Also, experiments show RGCNN is much more robust to both noise and point cloud density in comparison with other methods. We further apply RGCNN to point cloud classification and achieve competitive results on ModelNet40 dataset.


title: Securing the Internet of Things in the Age of Machine Learning and Software-defined Networking

abstract: The Internet of Things (IoT) realizes a vision where billions of interconnected devices are deployed just about everywhere, from inside our bodies to the most remote areas of the globe. As the IoT will soon pervade every aspect of our lives and will be accessible from anywhere, addressing critical IoT security threats is now more important than ever. Traditional approaches where security is applied as an afterthought and as a"patch"against known attacks are insufficient. Indeed, next-generation IoT challenges will require a new secure-by-design vision, where threats are addressed proactively and IoT devices learn to dynamically adapt to different threats. To this end, machine learning and software-defined networking will be key to provide both reconfigurability and intelligence to the IoT devices. In this paper, we first provide a taxonomy and survey the state of the art in IoT security research, and offer a roadmap of concrete research challenges related to the application of machine learning and software-defined networking to address existing and next-generation IoT security threats.


title: Behavior Change Design Sprints

abstract: While numerous design methods used in industry help designers rapidly brainstorm design ideas, few help them to use theory in the design process. Behavior change theories can support such design activities as understanding, ideating, sketching, and prototyping. We present the Behavior Change Design Sprint (BCDS), a design process for applying behavior change theories to the design process and for prototyping behavior change technologies. BCDS facilitates the application of theories into the design process through a series of exercises that help designers identify intervention placement and project behavioral outcomes, conduct more focused ideation, and advocate for their design rationale. We present our process to create the sprint and findings from a series of sprint deployments.


title: Technical Assessment of Load Commutation Switch in Hybrid HVDC Breaker

abstract: The development of a large scale high voltage direct current (HVDC) power grid requires a reliable, fast and lowloss circuit breaker. The load commutation switch (LCS) is an essential part of ABB's 1200 MW hybrid HVDC breaker concept which builds up a low-loss conducting path for the load current. The technical requirements for the LCS are expressed in this paper by studying the operation principle of the hybrid HVDC breaker. The voltage stress over the LCS is calculated and simulated based on a dc grid with 320kV and 2kA rated voltage and current. A system model of the hybrid HVDC breaker is developed in PSCAD/EMTDC software to study the design criteria for snubber circuit and arrester blocks. It is observed that conventional snubber circuits are not suitable for a bidirectional hybrid HVDC breaker as the current of snubber capacitors prevent the fast interruption action. A modified snubber circuit is proposed in this paper along with two more alternatives for the load commutation switch to overcome this problem. Moreover, the power loss model for a semiconductor device is discussed in this paper based on the 4.5 kV StakPak IGBT. The model is used to calculate the conduction power losses for different LCS topologies. Ultimately, a matrix of 3x3 IGBT modules is selected to provide a reliable LCS design which can handle several internal fault cases with no interruption of operation. A full-scale prototype has been constructed and tested in ABB HVDC Center, Ludvika, Sweden. The experimental test results are also included in the paper in order to verify the calculation and simulation study.


title: Heritability estimates versus large environmental effects: The IQ paradox resolved

abstract: Some argue that the high heritability of IQ renders purely environmental explanations for large IQ differences between groups implausible. Yet, large environmentally induced IQ gains between generations suggest an important role for environment in shaping IQ. The authors present a formal model of the process determining IQ in which people's IQs are affected by both environment and genes, but in which their environments are matched to their IQs. The authors show how such a model allows very large effects for environment, even incorporating the highest estimates of heritability. Besides resolving the paradox, the authors show that the model can account for a number of other phenomena, some of which are anomalous when viewed from the standard perspective.


title: Novel CPW-Fed Planar Monopole Antenna for WiMAX/WLAN Applications

abstract: A coplanar waveguide (CPW)-fed planar monopole antenna with triple-band operation for WiMAX and WLAN applications is presented. The antenna, which occupies a small size of 25(L) × 25(W) × 0.8(H) mm3, is simply composed of a pentagonal radiating patch with two bent slots. By carefully selecting the positions and lengths of these slots, good dual stopband rejection characteristic of the antenna can be obtained so that three operating bands covering 2.14-2.85, 3.29-4.08, and 5.02-6.09 GHz can be achieved. The measured results also demonstrate that the proposed antenna has good omnidirectional radiation patterns with appreciable gain across the operating bands and is thus suitable to be integrated within the portable devices for WiMAX/WLAN applications.


title: Awakened apparel: embedded soft actuators for expressive fashion and functional garments

abstract: Each morning we select an outfit meant to suit our mood and our plans. What if our clothes could seamlessly morph with us as our attitudes and activities change throughout the day? We created Awakened Apparel; one of the first shape-changing fashions to employ pneumatically actuated origami. Our prototype draws from diverse disciplines including soft robotics and fashion to present a design vision that advances the growing field of dynamic interactive garments. We explore technical and fabrication approaches for shape-changing technology held close to the body and identify areas for further innovation.


title: An information theoretic approach for extracting and tracing non-functional requirements

abstract: Non-functional requirements (NFRs) are high-level quality constraints that a software system should exhibit. Detecting such constraints early in the process is critical for the stability of software architectural design. However, due to their pervasive nature, and the lack of robust modeling and documentation techniques, NFRs are often overlooked during the requirements elicitation phase. Realizing such constraints at later stages of the development process often leads to architecture erosion and poor traceability. Motivated by these observations, we propose an unsupervised, computationally efficient, and scalable approach for extracting and tracing NFRs in software systems. Based on main assumptions of the cluster hypothesis and information theory, the proposed approach exploits the semantic knowledge embedded in the textual content of requirements specifications to discover, classify, and trace high-level software quality constraints imposed by the system's functional features. Three experimental systems are used to conduct the experimental analysis in this paper. Results show that the proposed approach can discover software NFRs with an average accuracy of 73%, enabling these NFRs to be traced to their implementations with accuracy levels adequate for practical applications.


title: A NEMO-LISP based architecture for mobility support in medical Body Area Networks

abstract: Internet of Things is an evolution from computer networks to a network of interconnected objects such as books, cars, sensors, etc… In recent years, interest in the applications of Wireless Body Area Networks (BANs), have grown significantly and this opens up an important number of challenges such as mobility management. Locator/Id Split Protocol (LISP) deals with the traditional IP problems like support of device mobility. This work outlines LISP and its properties for high-availability environments. This paper also suggests LISP use for mobility support in BANs. We propose a new architecture based on LISP Protocol coupled with NEMO protocol facilitating mobility management and communication with the nodes on movement.


Early Ganong effects


title: Indefinite Proximity Learning: A Review

abstract: Efficient learning of a data analysis task strongly depends on the data representation. Most methods rely on (symmetric) similarity or dissimilarity representations by means of metric inner products or distances, providing easy access to powerful mathematical formalisms like kernel or branch-and-bound approaches. Similarities and dissimilarities are, however, often naturally obtained by nonmetric proximity measures that cannot easily be handled by classical learning algorithms. Major efforts have been undertaken to provide approaches that can either directly be used for such data or to make standard methods available for these types of data. We provide a comprehensive survey for the field of learning with nonmetric proximities. First, we introduce the formalism used in nonmetric spaces and motivate specific treatments for nonmetric proximity data. Second, we provide a systematization of the various approaches. For each category of approaches, we provide a comparative discussion of the individual algorithms and address complexity issues and generalization properties. In a summarizing section, we provide a larger experimental study for the majority of the algorithms on standard data sets. We also address the problem of large-scale proximity learning, which is often overlooked in this context and of major importance to make the method relevant in practice. The algorithms we discuss are in general applicable for proximity-based clustering, one-class classification, classification, regression, and embedding approaches. In the experimental part, we focus on classification tasks.


title: Ensuring the Quality of the Findings of Qualitative Research: Looking at Trustworthiness Criteria

abstract: Full text can be found at the following link http://jeteraps.scholarlinkresearch.com/abstractview.php?id=19.


title: Investigating episodes of mobile phone activity as indicators of opportune moments to deliver notifications

abstract: We investigate whether opportune moments to deliver notifications surface at the endings of episodes of mobile interaction (making voice calls or receiving SMS) based on the assumption that the endings collocate with naturally occurring breakpoint in the user's primary task. Testing this with a naturalistic experiment we find that interruptions (notifications) are attended to and dealt with significantly more quickly after a user has finished an episode of mobile interaction compared to a random baseline condition, supporting the potential utility of this notification strategy. We also find that the workload and situational appropriateness of the secondary interruption task significantly affect subsequent delay and completion rate of the tasks. In situ self-reports and interviews reveal complexities in the subjective experience of the interruption, which suggest that a more nuanced classification of the particular call or SMS and its relationship to the primary task(s) would be desirable.


title: Big Data Analytics framework for Peer-to-Peer Botnet detection using Random Forests

abstract: Abstract Network traffic monitoring and analysis-related research has struggled to scale for massive amounts of data in real time. Some of the vertical scaling solutions provide good implementation of signature based detection. Unfortunately these approaches treat network flows across different subnets and cannot apply anomaly-based classification if attacks originate from multiple machines at a lower speed, like the scenario of Peer-to-Peer Botnets. In this paper the authors build up on the progress of open source tools like Hadoop, Hive and Mahout to provide a scalable implementation of quasi-real-time intrusion detection system. The implementation is used to detect Peer-to-Peer Botnet attacks using machine learning approach. The contributions of this paper are as follows: (1) Building a distributed framework using Hive for sniffing and processing network traces enabling extraction of dynamic network features; (2) Using the parallel processing power of Mahout to build Random Forest based Decision Tree model which is applied to the problem of Peer-to-Peer Botnet detection in quasi-real-time. The implementation setup and performance metrics are presented as initial observations and future extensions are proposed.


title: Online Identification of PMSM Parameters: Parameter Identifiability and Estimator Comparative Study

abstract: In this paper, a model-reference-based online identification method is proposed to estimate permanent-magnet synchronous machine (PMSM) parameters during transients and in steady state. It is shown that all parameters are not identifiable in steady state and a selection has to be made according to the user's objectives. Then, large signal convergence of the estimated parameters is analyzed using the second method of Lyapunov and the singular perturbations theory. It is illustrated that this method may be applied with a decoupling control technique that improves convergence dynamics and overall system stability. This method is compared with an extended Kalman filter (EKF)-based online identification approach, and it is shown that, in spite of its implementation complexity with respect to the proposed method, EKF does not give better results than the proposed method. It is also shown that the use of a simple PMSM model makes estimated parameters sensitive to those supposed to be known whatever the estimator is (both the proposed method and EKF). The simulation results as well as the experimental ones, implemented on a nonsalient pole PMSM, illustrate the validity of the analytic approach and confirm the same conclusions.


title: 3D Pose tracking of walker users' lower limb with a structured-light camera on a moving platform

abstract: Tracking and understanding human gait is an important step towards improving elderly mobility and safety. Our research team is developing a vision-based tracking system that estimates the 3D pose of a wheeled walker user's lower limbs with a depth sensor, Kinect, mounted on the moving walker. Our tracker estimates 3D poses from depth images of the lower limbs in the coronal plane in a dynamic, uncontrolled environment. We employ a probabilistic approach based on particle filtering, with a measurement model that works directly in the 3D space and another measurement model that works in the projected image space. Empirical results show that combining both measurements, assuming independence between them, yields tracking results that are better than with either one alone. Experiments are conducted to evaluate the performance of the tracking system with different users. We demonstrate that the tracker is robust against unfavorable conditions such as partial occlusion, missing observations, and deformable tracking target. Also, our tracker does not require user intervention or manual initialization commonly required in most trackers.


title: The association between mutations in the lysosomal protein glucocerebrosidase and parkinsonism.

abstract: A body of work has emerged over the past decade demonstrating a relationship between mutations in glucocerebrosidase gene (GBA), the gene implicated in Gaucher disease (GD), and the development of parkinsonism. Several different lines of research support this relationship. First, patients with GD who are homozygous for mutations in GBA have a higher than expected propensity to develop Parkinson's disease (PD). Furthermore, carriers of GBA mutations, particularly family members of patients with GD, have displayed an increased rate of parkinsonism. Subsequently, investigators from centers around the world screened cohorts of patients with parkinsonism for GBA mutations and found that overall, subjects with PD, as well as other Lewy body disorders, have at least a fivefold increase in the number of carriers of GBA mutations as compared to age-matched controls. In addition, neuropathologic studies of subjects with parkinsonism carrying GBA mutations demonstrate Lewy bodies, depletion of neurons of the substantia nigra, and involvement of hippocampal layers CA2-4. Although the basis for this association has yet to be elucidated, evidence continues to support the role of GBA as a PD risk factor across different centers, synucleinopathies, and ethnicities. Further studies of the association between GD and parkinsonism will stimulate new insights into the pathophysiology of the two disorders and will prove crucial for both genetic counseling of patients and family members and the design of relevant therapeutic strategies for specific patients with parkinsonism.


title: Design concept of detail musculoskeletal humanoid “Kenshiro” - Toward a real human body musculoskeletal simulator

abstract: We have developed and studied musculoskeletal humanoids. Our goal is to realize a more human-like humanoid as a real human simulator, which has the same muscle and joint arrangements as human's and can do natural and dynamic motions as well as humans. Especially, it is very challenging to design musculoskeletal structure which can contain a large number of high powered muscles. Now, we design new fullbody musculoskeletal humanoid Kenshiro. This paper presents the concepts of this new robot and also shows the outline of our latest results Kenshiro, which is the succeeding version of our previous robot Kojiro.


title: Taxes, uncertainty, and long-term growth

abstract: Abstract Uncertainty fundamentally alters the way in which taxes effect growth, because tax policies can change the riskiness of disposable income. An increase in the income tax rate, for example, reduces both the mean and variance of after-tax income. The reduction in the mean reduces savings, as predicted by models without uncertainty. However, the reduction in risk may decrease or increase savings depending upon whether consumers are averse to intertemporal substitution. If the elasticity of intertemporal substitution is small, then the fall in the variance reinforces the effects of the fall in the mean, so an increase in the tax rate reduces growth by much more than predicted by non-stochastic models. If the elasticity of intertemporal substitution is large, however, then the fall in the variance causes growth to decrease by less than predicted by non-stochastic models; theoretically, it is actually possible for a tax increase to increase growth.


title: Anomaly detection using sparse reconstruction in crowded scenes

abstract: In this paper, we propose an algorithm of anomaly detection in crowded scenes by using sparse representation over the normal bases. First, the histogram of maximal optical flow projection (HMOFP) features are extracted from a set of normal training data. Then, the online dictionary learning algorithm is used to train an optimal dictionary with proper redundancy, which is better than the dictionary simply composed by the HMOFP features of the whole training data. In order to detect the normalness of a frame, the l 1-norm of the sparse reconstruction coefficients is used as the Reconstruction Coefficient Sparsity (RCS). Our algorithm is effective for both global abnormal events (GAE) and local abnormal events (LAE). We evaluate our method on three benchmark datasets-the UMN dataset, the PETS2009 dataset and the UCSD Ped1 dataset. Compared with the most popular methods, experimental results show that our algorithm achieves good results especially for the pixel-level local abnormal event localization.


title: Meta-Analysis of the Relationship Between the Big Five and Academic Success at University

abstract: Abstract. Interest in the prediction of academic success in higher education has grown considerably in recent years in German-speaking countries. While the validity of school grades and admission tests has been investigated by meta-analyses and large-scale studies at least in the United States, less is known about noncognitive predictors of academic success. The present meta-analysis investigates the impact of the Big Five personality factors on academic success at university. A total of 258 correlation coefficients from 58 studies published since 1980 were included. Grades, retention, and satisfaction served as success criteria. Correlations were corrected for attenuation caused by measurement error. Results show that the influence of personality traits on academic achievement depends on the success criterion. While Neuroticism is related to academic satisfaction (? = -.369, k = 8), Conscientiousness correlates with grades (? = .269, k = 41). Extraversion, Openness to Experience, and Agreeableness have n...


title: A variable step-size frequency-domain adaptive filtering algorithm for stereophonic acoustic echo cancellation

abstract: In this paper a gradient adaptive step-size algorithm for frequency domain adaptive filtering is proposed: the step-size adaptation is performed by differentiating the time-domain block error at the output of the block adaptive filter with respect to the step-size. By using the results obtained through a rigorous mathematical treatment in the single-channel situation and in the presence of a partitioned adaptive filter, an adaptation rule valid for two-channel adaptive algorithms, which may be easily extended to multi-channel adaptive algorithms, is derived. Experimental results concerning stereophonic acoustic echo cancellation are reported, in order to validate the effectiveness of the proposed algorithm.


title: Stress Detection Using Wearable Physiological Sensors

abstract: As the population increases in the world, the ratio of health carers is rapidly decreasing. Therefore, there is an urgent need to create new technologies to monitor the physical and mental health of people during their daily life. In particular, negative mental states like depression and anxiety are big problems in modern societies, usually due to stressful situations during everyday activities including work. This paper presents a machine learning approach for stress detection on people using wearable physiological sensors with the final aim of improving their quality of life. The presented technique can monitor the state of the subject continuously and classify it into ”stressful” or ”non-stressful” situations. Our classification results show that this method is a good starting point towards real-time stress detection.


title: MUSE: Modularizing Unsupervised Sense Embeddings

abstract: This paper proposes to address the word sense ambiguity issue in an unsupervised manner, where word sense representations are learned along a word sense selection mechanism given contexts. Prior work focused on designing a single model to deliver both mechanisms, and thus suffered from either coarse-grained representation learning or inefficient sense selection. The proposed modular approach, MUSE, implements flexible modules to optimize distinct mechanisms, achieving the first purely sense-level representation learning system with linear-time sense selection. We leverage reinforcement learning to enable joint training on the proposed modules, and introduce various exploration techniques on sense selection for better robustness. The experiments on benchmark data show that the proposed approach achieves the state-of-the-art performance on synonym selection as well as on contextual word similarities in terms of MaxSimC.


title: A survey on multistage/multiphase statistical modeling methods for batch processes

abstract: In industrial manufacturing, most batch processes are inherently multistage/multiphase in nature. To ensure both quality consistency of the manufactured products and safe operation of this kind of batch process, different multivariate statistical process control (MSPC) methods have been proposed in recent years. This paper gives an overview of multistage/multiphase statistical process control methods used for process analysis, monitoring, quality prediction and online quality improvement. Different types of phase divisions and modeling strategies are introduced and the method properties are discussed. For comparisons, a selection guide to these methods for different application purposes is provided. Finally, some promising research directions are suggested based on existing works.


Experiments in Distant Influence


title: Learning from Simulated and Unsupervised Images through Adversarial Training

abstract: With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations. However, learning from synthetic images may not achieve the desired performance due to a gap between synthetic and real image distributions. To reduce this gap, we propose Simulated+Unsupervised (S+U) learning, where the task is to learn a model to improve the realism of a simulator's output using unlabeled real data, while preserving the annotation information from the simulator. We develop a method for S+U learning that uses an adversarial network similar to Generative Adversarial Networks (GANs), but with synthetic images as inputs instead of random vectors. We make several key modifications to the standard GAN algorithm to preserve annotations, avoid artifacts, and stabilize training: (i) a 'self-regularization' term, (ii) a local adversarial loss, and (iii) updating the discriminator using a history of refined images. We show that this enables generation of highly realistic images, which we demonstrate both qualitatively and with a user study. We quantitatively evaluate the generated images by training models for gaze estimation and hand pose estimation. We show a significant improvement over using synthetic images, and achieve state-of-the-art results on the MPIIGaze dataset without any labeled real data.


title: The GJK Distance Algorithm: An Interval Version for Incremental Motions

abstract: The tracking of distance between two convex polyhedra is commonly used in the field of robotics, including collision detection or path planning. One of the well-known algorithms in this area is the distance algorithm developed by Gilbert, Johnson and Keerthi. Although this algorithm is widely-used in robotics, up till now, there has been no verification of the computed results. This paper will present an interval version for tracking the distance between convex polyhedra using the C++ library PROFIL/BIAS.


title: An optimum quarter-wave impedance matching feedline for circular UWB array antenna with high gain performance

abstract: The goal of this paper is to designed ultra-wide band (UWB) array antenna for biomedical application. The array arrangement is introduced to improve the performance of the antenna in terms of return loss, gain, bandwidth, and directivity. In this paper, a four-element antenna array is presented. The antenna array is composed of quarter wave feed network along with four-identical circular patch antenna elements designed and simulated using Computer Simulation Tool (CST) Microwave Environment software. The performance of the designed array antenna evaluated in term of return loss, Voltage Standing Wave Ratio (VSWR), bandwidth, radiation pattern, directivity and gain. The array elements and its feed network are properly designed to have a very wide useful band and high gain antenna against frequency. Introduction of unique mitered structures along the feedline and low right side feed realize UWB array antenna with high gain. The simulation result of return loss shown that the operating bandwidth less than -10 dB is start from 4.5 GHz until more than 20 GHz at gain range of 4.2 dB until more than 8 dB at VSWR less than 2. The antenna has an excellent averaged directivity of 7 dBi and a widespread functioned fractional bandwidth of more than 129%. Simulated result shows that the array has a peak gain of more than 8 dB at 9 GHz. These features lead the UWB antenna array appropriate for microwave imaging purposes. The antenna is fabricated on the low cost FR-4 substrate with dielectric constant of 4.7 and thickness of 1.6 mm. Less complicated fabrication methods is possible due to the feeding network placement is on the same level with the antenna patch elements.


title: Modeling a hysteretic modulator's PFM and PWM modes

abstract: In this paper, the describing function approach is used to extend the modeling of hysteretic mode control from the PWM mode to the PWM/PFM dual mode. A detailed circuit configuration of the PFM modulator and derivation of the model are introduced. Based on the derived model, a single zero transfer function is presented and several engineering approximations are applied to represent the dynamic characteristics of the PWM/PFM hysteretic modulator. A closed loop analysis is performed to assist the system designer with understanding the modulator.


title: Design of a 3D-printed circularly polarized antenna for portable UHF RFID readers

abstract: In this paper, a compact, low-profile, circularly polarized UHF RFID reader antenna conformal to a dielectrically controlled plastic support is proposed. The radiating element for the UHF-RFID band (865–868 MHz) has been realized through a circular array of four inverted-F monopoles, where the array elements are excited with a 90-degree phase offset (sequential rotation feeding technique) through a microstrip feeding network. To allow for a friendly prototype realization, the antenna structure is thought to be shaped on adhesive copper and the dielectric support to be 3D-printed in Poly-Lactic Acid (PLA), whose permittivity and loss tangent have been measured and taken into account in the simulation phase. A significant antenna compactness (60mm × 60mm × 6.5mm) has been obtained. Reflection coefficient, port isolation and radiation patterns are evaluated by numerical simulations.


title: A Comparison of Document Clustering Techniques

abstract: This paper presents the results of an experimental study of some common document clustering techniques. In particular, we compare the two main approaches to document clustering, agglomerative hierarchical clustering and K-means. (For K-means we used a “standard” K-means algorithm and a variant of K-means, “bisecting” K-means.) Hierarchical clustering is often portrayed as the better quality clustering approach, but is limited because of its quadratic time complexity. In contrast, K-means and its variants have a time complexity which is linear in the number of documents, but are thought to produce inferior clusters. Sometimes K-means and agglomerative hierarchical approaches are combined so as to “get the best of both worlds.” However, our results indicate that the bisecting K-means technique is better than the standard K-means approach and as good or better than the hierarchical approaches that we tested for a variety of cluster evaluation metrics. We propose an explanation for these results that is based on an analysis of the specifics of the clustering algorithms and the nature of document


title: Why unbiased computational processes can lead to discriminative decision procedures (Chapter 3)

abstract: Nowadays, more and more decision procedures are supported or even guided by automated processes. An important technique in this automation is data mining. In this chapter we study how such automatically generated decision support models may exhibit discriminatory behavior towards certain groups based upon, e.g., gender or ethnicity. Surprisingly, such behavior may even be observed when sensitive information is removed or suppressed and the whole procedure is guided by neutral arguments such as predictive accuracy only. The reason for this phenomenon is that most data mining methods are based upon assumptions that are not always satisfied in reality, namely, that the data is correct and represents the population well. In this chapter we discuss the implicit modeling assumptions made by most data mining algorithms and show situations in which they are not satisfied. Then we outline three realistic scenarios in which an unbiased process can lead to discriminatory models. The effects of the implicit assumptions not being fulfilled are illustrated by examples. The chapter concludes with an outline of the main challenges and problems to be solved.


title: Learning object identification rules for information integration

abstract: When integrating information from multiple websites, the same data objects can exist in inconsistent text formats across sites, making it difficult to identify matching objects using exact text match. We have developed an object identification system called Active Atlas, which compares the objects' shared attributes in order to identify matching objects. Certain attributes are more important for deciding if a mapping should exist between two objects. Previous methods of object identification have required manual construction of object identification rules or mapping rules for determining the mappings between objects, as well as domain-dependent transformations for recognizing format inconsistencies. This manual process is time consuming and error-prone. In our approach, Active Atlas learns to simultaneously tailor both mapping rules and a set of general transformations to a specific application domain, through limited user input. The experimental results demonstrate that we achieve higher accuracy and require less user involvement than previous methods across various application domains.


Art Painting Identification using Convolutional Neural Network


title: Simulation within the railroad environment

abstract: A panel of individuals with expertise in the railroad industry provides an overview of Simulation within the Railroad Environment. The panelists discuss their work and highlight the issues, challenges, and benefits associated with application of simulation models. Topics presented are;• Model Development, the issues and challenges.• Role of Dispatch Model in Mainline Capacity Studies.• Benefits of Simulation tools in Train Dispatching.• Usage of Simulation in Strategic Decision Making.• Areas for Improvement and Increased Use.


Ectopic hidradenitis suppurativa on the dorsal foot of a road maker


title: A Systematic Literature Review of Open Government Data Research: Challenges, Opportunities and Gaps

abstract: Open Government Data (OGD) has been growing as a field of research in digital government. Using a systematic review method, this paper sought to capture the status of research in OGD over the last five years from 2012–2018. The key findings indicate that OGD is an emergent research area with no existing theoretical frameworks. The absence of design science methods suggests an opportunity to create digital government platforms based on OGD best practices. There are also strong legal concerns concerning the opening up of government data, the format of the data, and the privacy / security implications of such exposure. There is limited research on OGD for business benefit nor developing countries. We highlight some gaps for further research and identify potential opportunities.


title: Machine Learning Models Comparison for Bitcoin Price Prediction

abstract: In recent years, Bitcoin is the most valuable in the cryptocurrency market. However, prices of Bitcoin have highly fluctuated which make them very difficult to predict. Hence, this research aims to discover the most efficient and highest accuracy model to predict Bitcoin prices from various machine learning algorithms. By using 1-minute interval trading data on the Bitcoin exchange website named bitstamp from January 1, 2012 to January 8, 2018, some different regression models with scikit-Iearn and Keras libraries had experimented. The best results showed that the Mean Squared Error (MSE) was as low as 0.00002 and the R-Square (R2) was as high as 99.2%.


title: How a family is affected when a child is born with anorectal malformation. Interviews with three patients and their parents.

abstract: Increased knowledge about families with a malformed child affects nursing practice. This study illuminates the experiences and psychosocial situation of 3 patients with imperforate anus and their parents. A qualitative method has been used to analyze the data. We found that the parents' experiences of suffering were overwhelming and that the 3 patients had been depressed during periods. The patients were not as open as their parents; we need to devise better tools to explore and understand children's experiences. The children and their parents need special care and an opportunity to express their anxieties.


title: Self-Supervised Monocular Image Depth Learning and Confidence Estimation

abstract: Abstract We present a novel self-supervised framework for monocular image depth learning and confidence estimation. Our framework reduces the amount of ground truth annotation data required for training Convolutional Neural Networks (CNNs), which is often a challenging problem for the fast deployment of CNNs in many computer vision tasks. Our DepthNet adopts a novel fully differential patch-based cost function through the Zero-Mean Normalized Cross Correlation (ZNCC) to take multi-scale patches as matching and learning strategies. This approach greatly increases the accuracy and robustness of the depth learning. Whilst the proposed patch-based cost function naturally provides a 0-to-1 confidence, it is then used to self-supervise the training of a parallel network for confidence map learning and estimation by exploiting the fact that ZNCC is a normalized measure of similarity which can be approximated as the confidence of the depth estimation. Therefore, the proposed corresponding confidence map learning and estimation operate in a self-supervised manner and is a parallel network to the DepthNet. Evaluation on the KITTI depth prediction evaluation dataset and Make3D dataset show that our method outperforms the state-of-the-art results.


title: Mining frequent patterns without candidate generation

abstract: Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist prolific patterns and/or long patterns. ::: In this study, we propose a novel frequent pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develop an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. Efficiency of mining is achieved with three techniques: (1) a large database is compressed into a highly condensed, much smaller data structure, which avoids costly, repeated database scans, (2) our FP-tree-based mining adopts a pattern fragment growth method to avoid the costly generation of a large number of candidate sets, and (3) a partitioning-based, divide-and-conquer method is used to decompose the mining task into a set of smaller tasks for mining confined patterns in conditional databases, which dramatically reduces the search space. Our performance study shows that the FP-growth method is efficient and scalable for mining both long and short frequent patterns, and is about an order of magnitude faster than the Apriori algorithm and also faster than some recently reported new frequent pattern mining methods.


title: The impact of IS-marketing alignment on marketing performance and business performance

abstract: The importance of the alignment between information systems (IS) and the business has been emphasized for over a decade. To date, no empirical study has explored the impact of the alignment of IS and marketing, despite initial indications that such an alignment could impact favorably upon business performance. This study reports on a new conceptualization of alignment, together with the development and testing of a parsimonious model which addresses this issue. Data from a survey of 415 respondents from medium-large New Zealand companies were used to test the model. It was found that IS-marketing alignment had a positive impact on both business performance and marketing performance, and that marketing performance in turn had a modest but positive impact on business performance.


title: Analysis and design of asymmetric quad-active-bridge converter

abstract: The focus of this paper is on power flow control in the asymmetric quad-active-bridge (QAB) converter and its limitations imposed by uneven values of parasitic elements in the isolation transformer. The QAB converter under study is intended for solid-state transformer applications in asymmetric configuration, where one of the ports is placed at the low voltage side and rated for the threefold power of the medium voltage ports. Significant imbalance between leakage inductance could be observed between them. The steady-state analysis of the asymmetric QAB converter and assessment of power losses using numerical simulation are provided. Experimental waveforms were obtained using a prototype rated for cumulative MV dc link of 2400 V and power of 30 kW.


title: Surgical Management of Deep Gluteal Syndrome Causing Sciatic Nerve Entrapment: A Systematic Review

abstract: PURPOSE ::: To assess the causes, surgical indications, patient-reported clinical outcomes, and complications in patients with deep gluteal syndrome causing sciatic nerve entrapment. ::: ::: ::: METHODS ::: Three databases (PubMed, Ovid [MEDLINE], and Embase) were searched by 2 reviewers independently from database inception until September 7, 2016. The inclusion criteria were studies reporting on both arthroscopic and open surgery and those with Level I to IV evidence. Systematic reviews, conference abstracts, book chapters, and technical reports with no outcome data were excluded. The methodologic quality of the studies was assessed with the MINORS (Methodological Index for Non-randomized Studies) tool. ::: ::: ::: RESULTS ::: The search identified 1,539 studies, of which 28 (481 patients; mean age, 48 years) were included for assessment. Of the studies, 24 were graded as Level IV, 3 as Level III, and 1 as Level II. The most commonly identified causes were iatrogenic (30%), piriformis syndrome (26%), trauma (15%), and non-piriformis (hamstring, obturator internus) muscle pathology (14%). The decision to pursue surgical management was made based on clinical findings and diagnostic investigations alone in 50% of studies, whereas surgical release was attempted only after failed conservative management in the other 50%. Outcomes were positive, with an improvement in pain at final follow-up (mean, 23 months) reported in all 28 studies. The incidence of complications from these procedures was low: Fewer than 1% and 8% of open surgical procedures and 0% and fewer than 1% of endoscopic procedures resulted in major (deep wound infection) and minor complications, respectively. ::: ::: ::: CONCLUSIONS ::: Although most of the studies identified were case series and reports, the results consistently showed improvement in pain and a low incidence of complications, particularly for endoscopic procedures. These findings lend credence to surgical management as a viable option for buttock pain caused by deep gluteal syndrome and warrant further investigation. ::: ::: ::: LEVEL OF EVIDENCE ::: Level IV, systematic review of Level II through IV studies.


title: Data Modelling with Gaussian Process in Sensor Networks for Urban Environmental Monitoring

abstract: In this paper, the multidimensional output Gaussian process (GP) is applied to model urban environmental data collected by sensor networks. Measurements from sensors at different locations are correlated. Moreover, we observe that the pollution level in urban area is highly coupled with human activities and shows periodic patterns accordingly. Based on these observations, we discuss the design of mean and kernel functions with two approaches: (1) composed kernel and maximum likelihood estimation of hyper-parameters, (2) Wiener-Khinchin theorem based approximation of sample covariances. To validate the models, the accuracy of interpolations given by different approaches are compared. The experimental results show that, for the application of interpolation, the dependent GP with the approximated sample covariances as kernels can provide better performance than the independent GP model with composed kernels.


title: Spell checker for Myanmar language

abstract: Natural Language Processing (NLP) is one of the most important research areas carried out in the world of Human language. For every language, spell checker is an essential component of many of the common Desktop applications, Machine Translation system and Office Automation system. In Myanmar, Myanmar Language is used as an official language. Myanmar Pronunciation and orthography has differences because spelling is often not an accurate reflection of pronunciation. In this paper, we developed Myanmar Spell Checker which can handle Typographic Errors (Non-word Errors), Phonetic Errors and Sequence Errors of Myanmar words. If misspelled word contains in the input sentence, this system can provide suggestion for misspelled Myanmar words. We apply Myanmar text Corpus to check Myanmar words. And then we used String Cosine Similarity to generate suggestions list for mistyped Myanmar words. The system can improve the quality of suggestion for misspelled Myanmar words and users' efficiency when the users cannot figure out the correct spelling by themselves.


title: The Design and Implementation of Video Surveillance System Based on H.264, SIP, RTP/RTCP and RTSP

abstract: The streaming media protocols including SIP(Session Initiation Protocol), RTP(Real-time Transport Protocol), RTCP(RTP Control Protocol) and RTSP(Real-Time Streaming Protocol) is the basic technology of the video surveillance system. A video surveillance system based on SIP, RTP/RTCP and RTSP is designed and implemented in this paper. The video surveillance system uses the SIP protocol to connect the client and the server, and provides the service of the multimedia data transmission between them with the RTP/RTCP and RTSP protocol. The video images of the surveillance system is encoded by H.264 video coding standards. The play of the video data is based on the technology of Direct show. The experiment results show that the surveillance system provides good video quality and adaptability of the network conditions.


title: Robust euclidean alignment of 3D point sets: the trimmed iterative closest point algorithm

abstract: Abstract The problem of geometric alignment of two roughly pre-registered, partially overlapping, rigid, noisy 3D point sets is considered. A new natural and simple, robustified extension of the popular Iterative Closest Point (ICP) algorithm [IEEE Trans. Pattern Anal. Machine Intell. 14 (1992) 239] is presented, called Trimmed ICP (TrICP). The new algorithm is based on the consistent use of the Least Trimmed Squares approach in all phases of the operation. Convergence is proved and an efficient implementation is discussed. TrICP is fast, applicable to overlaps under 50%, robust to erroneous and incomplete measurements, and has easy-to-set parameters. ICP is a special case of TrICP when the overlap parameter is 100%. Results of a performance evaluation study on the SQUID database of 1100 shapes are presented. The tests compare TrICP and the Iterative Closest Reciprocal Point algorithm [Fifth International Conference on Computer Vision, 1995].


title: Production-run software failure diagnosis via hardware performance counters

abstract: Sequential and concurrency bugs are widespread in deployed software. They cause severe failures and huge financial loss during production runs. Tools that diagnose production-run failures with low overhead are needed. The state-of-the-art diagnosis techniques use software instrumentation to sample program properties at run time and use off-line statistical analysis to identify properties most correlated with failures. Although promising, these techniques suffer from high run-time overhead, which is sometimes over 100%, for concurrency-bug failure diagnosis and hence are not suitable for production-run usage. We present PBI, a system that uses existing hardware performance counters to diagnose production-run failures caused by sequential and concurrency bugs with low overhead. PBI is designed based on several key observations. First, a few widely supported performance counter events can reflect a wide variety of common software bugs and can be monitored by hardware with almost no overhead. Second, the counter overflow interrupt supported by existing hardware and operating systems provides a natural and effective mechanism to conduct event sampling at user level. Third, the noise and non-determinism in interrupt delivery complements well with statistical processing. We evaluate PBI using 13 real-world concurrency and sequential bugs from representative open-source server, client, and utility programs, and 10 bugs from a widely used software-testing benchmark. Quantitatively, PBI can effectively diagnose failures caused by these bugs with a small overhead that is never higher than 10%. Qualitatively, PBI does not require any change to software and presents a novel use of existing hardware performance counters.


title: Clustering Algorithm for Gujarati Language

abstract: Natural language processing area is still under research. But now a day it is on platform for worldwide researchers. Natural language processing includes analyzing the language based on its structure and then tagging of each word appropriately with its grammar base. Here we have 50,000 tagged words set and we try to cluster those Gujarati words based on proposed algorithm, we have defined our own algorithm for processing. Many clustering techniques are available Ex. Single linkage , complete, linkage ,average linkage, Hear no of clusters to be formed are not known, so it's all depends on the type of data set provided .Clustering is preprocess for stemming . Stemming is the process where root is extracted from its word. Ex. cats= cat+S, meaning. Cat: Noun and plural form.


title: Blind speech separation employing directional statistics in an Expectation Maximization framework

abstract: In this paper we propose to employ directional statistics in a complex vector space to approach the problem of blind speech separation in the presence of spatially correlated noise. We interpret the values of the short time Fourier transform of the microphone signals to be draws from a mixture of complexWatson distributions, a probabilistic model which naturally accounts for spatial aliasing. The parameters of the density are related to the a priori source probabilities, the power of the sources and the transfer function ratios from sources to sensors. Estimation formulas are derived for these parameters by employing the Expectation Maximization (EM) algorithm. The E-step corresponds to the estimation of the source presence probabilities for each time-frequency bin, while the M-step leads to a maximum signal-to-noise ratio (MaxSNR) beamformer in the presence of uncertainty about the source activity. Experimental results are reported for an implementation in a generalized sidelobe canceller (GSC) like spatial beamforming configuration for 3 speech sources with significant coherent noise in reverberant environments, demonstrating the usefulness of the novel modeling framework.


title: Process Equivalence: Comparing Two Process Models Based on Observed Behavior

abstract: In various application domains there is a desire to compare process models, e.g., to relate an organization-specific process model to a reference model, to find a web service matching some desired service description, or to compare some normative process model with a process model discovered using process mining techniques. Although many researchers have worked on different notions of equivalence (e.g., trace equivalence, bisimulation, branching bisimulation, etc.), most of the existing notions are not very useful in this context. First of all, most equivalence notions result in a binary answer (i.e., two processes are equivalent or not). This is not very helpful, because, in real-life applications, one needs to differentiate between slightly different models and completely different models. Second, not all parts of a process model are equally important. There may be parts of the process model that are rarely activated while other parts are executed for most process instances. Clearly, these should be considered differently. To address these problems, this paper proposes a completely new way of comparing process models. Rather than directly comparing two models, the process models are compared with respect to some typical behavior. This way we are able to avoid the two problems. Although the results are presented in the context of Petri nets, the approach can be applied to any process modeling language with executable semantics.


title: Optimized projection pattern supplementing stereo systems

abstract: Stereo camera systems are widely used in many real applications including indoor and outdoor robotics. They are very easy to use and provide accurate depth estimates on well-textured scenes, but often fail when the scene does not have enough texture. It is possible to help the system work better in this situation by actively projecting certain light patterns to the scene to create artificial texture on the scene surface. The question we try to answer in ths paper is what would be the best pattern(s) to project. This paper introduces optimized projection patterns based on a novel concept of (symmetric) non-recurring De Bruijn sequences, and describes algorithms to generate such sequences. A projected pattern creates an artificial texture which does not contain any duplicate patterns over epipolar lines within certain range, thus it makes the correspondence match simple and unique. The proposed patterns are compatible with most existing stereo algorithms, meaning that they can be used without any changes in the stereo algorithm and one can immediately get much denser depth estimates without any additional computational cost. It is also argued that the proposed patterns are optimal binary patterns, and finally a few experimental result using stereo and space-time stereo algorithms are presented.


title: The Three-Dimensional Bin Packing Problem

abstract: The problem addressed in this paper is that of orthogonally packing a given set of rectangular-shaped items into the minimum number of three-dimensional rectangular bins. The problem is strongly NP-hard and extremely difficult to solve in practice. Lower bounds are discussed, and it is proved that the asymptotic worst-case performance ratio of the continuous lower bound is ?. An exact algorithm for filling a single bin is developed, leading to the definition of an exact branch-and-bound algorithm for the three-dimensional bin packing problem, which also incorporates original approximation algorithms. Extensive computational results, involving instances with up to 90 items, are presented: It is shown that many instances can be solved to optimality within a reasonable time limit.


title: Breast cancer diagnosis using adaptive voting ensemble machine learning algorithm

abstract: According to Breast Cancer Institute (BCI), Breast Cancer is one of the most dangerous type of diseases that is very effective for women in the world. As per clinical expert detecting this cancer in its first stage helps in saving lives. As per cancer.net offers individualized guides for more than 120 types of cancer and related hereditary syndromes. For detecting breast cancer mostly machine learning techniques are used. In this paper we proposed adaptive ensemble voting method for diagnosed breast cancer using Wisconsin Breast Cancer database. The aim of this work is to compare and explain how ANN and logistic algorithm provide better solution when its work with ensemble machine learning algorithms for diagnosing breast cancer even the variables are reduced. In this paper we used the Wisconsin Diagnosis Breast Cancer dataset. When compared to related work from the literature. It is shown that the ANN approach with logistic algorithm is achieved 98.50% accuracy from another machine learning algorithm.


title: AMI-Net: Convolution Neural Networks With Affine Moment Invariants

abstract: Affine moment invariant (AMI) is a kind of hand-crafted image feature, which is invariant to affine transformations. This property is precisely what the standard convolution neural network (CNN) is difficult to achieve. In this letter, we present a kind of network architecture to introduce AMI into CNN, which is called AMI-Net. We achieved this by calculating AMI on the feature maps of the hidden layers. These AMIs will be concatenated with the standard CNN's FC layer to determine the network's final output. By calculating AMI on the feature maps, we can not only extend the dimension of AMIs, but also introduce affine transformation invariant into CNN. Two network architectures and training strategies of AMI-Net are illuminated, one is two-stage, and the other is end-to-end. To prove the effectiveness of the AMI-Net, several experiments have been conducted on common image datasets, MNIST, MNIST-rot, affNIST, SVHN, and CIFAR-10. By comparing with the corresponding standard CNN, respectively, we verify the validity of AMI-net.


title: Depth estimation from a single 2D image

abstract: With the growth of the 3D market, the demand for providing 3D content from already existing 2D content has increased. However, the availability of 3D content is very limited, since estimating 3D structure from a monocular image is a challenging task. In this paper, we propose a new nonparametric learning-based method for 2D-to-3D conversion from a monocular image. Our method follows three stages. First, we select K similar images to the input image from an RGBD database. Then, we infer the depth map using the K selected images and their corresponding depths maps. Finally, we refine the estimated depth map. Experiments on dataset were conducted and comparative evaluations with the state of the art are presented.


title: Hydrogenated Amorphous Silicon Thin-Film Transistor-Based Optical Pixel Sensor With High Sensitivity Under Ambient Illumination

abstract: This letter develops an optical pixel sensor that is based on hydrogenated amorphous silicon thin-film transistors. Exploiting the photo sensitivity of the photo TFTs and combining different color filters, the proposed sensor can sense an optical input signal of a specified color under high ambient illumination conditions. Measurements indicate that the proposed pixel sensor effectively reacts to the optical input signal under light intensities from 873 to 12,910 lux, proving that the sensor is highly reliable under strong ambient illumination.


title: Optimizing Expected Intersection-Over-Union with Candidate-Constrained CRFs

abstract: We study the question of how to make loss-aware predictions in image segmentation settings where the evaluation function is the Intersection-over-Union (IoU) measure that is used widely in evaluating image segmentation systems. Currently, there are two dominant approaches: the first approximates the Expected-IoU (EIoU) score as Expected-Intersection-over-Expected-Union (EIoEU), and the second approach is to compute exact EIoU but only over a small set of high-quality candidate solutions. We begin by asking which approach we should favor for two typical image segmentation tasks. Studying this question leads to two new methods that draw ideas from both existing approaches. Our new methods use the EIoEU approximation paired with high quality candidate solutions. Experimentally we show that our new approaches lead to improved performance on both image segmentation tasks.


title: How Important Are Enjoyment and Mobility for Mobile Applications

abstract: ABSTRACTThis article investigates how perceived enjoyment and mobility drive user continuance intentions toward using mobile applications. A second-stage continuance model was developed after a thorough literature review. A survey instrument was deployed to collect data from 584 smartphone users. The model was empirically tested using structural equation modeling procedures. Data analyses show that the salience of disconfirmation and beliefs in enjoyment and mobility serve as the primary driver of the changes in satisfaction and attitude toward continuance intentions. Furthermore, perceived enjoyment, mobility and satisfaction jointly explained over 60% of the variance in post-usage attitude. As an initial effort to revise and test the expanded IS continuance model, this study deepens our understanding of enjoyment and mobility at post-usage stage of mobile experience. It urges mobile application providers to forge continuance by devoting more resources and efforts toward creating a truly enjoyable as wel...


Hierarchical Bayesian Nonparametric Models with Applications


title: The Situational Humour Response Questionnaire (SHRQ) as a test of “sense of humour”: a validity study in the field of humour appreciation☆

abstract: Abstract The Situational Humour Response Questionnaire [SHRQ, Martin & Lefcourt (1984) Journal of Personality and Social Psychology, 47, 145–155] measures the propensity to smile and laugh in a variety of daily life situations. Since the SHRQ is regarded as a test of “sense of humour”, its validity in the field of humour appreciation was investigated. Two student samples (N = 105 and 101) from Indiana answered the SHRQ and rated the funniness and aversiveness of one of two sets of 35 jokes and cartoons taken from Form A and Form B of the 3-WD Humour Test [Ruch (1983) Humour Test 3-WD (Form A, B and K). Unpublished Manuscript]. Product-moment correlations between the SHRQ and humour appreciation was computed at the level of funniness and aversiveness for individual items, for humour categories as well as for total scores. Contrary to expectations, the SHRQ did not correlate with any level of 3-WD Humour Test scores suggesting that these two tests apparently tap totally different domains of humour. It may be that the SHRQ measures laughter that is only partially accompanied by the humour experience with that experience being more fully measured in the 3-WD Test. It appears that the SHRQ fails to fulfil the criterion that a test of “sense of humour” should be able to account for individual differences in humour appreciation. A hypothesis is proposed suggesting that the relationship between the SHRQ and humour appreciation might be mediated by social factors.


title: WHAT IS ASTRAGALUS LEUCARGYREUS BORNM. AND WHERE IS ITS SYSTEMATIC POSITION

abstract: Astragalus leucargyreus Bornm. was the first time recollected from the type locality, Hamadan province after the 110 years. Morphological characters, habitat and geographical distribution are presented. Further studies showed that A. leucargyreus should be excluded from the sect. Hymenostegis Bunge and transferred to the sect. Adiaspastus Bunge, then treated as a synonymous with A. erythrolepis Boiss.. This study shows that A. leucargyreus Bornm. with its morphological feature as a valid species now is transferred from the sect. Adiaspastus to its previous the sect. Hymenostegis and excluded from synonymous status.


title: Working in the Space Between

abstract: All of the contributors to this special issue have reflected on the stakes involved in negotiating differences in language and culture. In their research and professional practice they inhabit the ‘space between’: the space between languages, the space between cultures, and the space between academic disciplines. While many of our contributors are located in the Australian university system, we also have contributors from outside that system, as well as contributors who are theorising disparate sites for the negotiation of difference. The most exciting aspect of the papers presented here is the ability to move between the spheres of cultural theory and the everyday. Analytical techniques originally developed for literary and cultural analysis are brought to bear on the texts and practices of everyday life. ::: ::: The loci for these investigations include the classroom, the police station, the streets, local government and the university itself. The practices examined include translating and interpreting, language teaching, academic writing, literary production and critique, language planning and small business and shadow economies. The academic disciplines drawn on include theoretical and applied linguistics, discourse analysis, language teaching pedagogy, policy studies, cultural studies, literary studies, political science, gender studies and postcolonial theory.


A Differential Approach for Gaze Estimation with Calibration


The Power of the Picture: How Narrative Film Captures Attention and Disrupts Goal Pursuit


title: Theory of how to design the site easy to navigate

abstract: Web design is a kind balance art. On the one hand, you want the page beautiful and vivid, but on the other hand, you need it can quickly be downloaded otherwise people will go to other sites. A successful website can attract visitors, in addition to the rich content and the characteristic columns, a cordial and friendly interface, unique style web design, reasonable man-machine interface can make them linger. For surfers who looking for information online, they hope quickly and efficiently find information they need, web designers need to provide a convenient, quick links and navigation.This paper analyzes how to establish reasonable and effective man-machine interface in the website from the perspective of consumer psychology, especially interpret view of the problem of navigating human-computer interaction system and how to create a convenient navigation system.


title: Real-Time Synchronization on Multiprocessors: To Block or Not to Block, to Suspend or Spin?

abstract: In the domain of multiprocessor real-time systems, there has been a wealth of recent work on scheduling, but relatively little work on the equally-important topic of synchronization. When synchronizing accesses to shared resources, four basic options exist: lock-free execution, wait-free execution, spin- based locking, and suspension-based locking. To our knowledge, no empirical multiprocessor-based evaluation of these basic techniques that focuses on real-time systems has ever been conducted before. In this paper, we present such an evaluation and report on our efforts to incorporate synchronization support in the testbed used in this effort.


Security in IPv 6-enabled Wireless Sensor Networks : An Implementation of TLS / DTLS for the Contiki Operating System


title: Tiny Descriptors for Image Retrieval with Unsupervised Triplet Hashing

abstract: A typical image retrieval pipeline starts with the comparison of global descriptors from a large database to find a short list of candidate matches. A good image descriptor is key to the retrieval pipeline and should reconcile two contradictory requirements: providing recall rates as high as possible and being as compact as possible for fast matching. Following the recent successes of Deep Convolutional Neural Networks (DCNN) for large scale image classification, descriptors extracted from DCNNs are increasingly used in place of the traditional hand crafted descriptors such as Fisher Vectors (FV) with better retrieval performances. Nevertheless, the dimensionality of a typical DCNN descriptor --extracted either from the visual feature pyramid or the fully-connected layers-- remains quite high at several thousands of scalar values. In this paper, we propose Unsupervised Triplet Hashing (UTH), a fully unsupervised method to compute extremely compact binary hashes --in the 32-256 bits range-- from high-dimensional global descriptors. UTH consists of two successive deep learning steps. First, Stacked Restricted Boltzmann Machines (SRBM), a type of unsupervised deep neural nets, are used to learn binary embedding functions able to bring the descriptor size down to the desired bitrate. SRBMs are typically able to ensure a very high compression rate at the expense of loosing some desirable metric properties of the original DCNN descriptor space. Then, triplet networks, a rank learning scheme based on weight sharing nets is used to fine-tune the binary embedding functions to retain as much as possible of the useful metric properties of the original space. A thorough empirical evaluation conducted on multiple publicly available dataset using DCNN descriptors shows that our method is able to significantly outperform state-of-the-art unsupervised schemes in the target bit range.


title: Performance modeling and system management for multi-component online services

abstract: Many dynamic-content online services are comprised of multiple interacting components and data partitions distributed across server clusters. Understanding the performance of these services is crucial for efficient system management. This paper presents a profile-driven performance model for cluster-based multi-component online services. Our offline constructed application profiles characterize component resource needs and inter-component communications. With a given component placement strategy, the application profile can be used to predict system throughput and average response time for the online service. Our model differentiates remote invocations from fast-path calls between co-located components and we measure the network delay caused by blocking inter-component communications. Validation with two J2EE-based online applications show that our model can predict application performance with small errors (less than 13% for throughput and less than 14% for the average response time). We also explore how this performance model can be used to assist system management functions for multi-component online services, with case examinations on optimized component placement, capacity planning, and cost-effectiveness analysis.


title: Theoretical Foundation for CMA-ES from Information Geometry Perspective

abstract: This paper explores the theoretical basis of the covariance matrix adaptation evolution strategy (CMA-ES) from the information geometry viewpoint. ::: To establish a theoretical foundation for the CMA-ES, we focus on a geometric structure of a Riemannian manifold of probability distributions equipped with the Fisher metric. We define a function on the manifold which is the expectation of fitness over the sampling distribution, and regard the goal of update of the parameters of sampling distribution in the CMA-ES as maximization of the expected fitness. We investigate the steepest ascent learning for the expected fitness maximization, where the steepest ascent direction is given by the natural gradient, which is the product of the inverse of the Fisher information matrix and the conventional gradient of the function. ::: Our first result is that we can obtain under some types of parameterization of multivariate normal distribution the natural gradient of the expected fitness without the need for inversion of the Fisher information matrix. We find that the update of the distribution parameters in the CMA-ES is the same as natural gradient learning for expected fitness maximization. Our second result is that we derive the range of learning rates such that a step in the direction of the exact natural gradient improves the parameters in the expected fitness. We see from the close relation between the CMA-ES and natural gradient learning that the default setting of learning rates in the CMA-ES seems suitable in terms of monotone improvement in expected fitness. Then, we discuss the relation to the expectation-maximization framework and provide an information geometric interpretation of the CMA-ES.


The Homelessness Individuals and Families Information System: A Case Study in Canadian Capacity Building (1)


title: Combining Convolutional and Recurrent Neural Networks for Human Skin Detection

abstract: Skin detection from images, typically used as a preprocessing step, has a wide range of applications such as dermatology diagnostics, human computer interaction designs, and etc. It is a challenging problem due to many factors such as variation in pigment melanin, uneven illumination, and differences in ethnicity geographics. Besides, age and gender introduce additional difficulties to the detection process. It is hard to determine whether a single pixel is skin or nonskin without considering the context. An efficient traditional hand-engineered skin color detection algorithm requires extensive work by domain experts. Recently, deep learning algorithms, especially convolutional neural networks (CNNs), have achieved great success in pixel-wise labeling tasks. However, CNN-based architectures are not sufficient for modeling the relationship between pixels and their neighbors. In this letter, we integrate recurrent neural networks (RNNs) layers into the fully convolutional neural networks (FCNs), and develop an end-to-end network for human skin detection. In particular, FCN layers capture generic local features, while RNN layers model the semantic contextual dependencies in images. Experimental results on the COMPAQ and ECU skin datasets validate the effectiveness of the proposed approach, where RNN layers enhance the discriminative power of skin detection in complex background situations.


title: Planar Omnidirectional Crawler Mobile Mechanism—Development of Actual Mechanical Prototype and Basic Experiments

abstract: This letter proposes a planar omnidirectional crawler mobile mechanism. This mechanism is the basis for the mobility of search and rescue robots. A planar omnidirectional crawler aims to facilitate penetration into a narrow path and soft or fragile ground. In this letter, the effectiveness of the proposed transmission mechanism is experimentally determined. In addition, a planar omnidirectional crawler equipped with a transmission mechanism was developed, and its characteristics were investigated through experiments for the pressure applied to the ground.


title: Pattern Classification Using Ensemble Methods

abstract: Researchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methodology since the late seventies. Thus, they are faced with a wide variety of methods, given the growing interest in the field. This book aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of ensemble methods, theories, trends, challenges and applications. The book describes in detail the classical methods, as well as the extensions and novel approaches developed recently. Along with algorithmic descriptions of each method, it also explains the circumstances in which this method is applicable and the consequences and the trade-offs incurred by using the method.