title: Development of autonomous navigation robotic wheelchairs using programmable System-on-Chip based distributed computing architecture

abstract: Autonomous robotic wheelchairs are widely discussed in recent years. Most of autonomous robotic wheelchairs were developed based on rich computation architecture to deal with complicated navigation efforts such as personal computers or embedded computers. However, such rich computation architectures increase cost of robotic wheelchair when compared to conventional powered wheelchairs. At the same time, large amounts of energy consumptions, lower reliability, and inefficient size of personal computers reduce the practicability of robotic wheelchair. In this paper, we present a low cost solution to solve complicated wheelchair navigation problem based on distributed computing architecture. This solution is developed based on the programmable System-on-Chip (PSoC) computing and control architecture. The tasks within autonomous navigations are categorized into human machine interface, sensor collection, fuzzy logic based navigation functions, closed loop motion controller, and these tasks are individually implemented using the PSoC. Finally, the task oriented PSoC are integrated and fused to perform autonomous navigations of robotic wheelchairs. Consequently, the proposed PSoC based distributed computing architecture was successfully implemented and experimentally tested. The navigation results were verified using the results inferred from the personal computer based computing architecture.


title: A Linear Regression Framework for the Receiver Operating Characteristic (ROC) Curve Analysis.

abstract: The receiver operating characteristic (ROC) curve has been a popular statistical tool for characterizing the ::: discriminating power of a classifier, such as a biomarker or an imaging modality for disease screening or diagnosis. It ::: has been recognized that the accuracy of a given procedure may depend on some underlying factors, such as subject’s ::: demographic characteristics or disease risk factors, among others. Non-parametric- or parametric-based methods tend ::: to be either inefficient or cumbersome when evaluating effect of multiple covariates is the main focus. Here we propose a semi-parametric linear regression framework to model covariate effect. It allows the estimation of sensitivity at given specificity to vary according to the covariates and provides a way to model the area under the ROC curve indirectly. Estimation procedure and asymptotic theory are presented. Extensive simulation studies have been conducted to investigate the validity of the proposed method. We illustrate the new method on a diagnostic test dataset.


title: Biochemical and Clinical Assessments of Segmental Maxillary Posterior Tooth Intrusion

abstract: Objective. To compare chondroitin sulphate (CS) levels around maxillary second premolars, first molars, and second molars between the unloaded and the loaded periods and to measure the rates of intrusion of maxillary posterior teeth during segmental posterior tooth intrusion. Materials and Methods. In this prospective clinical study, 105 teeth (from 15 patients exhibiting anterior open bite and requiring maxillary posterior tooth intrusion) were studied. Competitive ELISA was used to detect CS levels. Dental casts (during the unloaded and loaded periods) were scanned, and posterior tooth intrusion distances were measured. Results. During the unloaded period, the median CS levels around maxillary second premolars, first molars, second molars (experimental teeth), and mandibular first molars (negative control) were 0.006, 0.055, 0.056, and 0.012 and during the loaded period were 2.592, 5.738, 4.727, and 0.163 ng/μg of total protein, respectively. The median CS levels around experimental teeth were significantly elevated during the loaded period. The mean rates of maxillary second premolar and first and second molar intrusion were 0.72, 0.58, and 0.40 mm/12 weeks, respectively. Conclusions. Biochemical and clinical assessments suggested that the segmental posterior tooth intrusion treatment modality with 50 g of vertical force per side was sufficient. Trial Registration. The study is registered as TCTR20170206006.


title: An expert system for detecting automobile insurance fraud using social network analysis

abstract: The article proposes an expert system for detection, and subsequent investigation, of groups of collaborating automobile insurance fraudsters. The system is described and examined in great detail, several technical difficulties in detecting fraud are also considered, for it to be applicable in practice. Opposed to many other approaches, the system uses networks for representation of data. Networks are the most natural representation of such a relational domain, allowing formulation and analysis of complex relations between entities. Fraudulent entities are found by employing a novel assessment algorithm, Iterative Assessment Algorithm (IAA), also presented in the article. Besides intrinsic attributes of entities, the algorithm explores also the relations between entities. The prototype was evaluated and rigorously analyzed on real world data. Results show that automobile insurance fraud can be efficiently detected with the proposed system and that appropriate data representation is vital.


title: A Drain-Extended MOS Device With Spreading Filament Under ESD Stress

abstract: Based on 3-D TCAD simulations, a ten-times improvement in the ESD performance of drain-extended NMOS device is predicted by incorporating deep p-implant underneath the n+ drain region. The proposed modification does not degrade the intrinsic MOS characteristics, thus enabling a self-protection ESD concept. Moreover, a detailed physical insight toward the achieved improvement is given.


title: AUGMENTED REALITY SYSTEM USING LIDAR POINT CLOUD DATA FOR DISPLAYING DIMENSIONAL INFORMATION OF OBJECTS ON MOBILE PHONES

abstract: Abstract. Mobile augmented reality system is the next generation technology to visualise 3D real world intelligently. The technology is expanding at a fast pace to upgrade the status of a smart phone to an intelligent device. The research problem identified and presented in the current work is to view actual dimensions of various objects that are captured by a smart phone in real time. The methodology proposed first establishes correspondence between LiDAR point cloud, that are stored in a server, and the image t hat is captured by a mobile. This correspondence is established using the exterior and interior orientation parameters of the mobile camera and the coordinates of LiDAR data points which lie in the viewshed of the mobile camera. A pseudo intensity image is generated using LiDAR points and their intensity. Mobile image and pseudo intensity image are then registered using image registration method SIFT thereby generating a pipeline to locate a point in point cloud corresponding to a point (pixel) on the mobile image. The second part of the method uses point cloud data for computing dimensional information corresponding to the pairs of points selected on mobile image and fetch the dimensions on top of the image. This paper describes all steps of the proposed method. The paper uses an experimental setup to mimic the mobile phone and server system and presents some initial but encouraging results


title: Interlocking Folded Plate – Integral Mechanical Attachment for Structural Wood Panels

abstract: Automatic joinery has become a common technique for the jointing of beams in timber framing and roofing. It has revived traditional, integrated joints such as mortise and tenon connections. Similarly, but only recently, the automatic fabrication of traditional cabinetmaking joints has been introduced for the assembly of timber panel shell structures. First prototypes have used such integrated joints for the alignment and assembly of components, while additional adhesive bonding was used for the load-bearing connection. However, glued joints cannot be assembled on site, which results in several design constraints.In this paper, we propose the use of dovetail joints without adhesive bonding, on the case study of a timber folded plate structure. Through their single-degree-of-freedom (1DOF) geometry, these joints block the relative movement of two parts in all but one direction. This presents the opportunity for an interlocking connection of plates, as well as a challenge for the assembly of folded plate shells, where multiple non-parallel edges per plate must be jointed simultaneously.


title: Smart urban planning using big data analytics based internet of things

abstract: The extensive growth of the Internet of Things (IoT) is providing direction towards the smart urban. The smart urban is favored because it improves the standard of living of the citizens and provides excellence in the community services. The services may include but not limited to health, parking, transport, water, environment, power, and so forth. The diverse and heterogeneous environment of IoT and smart urban is challenged by real-time data processing and decision-making. In this research article, we propose IoT based smart urban architecture using Big Data analytics. The proposed architecture is divided into three different tiers: (1) data acquisition and aggregation, (2) data computation and processing, and (3) decision making and application. The proposed architecture is implemented and validated on Hadoop Ecosystem using reliable and authentic datasets. The research shows that the proposed system presents valuable imminent into the community development systems to get better the existing smart urban architecture.


title: Emotional responses to art: From collation and arousal to cognition emotion

abstract: Emotions and art are intimately related (Tan, 2000). From ancient to modern times, theories of aesthetics have emphasized the role of art in evoking, shaping, and modifying human feelings. The experimental study of preferences, evaluations, and feelings related to art has a long history in psychology. Aesthetics is one of the oldest areas of psychological research, dating to Fechner's (1876) landmark work. Psychology has had a steady interest in aesthetic problems since then, but art has never received as much attention as one would expect (see Berlyne, 1971a; Tan, 2000; Valentine, 1962). The study of art and the study of emotions, as areas of scientific inquiry, both languished during much of the last century. It is not surprising that the behavioral emphasis on observable action over inner experience would lead to a neglect of research on aesthetics. In an interesting coincidence, both art and emotion resurfaced in psychology at about the same time. As emotion psychologists began developing theories of basic emotions (Ekman & Friesen, 1971; Izard, 1971; Tomkins, 1962), experimental psychologists began tackling hedonic qualities of art (Berlyne, 1971a, 1972, 1974). Since then, the psychology of emotion and the psychology of art have had little contact (see Silvia, in press-b; Tan, 2000).


title: Scalable Edge Computing for Low Latency Data Dissemination in Topic-Based Publish/Subscribe

abstract: Advances in Internet of Things (IoT) give rise to a variety of latency-sensitive, closed-loop applications that reside at the edge. These applications often involve a large number of sensors that generate volumes of data, which must be processed and disseminated in real-time to potentially a large number of entities for actuation, thereby forming a closed-loop, publish-process-subscribe system. To meet the response time requirements of such applications, this paper presents techniques to realize a scalable, fog/edge-based broker architecture that balances data publication and processing loads for topic-based, publish-process-subscribe systems operating at the edge, and assures the Quality-of-Service (QoS), specified as the 90th percentile latency, on a per-topic basis. The key contributions include: (a) a sensitivity analysis to understand the impact of features such as publishing rate, number of subscribers, per-sample processing interval and background load on a topic's performance; (b) a latency prediction model for a set of co-located topics, which is then used for the latency-aware placement of topics on brokers; and (c) an optimization problem formulation for k-topic co-location to minimize the number of brokers while meeting each topic's QoS requirement. Here, k denotes the maximum number of topics that can be placed on a broker. We show that the problem is NP-hard for k >=3 and present three load balancing heuristics. Empirical results are presented to validate the latency prediction model and to evaluate the performance of the proposed heuristics.


title: Predicate learning in neural systems: Discovering latent generative structures

abstract: Humans learn complex latent structures from their environments (e.g., natural language, mathematics, music, social hierarchies). In cognitive science and cognitive neuroscience, models that infer higher-order structures from sensory or first-order representations have been proposed to account for the complexity and flexibility of human behavior. But how do the structures that these models invoke arise in neural systems in the first place? To answer this question, we explain how a system can learn latent representational structures (i.e., predicates) from experience with wholly unstructured data. During the process of predicate learning, an artificial neural network exploits the naturally occurring dynamic properties of distributed computing across neuronal assemblies in order to learn predicates, but also to combine them compositionally, two computational aspects which appear to be necessary for human behavior as per formal theories in multiple domains. We describe how predicates can be combined generatively using neural oscillations to achieve human-like extrapolation and compositionality in an artificial neural network. The ability to learn predicates from experience, to represent structures compositionally, and to extrapolate to unseen data offers an inroads to understanding and modeling the most complex human behaviors.


title: Crops Disease Diagnosing Using Image-Based Deep Learning Mechanism

abstract: To increase the crop productivity environmental factors or product resource, such as temperature, humidity, labor and electrical costs are important. However, above all, crop disease is the crucial factor and causes 20–30% reduction of the productivity in case of its infection. Thus, the disease of the crop is much more important factor affecting the productivity of the crops. Therefore, the farmer concentrates on the cause of the disease in the crops during its growth, but it is not easy to recognize the disease on the spot. Until now, they just relied on the opinion of the experts or their own experiences when the disease is doubtful. However, it triggers a decrease in productivity as no taking appropriate action and time. In this paper, to address this problem we provide the mechanism, which dynamically analyses the images of the disease. The analysis result is immediately sent to the farmer required the decision and then feedback from the farmer is reflected to the model. The mechanism performs the diagnosing of the disease, especially for the strawberry fruits and leaves, with data set of images using deep learning. Thus, it encourages increasing of the productivity through the fast recognition of disease and the consequent action.


title: Don't fall into a trap: Physical side-channel analysis of ChaCha20-Poly1305

abstract: The stream cipher ChaCha20 and the MAC function Poly1305 have been published as IETF RFC 7539. Since then, the industry is starting to use it more often. For example, it has been implemented by Google in their Chrome browser for TLS and also support has been added to OpenSSL, as well as OpenSSH. It is often claimed, that the algorithms are designed to be resistant to side-channel attacks. However, this is only true, if the only observable side-channel is the timing behavior. In this paper, we show that ChaCha20 is susceptible to power and EM side-channel analysis, which also translates to an attack on Poly1305, if used together with ChaCha20 for key generation. As a first countermeasure, we analyze the effectiveness of randomly shuffling the operations of the ChaCha round function.


title: The Role of Aging on Dynamic Failure Envelopes of OSP-Sn37Pb Interconnects in Plastic Ball Grid Array (PBGA) Packages

abstract: This paper quantifies the effect of aging on the durability of printed wiring assemblies (PWAs) subjected to dynamic loading conditions. The test specimen is a FR4 board with a single 256 I/O, full grid, PBGA component at the center. The pad finish on the board and component side of the Sn37Pb eutectic solder interconnects is organic solderability preservative (OSP) and Sn15Pb, respectively. The test matrix is designed to cover one order of magnitude of PWA flexural strain (1E-3 to 1E-2), three orders of magnitude of PWA flexural strain rate (1E-3 to 1E0 s-1), and two aging conditions (as-reflowed and 125 degC for 100 h). Fatigue failure envelopes, based on a mechanics-inspired empirical rate-dependent model, are used to characterize the durability in terms of PWA flexural strain and strain rate. A failure site transition zone (FSTZ) is defined in terms of the damage parameters, beyond which the failure site changes from the solder to other parts of the PWA. The combined effect of load amplitude, loading rate, and thermal aging on the FSTZ and solder durability is quantified and presented.


title: Fast Training of Convolutional Networks through FFTs

abstract: Convolutional networks are one of the most widely employed architectures in computer vision and machine learning. In order to leverage their ability to learn complex functions, large amounts of data are required for training. Training a large convolutional network to produce state-of-the-art results can take weeks, even when using modern GPUs. Producing labels using a trained network can also be costly when dealing with web-scale datasets. In this work, we present a simple algorithm which accelerates training and inference by a significant factor, and can yield improvements of over an order of magnitude compared to existing state-of-the-art implementations. This is done by computing convolutions as pointwise products in the Fourier domain while reusing the same transformed feature map many times. The algorithm is implemented on a GPU architecture and addresses a number of related challenges.


The use of mastoid fascia for dorsal nasal augmentation


title: Flow‐Induced Inertial Steady Vector Field Topology

abstract: Traditionally, vector field visualization is concerned with 2D and 3D flows. Yet, many concepts can be extended to general dynamical systems, including the higher-dimensional problem of modeling the motion of finite-sized objects in fluids. In the steady case, the trajectories of these so-called inertial particles appear as tangent curves of a 4D or 6D vector field. These higher-dimensional flows are difficult to map to lower-dimensional spaces, which makes their visualization a challenging problem. We focus on vector field topology, which allows scientists to study asymptotic particle behavior. As recent work on the 2D case has shown, both extraction and classification of isolated critical points depend on the underlying particle model. In this paper, we aim for a model-independent classification technique, which we apply to two different particle models in not only 2D, but also 3D cases. We show that the classification can be done by performing an eigenanalysis of the spatial derivatives' velocity subspace of the higher-dimensional 4D or 6D flow. We construct glyphs that depict not only the types of critical points, but also encode the directional information given by the eigenvectors. We show that the eigenvalues and eigenvectors of the inertial phase space have sufficient symmetries and structure so that they can be depicted in 2D or 3D, instead of 4D or 6D.


title: Treatment of thoracolumbar burst fractures without neurologic deficit by indirect reduction and posterior instrumentation: bisegmental stabilization with monosegmental fusion

abstract: This study retrospectively reviews 20 sequential patients with thoracolumbar burst fractures without neurologic deficit. All patients were treated by indirect reduction, bisegmental posterior transpedicular instrumentation and monosegmental fusion. Clinical and radiological outcome was analyzed after an average follow-up of 6.4 years. Re-kyphosis of the entire segment including the cephaled disc was significant with loss of the entire postoperative correction over time. This did not influence the generally benign clinical outcome. Compared to its normal height the fused cephalad disc was reduced by 70% and the temporarily spanned caudal disc by 40%. Motion at the temporarily spanned segment could be detected in 11 patients at follow-up, with no relation to the clinical result. Posterior instrumentation of thoracolumbar burst fractures can initially reduce the segmental kyphosis completely. The loss of correction within the fractured vertebral body is small. However, disc space collapse leads to eventual complete loss of segmental reduction. Therefore, posterolateral fusion alone does not prevent disc space collapse. Nevertheless, clinical long-term results are favorable. However, if disc space collapse has to prevented, an interbody disc clearance and fusion is recommended.


title: Recommender Systems: Do They Have a Viable Business Model in e-Tourism?

abstract: The evolution of the internet over the past years has given destinations, suppliers of tourism services and intermediaries a wide range of new possibilities to get into contact with their customers. Destinations are at the heart of travel decisions but yet it is difficult for travelers to find quality information relating to destinations and even more for Destination Marketing Organizations (DMOs) to distribute their tourism offer. Thus, a wide range of new technologies - so called recommendation technologies - have emerged aligning destination information with booking facilities to convert destination search into online bookings. Recommender systems have already been applied successfully in the private market but yet it is difficult to determine the added value for the service offered. This paper discusses different recommendation technologies, presents current business models applied in e-commerce and provides suggestions on how those could be applied to recommender systems.


Who Is Going to Get Hurt? Predicting Injuries in Professional Soccer


VISP Testbed - A Toolkit for Modeling and Evaluating Resource Provisioning Algorithms for Stream Processing Applications


title: Perceptions of Information Security at the Workplace : Linking Information Security Climate to Compliant Behavior

abstract: AbstractA large number of information security breaches in the workplace result from employees’ failure to comply with organizational information security guidelines. Recent surveys report that 78% of computer attacks appear in the form of viruses embedded in email attachments. Employees who open e-mail attachments from unknown sources risk infecting their own computers as well as other computers sharing the same network. Therefore, more attention needs to be paid to learning why non-compliant behavior takes place so that appropriate measures for curbing the occurrence of such behavior can be found. With such motivation in mind, this study examines the effects of social contextual factors on employees’ compliance with organizational security policies. The research model is developed based on concepts adapted from safety climate literature that has been used to explain the safe behavior of employees in organizations. Data was collected from a sample of 140 employees from two large IT intensive organization...


title: Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks

abstract: Traditional approaches to the task of ACE event extraction primarily rely on elaborately designed features and complicated natural language processing (NLP) tools. These traditional approaches lack generalization, take a large amount of human effort and are prone to error propagation and data sparsity problems. This paper proposes a novel event-extraction method, which aims to automatically extract lexical-level and sentence-level features without using complicated NLP tools. We introduce a word-representation model to capture meaningful semantic regularities for words and adopt a framework based on a convolutional neural network (CNN) to capture sentence-level clues. However, CNN can only capture the most important information in a sentence and may miss valuable facts when considering multiple-event sentences. We propose a dynamic multi-pooling convolutional neural network (DMCNN), which uses a dynamic multi-pooling layer according to event triggers and arguments, to reserve more crucial information. The experimental results show that our approach significantly outperforms other state-of-the-art methods.


title: Disentangling Disentanglement in Variational Auto-Encoders

abstract: We develop a generalisation of disentanglement in VAEs---decomposition of the latent representation---characterising it as the fulfilment of two factors: a) the latent encodings of the data having an appropriate level of overlap, and b) the aggregate encoding of the data conforming to a desired structure, represented through the prior. Decomposition permits disentanglement, i.e. explicit independence between latents, as a special case, but also allows for a much richer class of properties to be imposed on the learnt representation, such as sparsity, clustering, independent subspaces, or even intricate hierarchical dependency relationships. We show that the $\beta$-VAE varies from the standard VAE predominantly in its control of latent overlap and that for the standard choice of an isotropic Gaussian prior, its objective is invariant to rotations of the latent representation. Viewed from the decomposition perspective, breaking this invariance with simple manipulations of the prior can yield better disentanglement with little or no detriment to reconstructions. We further demonstrate how other choices of prior can assist in producing different decompositions and introduce an alternative training objective that allows the control of both decomposition factors in a principled manner.


title: The design and analysis of a 5-level cascaded voltage source inverter with low THD

abstract: Multilevel inverters have been important devices developed in recent years, owing to their capability to increase the voltage and power delivered to the load. Researches done based on basic inverter topologies show that, multilevel inverters have many advantages such as low power dissipation on power switches, low harmonic and low electromagnetic interference (EMI) outputs. A modified Sinusoidal Pulse Width Modulation (SPWM) modulator that reduces output harmonics is presented in this paper. The proposed modulation technique can be easily applied to any multilevel inverter topology carrying out the necessary calculations. The most common multilevel inverter topologies have been studied to define the best topology for SPWM modulation strategy. It is seen that cascaded H-bridges are the most convenient solution. The cascaded H-bridge cells have been constituted by IGBT semiconductors, and switched by the proposed 24-channel SPWM modulator to obtain 5-level output at the back-end of the 3-phase voltage source inverter (VSI). The designed H-bridge cells have a strong switching bandwidth up to 40 KHz, owing to its robustly designed modulator block. The proposed VSI in this paper also has a Total Harmonic Distortion ratio of output current (THDi) around at 0.1% without requiring any filtering circuit. The harmonic analysis of proposed design has been executed under several working conditions such as various switching frequencies and modulation indexes. The detailed comparisons have been performed to determine the best working conditions of VSI and presented in this paper.


title: News impact on stock price return via sentiment analysis

abstract: Financial news articles are believed to have impacts on stock price return. Previous works model news pieces in bag-of-words space, which analyzes the latent relationship between word statistical patterns and stock price movements. However, news sentiment, which is an important ring on the chain of mapping from the word patterns to the price movements, is rarely touched. In this paper, we first implement a generic stock price prediction framework, and plug in six different models with different analyzing approaches. To take one step further, we use Harvard psychological dictionary and Loughran–McDonald financial sentiment dictionary to construct a sentiment space. Textual news articles are then quantitatively measured and projected onto the sentiment space. Instance labeling method is rigorously discussed and tested. We evaluate the models' prediction accuracy and empirically compare their performance at different market classification levels. Experiments are conducted on five years historical Hong Kong Stock Exchange prices and news articles. Results show that (1) at individual stock, sector and index levels, the models with sentiment analysis outperform the bag-of-words model in both validation set and independent testing set; (2) the models which use sentiment polarity cannot provide useful predictions; (3) there is a minor difference between the models using two different sentiment dictionaries.


title: Why Do Men Get More Attention? Exploring Factors Behind Success in an Online Design Community

abstract: Online platforms are an increasingly popular tool for people to produce, promote or sell their work. However recent studies indicate that social disparities and biases present in the real world might transfer to online platforms and could be exacerbated by seemingly harmless design choices on the site (e.g., recommendation systems or publicly visible success measures). In this paper we analyze an exclusive online community of teams of design professionals called Dribbble and investigate apparent differences in outcomes by gender. Overall, we find that men produce more work, and are able to show it to a larger audience thus receiving more likes. Some of this effect can be explained by the fact that women have different skills and design different images. Most importantly however, women and men position themselves differently in the Dribbble community. Our investigation of users' position in the social network shows that women have more clustered and gender homophilous following relations, which leads them to have smaller and more closely knit social networks. Overall, our study demonstrates that looking behind the apparent patterns of gender inequalities in online markets with the help of social networks and product differentiation helps us to better understand gender differences in success and failure.


IMMORTAL Verification , debugging and testing tools Table of Revisions


title: Exploring Local Community Structures in Large Networks

abstract: In this paper, three new algorithms, a greedy algorithm, a KL-like algorithm, and an add-all algorithm, are proposed to find local optimal community structures in large networks starting from a given source vertex. The time complexity for finding a local community of all these algorithms is O(K 2d), where K is the number of vertices to be explored in the sub-graph and d is the average degree of the vertices in the sub-graph. A JAVA tool is developed based on these algorithms to identify local community structures in large networks. The results of using this tool to analyze a co-purchase network from Amazon.com show that local community structures exist in this large-scale co-purchase network. Further analyses of the identified local communities show that purchases of media items form more compact local communities than purchases of book items do, indicating that recommending digital media items to customers based on co-purchasing information in the online store is more efficient than recommending books.


title: Visual Exploration of Big Spatio-Temporal Urban Data: A Study of New York City Taxi Trips

abstract: As increasing volumes of urban data are captured and become available, new opportunities arise for data-driven analysis that can lead to improvements in the lives of citizens through evidence-based decision making and policies. In this paper, we focus on a particularly important urban data set: taxi trips. Taxis are valuable sensors and information associated with taxi trips can provide unprecedented insight into many different aspects of city life, from economic activity and human behavior to mobility patterns. But analyzing these data presents many challenges. The data are complex, containing geographical and temporal components in addition to multiple variables associated with each trip. Consequently, it is hard to specify exploratory queries and to perform comparative analyses (e.g., compare different regions over time). This problem is compounded due to the size of the data-there are on average 500,000 taxi trips each day in NYC. We propose a new model that allows users to visually query taxi trips. Besides standard analytics queries, the model supports origin-destination queries that enable the study of mobility across the city. We show that this model is able to express a wide range of spatio-temporal queries, and it is also flexible in that not only can queries be composed but also different aggregations and visual representations can be applied, allowing users to explore and compare results. We have built a scalable system that implements this model which supports interactive response times; makes use of an adaptive level-of-detail rendering strategy to generate clutter-free visualization for large results; and shows hidden details to the users in a summary through the use of overlay heat maps. We present a series of case studies motivated by traffic engineers and economists that show how our model and system enable domain experts to perform tasks that were previously unattainable for them.


title: Adaptive Somatic Mutations Calls with Deep Learning and Semi-Simulated Data

abstract: A number of approaches have been developed to call somatic variation in high-throughput sequencing data. Here, we present an adaptive approach to calling somatic variations. Our approach trains a deep feed-forward neural network with semi-simulated data. Semi-simulated datasets are constructed by planting somatic mutations in real datasets where no mutations are expected. Using semi-simulated data makes it possible to train the models with millions of training examples, a usual requirement for successfully training deep learning models. We initially focus on calling variations in RNA-Seq data. We derive semi-simulated datasets from real RNA-Seq data, which offer a good representation of the data the models will be applied to. We test the models on independent semi-simulated data as well as pure simulations. On independent semi-simulated data, models achieve an AUC of 0.973. When tested on semi-simulated exome DNA datasets, we find that the models trained on RNA-Seq data remain predictive (sens ~0.4 & spec ~0.9 at cutoff of P>=0.9), albeit with lower overall performance (AUC=0.737). Interestingly, while the models generalize across assay, training on RNA-Seq data lowers the confidence for a group of mutations. Haloplex exome specific training was also performed, demonstrating that the approach can produce probabilistic models tuned for specific assays and protocols. We found that the method adapts to the characteristics of experimental protocol. We further illustrate these points by training a model for a trio somatic experimental design when germline DNA of both parents is available in addition to data about the individual. These models are distributed with Goby (http://goby.campagnelab.org).


title: Emotional voice conversion using deep neural networks with MCC and F0 features

abstract: An artificial neural network is one of the most important models for training features in a voice conversion task. Typically, Neural Networks (NNs) are not effective in processing low-dimensional F0 features, thus this causes that the performance of those methods based on neural networks for training Mel Cepstral Coefficients (MCC) are not outstanding. However, F0 can robustly represent various prosody signals (e.g., emotional prosody). In this study, we propose an effective method based on the NNs to train the normalized-segment-F0 features (NSF0) for emotional prosody conversion. Meanwhile, the proposed method adopts deep belief networks (DBNs) to train spectrum features for voice conversion. By using these approaches, the proposed method can change the spectrum and the prosody for the emotional voice at the same time. Moreover, the experimental results show that the proposed method outperforms other state-of-the-art methods for voice emotional conversion.


title: MIRPLib - A library of maritime inventory routing problem instances: Survey, core model, and benchmark results

abstract: This paper presents a detailed description of a particular class of deterministic single product Maritime Inventory Routing Problems (MIRPs), which we call deep-sea MIRPs with inventory tracking at every port. This class involves vessel travel times between ports that are significantly longer than the time spent in port and require inventory levels at all ports to be monitored throughout the planning horizon. After providing a comprehensive literature survey of this class, we introduce a core model for it cast as a mixed-integer linear program. This formulation is quite general and incorporates assumptions and families of constraints that are most prevalent in practice. We also discuss other modeling features commonly found in the literature and how they can be incorporated into the core model. We then offer a unified discussion of some of the most common advanced techniques used for improving the bounds of these problems. Finally, we present a library, called MIRPLib, of publicly available test problem instances for MIRPs with inventory tracking at every port. Despite a growing interest in combined routing and inventory management problems in a maritime setting, no data sets are publicly available, which represents a significant “barrier to entry” for those interested in related research. Our main goal for MIRPLib is to help maritime inventory routing gain maturity as an important and interesting class of planning problems. As a means to this end, we (1) make available benchmark instances for this particular class of MIRPs; (2) provide the mixed-integer linear programming community with a set of optimization problem instances from the maritime transportation domain in LP and MPS format; and (3) provide a template for other researchers when specifying characteristics of MIRPs arising in other settings. Best known computational results are reported for each instance.


title: System-Level Virtualization for High Performance Computing

abstract: System-level virtualization has been a research topic since the 70's but regained popularity during the past few years because of the availability of efficient solution such as Xen and the implementation of hardware support in commodity processors (e.g. Intel-VT, AMD-V). However, a majority of system-level virtualization projects is guided by the server consolidation market. As a result, current virtualization solutions appear to not be suitable for high performance computing (HPC) which is typically based on large-scale systems. On another hand there is significant interest in exploiting virtual machines (VMs) within HPC for a number of other reasons. By visualizing the machine, one is able to run a variety of operating systems and environments as needed by the applications. Virtualization allows users to isolate workloads, improving security and reliability. It is also possible to support non-native environments and/or legacy operating environments through virtualization. In addition, it is possible to balance work loads, use migration techniques to relocate applications from failing machines, and isolate fault systems for repair. This document presents the challenges for the implementation of a system-level virtualization solution for HPC. It also presents a brief survey of the different approaches and techniques to address these challenges.


title: Soft-switched bidirectional buck-boost converters

abstract: A bidirectional buck-boost converter with a new soft-switching active-snubber cell that reduces switching losses is introduced. Soft-switching cell consists of an active snubber switch, a snubber inductor, and a two-winding transformer with associated magnetizing current reset circuit. The soft-switching cells enable the buck and boost rectifier to turn off with a controlled turn-off rate of their current to minimize corresponding reverse-recovery losses. In addition, in the introduced soft-switching cell, the power-controlling buck and boost switches turn on with zero-voltage switching (ZVS) and the snubber switches turn off with zero-current switching (ZCS). The performance of the proposed bidirectional converter was evaluated on a 5-kW prototype exchanging energy between a 400-V bus and a battery with voltage range between 200 V and 300 V. The 100-kHz prototype circuit exhibits the maximum full-load efficiency of 99.1% in the boost-mode and 98.2% in the buck-mode operation.


title: Implications of generator siting for CO2 pipeline infrastructure

abstract: The location of a new electric power generation system with carbon capture and sequestration (CCS) affects the profitability of the facility and determines the amount of infrastructure required to connect the plant to the larger world. Using a probabilistic analysis, we examine where a profit-maximizing power producer would locate a new generator with carbon capture in relation to a fuel source, electric load, and CO2 sequestration site. Based on models of costs for transmission lines, CO2 pipelines, and fuel transportation, we find that it is always preferable to locate a CCS power facility nearest the electric load, reducing the losses and costs of bulk electricity transmission. This result suggests that a power system with significant amounts of CCS requires a very large CO2 pipeline infrastructure.


title: Exploring the Space of Topic Coherence Measures

abstract: Quantifying the coherence of a set of statements is a long standing problem with many potential applications that has attracted researchers from different sciences. The special case of measuring coherence of topics has been recently studied to remedy the problem that topic models give no guaranty on the interpretablity of their output. Several benchmark datasets were produced that record human judgements of the interpretability of topics. We are the first to propose a framework that allows to construct existing word based coherence measures as well as new ones by combining elementary components. We conduct a systematic search of the space of coherence measures using all publicly available topic relevance data for the evaluation. Our results show that new combinations of components outperform existing measures with respect to correlation to human ratings. nFinally, we outline how our results can be transferred to further applications in the context of text mining, information retrieval and the world wide web.


title: Multiclass spectral clustering

abstract: We propose a principled account on multiclass spectral clustering. Given a discrete clustering formulation, we first solve a relaxed continuous optimization problem by eigen-decomposition. We clarify the role of eigenvectors as a generator of all optimal solutions through orthonormal transforms. We then solve an optimal discretization problem, which seeks a discrete solution closest to the continuous optima. The discretization is efficiently computed in an iterative fashion using singular value decomposition and nonmaximum suppression. The resulting discrete solutions are nearly global-optimal. Our method is robust to random initialization and converges faster than other clustering methods. Experiments on real image segmentation are reported.


title: Patient classification as an outlier detection problem: An application of the One-Class Support Vector Machine

abstract: article i nfo Pattern recognition approaches, such as the Support Vector Machine (SVM), have been successfully used to classify groups of individuals based on their patterns of brain activity or structure. However these approaches focus on finding group differences and are not applicable to situations where one is interested in accessing deviations from a specific class or population. In the present work we propose an application of the one-class SVM (OC-SVM) to investigate if patterns of fMRI response to sad facial expressions in depressed patients would be classified as outliers in relation to patterns of healthy control subjects. We defined features based on whole brain voxels and anatomical regions. In both cases we found a significant correlation between the OC- SVM predictions and the patients' Hamilton Rating Scale for Depression (HRSD), i.e. the more depressed the patients were the more of an outlier they were. In addition the OC-SVM split the patient groups into two subgroups whose membership was associated with future response to treatment. When applied to region- based features the OC-SVM classified 52% of patients as outliers. However among the patients classified as outliers 70% did not respond to treatment and among those classified as non-outliers 89% responded to treatment. In addition 89% of the healthy controls were classified as non-outliers.


A Comprehensive Taxonomy of DDoS Attacks and Defense Mechanism Applying in a Smart Classification


title: Unwrapping and visualizing cuneiform tablets

abstract: Cuneiform inscriptions, which scholars consider the first written language, were made in moist, clay tablets. We've developed a semiautomatic method for concisely displaying the tablets' inscribed writing, thereby providing a clear visualization that can be printed on paper. We first scan the tablets with 3D range scanners and use the scan data to construct a high-resolution 3D model (at a resolution of 50 microns). Next, we unwrap and warp the tablet surface to form a set of flat rectangles, one per side or edge of the tablet. This process permits all the writing to be seen at once, although necessarily slightly distorted. Finally, we apply curvature coloring and accessibility coloring to the unwrapped text, thereby replacing raking illumination with a nonphotorealistic rendering technique.


title: Articulated mesh animation from multi-view silhouettes

abstract: Details in mesh animations are difficult to generate but they have great impact on visual quality. In this work, we demonstrate a practical software system for capturing such details from multi-view video recordings. Given a stream of synchronized video images that record a human performance from multiple viewpoints and an articulated template of the performer, our system captures the motion of both the skeleton and the shape. The output mesh animation is enhanced with the details observed in the image silhouettes. For example, a performance in casual loose-fitting clothes will generate mesh animations with flowing garment motions. We accomplish this with a fast pose tracking method followed by nonrigid deformation of the template to fit the silhouettes. The entire process takes less than sixteen seconds per frame and requires no markers or texture cues. Captured meshes are in full correspondence making them readily usable for editing operations including texturing, deformation transfer, and deformation model learning.


title: Deep Active Contours

abstract: We propose a method for interactive boundary extraction which combines a deep, patch-based representation with an active contour framework. We train a class-specific convolutional neural network which predicts a vector pointing from the respective point on the evolving contour towards the closest point on the boundary of the object of interest. These predictions form a vector field which is then used for evolving the contour by the Sobolev active contour framework proposed by Sundaramoorthi et al. The resulting interactive segmentation method is very efficient in terms of required computational resources and can even be trained on comparatively small graphics cards. We evaluate the potential of the proposed method on both medical and non-medical challenge data sets, such as the STACOM data set and the PASCAL VOC 2012 data set.


title: It depends on the context! A decidable logic of actions and plans based on a ternary dependence relation

abstract: In this paper we argue for a weak form of causality in terms of a dependence relation involving actions, atoms and formulae in order to deal with the frame and rami cation problems. This relation allows the atoms to change their value without forcing or causing it. Once integrated in the framework of the Logic of Actions and Plans LAP , it gives us a simple and powerful formalism to reasoning about actions and a decision procedure in terms of tableau methods. We also show how to deal with scenarios involving indeterminate and indirect e ects which no other causal framework can handle.


A German Twitter Snapshot


Analysis on Requirement Elicitation and its Issues


title: Dynamic Matching via Weighted Myopia with Application to Kidney Exchange

abstract: In many dynamic matching applications--especially high-stakes ones--the competitive ratios of prior-free online algorithms are unacceptably poor. The algorithm should take distributional information about possible futures into account in deciding what action to take now. This is typically done by drawing sample trajectories of possible futures at each time period, but may require a prohibitively large number of trajectories or prohibitive memory and/or computation to decide what action to take. Instead, we propose to learn potentials of elements (e.g., vertices) of the current problem. Then, at run time, we simply run an offline matching algorithm at each time period, but subtracting out in the objective the potentials of the elements used up in the matching. ::: ::: We apply the approach to kidney exchange. Kidney exchanges enable willing but incompatible patient-donor pairs (vertices) to swap donors. These swaps typically include cycles longer than two pairs and chains triggered by altruistic donors. Fielded exchanges currently match myopically, maximizing the number of patients who get kidneys in an offline fashion at each time period. Myopic matching is sub-optimal; the clearing problem is dynamic since patients, donors, and altruists appear and expire over time. We theoretically compare the power of using potentials on increasingly large elements: vertices, edges, cycles, and the entire graph (optimum). Then, experiments show that by learning vertex potentials, our algorithm matches more patients than the current practice of clearing myopically. It scales to exchanges orders of magnitude beyond those handled by the prior dynamic algorithm.


title: Nonlinear spatial normalization using basis functions

abstract: We describe a comprehensive framework for performing rapid and automatic nonlabel-based nonlinear spatial normalizations. The approach adopted minimizes the residual squared difference between an image and a template of the same modality. In order to reduce the number of parameters to be fitted, the nonlinear warps are described by a linear combination of low spatial frequency basis functions. The objective is to determine the optimum coefficients fur each of the bases by minimizing the sum of squared differences between the image and template, while simultaneously maximizing the smoothness of the transformation using a maximum a posteriori (MAP) approach. Most MAT approaches assume that the variance associated with each voxel is already known and that there is no covariance between neighboring voxels. The approach described here attempts to estimate this variance from the data, and also corrects fur the correlations between neighboring voxels. This makes the same approach suitable for the spatial normalization of both high-quality magnetic resonance images, and low-resolution noisy positron emission tomography images. A fast algorithm has been developed that utilizes Taylor's theorem and the separable nature of the basis functions, meaning that most of the nonlinear spatial variability between images can be automatically corrected within a few minutes. Hum. Brain Mapping 7:254-266, 1999. (C) 1999 Wiley-Liss, Inc.


title: Culture as a Configuration of Values: An Archetypal Perspective

abstract: The aim of this chapter is to: (1) model culture as a configuration of multiple values, (2) identify different culture archetypes across the globe, and (3) empirically demonstrate heterogeneity in culture archetypes within and across 52 countries. We use Schwartz values from the World Values Survey (WVS) and the archetypal analysis (AA) method to identify diverse culture archetypes within and across countries. We find significant heterogeneity in culture values archetypes within countries and homogeneity across countries, calling into question the assumption of uniform national culture values in economics and other fields. We show how the heterogeneity in culture values across the globe can be represented with a small number of distinctive archetypes. The study could be extended to include a larger set of countries, and/or cover a broader range of theoretically grounded values than those available in the Schwartz values model in the WVS. Research and practice often assume cultural homogeneity within nations and cultural diversity across nations. Our finding of different culture archetypes within countries and similar archetypes across countries demonstrates the important role of culture sharing and exchange as a source of reducing cultural conflicts between nations and enhancing creativity and innovation through interaction and integration in novel ways. We examine culture as a configuration of multiple values, and use a novel AA method to capture heterogeneity in culture values within and across countries.


title: A Short-Term Photovoltaic Power Prediction Model Based on an FOS-ELM Algorithm

abstract: With the increasing proportion of photovoltaic (PV) power in power systems, the problem of its fluctuation and intermittency has become more prominent. To reduce the negative influence of the use of PV power, we propose a short-term PV power prediction model based on the online sequential extreme learning machine with forgetting mechanism (FOS-ELM), which can constantly replace outdated data with new data. We use historical weather data and historical PV power data to predict the PV power in the next period of time. The simulation result shows that this model has the advantages of a short training time and high accuracy. This model can help the power dispatch department schedule generation plans as well as support spatial and temporal compensation and coordinated power control, which is important for the security and stability as well as the optimal operation of power systems.


Enterprise Architecture Development and Modelling Combining TOGAF and ArchiMate


title: Advanced control of wind turbines: Brief survey, categorization, and challenges

abstract: To pave the way for higher penetration of wind turbines in the power grid, it is necessary to improve efficiency of wind energy conversion systems in terms of production rate maximization and operational cost minimization. In turn, with available technologies, this calls for advanced control strategies in wind industry. In this article, some basic concepts related to the control of wind turbines are introduced, a control-theoretic categorization is proposed, the literature of wind turbine control is briefly surveyed, and some challenges in the control of wind turbines are determined from the literature at the end.


Reading human relationships from their interaction with an interactive humanoid robot


title: An adaptive FPGA implementation of multi-core K-nearest neighbour ensemble classifier using dynamic partial reconfiguration

abstract: Classification of highly dimensional Microarray data using K-nearest neighbour (K-NN) is a time-consuming task when implemented on general purpose processors (GPPs), and such it can benefit greatly from a parallel hardware implementation. In this work, an FPGA implementation of the K-NN classifier is presented and compared with an equivalent implementation running on GPP. Then, a novel FPGA-based multi-core implementation of the K-NN ensemble classifier, which exploits dynamic partial reconfiguration (DPR) is presented. The FPGA implementation of the single core K-NN classifier was found to be 92× faster than a GPP implementation, and the ensemble implementation was found to offer ∼5× speed-up of the FPGA reconfiguration time. In addition, the paper investigates the effect of data dimensionality on classification time on both FPGAs and GPPs, showing that FPGAs scale up better than GPPs with higher data dimensionality.


title: ClearView: Data cleaning for online review mining

abstract: How can we automatically clean and curate online reviews to better mine them for knowledge discovery? Typical online reviews are full of noise and abnormalities, hindering semantic analysis and leading to a poor customer experience. Abnormalities include non-standard characters, unstructured punctuation, different/multiple languages, and misspelled words. Worse still, people will leave "junk" text, which is either completely nonsensical, spam, or fraudulent. In this paper, we describe three types of noisy and abnormal reviews, discuss methods to detect and filter them, and, finally, show the effectiveness of our cleaning process by improving the overall distributional characteristics of review datasets.


title: Large-Scale Ontology Matching: State-of-the-Art Analysis

abstract: Ontologies have become a popular means of knowledge sharing and reuse. This has motivated the development of large-sized independent ontologies within the same or different domains with some overlapping information among them. To integrate such large ontologies, automatic matchers become an inevitable solution. However, the process of matching large ontologies has high space and time complexities. Therefore, for a tool to efficiently and accurately match these large ontologies within the limited computing resources, it must have techniques that can significantly reduce the high space and time complexities associated with the ontology matching process. This article provides a review of the state-of-the-art techniques being applied by ontology matching tools to achieve scalability and produce high-quality mappings when matching large ontologies. In addition, we provide a direct comparison of the techniques to gauge their effectiveness in achieving scalability. A review of the state-of-the-art ontology matching tools that employ each strategy is also provided. We also evaluate the state-of-the-art tools to gauge the progress they have made over the years in improving alignment’s quality and reduction of execution time when matching large ontologies.


title: Improving Optimization Bounds using Machine Learning: Decision Diagrams meet Deep Reinforcement Learning

abstract: Finding tight bounds on the optimal solution is a critical element of practical solution methods for discrete optimization problems. In the last decade, decision diagrams (DDs) have brought a new perspective on obtaining upper and lower bounds that can be significantly better than classical bounding mechanisms, such as linear relaxations. It is well known that the quality of the bounds achieved through this flexible bounding method is highly reliant on the ordering of variables chosen for building the diagram, and finding an ordering that optimizes standard metrics is an NP-hard problem. In this paper, we propose an innovative and generic approach based on deep reinforcement learning for obtaining an ordering for tightening the bounds obtained with relaxed and restricted DDs. We apply the approach to both the Maximum Independent Set Problem and the Maximum Cut Problem. Experimental results on synthetic instances show that the deep reinforcement learning approach, by achieving tighter objective function bounds, generally outperforms ordering methods commonly used in the literature when the distribution of instances is known. To the best knowledge of the authors, this is the first paper to apply machine learning to directly improve relaxation bounds obtained by general-purpose bounding mechanisms for combinatorial optimization problems.


Dysarthric Speech Recognition Using Convolutional LSTM Neural Network


title: Gains from Others’ Losses: Technology Trajectories and the Global Division of Firms

abstract: This paper offers new insights into the role of firms versus individuals in driving technology directions, and the extent to which human capital may be lost during industrial shifts. We explore in particular whether (1) firms who move manufacturing offshore slow U.S.-based R&D activities in an emerging technology and (2) the inventors originally within these offshoring firms, leave, and continue innovating in the emerging technology at different institutions. We focus on the 28 leading U.S. optoelectronic component manufactures for telecommunications and the inventors who patent at these firms. In the case of U.S. optoelectronic component manufacturers for telecommunications, offshoring is associated with a decrease in innovation in the emerging technology, but an increase in all other types of patenting. The majority of inventors depart to firms outside the industry and stop work in the emerging technology. However, an important minority of emerging technology inventors at the offshoring firms go to a single onshore firm in the same industry (which gains from others’ losses and subsequently dominates this space). Our results suggest a strong role for firms and firm strategy in driving innovation directions, and the corresponding opportunities faced by individuals.


title: Prevalence of prenatally diagnosed congenital cystic adenomatoid malformation among fetuses in China

abstract: The prevalence of congenital cystic adenomatoid malformation among fetuses still varies in different studies in China. The present meta-analysis was intended to evaluate the pooled prevalence of fetuses in China. Four English (Pubmed, Elsevier Science Direct, Web of Science and the Cochrane Library) and four Chinese (the Chinese Biological Medical Literature database, the Chinese National Knowledge Infrastructure database, VIP database and Wanfang Data) databases were searched from inception to July 2016. Meta-analyses were performed using Stata (version 12.0), with prevalence and corresponding 95% confidence intervals using the random effect model. Five studies with 393496 fetuses were chosen for this meta-analysis. The overall pooled prevalence was 4.01/10000 (2.03/10000 - 6.00/10000) fetuses. Sensitivity analysis revealed that the results were stable, and Begg's test and Egg's test showed no potential risk of publication bias. This is, to our knowledge, the first study to systematically evaluate the literature of the prevalence of congenital cystic adenomatoid malformation among fetuses in China. Results showed that the prevalence among fetuses should be considerable. A large-scale multicenter study on the epidemiology across different areas in China is required.


Engaging Residents in Community Change: The Critical Role of Trust in the Development of a Promise Neighborhood.


Neural Joint Learning for Classifying Wikipedia Articles into Fine-grained Named Entity Types


title: Estimating Treatment Effects Using Observational Data

abstract: THE RANDOMIZED CLINICAL TRIAL (RCT) IS THE ideal method for measuring treatment effects. Participants in clinical trials are randomly assigned to a treatment or control group. Randomization reduces biases by making treatment and control groups “equal with respect to all features,” except the treatment assignment. When randomization is performed correctly, differences in efficacy found by statistical comparisons can be attributed to the difference between the treatment and control. However, the RCT does not necessarily provide the final answer to treatment effectiveness, as there are many restrictions that limit generalizability. For example, RCTs are often restricted to patients with limited disease, comorbidity, and concomitant medications. Thus, RCTs generally demonstrate efficacy rather than effectiveness, where efficacy is the treatment effect under the restricted conditions of the RCT and effectiveness is the treatment effect under the conditions of usual practice. Observational, nonrandomized studies have a role when RCTs are not available, and, even when RCTs are available, to quantify effectiveness and other real world experiences. A contemporary example of this is the evaluation of drugeluting stents, for which RCTs have demonstrated shortterm efficacy for relatively healthy patients and observational studies are beginning to address long-term effectiveness and safety problems and use of clopidogrel in a broader array of patients. There are many approaches for making statistical inferences from observational data. Some approaches focus on study design, others on statistical techniques. However, even with the best of designs, observational studies, unlike the RCTs, do not automatically control for selection biases. Therefore, statistical methods involving matching, stratification, and/or covariance adjustment are needed. Lack of randomization in observational studies may result in large differences on the observed (and unobserved) participant characteristics between the treatment and control groups. These differences can lead to biased estimates of treatment effects. The goal of the statistical techniques that focus on observational data is to create an analysis that resembles what would occur had the treatment been randomly assigned. In RCTs, the balance is achieved on participant characteristics that occur before the treatment is administered. The success of randomization in creating balance can be assessed before any outcome measurements are taken. Therefore, in observational studies, the first goal of a statistical technique is to create balance between treatments on characteristics that are assessed before the actual treatment is administered. Once this balance has been achieved, outcome measurements can be ascertained and compared between groups. In practice, this goal is often difficult to achieve because the data available for observational studies usually contain measured patient characteristics that are obtained before, during, and after treatment administration, and it is often difficult to determine exactly which patient characteristics are pretreatment or not. Furthermore, there frequently are unmeasured characteristics that are not available, inadequately measured, or unknown. Thus, the statistical methods in observational studies need first to be judged based on their performance in creating a balance on background characteristics between treated and control groups and the impact of outcome data should play no role in this assessment. To adjust for pretreatment imbalances, 2 statistical approaches often used are analysis of covariance methods and propensity score methods. These 2 methods complement each other and generally should be used together rather than choosing between one or the other. Analysis of covariance refers here to standard statistical analyses that produce estimates of treatment effects adjusted for background characteristics (covariates), which are included explicitly in a statistical (regression) model. With observational studies, this technique can produce biased estimates of treatment effects if there is extreme imbalance of the background characteristics and/or the treatment effect


title: Extracting surfaces from fuzzy 3D-ultrasound data

abstract: Rendering 3D models from 3D-ultrasonic data is a complicated task due to the noisy, fuzzy nature of ultrasound imaging containing a lot of artifacts, speckle etc. In the method presented in this paper we first apply several filtering techniques (low-pass, mathematical morphology, multi-resolution analysis) to separate the areas of low coherency containing mostly noise and speckle from those of useful information. Our novel BLTP filtering can be applied at interactive times on-the-fly under user control & feed-back. Goal of this processing is to create a ’region-of-interest’ (ROI) mask, whereas the data itself remains unaltered. Secondly,we examine several alternatives to the original Levoy contouring method. Finally we introduce an improved surface-extraction volume rendering procedure, applied on the original data within the ROI areas for visualizing high quality images within a few seconds on a normal workstation, or even on a PC, thus making the complete system suitable for routine clinical applications. CR Descriptors: General Terms: Algorithms. I.3.3 [Computer Graphics]: Picture/image generation; I.3.8 [Computer Graphics]: Applications; I.4.3 [Image Processing]: Enhancement, Smoothing, Filtering; I.4.6 [Image Processing]: Segmentation, Edge and Feature Detection, Pixel Classification; J.3 [Life and Medical Sciences]. Additional


title: $LCL$ Filter Design and Performance Analysis for Grid-Interconnected Systems

abstract: The use of power converters is very important in maximizing the power transfer from renewable energy sources such as wind, solar, or even a hydrogen-based fuel cell to the utility grid. An LCL filter is often used to interconnect an inverter to the utility grid in order to filter the harmonics produced by the inverter. Although there is an extensive amount of literature available describing LCL filters, there has been a gap in providing a systematic design methodology. Furthermore, there has been a lack of a state-space mathematical modeling approach that considers practical cases of delta- and wye-connected capacitors showing their effects on possible grounding alternatives. This paper describes a design methodology of an LCL filter for grid-interconnected inverters along with a comprehensive study of how to mitigate harmonics. The procedures and techniques described in this paper may be used in small-scale renewable energy conversion systems and may be also retrofitted for medium- and large-scale grid-connected systems.


title: Software Cost Estimation using Fuzzy Decision Trees

abstract: This paper addresses the issue of software cost estimation through fuzzy decision trees, aiming at acquiring accurate and reliable effort estimates for project resource allocation and control. Two algorithms, namely CHAID and CART, are applied on empirical software cost data recorded in the ISBSG repository. Approximately 1000 project data records are selected for analysis and experimentation, with fuzzy decision trees instances being generated and evaluated based on prediction accuracy. The set of association rules extracted is used for providing mean effort value ranges. The experimental results suggest that the proposed approach may provide accurate cost predictions in terms of effort. In addition, there is strong evidence that the fuzzy transformation of cost drivers contribute to enhancing the estimation process.


title: A hybrid dragonfly algorithm with extreme learning machine for prediction

abstract: In this work, a proposed hybrid dragonfly algorithm (DA) with extreme learning machine (ELM) system for prediction problem is presented. ELM model is considered a promising method for data regression and classification problems. It has fast training advantage, but it always requires a huge number of nodes in the hidden layer. The usage of a large number of nodes in the hidden layer increases the test/evaluation time of ELM. Also, there is no guarantee of optimality of weights and biases settings on the hidden layer. DA is a recently promising optimization algorithm that mimics the moving behavior of moths. DA is exploited here to select less number of nodes in the hidden layer to speed up the performance of the ELM. It also is used to choose the optimal hidden layer weights and biases. A set of assessment indicators is used to evaluate the proposed and compared methods over ten regression data sets from the UCI repository. Results prove the capability of the proposed DA-ELM model in searching for optimal feature combinations in feature space to enhance ELM generalization ability and prediction accuracy. The proposed model was compared against the set of commonly used optimizers and regression systems. These optimizers are namely, particle swarm optimization (PSO) and genetic algorithm (GA). The proposed DA-ELM model proved an advance overall compared methods in both accuracy and generalization ability.


title: Investigating the additive manufacture of extra-terrestrial materials

abstract: The Powder Bed Fusion (PBF) additive manufacturing process category, consists of a group of key enabling technologies allowing the fabrication of both intrinsic and complex structures for a series ...


title: JPEG on STEROIDS: Common optimization techniques for JPEG image compression

abstract: Despite its age, JPEG (formally, Rec. ITU-T T.81 — ISO/IEC 10918-1) is still the omnipresent image file format for lossy compression of photographic images. While its rate-distortion performance is not competitive with state-of-the-art schemes like JPEG 2000 or HEVC, manifold techniques have been developed over the years to improve its compression performance. This article provides a short review of the known technologies and evaluates them on the basis of the JPEG XT demo implementation available on the home page of the JPEG committee. It also puts the compression gains into perspective of more modern compression formats such as JPEG 2000.


title: Image Processing Approach to Automatic Scoring System for Archery Targets

abstract: This paper proposes an image processing approach to automatic scoring system for archery targets. Specifically, the proposed system first performs Morphological operations on the target image to obtain thick boundaries of the arrow hits and then the image segmentation process is applied to segment the target area by using color and shape features. In doing so, a dynamic thresholding is applied to handle the possible effects of illumination variations and the bull's eyes or the target concentric circles are segmented by using geometrical properties of circles and ellipses to calculate hit scores. Thus the system can have a capability of scoring inside and outside the concentric circles separately. The proposed method is tested by using our own video sequences taken in the self-conducted experiments performed by four archers on two different days shooting 6 arrows. The test results show that the proposed scoring system is promising with an accuracy rate of 100%.


title: The Role of Posterior Parietal Cortex in Beat-based Timing Perception: A Continuous Theta Burst Stimulation Study

abstract: There is a growing interest in how the brain's motor systems contribute to the perception of musical rhythms. The Action Simulation for Auditory Prediction hypothesis proposes that the dorsal auditory stream is involved in bidirectional interchange between auditory perception and beat-based prediction in motor planning structures via parietal cortex [Patel, A. D., & Iversen, J. R. The evolutionary neuroscience of musical beat perception: The Action Simulation for Auditory Prediction (ASAP) hypothesis. Frontiers in Systems Neuroscience, 8, 57, 2014]. We used a TMS protocol, continuous theta burst stimulation (cTBS), that is known to down-regulate cortical activity for up to 60 min following stimulation to test for causal contributions to beat-based timing perception. cTBS target areas included the left posterior parietal cortex (lPPC), which is part of the dorsal auditory stream, and the left SMA (lSMA). We hypothesized that down-regulating lPPC would interfere with accurate beat-based perception by disrup...


title: Pixel Recursive Super Resolution

abstract: We present a pixel recursive super resolution model that synthesizes realistic details into images while enhancing their resolution. A low resolution image may correspond to multiple plausible high resolution images, thus modeling the super resolution process with a pixel independent conditional model often results in averaging different details--hence blurry edges. By contrast, our model is able to represent a multimodal conditional distribution by properly modeling the statistical dependencies among the high resolution image pixels, conditioned on a low resolution input. We employ a PixelCNN architecture to define a strong prior over natural images and jointly optimize this prior with a deep conditioning convolutional network. Human evaluations indicate that samples from our proposed model look more photo realistic than a strong L2 regression baseline.


title: A Survey on the Security of Blockchain Systems

abstract: Since its inception, the blockchain technology has shown promising application prospects. From the initial cryptocurrency to the current smart contract, blockchain has been applied to many fields. Although there are some studies on the security and privacy issues of blockchain, there lacks a systematic examination on the security of blockchain systems. In this paper, we conduct a systematic study on the security threats to blockchain and survey the corresponding real attacks by examining popular blockchain systems. We also review the security enhancement solutions for blockchain, which could be used in the development of various blockchain systems, and suggest some future directions to stir research efforts into this area.


title: DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks

abstract: This paper studies monocular visual odometry (VO) problem. Most of existing VO algorithms are developed under a standard pipeline including feature extraction, feature matching, motion estimation, local optimisation, etc. Although some of them have demonstrated superior performance, they usually need to be carefully designed and specifically fine-tuned to work well in different environments. Some prior knowledge is also required to recover an absolute scale for monocular VO. This paper presents a novel end-to-end framework for monocular VO by using deep Recurrent Convolutional Neural Networks (RCNNs). Since it is trained and deployed in an end-to-end manner, it infers poses directly from a sequence of raw RGB images (videos) without adopting any module in the conventional VO pipeline. Based on the RCNNs, it not only automatically learns effective feature representation for the VO problem through Convolutional Neural Networks, but also implicitly models sequential dynamics and relations using deep Recurrent Neural Networks. Extensive experiments on the KITTI VO dataset show competitive performance to state-of-the-art methods, verifying that the end-to-end Deep Learning technique can be a viable complement to the traditional VO systems.


Deep Learning and Transfer Learning in the Classification of EEG Signals


title: Multi-omic data integration and analysis using systems genomics approaches: methods and applications in animal production, health and welfare

abstract: In the past years, there has been a remarkable development of high-throughput omics (HTO) technologies such as genomics, epigenomics, transcriptomics, proteomics and metabolomics across all facets of biology. This has spearheaded the progress of the systems biology era, including applications on animal production and health traits. However, notwithstanding these new HTO technologies, there remains an emerging challenge in data analysis. On the one hand, different HTO technologies judged on their own merit are appropriate for the identification of disease-causing genes, biomarkers for prevention and drug targets for the treatment of diseases and for individualized genomic predictions of performance or disease risks. On the other hand, integration of multi-omic data and joint modelling and analyses are very powerful and accurate to understand the systems biology of healthy and sustainable production of animals. We present an overview of current and emerging HTO technologies each with a focus on their applications in animal and veterinary sciences before introducing an integrative systems genomics framework for analysing and integrating multi-omic data towards improved animal production, health and welfare. We conclude that there are big challenges in multi-omic data integration, modelling and systems-level analyses, particularly with the fast emerging HTO technologies. We highlight existing and emerging systems genomics approaches and discuss how they contribute to our understanding of the biology of complex traits or diseases and holistic improvement of production performance, disease resistance and welfare.


title: Design of a Novel Portable ECG Monitor for Heart Health

abstract: Using the ECG analog front-end and ARM Cortex-M3 processor to develop a portable ECG monitor. The STM32 as the core unit, the ADS1292 as the acquisition analog front-end, it also includes a touch screen display module, an SD card storage module and a voltage conversion module. Automatic ECG analysis algorithms including QRS complex detection, QRS width detection and ST segment detection. ECG can be divided into four kinds of heart beat and eight kinds of arrhythmia rhythm using the extracted ECG parameters. The results have been evaluated on the MIT-BIH Arrhythmia Database, the sensitivity of QRS complex detection was 99% and the sensitivity of heart beat classification was above 95%. The monitor can display the real-time ECG waveform and the current heart rate, to make recommendations for the subjects, and it stored the abnormal ECG waveform that provided to physicians for further analysis and diagnosis. Ultimately the monitor gives a composite score based on heart rate, arrhythmia and ST segment to facilitate subjects for heart health.


title: Intelligent Parking System Design Using FPGA

abstract: In this research, we introduce FPGA based fuzzy logic controller (FLC). The benefit of using FPGA based FLC compare to software FLC is that the computation time reduction. Using this FLC, we design automated car back parallel parking system also with complete FPGA based controller. We build a small-scaled robot car and test on a real environment with VHDL code for wall following and parking. This paper describes the background of fuzzy logic system, the design of fuzzy logic system with FPGA and the experimental results.


An Unsupervised Speaker Clustering Technique based on SOM and I-vectors for Speech Recognition Systems


title: Web Content Mining Techniques: A Survey

abstract: The Quest for knowledge has led to new discoveries and inventions. With the emergence of World Wide Web, it became a hub for all these discoveries and inventions. Web browsers became a tool to make the information available at our finger tips. As years passed World Wide Web became overloaded with information and it became hard to retrieve data according to the need. Web mining came as a rescue for the above problem. Web content mining is a subdivision under web mining. This paper deals with a study of different techniques and pattern of content mining and the areas which has been influenced by content mining. The web contains structured, unstructured, semi structured and multimedia data. This survey focuses on how to apply content mining on the above data. It also points out how web content mining can be utilized in web usage mining.


title: Automated Retail Store Based on RFID

abstract: Radio Frequency Identification (RFID) is one of the most exciting and promising technologies in the field of automated identification. This technology is being increasingly used in various areas such as supply-chain management, security, library, airline, military, animal farms, sports, etc. Common application examples are equipment tracking, personnel tracking, vehicle access controls, and items security in departmental stores. Resource optimization, quality customer care, enhanced accuracy, and efficient business processes are the motivational factors behind the enormous use of RFID technology. In this paper, we present the design, development and implementation of an Automated Future Retail Store based on RFID technology.


title: Place recognition using straight lines for vision-based SLAM

abstract: Most visual simultaneous localization and mapping systems use point features as their landmarks and adopt point-based feature descriptors to recognize them. Compared to point landmarks, however, lines have strength in conveying the structural information of the environment. Despite the benefit, they have not been widely used because lines are more difficult in detecting, tracking, and recognizing, and this delayed the use of lines as landmarks. In this paper, we propose a place recognition algorithm using straight line features, which enables reliable loop closure detections in large complex environments under significant illumination changes. A vocabulary tree trained with mean standard-deviation line descriptor is used in finding the candidate matches between keyframes, and a Bayesian filtering framework enables reliable keyframe matching for large-scale loop closures. The proposed algorithm is compared with state-of-the-art point-based methods using scale-invariant feature transform or speeded up robust features. The experimental results show that the proposed method outperforms the others in challenging indoor environments.


title: Designing crowdsourcing community for the enterprise

abstract: In this paper, we describe the design principles used for implementing crowdsourcing within the enterprise. This is based on our distinction between two kinds of crowdsourcing: enterprise (inside a firewall) versus the public domain. Whereas public domain crowdsourcing offers monetary rewards in exchange for participation, we show that identifying the right social objects and using these in designing the incentive model is sufficient to incent, motivate, and sustain participation levels in enterprise crowdsourcing. Finally, we show that the systematic integration of the characteristics of levels of participation into the design, e.g., the distinction between direct and indirect crowdsourcing, is sufficient for optimizing users' participation and contributions.


title: Restoring camera-captured distorted document images

abstract: This article discusses restoration of camera-captured distorted document images. Without the assistance of 3D data or model, our algorithm estimates and rectifies document warping just from 2D image based on line segmentation. Warping shape of each text line is acquired by estimating baselines' shape and characters' slant angles after line segmentation. In order to get fluent recovery result, thin-plate splines are exploited whose key points are determined through the result of warping estimation. Such process can effectively depict document warping and successfully restore warped document images to be flat. Comparison of OCR recognition rate between original camera-captured images and restored images shows the effectiveness of the algorithm proposed. We also demonstrate evaluation on DFKI dewarping contest dataset with some related algorithms. Besides desirable restoration result, processing speed of the whole procedure is satisfactory as well. In conclusion, it is applicable to be performed in OCR application to achieve better understanding of camera-captured document images.


title: The immune system's role in sepsis progression, resolution, and long-term outcome.

abstract: Sepsis occurs when an infection exceeds local tissue containment and induces a series of dysregulated physiologic responses that result in organ dysfunction. A subset of patients with sepsis progress to septic shock, defined by profound circulatory, cellular, and metabolic abnormalities, and associated with a greater mortality. Historically, sepsis-induced organ dysfunction and lethality were attributed to the complex interplay between the initial inflammatory and later anti-inflammatory responses. With advances in intensive care medicine and goal-directed interventions, early 30-day sepsis mortality has diminished, only to steadily escalate long after "recovery" from acute events. As so many sepsis survivors succumb later to persistent, recurrent, nosocomial, and secondary infections, many investigators have turned their attention to the long-term sepsis-induced alterations in cellular immune function. Sepsis clearly alters the innate and adaptive immune responses for sustained periods of time after clinical recovery, with immune suppression, chronic inflammation, and persistence of bacterial representing such alterations. Understanding that sepsis-associated immune cell defects correlate with long-term mortality, more investigations have centered on the potential for immune modulatory therapy to improve long-term patient outcomes. These efforts are focused on more clearly defining and effectively reversing the persistent immune cell dysfunction associated with long-term sepsis mortality.


Knowledge Factorization


title: Comparison of Unigram, Bigram, HMM and Brill’s POS Tagging Approaches for some South Asian Languages

abstract: Part-of-Speech (POS) Tagging is a process that attaches each word in a sentence with a suitable tag from a given set of tags. POS Tagging is important in various areas of Natural Language Processing. Different methods of automating the process have been developed and employed for English and other Western languages. Some similar work, most of which utilize the stochastic approaches for POS Tagging has also been done in the same area for South Asian languages. We experimented with some of the widelyused approaches for POS Tagging on three South Asian languages, Bangla, Hindi and Telegu, using corpora of different sizes. We observed the performance of the approaches and found the Brill’s transformation based tagger’s performance to be superior to the other approaches in all of our experiments, though the use of this approach has been very limited until recently.


title: Evaluating Capability of Deep Neural Networks for Image Classification via Information Plane

abstract: Inspired by the pioneering work of information bottleneck principle for Deep Neural Networks (DNNs) analysis, we design an information plane based framework to evaluate the capability of DNNs for image classification tasks, which not only helps understand the capability of DNNs, but also helps us choose a neural network which leads to higher classification accuracy more efficiently. Further, with experiments, the relationship among the model accuracy, I(X; T) and I(T; Y) are analyzed, where I(X; T) and I(T; Y) are the mutual information of DNN’s output T with input X and label Y. We also show the information plane is more informative than loss curve and apply mutual information to infer the model’s capability for recognizing objects of each class. Our studies would facilitate a better understanding of DNNs.


title: Dynamic Filtering with Large Sampling Field for ConvNets

abstract: We propose a dynamic filtering strategy with large sampling field for ConvNets (LS-DFN), where the position-specific kernels learn from not only the identical position but also multiple sampled neighbour regions. During sampling, residual learning is introduced to ease training and an attention mechanism is applied to fuse features from different samples. Such multiple samples enlarge the kernels’ receptive fields significantly without requiring more parameters. While LS-DFN inherits the advantages of DFN [5], namely avoiding feature map blurring by positionwise kernels while keeping translation invariance, it also efficiently alleviates the overfitting issue caused by much more parameters than normal CNNs. Our model is efficient and can be trained end-to-end via standard back-propagation. We demonstrate the merits of our LS-DFN on both sparse and dense prediction tasks involving object detection, semantic segmentation and flow estimation. Our results show LS-DFN enjoys stronger recognition abilities in object detection and semantic segmentation tasks on VOC benchmark [8] and sharper responses in flow estimation on FlyingChairs dataset [6] compared to strong baselines.


title: Remote Photoplethysmography Correspondence Feature for 3D Mask Face Presentation Attack Detection

abstract: 3D mask face presentation attack, as a new challenge in face recognition, has been attracting increasing attention. Recently, remote Photoplethysmography (rPPG) is employed as an intrinsic liveness cue which is independent of the mask appearance. Although existing rPPG-based methods achieve promising results on both intra and cross dataset scenarios, they may not be robust enough when rPPG signals are contaminated by noise. In this paper, we propose a new liveness feature, called rPPG correspondence feature (CFrPPG) to precisely identify the heartbeat vestige from the observed noisy rPPG signals. To further overcome the global interferences, we propose a novel learning strategy which incorporates the global noise within the CFrPPG feature. Extensive experiments indicate that the proposed feature not only outperforms the state-of-the-art rPPG based methods on 3D mask attacks but also be able to handle the practical scenarios with dim light and camera motion.


title: Learning Urban Navigation via Value Iteration Network

abstract: Choosing an appropriate route is a critical problem in urban navigation. Being familiar with roads topology and other vehicles’ routes, experienced drivers could usually find a near optimal route. However, the nowadays navigation applications hardly catch the domain knowledge and drivers’ interaction in this scenario. In fact, they only recommend several routes and leave the most difficult decision to driver. Hence the route is often congested by many vehicles whose drivers make a similar choose. To intelligently make right decision on navigation and improve traffic efficiency for each vehicle, we propose a neural network structure which learns to plan coarse-grained route in complex urban areas.Focused on learning to navigate, this paper first formalizes urban map and vehicle route model. The city map is segmented into grids and each vehicle’s route is mapped to grids. Based on grid-world model, we solicit both global traffic status and driving actions from large-scale taxicab GPS data. The learn-to-plan problem is therefore to find a policy function from a global status representation to an experienced driver’s action under that status. Traditional neural network is difficult to learn to this plan-involved function, so we leverage and modify value iteration network (VIN), which explicitly takes long-term plan into consideration. Finally we evaluate the performance of proposed network on real map and trajectory data in Beijing, China. The results show that VIN can achieve human driver performance in most cases, with high success rate and less commuting time.


title: Batch Normalization Sampling

abstract: Deep Neural Networks (DNNs) thrive in recent years in which Batch Normalization (BN) plays an indispensable role. However, it has been observed that BN is costly due to the reduction operations. In this paper, we propose alleviating this problem through sampling only a small fraction of data for normalization at each iteration. Specifically, we model it as a statistical sampling problem and identify that by sampling less correlated data, we can largely reduce the requirement of the number of data for statistics estimation in BN, which directly simplifies the reduction operations. Based on this conclusion, we propose two sampling strategies, "Batch Sampling" (randomly select several samples from each batch) and "Feature Sampling" (randomly select a small patch from each feature map of all samples), that take both computational efficiency and sample correlation into consideration. Furthermore, we introduce an extremely simple variant of BN, termed as Virtual Dataset Normalization (VDN), that can normalize the activations well with few synthetical random samples. All the proposed methods are evaluated on various datasets and networks, where an overall training speedup by up to 20% on GPU is practically achieved without the support of any specialized libraries, and the loss on accuracy and convergence rate are negligible. Finally, we extend our work to the "micro-batch normalization" problem and yield comparable performance with existing approaches at the case of tiny batch size.