title: Discrete Exterior Calculus

abstract: This thesis presents the beginnings of a theory of discrete exterior calculus (DEC). Our approach is to develop DEC using only discrete combinatorial and geometric operations on a simplicial complex and its geometric dual. The derivation of these may require that the objects on the discrete mesh, but not the mesh itself, are interpolated. ::: Our theory includes not only discrete equivalents of differential forms, but also discrete vector fields and the operators acting on these objects. Definitions are given for discrete versions of all the usual operators of exterior calculus. The presence of forms and vector fields allows us to address their various interactions, which are important in applications. In many examples we find that the formulas derived from DEC are identical to the existing formulas in the literature. We also show that the circumcentric dual of a simplicial complex plays a useful role in the metric dependent part of this theory. The appearance of dual complexes leads to a proliferation of the operators in the discrete theory. ::: One potential application of DEC is to variational problems which come equipped with a rich exterior calculus structure. On the discrete level, such structures will be enhanced by the availability of DEC. One of the objectives of this thesis is to fill this gap. There are many constraints in numerical algorithms that naturally involve differential forms. Preserving such features directly on the discrete level is another goal, overlapping with our goals for variational problems. ::: In this thesis we have tried to push a purely discrete point of view as far as possible. We argue that this can only be pushed so far, and that interpolation is a useful device. For example, we found that interpolation of functions and vector fields is a very convenient. In future work we intend to continue this interpolation point of view, extending it to higher degree forms, especially in the context of the sharp, Lie derivative and interior product operators. Some preliminary ideas on this point of view are presented in the thesis. We also present some preliminary calculations of formulas on regular nonsimplicial complexes.


Image Enhancement Using Recursive Adaptive Gamma Correction


title: Test Results and Torque Improvement of the 50-kW Switched Reluctance Motor Designed for Hybrid Electric Vehicles

abstract: A switched reluctance motor (SRM) has been developed as one of the possible candidates of rare-earth-free electric motors. A prototype machine has been built and tested. It has competitive dimensions, torque, power, and efficiency with respect to the 50-kW interior permanent magnet synchronous motor employed in the hybrid electric vehicles (Toyota Prius 2003). It is found that competitive power of 50-kW rating and efficiency of 95% are achieved. The prototype motor provided 85% of the target torque. Except the maximum torque, the most speed-torque region is found to be covered by the test SRM. The cause of discrepancy in the measured and calculated torque values is examined. An improved design is attempted, and a new experimental switched reluctance machine is designed and built for testing. The results are given in this paper.


title: A System for new event detection

abstract: We present a new method and system for performing the New Event Detection task, i.e., in one or multiple streams of news stories, all stories on a previously unseen (new) event are marked. The method is based on an incremental TF-IDF model. Our extensions include: generation of source-specific models, similarity score normalization based on document-specific averages, similarity score normalization based on source-pair specific averages, term reweighting based on inverse event frequencies, and segmentation of the documents. We also report on extensions that did not improve results. The system performs very well on TDT3 and TDT4 test data and scored second in the TDT-2002 evaluation.


Driver Drowsiness Detection System and Techniques : A Review


title: Self-adaptation in software-intensive cyber-physical systems

abstract: A method for designing self-adaptive software-intensive cyber-physical systems.System variability is described by alternative invariant decompositions.Traceability between system requirements and runtime entities is preserved. Design of self-adaptive software-intensive cyber-physical systems (siCPS) operating in dynamic environments is a significant challenge when a sufficient level of dependability is required. This stems partly from the fact that the concerns of self-adaptivity and dependability are to an extent contradictory. In this paper, we introduce IRM-SA (Invariant Refinement Method for Self-Adaptation)-a design method and associated formally grounded model targeting siCPS-that addresses self-adaptivity and supports dependability by providing traceability between system requirements, distinct situations in the environment, and predefined configurations of system architecture. Additionally, IRM-SA allows for architecture self-adaptation at runtime and integrates the mechanism of predictive monitoring that deals with operational uncertainty. As a proof of concept, it was implemented in DEECo, a component framework that is based on dynamic ensembles of components. Furthermore, its feasibility was evaluated in experimental settings assuming decentralized system operation.


Concepts and Practice of Verification, Validation, and Uncertainty Quantification


title: Selective Wireless Power Transfer for Smart Power Distribution in a Miniature-Sized Multiple-Receiver System

abstract: In a multiple-receiver wireless power transfer (WPT) system, determining the condition for uniform power distribution at a high transfer efficiency is a challenging issue. In this paper, a selective WPT technique using magnetic resonance coupling (MRC) is introduced for smart power delivery in a multiple-receiver system. The proposed method selectively and exclusively delivers power to only one designated receiver among multiple receivers, eliminating the cross-coupling effect and unbalanced power division problem across the receivers. This is achieved by separating the resonant frequencies of the receivers to isolate the coupling effects between coils. The power division ratio of the receivers is controlled by changing the duration time ratio for power transfer. In this paper, a one-transmitter three-receiver selective MRC system is designed and fabricated. The power distribution is demonstrated under impedance matched condition, showing a power transfer efficiency of 24%-29% at a very small coupling coefficient of 0.01 with a 12-mm-diameter receiver coil. Distance compensation and a one-way communication of time division multiple access are demonstrated for a multiple-receiver system, using the proposed method.


title: Junction Temperature Extraction Approach With Turn-Off Delay Time for High-Voltage High-Power IGBT Modules

abstract: Thermo-sensitive electrical parameter (TSEP) approaches are widely employed in the junction temperature extraction and prediction of power semiconductor devices. In this paper, the turn-off delay time is explored as an indicator of a TSEP to extract the junction temperature from high-power insulated gate bipolar transistor (IGBT) modules. The parasitic inductor $L_{\rm eE}$ between the Kelvin and power emitter terminals of an IGBT module is utilized to extract the turn-off delay time. Furthermore, the monotonic dependence between the junction temperature and turn-off delay time is investigated. The beginning and end point of the turn-off delay time can be determined by monitoring the induced voltage $v_{\rm eE}$ across the inductor $L_{\rm eE}$ . A dynamic switching characteristic test platform for high-power IGBT modules is used to experimentally verify the theoretical analysis. The experimental results show that the dependency between IGBT junction temperature and turn-off delay time is near linear. It is established that the turn-off delay time is a viable TSEP with good linearity, fixed sensitivity, and offers nondestruction on-line IGBT junction temperature extraction.


title: RecSys Challenge 2017: Offline and Online Evaluation

abstract: The ACM Recommender Systems Challenge 20171 focused on the problem of job recommendations: given a new job advertisement, the goal was to identify those users who are both (a) interested in getting notified about the job advertisement, and (b) appropriate candidates for the given job. Participating teams had to balance between user interests and requirements for the given job as well as dealing with the cold-start situation. For the first time in the history of the conference, the RecSys challenge offered an online evaluation: teams first had to compete as part of a traditional offline evaluation and the top 25 teams were then invited to evaluate their algorithms in an online setting, where they could submit recommendations to real users. Overall, 262 teams registered for the challenge, 103 teams actively participated and submitted together more than 6100 solutions as part of the offline evaluation. Finally, 18 teams participated and rolled out recommendations to more than 900,000 users on XING2.


title: 368 PSYCHOLOGICAL SCIENCE Research Article TO SEE OR NOT TO SEE: The Need for Attention to Perceive Changes in Scenes

abstract: When looking at a scene, observers feel that they see its entire structure in great detail and can immediately notice any changes in it However, when brief blank fields are placed between alternating displays of an original and a modified scene, a striking failure of perception is induced Identification of changes becomes extremely difficult, even when changes are large and made repeatedly Identification is much faster when a verbal cue is provided showing that poor visibility is not the cause of this difficulty Identification is also faster for objects considered to be important in the scene These results support the idea that observers never form a complete, detailed representation of their surroundings In addition, the results indicate that attention is required to perceive change, and that in the absence of localized motion signals attention is guided on the basis of high-level interest


title: A Deep Sum-Product Architecture for Robust Facial Attributes Analysis

abstract: Recent works have shown that facial attributes are useful in a number of applications such as face recognition and retrieval. However, estimating attributes in images with large variations remains a big challenge. This challenge is addressed in this paper. Unlike existing methods that assume the independence of attributes during their estimation, our approach captures the interdependencies of local regions for each attribute, as well as the high-order correlations between different attributes, which makes it more robust to occlusions and misdetection of face regions. First, we have modeled region interdependencies with a discriminative decision tree, where each node consists of a detector and a classifier trained on a local region. The detector allows us to locate the region, while the classifier determines the presence or absence of an attribute. Second, correlations of attributes and attribute predictors are modeled by organizing all of the decision trees into a large sum-product network (SPN), which is learned by the EM algorithm and yields the most probable explanation (MPE) of the facial attributes in terms of the region's localization and classification. Experimental results on a large data set with 22,400 images show the effectiveness of the proposed approach.


title: Hindi character recognition using RBF neural network and directional group feature extraction technique

abstract: In this paper, Radial Basis Function (RBF) neural Network has been implemented on eight directional values of gradient features for handwritten Hindi character recognition. The character recognition system was trained by using different samples in different handwritings collected of various people of different age groups. The Radial Basis Function network with one input and one output layer has been used for the training of RBF Network. Experiment has been performed to study the recognition accuracy, training time and classification time of RBF neural network. The recognition accuracy, training time and classification time achieved by implementing the RBF network have been compared with the result achieved in previous related work i.e. Back propagation Neural Network. Comparative result shows that the RBF with directional feature provides slightly less recognition accuracy, reduced training and classification time.


SIS: A System for Sequential Circuit Synthesis


a two point linear boundary value problem for system of ODEs with deviating arguments


title: Information privacy risk assessment of Facebook Graph Search

abstract: This Social networking sites remain a popular method of interaction and channel of communication for modern society. As the number of social networking users increase, the size of social networks is significantly increasing. To enhance the searching technique, graph based search algorithm is adopted but the adoption of this searching technique has increased the security issues for the users and their data. There is a necessity for strong privacy tools which will protect the privacy of users in graph based search. In this poster, we present the privacy risks of Facebook (FB) Graph Search.


title: Adaptive Temporal Encoding Network for Video Instance-level Human Parsing

abstract: Beyond the existing single-person and multiple-person human parsing tasks in static images, this paper makes the first attempt to investigate a more realistic video instance-level human parsing that simultaneously segments out each person instance and parses each instance into more fine-grained parts (e.g., head, leg, dress). We introduce a novel Adaptive Temporal Encoding Network (ATEN) that alternatively performs temporal encoding among key frames and flow-guided feature propagation from other consecutive frames between two key frames. Specifically, ATEN first incorporates a Parsing-RCNN to produce the instance-level parsing result for each key frame, which integrates both the global human parsing and instance-level human segmentation into a unified model. To balance between accuracy and efficiency, the flow-guided feature propagation is used to directly parse consecutive frames according to their identified temporal consistency with key frames. On the other hand, ATEN leverages the convolution gated recurrent units (convGRU) to exploit temporal changes over a series of key frames, which are further used to facilitate the frame-level instance-level parsing. By alternatively performing direct feature propagation between consistent frames and temporal encoding network among key frames, our ATEN achieves a good balance between frame-level accuracy and time efficiency, which is a common crucial problem in video object segmentation research. To demonstrate the superiority of our ATEN, extensive experiments are conducted on the most popular video segmentation benchmark (DAVIS) and a newly collected Video Instance-level Parsing (VIP) dataset, which is the first video instance-level human parsing dataset comprised of 404 sequences and over 20k frames with instance-level and pixel-wise annotations.


title: Contingency Filtering Techniques for Preventive Security-Constrained Optimal Power Flow

abstract: This paper focuses on contingency filtering to accelerate the iterative solution of preventive security-constrained optimal power flow (PSCOPF) problems. To this end, we propose two novel filtering techniques relying on the comparison at an intermediate PSCOPF solution of post-contingency constraint violations among postulated contingencies. We assess these techniques by comparing them with severity index-based filtering schemes, on a 60-and a 118-bus system. Our results show that the proposed contingency filtering techniques lead to faster solution of the PSCOPF, while being more robust and meaningful, than severity-index based ones.


title: Generalizing Over Uncertain Dynamics for Online Trajectory Generation

abstract: We present an algorithm which learns an online trajectory generator that can generalize over varying and uncertain dynamics. When the dynamics is certain, the algorithm generalizes across model parameters. When the dynamics is partially observable, the algorithm generalizes across different observations. To do this, we employ recent advances in supervised imitation learning to learn a trajectory generator from a set of example trajectories computed by a trajectory optimizer. In experiments in two simulated domains, it finds solutions that are nearly as good as, and sometimes better than, those obtained by calling the trajectory optimizer on line. The online execution time is dramatically decreased, and the off-line training time is reasonable.


title: Polarization-Based Angle Sensitive Pixels for Light Field Image Sensors With High Spatio-Angular Resolution

abstract: This paper presents a new angle sensitive pixel design for use in light field image sensors. This new pixel, referred to as the macro-pixel, is comprised of seven sub-pixels, and can determine the light angles in the range from −45° to +45°. The range of detectable angles is only limited by the presence of routing metals in the vicinity of the pixels. Each macro-pixel captures incident light angle through the aid of sub-wavelength metallic structures at its focal plane. Unlike previous designs that needed a large number of sub-pixels to determine a limited range of the incident angles, the presented design requires only half as many sub-pixels while offering a comparatively large angle detection range.


title: Business Intelligence Maturity Models Toward New Integrated Model

abstract: Business Intelligence (BI) has become one of the most important research areas that helps organizations and managers to better decision making process. This paper aims to show the barriers to BI adoption and discusses the most commonly used Business Intelligence Maturity Models (BIMMs). The aim also is to highlight the pitfalls of these BIMMs in order reach a solution. Using new techniques such as Service Oriented Architecture (SOA), Service Oriented Business Intelligence (SOBI) or Event Driven Architecture (EDA) leads to a new model. The proposed model named Service-Oriented Business Intelligence Maturity Model (SOBIMM) is briefly described in this paper.


title: Skyline with presorting

abstract: The skyline, or Pareto, operator selects those tuples that are not dominated by any others. Extending relational systems with the skyline operator would offer a basis for handling preference queries. Good algorithms are needed for skyline, however, to make this efficient in a relational setting. We propose a skyline algorithm, SFS, based on presorting that is general, for use with any skyline query, efficient, and well behaved in a relational setting.


title: Business: Do you wanna sell more? Discovering Topics, Sentiments and Prediction of Ratings

abstract: In the era of Social Computing, the role of customer reviews and ratings can be instrumental in predicting the success and sustainability of businesses as customers and even competitors use them to judge the quality of a business. Yelp is one of the most popular websites for users to write such reviews. This rating can be subjective and biased toward user's personality. Business preferences of a user can be decrypted based on his/ her past reviews. In this paper, we deal with (i) uncovering latent topics in Yelp data based on positive and negative reviews using topic modeling to learn which topics are the most frequent among customer reviews, (ii) sentiment analysis of users' reviews to learn how these topics associate to a positive or negative rating which will help businesses improve their offers and services, and (iii) predicting unbiased ratings from user-generated review text alone, using Linear Regression model. We also perform data analysis to get some deeper insights into customer reviews.


title: Subject-independent emotion recognition based on physiological signals: a three-stage decision method

abstract: Collaboration between humans and computers has become pervasive and ubiquitous, however current computer systems are limited in that they fail to address the emotional component. An accurate understanding of human emotions is necessary for these computers to trigger proper feedback. Among multiple emotional channels, physiological signals are synchronous with emotional responses; therefore, analyzing physiological changes is a recognized way to estimate human emotions. In this paper, a three-stage decision method is proposed to recognize four emotions based on physiological signals in the multi-subject context. Emotion detection is achieved by using a stage-divided strategy in which each stage deals with a fine-grained goal. The decision method consists of three stages. During the training process, the initial stage transforms mixed training subjects to separate groups, thus eliminating the effect of individual differences. The second stage categorizes four emotions into two emotion pools in order to reduce recognition complexity. The third stage trains a classifier based on emotions in each emotion pool. During the testing process, a test case or test trial will be initially classified to a group followed by classification into an emotion pool in the second stage. An emotion will be assigned to the test trial in the final stage. In this paper we consider two different ways of allocating four emotions into two emotion pools. A comparative analysis is also carried out between the proposal and other methods. An average recognition accuracy of 77.57% was achieved on the recognition of four emotions with the best accuracy of 86.67% to recognize the positive and excited emotion. Using differing ways of allocating four emotions into two emotion pools, we found there is a difference in the effectiveness of a classifier on learning each emotion. When compared to other methods, the proposed method demonstrates a significant improvement in recognizing four emotions in the multi-subject context. The proposed three-stage decision method solves a crucial issue which is ’individual differences’ in multi-subject emotion recognition and overcomes the suboptimal performance with respect to direct classification of multiple emotions. Our study supports the observation that the proposed method represents a promising methodology for recognizing multiple emotions in the multi-subject context.


title: Nonlinear controller design for a magnetic levitation device

abstract: Various applications of micro-robotic technology suggest the use of new actuator systems which allow motions to be realized with micrometer accuracy. Conventional actuation techniques such as hydraulic or pneumatic systems are no longer capable of fulfilling the demands of hi-tech micro-scale areas such as miniaturized biomedical devices and MEMS production equipment. These applications pose significantly different problems from actuation on a large scale. In particular, large scale manipulation systems typically deal with sizable friction, whereas micro manipulation systems must minimize friction to achieve submicron precision and avoid generation of static electric fields. Recently, the magnetic levitation technique has been shown to be a feasible actuation method for micro-scale applications. In this paper, a magnetic levitation device is recalled from the authors’ previous work and a control approach is presented to achieve precise motion control of a magnetically levitated object with sub-micron positioning accuracy. The stability of the controller is discussed through the Lyapunov method. Experiments are conducted and showed that the proposed control technique is capable of performing a positioning operation with rms accuracy of 16 μm over a travel range of 30 mm. The nonlinear control strategy proposed in this paper showed a significant improvement in comparison with the conventional control strategies for large gap magnetic levitation systems.


title: Gearbox Tooth Cut Fault Diagnostics Using Acoustic Emission and Vibration Sensors — A Comparative Study

abstract: In recent years, acoustic emission (AE) sensors and AE-based techniques have been developed and tested for gearbox fault diagnosis. In general, AE-based techniques require much higher sampling rates than vibration analysis-based techniques for gearbox fault diagnosis. Therefore, it is questionable whether an AE-based technique would give a better or at least the same performance as the vibration analysis-based techniques using the same sampling rate. To answer the question, this paper presents a comparative study for gearbox tooth damage level diagnostics using AE and vibration measurements, the first known attempt to compare the gearbox fault diagnostic performance of AE- and vibration analysis-based approaches using the same sampling rate. Partial tooth cut faults are seeded in a gearbox test rig and experimentally tested in a laboratory. Results have shown that the AE-based approach has the potential to differentiate gear tooth damage levels in comparison with the vibration-based approach. While vibration signals are easily affected by mechanical resonance, the AE signals show more stable performance.


title: Linked Data and multimedia: the state of affairs

abstract: Linked Data is a way of exposing and sharing data as resources on the Web and interlinking them with semantically related resources. In the last three years significant amounts of data have been generated, increasingly forming a globally connected, distributed data space. For multimedia content, metadata are a key factor for efficient management, organization, and retrieval. However, the relationship between multimedia and Linked Data has been rarely studied, leading to a lack of mutual awareness and, as a consequence thereof, technological deficiencies. This article introduces the basic concepts of Linked Data in the context of multimedia metadata, and discusses techniques to generate, expose, discover, and consume Linked Data. It shows that a large amount of data sources exist, which are ready to be exploited by multimedia applications. The benefit of Linked Data in two multimedia-related applications is discussed and open research issues are outlined with the goal of bringing the research fields of multimedia and Linked Data closer together in order to facilitate mutual benefit.


title: A column generation heuristic for the two-dimensional two-staged guillotine cutting stock problem with multiple stock size

abstract: We consider a two-dimensional cutting stock problem where stock of different sizes is available, and a set of rectangular items has to be obtained through two-staged guillotine cuts. We propose a heuristic algorithm, based on column generation, which requires as its subproblem the solution of a two-dimensional knapsack problem with two-staged guillotines cuts. A further contribution of the paper consists in the definition of a mixed integer linear programming model for the solution of this knapsack problem, as well as a heuristic procedure based on dynamic programming. Computational experiments show the effectiveness of the proposed approach, which obtains very small optimality gaps and outperforms the heuristic algorithm proposed by Cintra et al. [3].


title: Non-uniform Subset Selection for Active Learning in Structured Data

abstract: Several works have shown that relationships between data points (i.e., context) in structured data can be exploited to obtain better recognition performance. In this paper, we explore a different, but related, problem: how can these inter-relationships be used to efficiently learn and continuously update a recognition model, with minimal human labeling effort. Towards this goal, we propose an active learning framework to select an optimal subset of data points for manual labeling by exploiting the relationships between them. We construct a graph from the unlabeled data to represent the underlying structure, such that each node represents a data point, and edges represent the inter-relationships between them. Thereafter, considering the flow of beliefs in this graph, we choose those samples for labeling which minimize the joint entropy of the nodes of the graph. This results in significant reduction in manual labeling effort without compromising recognition performance. Our method chooses non-uniform number of samples from each batch of streaming data depending on its information content. Also, the submodular property of our objective function makes it computationally efficient to optimize. The proposed framework is demonstrated in various applications, including document analysis, scene-object recognition, and activity recognition.


title: A comparative assessment of classification methods

abstract: Classification systems play an important role in business decision-making tasks by classifying the available information based on some criteria. The objective of this research is to assess the relative performance of some well-known classification methods. We consider classification techniques that are based on statistical and AI techniques. We use synthetic data to perform a controlled experiment in which the data characteristics are systematically altered to introduce imperfections such as nonlinearity, multicollinearity, unequal covariance, etc. Our experiments suggest that data characteristics considerably impact the classification performance of the methods. The results of the study can aid in the design of classification systems in which several classification methods can be employed to increase the reliability and consistency of the classification.


title: Fault Tolerance and Resilience in Cloud Computing Environments

abstract: The increasing demand for flexibility and scalability in dynamically obtaining and releasing computing resources in a cost-effective and device-independent manner, and easiness in hosting applications without the burden of installation and maintenance, has resulted in a wide adoption of the cloud computing paradigm. While the benefits are immense, this computing paradigm is still vulnerable to a large number of system failures; as a consequence, users have become increasingly concerned about the reliability and availability of cloud computing services. Fault tolerance and resilience serve as an effective means to address users’ reliability and availability concerns. In this chapter, we focus on characterizing the recurrent failures in a typical cloud computing environment, analyzing the effects of failures on users’ applications and surveying fault tolerance solutions corresponding to each class of failures. We also discuss the perspective of offering fault tolerance as a service to users’ applications as one of the effective means of addressing users’ reliability and availability concerns.


title: A generic dynamic programming Matlab function

abstract: This paper introduces a generic dynamic programming function for Matlab. This function solves discretetime optimal-control problems using Bellman's dynamic programming algorithm. The function is implemented such that the user only needs to provide the objective function and the model equations. The function includes several options for solving optimal-control problems. The model equations can include several state variables and input variables. Furthermore, the model equations can be time-variant and include time-variant state and input constraints. The syntax of the function is explained using two examples. The first is the well-known Lotka-Volterra fishery problem and the second is a parallel hybrid-electric vehicle optimization problem.


title: Digital Beamforming in a large conformal Phased Array Antenna for satellite operations support — Architecture, design, and development

abstract: This paper describes the development of Digital Beamforming (DBF) architectures, techniques, and hardware performance demonstrations that can be incorporated into the Geodesic Dome Phased Array Antenna (GDPAA), a large multi-beam conformal phasedarray antenna. The DBF system can provide functions for satellite Tracking, Telemetry and Commanding (TT&C), with significant improvements in adaptive pattern control for anti-jamming or interference suppression control, direction finding with high-resolution, ultra low sidelobes, and flexible antenna resource management and beam scheduler. For most of the design, Commercial Off-the-Shelf (COTS) components were used to reduce costs and complexity. Three (3) candidates DBF architectures were analyzed and compared with respect to their performance, complexity and cost. For the selected (optimal) architecture the key COTS components were evaluated and the results are reported.


title: Understanding barriers to delirium care: a multicentre survey of knowledge and attitudes amongst UK junior doctors

abstract: Background: delirium is under-diagnosed and under-treated in comparison to other common and serious acute disorders. The reasons for this are unclear. Objective: we conducted a multicentre survey of knowledge of and attitudes to delirium in trainee general physicians. Design: questionnaire-based survey in 34 acute hospitals in the UK. Methods: we developed a questionnaire designed to test knowledge of delirium prevalence, DSM-IV diagnostic criteria, use of specific screening tools, association with adverse outcomes, and pharmacological management. Questionnaires were completed and returned by hand to the researchers immediately after recruitment. Participants were a convenience sample of trainee doctors in general and emergency medicine. Results: 784 trainee physicians participated. Most participants expressed the view that delirium has a high prevalence and that it is associated with serious adverse outcomes. However, they had poor knowledge of its diagnosis and treatment, reporting the need for better training. Experience working in geriatric medicine had only a modest effect on the ability to diagnose delirium. Conclusions: UK training doctors’ lack of basic knowledge of the diagnosis and management of delirium, rather than a lack of awareness of its high prevalence and clinical significance, appears to be important in determining its under-recognition.


title: A dynamic instruction set computer

abstract: A dynamic instruction set computer (DISC) has been developed that supports demand-driven modification of its instruction set. Implemented with partially reconfigurable FPGAs, DISC treats instructions as removable modules paged in and out through partial reconfiguration as demanded by the executing program. Instructions occupy FPGA resources only when needed and FPGA resources can be reused to implement an arbitrary number of performance-enhancing application-specific instructions. DISC further enhances the functional density of FPGAs by physically relocating instruction modules to available FPGA space.


title: Enhancing Sketch-Based Image Retrieval by Re-Ranking and Relevance Feedback

abstract: A sketch-based image retrieval often needs to optimize the tradeoff between efficiency and precision. Index structures are typically applied to large-scale databases to realize efficient retrievals. However, the performance can be affected by quantization errors. Moreover, the ambiguousness of user-provided examples may also degrade the performance, when compared with traditional image retrieval methods. Sketch-based image retrieval systems that preserve the index structure are challenging. In this paper, we propose an effective sketch-based image retrieval approach with re-ranking and relevance feedback schemes. Our approach makes full use of the semantics in query sketches and the top ranked images of the initial results. We also apply relevance feedback to find more relevant images for the input query sketch. The integration of the two schemes results in mutual benefits and improves the performance of the sketch-based image retrieval.


title: Efficiently making (almost) any concurrency control mechanism serializable

abstract: Concurrency control (CC) algorithms must trade off strictness for performance. In particular, serializable CC schemes generally pay higher cost to prevent anomalies, both in runtime overhead such as the maintenance of lock tables and in efforts wasted by aborting transactions. We propose the serial safety net (SSN), a serializability-enforcing certifier which can be applied on top of various CC schemes that offer higher performance but admit anomalies, such as snapshot isolation and read committed. The underlying CC mechanism retains control of scheduling and transactional accesses, while SSN tracks the resulting dependencies. At commit time, SSN performs a validation test by examining only direct dependencies of the committing transaction to determine whether it can commit safely or must abort to avoid a potential dependency cycle. SSN performs robustly for a variety of workloads. It maintains the characteristics of the underlying CC without biasing toward a certain type of transactions, though the underlying CC scheme might. Besides traditional OLTP workloads, SSN also efficiently handles heterogeneous workloads which include a significant portion of long, read-mostly transactions. SSN can avoid tracking the vast majority of reads (thus reducing the overhead of serializability certification) and still produce serializable executions with little overhead. The dependency tracking and validation tests can be done efficiently, fully parallel and latch-free, for multi-version systems on modern hardware with substantial core count and large main memory. We demonstrate the efficiency, accuracy and robustness of SSN using extensive simulations and an implementation that overlays snapshot isolation in ERMIA, a memory-optimized OLTP engine that supports multiple CC schemes. Evaluation results confirm that SSN is a promising approach to serializability with robust performance and low overhead for various workloads.


title: Finding Tiny Faces in the Wild with Generative Adversarial Network

abstract: Face detection techniques have been developed for decades, and one of remaining open challenges is detecting small faces in unconstrained conditions. The reason is that tiny faces are often lacking detailed information and blurring. In this paper, we proposed an algorithm to directly generate a clear high-resolution face from a blurry small one by adopting a generative adversarial network (GAN). Toward this end, the basic GAN formulation achieves it by super-resolving and refining sequentially (e.g. SR-GAN and cycle-GAN). However, we design a novel network to address the problem of super-resolving and refining jointly. We also introduce new training losses to guide the generator network to recover fine details and to promote the discriminator network to distinguish real vs. fake and face vs. non-face simultaneously. Extensive experiments on the challenging dataset WIDER FACE demonstrate the effectiveness of our proposed method in restoring a clear high-resolution face from a blurry small one, and show that the detection performance outperforms other state-of-the-art methods.


Neural Math Word Problem Solver with Reinforcement Learning


title: Capacity Analysis of IEEE 802.11ah WLANs for M2M Communications

abstract: Focusing on the increasing market of the sensors and actuators networks, the IEEE 802.11ah Task Group is currently working on the standardization of a new amendment. This new amendment will operate at the sub-1GHz band, ensure transmission ranges up to 1 Km, data rates above 100 kbps and very low power operation. With IEEE 802.11ah, the WLANs will offer a solution for applications such as smart metering, plan automation, eHealth or surveillance. Moreover, thanks to a hierarchical signalling, the IEEE 802.11ah will be able to manage a higher number of stations (STAs) and improve the 802.11 Power Saving Mechanisms. In order to support a high number of STAs, two different signalling modes are proposed, TIM and Non-TIM Offset. In this paper we present a theoretical model to predict the maximum number of STAs supported by both modes depending on the traffic load and the data rate used. Moreover, the IEEE 802.11ah performance and energy consumption for both signalling modes and for different traffic patterns and data rates is evaluated. Results show that both modes achieve similar Packet Delivery Ratio values but the energy consumed with the TIM Offset is, in average, 11.7% lower.


title: Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks

abstract: Pancreas segmentation in computed tomography imaging has been historically difficult for automated methods because of the large shape and size variations between patients. In this work, we describe a custom-build 3D fully convolutional network (FCN) that can process a 3D image including the whole pancreas and produce an automatic segmentation. We investigate two variations of the 3D FCN architecture; one with concatenation and one with summation skip connections to the decoder part of the network. We evaluate our methods on a dataset from a clinical trial with gastric cancer patients, including 147 contrast enhanced abdominal CT scans acquired in the portal venous phase. Using the summation architecture, we achieve an average Dice score of 89.7 ± 3.8 (range [79.8, 94.8])% in testing, achieving the new state-of-the-art performance in pancreas segmentation on this dataset.


title: Making machine learning robust against adversarial inputs

abstract: Such inputs distort how machine-learning-based systems are able to function in the world as it is.


title: Pushing the boundaries of parallel Deep Learning -- A practical approach

abstract: This work aims to assess the state of the art of data parallel deep neural network training, trying to identify potential research tracks to be exploited for performance improvement. Beside, it presents a design for a practical C++ library dedicated at implementing and unifying the current state of the art methodologies for parallel training in a performance-conscious framework, allowing the user to explore novel strategies without departing significantly from its usual work-flow.


title: The RIN: an RNA integrity number for assigning integrity values to RNA measurements

abstract: BackgroundThe integrity of RNA molecules is of paramount importance for experiments that try to reflect the snapshot of gene expression at the moment of RNA extraction. Until recently, there has been no reliable standard for estimating the integrity of RNA samples and the ratio of 28S:18S ribosomal RNA, the common measure for this purpose, has been shown to be inconsistent. The advent of microcapillary electrophoretic RNA separation provides the basis for an automated high-throughput approach, in order to estimate the integrity of RNA samples in an unambiguous way.MethodsA method is introduced that automatically selects features from signal measurements and constructs regression models based on a Bayesian learning technique. Feature spaces of different dimensionality are compared in the Bayesian framework, which allows selecting a final feature combination corresponding to models with high posterior probability.ResultsThis approach is applied to a large collection of electrophoretic RNA measurements recorded with an Agilent 2100 bioanalyzer to extract an algorithm that describes RNA integrity. The resulting algorithm is a user-independent, automated and reliable procedure for standardization of RNA quality control that allows the calculation of an RNA integrity number (RIN).ConclusionOur results show the importance of taking characteristics of several regions of the recorded electropherogram into account in order to get a robust and reliable prediction of RNA integrity, especially if compared to traditional methods.


title: Hierarchical Multi-Beam Search for Millimeter-Wave MIMO Systems

abstract: In millimeter-wave (mmWave) MIMO systems, while a hybrid digital/analog precoding structure offers the potential to increase the achievable rate, it also faces the challenge to lower channel estimation time due to a large number of antennas at both Tx/Rx sides. In this paper, the channel estimation is realized via multi-beam search, and a new hierarchical multi-beam search scheme, which uses a pre- designed analog hierarchical codebook, is proposed. Performance evaluations show that, compared with a state-of- the-art scheme, the proposed scheme not only achieves a higher success rate to acquired multiple beams under typical system settings, but also greatly reduces the channel estimation time.


title: Aggregated parallel coordinates: integrating hierarchical dimensions into parallel coordinates visualisations

abstract: Aggregated Parallel Coordinates (APC) are an extension of standard parallel coordinates, which supports the visualisation and exploration of hierarchies within numerical dimensions. Such datasets can occur when data is available at several granularities and these can be grouped or aggregated in some way (mean, sum, max) to form higher levels of abstraction. While existing parallel coordinates techniques can be used to visualise individual dimensions of such data, they have no provision for interactively expanding and collapsing such hierarchically aggregated dimensions. In race car engineering, specialised simulation software is used to derive a car setup for optimal performance during a race. Such simulations generate complex, high-dimensional datasets with a large number of records, many of which are inherently hierarchically grouped. The APC software was implemented in C♯ as a Microsoft WPF user control library. It has been integrated into AVL's data visualisation tool called SimBook and is being used to explore the output of race car simulations.


title: Twitter sentiment classification using machine learning techniques for stock markets

abstract: Sentiment classification of Twitter data has been successfully applied in finding predictions in a variety of domains. However, using sentiment classification to predict stock market variables is still challenging and ongoing research. The main objective of this study is to compare the overall accuracy of two machine learning techniques (logistic regression and neural network) with respect to providing a positive, negative and neutral sentiment for stock-related tweets. Both classifiers are compared using Bigram term frequency (TF) and Unigram term frequency - inverse document term frequency (TF-IDF) weighting schemes. Classifiers are trained using a dataset that contains 42,000 automatically annotated tweets. The training dataset forms positive, negative and neutral tweets covering four technology-related stocks (Twitter, Google, Facebook, and Tesla) collected using Twitter Search API. Classifiers give the same results in terms of overall accuracy (58%). However, empirical experiments show that using Unigram TF-IDF outperforms TF.


title: Why We Filter Our Photos and How It Impacts Engagement.

abstract: A variety of simple graphical filters are available to camera phone users to enhance their photos on the fly; these filters often stylize, saturate or age a photo. In this paper, we present a combination of large-scale data analysis and small scale in-depth interviews to understand filter-work. We look at producers’ practices of photo filtering and gain insights in the roles filters play in engaging photo consumers’ by driving their social interactions. We first interviewed 15 Flickr mobile app users (photo producers) to understand their use and perception of filters. Next, we analyzed how filters affect a photo’s engagement (consumers’ perspective) using a corpus of 7.6 million Flickr photos. We find two groups of serious and casual photographers among filter users. The serious see filters as correction tools and prefer milder effects. Casual photographers, by contrast, use filters to significantly transform their photos with bolder effects. We also find that filtered photos are 21% more likely to be viewed and 45% more likely to be commented on by consumers of photographs. Specifically, filters that increase warmth, exposure and contrast boost engagement the most. Towards the ongoing research in social engagement and photo-work, these findings suggest several practical implications such as designing filters for both serious and casual photographers or designing methods to prioritize and rank content in order to maximize engagement.


title: LTP: A Chinese Language Technology Platform

abstract: LTP (Language Technology Platform) is an integrated Chinese processing platform which includes a suite of high performance natural language processing (NLP) modules and relevant corpora. Especially for the syntactic and semantic parsing modules, we achieved good results in some relevant evaluations, such as CoNLL and SemEval. Based on XML internal data representation, users can easily use these modules and corpora by invoking DLL (Dynamic Link Library) or Web service APIs (Application Program Interface), and view the processing results directly by the visualization tool.


title: An observability-constrained sliding window filter for SLAM

abstract: A sliding window filter (SWF) is an appealing smoothing algorithm for nonlinear estimation problems such as simultaneous localization and mapping (SLAM), since it is resource-adaptive by controlling the size of the sliding window, and can better address the nonlinearity of the problem by relinearizing available measurements. However, due to the marginalization employed to discard old states from the sliding window, the standard SWF has different parameter observability properties from the optimal batch maximum-a-posterior (MAP) estimator. Specifically, the nullspace of the Fisher information matrix (or Hessian) has lower dimension than that of the batch MAP estimator. This implies that the standard SWF acquires spurious information, which can lead to inconsistency. To address this problem, we propose an observability-constrained (OC)-SWF where the linearization points are selected so as to ensure the correct dimension of the nullspace of the Hessian, as well as minimize the linearization errors. We present both Monte Carlo simulations and real-world experimental results which show that the OC-SWF's performance is superior to the standard SWF, in terms of both accuracy and consistency.


abstract: JMUI (Journal on Multimodal User Interfaces), Special issue “Best of affective computing and intelligent Guidelines for multimodal user interface design. support, human multi-modal information processing. characteristics to the design of a user-oriented and guidelines of multimodal interface design. Artifact lifecycle management, Consumer and user, Interfaces in Automated.Aug 2 Aug 7Los Angeles, CA, USAThursday, 6 August 2015 / HCI International 20152015.hci.international/thursdayCachedDefining and Optimizing User Interfaces Information Complexity for AI Design and Development of Multimodal Applications: A Vision on Key Issues and Traditional Heuristics and Industry Guidelines to Evaluate Multimodal Digital Artifacts


Option Pricing under Heston and 3 / 2 Stochastic Volatility Models : an Approximation to the Fast Fourier Transform


title: Extending the Stansfield Algorithm to Three Dimensions: Algorithms and Implementations

abstract: The Stansfield algorithm is a well-known geolocation method for two-dimensional (2-D), azimuth-only bearing measurements of a stationary emitter. It is known to suffer from estimation bias, and its performance in terms of optimality criteria is unknown. Strategies that accurately combine both azimuth and elevation measurements for 3-D position estimation are generally required in many applications. This paper introduces two novel bearings-only geolocation algorithms: the weighted Stansfield algorithm in three dimensions (WS3D) and the instrumental variable weighted Stansfield algorithm in three dimensions (IVWS3D). In these two procedures, bearing quality weighting and estimated range weighting are used to minimize the maximum-likelihood cost function in pairs of coupled least-squares procedures. Measurement weighting is shown to be important to obtain best performance. Moreover, the use of updated bearing measurements in the instrumental variable method in IVWS3D is shown to decrease estimation bias. Simulations of the new algorithms show that the WS3D algorithm asymptotically outperforms existing geolocation algorithms, whereas the IVWS3D algorithm provides the least-biased position estimation.


title: Perceptions of water use

abstract: In a national online survey, 1,020 participants reported their perceptions of water use for household activities. When asked for the most effective strategy they could implement to conserve water in their lives, or what other Americans could do, most participants mentioned curtailment (e.g., taking shorter showers, turning off the water while brushing teeth) rather than efficiency improvements (e.g., replacing toilets, retrofitting washers). This contrasts with expert recommendations. Additionally, some participants are more likely to list curtailment actions for themselves, but list efficiency actions for other Americans. For a sample of 17 activities, participants underestimated water use by a factor of 2 on average, with large underestimates for high water-use activities. An additional ranking task showed poor discrimination of low vs. high embodied water content in food products. High numeracy scores, older age, and male sex were associated with more accurate perceptions of water use. Overall, perception of water use is more accurate than the perception of energy consumption and savings previously reported. Well-designed efforts to improve public understanding of household water use could pay large dividends for behavioral adaptation to temporary or long-term decreases in availability of fresh water.


A Cloud Qur'an Application Using Drupal Technology


title: A Comprehensive Review of Stability Analysis of Continuous-Time Recurrent Neural Networks

abstract: Stability problems of continuous-time recurrent neural networks have been extensively studied, and many papers have been published in the literature. The purpose of this paper is to provide a comprehensive review of the research on stability of continuous-time recurrent neural networks, including Hopfield neural networks, Cohen-Grossberg neural networks, and related models. Since time delay is inevitable in practice, stability results of recurrent neural networks with different classes of time delays are reviewed in detail. For the case of delay-dependent stability, the results on how to deal with the constant/variable delay in recurrent neural networks are summarized. The relationship among stability results in different forms, such as algebraic inequality forms, \(M\) -matrix forms, linear matrix inequality forms, and Lyapunov diagonal stability forms, is discussed and compared. Some necessary and sufficient stability conditions for recurrent neural networks without time delays are also discussed. Concluding remarks and future directions of stability analysis of recurrent neural networks are given.


title: MEgo2Vec: Embedding Matched Ego Networks for User Alignment Across Social Networks

abstract: Aligning users across multiple heterogeneous social networks is a fundamental issue in many data mining applications. Methods that incorporate user attributes and network structure have received much attention. However, most of them suffer from error propagation or the noise from diverse neighbors in the network. To effectively model the influence from neighbors, we propose a graph neural network to directly represent the ego networks of two users to be aligned into an embedding, based on which we predict the alignment label. Three major mechanisms in the model are designed to unitedly represent different attributes, distinguish different neighbors and capture the structure information of the ego networks respectively. Systematically, we evaluate the proposed model on a number of academia and social networking datasets with collected alignment labels. Experimental results show that the proposed model achieves significantly better performance than the state-of-the-art comparison methods (+3.12-30.57% in terms of F1 score).


title: Light as a central modulator of circadian rhythms, sleep and affect

abstract: Light has profoundly influenced the evolution of life on earth. As widely appreciated, light enables us to generate images of our environment. However, light - through intrinsically photosensitive retinal ganglion cells (ipRGCs) - also influences behaviours that are essential for our health and quality of life but are independent of image formation. These include the synchronization of the circadian clock to the solar day, tracking of seasonal changes and the regulation of sleep. Irregular light environments lead to problems in circadian rhythms and sleep, which eventually cause mood and learning deficits. Recently, it was found that irregular light can also directly affect mood and learning without producing major disruptions in circadian rhythms and sleep. In this Review, we discuss the indirect and direct influence of light on mood and learning, and provide a model for how light, the circadian clock and sleep interact to influence mood and cognitive functions.


title: Food vs Non-Food Classification

abstract: Automatic understanding of food is an important research challenge. Food recognition engines can provide a valid aid for automatically monitoring the patient's diet and food-intake habits directly from images acquired using mobile or wearable cameras. One of the first challenges in the field is the discrimination between images containing food versus the others. Existing approaches for food vs non-food classification have used both shallow and deep representations, in combination with multi-class or one-class classification approaches. However, they have been generally evaluated using different methodologies and data, making a real comparison of the performances of existing methods unfeasible. In this paper, we consider the most recent classification approaches employed for food vs non-food classification, and compare them on a publicly available dataset. Different deep-learning based representations and classification methods are considered and evaluated.


title: Mental practice and verbal instructions execution: A cognitive robotics study

abstract: Understanding the tight relationship that exists between mental imagery and motor activities (i.e. how images in the mind can influence movements and motor skills) has become a topic of interest and is of particular importance in domains in which improving those skills is crucial for obtaining better performance, such as in sports and rehabilitation. In this paper, using an embodied cognition approach and a cognitive robotics platform, we introduce initial results of an ongoing study that explores the impact linguistic stimuli could have in processes of mental imagery practice and subsequent motor execution and performance. Results are presented to show that the robot used, is able to “imagine” or “mentally” recall and accurately execute movements learned in previous training phases, strictly on the basis of the verbal commands issued. Further tests show that data obtained with “imagination” could be used to simulate “mental training” processes such as those that have been employed with human subjects in sports training, in order to enhance precision in the performance of new tasks, through the association of different verbal commands.


title: SemEval 2014 Task 8: Broad-Coverage Semantic Dependency Parsing

abstract: Task 18 at SemEval 2015 defines Broad-Coverage Semantic Dependency Parsing (SDP) as the problem of recovering sentence-internal predicate–argument relationships for all content words, i.e. the sema ...


title: Passive Hierarchical Impedance Control Via Energy Tanks

abstract: Modern robotic systems with a large number of actuated degrees of freedom can be utilized to perform several tasks at the same time while following a given order of priority. The most frequently used method is to apply null space projections to realize such a strict hierarchy, where lower priority tasks are executed as long as they do not interfere with any higher priority objectives. However, introducing null space projectors inevitably destroys the beneficial and safety-relevant feature of passivity. Here, two controllers are proposed to restore the passivity: one with local energy tanks on each hierarchy level and one with a global tank for the entire system. The formal proofs of passivity show that no energy is generated by these controllers. Once the tanks are empty, passivity is still guaranteed at the cost of some control performance. Simulations and experiments on a torque-controlled robot validate the approaches and predestine them for the usage in safety-relevant applications.


English Multiword Expression-aware Dependency Parsing Including Named Entities


title: Pado: A Data Processing Engine for Harnessing Transient Resources in Datacenters

abstract: Datacenters are under-utilized, primarily due to unused resources on over-provisioned nodes of latency-critical jobs. Such idle resources can be used to run batch data analytic jobs to increase datacenter utilization, but these transient resources must be evicted whenever latency-critical jobs require them again. Resource evictions often lead to cascading recomputations, which is usually handled by checkpointing intermediate results on stable storages of eviction-free reserved resources. However, checkpointing has major shortcomings in its substantial overhead of transferring data back and forth. In this work, we step away from such approaches and focus on observing the job structure and the relationships between computations of the job. We carefully mark the computations that are most likely to cause a large number of recomputations upon evictions, to run them reliably using reserved resources. This lets us retain corresponding intermediate results effortlessly without any additional checkpointing. We design Pado, a general data processing engine, which carries out our idea with several optimizations that minimize the number of additional reserved nodes. Evaluation results show that Pado outperforms Spark 2.0.0 by up to 5.1×, and checkpoint-enabled Spark by up to 3.8×.


Domain-specific Question Generation from a Knowledge Base.


title: Glyphmaker: creating customized visualizations of complex data

abstract: Glyphmaker allows nonexpert users to customize their own graphical representations using a simple glyph editor and a point-and-click binding mechanism. In particular, users can create and then alter bindings to visual representations, bring in new data or glyphs with associated bindings, change ranges for bound data, and do these operations interactively. They can also focus on data down to any level of detail, including individual elements, and then isolate or highlight the focused region. These features empower users, letting them employ their specialized domain knowledge to create customized visual representations for further exploration and analysis. For ease of design and use, we built Glyphmaker on top of Iris Explorer, the Silicon Graphics Inc. (SGI) dataflow visualization system. The current version of Glyphmaker has been successfully tested on a materials system simulation. We are planning a series of tests and evaluations by scientists and engineers using real data. >


title: A review of wireless and PLC propagation channel characteristics for smart grid environments

abstract: Wireless, power line communication (PLC), fiber optic, Ethernet, and so forth are among the communication technologies on which smart grid communication infrastructure is envisioned to be built. Among these, wireless and PLC-based solutions are attractive considering the cost of initial deployment. Wireless communication deployment in smart grid covers a variety of environments such as indoor, outdoor, and electric-power-system facilities. Similar diversity is expected in PLC deployment as well covering low voltage (LV), medium voltage (MV), and high voltage (HV) segments of the grid. In spite of being attractive, wireless and PLC channels are very harsh posing great challenges to performance of communication systems. In proposing solutions to smart grid communication needs, two approaches are likely to be followed. One is based on the use of existing wireless and PLC technologies with some modifications, and the other relies upon developing novel communication protocols particularly addressing the smart grid needs. Both of these approaches require an in-depth knowledge of communication channel characteristics. The aim of this study is to reveal the wireless and PLC channel characteristics of smart grid environments in terms of several parameters such as path loss and attenuation, time dispersion, time selectivity, amplitude statistics, and noise characteristics.


title: Bayesian stackelberg games and their application for security at Los Angeles international airport

abstract: Many multiagent settings are appropriately modeled as Stackelberg games [Fudenberg and Tirole 1991; Paruchuri et al. 2007], where a leader commits to a strategy first, and then a follower selfishly optimizes its own reward, considering the strategy chosen by the leader. Stackelberg games are commonly used to model attacker-defender scenarios in security domains [Brown et al. 2006] as well as in patrolling [Paruchuri et al. 2007; Paruchuri et al. 2008]. For example, security personnel patrolling an infrastructure commit to a patrolling strategy first, before their adversaries act taking this committed strategy into account. Indeed, Stackelberg games are being used at the Los Angeles International Airport to schedule security checkpoints and canine patrols [Murr 2007; Paruchuri et al. 2008; Pita et al. 2008a]. They could potentially be used in network routing, pricing in transportation systems and many other situations [Korilis et al. 1997; Cardinal et al. 2005]. Although the follower in a Stackelberg game is allowed to observe the leader’s strategy before choosing its own strategy, there is often an advantage for the leader over the case where both players must choose their moves simultaneously. To see the advantage of being the leader in a Stackelberg game, consider the game with the payoff as shown in Table I. The leader is the row player and the follower is the column player. The only pure-strategy Nash equilibrium for this game is when the leader plays a and the follower plays c which gives the leader a payoff of 2. However, if the leader commits to a mixed strategy of playing a and b with equal (0.5) probability, then the follower will play d, leading to an expected payoff for the leader of 3.5.


title: A Fast Hierarchical Clustering Algorithm for Functional Modules Discovery in Protein Interaction Networks

abstract: As advances in the technologies of predicting protein interactions, huge data sets portrayed as networks have been available. Identification of functional modules from such networks is crucial for understanding principles of cellular organization and functions. However, protein interaction data produced by high-throughput experiments are generally associated with high false positives, which makes it difficult to identify functional modules accurately. In this paper, we propose a fast hierarchical clustering algorithm HC-PIN based on the local metric of edge clustering value which can be used both in the unweighted network and in the weighted network. The proposed algorithm HC-PIN is applied to the yeast protein interaction network, and the identified modules are validated by all the three types of Gene Ontology (GO) Terms: Biological Process, Molecular Function, and Cellular Component. The experimental results show that HC-PIN is not only robust to false positives, but also can discover the functional modules with low density. The identified modules are statistically significant in terms of three types of GO annotations. Moreover, HC-PIN can uncover the hierarchical organization of functional modules with the variation of its parameter's value, which is approximatively corresponding to the hierarchical structure of GO annotations. Compared to other previous competing algorithms, our algorithm HC-PIN is faster and more accurate.


title: Mechanical design of humanoid robot platform KHR-3 (KAIST Humanoid Robot 3: HUBO)

abstract: KHR-I has been developed on the purpose of research about biped walking. It has 21 DOF without hands and head, which has 12 DOF in legs, 1 DOF in torso, and 8 DOF in arms. The objective of KHR-2 (41 DOF) was to develop the humanoid which can walk on the living-floor with human-like appearance and movement. KHR-3 has the purpose that it has more human-like features, movements and human-friendly character. Mechanical design of KHR-3 is presented on this paper. The design concept, lower body design, upper body design and actuator selection of joints are included in this paper. We have developed and published KHR-I and 2 in last three years. KHR3 platform is based on KHR-2. It has 41 degree of freedom (DOF), 125 cm height, and 55 kg weight. The differences from KHR-2 are mechanical stiffness and detailed design of the frame in the mechanical point of view. Stiffness of the frame is increased and detailed design about joints and link frame has been modified or redesigned. We introduced exterior art design concept on KHR-2 in beginning, and the concept has been implemented on KHR-3 in mechanical design stage


title: Medical Cyber-Physical Systems Development: A Forensics-Driven Approach

abstract: The synthesis of technology and the medical industry has partly contributed to the increasing interest in Medical Cyber-Physical Systems (MCPS). While these systems provide benefits to patients and professionals, they also introduce new attack vectors for malicious actors (e.g. financially-and/or criminally-motivated actors). A successful breach involving a MCPS can impact patient data and system availability. The complexity and operating requirements of a MCPS complicates digital investigations. Coupling this information with the potentially vast amounts of information that a MCPS produces and/or has access to is generating discussions on, not only, how to compromise these systems but, more importantly, how to investigate these systems. The paper proposes the integration of forensics principles and concepts into the design and development of a MCPS to strengthen an organization's investigative posture. The framework sets the foundation for future research in the refinement of specific solutions for MCPS investigations.


A Comparison of SIFT, PCA-SIFT and SURF


title: CHARACTER RECOGNITION IN NATURAL IMAGES

abstract: This paper tackles the problem of recognizing characters in images of natural scenes. In particular, we focus on recognizing characters in situations that would traditionally not be handled well by OCR techniques. We present an annotated database of images containing English and Kannada characters. The database comprises of images of street scenes taken in Bangalore, India using a standard camera. The problem is addressed in an object cateogorization framework based on a bag-of-visual-words representation. We assess the performance of various features based on nearest neighbour and SVM classification. It is demonstrated that the performance of the proposed method, using as few as 15 training images, can be far superior to that of commercial OCR systems. Furthermore, the method can benefit from synthetically generated training data obviating the need for expensive data collection and annotation.


title: A Planar Dual Band GPS and DSRC Antenna for Road Vehicles

abstract: The results of a prototype novel planar antenna providing dual-band performance for road-vehicle applications are reported. Multilayer stacked patches provide simultaneous RHCP polarization for GPS and vertically polarized low elevation coverage for 5.9 GHz DSRC operations. The results show good correlation between measurement and theory.


Captcha and Its Techniques : A Review


title: Video fire detection based on Gaussian Mixture Model and multi-color features

abstract: This paper proposes a new approach to detect fire from a video stream. It takes full advantage of the motion feature and color information of fire. Firstly, motion detection using Gaussian Mixture Model-based background subtraction is applied to extract moving objects from a video stream. Then, multi-color-based detection combining the RGB, HSI and YUV color space is employed to obtain possible fire regions. Finally, the results of the above two steps are combined to identify the accurate fire areas. The experimental results obtained by applying this method on different fire videos show that the proposed method can achieve better effectiveness, adaptability and robustness.


title: Junction Temperature Measurement of IGBTs Using Short-Circuit Current as a Temperature-Sensitive Electrical Parameter for Converter Prototype Evaluation

abstract: This paper proposes a method to measure the junction temperatures of insulated-gate bipolar transistors (IGBTs) during the converter operation for prototype evaluation. The IGBT short-circuit current is employed as the temperature-sensitive electrical parameter (TSEP). The calibration experiments show that the short-circuit current has an adequate temperature sensitivity of 0.35 ${\rm A}/^{\circ}\hbox{C} $ . The parameter also has good selectivity and linearity, which makes it suitable to be used as a TSEP. Test circuit and hardware design are proposed for the IGBT junction temperature measurement in various power electronics dc–dc and ac–dc converter applications. By connecting a temperature measurement unit to the converter and giving a short-circuit pulse during the converter operation, the short-circuit current is measured, and the IGBT junction temperature can be derived from the calibration curve. The proposed temperature measurement method is a valuable tool for prototype evaluation and avoids the unnecessary safety margin regarding device operating temperatures, which is significant particularly for high-temperature/high-density converter applications.


title: Influence of the Amount of Permanent-Magnet Material in Fractional-Slot Permanent-Magnet Synchronous Machines

abstract: The efficiency of permanent-magnet (PM) synchronous machines with outer rotor and concentrated windings is investigated as a function of the mass of magnets used, keeping the power, volume, and mechanical air-gap thickness constant. In order to be useful for electric vehicle motors and wind turbine generators, the efficiency is computed in wide speed and torque ranges, including overload. For a given type and amount of magnets, the geometry of the machine and the efficiency map are computed by analytical models and finite-element models, taken into account the iron loss, copper loss, magnet loss, and pulsewidth-modulation loss. The models are validated by experiments. Furthermore, the demagnetization risk and torque ripple are studied as functions of the mass of magnets in the machine. The effect of the mass of magnets is investigated for several soft magnetic materials, for several combinations of number of poles and number of stator slots, and for both rare earth (NdFeB) magnets and ferrite magnets. It is observed that the amount of PM material can vary in a wide range with a minor influence on the efficiency, torque density, and torque ripple and with a limited demagnetization risk.


title: Requirements for Internet Hosts -- Communication Layers

abstract: This RFC is an official specification for the Internet community. It ::: incorporates by reference, amends, corrects, and supplements the ::: primary protocol standards documents relating to hosts. [STANDARDS- ::: TRACK]


title: Auditing and Assessment of Data Traffic Flows in an IoT Architecture

abstract: Recent advances in the development of the Internet of Things and ICT have completely changed the way citizens interact with Smart City environments increasing the demand of more services and infrastructures in many different contexts. Furthermore, citizens require to be active users in a flexible smart living lab, with the possibility to access Smart City data, analyze them, perform actions and receive notifications based on automated decision-making processes. Critical problems could arise if the continuity of data flows and communication among connected IoT devices and data-driven applications is interrupted or lost, due to some devices or system malfunction or unexpected behavior. The proposed solution is a set of instruments, aimed at real-time collecting and storing IoT and Smart City data (data shadow), as well as auditing data traffic flows in an IoT Smart City Architecture, with the purpose of quantitatively monitoring the status and detecting potential anomalies and malfunctions at level of single IoT device and/or service. These instruments are the DevDash and AMMA tools, designed and realized within the Snap4City framework. Specific use cases have been provided to highlight the capabilities of these instruments in terms of data indexing, monitoring and analysis.


title: Video games as a tool to train visual skills

abstract: Purpose ::: Adult brain plasticity, although possible, is often difficult to elicit. Training regimens in adults can produce specific improvements on the trained task without leading to general enhancements that would improve quality of life. This paper considers the case of playing action video games as a way to induce widespread enhancement in vision.


title: A PERSONALIZED WEB SEARCH BASED ON USER PROFILE AND USER CLICKS

abstract: Generally web search engines are built to serve all users, independent of the individual needs of any user. Personalization of web search is to carry out retrieval for each user incorporating their interest. This has become an important factor in daily usage as it improves the retrieval effectiveness on topics that the user would look for. There are several studies that have been done in this field. This paper proposes a new idea on personalized web search which is based on user profile and user clicks. Based on the rapid increase in Internet usage, users are more dependent on the web search engine for various information needs. The query submitted by the user has the features of shortness, ambiguousness and incompleteness(10). As the amount of information on the Web increases rapidly, it creates newer challenges for Web search. When similar queries are submitted by multiple users, a general search engine could return the same result, In the current scenario, where users expect systems to be intelligent and support their information search, the general search engine is no longer workable. Systems have to be made sensitive to user likes by understanding their search patterns over a period of time. This will enable users to obtain more relevant results to searches on similar queries posted in the internet search engines. Personalization of web search has been area of research in the recent times several efficient approaches have been derived based on related studies and observations.Of the several methods that have been proposed and imple-mented, this paper focuses on two methods -User Clicks and User Profile. This paper proposes an novel idea of combining two crucial methods - user profile and user clicks to enhance the web based search and increase the level of personalization.


title: Changes in lens opacities on the age-related eye disease study grading scale predict progression to cataract surgery and vision loss: age-related eye disease study report no. 34.

abstract: PURPOSE ::: To investigate whether the 2-year change in lens opacity severity on the Age-Related Eye Disease Study (AREDS) lens grading scale predicts progression to cataract surgery or loss of visual acuity by 5 years. ::: ::: ::: DESIGN ::: Prospective cohort study within a randomized clinical trial of oral supplements. ::: ::: ::: PARTICIPANTS ::: The AREDS participants whose eyes were phakic at baseline and free of late age-related macular degeneration throughout the study. ::: ::: ::: METHODS ::: Baseline and annual lens photographs of AREDS participants (n = 3466/4757; 73%) were graded for severity of cataracts using the AREDS system for classifying cataracts from photographs. Clinical examinations conducted semiannually collected data on cataract surgery and visual acuity. Association of the change in lens opacities at 2 years with these outcomes at 5 years was analyzed with adjusted Cox proportional hazard models. ::: ::: ::: MAIN OUTCOME MEASUREMENTS ::: Progression of lens opacities on stereoscopic lens photographs at 2 years, cataract surgery, and visual acuity loss of 2 lines or more at 5 years. ::: ::: ::: RESULTS ::: The adjusted hazard ratios (HRs) for association of progression to cataract surgery at 5 years were: nuclear cataract increase of 1.0 unit or more compared with less than 1.0-unit change at 2 years, 2.77 (95% confidence interval [CI], 2.07-3.70; P < 0.001); cortical cataract increase of 5% or more in lens opacity in the central 5 mm of the lens compared with less than 5% increase at 2 years, 1.91 (95% CI, 1.27-2.87; P = 0.002); and posterior subcapsular cataract increase of 5% or more versus less than 5% in the central 5 mm of the lens, 8.25 (95% CI, 5.55-12.29; P < 0.001). Similarly, HRs of vision loss of 2 lines or more at 5 years for this degree of lens changes at 2 years were the following: nuclear, 1.83 (95% CI, 1.49-2.25; P < 0.001); cortical, 1.13 (95% CI, 0.78-1.65; P = 0.519); and posterior subcapsular cataract, 3.05 (95% CI, 1.79-5.19; P < 0.001). ::: ::: ::: CONCLUSIONS ::: Two-year changes in severity of lens opacities on the AREDS lens grading scale are predictive of long-term clinically relevant outcomes, making them potential surrogate end points in follow-up studies.


title: Hematocolpos causing severe urinary retention in an adolescent girl with imperforate hymen: an uncommon presentation

abstract: INTRODUCTION ::: Hematocolpos is rarely presented as a pelvic mass which mechanically compresses the bladder and the urethra thereby causing urinary retention. ::: ::: ::: CASE REPORT ::: A 12-year-old girl referred with the history of lower abdominal pain and retention of urine for 24 h. The patient had not started her menses yet. Three weeks before she also complained of discomfort on passing urine, frequency and urgency and was taken to a local outpatient clinic where she was given antibiotics with the diagnosis of urinary tract infection, she had also the history of intermittent urinary catheterization (three times before) in an emergency department because of acute severe urinary retention. Transabdominal ultrasonography revealed a pelvic semi-solid mass suggestive of hematocolpos. Pelvic examination revealed a pale blue imperforate hymen bulging from the vaginal introitus outwards. A cruciate incision was made over the hymen. Postoperative period was uneventful. ::: ::: ::: CONCLUSION ::: In case of acute severe urinary retention in an adolescent girl, the clinicians should keep in mind that imperforate hymen may be a causative factor and this condition may easily be treated surgically.


title: Interference management in LTE femtocell systems using Fractional Frequency Reuse

abstract: Recently, Long Term Evolution (LTE) has developed a femtocell for indoor coverage extension. However, interference problem between the femtocell and the macrocell should be solved in advance. In this paper, we propose an interference management scheme in the LTE femtocell systems using Fractional Frequency Reuse (FFR). Under the macrocell allocating frequency band by the FFR, the femtocell chooses sub-bands which are not used in the macrocell sub-area to avoid interference. Simulation results show that proposed scheme enhances total/edge throughputs and reduces the outage probability in overall network, especially for the cell edge users.


title: Reflections on 25 Years of Ethnography in CSCW

abstract: In this article we focus attention on ethnography's place in CSCW by reflecting on how ethnography in the context of CSCW has contributed to our understanding of the sociality and materiality of work and by exploring how the notion of the `field site' as a construct in ethnography provides new ways of conceptualizing `work' that extends beyond the workplace. We argue that the well known challenges of drawing design implications from ethnographic research have led to useful strategies for tightly coupling ethnography and design. We also offer some thoughts on recent controversies over what constitutes useful and proper ethnographic research in the context of CSCW. Finally, we argue that as the temporal and spatial horizons of inquiry have expanded, along with new domains of collaborative activity, ethnography continues to provide invaluable perspectives.


title: MBZip: Multiblock Data Compression

abstract: Compression techniques at the last-level cache and the DRAM play an important role in improving system performance by increasing their effective capacities. A compressed block in DRAM also reduces the transfer time over the memory bus to the caches, reducing the latency of a LLC cache miss. Usually, compression is achieved by exploiting data patterns present within a block. But applications can exhibit data locality that spread across multiple consecutive data blocks. We observe that there is significant opportunity available for compressing multiple consecutive data blocks into one single block, both at the LLC and DRAM. Our studies using 21 SPEC CPU applications show that, at the LLC, around 25% (on average) of the cache blocks can be compressed into one single cache block when grouped together in groups of 2 to 8 blocks. In DRAM, more than 30% of the columns residing in a single DRAM page can be compressed into one DRAM column, when grouped together in groups of 2 to 6. Motivated by these observations, we propose a mechanism, namely, MBZip, that compresses multiple data blocks into one single block (called a zipped block), both at the LLC and DRAM. At the cache, MBZip includes a simple tag structure to index into these zipped cache blocks and the indexing does not incur any redirectional delay. At the DRAM, MBZip does not need any changes to the address computation logic and works seamlessly with the conventional/existing logic. MBZip is a synergistic mechanism that coordinates these zipped blocks at the LLC and DRAM. Further, we also explore silent writes at the DRAM and show that certain writes need not access the memory when blocks are zipped. MBZip improves the system performance by 21.9%, with a maximum of 90.3% on a 4-core system.


title: Acupuncture modulates the neuro-endocrine-immune network

abstract: As a nonspecific physical stimulation, the effect of acupuncture on diseases is produced by motivating the inherent regulatory system in the body, having the characteristics of whole regulation, dual directional regulation, etc. Modern scientific researches show that body’s inherent regulatory system is neuro–endocrine–immune (NEI) network. Hence, we speculate that the regulatory effect of acupuncture may be produced through its regulation of NEI network. In this article, we reviewed the recent researches about acupuncture’s effect on the NEI network, to find out the evidence of acupuncture adjusting NEI network and provide some evidences for revealing the mechanism of acupuncture.


title: High-Efficiency GaN Doherty Power Amplifier for 100-MHz LTE-Advanced Application Based on Modified Load Modulation Network

abstract: This paper presents a high-efficiency GaN Doherty power amplifier (PA) with 100-MHz instantaneous bandwidth for 3.5-GHz long-term-evolution (LTE)-advanced application. A modified load modulation network, employing an enlarged peaking amplifier to carrier amplifier power ratio and moderately increased load impedance of the carrier amplifier, is proposed for enhancing efficiency and achieving improved load modulation. To increase the power ratio and alleviate the influence of slight impedance mismatch, a proposed load impedance strategy and corresponding stepped-impedance matching network are adopted for high-efficiency and wideband operation. By tuning the carrier offset line, the inconsistency of efficiency, gain, and output power in the operation band can be alleviated. Measurement results show that the Doherty PA has a drain efficiency of approximately 40% with gain fluctuation less than 0.5 dB at 9-dB back-off power, and maximum efficiency of about 60% at saturation in the signal band of 3.4-3.5 GHz. By using the digital pre-distortion (DPD) technique, the Doherty PA achieves adjacent channel leakage ratio of about -48 dBc at an average output power of 40.4 dBm with efficiency of 42.5%, when driven by 100-MHz LTE-advanced signal. To the best of the authors' knowledge, this is the first high-performance result of linearization using conventional DPD technique with 100-MHz bandwidth signals for the GaN Doherty PA at 3.5-GHz frequency band thus far.


Anomaly detection in banking operations


title: End-Task Oriented Textual Entailment via Deep Explorations of Inter-Sentence Interactions

abstract: This work deals with SciTail, a natural entailment challenge derived from a multi-choice question answering problem. The premises and hypotheses in SciTail were generated with no awareness of each other, and did not specifically aim at the entailment task. This makes it more challenging than other entailment data sets and more directly useful to the end-task -- question answering. We propose DEISTE (deep explorations of inter-sentence interactions for textual entailment) for this entailment task. Given word-to-word interactions between the premise-hypothesis pair ($P$, $H$), DEISTE consists of: (i) a parameter-dynamic convolution to make important words in $P$ and $H$ play a dominant role in learnt representations; and (ii) a position-aware attentive convolution to encode the representation and position information of the aligned word pairs. Experiments show that DEISTE gets $\approx$5\% improvement over prior state of the art and that the pretrained DEISTE on SciTail generalizes well on RTE-5.


title: Faceshop: deep sketch-based face image editing

abstract: We present a novel system for sketch-based face image editing, enabling users to edit images intuitively by sketching a few strokes on a region of interest. Our interface features tools to express a desired image manipulation by providing both geometry and color constraints as user-drawn strokes. As an alternative to the direct user input, our proposed system naturally supports a copy-paste mode, which allows users to edit a given image region by using parts of another exemplar image without the need of hand-drawn sketching at all. The proposed interface runs in real-time and facilitates an interactive and iterative workflow to quickly express the intended edits. Our system is based on a novel sketch domain and a convolutional neural network trained end-to-end to automatically learn to render image regions corresponding to the input strokes. To achieve high quality and semantically consistent results we train our neural network on two simultaneous tasks, namely image completion and image translation. To the best of our knowledge, we are the first to combine these two tasks in a unified framework for interactive image editing. Our results show that the proposed sketch domain, network architecture, and training procedure generalize well to real user input and enable high quality synthesis results without additional post-processing.


title: Greening Internet of Things for Smart Everythings with A Green-Environment Life: A Survey and Future Prospects

abstract: Tremendous technology development in the field of Internet of Things (IoT) has changed the way we work and live. Although the numerous advantages of IoT are enriching our society, it should be reminded that the IoT also consumes energy, embraces toxic pollution and E-waste. These place new stress on the environments and smart world. In order to increase the benefits and reduce the harm of IoT, there is an increasing desire to move toward green IoT. Green IoT is seen as the future of IoT that is environmentally friendly. To achieve that, it is necessary to put a lot of measures to reduce carbon footprint, conserve fewer resources, and promote efficient techniques for energy usage. It is the reason for moving towards green IoT, where the machines, communications, sensors, clouds, and internet are alongside energy efficiency and reducing carbon emission. This paper presents a thorough survey of the current on-going research work and potential technologies of green IoT with an intention to provide some clues for future green IoT research.


title: Commonsense mining as knowledge base completion? A study on the impact of novelty

abstract: Commonsense knowledge bases such as ConceptNet represent knowledge in the form of relational triples. Inspired by the recent work by Li et al., we analyse if knowledge base completion models can be used to mine commonsense knowledge from raw text. We propose novelty of predicted triples with respect to the training set as an important factor in interpreting results. We critically analyse the difficulty of mining novel commonsense knowledge, and show that a simple baseline method outperforms the previous state of the art on predicting more novel.


title: Intrinsic Decompositions for Image Editing

abstract: Intrinsic images are a mid-level representation of an image that decompose the image into reflectance and illumination layers. The reflectance layer captures the color/texture of surfaces in the scene, while the illumination layer captures shading effects caused by interactions between scene illumination and surface geometry. Intrinsic images have a long history in computer vision and recently in computer graphics, and have been shown to be a useful representation for tasks ranging from scene understanding and reconstruction to image editing. In this report, we review and evaluate past work on this problem. Specifically, we discuss each work in terms of the priors they impose on the intrinsic image problem. We introduce a new synthetic ground-truth dataset that we use to evaluate the validity of these priors and the performance of the methods. Finally, we evaluate the performance of the different methods in the context of image-editing applications.


title: Word Embeddings Go to Italy: A Comparison of Models and Training Datasets

abstract: In this paper we present some preliminary results on the generation of word embeddings for the Italian language. We compare two popular word representation models, word2vec and GloVe, and train them on two datasets with different stylistic properties. We test the generated word embeddings on a word analogy test derived from the one originally proposed for word2vec, adapted to capture some of the linguistic aspects that are specific of Italian. Results show that the tested models are able to create syntactically and semantically meaningful word embeddings despite the higher morphological complexity of Italian with respect to English. Moreover, we have found that the stylistic properties of the training dataset plays a relevant role in the type of information captured by the produced vectors.


title: PTLsim: A Cycle Accurate Full System x86-64 Microarchitectural Simulator

abstract: In this paper, we introduce PTLsim, a cycle accurate full system x86-64 microprocessor simulator and virtual machine. PTLsim models a modern superscalar out of order x86-64 processor core at a configurable level of detail ranging from RTL-level models of all key pipeline structures, caches and devices up to full-speed native execution on the host CPU. Unlike other microarchitectural simulators, PTLsim targets the real commercially available x86 ISA, rather than a discontinued architecture with limited tools and an uncertain future. PTLsim supports several flavors: a single threaded userspace version and a full system version providing an SMT model and the infrastructure for multi-core support. We first describe what it takes to perform cycle accurate modeling of a complete x86 machine at the muop (micro-operation) level, along with the challenges and requirements for effective full system multi-processor capable simulation. We then describe the internal architecture of full system PTLsim and how it interacts with the Xen hypervisor and PTLsim's native mode co-simulation technology. We experimentally evaluate PTLsim's real world accuracy by configuring it like an AMD Athlon 64 machine before running a demanding full system client-server networked benchmark inside PTLsim. We compare the statistics generated by our model with the actual numbers from the real processor to demonstrate PTLsim is accurate to within 5% across all major parameters. We provide a discussion of prior simulation tools, along with their strengths and weaknesses. We describe why PTLsim's x86 focus is highly relevant, and we use our full system simulation results to demonstrate the pitfalls of userspace only simulation. Finally, we conclude by detailing future work


title: Dataflow design for optimal incremental SVM training

abstract: This paper proposes a new parallel architecture for incremental training of a Support Vector Machine (SVM), which produces an optimal solution based on manipulating the Karush-Kuhn-Tucker (KKT) conditions. Compared to batch training methods, our approach avoids re-training from scratch when training dataset changes. The proposed architecture is the first to adopt an efficient dataflow organisation. The main novelty is a parametric description of the parallel dataflow architecture, which deploys customisable arithmetic units for dense linear algebraic operations involved in updating the KKT conditions. The proposed architecture targets on-line SVM training applications. Experimental evaluation with real world financial data shows that our architecture implemented on Stratix-V FPGA achieved significant speedup against LIBSVM on Core i7-4770 CPU.