title: Securing FPGA-based obsolete component replacement for legacy systems

abstract: Component-aging is unavoidable in legacy systems. Although re-designing the system typically results in a high cost, the need to replace aged components for legacy systems is an urgent priority. Unfortunately, the aged components are likely to be obsolete and not available on the current market. Obsolete component replacement with field-programmable gate array (FPGA) devices is emerging as a feasible option to extend the lifetime of legacy systems. While replacing the aged component, we traditionally only focus on matching the functionality and neglect the potential security threats from FPGA replacement. However, recent literature demonstrates that FPGA devices may contain hardware Trojans, which are induced during FPGA device fabrication or bitstream generation time. To prevent the Trojans on FPGA from receiving external inputs or leaking sensitive information, we propose a Runtime Pin Grounding (RPG) scheme to ground the unused pins and check the pin status at every clock cycle. Furthermore, we exploit the principle of moving target defense (MTD) and propose a hardware MTD (HMTD) method. In our method, the aged obsolete unit is replicated to multiple copies in the FPGA device, and two of the replicas are randomly selected for output comparison and thus Trojan detection. We successfully implemented the proposed RPG and HMTD methods on a Nexys-3 FPGA board. Our case study shows that the proposed RPG scheme increases the FPGA utilization rate by less than 0.1%. On average, our HMTD method reduces the hardware Trojan bypass rate by 61% over the existing method.


title: A Robot of My Own: Participatory Design of Socially Assistive Robots for Independently Living Older Adults Diagnosed with Depression

abstract: This paper presents an ongoing project using participatory design methods to develop design concepts for socially assistive robots SARs with older adults diagnosed with depression and co-occurring physical illness. We frame SARs development in the context of preventive patient-centered healthcare, which empowers patients as the primary drivers of health and aims to delay the onset of disease rather than focusing on treatment. After describing how SARs can be of benefit in this form of healthcare, we detail our participatory design study with older adults and therapists aimed at developing preventive SARs applications for this population. We found therapists and older adults to be willing and able to participate in assistive robot design, though hands-on participation was a challenge. Our findings suggest that important areas of concern for older adults with depression are social interaction and companionship, as well as technologies that are easy to use and require minimal intervention.


title: Mixed Graphical Models via Exponential Families

abstract: Markov Random Fields, or undirected graphical models are widely used to model highdimensional multivariate data. Classical instances of these models, such as Gaussian Graphical and Ising Models, as well as recent extensions (Yang et al., 2012) to graphical models specified by univariate exponential families, assume all variables arise from the same distribution. Complex data from high-throughput genomics and social networking for example, often contain discrete, count, and continuous variables measured on the same set of samples. To model such heterogeneous data, we develop a novel class of mixed graphical models by specifying that each node-conditional distribution is a member of a possibly different univariate exponential family. We study several instances of our model, and propose scalable M -estimators for recovering the underlying network structure. Simulations as well as an application to learning mixed genomic networks from next generation sequencing and mutation data demonstrate the versatility of our methods.


title: Framework for automated reconstruction of 3D model from multiple 2D aerial images

abstract: The paper considers a problem of 3D environment model reconstruction from a set of 2D images acquired by the Unmanned Aerial Vehicle (UAV) in near real-time. The designed framework combines the FAST (Features from Accelerated Segment Test) algorithm and optical flow approach for detection of interest image points and adjacent images reconstruction. The robust estimation of camera locations is performed using the image points tracking. The coordinates of 3D points and the projection matrix are computed simultaneously using Structure-from-Motion (SfM) algorithm, from which the 3D model of environment is generated. The designed framework is tested using real image data and video sequences captured with camera mounted on the UAV. The effectiveness and quality of the proposed framework are verified through analyses of accuracy of the 3D model reconstruction and its time execution.


title: Image similarity using Deep CNN and Curriculum Learning

abstract: Image similarity involves fetching similar looking images given a reference image. Our solution called SimNet, is a deep siamese network which is trained on pairs of positive and negative images using a novel online pair mining strategy inspired by Curriculum learning. We also created a multi-scale CNN, where the final image embedding is a joint representation of top as well as lower layer embedding's. We go on to show that this multi-scale siamese network is better at capturing fine grained image similarities than traditional CNN's.


title: Comprehensive DC Power Balance Management in High-Power Three-Level DC–DC Converter for Electric Vehicle Fast Charging

abstract: With the increasing popularity of electric vehicles, there is an urgent demand to shorten the charging time, so the development of high-power charging stations with fast chargers is necessary to alleviate range anxiety for drivers. The charging station based on the neutral-point-clamped (NPC) converter can bring many merits, but it has unbalanced power problems in the bipolar dc bus. To solve this issue, comprehensive dc power balance management (PBM) in conjunction with high-power three-level dc–dc converter based fast charger is proposed in this paper. The active dc power balance management (APBM) is proposed to assist the central NPC converter in balancing power so that the additional balancing circuit is eliminated; while the passive dc power balance management (PPBM) is proposed to eliminate the fluctuating neutral-point currents and to ensure the balanced operation of fast chargers. The principles of APBM and PPBM are researched, the efficient integration between them is studied, and the overall control scheme for the fast charger is proposed. The power balance limits of APBM are explored, while the circulating currents of PPBM are analyzed. Simulation and experimental results are presented to verify the effectiveness of the proposed fast charger with PBM functions.


title: A comparative study of winding configuration effect on the performance of low speed PMSG using FEM

abstract: This paper presents a comparative study of the effect of three different winding configurations on the performance of permanent magnet synchronous generators (PMSG). Three generator models with the same outer radius and stack length, and different winding configurations are simulated using Finite element method (FEM) software; the configurations are the long pitched, short the pitched distributed winding and the concentrated winding. The performance of the models is compared. The results show a better performance of concentrated winding over the traditional distributed windings in terms of efficiency and power to weight ratio.


title: Massive MIMO architecture for 5G networks: Co-located, or distributed?

abstract: Massive MIMO has been identified as one of the promising disruptive air interface technologies to address the massive capacity requirement demanded by 5G wireless communications. Most of the existing works showed different benefits of massive MIMO system but for the co-located deployment scenario. In this paper, we are interested to find out extra benefits that may be brought by distributing the massive number of antennas to different levels. Specifically, we compare the performance of co-located and distributed deployment scenarios in the framework of area spectrum efficiency (ASE) and area energy efficiency (AEE) tradeoff. Closed-form expressions are derived to help discover important design insights. For instance, it is interesting to see the distribution of the massive antennas always helps to improve ASE, but not the case for AEE. Moreover, with the large number of transmit antennas available and the practical cost of channel estimation considered, collaboration between cells is not always welcomed in each deployment scenario and the positive use cases depend on system parameters such as the number of antennas and the way they are distributed.


title: Active Bayesian Optimization: Minimizing Minimizer Entropy

abstract: The ultimate goal of optimization is to find the minimizer of a target function.However, typical criteria for active optimization often ignore the uncertainty about the minimizer. We propose a novel criterion for global optimization and an associated sequential active learning strategy using Gaussian processes.Our criterion is the reduction of uncertainty in the posterior distribution of the function minimizer. It can also flexibly incorporate multiple global minimizers. We implement a tractable approximation of the criterion and demonstrate that it obtains the global minimizer accurately compared to conventional Bayesian optimization criteria.


title: Expectation (and attention) in visual cognition

abstract: Visual cognition is limited by computational capacity, because the brain can process only a fraction of the visual sensorium in detail, and by the inherent ambiguity of the information entering the visual system. Two mechanisms mitigate these burdens: attention prioritizes stimulus processing on the basis of motivational relevance, and expectations constrain visual interpretation on the basis of prior likelihood. Of the two, attention has been extensively investigated while expectation has been relatively neglected. Here, we review recent work that has begun to delineate a neurobiology of visual expectation, and contrast the findings with those of the attention literature, to explore how these two central influences on visual perception overlap, differ and interact.


title: EM enhancement of 3D head pose estimated by point at infinity

abstract: Head pose estimation is a key task for visual surveillance, HCI and face recognition applications. In this paper, a new approach is proposed for estimating 3D head pose from a monocular image. The approach assumes the full perspective projection camera model. Our approach employs general prior knowledge of face structure and the corresponding geometrical constraints provided by the location of a certain vanishing point to determine the pose of human faces. To achieve this, eye-lines, formed from the far and near eye corners, and mouth-line of the mouth corners are assumed parallel in 3D space. Then the vanishing point of these parallel lines found by the intersection of the eye-line and mouth-line in the image can be used to infer the 3D orientation and location of the human face. In order to deal with the variance of the facial model parameters, e.g. ratio between the eye-line and the mouth-line, an EM framework is applied to update the parameters. We first compute the 3D pose using some initially learnt parameters (such as ratio and length) and then adapt the parameters statistically for individual persons and their facial expressions by minimizing the residual errors between the projection of the model features points and the actual features on the image. In doing so, we assume every facial feature point can be associated to each of features points in 3D model with some a posteriori probability. The expectation step of the EM algorithm provides an iterative framework for computing the a posterori probabilities using Gaussian mixtures defined over the parameters. The robustness analysis of the algorithm on synthetic data and some real images with known ground-truth are included.


title: A Software Defined Fog Node Based Distributed Blockchain Cloud Architecture for IoT

abstract: The recent expansion of the Internet of Things (IoT) and the consequent explosion in the volume of data produced by smart devices have led to the outsourcing of data to designated data centers. However, to manage these huge data stores, centralized data centers, such as cloud storage cannot afford auspicious way. There are many challenges that must be addressed in the traditional network architecture due to the rapid growth in the diversity and number of devices connected to the internet, which is not designed to provide high availability, real-time data delivery, scalability, security, resilience, and low latency. To address these issues, this paper proposes a novel blockchain-based distributed cloud architecture with a software defined networking (SDN) enable controller fog nodes at the edge of the network to meet the required design principles. The proposed model is a distributed cloud architecture based on blockchain technology, which provides low-cost, secure, and on-demand access to the most competitive computing infrastructures in an IoT network. By creating a distributed cloud infrastructure, the proposed model enables cost-effective high-performance computing. Furthermore, to bring computing resources to the edge of the IoT network and allow low latency access to large amounts of data in a secure manner, we provide a secure distributed fog node architecture that uses SDN and blockchain techniques. Fog nodes are distributed fog computing entities that allow the deployment of fog services, and are formed by multiple computing resources at the edge of the IoT network. We evaluated the performance of our proposed architecture and compared it with the existing models using various performance measures. The results of our evaluation show that performance is improved by reducing the induced delay, reducing the response time, increasing throughput, and the ability to detect real-time attacks in the IoT network with low performance overheads.


title: Power Quality Control and Design of Power Converter for Variable-Speed Wind Energy Conversion System with Permanent-Magnet Synchronous Generator

abstract: The control strategy and design of an AC/DC/AC IGBT-PMW power converter for PMSG-based variable-speed wind energy conversion systems (VSWECS) operation in grid/load-connected mode are presented. VSWECS consists of a PMSG connected to a AC-DC IGBT-based PWM rectifier and a DC/AC IGBT-based PWM inverter with LCL filter. In VSWECS, AC/DC/AC power converter is employed to convert the variable frequency variable speed generator output to the fixed frequency fixed voltage grid. The DC/AC power conversion has been managed out using adaptive neurofuzzy controlled inverter located at the output of controlled AC/DC IGBT-based PWM rectifier. In this study, the dynamic performance and power quality of the proposed power converter connected to the grid/load by output LCL filter is focused on. Dynamic modeling and control of the VSWECS with the proposed power converter is performed by using MATLAB/Simulink. Simulation results show that the output voltage, power, and frequency of VSWECS reach to desirable operation values in a very short time. In addition, when PMSG based VSWECS works continuously with the 4.5 kHz switching frequency, the THD rate of voltage in the load terminal is 0.00672%.


title: A Survey on Mobile Affective Computing

abstract: This survey presents recent progress on Affective Computing (AC) using mobile devices. AC has been one of the most active research topics for decades. The primary limitation of traditional AC research refers to as impermeable emotions. This criticism is prominent when emotions are investigated outside social contexts. It is problematic because some emotions are directed at other people and arise from interactions with them. The development of smart mobile wearable devices (e.g., Apple Watch, Google Glass, iPhone, Fitbit) enables the wild and natural study for AC in the aspect of computer science. This survey emphasizes the AC study and system using smart wearable devices. Various models, methodologies and systems are discussed in order to examine the state of the art. Finally, we discuss remaining challenges and future works.


title: Vehicle Routing And Scheduling Models, Simulation And City Logistics

abstract: The distribution of goods based on road services in urban areas, usually known as City Logistics, contributes to traffic congestion and is affected by traffic congestion, generates environmental impacts and incurs in high logistics costs. Therefore a holistic approach to the design and evaluation of City Logistics applications requires an integrated framework in which all components could work together that is must be modelled not only in terms of the core models for vehicle routing and fleet management, but also in terms of models able of including the dynamic aspects of traffic on the underlying road network, namely if Information and Communication Technologies (ICT) applications are taken into account. This paper reports on the modelling framework developed in the national projects SADERYL-I and II, sponsored by the Spanish “Direccion General de Ciencia y Tecnologia” (DGCYT) and tested in the European Project MEROPE of the INTERREG IIIB Programme. The modelling framework consists of a Decision Support System whose core architecture is composed by a Data Base, to store all the data required by the implied models: location of logistic centres and customers, capacities of warehouses and depots, transportation costs, operational costs, fleet data, etc.; a Database Management System, for the updating of the information stored in the data base; a Model Base, containing the family of models and algorithms to solve the related problems, discrete location, network location, street vehicle routing and scheduling; a Model Base Management System, to update, modify, add or delete models from the Model Base; a GIS based Graphic User Interface supporting the dialogues to define and update data, select the model suitable to the intended problem, generate automatically from the digital map of the road network the input graph for the Network Location and Vehicle Routing models, apply the corresponding algorithm, visualize the problem and the results, etc. To account for the dynamics of urban traffic flows the system includes an underlying dynamic traffic simulation model (AIMSUN in this case) which is able to track individually the fleet vehicles, emulating in this way the monitoring of fleet vehicles in a real time fleet management system, gathering dynamic data (i.e. current position, previous position, current speed, previous speed, etc.) while following the vehicle, in a similar way as the data that in real life an equipped vehicle could provide. This is the information required by a “Dynamic Router and Scheduler” to determine which vehicle will be assigned to the new service and which will be the new route for the selected vehicle


title: 3LC: Lightweight and Effective Traffic Compression for Distributed Machine Learning

abstract: The performance and efficiency of distributed machine learning (ML) depends significantly on how long it takes for nodes to exchange state changes. Overly-aggressive attempts to reduce communication often sacrifice final model accuracy and necessitate additional ML techniques to compensate for this loss, limiting their generality. Some attempts to reduce communication incur high computation overhead, which makes their performance benefits visible only over slow networks. ::: We present 3LC, a lossy compression scheme for state change traffic that strikes balance between multiple goals: traffic reduction, accuracy, computation overhead, and generality. It combines three new techniques---3-value quantization with sparsity multiplication, quartic encoding, and zero-run encoding---to leverage strengths of quantization and sparsification techniques and avoid their drawbacks. It achieves a data compression ratio of up to 39--107X, almost the same test accuracy of trained models, and high compression speed. Distributed ML frameworks can employ 3LC without modifications to existing ML algorithms. Our experiments show that 3LC reduces wall-clock training time of ResNet-110--based image classifiers for CIFAR-10 on a 10-GPU cluster by up to 16--23X compared to TensorFlow's baseline design.


title: Bi-directional input/output circuits with integrated level shifters for near-threshold computing

abstract: Interfacing techniques for near-threshold computing are described in this paper. A bi-directional input/output circuit with integrated level shifters is proposed for multiple near-threshold power domains. The circuit provides conversion ranges of 0.38 V to 1.2 V and 0.45 V to 3.3 V depending on the targeted output voltage. Eight different configurations of I/O circuits are evaluated with level shifters implemented with a standard current mirror, cross coupled, and the proposed single ended topology. The use of a single ended level shifter provides the optimum power-delay point. The I/O circuit, at a core voltage of 0.45 V, consumes 4.87 mW with a fan out of four (FO4) delay of 0.46 for a V DOUT and V PAD voltage of, respectively, 0.45 V and 3.3 V. For a V DD, L of 0.625 V and V DD, H of 1.5 V, the delay and total power consumption of the single ended level shifter are, respectively, 0.74x and 0.4x that of a current mirror level shifter.


title: Trainable Greedy Decoding for Neural Machine Translation

abstract: Recent research in neural machine translation has largely focused on two aspects; neural network architectures and end-to-end learning algorithms. The problem of decoding, however, has received relatively little attention from the research community. In this paper, we solely focus on the problem of decoding given a trained neural machine translation model. Instead of trying to build a new decoding algorithm for any specific decoding objective, we propose the idea of trainable decoding algorithm in which we train a decoding algorithm to find a translation that maximizes an arbitrary decoding objective. More specifically, we design an actor that observes and manipulates the hidden state of the neural machine translation decoder and propose to train it using a variant of deterministic policy gradient. We extensively evaluate the proposed algorithm using four language pairs and two decoding objectives and show that we can indeed train a trainable greedy decoder that generates a better translation (in terms of a target decoding objective) with minimal computational overhead.


title: Non-linear Label Ranking for Large-scale Prediction of Long-Term User Interests

abstract: We consider the problem of personalization of online services from the viewpoint of ad targeting, where we seek to find the best ad categories to be shown to each user, resulting in improved user experience and increased advertisers' revenue. We propose to address this problem as a task of ranking the ad categories depending on a user's preference, and introduce a novel label ranking approach capable of efficiently learning non-linear, highly accurate models in large-scale settings. Experiments on a real-world advertising data set with more than 3.2 million users show that the proposed algorithm outperforms the existing solutions in terms of both rank loss and top-K retrieval performance, strongly suggesting the benefit of using the proposed model on large-scale ranking problems.


title: HIGH YIELDING HYBRID RICE CULTIVAR 'FENGHUAYOU No.2' BRED FROM MARKER-LABLED MALE STERILE LINE

abstract: 'Fenghuayou No.2' is a new high yielding hybrid rice cultivar that was bred from the cytoplasmic male sterile line 'Baifeng A' labeled with green-revertible albino leaf color marker and elite restorer line 'NHR4' expressing the life-time yellow leaves.This new rice cultivar could be recommended to Zhejiang, Jiangxi,and Anhui Provinces as single-season and late-season hybrid rice.


title: Agile requirements engineering practices and challenges: an empirical study

abstract: This paper describes empirical research into agile requirements engineering (RE) practices. Based on an analysis of data collected in 16 US software development organizations, we identify six agile practices. We also identify seven challenges that are created by the use of these practices. We further analyse how this collection of practices helps mitigate some, while exacerbating other risks in RE. We provide a framework for evaluating the impact and appropriateness of agile RE practices by relating them to RE risks. Two risks that are intractable by agile RE practices emerge from the analysis. First, problems with customer inability and a lack of concurrence among customers significantly impact agile development. Second, risks associated with the neglecting non-functional requirements such as security and scalability are a serious concern. Developers should carefully evaluate the risk factors in their project environment to understand whether the benefits of agile RE practices outweigh the costs imposed by the challenges.


title: Association mining

abstract: The task of finding correlations between items in a dataset, association mining, has received considerable attention over the last decade. This article presents a survey of association mining fundamentals, detailing the evolution of association mining algorithms from the seminal to the state-of-the-art. This survey focuses on the fundamental principles of association mining, that is, itemset identification, rule generation, and their generic optimizations.


title: Session guarantees for weakly consistent replicated data

abstract: Four per-session guarantees are proposed to aid users and applications of weakly consistent replicated data: "read your writes", "monotonic reads", "writes follow reads", and "monotonic writes". The intent is to present individual applications with a view of the database that is consistent with their own actions, even if they read and write from various, potentially inconsistent servers. The guarantees can be layered on existing systems that employ a read-any/write-any replication scheme while retaining the principal benefits of such a scheme, namely high availability, simplicity, scalability, and support for disconnected operation. These session guarantees were developed in the context of the Bayou project at Xerox PARC in which we are designing and building a replicated storage system to support the needs of mobile computing users who may be only intermittently connected. >


title: Entanglements in Practice: Performing Anonymity Through Social Media

abstract: Information systems researchers have shown an increasing interest in the notion of sociomateriality. In this paper, we continue this exploration by focusing specifically on entanglement: the inseparability of meaning and matter. Our particular approach is differentiated by its grounding in a relational and performative ontology, and its use of agential realism. We explore some of the key ideas of entanglement through a comparison of two phenomena in the travel sector: an institutionalized accreditation scheme offered by the AA and an online social media website hosted by TripAdvisor. Our analysis centers on the production of anonymity in these two practices of hotel evaluation. By examining how anonymity is constituted through an entanglement of matter and meaning, we challenge the predominantly social treatments of anonymity to date and draw attention to the uncertainties and outcomes generated by specific performances of anonymity in practice. In closing, we consider what the particular agential realist concept of entanglement entails for understanding anonymity, and discuss its implications for research practice.


title: Support vector machines based on convex risk functions and general norms

abstract: This paper studies unified formulations of support vector machines (SVMs) for binary classification on the basis of convex analysis, especially, convex risk functions theory, which is recently developed in the context of financial optimization. Using the notions of convex empirical risk and convex regularizer, a pair of primal and dual formulations of the SVMs are described in a general manner. With the generalized formulations, we discuss reasonable choices for the empirical risk and the regularizer on the basis of the risk function’s properties, which are well-known in the financial context. In particular, we use the properties of the risk function’s dual representations to derive multiple interpretations. We provide two perspectives on robust optimization modeling, enhancing the known facts: (1) the primal formulation can be viewed as a robust empirical risk minimization; (2) the dual formulation is compatible with the distributionally robust modeling.


title: Perturbation results for some nonlinear equations involving fractional operators

abstract: By using a perturbation technique in critical point theory, we prove the existence of solutions for two types of nonlinear equations involving fractional differential operators.


title: Coarse Word-Sense Disambiguation Using Common Sense.

abstract: Coarse word sense disambiguation (WSD) is an NLP task that is both important and practical: it aims to distinguish senses of a word that have very different meanings, while avoiding the complexity that comes from trying to finely distinguish every possible word sense. Reasoning techniques that make use of common sense information can help to solve the WSD problem by taking word meaning and context into account. We have created a system for coarse word sense disambiguation using blending, a common sense reasoning technique, to combine information from SemCor, WordNet, ConceptNet and Extended WordNet. Within that space, a correct sense is suggested based on the similarity of the ambiguous word to each of its possible word senses. The general blending-based system performed well at the task, achieving an f-score of 80.8\% on the 2007 SemEval Coarse Word Sense Disambiguation task.


title: Demonstration of 20µm pitch micro-vias by excimer laser ablation in ultra-thin dry-film polymer dielectrics for multi-layer RDL on glass interposers

abstract: This paper describes the first demonstration of 8–10µm diameter micro-vias at 20µm pitch in ultra-thin dry-film polymer dielectrics to achieve high-density and low-cost redistribution layers (RDL) on panel-based glass and organic interposers. A polymer dielectric dry-film, ZEONIF ZS100, at 10µm thickness was double side laminated on thin and low CTE glass and organic substrates. Micro-via arrays at 20µm pitch were formed by 248nm KrF excimer laser ablation using mask projection scanning, and metallized by a semi-additive process (SAP) using electroless and electrolytic copper plating, with no chemical-mechanical polishing to form fully filled via structures. Fully-filled micro-vias at 20um were achieved using processes scalable to large panels for low-cost and high-density 2.5D and 3D interposers.


title: Software-Defined Networking (SDN): Layers and Architecture Terminology

abstract: Software-Defined Networking (SDN) refers to a new approach for network ::: programmability, that is, the capacity to initialize, control, change, ::: and manage network behavior dynamically via open interfaces. SDN ::: emphasizes the role of software in running networks through the ::: introduction of an abstraction for the data forwarding plane and, by ::: doing so, separates it from the control plane. This separation allows ::: faster innovation cycles at both planes as experience has already ::: shown. However, there is increasing confusion as to what exactly SDN ::: is, what the layer structure is in an SDN architecture, and how layers ::: interface with each other. This document, a product of the IRTF ::: Software-Defined Networking Research Group (SDNRG), addresses these ::: questions and provides a concise reference for the SDN research ::: community based on relevant peer-reviewed literature, the RFC series, ::: and relevant documents by other standards organizations.


title: Thermal fatigue analysis of automotive engine exhaust manifold

abstract: In the paper,thermodynamic analysis of Jetta 2V engine exhaust manifold is done by finite element analysis method, temperature field and thermal stress is calculated, and the low cycle fatigue life of the exhaust manifold is assessmented by Manson-Coffin formula. Using ANSYS software to research the thermodynamics of the exhaust manifold, is overcomed the limitations of the classical method of calculating the complex structure, is a better precise calculation method to simulate the actual operation condition of the exhaust manifold.


title: Characteristic analysis of Otsu threshold and its applications

abstract: This paper proves that Otsu threshold is equal to the average of the mean levels of two classes partitioned by this threshold. Therefore, when the within-class variances of two classes are different, the threshold biases toward the class with larger variance. As a result, partial pixels belonging to this class will be misclassified into the other class with smaller variance. To address this problem and based on the analysis of Otsu threshold, this paper proposes an improved Otsu algorithm that constrains the search range of gray levels. Experimental results demonstrate the superiority of new algorithm compared with Otsu method.


title: Time for a change in phased array radar architectures - part I: Planar vs. conformal arrays

abstract: The well-known phased array radar architecture with four planar faces presents significant limitations for the azimuth coverage of 360 degrees due to the beam scanning up to 45 degrees. Today, a better time and energy efficiency can be obtained by circularly-symmetrical arrays having cylindrical or conical shape. Using separate transmit/receive (Tx/Rx) arrays with Digital Beam Forming in the Rx array adds some further operational flexibility in terms of coverage and revisit time.


title: Using Image Flow to Detect Eye Blinks in Color Videos

abstract: Static computer vision techniques enable non-intrusive observation and analysis of biometrics such as eye blinks. However, ambiguous eye behaviors such as partial blinks and asymmetric eyelid movements present problems that computer vision techniques relying on static appearance alone cannot solve reliably. Image flow analysis enables reliable and efficient interpretation of these ambiguous eye blink behaviors. In this paper we present a method for using image flow analysis to compute problematic eye blink parameters. The flow analysis produces the magnitude and direction of the eyelid movement. A deterministic finite state machine uses the eyelid movement data to compute blink parameters (e.g., blink count, blink rate, and other transitional statistics) for use in human computer interaction applications across a wide range of disciplines. We conducted extensive experiments employing this method on approximately 750K color video frames of five subjects


title: The Butterfly Methodology: a gateway-free approach for migrating legacy information systems

abstract: The problems posed by mission-critical legacy systems-e.g., brittleness, inflexibility, isolation, non-extensibility, lack of openness-are well known, but practical solutions have been slow to emerge. Generally, organisations attempt to keep their legacy systems operational, while developing mechanisms which allow the legacy systems to interoperate with new, modern systems which provide additional functionality. The most mature approach employs gateways to provide this interoperability. However, gateways introduce considerable complexity in their attempt to maintain consistency between the legacy and target systems. This paper presents an innovative gateway-free approach to migrating legacy information systems in a mission-critical environment: the Butterfly Methodology. The fundamental premise of this methodology is to question the need for the parallel operation of the legacy and target systems during migration.


title: OCR-Algorithm for Detection of Subtitles in Television and Cinema

abstract: Two row electrical connector comprises a pair of connector housing modules which are hinged to each other and which are folded so that their base portions are against each other. Each module contains a single row of terminal receiving cavities in which terminals are contained. The two modules are in turn contained in a pair of cover members which form an external housing shell for the modules. Improved methods of connecting wires to two row connectors and thereafter assembling the parts of the connector are also disclosed.


title: Report on the SIGIR 2016 Workshop on Neural Information Retrieval (Neu-IR)

abstract: The SIGIR 2016 workshop on Neural Information Retrieval (Neu-IR) took place on 21 July, 2016 in Pisa. The goal of the Neu-IR (pronounced "New IR") workshop was to serve as a forum for academic and industrial researchers, working at the intersection of information retrieval (IR) and machine learning, to present new work and early results, compare notes on neural network toolkits, share best practices, and discuss the main challenges facing this line of research. In total, 19 papers were presented, including oral and poster presentations. The workshop program also included a session on invited "lightning talks" to encourage participants to share personal insights and negative results with the community. The workshop was well-attended with more than 120 registrations.


title: Computerized Temporal Handwriting Characteristics of Proficient and Non-Proficient Handwriters

abstract: Objectives ::: ::: The purpose of this study was to use computerized temporal measures to examine and compare the writing process of proficient and non-proficient third-grade handwriters. ::: Method ::: ::: A computerized digitizer system was used to compare the temporal handwriting measures of two groups of 8--9-year-old students. Classroom teachers used a questionnaire to identify 50 students who were non-proficient handwriters and 50 students who were proficient handwriters. Multivariate analysis of variance (MANOVA) analyses were used to test for the group differences across tasks for each dependent variable. Total time, "on paper" time, "in air" time, speed, and number of characters per minute were recorded as the participants performed graded writing tasks. ::: Results ::: ::: Non-proficient handwriters required significantly more time to perform handwriting tasks [F(4,91) = 14.83, p < .0001]; their "in air" time, was especially longer as compared to the proficient handwriters [F(4,91) = 13.63, p < .0001]. Their handwriting speed was slower [F(4,91) = 5.99, p < .0002], and they wrote fewer characters per minute (F(4,91) = 14.63, p < .0001). ::: Conclusion ::: ::: The use of a computerized handwriting system provides objective temporal measures of handwriting performance, and may lead to the development of additional tools for the evaluation and treatment of handwriting difficulties.


title: Discriminant ECOC: a heuristic method for application dependent design of error correcting output codes

abstract: We present a heuristic method for learning error correcting output codes matrices based on a hierarchical partition of the class space that maximizes a discriminative criterion. To achieve this goal, the optimal codeword separation is sacrificed in favor of a maximum class discrimination in the partitions. The creation of the hierarchical partition set is performed using a binary tree. As a result, a compact matrix with high discrimination power is obtained. Our method is validated using the UCI database and applied to a real problem, the classification of traffic sign images.


title: What Do Practitioners Vary in Using Scrum?

abstract: Background: Agile software development has become a popular way of developing software. Scrum is the most frequently used agile framework, but it is often reported to be adapted in practice. Objective: Thus, we aim to understand how Scrum is adapted in different contexts and what are the reasons for these changes. Method: Using a structured interview guideline, we interviewed ten German companies about their concrete usage of Scrum and analysed the results qualitatively. Results: All companies vary Scrum in some way. The least variations are in the Sprint length, events, team size and requirements engineering. Many users varied the roles, effort estimations and quality assurance. Conclusions: Many variations constitute a substantial deviation from Scrum as initially proposed. For some of these variations, there are good reasons. Sometimes, however, the variations are a result of a previous non-agile, hierarchical organisation.


title: Sieve-Based Entity Linking for the Biomedical Domain

abstract: We examine a key task in biomedical text processing, normalization of disorder mentions. We present a multi-pass sieve approach to this task, which has the advantage of simplicity and modularity. Our approach is evaluated on two datasets, one comprising clinical reports and the other comprising biomedical abstracts, achieving state-of-the-art results.


title: High Dimensional Human Guided Machine Learning

abstract: Have you ever looked at a machine learning classification model and thought, I could have made that? Well, that is what we test in this project, comparing XGBoost trained on human engineered features to training directly on data. The human engineered features do not outperform XGBoost trained di- rectly on the data, but they are comparable. This project con- tributes a novel method for utilizing human created classifi- cation models on high dimensional datasets.


title: Parameter Estimation Method to Improve the Accuracy of Photovoltaic Electrical Model

abstract: This paper proposes an estimation method to identify the electrical model parameters of photovoltaic (PV) modules and makes a comparison with other methods already popular in the technical literature. Based on the full single-diode model, the mathematical description of the I–V characteristic of modules is generally represented by a coupled nonlinear equation with five unknown parameters, which is difficult to solve by an analytical approach. The aim of the proposed method is to find the five unknown parameters that guarantee the minimum absolute error between the P–V curves generated by the electrical model and the P–V curves provided by the manufacturers' datasheets for different external conditions such as temperature and irradiance. The first advantage of the proposed method is that the parameters are estimated using the P–V curves instead of I–V curves, since most of the applications that use the electrical model want to accurately estimate the extracted power. The second advantage is that the value ranges of each unknown parameter respect their physical meaning. In order to prove the effectiveness of the proposition, a comparison among methods is carried out using both types of P–V and I–V curves: those obtained by manufacturers' datasheets and those extracted experimentally in the laboratory.


title: Deep Texture Features for Robust Face Spoofing Detection

abstract: Biometric systems are quite common in our everyday life. Despite the higher difficulty to circumvent them, nowadays criminals are developing techniques to accurately simulate physical, physiological, and behavioral traits of valid users, process known as spoofing attack. In this context, robust countermeasure methods must be developed and integrated with the traditional biometric applications in order to prevent such frauds. Despite face being a promising trait due to its convenience and acceptability, face recognition systems can be easily fooled with common printed photographs. Most of state-of-the-art antispoofing techniques for face recognition applications extract handcrafted texture features from images, mainly based on the efficient local binary patterns (LBP) descriptor, to characterize them. However, recent results indicate that high-level (deep) features are more robust for such complex tasks. In this brief, a novel approach for face spoofing detection that extracts deep texture features from images by integrating the LBP descriptor to a modified convolutional neural network is proposed. Experiments on the NUAA spoofing database indicate that such deep neural network (called LBPnet) and an extended version of it (n-LBPnet) outperform other state-of-the-art techniques, presenting great results in terms of attack detection.


title: Deep Generative Modeling of LiDAR Data

abstract: Building models capable of generating structured output is a key challenge for AI and robotics. While generative models have been explored on many types of data, little work has been done on synthesizing lidar scans, which play a key role in robot mapping and localization. In this work, we show that one can adapt deep generative models for this task by unravelling lidar scans into a 2D point map. Our approach can generate high quality samples, while simultaneously learning a meaningful latent representation of the data. We demonstrate significant improvements against state-of-the-art point cloud generation methods. Furthermore, we propose a novel data representation that augments the 2D signal with absolute positional information. We show that this helps robustness to noisy and imputed input; the learned model can recover the underlying lidar scan from seemingly uninformative data.


title: The Anatomy of the Aging Face: Volume Loss and Changes in 3-Dimensional Topography

abstract: Facial aging reflects the dynamic, cumulative effects of time on the skin, soft tissues, and deep structural components of the face, and is a complex synergy of skin textural changes and loss of facial volume. Many of the facial manifestations of aging reflect the combined effects of gravity, progressive bone resorption, decreased tissue elasticity, and redistribution of subcutaneous fullness. A convenient method for assessing the morphological effects of aging is to divide the face into the upper third (forehead and brows), middle third (midface and nose), and lower third (chin, jawline, and neck). The midface is an important factor in facial aesthetics because perceptions of facial attractiveness are largely founded on the synergy of the eyes, nose, lips, and cheek bones (central facial triangle). For aesthetic purposes, this area should be considered from a 3-dimensional rather than a 2-dimensional perspective, and restoration of a youthful 3-dimensional facial topography should be regarded as the primary goal in facial rejuvenation. Recent years have seen a significant increase in the number of nonsurgical procedures performed for facial rejuvenation. Patients seeking alternatives to surgical procedures include those who require restoration of lost facial volume, those who wish to enhance normal facial features, and those who want to correct facial asymmetry. Important factors in selecting a nonsurgical treatment option include the advantages of an immediate cosmetic result and a short recovery time.


title: Modelling serendipity in a computational context

abstract: Serendipity has played a role in many human discoveries, and an improved understanding of serendipity could help bring about breakthroughs in the field of computational discovery. The concept of serendipity has previously been adopted for users' benefit by many subfields of computer science, in work that focused on employing computer systems to support and catalyse a serendipitous experience for the user. In this article, we switch perspectives to focus on artificial systems that catalyse, evaluate and leverage serendipitous occurrences themselves. Since serendipity cannot be generated on demand, our analysis embraces the concept of $\mathit{serendipity}\ \mathit{potential}$. ::: We survey the literature on serendipity and creativity to distil the core common themes, which we then use as a conceptual framework. Specifically, we describe five operational dimensions of systems with serendipity potential: perception of a chance event, attention to salient detail, a focus shift achieved by interest, bridge to a problem, and valuation of the result. The focus shift is a central necessary condition, in which the system reevaluates or recontextualises something that had been given a low evaluation score, and subsequently finds it to be of increased value. ::: We use our framework to analyse several historical systems which included features of serendipity: Mueller's $\mathsf{DAYDREAMER}$, Figueiredo and Campos's $\mathsf{Max}$, Colton's $\mathsf{HR}$, and Pease's $\mathsf{HRL}$. We show that $\mathsf{DAYDREAMER}$ and $\mathsf{HRL}$ manifest all of the features of our framework. We discuss our framework in relationship to existing work on serendipity, surprise, and control in a computing context. We comment on how environmental factors and system features interact when designing for serendipity, and compare the evaluation of serendipity to the evaluation of creativity.


Motivational variables in second-language acquisition.


title: The Highly Adaptive SDM Hand: Design and Performance Evaluation

abstract: The inherent uncertainty associated with unstructured environments makes establishing a successful grasp difficult. Traditional approaches to this problem involve hands that are complex, fragile, require elaborate sensor suites, and are difficult to control. Alternatively, by carefully designing the mechanical structure of the hand to incorporate features such as compliance and adaptability, the uncertainty inherent in unstructured grasping tasks can be more easily accommodated. In this paper, we demonstrate a novel adaptive and compliant grasper that can grasp objects spanning a wide range of size, shape, mass, and position/orientation using only a single actuator. The hand is constructed using polymer-based Shape Deposition Manufacturing (SDM) and has superior robustness properties, making it able to withstand large impacts without damage. We also present the results of two experiments to demonstrate that the SDM Hand can reliably grasp objects in the presence of large positioning errors, while keeping acquisition contact forces low. In the first, we evaluate the amount of allowable manipulator positioning error that results in a successful grasp. In the second experiment, the hand autonomously grasps a wide range of spherical objects positioned randomly across the workspace, guided by only a single image from an overhead camera, using feed-forward control of the hand.


title: Evaluating usability of E - Learning Systems in Universities

abstract: The use of e-learning systems has increased significantly in the recent times. E-learning systems are supplementing teaching and learning in universities globally. Kenyan universities have adopted e-learning technologies as means for delivering course content. However despite adoption of these systems, there are considerable challenges facing the usability of the systems. Lecturers and students have different perceptions in regard to the usability of e-learning systems. The aim of this study was to evaluate usability attributes that affect e-learning systems in Kenyan universities. The study had two fold objectives; determining status of e-learning platforms and evaluating usability issues affecting e-learning adoption in Kenyan universities. The research took a case study of one of the public universities which has implemented Moodle e-learning system. The usability attributes evaluated were user-friendliness, learnability, technological infrastructure and policy. The research made recommendations which could help universities accelerate the adoption of e-learning systems.


title: Construal level theory of psychological distance

abstract: People are capable of thinking about the future, the past, remote locations, another person’s perspective, and counterfactual alternatives. Without denying the uniqueness of each process, it is proposed that they constitute different forms of traversing psychological distance. Psychological distance is egocentric: Its reference point is the self in the here and now, and the different ways in which an object might be removed from that point—in time, in space, in social distance, and in hypotheticality—constitute different distance dimensions. Transcending the self in the here and now entails mental construal, and the farther removed an object is from direct experience, the higher (more abstract) the level of construal of that object. Supporting this analysis, research shows (a) that the various distances are cognitively related to each other, (b) that they similarly influence and are influenced by level of mental construal, and (c) that they similarly affect prediction, preference, and action.


title: Quantitative determination and evaluation of Paris polyphylla var. yunnanensis with different harvesting times using UPLC-UV-MS and FT-IR spectroscopy in combination with partial least squares discriminant analysis

abstract: A rapid method was developed and validated by ultra-performance liquid chromatography–triple quadrupole mass spectroscopy with ultraviolet detection (UPLC-UV-MS) for simultaneous determination of paris saponin I, paris saponin II, paris saponin VI and paris saponin VII. Partial least squares discriminant analysis (PLS-DA) based on UPLC and Fourier transform infrared (FT-IR) spectroscopy was employed to evaluate Paris polyphylla var. yunnanensis (PPY) at different harvesting times. Quantitative determination implied that the various contents of bioactive compounds with different harvesting times may lead to different pharmacological effects; the average content of total saponins for PPY harvested at 8 years was higher than that from other samples. The PLS-DA of FT-IR spectra had a better performance than that of UPLC for discrimination of PPY from different harvesting times.


title: CoDraw: Visual Dialog for Collaborative Drawing

abstract: In this work, we propose a goal-driven collaborative task that contains vision, language, and action in a virtual environment as its core components. Specifically, we develop a collaborative `Image Drawing' game between two agents, called CoDraw. Our game is grounded in a virtual world that contains movable clip art objects. Two players, Teller and Drawer, are involved. The Teller sees an abstract scene containing multiple clip arts in a semantically meaningful configuration, while the Drawer tries to reconstruct the scene on an empty canvas using available clip arts. The two players communicate via two-way communication using natural language. We collect the CoDraw dataset of ~10K dialogs consisting of 138K messages exchanged between a Teller and a Drawer from Amazon Mechanical Turk (AMT). We analyze our dataset and present three models to model the players' behaviors, including an attention model to describe and draw multiple clip arts at each round. The attention models are quantitatively compared to the other models to show how the conventional approaches work for this new task. We also present qualitative visualizations.


title: Tools for Generating and Analyzing Attack Graphs

abstract: Attack graphs depict ways in which an adversary exploits system vulnerabilities to achieve a desired state. System administrators use attack graphs to determine how vulnerable their systems are and to determine what security measures to deploy to defend their systems. In this paper, we present details of an example to illustrate how we specify and analyze network attack models. We take these models as input to our attack graph tools to generate attack graphs automatically and to analyze system vulnerabilities. While we have published our generation and analysis algorithms in earlier work, the presentation of our example and toolkit is novel to this paper.


title: Design of a novel compact printed folded dipole antenna for biomedical applications

abstract: In this study, a human tissue-implantable compact folded dipole antenna of 19.6 * 2 * 0.254 mm 3 operating in the Medical Implant Communication Service (MICS) frequency band (402-405 MHz) is presented. The antenna design and analysis is carried out inside a homogeneous flat phantom with electrical properties equivalent to those of 2/3 human muscle tissue. The dipole antenna, printed on a high-dielectric substrate layer, exhibits a frequency resonance at 402 MHz with a wide 10-dB impedance bandwidth of 105 MHz. The proposed antenna radiates an omnidirectional far-field radiation pattern with a maximum realized gain of -31.2 dB. In addition, the Specific Absorption Rate (SAR) assessment indicates the maximum input power deliverable to the antenna in order to meet the required safety regulations.


To Go or Not to Go: A Case for Q-Learning at Unsignalized Intersections


title: ULTRA-WIDEBAND FOUR-WAY IN-PHASE MULTILAYER POWER DIVIDER WITH HIGH ISOLATION

abstract: A novel ultra-wideband four-way in-phase multilayer power divider based on the microstrip- to-slotline transition is proposed in this paper as a complemental slotline power divider with high isolation. Due to the introduction of three lumped isolation resistors, the isolations between output ports in the new structure have been improved. The design expressions have been derived by making use of odd-mode and even-mode method. Both simulated and measured results have proved that the proposed power divider has good impedance matching at all ports, high isolations between output ports, excellent amplitude and phase balance, as well as ∞at group delay over the wide frequency range from 3.8GHz to 11GHz.


title: Data interlinking through robust linkkey extraction

abstract: Links are important for the publication of RDF data on the web. Yet, establishing links between data sets is not an easy task. We develop an approach for that purpose which extracts weak linkkeys. Linkkeys extend the notion of a key to the case of different data sets. They are made of a set of pairs of properties belonging to two different classes. A weak linkkey holds between two classes if any resources having common values for all of these properties are the same resources. An algorithm is proposed to generate a small set of candidate linkkeys. Depending on whether some of the, valid or invalid, links are known, we define supervised and non supervised measures for selecting the appropriate linkkeys. The supervised measures approximate precision and recall, while the non supervised measures are the ratio of pairs of entities a linkkey covers (coverage), and the ratio of entities from the same data set it identifies (discrimination). We have experimented these techniques on two data sets, showing the accuracy and robustness of both approaches.


title: Learning Information Extraction Rules for Semi-Structured and Free Text

abstract: A wealth of on-line text information can be made available to automatic processing by information extraction (IE) systems. Each IE application needs a separate set of rules tuned to the domain and writing style. WHISK helps to overcome this knowledge-engineering bottleneck by learning text extraction rules automatically. ::: ::: WHISK is designed to handle text styles ranging from highly structured to free text, including text that is neither rigidly formatted nor composed of grammatical sentences. Such semi-structured text has largely been beyond the scope of previous systems. When used in conjunction with a syntactic analyzer and semantic tagging, WHISK can also handle extraction from free text such as news stories.


Development of a new front-of-pack nutrition label in France : the five-colour Nutri-Score


title: Real-time flood water level monitoring system with SMS notification

abstract: Metropolitan Manila, Philippines is one of the regions at high risk from flooding. Pandacan, is one of the districts in the city of Manila, located at the south of Pasig River which is prone to flooding. The purpose of this project is to provide a standalone flood water level monitoring system for the community in Kahilom Street Pandacan, Manila. The system is constructed through the use of Arduino Uno, GSM shield and sensors that will be powered by a solar panel with generator. The early warning device will be the three LED that is mounted to a PVC pipe and then the system will send an SMS notification to the people in the community. The functionality of the system was tested by the simulation of flooding. The results provided that the objectives of the design satisfied the needs of the client.


title: The Future of Scientific Simulations: from Artificial Life to Artificial Cosmogenesis

abstract: This philosophical paper explores the relation between modern scientific simulations and the future of the universe. We argue that a simulation of an entire universe will result from future scientific activity. This requires us to tackle the challenge of simulating open-ended evolution at all levels in a single simulation. The simulation should encompass not only biological evolution, but also physical evolution (a level below) and cultural evolution (a level above). The simulation would allow us to probe what would happen if we would “replay the tape of the universe” with the same or different laws and initial conditions. We also distinguish between realworld and artificial-world modelling. Assuming that intelligent life could indeed simulate an entire universe, this leads to two tentative hypotheses. Some authors have argued that we may already be in a simulation run by an intelligent entity. Or, if such a simulation could be made real, this would lead to the production of a new universe. This last direction is argued with a careful speculative philosophical approach, emphasizing the imperative to find a solution to the heat death problem in cosmology. The reader is invited to consult Annex 1 for an overview of the logical structure of this paper.


title: Improving Machine Translation Performance by Exploiting Non-Parallel Corpora

abstract: We present a novel method for discovering parallel sentences in comparable, non-parallel corpora. We train a maximum entropy classifier that, given a pair of sentences, can reliably determine whether or not they are translations of each other. Using this approach, we extract parallel data from large Chinese, Arabic, and English non-parallel newspaper corpora. We evaluate the quality of the extracted data by showing that it improves the performance of a state-of-the-art statistical machine translation system. We also show that a good-quality MT system can be built from scratch by starting with a very small parallel corpus (100,000 words) and exploiting a large non-parallel corpus. Thus, our method can be applied with great benefit to language pairs for which only scarce resources are available.


title: Generative probabilistic models for multimedia retrieval: query generation against document generation

abstract: This paper presents the use of generative probabilistic models for multimedia retrieval. Gaussian mixture models are estimated to describe the visual content of images (or video) and are explored in different ways of using them for retrieval. So-called query generation (how likely is the query given the document model) and document generation (how likely is the document given the query model) approaches are considered and how both fit in a common probabilistic framework is explained. Query generation is shown to be theoretically superior, and confirmed experimentally on the Trecvid search task. However, it is found that in some cases a document generation approach gives better results. Especially in the cases where queries are narrow and visual results are combined with textual results, the document generation approach seems to be better at setting a visual context than the query generation variant.


title: Unsupervised Single-Channel Music Source Separation by Average Harmonic Structure Modeling

abstract: Source separation of musical signals is an appealing but difficult problem, especially in the single-channel case. In this paper, an unsupervised single-channel music source separation algorithm based on average harmonic structure modeling is proposed. Under the assumption of playing in narrow pitch ranges, different harmonic instrumental sources in a piece of music often have different but stable harmonic structures; thus, sources can be characterized uniquely by harmonic structure models. Given the number of instrumental sources, the proposed algorithm learns these models directly from the mixed signal by clustering the harmonic structures extracted from different frames. The corresponding sources are then extracted from the mixed signal using the models. Experiments on several mixed signals, including synthesized instrumental sources, real instrumental sources, and singing voices, show that this algorithm outperforms the general nonnegative matrix factorization (NMF)-based source separation algorithm, and yields good subjective listening quality. As a side effect, this algorithm estimates the pitches of the harmonic instrumental sources. The number of concurrent sounds in each frame is also computed, which is a difficult task for general multipitch estimation (MPE) algorithms.


title: Toward a National Framework for the Secondary Use of Health Data: An American Medical Informatics Association White Paper

abstract: Secondary use of health data applies personal health information (PHI) for uses outside of direct health care delivery. It includes such activities as analysis, research, quality and safety measurement, public health, payment, provider certification or accreditation, marketing, and other business applications, including strictly commercial activities. Secondary use of health data can enhance health care experiences for individuals, expand knowledge about disease and appropriate treatments, strengthen understanding about effectiveness and efficiency of health care systems, support public health and security goals, and aid businesses in meeting customers' needs. Yet, complex ethical, political, technical, and social issues surround the secondary use of health data. While not new, these issues play increasingly critical and complex roles given current public and private sector activities not only expanding health data volume, but also improving access to data. Lack of coherent policies and standard “good practices” for secondary use of health data impedes efforts to strengthen the U.S. health care system. The nation requires a framework for the secondary use of health data with a robust infrastructure of policies, standards, and best practices. Such a framework can guide and facilitate widespread collection, storage, aggregation, linkage, and transmission of health data. The framework will provide appropriate protections for legitimate secondary use.


title: Nonparametric estimation of the precision-recall curve

abstract: The Precision-Recall (PR) curve is a widely used visual tool to evaluate the performance of scoring functions in regards to their capacities to discriminate between two populations. The purpose of this paper is to examine both theoretical and practical issues related to the statistical estimation of PR curves based on classification data. Consistency and asymptotic normality of the empirical counterpart of the PR curve in sup norm are rigorously established. Eventually, the issue of building confidence bands in the PR space is considered and a specific resampling procedure based on a smoothed and truncated version of the empirical distribution of the data is promoted. Arguments of theoretical and computational nature are presented to explain why such a bootstrap is preferable to a "naive" bootstrap in this setup.


title: Prediction of retweet cascade size over time

abstract: Retweet cascades play an essential role in information diffusion in Twitter. Popular tweets reflect the current trends in Twitter, while Twitter itself is one of the most important online media. Thus, understanding the reasons why a tweet becomes popular is of great interest for sociologists, marketers and social media researches. What is even more important is the possibility to make a prognosis of a tweet's future popularity. Besides the scientific significance of such possibility, this sort of prediction has lots of practical applications such as breaking news detection, viral marketing etc. In this paper we try to forecast how many retweets a given tweet will gain during a fixed time period. We train an algorithm that predicts the number of retweets during time T since the initial moment. In addition to a standard set of features we utilize several new ones. One of the most important features is the flow of the cascade. Another one is PageRank on the retweet graph, which can be considered as the measure of influence of users.


title: Printed Meandering Probe-Fed Circularly Polarized Patch Antenna With Wide Bandwidth

abstract: In this letter, a wideband compact circularly polarized (CP) patch antenna is proposed. This patch antenna consists of a printed meandering probe (M-probe) and truncated patches that excite orthogonal resonant modes to generate a wideband CP operation. The stacked patch is employed to further improve the axial-ratio (AR) bandwidth to fit the 5G Wi-Fi application. The proposed antenna achieves 42.3% impedance bandwidth and 16.8% AR bandwidth, respectively. The average gain within the AR bandwidth is 6.6 dBic with less than 0.5 dB variation. This work demonstrates a bandwidth broadening technique of an M-probe fed CP patch antenna. It is the first study to investigate and exhibit the M-probe could also provide the wideband characteristics in the dielectric loaded patch antenna. The potential applications of the antenna are 5G Wi-Fi and satellite communication systems.


title: Understanding IEEE 802.11n multi-hop communication in wireless networks

abstract: In this paper, we present a measurement study of the multi-hop behavior of the new IEEE 802.11n standard in an indoor mesh testbed. As main contribution, we quantitatively describe characteristics of IEEE 802.11n on multi-hop paths like throughput, aggregate size and utilized MIMO features. Furthermore, we show how to build an IEEE 802.11n indoor testbed capable of multi-hop communication. In an 8-hop chain topology, we observe that channel bonding nearly doubles the throughput for any fixed path length. The mean aggregate size in number of frames at each node is also doubled by channel bonding. The aggregate size decreases with increasing path length. Limiting the aggregate size severely impacts throughput both on single and multi-hop paths. The throughput degrades as the path length is increased as in legacy IEEE 802.11a/b/g. We believe that these findings will enhance the understanding of the performance of IEEE 802.11n in multi-hop networks.


title: Use of SCADA Data for Failure Detection in Wind Turbines

abstract: This paper discusses the use of existing wind turbine SCADA data for development of fault detection and diagnostic techniques for wind turbines.


title: Comparative analysis of power losses for 3-Level NPC and T-type inverter modules

abstract: In this paper, power losses of 3-level NPC inverter (3LNPCI) and 3-level T-type inverter (3LTI) are classified and calculated according to the switching states. The conduction loss depends on modulation index (MI) and power factor (PF), whereas the switching loss depends on switching frequency, so the power loss analyses have been done at the different operating points of MI, PF, and switching frequency. And power losses of 3LNPCI and 3LTI with NPC and T-type inverter dedicated modules are calculated based on the theoretical formula and PSiM thermal module by using the real parameter values of switching devices. For the comparison of power losses of NPC and T-type inverters, all parameters have been referred from the data sheets of the different manufacturing companies.


Technology Transfer of Renewable Energy Resources for Women’s Empowerment and Country Development


title: Lucid dreaming – dreams of clarity

abstract: Although the concept of ‘lucid dreaming’ was first coined by Frederik Willems van Eeden, a Dutch psychiatrist, and introduced at the meeting of The Society for Psychical Research held on 22 April 1913, the phenomenon of lucid dreaming was already known in earlier historical periods and its descriptions can be found in writings of Aristotle. However, modern lucid dreaming research was established only after LaBerge introduced his method for physiological investigation of lucid dreaming through eye signals in 1980. Current attempts in the field are directed at defining the neuro-physiological correlates of lucid dreaming, an aim that still has yet to be achieved. One of the possible applications of lucid dreaming is in the field of psychotherapy where it is usually used in treating nightmares. However, other possible therapeutic potentials of lucid dreaming are still in the process of being discovered and therefore constitute a fertile area for future researchers. Copyright © 2009 British Society of Experimental & Clinical Hypnosis. Published by John Wiley & Sons, Ltd.


title: Smooth Pole Tracking Technique by Power MOSFET Array in Low-Dropout Regulators

abstract: This paper proposes an advanced Q-reduction technique of pole-splitting compensations for low dropout (LDO) regulator. The output pole is load dependent and may cause the scenario of complex poles when load current changes. LDO regulators may oscillate because high-Q incurs the less gain and phase margins. The reasons of causing complex poles depend on the design methodology. For the design of capacitor-free LDO regulator, high-Q issue happens when load current changes from heavy to light. Recent literature provides a method to alleviate the high-Q problem. However, large dropout voltage in case of suddenly large loads forces designers to include a small load capacitor as an indispensable component for supplying system-on-chip (SoC) systems. Different to the case of capacitor-free LDO regulators, high- Q issue happens when load current changes from light to heavy. According to our proposed power MOSFET array, the high-Q problem can be alleviated and prevent LDO regulators from oscillating when load changes. Experimental results promise the stability and show the improvement of load and line regulations.


title: UCO: A Unified Cybersecurity Ontology

abstract: In this paper we describe the Unified Cybersecurity Ontology (UCO) that is intended to support information integration and cyber situational awareness in cybersecurity systems. The ontology incorporates and integrates heterogeneous data and knowledge schemas from different cybersecurity systems and most commonly used cybersecurity standards for information sharing and exchange. The UCO ontology has also been mapped to a number of existing cybersecurity ontologies as well as concepts in the Linked Open Data cloud. Similar to DBpedia which serves as the core for general knowledge in Linked Open Data cloud, we envision UCO to serve as the core for cybersecurity domain, which would evolve and grow with the passage of time with additional cybersecurity data sets as they become available. We also present a prototype system and concrete use cases supported by the UCO ontology. To the best of our knowledge, this is the first cybersecurity ontology that has been mapped to general world ontologies to support broader and diverse security use cases. We compare the resulting ontology with previous efforts, discuss its strengths and limitations, and describe potential future work directions.


title: Broadband antenna design using different 3D printing technologies and metallization processes

abstract: The purpose of this paper is to provide a comprehensive evaluation of how plastic materials, different metallization process, and thickness of the metal can influence the performance of 3D printed broadband antenna structures. A set of antennas were manufactured using Fused Deposition Modeling technology and Polyjet technology. Three different plastic materials ABS, PLA and Vero were employed in the tests. The antenna structures were metallized using three common metallization processes: vacuum metallization, electroplating, and conductive paint. In this project, the broadband performances of the metallized plastic antennas are compared to an original metal antenna by measuring VSWR, radiation patterns (H- and E-Planes), and gain. Measurements show that the performances of these plastic structures, regardless of production method, are very similar to the original metal antenna.


title: Interaction of Open Innovation and Business Ecosystem

abstract: Open innovation breaks the original innovation border of organization and emphasizes the use of suppliers, customers, partners, and other internal and external innovative thinking and resources. How to effectively implement and manage open innovation has become a new business problem. Business ecosystem is the network system of value creation and co-evolution achieved by suppliers, users, partner, and other groups with self-organization mode. This study began with the risk analysis of open innovation implementation; then innovation process was embedded into business ecosystem structure; open innovation mode based on business ecosystem was proposed; business ecosystem based on open innovation was built according to influence degree of each innovative object. Study finds that both sides have a mutual promotion relationship, which provides a new analysis perspective for open innovation and business ecosystem; at the same time, it is also conducive to guiding the concrete practice of implementing open innovation.


Expression of C aspase-3 and cm yc in N on-Sm all C ell Lung C ancer


title: The recognition and tracking of traffic lights based on color segmentation and CAMSHIFT for intelligent vehicles

abstract: The recognition and tracking of traffic lights for intelligent vehicles based on a vehicle-mounted camera are studied in this paper. The candidate region of the traffic light is extracted using the threshold segmentation method and the morphological operation. Then, the recognition algorithm of the traffic light based on machine learning is employed. To avoid false negatives and tracking loss, the target tracking algorithm CAMSHIFT (Continuously Adaptive Mean Shift), which uses the color histogram as the target model, is adopted. In addition to traffic signal pre-processing and the recognition method of learning, the initialization problem of the search window of CAMSHIFT algorithm is resolved. Moreover, the window setting method is used to shorten the processing time of the global HSV color space conversion. The real vehicle experiments validate the performance of the presented approach.


title: The Effects of Interaction Goals on Negotiation Tactics and Outcomes: A Dyad-Level Analysis Across Two Cultures

abstract: This study investigates how negotiators’ interaction goals influence their own and their counterparts’ negotiation tactics and outcomes across two cultures using a simulated employment contract negotiation. Results show that when negotiators placed greater importance on competitive goals, they used more distributive persuasion and fewer priority information exchange tactics, which reduced their counterparts’ profit; negotiators’ competitive goals also caused their counterparts to use fewer priority information exchange tactics, which in turn hurt their own profit. Dyad members’ competitive goals have an indirect, negative impact on joint profit. In addition, Chinese negotiators placed greater importance on competitive goals and used more distributive and fewer integrative tactics than Americans, but the associations between goals and tactics did not differ across cultures. Nevertheless, members of the two cultures took different paths to improve joint profit; as a result, Chinese dyads achieved no less jo...


title: Embodied cognition and the magical future of interaction design

abstract: The theory of embodied cognition can provide HCI practitioners and theorists with new ideas about interaction and new principles for better designs. I support this claim with four ideas about cognition: (1) interacting with tools changes the way we think and perceive -- tools, when manipulated, are soon absorbed into the body schema, and this absorption leads to fundamental changes in the way we perceive and conceive of our environments; (2) we think with our bodies not just with our brains; (3) we know more by doing than by seeing -- there are times when physically performing an activity is better than watching someone else perform the activity, even though our motor resonance system fires strongly during other person observation; (4) there are times when we literally think with things. These four ideas have major implications for interaction design, especially the design of tangible, physical, context aware, and telepresence systems.


title: A New Segmentation Algorithm to Stock Time Series Based on PIP Approach

abstract: Stock time series segmentation is one of the fundamental components in stock time series data mining. And the segmentation is often used for the trend analysis. In this paper, a new stock time series segmentation algorithm is proposed based on PIP(Perceptually Important Point) approach. This proposed segmentation method contributes to containing both the important data points and the primitive trends like uptrend and downtrend, while most of the current algorithms only contain one aspect of that. The proposed segmentation algorithm is proved to be more efficient and effective in reserving the trends and less complexity than those combined split-and- merge piecewise linear approximation segmentation algorithms.


title: Morphable Word Clouds for Time-Varying Text Data Visualization

abstract: A word cloud is a visual representation of a collection of text documents that uses various font sizes, colors, and spaces to arrange and depict significant words. The majority of previous studies on time-varying word clouds focuses on layout optimization and temporal trend visualization. However, they do not fully consider the spatial shapes and temporal motions of word clouds, which are important factors for attracting people’s attention and are also important cues for human visual systems in capturing information from time-varying text data. This paper presents a novel method that uses rigid body dynamics to arrange multi-temporal word-tags in a specific shape sequence under various constraints. Each word-tag is regarded as a rigid body in dynamics. With the aid of geometric, aesthetic, and temporal coherence constraints, the proposed method can generate a temporally morphable word cloud that not only arranges word-tags in their corresponding shapes but also smoothly transforms the shapes of word clouds over time, thus yielding a pleasing time-varying visualization. Using the proposed frame-by-frame and morphable word clouds, people can observe the overall story of a time-varying text data from the shape transition, and people can also observe the details from the word clouds in frames. Experimental results on various data demonstrate the feasibility and flexibility of the proposed method in morphable word cloud generation. In addition, an application that uses the proposed word clouds in a simulated exhibition demonstrates the usefulness of the proposed method.


title: Sparse and Unique Nonnegative Matrix Factorization Through Data Preprocessing

abstract: Nonnegative matrix factorization (NMF) has become a very popular technique in machine learning because it automatically extracts meaningful features through a sparse and part-based representation. However, NMF has the drawback of being highly ill-posed, that is, there typically exist many different but equivalent factorizations. In this paper, we introduce a completely new way to obtaining more well-posed NMF problems whose solutions are sparser. Our technique is based on the preprocessing of the nonnegative input data matrix, and relies on the theory of M-matrices and the geometric interpretation of NMF. This approach provably leads to optimal and sparse solutions under the separability assumption of Donoho and Stodden (NIPS, 2003), and, for rank-three matrices, makes the number of exact factorizations finite. We illustrate the effectiveness of our technique on several image datasets.


title: Temporal factors in tactile spatial acuity: evidence for RA interference in fine spatial processing.

abstract: We investigated the extent to which subjects’ ability to perceive the fine spatial structure of a stimulus depends on its temporal properties (namely the frequency at which it vibrates). Subjects were presented with static or vibrating gratings that varied in spatial period (1–8 mm) and vibratory frequency (5–80 Hz) and judged the orientation of the gratings, presented either parallel or perpendicular to the long axis of the finger. We found that the grating orientation threshold (GOT)—the spatial period at which subjects can reliably discriminate the orientation of the grating—increased as the vibratory frequency of the gratings increased. As the spatial modulation of SA1 and RA afferent fibers has been found to be independent of vibratory frequency, the frequency dependence of spatial acuity cannot be attributed to changes in the quality of the peripheral signal. Furthermore, we found GOTs to be relatively independent of stimulus amplitude, so the low spatial acuity at high flutter frequencies does not appear to be due to an inadequacy in the strength of the afferent response at those frequencies. We hypothesized that the RA signal, the strength of which increases with vibratory frequency, interfered with the spatially modulated signal conveyed by SA1 fibers. Consistent with this hypothesis, we found that adapting RA afferent fibers improved spatial acuity, as gauged by GOTs, at the high flutter frequencies.


title: The general inefficiency of batch training for gradient descent learning

abstract: Gradient descent training of neural networks can be done in either a batch or on-line manner. A widely held myth in the neural network community is that batch training is as fast or faster and/or more 'correct' than on-line training because it supposedly uses a better approximation of the true gradient for its weight updates. This paper explains why batch training is almost always slower than on-line training--often orders of magnitude slower--especially on large training sets. The main reason is due to the ability of on-line training to follow curves in the error surface throughout each epoch, which allows it to safely use a larger learning rate and thus converge with less iterations through the training data. Empirical results on a large (20,000-instance) speech recognition task and on 26 other learning tasks demonstrate that convergence can be reached significantly faster using on-line training than batch training, with no apparent difference in accuracy.


title: Design Aspects of an Active Electromagnetic Suspension System for Automotive Applications

abstract: This paper is concerned with the design aspects of an active electromagnet suspension system for automotive applications which combines a brushless tubular permanent magnet actuator (TPMA) with a passive spring. This system provides for additional stability and safety by performing active roll and pitch control during cornering and braking. Furthermore, elimination of the road irregularities is possible, hence passenger drive comfort is increased. Based upon measurements, static and dynamic specifications of the actuator are derived. The electro magnetic suspension is installed on a quarter car test setup, and the improved performance using roll control is measured and compared to a commercial passive system. An alternative design using a slotless external magnet tubular actuator is proposed which fulfills the derived performance, thermal and volume specifications.


title: Semantic Map Augmentation for Robot Navigation: A Learning Approach Based on Visual and Depth Data

abstract: In this paper, we propose a framework to build an improved metric representation of the environment with semantic information. We explore some of the recent advances of deep neural networks to object detection/semantic classification in a visual-based perception scheme. The output of this system is a map of the environment extended with semantic object classes and their positioning. This framework combines sensors available in commonly used mobile robotic platforms such as an RGB-D camera, 2D LIDAR and odometers. In short, a CNN-based object detector and a 3D model-based segmentation technique are used to localize and identify different classes of objects in the scene. Then the tracking of the semantic classes is performed with a Kalman filter approach. We show results for "door" objects and validate this approach with a collected dataset in an extensive indoor area, comprising corridors and offices. A dataset and the source code are made available to the community as ROS packages.


title: A Tutorial on Probabilistic Latent Semantic Analysis

abstract: In this tutorial, I will discuss the details about how Probabilistic Latent Semantic Analysis (PLSA) is formalized and how different learning algorithms are proposed to learn the model.


Development of a Fixture for Robotic Assembly Station for an Automotive BIW ( Body in White ) Line


title: Directional Cell Discovery in Millimeter Wave Cellular Networks

abstract: The acute disparity between increasing bandwidth demand and available spectrum, has brought millimeter wave (mmW) bands to the forefront of candidate solutions for the next-generation cellular networks. Highly directional transmissions are essential for cellular communication in these frequencies to compensate for high isotropic path loss. This reliance on directional beamforming, however, complicates initial cell search since the mobile and base station must jointly search over a potentially large angular directional space to locate a suitable path to initiate communication. To address this problem, this paper proposes a directional cell discovery procedure where base stations periodically transmit synchronization signals, potentially in time-varying random directions, to scan the angular space. Detectors for these signals are derived based on a Generalized Likelihood Ratio Test (GLRT) under various signal and receiver assumptions. The detectors are then simulated under realistic design parameters and channels based on actual experimental measurements at 28~GHz in New York City. The study reveals two key findings: (i) digital beamforming can significantly outperform analog beamforming even when the digital beamforming uses very low quantization to compensate for the additional power requirements; and (ii) omni-directional transmissions of the synchronization signals from the base station generally outperforms random directional scanning.


title: Cultural hybridity in participatory design

abstract: In this paper we examine challenges identified with participatory design research in the developing world and develop the postcolonial notion of cultural hybridity as a sensitizing concept. While participatory design intentionally addresses power relationships, its methodology does not to the same degree cover cultural power relationships, which extend beyond structural power and voice. The notion of cultural hybridity challenges the static cultural binary opposition between the self and the other, Western and non-Western, or the designer and the user---offering a more nuanced approach to understanding the malleable nature of culture. Drawing from our analysis of published literature in the participatory design community, we explore the complex relationship of participatory design to international development projects and introduce postcolonial cultural hybridity via postcolonial theory and its application within technology design thus far. Then, we examine how participatory approaches and cultural hybridity may interact in practice and conclude with a set of sensitizing insights and topics for further discussion in the participatory design community.


title: Positive and negative emotional verbal stimuli elicit activity in the left amygdala

abstract: The human amygdala’s involvement in negative emotion is well established, but relatively little is known regarding its role in positive emotion. Here we examined the neural response to emotionally positive, negative, and neutral words using fMRI.Relative to neutral words, positive and negative emotional words elicited greater activity in the left amygdala. Positive but not negative words elicited activity in dorsal and ventral striatal regions which have been linked in previous neuroimaging studies to reward and positive aiect, including caudate, putamen, globus pallidus, and accumbens. These ¢ndings provide the ¢rst direct evidence that the amygdala is involved in emotional reactions elicited by both positive and negative emotional words, and further indicate that positive words additionally activate brain regions related to reward. NeuroReport13:15^19 c 2002 Lippincott Williams & Wilkins.


title: Classification of CT brain images based on deep learning networks

abstract: Abstract While computerised tomography (CT) may have been the first imaging tool to study human brain, it has not yet been implemented into clinical decision making process for diagnosis of Alzheimer's disease (AD). On the other hand, with the nature of being prevalent, inexpensive and non-invasive, CT does present diagnostic features of AD to a great extent. This study explores the significance and impact on the application of the burgeoning deep learning techniques to the task of classification of CT brain images, in particular utilising convolutional neural network (CNN), aiming at providing supplementary information for the early diagnosis of Alzheimer's disease. Towards this end, three categories of CT images (N = 285) are clustered into three groups, which are AD, lesion (e.g. tumour) and normal ageing. In addition, considering the characteristics of this collection with larger thickness along the direction of depth (z) (~3–5 mm), an advanced CNN architecture is established integrating both 2D and 3D CNN networks. The fusion of the two CNN networks is subsequently coordinated based on the average of Softmax scores obtained from both networks consolidating 2D images along spatial axial directions and 3D segmented blocks respectively. As a result, the classification accuracy rates rendered by this elaborated CNN architecture are 85.2%, 80% and 95.3% for classes of AD, lesion and normal respectively with an average of 87.6%. Additionally, this improved CNN network appears to outperform the others when in comparison with 2D version only of CNN network as well as a number of state of the art hand-crafted approaches. As a result, these approaches deliver accuracy rates in percentage of 86.3, 85.6 ± 1.10, 86.3 ± 1.04, 85.2 ± 1.60, 83.1 ± 0.35 for 2D CNN, 2D SIFT, 2D KAZE, 3D SIFT and 3D KAZE respectively. The two major contributions of the paper constitute a new 3-D approach while applying deep learning technique to extract signature information rooted in both 2D slices and 3D blocks of CT images and an elaborated hand-crated approach of 3D KAZE.


title: Domain Adaptive Faster R-CNN for Object Detection in the Wild

abstract: Object detection typically assumes that training and test data are drawn from an identical distribution, which, however, does not always hold in practice. Such a distribution mismatch will lead to a significant performance drop. In this work, we aim to improve the cross-domain robustness of object detection. We tackle the domain shift on two levels: 1) the image-level shift, such as image style, illumination, etc, and 2) the instance-level shift, such as object appearance, size, etc. We build our approach based on the recent state-of-the-art Faster R-CNN model, and design two domain adaptation components, on image level and instance level, to reduce the domain discrepancy. The two domain adaptation components are based on H-divergence theory, and are implemented by learning a domain classifier in adversarial training manner. The domain classifiers on different levels are further reinforced with a consistency regularization to learn a domain-invariant region proposal network (RPN) in the Faster R-CNN model. We evaluate our newly proposed approach using multiple datasets including Cityscapes, KITTI, SIM10K, etc. The results demonstrate the effectiveness of our proposed approach for robust object detection in various domain shift scenarios.


title: The Use of Unmanned Aerial Systems (UAS) in Agriculture

abstract: Unmanned aerial vehicles (UAVs) represent technological developments used for precision agriculture. They provide high-resolution images taken from crops and when specific indices are applied, useful outputs for farm management decision-making are produced. The current paper provides a literature review on the use of UAVs in agriculture and specific applications are presented.