title: Routing and Wavelength Assignment in Optical WDM Networks

abstract: This article discusses the routing and wavelength assignment (RWA) problem in optical networks employing wavelength division multiplexing (WDM) technology. Two variants of the problem are studied: static RWA, whereby the traffic requirements are known in advance, and dynamic RWA in which connection requests arrive in some random fashion. Both point-to-point and multicast traffic demands are considered. ::: ::: ::: Keywords: ::: ::: wavelength division multiplexing (WDM); ::: optical networks; ::: routing and wavelength assignment (RWA); ::: virtual topology design; ::: optical multicast


title: Clustering feature decision trees for semi-supervised classification from high-speed data streams

abstract: Most stream data classification algorithms apply the supervised learning strategy which requires massive labeled data. Such approaches are impractical since labeled data are usually hard to obtain in reality. In this paper, we build a clustering feature decision tree model, CFDT, from data streams having both unlabeled and a small number of labeled examples. CFDT applies a micro-clustering algorithm that scans the data only once to provide the statistical summaries of the data for incremental decision tree induction. Micro-clusters also serve as classifiers in tree leaves to improve classification accuracy and reinforce the any-time property. Our experiments on synthetic and real-world datasets show that CFDT is highly scalable for data streams while generating high classification accuracy with high speed.


title: Social Fusion: Integrating Twitter and Instagram for Event Monitoring

abstract: This paper describes the implementation of a service to identify and geo-locate real world events that may be present as social activity signals in two different social networks. Specifically, we focus on content shared by users on Twitter and Instagram in order to design a system capable of fusing data across multiple networks. Past work has demonstrated that it is indeed possible to detect physical events using various social network platforms. However, many of these signals need corroboration in order to handle events that lack proper support within a single network. We leverage this insight to design an unsupervised approach that can correlate event signals across multiple social networks. Our algorithm can detect events and identify the location of the event occurrence. We evaluate our algorithm using both simulations and real world datasets collected using Twitter and Instagram. The results indicate that our algorithm significantly improves false positive elimination and attains high precision compared to baseline methods on real world datasets.


title: Informing Pedagogical Action: Aligning Learning Analytics With Learning Design

abstract: This article considers the developing field of learning analytics and argues that to move from small-scale practice to broad scale applicability, there is a need to establish a contextual framework that helps teachers interpret the information that analytics provides. The article presents learning design as a form of documentation of pedagogical intent that can provide the context for making sense of diverse sets of analytic data. We investigate one example of learning design to explore how broad categories of analytics—which we call checkpoint and process analytics—can inform the interpretation of outcomes from a learning design and facilitate pedagogical action.


title: What is procedural content generation?: Mario on the borderline

abstract: We try to clarify the concept of procedural content generation (PCG) through contrasting it to other forms of content generation in games with which it could easily be mistaken, and through discussing some properties of PCG which are sometimes thought of as necessary but are actually not. After drawing up some clear demarcations for what is and what is not PCG, we present two versions of a content generation system for Infinite Mario Bros which is intentionally designed to question these same demarcations. We argue that, according to our own definition, one version of the system is an example of PCG while the other is not, even though they are mostly identical. We hope that this paper answers some questions but raises others, and inspires researchers and developers to thread some less common ground in developing content generation techniques.


title: Split-transistor compensation: Application to a low-dropout voltage regulator (LDO)

abstract: A controlled comparison of three compensation techniques — nested Miller compensation (NMC), Split-Length compensation (SLC) and the new Split-Transistor compensation (STC) — is performed in a multi-stage low-dropout voltage regulator (LDO). The LDO with STC used 41% and 84% lower total compensation capacitance compared to the LDOs with SLC and NMC, respectively. To verify the proposed compensation technique, a micro-power LDO using STC has been fabricated in a 0.5-μm CMOS process. Experimental results show an undershoot of 126 mV for a 1 μΑ −50 mA load step and an overshoot of 556 mV for a 50 mA–1 μΑ load step. At a total quiescent current of less than 5 μA, the fabricated micro-power LDO with STC finds application in IoT devices.


title: Universal-filtered multi-carrier technique for wireless systems beyond LTE

abstract: In this paper, we propose a multi-carrier transmission scheme to overcome the problem of intercarrier interference (ICI) in orthogonal frequency division multiplexing (OFDM) systems. In the proposed scheme, called universal-filtered multi-carrier (UFMC), a filtering operation is applied to a group of consecutive subcarriers (e.g. a given allocation of a single user) in order to reduce out-of-band sidelobe levels and subsequently minimize the potential ICI between adjacent users in case of asynchronous transmissions. We consider a coordinated multi-point (CoMP) reception technique, where a number of base stations (BSs) send the received signals from user equipments (UEs) to a CoMP central unit (CCU) for joint detection and processing. We examine the impact of carrier frequency offset (CFO) on the performance of the proposed scheme and compare the results with the performance of cyclic prefix based orthogonal frequency division multiplexing (CP-OFDM) systems. We use computer experiments to illustrate the efficiency of the proposed multi-carrier scheme. The results indicate that the UFMC scheme outperforms the OFDM for both perfect and non-perfect frequency synchronization between the UEs and BSs.


title: Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results

abstract: Face detection has witnessed immense progress in the last few years, with new milestones being surpassed every year. While many challenges such as large variations in scale, pose, appearance are successfully addressed, there still exist several issues which are not specifically captured by existing methods or datasets. In this work, we identify the next set of challenges that requires attention from the research community and collect a new dataset of face images that involve these issues such as weather-based degradations, motion blur, focus blur and several others. We demonstrate that there is a considerable gap in the performance of state-of-the-art detectors and real-world requirements. Hence, in an attempt to fuel further research in unconstrained face detection, we present a new annotated Unconstrained Face Detection Dataset (UFDD) with several challenges and benchmark recent methods. Additionally, we provide an in-depth analysis of the results and failure cases of these methods. The dataset as well as baseline results will be made publicly available in due time. The UFDD dataset as well as baseline results are available at: www.ufdd.info/


title: Semi-Parametric Image Synthesis

abstract: We present a semi-parametric approach to photographic image synthesis from semantic layouts. The approach combines the complementary strengths of parametric and nonparametric techniques. The nonparametric component is a memory bank of image segments constructed from a training set of images. Given a novel semantic layout at test time, the memory bank is used to retrieve photographic references that are provided as source material to a deep network. The synthesis is performed by a deep network that draws on the provided photographic material. Experiments on multiple semantic segmentation datasets show that the presented approach yields considerably more realistic images than recent purely parametric techniques. The results are shown in the supplementary video at this https URL


GLUE : A MultiTask Benchmark and Analysis Platform for Natural Language Understanding


title: Solving Feature Subset Selection Problem by a Parallel Scatter Search

abstract: The aim of this paper is to develop a Parallel Scatter Search metaheuristic for solving the Feature Subset Selection Problem in classification. Given a set of instances characterized by several features, the classification problem consists of assigning a class to each instance. Feature Subset Selection Problem selects a relevant subset of features from the initial set in order to classify future instances. We propose two methods for combining solutions in the Scatter Search metaheuristic. These methods provide two sequential algorithms that are compared with a recent Genetic Algorithm and with a parallelization of the Scatter Search. This parallelization is obtained by running simultaneously the two combination methods. Parallel Scatter Search presents better performance than the sequential algorithms.


title: Kinetic: verifiable dynamic network control

abstract: Network conditions are dynamic; unfortunately, current approaches to configuring networks. Network operators need tools to express how a network's data-plane behavior should respond to a wide range of events and changing conditions, ranging from unexpected failures to shifting traffic patterns to planned maintenance. Yet, to update the network configuration today, operators typically rely on a combination of manual intervention and ad hoc scripts. In this paper, we present Kinetic, a domain specific language and network control system that enables operators to control their networks dynamically in a concise, intuitive way. Kinetic also automatically verifies the correctness of these control programs with respect to user-specified temporal properties. Our user study of Kinetic with several hundred network operators demonstrates that Kinetic is intuitive and usable, and our performance evaluation shows that realistic Kinetic programs scale well with the number of policies and the size of the network.


title: The Stixel world: A medium-level representation of traffic scenes

abstract: Recent progress in advanced driver assistance systems and the race towards autonomous vehicles is mainly driven by two factors: (1) increasingly sophisticated algorithms that interpret the environment around the vehicle and react accordingly, and (2) the continuous improvements of sensor technology itself. In terms of cameras, these improvements typically include higher spatial resolution, which as a consequence requires more data to be processed. The trend to add multiple cameras to cover the entire surrounding of the vehicle is not conducive in that matter. At the same time, an increasing number of special purpose algorithms need access to the sensor input data to correctly interpret the various complex situations that can occur, particularly in urban traffic.By observing those trends, it becomes clear that a key challenge for vision architectures in intelligent vehicles is to share computational resources. We believe this challenge should be faced by introducing a representation of the sensory data that provides compressed and structured access to all relevant visual content of the scene. The Stixel World discussed in this paper is such a representation. It is a medium-level model of the environment that is specifically designed to compress information about obstacles by leveraging the typical layout of outdoor traffic scenes. It has proven useful for a multitude of automotive vision applications, including object detection, tracking, segmentation, and mapping.In this paper, we summarize the ideas behind the model and generalize it to take into account multiple dense input streams: the image itself, stereo depth maps, and semantic class probability maps that can be generated, e.g., by deep convolutional neural networks. Our generalization is embedded into a novel mathematical formulation for the Stixel model. We further sketch how the free parameters of the model can be learned using structured SVMs.


title: Analysis, Design and Realization of a Novel Directive Ultrawideband Antenna

abstract: In this paper, we present a simple log-periodic-dipole-array (LPDA) solution that allows us to achieve good ultrawideband (UWB) performances. The antenna has been manufactured and the measurements agree well with the theoretical predictions. The antenna presents an average gain of 8 dB and a return loss better than -10 dB over the band from 4.2 to 10.6 GHz. Both the measured antenna transfer function and the computed effect on pulse transmission show good performances in comparison with already known UWB antennas.


title: Espresso: Leveraging Generic Patterns For Automatically Harvesting Semantic Relations

abstract: In this paper, we present Espresso, a weakly-supervised, general-purpose, and accurate algorithm for harvesting semantic relations. The main contributions are: i) a method for exploiting generic patterns by filtering incorrect instances using the Web; and ii) a principled measure of pattern and instance reliability enabling the filtering algorithm. We present an empirical comparison of Espresso with various state of the art systems, on different size and genre corpora, on extracting various general and specific relations. Experimental results show that our exploitation of generic patterns substantially increases system recall with small effect on overall precision.


title: Evolutionary robotics: the Sussex approach

abstract: We give an overview of evolutionary robotics research at Sussex over the last five years. We explain and justify our distinctive approaches to (artificial) evolution, and to the nature of robot control systems that are evolved. Results are presented from research with evolved controllers for autonomous mobile robots, simulated robots, co-evolved animats, real robots with software controllers, and a real robot with a controller directly evolved in hardware.


Development Kits for 3 D Optical Metrology Systems


On cyclic codes and quasi-cyclic codes over Z q + u Z q


title: PEM-PCA: A Parallel Expectation-Maximization PCA Face Recognition Architecture

abstract: Principal component analysis or PCA has been traditionally used as one of the feature extraction techniques in face recognition systems yielding high accuracy when requiring a small number of features. However, the covariance matrix and eigenvalue decomposition stages cause high computational complexity, especially for a large database. Thus, this research presents an alternative approach utilizing an Expectation-Maximization algorithm to reduce the determinant matrix manipulation resulting in the reduction of the stages' complexity. To improve the computational time, a novel parallel architecture was employed to utilize the benefits of parallelization of matrix computation during feature extraction and classification stages including parallel preprocessing, and their combinations, so-called a Parallel Expectation-Maximization PCA architecture. Comparing to a traditional PCA and its derivatives, the results indicate lower complexity with an insignificant difference in recognition precision leading to high speed face recognition systems, that is, the speed-up over nine and three times over PCA and Parallel PCA.


title: An Automatic Identification System (AIS) Database for Maritime Trajectory Prediction and Data Mining

abstract: In recent years, maritime safety and efficiency become very important across the world. Automatic Identification System (AIS) tracks vessel movement by onboard transceiver and terrestrial and/or satellite base stations. The data collected by AIS contain broadcast kinematic information and static information. Both of them are useful for maritime anomaly detection and vessel route prediction which are key techniques in maritime intelligence. This paper is devoted to construct a standard AIS database for maritime trajectory learning, prediction and data mining. A path prediction method based on Extreme Learning Machine (ELM) is tested on this AIS database and the testing results show this database can be used as a standardized training resource for different trajectory prediction algorithms and other AIS data based mining applications.


title: Fundamental Green Tradeoffs: Progresses, Challenges, and Impacts on 5G Networks

abstract: With years of tremendous traffic and energy consumption growth, green radio has been valued not only for theoretical research interests but also for the operational expenditure reduction and the sustainable development of wireless communications. Fundamental green tradeoffs, served as an important framework for analysis, include four basic relationships: 1) spectrum efficiency versus energy efficiency; 2) deployment efficiency versus energy efficiency; 3) delay versus power; and 4) bandwidth versus power. In this paper, we first provide a comprehensive overview on the extensive on-going research efforts and categorize them based on the fundamental green tradeoffs. We will then focus on research progresses of 4G and 5G communications, such as orthogonal frequency division multiplexing and non-orthogonal aggregation, multiple input multiple output, and heterogeneous networks. We will also discuss potential challenges and impacts of fundamental green tradeoffs, to shed some light on the energy efficient research and design for future wireless networks.


title: Physical One-Way Functions

abstract: Modern cryptographic practice rests on the use of one-way functions, which are easy to evaluate but difficult to invert. Unfortunately, commonly used one-way functions are either based on unproven conjectures or have known vulnerabilities. We show that instead of relying on number theory, the mesoscopic physics of coherent transport through a disordered medium can be used to allocate and authenticate unique identifiers by physically reducing the medium's microstructure to a fixed-length string of binary digits. These physical one-way functions are inexpensive to fabricate, prohibitively difficult to duplicate, admit no compact mathematical representation, and are intrinsically tamper-resistant. We provide an authentication protocol based on the enormous address space that is a principal characteristic of physical one-way functions.


title: Show Me or Tell Me: Designing Avatars for Feedback

abstract: Avatarscanbeemployedasamotivationaltool,forexample,allowingnon-verbalcommunicationthat can be close to human communication. We describe two lab studies where we presented participants with avatars that communicated verbally via text and visually via expressions. In the first study, participants rated five different categories of captions and corresponding avatars. Results showed that the most persuasive, consistent and trustworthy verbal feedback was given in a humanized form. The second study was an exhaustive forced choice experiment where participants chose the happiest avatar from a pair displayed. Results showed participants found visual avatars more expressive and easier to understand than their verbal counterparts, and that users respond differently when presented with negative or positive emotions. This paper contributes to a better understanding of how to design feedback for expressive avatars.


title: Multidisciplinary design and optimization of a plastic injection mold using an integrated design and engineering environment

abstract: In order to remain competitive with respect to low-cost overseas markets, domestic moldmakers will have to increase the productivity of their engineers and maintain high quality standard, while dealing with the problem of an aging workforce. To increase the competitiveness of the European automotive industry, the concept of and Integrated Design and Engineering Environment (IDEE) has been developed within the 6th framework European project Pegasus - Integrated engineering processing & materials technologies for the European sector. The IDEE consists of a distributed set of software applications able to accelerate the design process of plastic injection molds by means of process automation and multidisciplinary optimization techniques. The IDEE combines into a single user-friendly platform different technologies ranging from Knowledge Management (KM) and Knowledge-Based Engineering (KBE) to Process Integration and Automation (PI&A). Central to the IDEE framework is a KBE application, which consists of a fully parametric rule-based mold product model. On the basis of the geometry of the plastic part to be molded and other (user) inputs, the KBE application can automatically produce different mold configurations and variants. In this way, product knowledge is leveraged to automate the repetitive and therefore time-consuming CAD modelling activities typically encountered in industry. Next to the mold geometry, the KBE application is able to generate different reports that serve as input to in-house developed or commercially-of-the-shelf CAE applications, like Autodesk® Moldflow® Synergy. The resulting software framework enables the simulation and analysis of the injection molding process, from which important performance characteristics can be retrieved. Other disciplinary applications are used to assess the cost and the environmental impact of the molding process and tooling. All the IDEE software applications are provided as web services on remote servers according to the Software as a Service (SaaS) paradigm. Noesis’s Optimus software has been used to link them into a seamless framework, control their execution and support all data exchange in an automated fashion over the internet. The scope of this paper is to describe the architecture and main functionalities of the IDEE and provide the results of studies performed on representative plastic parts for the automotive industry. It is demonstrated how the IDEE can enable multidisciplinary optimization of the injection molding process and find solutions that best balance manufacturing performance and product quality. By means of the IDEE many concepts and customized variants can be quickly generated and evaluated in terms of hours or days instead of weeks or months, thereby supporting a more efficient development of innovative and complex parts. The reliability of the evaluation is enhanced by the early use of computer-based simulations in the design process, and the success rate of the overall component increased because of the more disciplines included in the evaluation (tooling design, material processing, cost, environmental impact). The IDEE can help the designer to virtually prototype and analyze the product and, eventually, support the “first-time-right” design principle.


title: SITAR: GUI Test Script Repair

abstract: System testing of a GUI-based application requires that test cases, consisting of sequences of user actions/events, be executed and the software's output be verified. To enable automated re-testing, such test cases are increasingly being coded as low-level test scripts, to be replayed automatically using test harnesses. Whenever the GUI changes—widgets get moved around, windows get merged—some scripts become unusable because they no longer encode valid input sequences. Moreover, because the software's output may have changed, their test oracles—assertions and checkpoints—encoded in the scripts may no longer correctly check the intended GUI objects. We present ScrIpT repAireR (SITAR), a technique to automatically repair unusable low-level test scripts. SITAR uses reverse engineering techniques to create an abstract test for each script, maps it to an annotated event-flow graph (EFG), uses repairing transformations and human input to repair the test, and synthesizes a new “repaired” test script. During this process, SITAR also repairs the reference to the GUI objects used in the checkpoints yielding a final test script that can be executed automatically to validate the revised software. SITAR amortizes the cost of human intervention across multiple scripts by accumulating the human knowledge as annotations on the EFG. An experiment using QTP test scripts suggests that SITAR is effective in that 41-89 percent unusable test scripts were repaired. Annotations significantly reduced human cost after 20 percent test scripts had been repaired.


title: Learning by observation for surgical subtasks: Multilateral cutting of 3D viscoelastic and 2D Orthotropic Tissue Phantoms

abstract: Automating repetitive surgical subtasks such as suturing, cutting and debridement can reduce surgeon fatigue and procedure times and facilitate supervised tele-surgery. Programming is difficult because human tissue is deformable and highly specular. Using the da Vinci Research Kit (DVRK) robotic surgical assistant, we explore a “Learning By Observation” (LBO) approach where we identify, segment, and parameterize motion sequences and sensor conditions to build a finite state machine (FSM) for each subtask. The robot then executes the FSM repeatedly to tune parameters and if necessary update the FSM structure. We evaluate the approach on two surgical subtasks: debridement of 3D Viscoelastic Tissue Phantoms (3d-DVTP), in which small target fragments are removed from a 3D viscoelastic tissue phantom; and Pattern Cutting of 2D Orthotropic Tissue Phantoms (2d-PCOTP), a step in the standard Fundamentals of Laparoscopic Surgery training suite, in which a specified circular area must be cut from a sheet of orthotropic tissue phantom. We describe the approach and physical experiments with repeatability of 96% for 50 trials of the 3d-DVTP subtask and 70% for 20 trials of the 2d-PCOTP subtask. A video is available at: http://j.mp/Robot-Surgery-Video-Oct-2014.


title: AI-2 analogs and antibiotics: a synergistic approach to reduce bacterial biofilms

abstract: Quorum sensing (QS), the process of autoinducer-mediated cell-cell signaling among bacteria, facilitates biofilm formation, virulence, and many other multicellular phenotypes. QS inhibitors are being investigated as antimicrobials because of their potential to reduce symptoms of infectious disease while slowing the emergence of resistant strains. Autoinducer-2 (AI-2) analogs have been shown to inhibit genotypic QS responses among many bacteria. We demonstrate for the first time, the ability of C1-alkyl AI-2 analog, isobutyl-DPD, to significantly inhibit the maturation of Escherichia coli biofilms grown in vitro. Using a novel microfluidic device that incorporates dynamic, real-time measurements of biofilm density, we also show that a combinatorial approach wherein isobutyl-DPD ((S)-4,5-dihydroxy-2,3-pentanedione) is used with the antibiotic gentamicin is quite effective in rendering near complete clearance of pre-existing E. coli biofilms. Similarly, another AI-2 analog, phenyl-DPD, also used in combination with near MIC levels of gentamicin, resulted in clearance of preformed Pseudomonas aeruginosa biofilms. Clearance of pre-existing biofilms has remained a significant health care challenge; these results warrant consideration of a new approach based on the combination of "quenching" QS signal transduction processes with traditional antibiotic treatment.


title: Dimensional anchors: a graphic primitive for multidimensional multivariate information visualizations

abstract: We introduce a graphic primitive, called a dimensional anchor (DA), which facilitates the creation of new visualizations and provides insight into the analysis of information visualizations. The DA represents an attempt to provide a unified framework or model for a variety of visualizations, including Parallel Coordinates, scatter plot matrices, Radviz, Survey Plots and Circle Segments A dimensional anchor is constructed by assigning values to parameters associated with various geometric graphic elements that encode the basics of the above visualizations. We define a visualization vector space in which all of the above visualizations and many new ones are represented by vectors. These encodings make it possible to perform a Grand Tour traveling from Parallel Coordinates to Survey Plot, and visiting many other visualizations in between


spTimer: Spatio-Temporal Bayesian Modelling Using R


title: Enforcing kernel constraints by hardware-assisted virtualization

abstract: This article deals with kernel security protection. We propose a characterization of malicious kernel-targeted actions, based on how the way they act to corrupt the kernel. Then, we discuss security measures able to counter such attacks. We finally expose our approach based on hardware-virtualization that is partially implemented into our demonstrator Hytux, which is inspired from bluepill (Rutkowska in subverting vista kernel for fun and profit. In: Black Hat in Las Vegas, 2006), a malware that installs itself as a lightweight hypervisor--on a hardware-virtualization compliant CPU--and puts a running Microsoft Windows Operating System into a virtual machine. However, in contrast with bluepill, Hytux is a lightweight hypervisor that implements protection mechanisms in a more privileged mode than the Linux kernel.


title: Design and Performance Analysis of Superconducting Rim-Driven Synchronous Motors for Marine Propulsion

abstract: Rim-driven thrusters have received much attention concerning the potential benefits in vibration and hydrodynamic characteristics, which are of great importance in marine transportation systems. In this sense, the rim-driven permanent magnet, brushless dc, and induction motors have been recently suggested to be employed as marine propulsion motors. On the other hand, high-temperature superconducting (HTS) synchronous motors are becoming much fascinating, particularly in transport applications, regarding some considerable advantages such as low loss, high efficiency, and compactness. However, the HTS-type rim-driven synchronous motor has not been studied yet. Therefore, this paper is devoted to a design practice of rim-driven synchronous motors with HTS field winding. A detailed design procedure is developed for the HTS rim-driven motors, and the design algorithm is validated applying the finite element (FE) method. The FE model of a three-phase 2.5-MW HTS rim-driven synchronous motor is utilized, and the electromagnetic characteristics of the motor are then evaluated. The goal is to design an HTS machine fitted in a thin duct to minimize the hydrodynamic drag force. The design problem exhibits some difficulties while considering various constraints.


title: Analysis and comparison of classical and flex-PCB slotless windings in BLDC motors

abstract: Slotless brushless DC motors find more and more applications due to their high performance and their low production cost. This paper focuses on the windings inserted in the airgap of these motors, and particularly, to an original production technique which consists in printing the windings on a flexible printed circuit board. It shows that this technique, when coupled with an optimization of the winding shape, can improve the power density of about 30 % compared with classical skewed and rhombic windings made of round wire.


AVICAR: Audio-Visual Speech Corpus in a Car Environment


title: Multi-label classification: An overview

abstract: Multi-label classification methods are increasingly required by modern applications, such as protein function classification, music categorization, and semantic scene classification. This article introduces the task of multi-label classification, organizes the sparse related literature into a structured presentation and performs comparative experimental results of certain multilabel classification methods. It also contributes the definition of concepts for the quantification of the multi-label nature of a data set.


title: An overlay network approach to find objects in the future internet

abstract: The discovery of network objects (content, services, devices, persons, etc.) is an important challenge to face when researching towards the Future Internet. In this paper we give an overview of the challenge regarding network object discovery for the Future Internet, proposing a new architecture that provides mechanisms to achieve network object discovery from partial object descriptions, building an overlay network to organize object descriptions and using strong statistical mechanisms to find them from partial descriptions. With a prototype implementation of the architecture we demonstrate experimentally its feasibility and general behavior in different scenarios.


title: Goal Setting and Self-Efficacy During Self-Regulated Learning

abstract: This article focuses on the self-regulated learning processes of goal setting and perceived self-efficacy. Students enter learning activities with goals and self-efficacy for goal attainment. As learners work on tasks, they observe their own performances and evaluate their own goal progress. Self-efficacy and goal setting are affected by self-observation, self-judgment, and self-reaction. When students perceive satisfactory goal progress, they feel capable of improving their skills; goal attainment, coupled with high self-efficacy, leads students to set new challenging goals. Research is reviewed on goal properties (specificity, proximity, difficulty), self-set goals, progress feedback, contracts and conferences, and conceptions of ability. Ways of teaching students to set realistic goals and evaluate progress include establishing upper and lower goal limits and employing games, contracts, and conferences. Future research might clarify the relation of goal setting and self-efficacy to transfer, goal orien...


title: Architectural optimizations for high performance and energy efficient Smith-Waterman implementation on FPGAs using OpenCL

abstract: Smith-Waterman is a dynamic programming algorithm that plays a key role in the modern genomics pipeline as it is guaranteed to find the optimal local alignment between two strings of data. The state of the art presents many hardware acceleration solutions that have been implemented in order to exploit the high degree of parallelism available in this algorithm. The majority of these implementations use heuristics to increase the performance of the system at the expense of the accuracy of the result. In this work, we present an implementation of the pure version of the algorithm. We include the key architectural optimizations to achieve highest possible performance for a given platform and leverage the Berkeley roofline model to track the performance and guide the optimizations. To achieve scalability, our custom design comprises of systolic arrays, data compression features and shift registers, while a custom port mapping strategy aims to maximize performance. Our designs are built leveraging an OpenCL-based design entry, namely Xilinx SDAccel, in conjunction with a Xilinx Virtex 7 and Kintex Ultrascale platform. Our final design achieves a performance of 42.47 GCUPS (giga cell updates per second) with an energy efficiency of 1.6988 GCUPS/W. This represents an improvement of 1.72x in performance and energy efficiency over previously published FPGA implementations and 8.49x better in energy efficiency over comparable GPU implementations.


title: Closure and Convergence: A Foundation of Fault-Tolerant Computing

abstract: The authors formally define what it means for a system to tolerate a class of faults. The definition consists of two conditions. The first is that if a fault occurs when the system state is within the set of legal states, the resulting state is within some larger set and, if faults continue to occur, the system state remains within that larger set (closure). The second is that if faults stop occurring, the system eventually reaches a state within the legal set (convergence). The applicability of the definition for specifying and verifying the fault-tolerance properties of a variety of digital and computer systems is demonstrated. Using the definition, the authors obtain a simple classification of fault-tolerant systems. Methods for the systematic design of such systems are discussed. >


title: Prototyping sonic interaction for walking

abstract: Sounds play a substantial role in the experience of movement activities such as walking. Drawing on the movement inducing effects of sound, sonic interaction opens up numerous possibilities to modify the walker's movements and experience. We argue that designing sonic interaction for movement activities demands an experiential awareness of the interplay of sound, body movement and use situation, and, propose a prototyping method to understand possibilities and challenges related to the design of mobile sonic interaction. In this paper, we present a rapid prototyping system that enables non-expert users to design sonic interaction for walking and to experience their design in the real-world context. We discuss the way this prototyping system allows designers to experience how their design ideas unfold in mobile use and affect the walking.


title: Grading and Classification of Anthracnose Fungal Disease of Fruits based on Statistical Texture Features

abstract: In this paper, lesion areas affected by anthracnose are segmented using segmentation techniques, graded based on percentage of affected area and neural network classifier is used to classify normal and anthracnose affected on fruits. We have considered three types of fruit namely mango, grape and pomegranate for our work. The developed processing scheme consists of two phases. In the first phase, segmentation techniques namely thresholding, region growing, K-means clustering and watershed are employed for separating anthracnose affected lesion areas from normal area. Then these affected areas are graded by calculating the percentage of affected area. In the second phase texture features are extracted using Runlength Matrix. These features are then used for classification purpose using ANN classifier. We have conducted experimentation on a dataset of 600 fruits’ image samples. The classification accuracies for normal and affected anthracnose fruit types are 84.65% and 76.6% respectively. The work finds application in developing a machine vision system in horticulture field.


title: Efficient ambient WiFi energy harvesting technology and its applications

abstract: This paper is focused on powering wireless devices (including sensors) with high efficiency circuitry that can harvest ambient WiFi/Bluetooth energy and convert into direct current (dc). Herewith, we introduce a novel circuitry to generate a battery-like voltage from very low incoming RF energy (<−20 dBm). The new RF energy harvesting front-end is demonstrated to have much better conversion efficiency than state-of-the-art commercial RF harvesting modules, notably for low power levels. Realization of this rectification circuitry is a game-changing technology in powering mobile devices. To verify our energy harvesting design, we carry out an experiment where our RF harvesting module provides enough power to drive a commercial temperature/humidity meter with an LCD display using nothing more than ambient WiFi signals in a typical office environment.


title: A Cadaveric Study of Ultrasound-Guided Subpectineal Injectate Spread Around the Obturator Nerve and Its Hip Articular Branches

abstract: BACKGROUND AND OBJECTIVES ::: The femoral and obturator nerves are assumed to account for the primary nociceptive innervation of the hip joint capsule. The fascia iliaca compartment block and the so-called 3-in-1-block have been used in patients with hip fracture based on a presumption that local anesthetic spreads to anesthetize both the femoral and the obturator nerves. Evidence demonstrates that this presumption is unfounded, and knowledge about the analgesic effect of obturator nerve blockade in hip fracture patients presurgically is thus nonexistent. The objectives of this cadaveric study were to investigate the proximal spread of the injectate resulting from the administration of an ultrasound-guided obturator nerve block and to evaluate the spread around the obturator nerve branches to the hip joint capsule. ::: ::: ::: METHODS ::: Fifteen milliliters of methylene blue was injected into the interfascial plane between the pectineus and external obturator muscles in 7 adult cadavers. The spread of the injectate into the obturator canal and around the obturator and accessory obturator nerve branches to the hip joint was evaluated by subsequent dissection. ::: ::: ::: RESULTS ::: The injected dye spread into the obturator canal and colored all obturator branches to the hip joint capsule in all 14 sides. Furthermore, the accessory obturator nerve was present in 3 sides (21%), and the nerve and its branches to the hip joint capsule were colored in all cases. ::: ::: ::: CONCLUSIONS ::: In cadavers, injection of 15 mL of methylene blue into the interfascial plane between the pectineus and the external obturator muscle effectively spreads proximally to reach the obturator canal, as well as the obturator nerve branches to the hip joint capsule and the accessory obturator nerve.


title: A wideband array of stacked patch antennas using embedded air cavities in LTCC

abstract: An embedded air cavity in low temperature cofired ceramics (LTCC) is used to achieve a wideband response with stacked patch antennas. A single LTCC stacked patch antenna achieves a gain of better than 5dBi over an 18% bandwidth with a good impedance match. A 2/spl times/2 element array of stacked patches is also realized, and achieves a 3-dB gain bandwidth from 25 to 30GHz with a maximum gain of 11.1dBi.


title: Type-2 Fuzzy Arithmetic using Alpha-planes

abstract: This paper examines type-2 fuzzy arithmetic using in- terval analysis. It relies heavily on alpha-cuts and alpha-planes. Fur- thermore, we discuss the use of quasi type-2 fuzzy sets proposed by Mendel and Liu and define quasi type-2 fuzzy numbers. Arithmetic operations of such numbers are defined and a worked example is pre-


title: Multi-layer neural networks for image analysis of agricultural products

abstract: Neural networks have recently gained popularity as an alternative to regression models to characterize biological processes. Their decision-making capabilities can be best used in image analysis of biological products where the shape and size classification is not governed by any mathematical function. This paper reviews the technique of image analysis of agricultural products with reference to use of neural network classifiers for decision making. A thorough review of published literature reveals that, although many neural network classifiers have been used and evaluated for classifying agricultural products, multi-layer neural network classifiers perform the best for such tasks.


title: IoT-aware business processes for logistics: limitations of current approaches

abstract: The Internet of Things (IoT) aims at bridging the gap between real- world business processes and information systems. Supply chain management is one of the major application areas that can benefit from the IoT. When attached to physical items, the IoT technologies such as RFID and sensor networks transform objects of the supply chain into smart items. These items have the ability to capture context data and provide information systems with a representation of ‗things'. This allows information systems to monitor the supply chain processes through process aware information systems. Smart items can also execute parts of the business processes. In distributed environments, they can exchange data among them and make decisions based on business logic. However, this logic only acts according to pre-planned behaviour. Unpredicted exceptions based on real life events require dynamic process adaption in process definitions and corresponding instances. In this paper we review the main technologies of the IoT associated with automated support of business processes in logistics. We also identify the main limitations in the Business Process Execution Language (BPEL), regarding the support of design and runtime changes in these processes with smart items.


Facial Action Coding System


title: Securing Wireless Mesh Networks

abstract: Using wireless mesh networks to offer Internet connectivity is becoming a popular choice for wireless Internet service providers as it allows fast, easy, and inexpensive network deployment. However, security in WMNs is still in its infancy as very little attention has been devoted thus far to this topic by the research community. In this article we describe the specifics of WMNs and identify three fundamental network operations that need to be secured


title: A 3-axis optical force/torque sensor for prostate needle placement in Magnetic resonance imaging environments

abstract: The work presented in this paper has been performed in furtherance of developing an MRI (Magnetic resonance imaging) compatible fiber optical force sensor. In this paper, we discuss the design criteria and sensing principle of this optical sensor for monitoring forces in the 0–20 Newton range with an sub-Newton resolution. This instrumentation enables two degrees-of-freedom (DOF) torque measurement and one DOF force measurement. A novel flexure mechanism is designed and the finite element analysis is performed to aid the optimization of the design parameters. This 3 axis force/torque sensor with this range and resolution is an ideal tool for interventional procedures, e.g. needle biopsy and brachytherapy. The sensor is experimentally investigated and calibrated. Calibration results demonstrate that this sensor is a practical and accurate measurement apparatus.


title: Constructing deterministic finite-state automata in recurrent neural networks

abstract: Recurrent neural networks that are trained to behave like deterministic finite-state automata (DFAs) can show deteriorating performance when tested on long strings. This deteriorating performance can be attributed to the instability of the internal representation of the learned DFA states. The use of a sigmoidel discriminant function together with the recurrent structure contribute to this instability. We prove that a simple algorithm can construct second-order recurrent neural networks with a sparse interconnection topology and sigmoidal discriminant function such that the internal DFA state representations are stable, that is, the constructed network correctly classifies strings of arbitrary length . The algorithm is based on encoding strengths of weights directly into the neural network. We derive a relationship between the weight strength and the number of DFA states for robust string classification. For a DFA with n state and m input alphabet symbols, the constructive algorithm generates a “programmed” neural network with O ( n ) neurons and O ( mn ) weights. We compare our algorithm to other methods proposed in the literature.


Loss Functions


title: Semantics-aware malware detection

abstract: A malware detector is a system that attempts to determine whether a program has malicious intent. In order to evade detection, malware writers (hackers) frequently use obfuscation to morph malware. Malware detectors that use a pattern-matching approach (such as commercial virus scanners) are susceptible to obfuscations used by hackers. The fundamental deficiency in the pattern-matching approach to malware detection is that it is purely syntactic and ignores the semantics of instructions. In this paper, we present a malware-detection algorithm that addresses this deficiency by incorporating instruction semantics to detect malicious program traits. Experimental evaluation demonstrates that our malware-detection algorithm can detect variants of malware with a relatively low run-time overhead. Moreover our semantics-aware malware detection algorithm is resilient to common obfuscations used by hackers.


title: Experiencing SAX: a novel symbolic representation of time series

abstract: Many high level representations of time series have been proposed for data mining, including Fourier transforms, wavelets, eigenwaves, piecewise polynomial models, etc. Many researchers have also considered symbolic representations of time series, noting that such representations would potentiality allow researchers to avail of the wealth of data structures and algorithms from the text processing and bioinformatics communities. While many symbolic representations of time series have been introduced over the past decades, they all suffer from two fatal flaws. First, the dimensionality of the symbolic representation is the same as the original data, and virtually all data mining algorithms scale poorly with dimensionality. Second, although distance measures can be defined on the symbolic approaches, these distance measures have little correlation with distance measures defined on the original time series. ::: ::: In this work we formulate a new symbolic representation of time series. Our representation is unique in that it allows dimensionality/numerosity reduction, and it also allows distance measures to be defined on the symbolic approach that lower bound corresponding distance measures defined on the original series. As we shall demonstrate, this latter feature is particularly exciting because it allows one to run certain data mining algorithms on the efficiently manipulated symbolic representation, while producing identical results to the algorithms that operate on the original data. In particular, we will demonstrate the utility of our representation on various data mining tasks of clustering, classification, query by content, anomaly detection, motif discovery, and visualization.


title: Cognitive-behavioral therapy of depression and depressive symptoms during adolescence: a review and meta-analysis.

abstract: Objective: Outcome studies support the effectiveness of cognitive-behavioral approaches for treating depression among adults. The effectiveness of these approaches for adolescents, however, has received less empirical attention. This article critically reviews the literature on cognitive-behavioral therapy with depressed and dysphoric adolescents. Method: A meta-analysis was conducted to assess the effectiveness of these approaches and the stability of therapeutic gains. Results: Fourteen posttreatment-control comparisons and 10 follow-up-control comparisons resulted from six studies containing 217 subjects. The overall effect size posttreatment was -1.02, whereas the overall effect size at follow-up was -0.61. The effect sizes in both of these analyses were homogeneous and were supported by Fail-Safe-N calculations. Conclusion: Results suggest the short- and long-term effectiveness of cognitive-behavioral approaches for treating depressive symptoms with this population.


Regular Paper Folding and Punching Paper


title: A novel social media competitive analytics framework with sentiment benchmarks

abstract: Proposed a social media competitive analytics framework with sentiment benchmarks.Implemented an innovative business-driven social media competitive analytics tool-VOZIQ.Used VOZIQ to glean industry-specific marketing intelligence. In today's competitive business environment, there is a strong need for businesses to collect, monitor, and analyze user-generated data on their own and on their competitors' social media sites, such as Facebook, Twitter, and blogs. To achieve a competitive advantage, it is often necessary to listen to and understand what customers are saying about competitors' products and services. Current social media analytics frameworks do not provide benchmarks that allow businesses to compare customer sentiment on social media to easily understand where businesses are doing well and where they need to improve. In this paper, we present a social media competitive analytics framework with sentiment benchmarks that can be used to glean industry-specific marketing intelligence. Based on the idea of the proposed framework, new social media competitive analytics with sentiment benchmarks can be developed to enhance marketing intelligence and to identify specific actionable areas in which businesses are leading and lagging to further improve their customers' experience using customer opinions gleaned from social media. Guided by the proposed framework, an innovative business-driven social media competitive analytics tool named VOZIQ is developed. We use VOZIQ to analyze tweets associated with five large retail sector companies and to generate meaningful business insight reports.


title: BugBox: A Vulnerability Corpus for {PHP} Web Applications

abstract: Web applications are a rich source of vulnerabilities due to their high exposure, diversity, and popularity. Accordingly, web application vulnerabilities are useful subjects for empirical security research. Although some information on vulnerabilities is publicly available, there are no publicly available datasets that couple vulnerabilities with their source code, metadata, and exploits through an executable test environment. We describe BugBox, a corpus and exploit simulation environment for PHP web application vulnerabilities. BugBox provides a test environment and a packaging mechanism that allows for the distribution and sharing of vulnerability data. The goal is to facilitate empirical vulnerability studies, security tool evaluation, and security metrics research. In addition, the framework promotes developer education by demonstrating exploits and providing a sandbox where they can be run safely. BugBox and its modules are opensource and available online, and new modules may be contributed by community members.


title: Human-level AI's Killer Application: Interactive Computer Games

abstract: Although one of the fundamental goals of AI is to understand and develop intelligent systems that have all the capabilities of humans, there is little active research directly pursuing this goal. We propose that AI for interactive computer games is an emerging application area in which this goal of human-level AI can successfully be pursued. Interactive computer games have increasingly complex and realistic worlds and increasingly complex and intelligent computer-controlled characters. In this article, we further motivate our proposal of using interactive computer games for AI research, review previous research on AI and games, and present the different game genres and the roles that human-level AI could play within these genres. We then describe the research issues and AI techniques that are relevant to each of these roles. Our conclusion is that interactive computer games provide a rich environment for incremental research on human-level AI.


title: Automatic identification of fruit flies (Diptera: Tephritidae)

abstract: Description and learning techniques for fruit fly identification.Image analysis techniques for classifying wings and aculeus.A novel multimodal classification approach for fruit fly identification based on wing and aculeus.Good results in the identification of fruit flies based on wings, aculei, and their combination. Fruit flies are pests of major economic importance in agriculture. Among these pests it is possible to highlight some species of genus Anastrepha, which attack a wide range of fruits, and are widely distributed in the American tropics and subtropics. Researchers seek to identify fruit flies in order to implement management and control programs as well as quarantine restrictions. However, fruit fly identification is manually performed by scarce specialists through analysis of morphological features of the mesonotum, wing, and aculeus. Our objective is to find solid knowledge that can serve as a basis for the development of a sounding automatic identification system of the Anastrepha fraterculus group, which is of high economic importance in Brazil. Wing and aculeus images datasets from three specimens have been used in this work. The experiments using a classifier multimodal fusion approach shows promising effectiveness results for identification of these fruit flies, with more than 98% classification accuracy, a remarkable result for this difficult problem.


title: The Relationship among Young Adult College Students’ Depression, Anxiety, Stress, Demographics, Life Satisfaction, and Coping Styles

abstract: Recent research indicates that young adult college students experience increased levels of depression, anxiety, and stress. It is less clear what strategies college health care providers might use to assist students in decreasing these mental health concerns. In this paper, we examine the relative importance of coping style, life satisfaction, and selected demographics in predicting undergraduates’ depression, anxiety, and stress. A total of 508 full-time undergraduate students aged 18–24 years completed the study measures and a short demographics information questionnaire. Coping strategies and life satisfaction were assessed using the Brief COPE Inventory and an adapted version of the Brief Students’ Multidimensional Life Satisfaction Scale. Depression, anxiety, and stress were measured using the Depression Anxiety and Stress Scale-21 (DASS-21). Multiple regression analyses were used to examine the relative influence of each of the independent variables on depression, anxiety, and stress. Maladaptive co...


title: Design of sTetro: A Modular, Reconfigurable, and Autonomous Staircase Cleaning Robot

abstract: The mechanical, electrical, and autonomy aspects of designing a novel, modular, and reconfigurable cleaning robot, dubbed as sTetro (stair Tetro), are presented. The developed robotic platform uses a vertical conveyor mechanism to reconfigure itself and is capable of navigating over flat surfaces as well as staircases, thus significantly extending the automated cleaning capabilities as compared to conventional home cleaning robots. The mechanical design and system architecture are introduced first, followed by a detailed description of system modelling and controller design efforts in sTetro. An autonomy algorithm is also proposed for self-reconfiguration, locomotion, and autonomous navigation of sTetro in the controlled environment, for example, in homes/offices with a flat floor and a straight staircase. A staircase recognition algorithm is presented to distinguish between the surrounding environment and the stairs. The misalignment detection technique of the robot with a front staircase riser is also given, and a feedback from the IMU sensor for misalignment corrective measures is provided. The experiments performed with the sTetro robot demonstrated the efficacy and validity of the developed system models, control, and autonomy approaches.


title: Computational Thinking is Pervasive

abstract: A remarkable intellectual revolution is happening all around us, but few people are remarking on it. Computational thinking is influencing research in nearly all disciplines, both in the sciences and the humanities. Researchers are using computational metaphors to enrich theories as diverse as protoeomics and the mind-body problem. Computing has enabled researchers to ask new kinds of questions and to accept new kinds of answers, for instance, questions that require the processing of huge amounts of data. Of course, we all have computers on our desks nowadays. We all use them for email, web browsing, word processing, game playing, etc. But the computational thinking revolution goes much deeper than that; it is changing the way we think. Computational concepts provide a new language for describing hypotheses and theories. Computers provide an extension to our cognitive faculties. If you want to understand the 21 Century then you must first understand computation. At the University of Edinburgh, we have been exploring these themes in a series of seminars. At each seminar, one or more experts have discussed the influence of computational thinking on their discipline. We have enjoyed a stellar cast of speakers from a wide range of disciplines. Speakers have included the Principal of the University and three Vice Principals. The disciplines have ranged from Physics, Biology and Medicine to Philosophy, Architecture and Education. Often the influences described have been quite subtle. A common story is that the most direct approach does not work for some interesting reason, but that a less direct approach is having a more profound effect.


title: Architecting an Intelligent Tutoring System with an Affective Dialogue Module

abstract: The use of Intelligent Tutoring Systems has become more common in autodidactic education. There is significant work about the integration of the dialogue and affective characteristics to these systems. However, this integration could complicate the Intelligent Tutoring Systems structure. For that reason it is important to take care about architectural view of these systems, in order to organize the modular structure of these systems. Moreover, the architecture is an important element from the perspective of Software Engineering because it is a logical core of any software system. Based on this, in this paper we present a system called TIPOO, which our implementation of an intelligent tutoring system. Also we present the integration of an affective dialogue module (TIPOO Chat) into TIPOO. We describe the architecture of both systems. The design and implementation of these systems are described, looking forward making the development of Intelligent Tutoring Systems an easier, faster and standardized process.


title: Implementing neonatal screening for congenital cytomegalovirus: addressing the deafness of policy makers

abstract: Congenital cytomegalovirus (CMV) infection is an important public health problem with approximately 7 in 1,000 newborns infected and consequently at risk for hearing impairment. Newborn hearing screening will fail to detect this hearing impairment in approximately half of the cases because late onset hearing loss is frequent. Hearing impairment has profound impact on cognitive and social development of children and their families, determining most of the disease burden of congenital CMV infection. The potential value of newborn screening for congenital CMV is increasingly discussed. To date, many experts acknowledge the benefit of antiviral treatment in the prevention of hearing deterioration in newborns with neurological symptoms, and the benefit of early identification of late-onset hearing impairment by means of extensive audiological follow up of infected infants. These opinions imply that the potential of newborn screening for CMV would lie in the identification of the large proportion of asymptomatic congenitally infected newborns at risk for developing late-onset hearing loss. Experience with postnatal antiviral treatment of symptomatic newborns is encouraging, but has not been studied in asymptomatic congenitally infected newborns. A large-scale study on the safety and effectiveness of combined screening and antiviral therapy for congenital CMV infection is the necessary next step to take and should not be delayed.


title: Quantitative change of EEG and respiration signals during mindfulness meditation

abstract: BackgroundThis study investigates measures of mindfulness meditation (MM) as a mental practice, in which a resting but alert state of mind is maintained. A population of older people with high stress level participated in this study, while electroencephalographic (EEG) and respiration signals were recorded during a MM intervention. The physiological signals during meditation and control conditions were analyzed with signal processing.MethodsEEG and respiration data were collected and analyzed on 34 novice meditators after a 6-week meditation intervention. Collected data were analyzed with spectral analysis, phase analysis and classification to evaluate an objective marker for meditation.ResultsDifferent frequency bands showed differences in meditation and control conditions. Furthermore, we established a classifier using EEG and respiration signals with a higher accuracy (85%) at discriminating between meditation and control conditions than a classifier using the EEG signal only (78%).ConclusionSupport vector machine (SVM) classifier with EEG and respiration feature vector is a viable objective marker for meditation ability. This classifier should be able to quantify different levels of meditation depth and meditation experience in future studies.


title: Fused Deep Neural Networks for Efficient Pedestrian Detection

abstract: In this paper, we present an efficient pedestrian detection system, designed by fusion of multiple deep neural network (DNN) systems. Pedestrian candidates are first generated by a single shot convolutional multi-box detector at different locations with various scales and aspect ratios. The candidate generator is designed to provide the majority of ground truth pedestrian annotations at the cost of a large number of false positives. Then, a classification system using the idea of ensemble learning is deployed to improve the detection accuracy. The classification system further classifies the generated candidates based on opinions of multiple deep verification networks and a fusion network which utilizes a novel soft-rejection fusion method to adjust the confidence in the detection results. To improve the training of the deep verification networks, a novel soft-label method is devised to assign floating point labels to the generated pedestrian candidates. A deep context aggregation semantic segmentation network also provides pixel-level classification of the scene and its results are softly fused with the detection results by the single shot detector. Our pedestrian detector compared favorably to state-of-art methods on all popular pedestrian detection datasets. For example, our fused DNN has better detection accuracy on the Caltech Pedestrian dataset than all previous state of art methods, while also being the fastest. We significantly improved the log-average miss rate on the Caltech pedestrian dataset to 7.67% and achieved the new state-of-the-art.


title: Self-Attention Generative Adversarial Networks

abstract: In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps. In SAGAN, details can be generated using cues from all feature locations. Moreover, the discriminator can check that highly detailed features in distant portions of the image are consistent with each other. Furthermore, recent work has shown that generator conditioning affects GAN performance. Leveraging this insight, we apply spectral normalization to the GAN generator and find that this improves training dynamics. The proposed SAGAN achieves the state-of-the-art results, boosting the best published Inception score from 36.8 to 52.52 and reducing Frechet Inception distance from 27.62 to 18.65 on the challenging ImageNet dataset. Visualization of the attention layers shows that the generator leverages neighborhoods that correspond to object shapes rather than local regions of fixed shape.


title: A dual-feed circularly-polarized traveling-wave array antenna

abstract: A traveling-wave circularly-polarized microstrip array antenna is presented in this paper. It uses a circularly polarized dual-feed radiating element. The element is a rectangular patch with two chamfered corners. It is fed by microstrip lines, making it possible for the radiating element and feed lines to be realized and integrated in a single layer. A four-element array is designed, built and tested. Measured performance of the antenna is presented, where a good agreement between the simulated and measured results is obtained and demonstrated.


title: Keloids and Hypertrophic Scars: Pathophysiology, Classification, and Treatment

abstract: Abstract Keloid and hypertrophic scars represent an aberrant response to the wound healing process. These scars are characterized by dysregulated growth with excessive collagen formation, and can be cosmetically and functionally disruptive to patients. Objectives are to describe the pathophysiology of keloid and hypertrophic scar, and to compare differences with the normal wound healing process. The classification of keloids and hypertrophic scars are then discussed. Finally, various treatment options including prevention, conventional therapies, surgical therapies, and adjuvant therapies are described in detail. Literature review was performed identifying relevant publications pertaining to the pathophysiology, classification, and treatment of keloid and hypertrophic scars. Though the pathophysiology of keloid and hypertrophic scars is not completely known, various cytokines have been implicated, including interleukin (IL)-6, IL-8, and IL-10, as well as various growth factors including transforming growth factor-beta and platelet-derived growth factor. Numerous treatments have been studied for keloid and hypertrophic scars,which include conventional therapies such as occlusive dressings, compression therapy, and steroids; surgical therapies such as excision and cryosurgery; and adjuvant and emerging therapies including radiation therapy, interferon, 5-fluorouracil, imiquimod, tacrolimus, sirolimus, bleomycin, doxorubicin, transforming growth factor-beta, epidermal growth factor, verapamil, retinoic acid, tamoxifen, botulinum toxin A, onion extract, silicone-based camouflage, hydrogel scaffold, and skin tension offloading device. Keloid and hypertrophic scars remain a challenging condition, with potential cosmetic and functional consequences to patients. Several therapies exist which function through different mechanisms. Better understanding into the pathogenesis will allow for development of newer and more targeted therapies in the future.


title: Influence of compaction pressure on the morphology and phase evolution of porous NiTi alloy prepared by SHS technique

abstract: A locking swivel device is provided for connection with a proximal end of a drill string and for conducting a drilling fluid to the drill string. The device is comprised of a housing and a mandrel rotatably supported within the housing such that an annular space is defined between the housing and the mandrel, wherein one of the housing and the mandrel is connectable with the drill string such that the drill string rotates therewith. A clutch assembly is positioned within the annular space which is actuatable between an engaged condition in which the clutch assembly engages the housing and the mandrel in order to inhibit the rotation of the mandrel relative to the housing and a disengaged condition in which the clutch assembly disengages the housing and the mandrel in order to permit the rotation of the mandrel relative to the housing. The clutch assembly is actuated to the engaged condition by a pressure exerted by the drilling fluid.


title: A low cost hand-eye calibration method for arc welding robots

abstract: Hand-eye calibration is one of the most important and fundamental tasks in performing visual guided robot control tasks. Many techniques have been proposed that use various expensive equipment ranging from a three dimensional positioning device to a laser measurement device. The work presented in this paper aims at developing a low cost robot hand/eye calibration method that is accurate and robust enough to be used in a robotic arc welding system. The proposed calibration method allows the camera and object frames to be referenced directly to the robots base co-ordinate frame. Experimental results show that the developed method can be used to achieve a calibration accuracy of 1 mm (in the directions of interest, X and Y axes) that is acceptable for robotic welding applications.


title: Vehicle control system for automatic valet parking with infrastructure sensors

abstract: This paper presents vehicle control system for automatic valet parking with infrastructure sensors. First, we describe the automatic valet parking service system. In the service, vehicle moves autonomously based on sensing data generated by infrastructure sensors. Second, we implement vehicle control system for automatic valet parking. We design hardware and software components focusing on minimizing the add-on devices and maximizing data integration. Finally, we do a feasibility test for verification of the system.


title: Semantic Segmentation of Aerial Images With Shuffling Convolutional Neural Networks

abstract: Semantic segmentation of aerial images refers to assigning one land cover category to each pixel. This is a challenging task due to the great differences in the appearances of ground objects. Many attempts have been made during the past decades. In recent years, convolutional neural networks (CNNs) have been introduced in the remote sensing field, and various solutions have been proposed to realize dense semantic labeling with CNNs. In this letter, we propose shuffling CNNs to realize semantic segmentation of aerial images in a periodic shuffling manner. This approach is a supplement to current methods for semantic segmentation of aerial images. We propose a naive version and a deeper version of this method, and both are adept at detecting small objects. Additionally, we propose a method called field-of-view (FoV) enhancement that can enhance the predictions. This method can be applied to various networks, and our experiments verify its effectiveness. The final results are further improved through an ensemble method that averages the score maps generated by the models at different checkpoints of the same network. We evaluate our models using the ISPRS Vaihingen and Potsdam data sets, and we acquire promising results using these two data sets.


title: Microstrip Antenna Array With Series Feeding Network Designed With the Use of Slot-Coupled Three-Way Power Divider

abstract: A novel concept of a series feeding network of a microstrip antenna array has been shown. The proposed feeding network utilizes a four-port slot coupler as a three-way power divider, which is composed of two microstrip lines appropriately coupled through a slot within a common ground plane. The proposed power divider is used for simultaneous power distribution between two consecutive linear subarrays and between two 4 × 1 linear arrays constituting a single 8 × 1 linear subarray, where equal-amplitude and out-of-phase signals are required. Such a solution allows for realization of antenna arrays, in which all linear subarrays designed with the use of the “through-element” series feeding technique are fed at their centers from single transmission lines. The theoretical analysis as well as measurement results of the 8 × 8 antenna array operating within 10.5-GHz frequency range are shown.


title: Factors influencing the usage of 3G mobile services in Taiwan

abstract: Purpose – The purpose of this study is to analyse factors influencing subscribers' usage of 3G mobile services in Taiwan.Design/methodology/approach – The research model, based on a technology acceptance model (TAM) and added perceived enjoyment, was tested by means of a two‐stage structure equation modelling approach. Data were collected from 532 respondents via a web questionnaire survey.Findings – The findings indicate that perceived usefulness, perceived ease of use and perceived enjoyment are positively related to attitude, and perceived enjoyment has a positive influence on perceived usefulness.Practical implications – The findings suggest users of 3G mobile services need to be provided with more diverse and entertaining ways of communicating, which are at the same time easily accessible and convenient to use.Originality/value – A new correlation from perceived enjoyment to perceived usefulness was found to have a significant effect. This finding indicates enjoyment as a key factor influences custom...


title: Question Answering with Subgraph Embeddings

abstract: This paper presents a system which learns to answer questions on a broad range of topics from a knowledge base using few hand-crafted features. Our model learns low-dimensional embeddings of words and knowledge base constituents; these representations are used to score natural language questions against candidate answers. Training our system using pairs of questions and structured representations of their answers, and pairs of question paraphrases, yields competitive results on a recent benchmark of the literature.


title: Perfect diffusion primitives for block ciphers

abstract: Although linear perfect diffusion primitives, i.e. MDS matrices, are widely used in block ciphers, e.g. AES, very little systematic work has been done on how to find ``efficient'' ones. In this paper we attempt to do so by considering software implementations on various platforms. These considerations lead to interesting combinatorial problems: how to maximize the number of occurrences of 1 in those matrices, and how to minimize the number of pairwise different entries. We investigate these problems and construct efficient $4\times4$ and $8\times8$ MDS matrices to be used e.g. in block ciphers.


title: Efficiency Optimized Distributed Transformers for Broadband Monolithic Millimeter-Wave Integrated Power Amplifier Circuits

abstract: This paper revises the efficiency optimization equations for magnetic-transformer-based power combiner/ divider impedance transformation networks (e.g., distributed active transformer). It evaluates their usability for high millimeter-wave frequencies, where the distributed behavior of a transformer and its loss become relevant. As is demonstrated, the standard optimization equations do not lead to an optimum result. To enable the optimization of distributed transformers, an extended lumped equivalent circuit model is proposed and used for the derivation of new optimization equations. The novel equations enable the design of magnetic transformers with maximum efficiency. To demonstrate the performance that can be achieved with optimized distributed magnetic transformer networks at millimeter-wave frequencies, measurement results of an R-band (i.e., 170–260 GHz)/H-band (i.e., 220–325 GHz) power amplifier (PA) are presented. With an output power of approximately 6.2 dBm at 260 GHz, the amplifier is among the state of the art for mHEMT PAs operating in this frequency range. With its relative 3-dB bandwidth of 29%, however, it surpasses all those reported mHEMT PAs to date.


title: Electronically Reconfigurable Uniplanar Antenna With Polarization Diversity for Cognitive Radio Applications

abstract: A uniplanar, polarization diversity antenna with wideband/narrowband (WB/NB) switching capability is presented. The design utilizes an open annular slot with an orthogonal feeding mechanism to achieve polarization diversity performance across the operating bands. To attain high isolation between the ports, a strip is incorporated in the ground plane. Electronically reconfigurable slot resonator-based filters are also precisely embedded in the feedlines for the WB/NB switching. A fully functional prototype was fabricated, and the results were validated. The antenna provides a wide bandwidth mode from 3.4 to 8.0 GHz and a narrow bandwidth mode from 4.7 to 5.4 GHz with interport isolation better than 15 dB. Moreover, the design delivers polarization diversity with a low envelope correlation coefficient, a nearly omnidirectional radiation pattern, and a moderate gain while operating in WB and NB modes. These characteristics make the proposed antenna suitable for next-generation cognitive radio systems, where the WB antenna is used for spectrum sensing and the NB antenna for communication.


title: Coq: The world’s best macro assembler?

abstract: We describe a Coq formalization of a subset of the x86 architecture. One emphasis of the model is brevity: using dependent types, type classes and notation we give the x86 semantics a makeover that counters its reputation for baroqueness. We model bits, bytes, and memory concretely using functions that can be computed inside Coq itself; concrete representations are mapped across to mathematical objects in the SSReflect library (naturals, and integers modulo 2n) to prove theorems. Finally, we use notation to support conventional assembly code syntax inside Coq, including lexically-scoped labels. Ordinary Coq definitions serve as a powerful "macro" feature for everything from simple conditionals and loops to stack-allocated local variables and procedures with parameters. Assembly code can be assembled within Coq, producing a sequence of hex bytes. The assembler enjoys a correctness theorem relating machine code in memory to a separation-logic formula suitable for program verification.


title: Inside help: An integrative review of champions in healthcare-related implementation

abstract: Background/aims:The idea that champions are crucial to effective healthcare-related implementation has gained broad acceptance; yet the champion construct has been hampered by inconsistent use across the published literature. This integrative review sought to establish the current state of the literature on champions in healthcare settings and bring greater clarity to this important construct.Methods:This integrative review was limited to research articles in peer-reviewed, English-language journals published from 1980 to 2016. Searches were conducted on the online MEDLINE database via OVID and PubMed using the keyword “champion.” Several additional terms often describe champions and were also included as keywords: implementation leader, opinion leader, facilitator, and change agent. Bibliographies of full-text articles that met inclusion criteria were reviewed for additional references not yet identified via the main strategy of conducting keyword searches in MEDLINE. A five-member team abstracted all fu...


title: Rotation invariant distance measures for trajectories

abstract: For the discovery of similar patterns in 1D time-series, it is very typical to perform a normalization of the data (for example a transformation so that the data follow a zero mean and unit standard deviation). Such transformations can reveal latent patterns and are very commonly used in datamining applications. However, when dealing with multidimensional time-series, which appear naturally in applications such as video-tracking, motion-capture etc, similar motion patterns can also be expressed at different orientations. It is therefore imperative to provide support for additional transformations, such as rotation. In this work, we transform the positional information of moving data, into a space that is translation, scale and rotation invariant. Our distance measure in the new space is able to detect elastic matches and can be efficiently lower bounded, thus being computationally tractable. The proposed methods are easy to implement, fast to compute and can have many applications for real world problems, in areas such as handwriting recognition and posture estimation in motion-capture data. Finally, we empirically demonstrate the accuracy and the efficiency of the technique, using real and synthetic handwriting data.


title: OFDM Inspired Waveforms for 5G

abstract: As the standardization activities are being formed to lay the foundation of 5G wireless networks, there is a common consensus on the need to replace the celebrated orthogonal frequency division multiplexing (OFDM) by a more effective air interface that better serves the challenging needs of 5G. To this end, in the recent past, a number of new waveforms have been introduced in the literature. Interestingly, and at the same time not surprising, these methods share a common fundamental principle with OFDM: each data packet is made up of a number of complex-valued sinusoidals (pure tones) that are modulated by the information symbols. In this tutorial article, we build a common framework based on the said OFDM principle and derive these new waveforms from this point of view. This derivation provides a new perspective that facilitates straightforward understanding of channel equalization and the application of these new waveforms to multiple-input multiple-output channels. It also facilitates derivation of new structures for more efficient synthesis/analysis of these waveforms than those that have been reported in the literature.


title: Research-paper recommender systems: a literature survey

abstract: In the last 16 years, more than 200 research articles were published about research-paper recommender systems. We reviewed these articles and present some descriptive statistics in this paper, as well as a discussion about the major advancements and shortcomings and an overview of the most common recommendation concepts and approaches. We found that more than half of the recommendation approaches applied content-based filtering (55 %). Collaborative filtering was applied by only 18 % of the reviewed approaches, and graph-based recommendations by 16 %. Other recommendation concepts included stereotyping, item-centric recommendations, and hybrid recommendations. The content-based filtering approaches mainly utilized papers that the users had authored, tagged, browsed, or downloaded. TF-IDF was the most frequently applied weighting scheme. In addition to simple terms, n-grams, topics, and citations were utilized to model users' information needs. Our review revealed some shortcomings of the current research. First, it remains unclear which recommendation concepts and approaches are the most promising. For instance, researchers reported different results on the performance of content-based and collaborative filtering. Sometimes content-based filtering performed better than collaborative filtering and sometimes it performed worse. We identified three potential reasons for the ambiguity of the results. (A) Several evaluations had limitations. They were based on strongly pruned datasets, few participants in user studies, or did not use appropriate baselines. (B) Some authors provided little information about their algorithms, which makes it difficult to re-implement the approaches. Consequently, researchers use different implementations of the same recommendations approaches, which might lead to variations in the results. (C) We speculated that minor variations in datasets, algorithms, or user populations inevitably lead to strong variations in the performance of the approaches. Hence, finding the most promising approaches is a challenge. As a second limitation, we noted that many authors neglected to take into account factors other than accuracy, for example overall user satisfaction. In addition, most approaches (81 %) neglected the user-modeling process and did not infer information automatically but let users provide keywords, text snippets, or a single paper as input. Information on runtime was provided for 10 % of the approaches. Finally, few research papers had an impact on research-paper recommender systems in practice. We also identified a lack of authority and long-term research interest in the field: 73 % of the authors published no more than one paper on research-paper recommender systems, and there was little cooperation among different co-author groups. We concluded that several actions could improve the research landscape: developing a common evaluation framework, agreement on the information to include in research papers, a stronger focus on non-accuracy aspects and user modeling, a platform for researchers to exchange information, and an open-source framework that bundles the available recommendation approaches.


title: The effects of introducing the Klein-Bell ADL Scale in a rehabilitation service.

abstract: This study investigated the impact of introducing the Klein-Bell ADL Scale into a rehabilitation medicine service. A pretest and a posttest questionnaire of rehabilitation team members and a pretest and a posttest audit of occupational therapy documentation were completed. Results of the questionnaire suggested that the ADL scale influenced rehabilitation team members' observations in the combined area of occupational therapy involvement in self-care, improvement in the identification of treatment goals and plans, and communication between team members. Results of the audit suggested that the thoroughness and quantification of occupational therapy documentation improved. The clinical implications of these findings recommend the use of the Klein-Bell ADL Scale in rehabilitation services for improving occupational therapy documentation and for enhancing rehabilitation team effectiveness.


title: Cognitive Effort Drives Workspace Configuration of Human Brain Functional Networks

abstract: Effortful cognitive performance is theoretically expected to depend on the formation of a global neuronal workspace. We tested specific predictions of workspace theory, using graph theoretical measures of network topology and physical distance of synchronization, in magnetoencephalographic data recorded from healthy adult volunteers (N = 13) during performance of a working memory task at several levels of difficulty. We found that greater cognitive effort caused emergence of a more globally efficient, less clustered, and less modular network configuration, with more long-distance synchronization between brain regions. This pattern of task-related workspace configuration was more salient in the β-band (16-32 Hz) and γ-band (32-63 Hz) networks, compared with both lower (α-band; 8-16 Hz) and higher (high γ-band; 63-125 Hz) frequency intervals. Workspace configuration of β-band networks was also greater in faster performing participants (with correct response latency less than the sample median) compared with slower performing participants. Processes of workspace formation and relaxation in relation to time-varying demands for cognitive effort could be visualized occurring in the course of task trials lasting <2 s. These experimental results provide support for workspace theory in terms of complex network metrics and directly demonstrate how cognitive effort breaks modularity to make human brain functional networks transiently adopt a more efficient but less economical configuration.


Surprisingly Easy Hard-Attention for Sequence to Sequence Learning


title: Multi-task Adversarial Network for Disentangled Feature Learning

abstract: We address the problem of image feature learning for the applications where multiple factors exist in the image generation process and only some factors are of our interest. We present a novel multi-task adversarial network based on an encoder-discriminator-generator architecture. The encoder extracts a disentangled feature representation for the factors of interest. The discriminators classify each of the factors as individual tasks. The encoder and the discriminators are trained cooperatively on factors of interest, but in an adversarial way on factors of distraction. The generator provides further regularization on the learned feature by reconstructing images with shared factors as the input image. We design a new optimization scheme to stabilize the adversarial optimization process when multiple distributions need to be aligned. The experiments on face recognition and font recognition tasks show that our method outperforms the state-of-the-art methods in terms of both recognizing the factors of interest and generalization to images with unseen variations.


title: Security and Resilience in 5G: Current Challenges and Future Directions

abstract: 5G tends to be a multi-layered, multi-actor, and multi-access mobile network in order to fulfill the stringent availability, security, privacy and resilience requirements that are usually contradictory. In this paper, we propose a 5G vision based on softwarization. We provide a non-exhaustive list of current security, trust and resilience issues that are critical to be explored in 5G. We finally give some directions to overcome these issues.


title: Digital forensics for printed source identification

abstract: Recently, digital forensics, which involves the collection and analysis of the origin digital device, has become an important issue. Digital content can play a crucial role in identifying the source device, such as serve as evidence in court. To achieve this goal, we use different texture feature extraction methods such as gray-level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT), to analyze the Chinese printed source in order to find the source of printers. Furthermore, we also explore the optimum feature subset by using feature selection techniques and using support vector machine (SVM) to identify the source model of the documents. The average experimental results attain a 98.64% identification rate which is significantly superior to the existing known method by 1.2%. This higher testing performance demonstrates that the proposed identification method is very useful for source laser printer identification.


title: Designing for Workplace Reflection: A Chat and Voice-Based Conversational Agent

abstract: Conversational agents stand to play an important role in supporting behavior change and well-being in many domains. With users able to interact with conversational agents through both text and voice, understanding how designing for these channels supports behavior change is important. To begin answering this question, we designed a conversational agent for the workplace that supports workers' activity journaling and self-learning through reflection. Our agent, named Robota, combines chat-based communication as a Slack Bot and voice interaction through a personal device using a custom Amazon Alexa Skill. Through a 3-week controlled deployment, we examine how voice-based and chat-based interaction affect workers' reflection and support self-learning. We demonstrate that, while many current technical limitations exist, adding dedicated mobile voice interaction separate from the already busy chat modality may further enable users to step back and reflect on their work. We conclude with discussion of the implications of our findings to design of workplace self-tracking systems specifically and to behavior-change systems in general.


title: Ring x2: Designing Gestures for Smart Rings using Temporal Calculus

abstract: We introduce "Ring×2," a design space for gesture input with two smart rings. Wearing two rings at once opens new input opportunities, unexplored by the community so far, such as bimanual ring gestures or shifting input from one smart ring to the other to effectively manage situational impairments, such as encumbrance. To enable such developments and explorations, we present a formal description of designing two-ring gestures from the perspective of temporal calculus, a logic-based framework for reasoning about events and actions occurring in time, which we exemplify on a dataset of 83 bimanual gestures performed by 24 participants with two rings. We hope that our exploration of two-ring gestures and our design approach rooted in temporal calculus will be provocative and inspiring for the community, leading to new designs of input techniques for smart rings.


title: Fast Semi-dense Depth Map Estimation

abstract: We consider the problem of depth reconstruction from downsampled sparse depth values. We compare our approach with semi-dense depth map interpolation and direct RGB-to-Depth reconstruction solutions on several datasets, including Matterport 3D dataset containing RGB and depth images of 90 building-scale scenes. We demonstrate that the proposed model can produce approximate depth map for over two hundreds images per second.


title: Decision Guidance Models for Microservice Monitoring

abstract: Decision guidance models are a means for design space exploration and documentation. In this paper, we present decision guidance models for microservice monitoring. The selection of a monitoring system is an essential part of each microservice architecture due to the high level of dynamic structure and behavior of such a system. We present decision guidance models for generation of monitoring data, data management, processing monitoring data, and for disseminating and presenting monitoring information to stakeholders. The presented models have been derived from literature, our previous work on monitoring for distributed systems and microservice-based systems, and by analyzing existing monitoring systems. The developed models have been used for discussing monitoring requirements for a microservice-based system with a company in the process automation domain. They are part of a larger effort for developing decision guidance models for microservice architecture in general.


title: Medically unexplained symptoms in primary care

abstract: Adult patients with medically unexplained symptoms (somatisation) in primary care are numerous and make disproportionately high demands on health services. Most of these individuals are open to the suggestion that their illness reflects psychological needs. Empowering explanations from doctors can enable patient and doctor to work collaboratively in managing the problem and can reduce healthcare contacts. Parental medically unexplained symptoms, sexual and physical abuse in childhood and childhood neglect are associated with a greater risk of medically unexplained symptoms in adulthood. The overall prognosis for the majority of primary care patients with medically unexplained symptoms is very good. The two most common approaches to dealing with medically unexplained symptoms in primary care are stepped-care approaches and teaching general practitioners new skills to manage consultations more effectively.