title: Kineticons: using iconographic motion in graphical user interface design

text: Icons in graphical user interfaces convey information in a mostly universal fashion that allows users to immediately interact with new applications, systems and devices. In this paper, we define Kineticons - an iconographic scheme based on motion. By motion, we mean geometric manipulations applied to a graphical element over time (e.g., scale, rotation, deformation). In contrast to static graphical icons and icons with animated graphics, kineticons do not alter the visual content or "pixel-space" of an element. Although kineticons are not new - indeed, they are seen in several popular systems - we formalize their scope and utility. One powerful quality is their ability to be applied to GUI elements of varying size and shape from a something as small as a close button, to something as large as dialog box or even the entire desktop. This allows a suite of system-wide kinetic behaviors to be reused for a variety of uses. Part of our contribution is an initial kineticon vocabulary, which we evaluated in a 200 participant study. We conclude with discussion of our results and design recommendations.


title: Sample Size in Factor Analysis

text: The factor analysis literature includes a range of recommendations regarding the minimum sample size necessary to obtain factor solutions that are adequately stable and that correspond closely to population factors. A fundamental misconception about this issue is that the minimum sample size, or the minimum ratio of sample size to the number of variables, is invariant across studies. In fact, necessary sample size is dependent on several aspects of any given study, including the level of communality of the variables and the level of overdetermination of the factors. The authors present a theoretical and mathematical framework that provides a basis for understanding and predicting these effects. The hypothesized effects are verified by a sampling study using artificial data. Results demonstrate the lack of validity of common rules of thumb and provide a basis for establishing guidelines for sample size in factor analysis.


title: The Fox News Effect: Media Bias and Voting∗

text: Does media bias affect voting? We address this question by looking at the entry of Fox News in cable markets and its impact on voting. Between October 1996 and November 2000, the conservative Fox News Channel was introduced in the cable programming of 20 percent of US towns. Fox News availability in 2000 appears to be largely idiosyncratic. Using a data set of voting data for 9,256 towns, we investigate if Republicans gained vote share in towns where Fox News entered the cable market by the year 2000. We find a significant effect of the introduction of Fox News on the vote share in Presidential elections between 1996 and 2000. Republicans gain 0.4 to 0.6 percentage points in the towns which broadcast Fox News. The results are robust to town-level controls, district and county fixed effects, and alternative specifications. We also find a significant effect of Fox News on Senate vote share and, to a lesser extent, on voter turnout. Our estimates imply that Fox News convinced 3 to 8 percent of its viewers to vote Republican. The evidence is most consistent with voters suffering from persuasion bias. We also discuss a model of rational voter updating that fits most facts. ∗George Akerlof, Stephen Ansolabehere, Larry M. Bartels, Matthew Gentzkow, Alan Gerber, Jay Hamilton, Alan Krueger, Marco Manacorda, Enrico Moretti, Torsten Persson, Sam Popkin, Riccardo Puglisi, Matthew Rabin, Jesse Shapiro, David Stromberg, and audiences at Bonn University, EUI (Florence), Fuqua, Harvard University, IIES (Stockholm), Princeton University, UC Berkeley, University of Chicago GSB, and the NBER 2005 Political Economy and Labor Studies Meetings provided useful comments. We would like to specially thank Jim Collins and Matthew Gentzkow for providing the Scarborough data. Shawn Bananzadeh, Jessica Chan, Marguerite Converse, Neil Dandavati, Tatyana Deryugina, Monica Deza, Dylan Fox, Melissa Galicia, Calvin Ho, Sudhamas Khanchanawong, Richard Kim, Martin Kohan, Vipul Kumar, Jonathan Leung, Clarice Li, Tze Yang Lim, Ming Mai, Sameer Parekh, Sharmini Radakrishnan, Rohan Relan, Chanda Singh, Matthew Stone, Nan Zhang, Sibo Zhao, and Liya Zhu helped collect the voting and the cable data. Dan Acland, Saurabh Bhargava, Avi Ebenstein, and Devin Pope provided excellent research assistance.


title: Validation of LiDAR point clouds for classification of high-value crops using geometric-and reflectance-based extraction algorithm

text: This study provides an experimental analysis of high-value crop classification using geometric and reflectance-based feature extraction algorithms which can be used to validate extensive classification. The main goal of this study is to identify the class based on the 3D reconstruction of small scale LiDAR scanner and RGB images. The reference used in this study was obtained from the initially classified classes of Mapua-Phil LiDAR2 Project. In validating high-value crops, the classification results were compared to the results obtained using the reference data. The validation procedures involve comparing the locations of previously classified crops and analyzing the crop cycle in the tested fields. The proposed methodology used to identify specific class based on the geometric feature alone are found to be acceptable with an average accuracy of 92.5% while the reflectance based classification alone provides an average accuracy of 90%.


title: Gain enhancement of butler matrix fed antenna array system by using planar circular EBG units

text: In this paper planar circular Electromagnetic Band Gap (EBG) structures have been implemented in the Butler matrix fed antenna array system to improve the radiation performance of the system. Initially a conventional 4×4 Butler matrix fed antenna array system has been designed at 14.5 GHz. Asymmetricity in the gain values of major beams of the antenna array system has been observed both in simulation and measurement. Generally due to the design simplicity, light weight and compactness, the rectangular patch microstrip antenna has been used as the radiating element in the Butler matrix fed antenna array system. At higher frequencies microstrip antenna suffers from losses due to surface wave excitation. It degrades the radiation efficiency of the antenna array system and also increases the level of mutual coupling between the radiating elements in the antenna array. EBG (Electromagnetic Band Gap) units are used to improve the radiation performance of the system by the generation of stop band. The propagation of surface wave will be suppressed in this stop band region and may improve the performance of the system. To fulfill this requirement, 3 × 1 planar circular EBG unit has been designed. A stop band of 2.15 GHz with the centre frequency of 14.5 GHz has been obtained by the proposed EBG unit. The 3×1 planar circular EBG unit has been integrated between the radiating elements of the antenna array system. As a result the gain enhancement has been achieved for the 1R, 2L, 2R and 1L beams both in simulation and measurement at 14.5 GHz.


title: Wiggling through complex traffic: Planning trajectories constrained by predictions

text: The vision of autonomous driving is piecewise becoming reality. Still the problem of executing the driving task in a safe and comfortable way in all possible environments, for instance highway, city or rural road scenarios is a challenging task. In this paper we present a novel approach to planning trajectories for autonomous vehicles. Hereby we focus on the problem of planning a trajectory, given a specific behavior option, e.g. merging into a specific gap at a highway entrance or a roundabout. Therefore we explicitly take arbitrary road geometry and prediction information of other traffic participants into account. We extend former contributions in this field by providing a flexible problem description and a trajectory planner without specialization to distinct classes of maneuvers beforehand. Using a carefully chosen representation of the dynamic free space, the method is capable of considering multiple lanes including the predicted dynamics of other traffic participants, while being real-time capable at the same time. The combination of those properties in one general planning method represents the novelty of the proposed method. We demonstrate the capability of our algorithm to plan safe trajectories in simulation and in real traffic in real-time.


title: Privacy-Preserving Human Activity Recognition from Extreme Low Resolution

text: Privacy protection from unwanted video recordings is an important societal challenge. We desire a computer vision system (e.g., a robot) that can recognize human activities and assist our daily life, yet ensure that it is not recording video that may invade our privacy. This paper presents a fundamental approach to address such contradicting objectives: human activity recognition while only using extreme low-resolution (e.g., 16x12) anonymized videos. We introduce the paradigm of inverse super resolution (ISR), the concept of learning the optimal set of image transformations to generate multiple low-resolution (LR) training videos from a single video. Our ISR learns different types of sub-pixel transformations optimized for the activity classification, allowing the classifier to best take advantage of existing high-resolution videos (e.g., YouTube videos) by creating multiple LR training videos tailored for the problem. We experimentally confirm that the paradigm of inverse super resolution is able to benefit activity recognition from extreme low-resolution videos.


title: Coverage-Guided Fuzzing for Deep Neural Networks

text: In company with the data explosion over the past decade, deep neural network (DNN) based software has experienced unprecedented leap and is becoming the key driving force of many novel industrial applications, including many safety-critical scenarios such as autonomous driving. Despite great success achieved in various human intelligence tasks, similar to traditional software, DNNs could also exhibit incorrect behaviors caused by hidden defects causing severe accidents and losses. In this paper, we propose DeepHunter, an automated fuzz testing framework for hunting potential defects of general-purpose DNNs. DeepHunter performs metamorphic mutation to generate new semantically preserved tests, and leverages multiple plugable coverage criteria as feedback to guide the test generation from different perspectives. To be scalable towards practical-sized DNNs, DeepHunter maintains multiple tests in a batch, and prioritizes the tests selection based on active feedback. The effectiveness of DeepHunter is extensively investigated on 3 popular datasets (MNIST, CIFAR-10, ImageNet) and 7 DNNs with diverse complexities, under large set of 6 coverage criteria as feedback. The large-scale experiments demonstrate that DeepHunter can (1) significantly boost the coverage with guidance; (2) generate useful tests to detect erroneous behaviors and facilitate the DNN model quality evaluation; (3) accurately capture potential defects during DNN quantization for platform migration.


title: The design of early childhood makerspaces to support positive technological development: Two case studies

text: Purpose – With the advent of the maker movement, there has been a new push to explore how spaces of learning ought to be designed. The purpose of this paper is to integrate three approaches for thinking about the role of design of the learning environment: the makerspace movement, Reggio Emilia’s Third Teacher approach, and the positive technological development (PTD) framework. Design/methodology/approach – This paper describes two case studies that involved the design of two different early childhood makerspaces (ECMSs) through a co-participatory design experience: the Kindergarten Creator Space at the International School of Billund in Denmark; and the ECMS at (removed for blind review), a resource library in Medford, MA. Findings – Based on the foundational education framework of PTD, and ideas from the field of interior design, this paper describes the design principles of several successful makerspaces, and case examples of children who use them. Originality/value – By grounding the theoretical discussion in three approaches, the authors aim to suggest design elements of physical spaces in schools and libraries that can promote young children’s learning through making. Recommendations are discussed for practitioners and researchers interested in ECMSs.


title: Predicting the Structure of Cooking Recipes

text: Cooking recipes exist in abundance; but due to their unstructured text format, they are hard to study quantitatively beyond treating them as simple bags of words. In this paper, we propose an ingredientinstruction dependency tree data structure to represent recipes. The proposed representation allows for more refined comparison of recipes and recipe-parts, and is a step towards semantic representation of recipes. Furthermore, we build a parser that maps recipes into the proposed representation. The parser’s edge prediction accuracy of 93.5% improves over a strong baseline of 85.7% (54.5% error reduction).


title: Convolutional Monte Carlo Rollouts in Go

text: In this work, we present a MCTS-based Go-playing program which uses convolutional networks in all parts. Our method performs MCTS in batches, explores the Monte Carlo search tree using Thompson sampling and a convolutional network, and evaluates convnet-based rollouts on the GPU. We achieve strong win rates against open source Go programs and attain competitive results against state of the art convolutional net-based Go-playing programs.


title: AFRL-RQ-WP-TR-2014-0212 A BROADBAND HIGH-GAIN BILAYER LOG-PERIODIC DIPOLE ARRAY ( LPDA ) FOR ULTRA HIGH FREQUENCY ( UHF ) CONFORMAL LOAD BEARING ANTENNA STRUCTURES ( CLAS ) APPLICATIONS

text: A broadband high-gain bi-layer Log-PeriodicDipole-Array (LPDA) is introduced for Conformal Load Bearing Antenna Structures (CLAS) applications. Under the proposed scheme the two layers of the LPDA are printed on two separate thin dielectric substrates which are substantially separated from each other. A meander line geometry is adapted to achieve size reduction for the array. The fabricated and tested array easily exceeds more than an octave of gain, pattern and VSWR bandwidth. Index Terms — Broadband antenna, array, log-periodic, LPDA, endfire, ultrawideband, UWB, CLAS.


title: Docker Layer Placement for On-Demand Provisioning of Services on Edge Clouds

text: Driven by the increasing popularity of the microservice architecture, we see an increase in services with unknown demand pattern located in the edge network. Pre-deployed instances of such services would be idle most of the time, which is economically infeasible. Also, the finite storage capacity limits the amount of deployed instances we can offer. Instead, we present an on-demand deployment scheme using the Docker platform. In Docker, service images consist of layers, each layer adding specific functionality. This allows different services to reuse layers, avoiding cluttering the storages with redundant replicas. We propose a layer placement method which allows users to connect to a server, retrieve all necessary layers -possibly from multiple locations- and deploy an instance of the requested service within the desired response time. We search for the best layer placement which maximizes the satisfied demand given the storage and delay constraints. We developed an iterative optimization heuristic which is less exhaustive by dividing the global problem in smaller subproblems. Our simulation results show that our heuristic is able to solve the problem with less system resources. Last, we present interesting use-cases to use this approach in real-life scenarios.


title: Support vector regression with modified firefly algorithm for stock price forecasting

text: The support vector regression (SVR) has been employed to deal with stock price forecasting problems. However, the selection of appropriate kernel parameters is crucial to obtaining satisfactory forecasting performance. This paper proposes a novel approach for forecasting stock prices by combining the SVR with the firefly algorithm (FA). The proposed forecasting model has two stages. In the first stage, to enhance the global convergence speed, a modified version of the FA, which is termed the MFA, is developed in which the dynamic adjustment strategy and the opposition-based chaotic strategy are introduced. In the second stage, a hybrid SVR model is proposed and combined with the MFA for stock price forecasting, in which the MFA is used to optimize the SVR parameters. Finally, comparative experiments are conducted to show the applicability and superiority of the proposed methods. Experimental results show the following: (1) Compared with other algorithms, the proposed MFA algorithm possesses superior performance, and (2) The proposed MFA-SVR prediction procedure can be considered as a feasible and effective tool for forecasting stock prices.


title: Women with hyperandrogenism in elite sports: scientific and ethical rationales for regulating.

text: The recent implementation by some major sports-governing bodies of policies governing eligibility of females with hyperandrogenism to compete in women's sports has raised a lot of attention and is still a controversial issue. This short article addresses two main subjects of controversy: the existing scientific basis supporting performance enhancing of high blood T levels in elite female athletes, and the ethical rationale and considerations about these policies. Given the recently published data about both innate and acquired hyperandrogenic conditions and their prevalence in elite female sports, we claim that the high level of androgens are per se performance enhancing. Regulating women with clinical and biological hyperandrogenism is an invitation to criticism because biological parameters of sex are not neatly divided into only two categories in the real world. It is, however, the responsibility of the sports-governing bodies to do their best to guarantee a level playing field to all athletes. In order not cloud the discussions about the policies on hyperandrogenism in sports, issues of sports eligibility and therapeutic options should always be considered and explained separately, even if they may overlap. Finally, some proposals for refining the existing policies are made in the present article.


title: Design of Boost-Flyback Single-Stage PFC converter for LED power supply without electrolytic capacitor for energy-storage

text: Light emitting diodes (LEDs) are likely to be used for general lighting applications due to their high efficiency and longer life. This paper presents the concept of applying large voltage ripple for energy storage into the Boost-Flyback Single -Stage PFC converter for the elimination of the electrical capacitor. Following proposed design procedure, the single stage PFC circuit with small energy storage capacitance can still achieve good output voltage regulation while preserving desired input power factor. The detailed theoretical analysis and design procedure of the Single-Stage PFC converter is presented. The experimental results obtained on a 60W prototype converter along with waveforms are presented.


title: Multilevel single-phase inverter based on TNPC-type

text: A new single-phase 7-level pulse-width modulation (PWM) inverter using T-type Neutral-point-clamped (TNPC) celIs is presented to obtain high quality output voltage minimizing the number of switching devices. It includes two TNPC cells series-connected together and connected to with different voltage levels. To avoid inherent problems caused by voltage unbalance, modified Buck-boost converters are added at DC-link side to make DC voltage sources for the proposed inverter. Operational principles and the switching pattern are analyzed in this paper. To verify the performance of the proposed inverter, PSIM simulation and experimental results are also shown in this paper.


title: Development of a helical climbing modular snake robot

text: This research aims to study the grasping torque profile of the helical climbing robot on the cylindrical pole with constant radius. The modular snake robot is formed into a helical shape which can be described by various parameters such as the helical pitch angle, the radius and the pitch distance. The torque in each axis of rotation is affected by the helical pitch angle parameter. Five grasping configurations with different helical pitch angle were tested on the 18 degree of freedoms, 7 modules wheeled snake robot. The experimental result showed that the torque around yaw axis transferred to the pitch axis when the helical pitch angle was increasing. The profile of torque magnitude along the robot's body resulted in parabolic shape due to the unbalanced grasping force of the discrete points of contact between the robot and the pole.


title: Privacy and human behavior in the age of information

text: This Review summarizes and draws connections between diverse streams of empirical research on privacy behavior. We use three themes to connect insights from social and behavioral sciences: people’s uncertainty about the consequences of privacy-related behaviors and their own preferences over those consequences; the context-dependence of people’s concern, or lack thereof, about privacy; and the degree to which privacy concerns are malleable—manipulable by commercial and governmental interests. Organizing our discussion by these themes, we offer observations concerning the role of public policy in the protection of privacy in the information age.


title: Classifying latent user attributes in twitter

text: Social media outlets such as Twitter have become an important forum for peer interaction. Thus the ability to classify latent user attributes, including gender, age, regional origin, and political orientation solely from Twitter user language or similar highly informal content has important applications in advertising, personalization, and recommendation. This paper includes a novel investigation of stacked-SVM-based classification algorithms over a rich set of original features, applied to classifying these four user attributes. It also includes extensive analysis of features and approaches that are effective and not effective in classifying user attributes in Twitter-style informal written genres as distinct from the other primarily spoken genres previously studied in the user-property classification literature. Our models, singly and in ensemble, significantly outperform baseline models in all cases. A detailed analysis of model components and features provides an often entertaining insight into distinctive language-usage variation across gender, age, regional origin and political orientation in modern informal communication.


title: Stochastic gradient boosted distributed decision trees

text: Stochastic Gradient Boosted Decision Trees (GBDT) is one of the most widely used learning algorithms in machine learning today. It is adaptable, easy to interpret, and produces highly accurate models. However, most implementations today are computationally expensive and require all training data to be in main memory. As training data becomes ever larger, there is motivation for us to parallelize the GBDT algorithm. Parallelizing decision tree training is intuitive and various approaches have been explored in existing literature. Stochastic boosting on the other hand is inherently a sequential process and have not been applied to distributed decision trees. In this work, we present two different distributed methods that generates exact stochastic GBDT models, the first is a MapReduce implementation and the second utilizes MPI on the Hadoop grid environment.


title: Robust Sentiment Detection on Twitter from Biased and Noisy Data

text: In this paper, we propose an approach to automatically detect sentiments on Twitter messages (tweets) that explores some characteristics of how tweets are written and meta-information of the words that compose these messages. Moreover, we leverage sources of noisy labels as our training data. These noisy labels were provided by a few sentiment detection websites over twitter data. In our experiments, we show that since our features are able to capture a more abstract representation of tweets, our solution is more effective than previous ones and also more robust regarding biased and noisy data, which is the kind of data provided by these


title: Recognizing Stances in Ideological On-Line Debates

text: This work explores the utility of sentiment and arguing opinions for classifying stances in ideological debates. In order to capture arguing opinions in ideological stance taking, we construct an arguing lexicon automatically from a manually annotated corpus. We build supervised systems employing sentiment and arguing opinions and their targets as features. Our systems perform substantially better than a distribution-based baseline. Additionally, by employing both types of opinion features, we are able to perform better than a unigrambased system.


title: A Security Risk Assessment Framework for Smart Car

text: As the automobile industry has recently adopted information technologies, the latter are being used to replace mechanical systems with electronically-controlled systems. Moreover, automobiles are evolving into smart cars or connected cars as they are connected to various IT devices and networks such as VANET (Vehicular Ad hoc NETwork). Although there were no concerns about the hacking of automobiles in the past, various security threats are now emerging as electronic systems are gradually filling up the interiors of many automobiles, which are in turn being connected to external networks. As such, researchers have begun studying smart car security, leading to the disclosure of security threats through the testing or development of various automobile security technologies. However, the security threats facing smart cars do not occur frequently and, practically speaking, it is unrealistic to attempt to cope with every possible security threat when considering such factors as performance, compatibility, and so forth. Moreover, the excessive application of security technology will increase the overall vehicle cost and lower the effectiveness of investment. Therefore, smart car security risks should be assessed and prioritized to establish efficient security measures. To that end, this study constructed a security risk assessment framework in a bid to establish efficient measures for smart car security. The proposed security risk assessment framework configured the assessment procedure based on the conventional security risk analysis model GMITS (ISO13335) and utilized 'attack tree analysis' to assess the threats and vulnerabilities.


title: Learning Policies for Partially Observable Environments: Scaling Up

text: Partially observable Markov decision processes (pomdp's) model decision problems in which an agent tries to maximize its reward in the face of limited and/or noisy sensor feedback. While the study of pomdp's is motivated by a need to address realistic problems, existing techniques for nding optimal behavior do not appear to scale well and have been unable to nd satisfactory policies for problems with more than a dozen states. After a brief review of pomdp's, this paper discusses several simple solution methods and shows that all are capable of nding near-optimal policies for a selection of extremely small pomdp's taken from the learning literature. In contrast, we show that none are able to solve a slightly larger and noisier problem based on robot navigation. We nd that a combination of two novel approaches performs well on these problems and suggest methods for scaling to even larger and more complicated domains.


title: Acceptance of e-commerce services: the case of electronic brokerages

text: This paper examines human motivations underlying individual acceptance of business-to-consumer (B2C) electronic commerce services. Such acceptance is the key to the survival of firms in this intensely competitive industry. A modified theory of planned behavior (TPB) is used to hypothesize a model of e-commerce service acceptance, which is then tested using a field survey of 172 e-brokerage users. We found TPB useful in explaining e-commerce service acceptance; however acceptance motivations are significantly different from that of typical IS products. Based on a broader conceptualization of TPB’s subjective norm to include both external (mass-media) and interpersonal influences, we report that subjective norm is an important predictor of e-commerce acceptance, behavioral control has minimal impact on e-commerce acceptance, and external influence is a significant determinant of subjective norm. Implications of these findings in light of e-commerce research and practice are discussed.


title: SURVEY OF BIOMETRIC RECOGNITION SYSTEMS AND THEIR

text: The term Biometrics is becoming highly important in computer security world. The human physical characteristics like fingerprints, face, hand geometry, voice and iris are known as biometrics. These features are used to provide an authentication for computer based security systems. The existing computer security systems used at various places like banking, passport, credit cards, smart cards, PIN , access control and network security are using username and passwords for person identification. The username and passwords can be replaced and/or provide double authentication by using any one of the biometric features. In this paper, the main focus is on the various biometrics, their applications and the existing biometrics recognition systems.


title: Highly sensitive sensor for detection of initial slip and its application in a multi-fingered robot hand

text: Tactile sensors for slip detection are essential for implementing human-like gripping in a robot hand. In previous studies, we proposed flexible, thin and lightweight slip detection sensors utilizing the characteristics of pressure-sensitive conductive rubber. This was achieved by using the high-frequency vibration component generated in the process of slipping of the gripped object in order to distinguish between slipping of the object and changes in the normal force. In this paper, we design a slip detection sensor for a multi-fingered robot hand and examine the influence of noise caused by the operation of such a hand. Finally, we describe an experiment focusing on the adjustment of the gripping force of a multi-fingered robot hand equipped with the developed sensors


title: Multi-Information Source Optimization

text: We consider Bayesian methods for multi-information source optimization (MISO), in which we seek to optimize an expensive-to-evaluate black-box objective function while also accessing cheaper but biased and noisy approximations (“information sources”). We present a novel algorithm that outperforms the state of the art for this problem by using a Gaussian process covariance kernel better suited to MISO than those used by previous approaches, and an acquisition function based on a one-step optimality analysis supported by efficient parallelization. We also provide a novel technique to guarantee the asymptotic quality of the solution provided by this algorithm. Experimental evaluations demonstrate that this algorithm consistently finds designs of higher value at less cost than previous approaches.


Narrowband Cooperative Spectrum Sensing in Cognitive Networks


title: Intensely pleasurable responses to music correlate with activity in brain regions implicated in reward and emotion.

text: We used positron emission tomography to study neural mechanisms underlying intensely pleasant emotional responses to music. Cerebral blood flow changes were measured in response to subject-selected music that elicited the highly pleasurable experience of "shivers-down-the-spine" or "chills." Subjective reports of chills were accompanied by changes in heart rate, electromyogram, and respiration. As intensity of these chills increased, cerebral blood flow increases and decreases were observed in brain regions thought to be involved in reward/motivation, emotion, and arousal, including ventral striatum, midbrain, amygdala, orbitofrontal cortex, and ventral medial prefrontal cortex. These brain structures are known to be active in response to other euphoria-inducing stimuli, such as food, sex, and drugs of abuse. This finding links music with biologically relevant, survival-related stimuli via their common recruitment of brain circuitry involved in pleasure and reward.


title: SPECTRE : Serialization of Proof-of-work Events : Confirming Transactions via Recursive Elections

text: Bitcoin utilizes Nakamoto Consensus to achieve agreement on a consistent set of transactions, in the permissionless setting, where anyone can participate in the protocol anonymously. Since its rise, many other permissionless consensus protocols have been proposed. We present SPECTRE, a new protocol for the consensus core of cryptocurrencies that remains secure even under high throughput and fast confirmation times. At any throughput, SPECTRE is resilient to attackers with up to 50% of the computational power (reaching the limit defined by network congestion and bandwidth constraints). SPECTRE can operate at arbitrarily high block creation rates, which implies that its transactions confirm in mere seconds (limited mostly by the round-trip-time in the network). SPECTRE’s underlying model falls into the category of partial synchronous networks: its security depends on the existence of some bound on the delivery time of messages between honest participants, but the protocol itself does not contain any parameter that depends on this bound. Hence, while other protocols that do encode such parameters must operate with extreme safety margins, SPECTRE converges according to the actual network delay. Key to SPECTRE’s achievements is the fact that it satisfies weaker properties than classic consensus requires. In the conventional paradigm, the order between any two transactions must be decided and agreed upon by all non-corrupt nodes. In contrast, SPECTRE only satisfies this with respect to transactions performed by honest users. We observe that in the context of money, two conflicting payments that are published concurrently could only have been created by a dishonest user, hence we can afford to delay the acceptance of such transactions without harming the usability of the system. Our framework formalizes this weaker set of requirements for a cryptocurrency’s distributed ledger. We then provide a formal proof that SPECTRE satisfies these requirements.


title: 1 Marine anthropogenic litter on British beaches : a 10-year nationwide assessment using citizen science

text: Growing evidence suggests that anthropogenic litter, particularly plastic, represents a highly pervasive and persistent threat to global marine ecosystems. Multinational research is progressing to characterise its sources, distribution and abundance so that interventions aimed at reducing future inputs and clearing extant litter can be developed. Citizen science projects, whereby members of the public gather information, offer a low-cost method of collecting large volumes of data with considerable temporal and spatial coverage. Furthermore, such projects raise awareness of environmental issues and can lead to positive changes in behaviours and attitudes. We present data collected over a decade (2005-2014 inclusive) by Marine Conservation Society (MCS) volunteers during beach litter surveys carried along the British coastline, with the aim of increasing knowledge on the composition, spatial distribution and temporal trends of coastal debris. Unlike many citizen science projects, the MCS beach litter survey programme gathers information on the number of volunteers, duration of surveys and distances covered. This comprehensive information provides an opportunity to standardise data for variation in sampling effort among surveys, enhancing the value of outputs and robustness of findings. We found that plastic is the main constituent of anthropogenic litter on British beaches and the majority of traceable items originate from land-based sources, such as public littering. We identify the coast of the Western English Channel and Celtic Sea as experiencing the highest relative litter levels. Increasing trends over the 10-year time period were detected for a number of individual item categories, yet no statistically significant change in total (effort-corrected) litter was detected. We discuss the limitations of the dataset and make recommendations for future work. The study demonstrates the value of citizen science data in providing insights that would otherwise not be possible due to logistical and financial constraints of running government-funded sampling programmes on such large scales.


title: Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking

text: To help accelerate progress in multi-target, multi-camera tracking systems, we present (i) a new pair of precision-recall measures of performance that treats errors of all types uniformly and emphasizes correct identification over sources of error; (ii) the largest fully-annotated and calibrated data set to date with more than 2 million frames of 1080 p, 60 fps video taken by 8 cameras observing more than 2,700 identities over 85 min; and (iii) a reference software system as a comparison baseline. We show that (i) our measures properly account for bottom-line identity match performance in the multi-camera setting; (ii) our data set poses realistic challenges to current trackers; and (iii) the performance of our system is comparable to the state of the art.


title: Effective Design Parameters on the End Effect in Single-Sided Linear Induction Motors

text: Linear induction motors are used in various industries but they have some specific phenomena which are the causes for some problems. The most important phenomenon is called end effect. End effect decreases efficiency, power factor and output force and unbalances the phase currents. This phenomenon is more important in medium and high speeds machines. In this paper a factor, EEF , is obtained by an accurate equivalent circuit model, to determine the end effect intensity. In this way, all of effective design parameters on end effect is described. Accuracy of this equivalent circuit model is evaluated by two dimensional finite-element analysis using ANSYS. The results show the accuracy of the equivalent circuit model. Keywords—Linear induction motor, end effect, equivalent circuit model, finite-element method.


title: In-Cell Self-Capacitive-Type Mobile Touch System and Embedded Readout Circuit in Display Driver IC

text: A mobile display system, which includes a self- capacitive type in-cell touch screen panel and a display driver IC, in which the readout circuit is embedded, is developed for reduced cost, improved display performance, and superior touch performance. A touch electrode is implemented with a segmented common electrode of an LCD display. The display system of the in-cell touch panel is 5.7-in a-Si HD (720 × 1280), the number of touch electrodes is 576 (18 × 32), and the sensor pitch is approximately 4 mm. Compared to the display-only system, display performance degradation does not occur in the proposed system since in-cell technology is adopted. Despite using a self-capacitance sensing method in the panel, unlimited multitouch is available by using an all-point sensing scheme; this system shows a touch accuracy of less than 0.2 mm and signal-to-noise ratio is measured to be more than 48 dB in the entire panel without any digital filter. To enhance the touch performance, we proposed noise reduction techniques that are strongly related to the display operation, such as base line compensation, delta adding correlated double sampling, and the use of a high-voltage charge compensator. The power consumption including micro controller unit (MCU) is less than 30 mW.


title: LinkedIn skills: large-scale topic extraction and inference

text: "Skills and Expertise" is a data-driven feature on LinkedIn, the world's largest professional online social network, which allows members to tag themselves with topics representing their areas of expertise. In this work, we present our experiences developing this large-scale topic extraction pipeline, which includes constructing a folksonomy of skills and expertise and implementing an inference and recommender system for skills. We also discuss a consequent set of applications, such as Endorsements, which allows members to tag themselves with topics representing their areas of expertise and for their connections to provide social proof, via an "endorse" action, of that member's competence in that topic.


title: A Problem of Dimensionality: A Simple Example

text: In pattern recognition problems it has been noted that beyond a certain point the inclusion of additional parameters (that have been estimated) leads to higher probabilities of error. A simple problem has been formulated where the probability of error approaches zero as the dimensionality increases and all the parameters are known; on the other hand, the probability of error approaches one-half as the dimensionality increases and parameters are estimated.


title: Aggregates Caching in Columnar In-Memory Databases

text: The mixed database workloads found in enterprise applications are comprised of short-running transactional as well as analytical queries with resource-intensive data aggregations. In this context, caching the query results of long-running queries is desirable as it increases the overall performance. However, traditional caching approaches are inefficient in a way that changes in the base data result in invalidation or recalculation of cached results. Columnar in-memory databases with a main-delta architecture are optimized for a novel caching mechanism for aggregate queries that is the main contribution of this paper. With the separation into a readoptimized main storage and write-optimized delta storage, we do not invalidate cached query results when new data is inserted to the delta storage. Instead, we use the cached query result and combine it with the newly added records in the delta storage. We evaluate this caching mechanism with mixed database workloads and show how it compares to existing work in this area.


title: Computer-assisted pop-up design for children: computationally enriched paper engineering

text: Computationally-enriched crafts are activities that blend the advantages of computational media with the affective, social, and cognitive affordances of children's crafts. In this paper, we describe a design application, Popup Workshop, whose purpose is to introduce children to the craft (and engineering discipline) of pop-up design in paper. We describe the fundamental ideas behind computational crafts in general, present our application in its current implementation and offer a scenario for its use, explore the particular ways in which pop-up design serves as fertile ground for integrating computation and tangible design, and discuss our early pilot-tests with elementary-school children, as well as ongoing and related work.


title: Original Loop-Closure Detection Algorithm for Monocular vSLAM

text: Vision-based simultaneous localization and mapping (vSLAM) is a well-established problem in mobile robotics and monocular vSLAM is one of the most challenging variations of that problem nowadays. In this work we study one of the core post-processing optimization mechanisms in vSLAM, e.g. loop-closure detection. We analyze the existing methods and propose original algorithm for loop-closure detection, which is suitable for dense, semi-dense and feature-based vSLAM methods. We evaluate the algorithm experimentally and show that it contribute to more accurate mapping while speeding up the monocular vSLAM pipeline to the extent the latter can be used in real-time for controlling small multirotor vehicle (drone).


title: The Structure of the Relational Database Model

text: = (0,6., dom, M, SC)


title: Detection of Subconscious Face Recognition Using Consumer-Grade Brain-Computer Interfaces

text: We test the possibility of tapping the subconscious mind for face recognition using consumer-grade BCIs. To this end, we performed an experiment whereby subjects were presented with photographs of famous persons with the expectation that about 20% of them would be (consciously) recognized; and since the photos are of famous persons, we expected that subjects would have seen before some of the 80% they didn’t (consciously) recognize. Further, we expected that their subconscious would have recognized some of those in the 80% pool that they had seen before. An exit questionnaire and a set of criteria allowed us to label recognitions as conscious, false, no recognitions, or subconscious recognitions. We analyzed a number of event related potentials training and testing a support vector machine. We found that our method is capable of differentiating between no recognitions and subconscious recognitions with promising accuracy levels, suggesting that tapping the subconscious mind for face recognition is feasible.


title: Going Wild: Large-Scale Classification of Open DNS Resolvers

text:
Since several years, millions of recursive DNS resolvers are-deliberately or not-open to the public. This, however, is counter-intuitive, since the operation of such openly accessible DNS resolvers is necessary in rare cases only. Furthermore, open resolvers enable both amplification DDoS and cache snooping attacks, and can be abused by attackers in multiple other ways. We thus find open recursive DNS resolvers to remain one critical phenomenon on the Internet.
 In this paper, we illuminate this phenomenon by analyzing it from two different angles. On the one hand, we study the landscape of DNS resolvers based on empirical data we collected for over a year. We analyze the changes over time and classify the resolvers according to device type and software version. On the other hand, we take the viewpoint of a client and measure the response authenticity of these resolvers. Besides legitimate redirections (e.g., to captive portals or router login pages), we find millions of resolvers to deliberately manipulate DNS resolutions (i.e., return bogus IP address information). To understand this threat in more detail, we systematically analyze non-legitimate DNS responses and reveal open DNS resolvers that manipulate DNS resolutions to censor communication channels, inject advertisements, serve malicious files, perform phishing, or redirect to other kinds of suspicious or malicious activities.


title: System-level Max POwer (SYMPO) - a systematic approach for escalating system-level power consumption using synthetic benchmarks

text: To effectively design a computer system for the worst case power consumption scenario, system architects often use hand-crafted maximum power consuming benchmarks at the assembly language level. These stressmarks, also called power viruses, are very tedious to generate and require significant domain knowledge. In this paper, we propose SYMPO, an automatic SYstem level Max POwer virus generation framework, which maximizes the power consumption of the CPU and the memory system using genetic algorithm and an abstract workload generation framework. For a set of three ISAs, we show the efficacy of the power viruses generated using SYMPO by comparing the power consumption with that of MPrime torture test, which is widely used by industry to test system stability. Our results show that the usage of SYMPO results in the generation of power viruses that consume 14-41% more power compared to MPrime on SPARC ISA. The genetic algorithm achieved this result in about 70 to 90 generations in 11 to 15 hours when using a full system simulator. We also show that the power viruses generated in the Alpha ISA consume 9-24% more power compared to the previous approach of stressmark generation. We measure and provide the power consumption of these benchmarks on hardware by instrumenting a quad-core AMD Phenom II X4 system. The SYMPO power virus consumes more power compared to various industry grade power viruses on x86 hardware. We also provide a microarchitecture independent characterization of various industry standard power viruses.


title: Socially Aware Conference Participant Recommendation With Personality Traits

text: As a result of the importance of academic collaboration at smart conferences, various researchers have utilized recommender systems to generate effective recommendations for participants. Recent research has shown that the personality traits of users can be used as innovative entities for effective recommendations. Nevertheless, subjective perceptions involving the personality of participants at smart conferences are quite rare and have not gained much attention. Inspired by the personality and social characteristics of users, we present an algorithm called Socially and Personality Aware Recommendation of Participants (SPARP). Our recommendation methodology hybridizes the computations of similar interpersonal relationships and personality traits among participants. SPARP models the personality and social characteristic profiles of participants at a smart conference. By combining the aforementioned recommendation entities, SPARP then recommends participants to each other for effective collaborations. We evaluate SPARP using a relevant data set. Experimental results confirm that SPARP is reliable and outperforms other state-of-the-art methods.


title: Planar MIMO antennas for IoT and CR applications

text: In this paper, a planar, compact, single-substrate, multiband 2 sets of 2-elements each multiple-input-multiple-output (MIMO) antenna system is presented. The MIMO antenna system consists of a tunable 2-element meandered and folded MIMO antenna to cover the LTE Band (698 MHz–813 MHz) and a compact 2-element modified truncated cube wideband antenna to cover 754 MHz–971 MHz, 1.65–1.83 GHz and 2–3.66 GHz, respectively. The ground plane of this antenna behaves as a sensing antenna operating in 0.76–1.92 GHz, and 3.0–5.2 GHz. The upper band antennas operate in 0.728–1.08 GHz, 1.64–1.84 GHz, 2.1–3.69 GHz, and 5.01–5.55 GHz range to develop a complete antenna platform for cognitive radios (CR) and Internet of Things (IoT) applications. The antenna is fabricated on a low cost FR-4 substrate (ε<inf>r</inf>=4.4 tanδ=0.02) of dimensions 65 ×120 ×1.56 mm3.


title: Learning Latent Personas of Film Characters

text: We present two latent variable models for learning character types, or personas, in film, in which a persona is defined as a set of mixtures over latent lexical classes. These lexical classes capture the stereotypical actions of which a character is the agent and patient, as well as attributes by which they are described. As the first attempt to solve this problem explicitly, we also present a new dataset for the text-driven analysis of film, along with a benchmark testbed to help drive future work in this area.


title: A MATLAB-Simulink-Based PV Module Model and Its Application Under Conditions of Nonuniform Irradiance

text: The performance of a photovoltaic (PV) module is mostly affected by array configuration, irradiance, and module temperature. It is important to understand the relationship between these effects and the output power of the PV array. This paper presents a MATLAB-Simulink-based PV module model which includes a controlled current source and an S-Function builder. The modeling scheme in S-Function builder is deduced by some predigested functions. Under the conditions of nonuniform irradiance, the model is practically validated by using different array configurations in testing platform. The comparison experiments indicate that I-V and P-V characteristic curves of simulation match the measurements from outdoor experiment well. Under the conditions of nonuniform irradiance, both simulation and experiment show that the output power of a PV array gets more complicated due to multiple peaks. Moreover, the proposed model can also simulate electric circuit and its maximum power point tracking (MPPT) in the environment of MATLAB-Simulink. The experiments show that the proposed model has good predictability in the general behaviors of MPPT under the conditions of both nonuniform and uniform irradiance.


title: QuickCut: An Interactive Tool for Editing Narrated Video

text: We present QuickCut, an interactive video editing tool designed to help authors efficiently edit narrated video. QuickCut takes an audio recording of the narration voiceover and a collection of raw video footage as input. Users then review the raw footage and provide spoken annotations describing the relevant actions and objects in the scene. QuickCut time-aligns a transcript of the annotations with the raw footage and a transcript of the narration to the voiceover. These aligned transcripts enable authors to quickly match story events in the narration with semantically relevant video segments and form alignment constraints between them. Given a set of such constraints, QuickCut applies dynamic programming optimization to choose frame-level cut points between the video segments while maintaining alignments with the narration and adhering to low-level film editing guidelines. We demonstrate QuickCut's effectiveness by using it to generate a variety of short (less than 2 minutes) narrated videos. Each result required between 14 and 52 minutes of user time to edit (i.e. between 8 and 31 minutes for each minute of output video), which is far less than typical authoring times with existing video editing workflows.


title: Towards improving bug tracking systems with game mechanisms

text: Low bug report quality and human conflicts pose challenges to keep bug tracking systems productive. This work proposes to address these issues by applying game mechanisms to bug tracking systems. We investigate the use of game mechanisms in Stack Overflow, an online community organized to resolve computer programming related problems, for which the improvements we seek for bug tracking systems also turn out to be relevant. The results of our Stack Overflow investigation show that its game mechanisms could be used to address these issues by motivating contributors to increase contribution frequency and quality, by filtering useful contributions, and by creating an agile and dependable moderation system. We proceed by mapping these mechanisms to open-source bug tracking systems, and find that most benefits are applicable. Additionally, our results motivate tailoring a reward and reputation system and summarizing bug reports as future directions for increasing the benefits of game mechanisms in bug tracking systems.


title: Anti-jamming performance of cognitive radio networks under multiple uncoordinated jammers in fading environment

text: In this paper, we study both the jamming capability of the cognitive-radio-based jammers and the anti-jamming capability of the cognitive radio networks (CRN), by considering multiple uncooperative jammers and independent Rayleigh flat-fading propagations. A Markov model of CRN transmission is set up for the cross-layer analysis of the anti-jamming performance. The transitional probabilities are derived analytically by considering a smart jamming attack strategy. Average throughput expression is obtained and verified by simulations. The results indicate that CRN communications can be extremely susceptible to smart jamming attacks targeting the CRN spectrum sensing and channel switching procedures.


title: Usability Analysis of Visual Programming Environments: A 'Cognitive Dimensions' Framework

text: The cognitive dimensions framework is a broad-brush evaluation technique for interactive devices and for non-interactive notations. It sets out a small vocabulary of terms designed to capture the cognitively-relevant aspects of structure, and shows how they can be traded off against each other. The purpose of this paper is to propose the framework as an evaluation technique for visual programming environments. We apply it to two commercially-available dataflow languages (with further examples from other systems) and conclude that it is effective and insightful; other HCI-based evaluation techniques focus on different aspects and would make good complements. Insofar as the examples we used are representative, current VPLs are successful in achieving a good ‘closeness of match’, but designers need to consider the ‘viscosity’ (resistance to local change) and the ‘secondary notation’ (possibility of conveying extra meaning by choice of layout, colour, etc.).


title: Customer churn analysis : Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry

text: Retaining customers is one of the most critical challenges in the maturing mobile telecommunications service industry. Using customer transaction and billing data, this study investigates determinants of customer churn in the Korean mobile telecommunications service market. Results indicate that call quality-related factors influence customer churn; however, customers participating in membership card programs are also more likely to churn, which raises questions about program effectiveness. Furthermore, heavy users also tend to churn. In order to analyze partial and total defection, this study defines changes in a customer’s status from active use (using the service on a regular basis) to non-use (deciding not to use it temporarily without having churned yet) or suspended (being suspended by the service provider) as partial defection and from active use to churn as total defection. Thus, mediating effects of a customer’s partial defection on the relationship between the churn determinants and total defection are analyzed and their implications are discussed. Results indicate that some churn determinants influence customer churn, either directly or indirectly through a customer’s status change, or both; therefore, a customer’s status change explains the relationship between churn determinants and the probability of churn. r 2006 Elsevier Ltd. All rights reserved.


Building Skeleton Models via 3-D Medial Surface/Axis Thinning Algorithms


title: Machine learning applications in genetics and genomics

text: The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets. Here, we provide an overview of machine learning applications for the analysis of genome sequencing data sets, including the annotation of sequence elements and epigenetic, proteomic or metabolomic data. We present considerations and recurrent challenges in the application of supervised, semi-supervised and unsupervised machine learning methods, as well as of generative and discriminative modelling approaches. We provide general guidelines to assist in the selection of these machine learning methods and their practical application for the analysis of genetic and genomic data sets.


title: Real AdaBoost : boosting for credit scorecards and similarity to WOE logistic regression

text: Adaboost is a machine learning algorithm that builds a series of small decision trees, adapting each tree to predict difficult cases missed by the previous trees and combining all trees into a single model. We will discuss the AdaBoost methodology and introduce the extension called Real AdaBoost. Real AdaBoost comes from a strong academic pedigree: its authors are pioneers of machine learning and the method has well-established empirical and theoretical support spanning 15 years. Practically speaking, Real AdaBoost is able to produce readable credit scorecards and offers attractive features including variable interaction and adaptive, stage-wise binning. We will contrast Real AdaBoost to the dominant methodology for creating credit scorecards: stepwise weight of evidence logistic regression (SWOELR). Real AdaBoost is remarkably similar to SWOELR and is well positioned to serve as a benchmark for SWOELR models; it may even offer a statistical framework by which we can understand the power of SWOELR. We offer a macro to generate Real AdaBoost models in SAS. INTRODUCTION Financial institutions (FIs) must develop a wide range of models for marketing, fraud detection, loan adjudication, etc. Modeling has undergone a recent renaissance as machine learning has exploded – spurned by the availability of advanced statistical techniques, the ubiquity of powerful computers to execute these techniques, and the well-publicized successes of the companies who have embraced these methods (Parloff 2016). Modeling departments within some FIs face opposing demands: executives want some of the famed value of advanced methods, while government regulators, internal deployment teams and front-line staff want models that are easy to implement, interpret and understand. In this paper we review Real AdaBoost, a machine learning technique that may offer a middle-ground between powerful, but opaque machine learning methods and transparent conventional methods. CONSUMER RISK MODELS One field of modeling where FIs must often strike a balance between power and transparency is consumer risk modeling. Consumer risk modeling involves ranking customers by their credit worthiness (the likelihood they will repay a loan): first by identifying customer characteristics that indicate risk of delinquency, and then combining them mathematically to calculate a relative risk score for each customer (common characteristics include: past loan delinquency, high credit utilization, etc.). CREDIT SCORECARDS In order to keep the consumer risk models as transparent as possible, many FIs require that the final output of the model be in the form of a scorecard (an example is shown in Table 1). Credit scorecards are a popular way to represent customer risk models due to their simplicity, readability, and the ease with which business expertise can be incorporated during the modeling process (Maldonado et al. 2013). A scorecard lists a number of characteristics that indicate risk and each characteristic is subdivided into a small number of bins defined by ranges of values for that characteristic (e.g., credit utilization: 30-80% is a bin for the credit utilization characteristic). Each bin is assigned a number of score points, a value derived from a statistical model and proportional to the risk of that bin (SAS 2012). A customer will fall into one and only one bin per characteristic and the final score of the applicant is the sum of the points assigned by each bin (plus an intercept). This final score is proportional to consumer risk. The procedure for developing scorecards is termed stepwise weight of evidence logistic regression (SWOELR) and is implemented in the Credit Scoring add-on in SAS® Enterprise MinerTM.


title: Approximation Algorithms for Degree-Constrained Minimum-Cost Network-Design Problems

text: We study network-design problems with two different design objectives: the total cost of the edges and nodes in the network and the maximum degree of any node in the network. A prototypical example is the degree-constrained node-weighted Steiner tree problem: We are given an undirected graph G(V,E) , with a nonnegative integral function d that specifies an upper bound d(v) on the degree of each vertex v ∈ V in the Steiner tree to be constructed, nonnegative costs on the nodes, and a subset of k nodes called terminals. The goal is to construct a Steiner tree T containing all the terminals such that the degree of any node v in T is at most the specified upper bound d(v) and the total cost of the nodes in T is minimum. Our main result is a bicriteria approximation algorithm whose output is approximate in terms of both the degree and cost criteria—the degree of any node v ∈ V in the output Steiner tree is O(d(v) log  k) and the cost of the tree is O(log  k) times that of a minimum-cost Steiner tree that obeys the degree bound d(v) for each node v . Our result extends to the more general problem of constructing one-connected networks such as generalized Steiner forests. We also consider the special case in which the edge costs obey the triangle inequality and present simple approximation algorithms with better performance guarantees.


title: Construction and Refinement of Panoramic Mosaics with Global and Local Alignment

text: This paper presents techniques for constructing full view panoramic mosaics from sequences of images. Our representation associates a rotation matrix (and optionally a focal length) with each input image, rather than explicitly projecting all of the images onto a common surface (e.g., a cylinder). In order to reduce accumulated registration errors, we apply global alignment (block adjustment) to the whole sequence of images, which results in an optimal image mosaic (in the least-squares sense). To compensate for small amounts of motion parallax introduced by translations of the camera and other unmodeled distortions, we develop a local alignment (deghosting) technique which warps each image based on the results of pairwise local image registrations. By combining both global and local alignment, we significantly improve the quality of our image mosaics, thereby enabling the creation of full view panoramic mosaics with hand-held cameras.


[1] Synthetic aperture radar (SAR) interferometry is a technique that provides high-resolution measurements of the ground displacement associated with many geophysical processes. Advanced techniques involving the simultaneous processing of multiple SAR acquisitions in time increase the number of locations where a deformation signal can be extracted and reduce associated error. Currently there are two broad categories of algorithms for processing multiple acquisitions, persistent scatterer and small baseline methods, which are optimized for different models of scattering. However, the scattering characteristics of real terrains usually lay between these two end-member models. I present here a new method that combines both approaches, to extract the deformation signal at more points and with higher overall signal-to-noise ratio than can either approach alone. I apply the combined method to data acquired over Eyjafjallajökull volcano in Iceland, and detect time-varying ground displacements associated with two intrusion events. Citation: Hooper, A., (2008), A multi-temporal InSAR method incorporating both persistent scatterer and small baseline approaches, Geophys. Res. Lett., 35, L16302, doi:10.1029/ 2008GL034654.


title: Machine Learning Methods for Optical Character Recognition

text: Success of optical character recognition depends on a number of factors, two of which are feature extraction and classi cation algorithms. In this paper we look at the results of the application of a set of classi ers to datasets obtained through various basic feature extraction methods.


title: Type-2 fuzzy ontology-based semantic knowledge for collision avoidance of autonomous underwater vehicles

text: The volume of obstacles encountered in the marine environment is rapidly increasing, which makes the development of collision avoidance systems more challenging. Several fuzzy ontology-based simulators have been proposed to provide a virtual platform for the analysis of maritime missions. However, due to the simulators’ limitations, ontologybased knowledge cannot be utilized to evaluate maritime robot algorithms and to avoid collisions. The existing simulators must be equipped with smart semantic domain knowledge to provide an efficient framework for the decision-making system of AUVs. This article presents type-2 fuzzy ontology-based semantic knowledge (T2FOBSK) and a simulator for marine users that will reduce experimental time and the cost of marine robots and will evaluate algorithms intelligently. The system reformulates the user’s query to extract the positions of AUVs and obstacles and convert them to a proper format for the simulator. The simulator uses semantic knowledge to calculate the degree of collision risk and to avoid obstacles. The available type-1 fuzzy ontology-based approach cannot extract intensively blurred data from the hazy marine environment to offer actual solutions. Therefore, we propose a type-2 fuzzy ontology to provide accurate information about collision risk and the marine environment during real-time marine operations. Moreover, the type-2 fuzzy ontology is designed using Protégé OWL-2 tools. The DL query and SPARQL query are used to evaluate the ontology. The distance to closest point of approach (DCPA), time to closest point of approach (TCPA) and variation of compass degree (VCD) are used to calculate the degree of collision risk between AUVs and obstacles. The experimental and simulation results show that the proposed architecture is highly efficient and highly productive for marine missions and the real-time decision-making system of AUVs. 2014 Elsevier Inc. All rights reserved.


title: Tracking Time Evolving Data Streams for Short-Term Traffic Forecasting

text: Data streams have arisen as a relevant topic during the last few years as an efficient method for extracting knowledge from big data. In the robust layered ensemble model (RLEM) proposed in this paper for short-term traffic flow forecasting, incoming traffic flow data of all connected road links are organized in chunks corresponding to an optimal time lag. The RLEM model is composed of two layers. In the first layer, we cluster the chunks by using the Graded Possibilistic c-Means method. The second layer is made up by an ensemble of forecasters, each of them trained for short-term traffic flow forecasting on the chunks belonging to a specific cluster. In the operational phase, as a new chunk of traffic flow data presented as input to the RLEM, its memberships to all clusters are evaluated, and if it is not recognized as an outlier, the outputs of all forecasters are combined in an ensemble, obtaining in this a way a forecasting of traffic flow for a short-term time horizon. The proposed RLEM model is evaluated on a synthetic data set, on a traffic flow data simulator and on two real-world traffic flow data sets. The model gives an accurate forecasting of the traffic flow rates with outlier detection and shows a good adaptation to non-stationary traffic regimes. Given its characteristics of outlier detection, accuracy, and robustness, RLEM can be fruitfully integrated in traffic flow management systems.


title: Gujarati Script Recognition : A Review

text: This paper is step to locate the researchers, their track in the way for recognizing the characters from various regional scripts in India. This survey paper would provide a path for developing recognition tools for Indian scripts where there is still a scope of recognition accuracy. It provides the reasons for researchers to work for recognition of Indian scripts. In this paper, the various scripts along with their special properties, there feature extraction and recognition techniques are described. Simultaneously a detailed comparison is made on the fronts of techniques used and recognition of Gujarati script.


title: Survey on security threats of smartphones in Internet of Things

text: Internet of Things (IoT) is an emerging concept which will interconnect billions of devices (such as smartphones, sensors and other networking devices), to communicate with each other. This interconnection of devices can be beneficial in many ways such as improving location based services and timely coordination. However, this also impose severe security concerns specially on smartphones which contains sensitive data. In this paper, we provide a comprehensive analysis of different security issues in smartphones which are integral part of IoT. We provide an easy and concise overview of different security threats. Our categorization helps in identifying specific security breach and applying relevant countermeasures.


title: Text Document Clustering based on Phrase Similarity using Affinity Propagation

text: Affinity propagation (AP) was recently introduced as an un-supervised learning algorithm for exemplar based clustering. In this paper novel text document clustering algorithm has been developed based on vector space model, phrases and affinity propagation clustering algorithm. Proposed algorithm can be called Phrase affinity clustering (PAC). PAC first finds the phrase by ukkonen suffix tree construction algorithm, second finds the vector space model using tf-idf weighting scheme of phrase. Third calculate the similarity matrix form VSD using cosine similarity . In Last affinity propagation algorithm generate the clusters . F-Measure ,Purity and Entropy of Proposed algorithm is better than GAHC ,ST-GAHC and ST-KNN on OHSUMED ,RCV1 and News group data sets.


title: Development of a credible and integrated electronic voting machine based on contactless IC cards, biometrie fingerprint credentials and POS printer

text: In recent times there has been a decline in the confidence of common people over electronic voting machines (EVMs). More anticipated than what the reality actually is, today's automated vote casting methods have faced immense controversy related to being vulnerable to hacking and questions have been raised about their transparency and security. This paper proposes the design and development of a novel tamper resistant electronic voting system that aims to mitigate the recurring issues and flaws of existing voting machines of today. To elucidate the system in brief, multiple layered verification process would be carried out on a potential voter by the means of fingerprint recognition and a Near Field Communication (NFC) smart card entry in order to authenticate his or her identity. Subsequently, the person would cast the vote by pressing a button corresponding to a particular candidate which would be recorded in the system providing the vote caster a visual confirmation. The final vote would then be printed out spontaneously onto a ballot box using a POS (Point of Sales) printer for an added level of validation. This system not only prevents multiple vote casts, but also eliminates the discrepancies that commonly arise with a person claiming not to have voted, whereas his or her name is present in the list of vote casters. The project is an incremental extension of a previous research.


title: LLC resonant converter burst mode control with constant burst time and optimal switching pattern

text: In this paper, a novel burst mode control solution for LLC resonant converters is proposed for the highest efficiency and small output voltage ripple. The burst time is set to be constant, the off time is modulated by load conditions. During the burst time, three pulse switching pattern is implemented to keep output voltage low frequency ripple minimal. The switch first turned on is controlled to be last turned off to reduce the initial hard switching turn on loss when burst starts. The pulse width of first switch is optimized to settle LLC to the steady-state under the highest efficiency load condition in one pulse. Zero current detection (ZCD) of the resonant current is implemented to achieve first switch partial ZVS, further increasing light load efficiency.


title: A self-powered ultra-fast DC solid state circuit breaker using a normally-on SiC JFET

text: This paper introduces a new self-powered solid state circuit breaker (SSCB) concept using a normally-on SiC JFET as the main static switch and a fast-starting isolated DC/DC converter as the protection driver. The new SSCB detects short circuit faults by sensing its drain-source voltage rise, and draws power from the fault condition to turn and hold off the SiC JFET. The new two-terminal SSCB can be directly placed in a circuit branch without requiring any external power supply or additional wiring. A unique low power isolated DC/DC converter is designed and optimized to provide a fast reaction to a short circuit event. The SSCB prototypes have experimentally demonstrated a fault current interruption capability up to 180 amperes at a DC bus voltage of 400 volts within 0.8 microseconds. DC circuit protection applications provide a unique market opportunity for wide bandgap power semiconductor devices outside the conventional focus on power electronic converter applications.


title: Noninvasive in vivo glucose sensing on human subjects using mid-infrared light.

text: Mid-infrared quantum cascade laser spectroscopy is used to noninvasively predict blood glucose concentrations of three healthy human subjects in vivo. We utilize a hollow-core fiber based optical setup for light delivery and collection along with a broadly tunable quantum cascade laser to obtain spectra from human subjects and use standard chemo-metric techniques (namely partial least squares regression) for prediction analysis. Throughout a glucose concentration range of 80-160 mg/dL, we achieve clinically accurate predictions 84% of the time, on average. This work opens a new path to a noninvasive in vivo glucose sensor that would benefit the lives of hundreds of millions of diabetics worldwide.


title: FCM : THE FUZZY c-MEANS CLUSTERING ALGORITHM

text: nThis paper transmits a FORTRAN-IV coding of the fuzzy c-means (FCM) clustering program. The FCM program is applicable to a wide variety of geostatistical data analysis problems. This program generates fuzzy partitions and prototypes for any set of numerical data. These partitions are useful for corroborating known substructures or suggesting substructure in unexplored data. The clustering criterion used to aggregate subsets is a generalized least-squares objective function. Features of this program include a choice of three norms (Euclidean, Diagonal, or Mahalonobis), an adjustable weighting factor that essentially controls sensitivity to noise, acceptance of variable numbers of clusters, and outputs that include several measures of cluster validity.


title: Relational versus non-relational database systems for data warehousing

text: Relational database systems have been the dominating technology to manage and analyze large data warehouses. Moreover, the ER model, the standard in database design has a close relationship with the relational model. Recently, there has been a surge of alternative technologies for large scale analytic processing, most of which are not based on the relational model. Out of these proposals, distributed file systems together with MapReduce have become strong competitors to relational database systems to analyze large data sets, exploiting parallel processing. Moreover, there is progress on using MapReduce to evaluate relational queries. With that motivation in mind, this panel will compare pros and cons of each technology for data warehousing and will identify research issues, considering practical aspects like ease of use, programming flexibility and cost; as well as technical aspects like data modeling, storage, hardware, scalability, query processing, fault tolerance and data mining.


title: Variance Reduction Methods II 1 Importance Sampling

text: For this problem, however, the usual approach would be completely inadequate since approximating θ to any reasonable degree of accuracy would require n to be inordinately large. For example, on average we would have to set n ≈ 2.7014× 10 in order to obtain just one non-zero value of I. Clearly this is impractical and a much smaller value of n would have to be used. Using a much smaller value of n, however, would almost inevitably result in an estimate, θ̂n = 0, and an approximate confidence interval [L,U ] = [0, 0]! So the naive approach does not work. We could try to use the variance reduction techniques we have seen in the course so far, but they would provide little, if any, help.


title: The Effect of Internet Security Breach Announcements on Market Value: Capital Market Reactions for Breached Firms and Internet Security Developers

text: Assessing the value of information technology (IT) security is challenging because of the difficulty of measuring the cost of security breaches. An event-study analysis, using market valuations, was used to assess the impact of security breaches on the market value of breached firms. The information-transfer effect of security breaches (i.e., their effect on the market value of firms that develop security technology) was also studied. The results show that announcing an Internet security breach is negatively associated with the market value of the announcing firm. The breached firms in the sample lost, on average, 2.1 percent of their market value within two days of the announcement—an average loss in market capitalization of $1.65 billion per breach. Firm type, firm size, and the year the breach occurred help explain the cross-sectional variations in abnormal returns produced by security breaches. The effects of security breaches are not restricted to the breached firms. The market value of security developers is positively associated with the disclosure of security breaches by other firms. The security developers in the sample realized an average abnormal return of 1.36 percent during the two-day period after the announcement—an average gain of $1.06 billion in two days. The study suggests that the cost of poor security is very high for investors.


title: Explaining and Predicting the Impact of Branding Alliances and Web Site Quality on Initial Consumer Trust of E-Commerce Web Sites

text: Trust is a crucial factor in e-commerce. However, consumers are less likely to trust unknown Web sites. This study explores how less-familiar e-commerce Web sites can use branding alliances and Web site quality to increase the likelihood of initial consumer trust. We use the associative network model of memory to explain brand knowledge and to show how the mere exposure effect can be leveraged to improve a Web site’s brand image. We also extend information integration theory to explain how branding alliances are able to increase initial trust and transfer positive effects to Web sites. Testing of our model shows that the most important constructs for increasing initial trust in our experimental context are branding and Web site quality. Finally, we discuss future research ideas, limitations, implications, and ideas for practitioners.


title: A critical analysis of decision support systems research

text: This paper critically analyses the nature and state of decision support systems (DSS) research. To provide context for the analysis, a history of DSS is presented which focuses on the evolution of a number of sub-groupings of research and practice: personal decision support systems, group support systems, negotiation support systems, intelligent decision support systems, knowledge managementbased DSS, executive information systems/business intelligence, and data warehousing. To understand the state of DSS research an empirical investigation of published DSS research is presented. This investigation is based on the detailed analysis of 1,020 DSS articles published in 14 major journals from 1990 to 2003. The analysis found that DSS publication has been falling steadily since its peak in 1994 and the current publication rate is at early 1990s levels. Other findings include that personal DSS and group support systems dominate research activity and data warehousing is the least published type of DSS. The journal DSS is the major publishing outlet; US ‘Other’ journals dominate DSS publishing and there is very low exposure of DSS in European journals. Around two-thirds of DSS research is empirical, a much higher proportion than general IS research. DSS empirical research is 1 Acknowledgements: We would like to thank Gemma Dodson for research assistance, especially in obtaining articles and coding, and Peter O’Donnell for discussions about DSS history. The research was partially supported by a Monash University Small Grant and a Curtin University Visiting Professorship. Preliminary results from the empirical study were presented at the 2004 IFIP International Conference on Decision Support Systems, Prato, Italy and the 2004 Australasian Conference on Information Systems, Hobart, Australia.


title: A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research

text: A review of the literature suggests that few studies use formative indicator measurement models, even though they should. Therefore, the purpose of this research is to (a) discuss the distinction between formative and reflective measurement models, (b) develop a set of conceptual criteria that can be used to determine whether a construct should be modeled as having formative or reflective indicators, (c) review the marketing literature to obtain an estimate of the extent of measurement model misspecification in the field, (d ) estimate the extent to which measurement model misspecification biases estimates of the relationships between constructs using a Monte Carlo simulation, and (e) provide recommendations for modeling formative indicator constructs.


title: Service quality delivery through Web sites : A critical review of extant know

text: Evidence exists that service quality delivery through Web sites is an essential strategy to success, possibly more important than low price and Web presence. To deliver superior service quality, managers of companies with Web presences must first understand how customers perceive and evaluate online customer service. Information on this topic is beginning to emerge from both academic and practitioner sources, but this information has not yet been examined as a whole. The goals of this article are to review and synthesize the literature about service quality delivery through Web sites, describe what is known about the topic, and develop an agenda for needed research.


title: Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation

text: Histogram equalization (HE) is widely used for contrast enhancement. However, it tends to change the brightness of an image and hence, not suitable for consumer electronic products, where preserving the original brightness is essential to avoid annoying artifacts. Bi-histogram equalization (BBHE) has been proposed and analyzed mathematically that it can preserve the original brightness to a certain extend. However, there are still cases that are not handled well by BBHE, as they require higher degree of preservation. This paper proposes a generalization of BBHE referred to as recursive mean-separate histogram equalization (RMSHE) to provide not only better but also scalable brightness preservation. BBHE separates the input image's histogram into two based on its mean before equalizing them independently. While the separation is done only once in BBHE, this paper proposes to perform the separation recursively; separate each new histogram further based on their respective mean. It is analyzed mathematically that the output image's mean brightness will converge to the input image's mean brightness as the number of recursive mean separation increases. Besides, the recursive nature of RMSHE also allows scalable brightness preservation, which is very useful in consumer electronics. Simulation results show that the cases which are not handled well by HE, BBHE and dualistic sub image histogram equalization (DSIHE), have been properly enhanced by RMSHE. Keyword: Bi-histogram equalization; Dualistic sub-image; Histogram equalization; Recursive mean-separate; Scalable brightness preservation


title: Sales Forecasting for Retail Chains

text: This paper presents a use case of data mining for sales forecasting in retail demand and sales prediction. In particular, the Extreme Gradient Boosting algorithm is used to design a prediction model to accurately estimate probable sales for retail outlets of a major European Pharmacy retailing company. The forecast of potential sales is based on a mixture of temporal and economical features including prior sales data, store promotions, retail competitors, school and state holidays, location and accessibility of the store as well as the time of year. The model building process was guided by common sense reasoning and by analytic knowledge discovered during data analysis and definitive conclusions were drawn. The performances of the XGBoost predictor were compared with those of more traditional regression algorithms like Linear Regression and Random Forest Regression. Findings not only reveal that the XGBoost algorithm outperforms the traditional modeling approaches with regard to prediction accuracy, but it also uncovers new knowledge that is hidden in data which help in building a more robust feature set and strengthen the sales prediction model.


title: "Applying TAM to E-Services Adoption: The Moderating Role of Perceived Risk"

text: Consumer adoption of e-services is an important goal for many service providers, however little is known about how different consumer segments perceive and evaluate them for adoption. The Technology Acceptance Model (TAM) explains information systems evaluation and adoption, however the Internetdelivered e-services context presents additional variance that requires supplemental measures to be added to TAM. This research extends TAM to include a perceived usage risk main effect and also tested whether perceived risk moderated several of TAM’s relationships. Results indicate that higher levels of perceived risk deflated ease of use’s effect and inflated subjective norm’s effect on perceived usefulness and adoption intention.


title: CRF-CNN: Modeling Structured Information in Human Pose Estimation

text: Deep convolutional neural networks (CNN) have achieved great success. On the other hand, modeling structural information has been proved critical in many vision problems. It is of great interest to integrate them effectively. In a classical neural network, there is no message passing between neurons in the same layer. In this paper, we propose a CRF-CNN framework which can simultaneously model structural information in both output and hidden feature layers in a probabilistic way, and it is applied to human pose estimation. A message passing scheme is proposed, so that in various layers each body joint receives messages from all the others in an efficient way. Such message passing can be implemented with convolution between features maps in the same layer, and it is also integrated with feedforward propagation in neural networks. Finally, a neural network implementation of endto-end learning CRF-CNN is provided. Its effectiveness is demonstrated through experiments on two benchmark datasets.


title: Diagnosis Code Prediction from Electronic Health Records as Multilabel Text Classification : A Survey

text: This article presents a survey on diagnosis code prediction from various information in Electronic Health Records (EHR): both unstructured free text and structured data. Particularly, our interests are in casting the problem as text classification with multiple sources and using neural network based models. We will first present previous work in this area and describe some simple baseline models for the relevant tasks.


title: Bandwidth Improvement of Microstrip Patch Antenna Using H- Shaped Patch

text: Despite the many advantages of microstrip patch antennas, they do have some considerable drawbacks. One of the main limitations with patch antennas is their inherently narrowband performance due to its resonant nature. With bandwidth as low as a few percent; broadband applications using conventional patch designs are limited. So for the antenna miniaturization and bandwidth improvement H-shaped microstrip patch antenna used. In this paper, authors cover two aspects of microstrip antenna designs. The first is the analysis of single element narrowband rectangular microstrip antenna which operates at the central frequency of 3.3 GHz. The second aspect is the analysis and design of slot cut H-shaped microstrip antenna. The simulation process has been done through high frequency structure simulator (HFSS). The properties of antenna such as bandwidth, S parameter, VSWR has been investigated and compared between a single element rectangular and H-shaped microstrip antenna.


title: A Low-Complexity Full-Duplex Radio Implementation With a Single Antenna

text: This paper presents a novel low-complexity full-duplex radio design, which only uses a single patch antenna without any duplexer or circulator for passive suppression of self-interference, and a computationally efficient technique for linear digital cancellation. The proposed full-duplex design is tested for IEEE 802.11g Wireless Standard on the WARP (v3) software-defined radio implementation platform. It is shown that this design provides a total suppression of 88 dB, which is sufficient for low-power or short-range full-duplex communication. The dual polarized slot coupled patch antenna used in our design provides an interport isolation as high as 60 dB in 2.4 GHz band. Additionally, the digital domain cancellation utilizes a frequency domain-based estimation and reconstruction approach, which not only offers up to <inline-formula><tex-math notation="LaTeX">$61\%$</tex-math></inline-formula> reduction in the computational complexity but also provides a <inline-formula><tex-math notation="LaTeX">$5-7$ </tex-math></inline-formula> dB better digital cancellation performance in highly selective channel conditions, as compared to the time-domain-based techniques. The proposed full-duplex implementation can be easily applied in OFDM-based wireless systems, such as IEEE 802.11, which is the considered air interface in this paper.


2 Foreword This textbook provides study materials for the participants of the course named Multivariate Analysis of Ecological Data that we teach at our university for the third year. Material provided here should serve both for the introductory and the advanced versions of the course. We admit that some parts of the text would profit from further polishing, they are quite rough but we hope in further improvement of this text. We hope that this book provides an easy-to-read supplement for the more exact and detailed publications like the collection of the Dr. Ter Braak' papers and the Canoco for Windows 4.0 manual. In addition to the scope of these publications, this textbook adds information on the classification methods of the multivariate data analysis and introduces some of the modern regression methods most useful in the ecological research. Wherever we refer to some commercial software products, these are covered by trademarks or registered marks of their respective producers. This publication is far from being final and this is seen on its quality: some issues appear repeatedly through the book, but we hope this provides, at least, an opportunity to the reader to see the same topic expressed in different words.


title: A route planning method based on improved artificial potential field algorithm

text: The route planning method based on artificial potential field is an important branch and the hot research focus of assignment planning. The traditional artificial potential field method is easy to fall into the local minimum, and the paper analyzed the causes, and then did some research on the improved artificial potential field method, and then proposed a method modifying the repulsion direction based on the improved artificial potential field to solve the three typical local minimum problem. The simulation results show that the proposed method is effective.


title: Building a Virtual HPC Cluster with Auto Scaling by the Docker

text: Solving the software dependency issue under the HPC environment has always been a difficult task for both computing system administrators and application scientists. This work would like to tackle the issue by introducing the modern container technology, the Docker, to be specific. By integrating the auto-scaling feature of service discovery with the light-weight virtualization tool, the Docker, the construction of a virtual cluster on top of physical cluster hardware is attempted. Thus, through the isolation of computing environment, a remedy of software dependency of HPC environment is possible. Keywords—HPC; Docker; auto-scaling; service discovery; MPI; Consul;


title: GeoClustering: A web service for geospatial clustering

text: Geospatial clustering is a very important research topic in the field of „Geospatial Knowledge Discovery.‟ Its main purpose is to group similar objects, from large geospatial datasets, into clusters, based on the objects‟ spatial and non-spatial attributes. This capability is very useful for better understanding the patterns and distributions of geographical phenomena. With the emerging availability of large amounts of geospatial data on the Internet, the demand for powerful methods of extracting meaningful information from large amounts of web-based geospatial data is thus also rapidly increasing. In this chapter, we present a geospatial clustering web service called GeoClustering. With this service, users are able to cluster online data sources easily and to then conveniently visualize the clustering results through a friendly map-centric user interface. Since it is web-based, GeoClustering offers a platform that can be accessed by anyone from anywhere at any time. To achieve better interoperability, GeoClustering offers an API and follows the OGC standard data format for spatial data exchange.


title: Compact Duplexing for a 680-GHz Radar Using a Waveguide Orthomode Transducer

text: A compact 680-GHz waveguide orthomode transducer (OMT) and circular horn combination has been designed, tested, and characterized in a radar transceiver's duplexer. The duplexing capability is implemented by a hybrid waveguide quasi optical solution, combining a linear polarization OMT and an external grating polarizer. Isolation between the OMT's orthogonal ports' flanges was measured with a vector network analyzer to exceed 33 dB over a >10% bandwidth between 630 and 710 GHz. Calibrated Y-factor measurements using a mixer attached to the OMT ports reveal losses through the transmit and receive paths that sum to an average of 4.7 dB of two-way loss over 660-690 GHz. This is consistent with radar sensitivity measurements comparing the new OMT/horn with a quasi-optical wire grid beam splitter. Moreover, the radar performance assessment validates the OMT as a suitable compact substitute of the wire grid for the JPL's short-range 680-GHz imaging radar.