title: Dimensionality reduction for probabilistic movement primitives

abstract: Humans as well as humanoid robots can use a large number of degrees of freedom to solve very complex motor tasks. The high-dimensionality of these motor tasks adds difficulties to the control problem and machine learning algorithms. However, it is well known that the intrinsic dimensionality of many human movements is small in comparison to the number of employed DoFs, and hence, the movements can be represented by a small number of synergies encoding the couplings between DoFs. In this paper, we want to apply Dimensionality Reduction (DR) to a recent movement representation used in robotics, called Probabilistic Movement Primitives (ProMP). While ProMP have been shown to have many benefits, they suffer with the high-dimensionality of a robotic system as the number of parameters of a ProMP scales quadratically with the dimensionality. We use probablistic dimensionality reduction techniques based on expectation maximization to extract the unknown synergies from a given set of demonstrations. The ProMP representation is now estimated in the low-dimensional space of the synergies. We show that our dimensionality reduction is more efficient both for encoding a trajectory from data and for applying Reinforcement Learning with Relative Entropy Policy Search (REPS).


title: Small Solutions to Polynomial Equations, and Low Exponent RSA Vulnerabilities

abstract: We show how to find sufficiently small integer solutions to a polynomial in a single variable modulo N, and to a polynomial in two variables over the integers. The methods sometimes extend to more variables. As applications: RSA encryption with exponent 3 is vulnerable if the opponent knows two-thirds of the message, or if two messages agree over eight-ninths of their length; and we can find the factors of N=PQ if we are given the high order $\frac{1}{4} \log_2 N$ bits of P.


title: Female and male perceptions of female physical attractiveness in front-view and profile.

abstract: Two important cues to female physical attractiveness are body mass index (BMI) and shape. In front view, it seems that BMI may be more important than shape; however, is it true in profile where shape cues may be stronger? There is also the question of whether men and women have the same perception of female physical attractiveness. Some studies have suggested that they do not, but this runs contrary to mate selection theory. This predicts that women will have the same perception of female attractiveness as men do. This allows them to judge their own relative value, with respect to their peer group, and match this value with the value of a prospective mate. To clarify these issues we asked 40 male and 40 female undergraduates to rate a set of pictures of real women (50 in front-view and 50 in profile) for attractiveness. BMI was the primary predictor of attractiveness in both front and profile, and the putative visual cues to BMI showed a higher degree of view-invariance than shape cues such as the waist-hip ratio (WHR). Consistent with mate selection theory, there were no significant differences in the rating of attractiveness by male and female raters.


title: Design and analysis of a hybrid energy harvester for self-powered sensor

abstract: This paper presents a slightly different concept of energy harvesting method as compared to the conventional energy harvesters. The new energy harvester of interest is known as Hybrid Energy Harvester (HEH). It comprises of the Piezoelectric and Magnetostrictive/Electromagnetic transducers integrated into a system. Theoretically, the hybrid energy harvester device developed is targeted to expand the vibration bandwidth as compared to the standalone energy harvesters, hence higher output power will be generated. To develop the hybrid energy harvester, mathematical models of each respective transducers are formulated, then simulations are carried out using the MATLAB/Simulink analysis tool. Next, a macro-scale prototype device is designed, fabricated and validated. From the experimental characterizations, it is unveiled that the maximum open circuit output voltage produced by the hybrid energy harvester is 6.23 V at a resonant frequency of 59.6 Hz.


title: The Third International Chinese Language Processing Bakeoff: Word Segmentation And Named Entity Recognition

abstract: The Third International Chinese Language Processing Bakeoff was held in Spring 2006 to assess the state of the art in two important tasks: word segmentation and named entity recognition. Twenty-nine groups submitted result sets in the two tasks across two tracks and a total of five corpora. We found strong results in both tasks as well as continuing challenges.


title: JavaScript as a first programming language for multimedia students

abstract: Whilst there has been much discussion of Java as a first programming language, JavaScript has been comparatively ignored. This paper argues the merits of JavaScript as a first programming language for Multimedia students. It then describes the content of modules that use this approach, and reports experiences and findings on first delivery. As a result of positive experiences we are now planning to use JavaScript as precursor to Java for mainstream Computing students.


title: LBP features for breast cancer detection

abstract: Cancer is nowadays considered as one of the most dangerous diseases in the world. Especially, breast cancer represents for women the second most common type of cancer and is a main cause of cancer dead. This paper presents a novel method for breast cancer detection from mammographic images based on Local Binary Patterns (LBP). This approach successfully uses LBP based features with a classifier and thresholding. The proposed method is evaluated on a set composed of images extracted from MIAS and DDSM databases. We have experimentally shown that the proposed method is efficient and effective because the achieved accuracy is about 84%.


title: Introduction to 3D Non-Photorealistic Rendering: Silhouettes and Outlines

abstract: In these notes, we survey some of the basic tools used for non-photorealistic rendering of 3D scenes. We will primarily focus on detecting outlines of object shape: silhouettes, boundaries, and creases. (Hatching and shading, which are also very important for communicating shape, are discussed elsewhere in the course notes.) The algorithms in this section can be divided into algorithms that operate in the image space (2D), and algorithms that operate in world space (3D).


title: Disaster forecasting using convex hull & K-median approach

abstract: Data mining is a concept to find knowledge from a large set of data. In this proposed model initially disaster, time, place from all over the world have been collected. Then the database has been divided into two parts - natural disaster and man-made disaster, then the outer region of the unstructured data using convex hull has been identified. According to month of occurrence, previous structural data are passed through K-median and the clustered data is passed through priority based protocol and then the resultant data can be used to predict natural disaster or man-made disaster by the analysis of the previous data.


Automatic Rapid Segmentation of Human Lung from 2D Chest X-Ray Images


title: Spectre is here to stay: An analysis of side-channels and speculative execution

abstract: The recent discovery of the Spectre and Meltdown attacks represents a watershed moment not just for the field of Computer Security, but also of Programming Languages. This paper explores speculative side-channel attacks and their implications for programming languages. These attacks leak information through micro-architectural side-channels which we show are not mere bugs, but in fact lie at the foundation of optimization. We identify three open problems, (1) finding side-channels, (2) understanding speculative vulnerabilities, and (3) mitigating them. For (1) we introduce a mathematical meta-model that clarifies the source of side-channels in simulations and CPUs. For (2) we introduce an architectural model with speculative semantics to study recently-discovered vulnerabilities. For (3) we explore and evaluate software mitigations and prove one correct for this model. Our analysis is informed by extensive offensive research and defensive implementation work for V8, the production JavaScript virtual machine in Chrome. Straightforward extensions to model real hardware suggest these vulnerabilities present formidable challenges for effective, efficient mitigation. As a result of our work, we now believe that speculative vulnerabilities on today's hardware defeat all language-enforced confidentiality with no known comprehensive software mitigations, as we have discovered that untrusted code can construct a universal read gadget to read all memory in the same address space through side-channels. In the face of this reality, we have shifted the security model of the Chrome web browser and V8 to process isolation.


title: Product platform design and customization : Status and promise

abstract: In an effort to improve customization for today's highly competitive global marketplace, many companies are utilizing product families and platform-based product development to increase variety, shorten lead times, and reduce costs. The key to a successful product family is the product platform from which it is derived either by adding, removing, or substituting one or more modules to the platform or by scaling the platform in one or more dimensions to target specific market niches. This nascent field of engineering design has matured rapidly in the past decade, and this paper provides a comprehensive review of the flurry of research activity that has occurred during that time to facilitate product family design and platform-based product development for mass customization. Techniques for identifying platform leveraging strategies within a product family are reviewed along with metrics for assessing the effectiveness of product platforms and product families. Special emphasis is placed on optimization approaches and artificial intelligence techniques to assist in the process of product family design and platform-based product development. Web-based systems for product platform customization are also discussed. Examples from both industry and academia are presented throughout the paper to highlight the benefits of product families and product platforms. The paper concludes with a discussion of potential areas of research to help bridge the gap between planning and managing families of products and designing and manufacturing them.


RFID (Radio Frequency Identification): Principles and Applications


title: Revealing Patterns and Trends of Mass Mobility Through Spatial and Temporal Abstraction of Origin-Destination Movement Data

abstract: Origin-destination (OD) movement data describe moves or trips between spatial locations by specifying the origins, destinations, start, and end times, but not the routes travelled. For studying the spatio-temporal patterns and trends of mass mobility, individual OD moves of many people are aggregated into flows (collective moves) by time intervals. Time-variant flow data pose two difficult challenges for visualization and analysis. First, flows may connect arbitrary locations (not only neighbors), thus making a graph with numerous edge intersections, which is hard to visualize in a comprehensible way. Even a single spatial situation consisting of flows in one time step is hard to explore. The second challenge is the need to analyze long time series consisting of numerous spatial situations. We present an approach facilitating exploration of long-term flow data by means of spatial and temporal abstraction. It involves a special way of data aggregation, which allows representing spatial situations by diagram maps instead of flow maps, thus reducing the intersections and occlusions pertaining to flow maps. The aggregated data are used for clustering of time intervals by similarity of the spatial situations. Temporal and spatial displays of the clustering results facilitate the discovery of periodic patterns and longer-term trends in the mass mobility behavior.


title: The clinical atlas of Greig cephalopolysyndactyly syndrome

abstract: Greig cephalopolysyndactyly syndrome (GCPS) is a rare multiple congenital anomaly syndrome that is inherited in an autosomal dominant pattern and is caused by haploinsufficiency of the GLI3 gene. The syndrome typically includes preaxial or mixed pre- and postaxial polydactyly and cutaneous syndactyly, ocular hypertelorism, and macrocephaly in its typical forms, but sometimes includes hydrocephalus, seizures, mental retardation, and developmental delay in more severe cases. Patients with milder forms of GCPS can have subtle craniofacial dysmorphic features that are difficult to distinguish from normal variation. This article presents the spectrum of dysmorphic findings in GCPS highlighting some of its key presenting features to familiarize clinicians with the variable expressivity of the condition. © 2008 Wiley-Liss, Inc.


title: A mathematical programming approach to the kernel fisher algorithm

abstract: We investigate a new kernel-based classifier: the Kernel Fisher Discriminant (KFD). A mathematical programming formulation based on the observation that KFD maximizes the average margin permits an interesting modification of the original KFD algorithm yielding the sparse KFD. We find that both, KFD and the proposed sparse KFD, can be understood in an unifying probabilistic context. Furthermore, we show connections to Support Vector Machines and Relevance Vector Machines. From this understanding, we are able to outline an interesting kernel-regression technique based upon the KFD algorithm. Simulations support the usefulness of our approach.


title: Perceptual Losses for Real-Time Style Transfer and Super-Resolution

abstract: We consider image transformation problems, where an input image is transformed into an output image. Recent methods for such problems typically train feed-forward convolutional neural networks using a per-pixel loss between the output and ground-truth images. Parallel work has shown that high-quality images can be generated by defining and optimizing perceptual loss functions based on high-level features extracted from pretrained networks. We combine the benefits of both approaches, and propose the use of perceptual loss functions for training feed-forward networks for image transformation tasks. We show results on image style transfer, where a feed-forward network is trained to solve the optimization problem proposed by Gatys et al. in real-time. Compared to the optimization-based method, our network gives similar qualitative results but is three orders of magnitude faster. We also experiment with single-image super-resolution, where replacing a per-pixel loss with a perceptual loss gives visually pleasing results.


title: An IT management assessment framework- evaluating enterprise architecture scenarios

abstract: Enterprise architecture, EA, is an established approach for the model-based and holistic management of IT. The scope of EA is however wide and the predominant EA frameworks suggest the creation of broad and detailed models. IT management cannot control all areas spanned by the present frameworks for EA models. In order to ensure well-informed decisions, IT management has a series of questions that need to be answered. This paper proposes an assessment framework that can be used to identify relevant questions for assessments of EA and EA scenarios, within the areas of EA that IT management can control. Three top dimensions in the proposed framework are presented: IT organization, IT systems, and Business organization. The framework further includes sub dimensions for identifying questions. An application of the assessment framework, as it was applied to assess EA scenarios in a power company, is also described.


title: Do high-top shoes reduce ankle inversion? A dynamic x-ray analysis of aggressive cutting in a high-top and low-top shoe

abstract: Ankle strains and sprains are one of the most common basketball injuries. These injuries are often the result of excessive ankle inversion. High-top shoes are worn because it is thought that they limit inversion and, therefore, reduce the risk of inversion injuries. However, it is difficult to quantify inversion of the ankle inside the shoe with traditional measurement techniques, making comparison between footwear conditions difficult. The purpose of this study was to compare inversion of the ankle and shoe in a high-top and a low-top shoe during aggressive lateral cutting. It was hypothesised that ankle inversion would be lower than shoe inversion in both a high-top shoe and a low-top shoe. Six male basketball players were analysed over three dynamic cutting trials in each condition. As hypothesised, shoe inversion during aggressive cutting was higher than ankle inversion in the high-top shoe (17°) and the low-top shoe (25.5°). Future research should focus on determining the relative contribution of ank...


title: Statistical affect detection in collaborative chat

abstract: Geographically distributed collaborative teams often rely on synchronous text-based online communication for accomplishing tasks and maintaining social contact. This technology leaves a trace that can help researchers understand affect expression and dynamics in distributed groups. Although manual labeling of affect in chat logs has shed light on complex group communication phenomena, scaling this process to larger data sets through automation is difficult. We present a pipeline of natural language processing and machine learning techniques that can be used to build automated classifiers of affect in chat logs. Interpreting affect as a dynamic, contextualized process, we explain our development and application of this method to four years of chat logs from a longitudinal study of a multi-cultural distributed scientific collaboration. With ground truth generated through manual labeling of affect over a subset of the chat logs, our approach can successfully identify many commonly occurring types of affect.


title: Brain–machine interfaces in space: Using spontaneous rather than intentionally generated brain signals

abstract: De auteurs bespreken de beperkingen en mogelijkheden van gesuggereerde BMI toepassingen in een ruimtevaart en breken een lans voor BMIs die zijn gebaseerd op spontane in plaats van op doelbewuste hersensignalen


title: Chromatography of tobacco mosaic virus, its constituents, and nucleic acids extracted from infected tobacco leaf tissues

abstract: Abstract Purified tobacco mosaic virus (TMV), TMV-ribonucleic acid (RNA), and aggregated TMV-protein were absorbed and eluted at specific NaCl concentrations by chromatography with methylated albumin kieselguhr columns. The A-protein, an alkaline hydrolyzate of the aggregated TMV-protein, was not absorbed. It is suggested that the specific elution of TMV and the aggregated TMV-protein were due to their quaternary structures. In the chromatography of the nucleic acids extracted from TMV-infected tobacco leaf tissues by the pyrophosphate-phenol method, infectious RNA was eluted later than ribosomal RNA as a single peak. There were no other differences between the chromatographs of nucleic acids from infected tissues and those from healthy tissues. Extraction by pyrophosphate and phenol directly from infected leaves yielded more infectious RNA than extraction by sodium dodecyl sulfate and phenol after homogenization.


title: Toward autonomous architecture: The convergence of digital design, robotics, and the built environment

abstract: The way we design, construct, and inhabit buildings is changing—moving toward greater integration of robotic and autonomous systems that challenge our preconceived notions of how buildings are made, what they are, or what they should be.


title: EFRS:Enabling Efficient and Fine-Grained Range Search on Encrypted Spatial Data

abstract: Range search of spatial data, has been applied in many scenarios such as geometric queries, location-based services, and computational geometry, etc. Due to the increasing amount of spatial data, which are usually outsourced to the cloud for saving storage and computational overhead. However, a common privacy issue is that the cloud server may steal user's sensitive information utilizing its powerful computing advantages. A feasible way of managing this bottleneck is to encrypt spatial data before outsourcing it. Nevertheless, the availability of data will be significantly reduced because of the query difficulty over the encrypted cloud data. In this paper, we propose an Efficient Range Search scheme (EFRS) which can achieve fine- grained query over encrypted spatial data. We original contributions are threefold. First, polynomial fitting technique and orderpreserving encryption are introduced to realize the efficient and fine- grained range query over encrypted cloud data. Then, in order to improve the search efficiency, we exploit the Rtree to significantly decreased the search space. Finally, we theoretically proved the security of our proposed scheme in terms of confidentially of spatial data, privacy protection of index and trapdoor, and the unlinkability of trapdoor. Besides, extensive experiments demonstrate the high efficiency of our proposed model compared with existing schemes.


title: Action Detection from a Robot-Car Perspective

abstract: We present the new Road Event and Activity Detection (READ) dataset, designed and created from an autonomous vehicle perspective to take action detection challenges to autonomous driving. READ will give scholars in computer vision, smart cars and machine learning at large the opportunity to conduct research into exciting new problems such as understanding complex (road) activities, discerning the behaviour of sentient agents, and predicting both the label and the location of future actions and events, with the final goal of supporting autonomous decision making.


title: A Compact Dual-Band Dielectric Resonator Antenna Using a Parasitic Slot

abstract: A compact dual-band dielectric resonator antenna (DRA) using a parasitic c-slot fed by a microstrip line is proposed. In this configuration, the DR performs the functions of an effective radiator and the feeding structure of the parasitic c-slot in the ground plane. By optimizing the proposed structure parameters, the structure resonates at two different frequencies. One is from the DRA with the broadside patterns and the other from the c-slot with the dipole-like patterns. In order to determine the performance of varying design parameters on bandwidth and resonance frequency, the parametric study is carried out using simulation software High-Frequency Structure Simulator and experimental results. The measured and simulated results show excellent agreement.


title: Persistent Aerial Tracking system for UAVs

abstract: In this paper, we propose a persistent, robust and autonomous object tracking system for unmanned aerial vehicles (UAVs) called Persistent Aerial Tracking (PAT). A computer vision and control strategy is applied to a diverse set of moving objects (e.g. humans, animals, cars, boats, etc.) integrating multiple UAVs with a stabilized RGB camera. A novel strategy is employed to successfully track objects over a long period, by ‘handing over the camera’ from one UAV to another. We evaluate several state-of-the-art trackers on the VIVID aerial video dataset and additional sequences that are specifically tailored to low altitude UAV target tracking. Based on the evaluation, we select the leading tracker and improve upon it by optimizing for both speed and performance, integrate the complete system into an off-the-shelf UAV, and obtain promising results showing the robustness of our solution in real-world aerial scenarios.


title: Multi-Agent Systems for Power Engineering Applications—Part I: Concepts, Approaches, and Technical Challenges

abstract: This is the first part of a two-part paper that has arisen from the work of the IEEE Power Engineering Society's Multi-Agent Systems (MAS) Working Group. Part I of this paper examines the potential value of MAS technology to the power industry. In terms of contribution, it describes fundamental concepts and approaches within the field of multi-agent systems that are appropriate to power engineering applications. As well as presenting a comprehensive review of the meaningful power engineering applications for which MAS are being investigated, it also defines the technical issues which must be addressed in order to accelerate and facilitate the uptake of the technology within the power and energy sector. Part II of this paper explores the decisions inherent in engineering multi-agent systems for applications in the power and energy sector and offers guidance and recommendations on how MAS can be designed and implemented.


title: Education for Growth: Why and For Whom?

abstract: This paper tries to reconcile evidence from the microeconometric and empirical macro growth literatures on the effect of schooling on income and GDP growth. Much microeconometric evidence suggest that education is an important causal determinant of income for individuals within countries. At a national level, however, recent studies have found that increases in educational attainment are unrelated to economic growth. This finding appears to be a spurious result of the extremely high rate of measurement error in first-differenced cross-country education data. After accounting for measurement error, the effect of changes in educational attainment on income growth in cross-country data is at least as great as microeconometric estimates of the rate of return to years of schooling. Another finding of the macro growth literature - that economic growth depends positively on the initial stock of human capital - is shown to result from imposing linearity and constant-coefficient assumptions on the estimates. These restrictions are often rejected by the data, and once either assumption is relaxed the initial level of education has little effect on economic growth for the average country.


title: Security Assurance for System-on-Chip Designs With Untrusted IPs

abstract: Modern system-on-chip (SoC) designs involve integration of a large number of intellectual property (IP) blocks, many of which are acquired from untrusted third-party vendors. An IP containing a security vulnerability—whether inadvertent or malicious—may compromise the trustworthiness of the entire SoC, e.g. , by leaking sensitive information or causing execution failures at key points. Existing functional validation approaches, post-manufacturing tests, and IP trust verification techniques are inadequate to accomplish comprehensive system-level security assurance in the presence of untrusted IPs. In this paper, we analyze security issues at the SoC level caused by untrusted IPs. We also propose a novel, resilient SoC security architecture to ensure trusted SoC operation with untrusted IPs. Our architecture realizes fine-grained IP-trust aware security policies in an efficient security policy checker that enables run-time monitoring of security issues arising from untrusted IPs. It also exploits on-chip design-for-debug architecture to ensure trusted information flow from IP blocks to the security policy checker. Unlike existing solutions to the untrusted IP problem, which rely on verification of IP trust before they are integrated into an SoC, the proposed approach follows a fundamentally different architecture-level solution based on run-time resilience. We demonstrate the effectiveness of this framework for system protection using several illustrative practical use cases. We also provide experimental results to show that the overhead of the proposed architecture is modest on representative SoC designs.


title: A 40-MHz current-mode hysteretic controlled switching converter with digital push-pull current pumping technique for high performance microprocessors

abstract: Ever-increasing power demands of microprocessors have necessitated fast-response, high-speed power converters. This paper presents a fast transient, hysteretic current-mode converter design with a push-pull current pump. By using a current feedback technique in contrast with the parallel feedback control approaches by the prior art, performance and stability are greatly enhanced. The system is implemented and verified on a 0.18 μm CMOS process through fully transistorlevel simulations, operating up to 40 MHz. In comparison with the buck converter by itself, the current pump improves the transient response by reducing V OUT undershoot magnitude and settling time by 33.3% and 25.6%, respectively, and overshoot magnitude and settling time by 18.3% and 40.4%, respectively. Undershoot and overshoot are maintained below 3%, at 2.8% and 2.9%, respectively.


title: Quantifying retinal blood vessels' tortuosity — Review

abstract: Tortuosity of retinal blood vessels has been identified as one of earliest indicators to a number of vascular and nonvascular diseases; therefore, early detection and grading blood vessel tortuosity could help for early diseases prevention of further complications. There have been many attempts to develop an accurate automated tortuosity grading measure or system. These attempts have varied, from classifying vessels as either tortuous or non-tortuous or classifying/grading a number of retinal vessels in increased tortuosity, to evaluate the collective tortuosity of a whole vascular tree. Yet, seem none of these systems has gained a universal acceptance. This paper provides an overview of systems and measures, either automatic or manual, which are most proposed, to quantify tortuosity, and to critically evaluate the strength and limitations of those systems and measures; also it shed light on problems encountered by researchers in this field, such as the absence of unified, publicly available datasets and the limitations of the existing ones in terms of datasets sizes, variety based on pathologies and the suitability of vessels segments used in these datasets.


title: DC link harmonics of three phase voltage source converters influenced by the pulse width modulation strategy-an analysis

abstract: DC-link current harmonics are the predominant factor to be considered for dimensioning dc capacitors in three-phase pulsewidth-modulation (PWM) voltage-source converters. In this paper, an analysis of the dc-link current harmonics applying a double Fourier series is derived. The analytical results for the dc-link current spectra of continuous and discontinuous PWMs are presented and compared with measurement results taken from a converter test setup. A good match between theoretically expected and actually obtained experimental results can be stated. Moreover, characteristic differences between the investigated modulation strategies regarding their dc-link current spectra become evident.


title: On Bayesian Adaptive Video Super Resolution

abstract: Although multiframe super resolution has been extensively studied in past decades, super resolving real-world video sequences still remains challenging. In existing systems, either the motion models are oversimplified or important factors such as blur kernel and noise level are assumed to be known. Such models cannot capture the intrinsic characteristics that may differ from one sequence to another. In this paper, we propose a Bayesian approach to adaptive video super resolution via simultaneously estimating underlying motion, blur kernel, and noise level while reconstructing the original high-resolution frames. As a result, our system not only produces very promising super resolution results outperforming the state of the art, but also adapts to a variety of noise levels and blur kernels. To further analyze the effect of noise and blur kernel, we perform a two-step analysis using the Cramer-Rao bounds. We study how blur kernel and noise influence motion estimation with aliasing signals, how noise affects super resolution with perfect motion, and finally how blur kernel and noise influence super resolution with unknown motion. Our analysis results confirm empirical observations, in particular that an intermediate size blur kernel achieves the optimal image reconstruction results.


title: Of Enemies and Pets: Warfare and Shamanism in Amazonia

abstract: Indigenous warfare in tropical South America, frequently involving cannibalism and trophy hunting, has been a recurrent theme in Americanist literature. Since the 16th century, conquerors, missionaries, chroniclers, and more recently anthropologists have striven to make sense of the phenomenon. In this article, I propose a model of social reproduction that subsumes warfare and shamanism within a generalized economy. I show the existence of a dialectic operating between two relational forms, predation and familiarization. This dialectic functions as a general schema of the mode for producing persons and groups in the region, [warfare, shamanism, ritual, exchange theory, animal familiarization, Amazonian Indians]


title: Serpentine Coil Topology for BNL Direct Wind Superconducting Magnets

abstract: Serpentine winding, a recent innovation developed at BNL for direct winding superconducting magnets, allows winding a coil layer of arbitrary multipolarity in one continuous winding process and greatly simplifies magnet design and production compared to the planar patterns used before. Serpentine windings were used for the BEPC-II Upgrade and JPARC magnets and are proposed to make compact final focus magnets for the ILC. Serpentine patterns exhibit a direct connection between 2D body harmonics and harmonics derived from the integral fields. Straightforward 2D optimization yields good integral field quality with uniformly spaced (natural) coil ends. This and other surprising features of Serpentine windings are addressed in this paper.


title: No More Pesky Learning Rates

abstract: The performance of stochastic gradient descent (SGD) depends critically on how learning rates are tuned and decreased over time. We propose a method to automatically adjust multiple learning rates so as to minimize the expected error at any one time. The method relies on local gradient variations across samples. In our approach, learning rates can increase as well as decrease, making it suitable for non-stationary problems. Using a number of convex and non-convex learning tasks, we show that the resulting algorithm matches the performance of SGD or other adaptive approaches with their best settings obtained through systematic search, and effectively removes the need for learning rate tuning.


title: Big data and machine learning driven handover management and forecasting

abstract: Handover (HO), as a key aspect of mobility management, plays an important role in improving network quality and mobility performance in mobile networks. Especially, in 5G networks, heterogeneous networks (HetNets) deployment of macro cells and small cells, and the deployment of ultra-dense networks (UDNs) make HO management become more challenging. Besides, the understanding of HO behavior in a cell is quite limited in existing studies, thus the forecasting HO for an individual cell is complicated, even impossible. This challenge led the authors to propose a practical process for managing and forecasting HO for a huge number of cells, based on machinelearning (ML) algorithms and big data. Moreover, based on HO forecasting, the authors also propose an approach to detect any abnormal HO in cells. The performance of the proposed approaches was evaluated by applying it to a real dataset that collected HO KPI of more than 6000 cells of a real network during the years, 2016 and 2017. The results show that the study was successful in identifying, separating HO behavior, forecasting the future number of HO attempts, and detecting abnormal HO behaviors of cells.


title: Axially chiral amino acid scaffolds as efficient fluorescent discriminators of methanol–ethanol

abstract: We report a unique fluorescence sensor based on an axially chiral unnatural triazolyl aromatic amino acid scaffold (ArTAA) for discrimination of methanol from ethanol via a switch on fluorescence response. All three sensors, the simple scaffold (ArTAA), and the mono- (PyAm-ArTAA) and bis-pyrenyl- (Py2Am-ArTAA) amides, show similar sensitivity and detection limit ranging from 0.5–2.1 v/v% of ethanol. The solid films of these sensors are also found to be effective in sensing ethanol vapour via generation of a distinct and enhanced fluorescence signal. All our experimental results suggest the role of axial chirality of the hairpin-shaped scaffold in differential solvation guided H-bonding interaction and discriminating between ethanol and methanol with a switch-on fluorescence response.


title: A 1-V, 800-MHz CMOS class-E power amplifier with power control for wireless sensor network

abstract: This paper presents a 1-V class-E power amplifier for Wireless Sensor Network in a 0.18-μm CMOS process. The design exploits a cross-coupled structure to achieve high operating frequency and reduce the size of the transistors. Pairs of digital MOS switches manipulate the cross-coupled structure to work or not. To control the output power, a topology of three parallel units with different output power is adopted. An off-chip balun is also proposed for output differential-to-single ended conversion and impedance matching. At 800 MHz, the power amplifier can deliver a range of output power from -5.5 to 6.6 dBm and achieve maximal power-added efficiency (PAE) of 52.7% at 6.6 dBm.


title: Staying engaged during the week: The effect of off-job activities on next day work engagement.

abstract: Although studies on employee recovery accumulate at a stunning pace, the commonly used theory (Effort-Recovery model) that explains how recovery occurs has not been explicitly tested. We aimed to unravel the recovery process by examining whether off-job activities enhance next morning vigor to the extent that they enable employees to relax and detach from work. In addition, we investigated whether adequate recovery also helps employees to work with more enthusiasm and vigor on the next workday. On five consecutive days, a total of 74 employees (356 data points) reported the hours they spent on various off-job activities, their feelings of psychological detachment, and feelings of relaxation before going to sleep. Feelings of vigor were reported on the next morning, and day-levels of work engagement were reported after work. As predicted, leisure activities (social, low-effort, and physical activities) increased next morning vigor through enhanced psychological detachment and relaxation. High-duty off-job activities (work and household tasks) reduced vigor because these activities diminished psychological detachment and relaxation. Moreover, off-job activities significantly affected next day work engagement. Our results support the assumption that recovery occurs when employees engage in off-job activities that allow for relaxation and psychological detachment. The findings also underscore the significance of recovery after work: Adequate recovery not only enhances vigor in the morning, but also helps employees to stay engaged during the next workday.


title: Non-Parametric Extrinsic and Intrinsic Calibration of Visual-Inertial Sensor Systems

abstract: This paper presents a solution for the extrinsic and intrinsic calibration of visual-inertial sensor systems. Calibration is formulated as a joint state and parameter estimation problem of a continuous-time system with discrete-time measurements. A maximum-likelihood estimator is derived to estimate the transform between cameras and inertial sensors, temporal alignment, and inertial sensor intrinsic parameters, such as scale factors, axes misalignment, and sensor noise characteristics. The estimator is simple to implement, consistent, and asymptotically attains the Cramer-Rao lower bound. In contrast to the existing methods, it requires no tuning parameters. Detailed results from repeated calibration experiments with a camera-inertial measurement unit system are reported and compared with the results obtained from a modern, parametric method. We reach a precision of $\mu \text{s}$ in time shift—within a calibration window of 20 s.


title: Autoencoders and Probabilistic Inference with Missing Data: An Exact Solution for The Factor Analysis Case

abstract: Latent variable models can be used to probabilistically "fill-in" missing data entries. The variational autoencoder architecture (Kingma and Welling, 2014; Rezende et al., 2014) includes a "recognition" or "encoder" network that infers the latent variables given the data variables. However, it is not clear how to handle missing data variables in this network. We show how to calculate exactly the latent posterior distribution for the factor analysis (FA) model in the presence of missing data, and note that this solution exhibits a non-trivial dependence on the pattern of missingness. Experiments compare the effectiveness of various approaches to filling in the missing data.


title: libDAI: A free/open source C++ library for discrete approximate inference methods

abstract: This paper describes the software package libDAI, a free & open source C++ library that provides implementations of various exact and approximate inference methods for graphical models with discrete-valued variables. libDAI supports directed graphical models (Bayesian networks) as well as undirected ones (Markov random fields and factor graphs). It offers various approximations of the partition sum, marginal probability distributions and maximum probability states. Parameter learning is also supported. A feature comparison with other open source software packages for approximate inference is given. libDAI is licensed under the GPL v2+ license and is available at http://www.libdai.org.


title: Communication in the face of a school crisis: Examining the volume and content of social media mentions during active shooter incidents

abstract: Little is known about the effectiveness of social media in delivering information during active shooter incidents at the P-12 level. This study analyzed social media activity that occurred during and after two active shooter events on September 30, 2014. Over 5000 social media posts from Facebook, Twitter, blogs, and mainstream news outlets were analyzed. Social media analysis outlined the scope of online communication during the first week following the incidents, revealed social media frequency, increases in conversation, misinformation, and differences between parent and student posts. Results revealed spikes in social media chatter following the release of the identities of shooters and victims. Consistent with media dependency theory and the high levels of uncertainty characteristic of the incident, users’ social media posts contained more information than affect displays during the active shooter event. Implications for scholars and P-12 administrators are discussed.


title: Developing and delivering hands-on information assurance exercises: experiences with the cyber defense lab at UMBC

abstract: In summer 2003, we developed four new hands-on information assurance educational exercises for use in the UMBC undergraduate and graduate curricula. Exercise topics comprise buffer overflow attacks, vulnerability scanning, password security and policy, and flaws in the wired equivalent privacy (WEP) protocol. During each exercise, each student carries out structured activities using a laptop from a mobile cart that can be rolled into any classroom. These dedicated, isolated machines permit a student to make mistakes safely, even while acting as the system administrator, without adversely affecting any other user. Each exercise is organized in a modular fashion to facilitate varied use for different courses, levels, and available time. Our experiences delivering these exercises show that practical hands-on activities motivate students and enhance learning. In this paper we describe our exercises and share lessons learned, including the importance of careful planning, ethical considerations, the rapid obsolescence of tools, and the difficulty of including exercises in already busy courses.


title: Feature Selection as a One-Player Game

abstract: This paper formalizes Feature Selection as a Reinforcement Learning problem, leading to a provably optimal though intractable selection policy. As a second contribution, this paper presents an approximation thereof, based on a one-player game approach and relying on the Monte-Carlo tree search UCT (Upper Confidence Tree) proposed by Kocsis and Szepesvari (2006). The Feature Uct SElection (FUSE) algorithm extends UCT to deal with i) a finite unknown horizon (the target number of relevant features); ii) the huge branching factor of the search tree, reflecting the size of the feature set. Finally, a frugal reward function is proposed as a rough but unbiased estimate of the relevance of a feature subset. A proof of concept of FUSE is shown on benchmark data sets.


title: Towards large-scale twitter mining for drug-related adverse events

abstract: Drug-related adverse events pose substantial risks to patients who consume post-market or Drug-related adverse events pose substantial risks to patients who consume post-market or investigational drugs. Early detection of adverse events benefits not only the drug regulators, but also the manufacturers for pharmacovigilance. Existing methods rely on patients' "spontaneous" self-reports that attest problems. The increasing popularity of social media platforms like the Twitter presents us a new information source for finding potential adverse events. Given the high frequency of user updates, mining Twitter messages can lead us to real-time pharmacovigilance. In this paper, we describe an approach to find drug users and potential adverse events by analyzing the content of twitter messages utilizing Natural Language Processing (NLP) and to build Support Vector Machine (SVM) classifiers. Due to the size nature of the dataset (i.e., 2 billion Tweets), the experiments were conducted on a High Performance Computing (HPC) platform using MapReduce, which exhibits the trend of big data analytics. The results suggest that daily-life social networking data could help early detection of important patient safety issues.


title: The self-fulfilling nature of positive illusions in romantic relationships: love is not blind, but prescient.

abstract: It is proposed that satisfying, stable relationships reflect intimates' ability to see imperfect partners in idealized ways. In this study of the long-term benefits (or possible costs) of positive illusions, both members of dating couples completed measures of idealization and well-being 3 times in a year. Path analyses revealed that idealization had a variety of self-fulfilling effects. Relationships were most likely to persist-even in the face of conflicts and doubts- when intimates idealized one another the most. Intimates who idealized one another more initially also reported relatively greater increases in satisfaction and decreases in conflicts and doubts over the year. Finally, individuals even came to share their partners' idealized images of them. In summary, intimates who idealized one another appeared more prescient than blind, actually creating the relationships they wished for as romances progressed.


HEART RATE MEASUREMENT FROM THE FINGER USING A LOW-COST MICROCONTROLLER


title: An analysis of a large scale habitat monitoring application

abstract: Habitat and environmental monitoring is a driving application for wireless sensor networks. We present an analysis of data from a second generation sensor networks deployed during the summer and autumn of 2003. During a 4 month deployment, these networks, consisting of 150 devices, produced unique datasets for both systems and biological analysis. This paper focuses on nodal and network performance, with an emphasis on lifetime, reliability, and the the static and dynamic aspects of single and multi-hop networks. We compare the results collected to expectations set during the design phase: we were able to accurately predict lifetime of the single-hop network, but we underestimated the impact of multi-hop traffic overhearing and the nuances of power source selection. While initial packet loss data was commensurate with lab experiments, over the duration of the deployment, reliability of the backend infrastructure and the transit network had a dominant impact on overall network performance. Finally, we evaluate the physical design of the sensor node based on deployment experience and a post mortem analysis. The results shed light on a number of design issues from network deployment, through selection of power sources to optimizations of routing decisions.


title: Automatic recipe cuisine classification by ingredients

abstract: With the growth of recipe sharing services, online cooking recipes associated with ingredients and cooking procedures are available. Many recipe sharing sites have devoted to the development of recipe recommendation mechanism. While most food related research has been on recipe recommendation, little effort has been done on analyzing the correlation between recipe cuisines and ingredients. In this paper, we aim to investigate the underlying cuisine-ingredient connections by exploiting the classification techniques, including associative classification and support vector machine. Our study conducted on food.com data provides insights about which cuisines are the most similar and what are the essential ingredients for a cuisine, with an application to automatic cuisine labeling for recipes.


title: Digree: A middleware for a graph databases polystore

abstract: In this paper we present Digree, an experimental middleware system that can execute graph pattern matching queries over databases hosting voluminous graph datasets. First, we formally present the employed data model and the processes of re-writing a query into an equivalent set of subqueries and subsequently combining the partial results into the final result set. Our framework guarantees the correctness and completeness of the produced answers. Then, we present a prototype implementation of Digree, which is agnostic to the underlying data processing engines used at the endpoints. As the experimental results show, in many cases Digree outperforms a single node graph database deployment in execution speed, up to 20 times depending on the query type.


title: Contribution of stress and gender to exploratory preferences for familiar versus unfamiliar conspecifics

abstract: Abstract An apparatus for measuring the exploratory preferences of rats for familiar and unfamiliar conspecifics in a novel environment was designed. The exploratory behavior of males and females was compared and contrasted to that elicited in response to an acute aversive event. Sprague-Dawley male and female rats were exposed to restraint and 60, 1 s, 1 mA tailshocks and returned to their home cage. Either 2 or 24 h later, they were placed in a novel environment with a familiar cage-mate and an unfamiliar conspecific of the same sex. Relative to unstressed controls and females, males stressed 2 h previously decreased the exploration of the unfamiliar conspecific, exhibiting a rapid decrease over the course of the trial. In response to the stressor, however both sexes, however, decreased the exploration of the familiar conspecific, decreased their overall activity, and returned preferentially to their starting quadrant. None of these stress-induced effects were evident 24 h later upon the first or second exposure to the apparatus. Thus, exposure to the stressor transiently increased perseveration and decreased activity in males and females, but only decreased the exploration of novel conspecifcs in males. These results indicate that a number of behavioral responses to stressors are conserved across gender, but those relating to novelty are more pronounced in males.


title: Why phishing still works : user strategies for combating phishing attacks

abstract: We have conducted a user study to assess whether improved browser security indicators and increased awareness of phishing have led to users' improved ability to protect themselves against such attacks. Participants were shown a series of websites and asked to identify the phishing websites. We use eye tracking to obtain objective quantitative data on which visual cues draw users' attention as they determine the legitimacy of websites. Our results show that users successfully detected only 53% of phishing websites even when primed to identify them and that they generally spend very little time gazing at security indicators compared to website content when making assessments. However, we found that gaze time on browser chrome elements does correlate to increased ability to detect phishing. Interestingly, users' general technical proficiency does not correlate with improved detection scores. HighlightsUsers successfully detected only 53% of phishing websites.Gaze time on browser chrome elements correlates to increased ability to detect phishing.Users' technical proficiency is not correlated with the ability to detect phishing.Users spend little time gazing at security indicators when assessing legitimacy of websites.


title: A compiler for throughput optimization of graph algorithms on GPUs

abstract: Writing high-performance GPU implementations of graph algorithms can be challenging. In this paper, we argue that three optimizations called throughput optimizations are key to high-performance for this application class. These optimizations describe a large implementation space making it unrealistic for programmers to implement them by hand. To address this problem, we have implemented these optimizations in a compiler that produces CUDA code from an intermediate-level program representation called IrGL. Compared to state-of-the-art handwritten CUDA implementations of eight graph applications, code generated by the IrGL compiler is up to 5.95x times faster (median 1.4x) for five applications and never more than 30% slower for the others. Throughput optimizations contribute an improvement up to 4.16x (median 1.4x) to the performance of unoptimized IrGL code.


title: Improving proactive information systems

abstract: Proactive contextual information systems help people locate information by automatically suggesting potentially relevant resources based on their current tasks or interests. Such systems are becoming increasingly popular, but designing user interfaces that effectively communicate recommended information is a challenge: the interface must be unobtrusive, yet communicate enough information at the right time to provide value to the user. In this paper we describe our experience with the FXPAL Bar, a proactive information system designed to provide contextual access to corporate and personal resources. In particular, we present three features designed to communicate proactive recommendations more effectively: translucent recommendation windows increase the user's awareness of particularly highly-ranked recommendations, query term highlighting communicates the relationship between a recommended document and the user's current context, and a novel recommendation digest function allows users to return to the most relevant previously recommended resources. We present empirical evidence supporting our design decisions and relate lessons learned for other designers of contextual recommendation systems.


title: The Automated Learning of Deep Features for Breast Mass Classification from Mammograms

abstract: The classification of breast masses from mammograms into benign or malignant has been commonly addressed with machine learning classifiers that use as input a large set of hand-crafted features, usually based on general geometrical and texture information. In this paper, we propose a novel deep learning method that automatically learns features based directly on the optmisation of breast mass classification from mammograms, where we target an improved classification performance compared to the approach described above. The novelty of our approach lies in the two-step training process that involves a pre-training based on the learning of a regressor that estimates the values of a large set of hand-crafted features, followed by a fine-tuning stage that learns the breast mass classifier. Using the publicly available INbreast dataset, we show that the proposed method produces better classification results, compared with the machine learning model using hand-crafted features and with deep learning method trained directly for the classification stage without the pre-training stage. We also show that the proposed method produces the current state-of-the-art breast mass classification results for the INbreast dataset. Finally, we integrate the proposed classifier into a fully automated breast mass detection and segmentation, which shows promising results.


title: Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables

abstract: Human activity recognition (HAR) in ubiquitous computing is beginning to adopt deep learning to substitute for well-established analysis techniques that rely on hand-crafted feature extraction and classification techniques. From these isolated applications of custom deep architectures it is, however, difficult to gain an overview of their suitability for problems ranging from the recognition of manipulative gestures to the segmentation and identification of physical activities like running or ascending stairs. In this paper we rigorously explore deep, convolutional, and recurrent approaches across three representative datasets that contain movement data captured with wearable sensors. We describe how to train recurrent approaches in this setting, introduce a novel regularisation approach, and illustrate how they outperform the state-of-the-art on a large benchmark dataset. Across thousands of recognition experiments with randomly sampled model configurations we investigate the suitability of each model for different tasks in HAR, explore the impact of hyperparameters using the fANOVA framework, and provide guidelines for the practitioner who wants to apply deep learning in their problem setting.


title: Retailers’ communication on ugly fruits and vegetables: What are consumers’ perceptions?

abstract: Abstract This research investigates retailers’ societal advertisements on ugly fruits and vegetables. A three (retailer's claim is focusing on consumers’ health, on food taste, on food price) x 2 (the retailer studied is Intermarche, Biocoop) between subjects design has been conducted. Several paths that retailers can follow in order to build the credibility of their message and consumers’ attitude toward the ad (the two explanatory variables relating to the message) as well as consumers’ trust in the retailer and their attitude toward this retailer (two explained variables relating to the retailer) are indicated. The direct and indirect impacts of societal advertisements on ugly fruits and vegetables on consumers’ relationship with retailers are thus pointed out.


title: A novel algorithm for shuttlecock tracking

abstract: Real-time accurate ball tracking in sport games is important for automated game analysis and augment reality display. It is very difficult because the ball is usually very small, with few features and large variance appearance, sudden motion change and occlusion occurs quite often as well as the background is noisy. Badminton is one of the fastest ball games in the world and so tracking a shuttlecock accurately is a challenging work. In this paper, we propose a novel real-time non-recursive tracking algorithm to addressing this issue by formulating the motion model and the correlation within scene context, which is different from standard tracking approaches. We build a multi-layer filters focusing on the rationality to eliminate false candidates step by step and choose the optimal candidate without previous results. Experiment results show that the proposed tracker can track the shuttlecocks in real-time with satisfying performance.


title: Syntactic Annotation Guidelines for the Quranic Arabic Dependency Treebank

abstract: The Quranic Arabic Dependency Treebank (QADT) is part of the Quranic Arabic Corpus (http://corpus.quran.com), an online linguistic resource organized by the University of Leeds, and developed through online collaborative annotation. The website has become a popular study resource for Arabic and the Quran, and is now used by over 1,500 researchers and students daily. This paper presents the treebank, explains the choice of syntactic representation ( ), and highlights key parts of the annotation guidelines. The text being analyzed is the Quran, the central religious book of Islam, written in classical Quranic Arabic (c. 600 CE). To date, all 77,430 words of the Quran have a manually verified morphological analysis, and syntactic analysis is in progress. 11,000 words of Quranic Arabic have been syntactically annotated as part of a gold standard treebank ( ). Annotation guidelines are especially important to promote consistency for a corpus which is being developed through online collaboration, since often many people will participate from different backgrounds and with different levels of linguistic expertise. The treebank is available online for collaborative correction to improve accuracy, with suggestions reviewed by expert Arabic linguists, and compared against existing published books of Quranic Syntax.


A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking


title: Noisy Depth Maps Fusion for Multiview Stereo Via Matrix Completion

abstract: This paper introduces a general framework to fuse noisy point clouds from multiview images of the same object. We solve this classical vision problem using a newly emerging signal processing technique known as matrix completion. With this framework, we construct the initial incomplete matrix from the observed point clouds by all the cameras, with the invisible points by any camera denoted as unknown entries. The observed points corresponding to the same object point are put into the same row. When properly completed, the recovered matrix should have rank one, since all the columns describe the same object. Therefore, an intuitive approach to complete the matrix is by minimizing its rank subject to consistency with observed entries. In order to improve the fusion accuracy, we propose a general noisy matrix completion method called log-sum penalty completion (LPC), which is particularly effective in removing outliers. Based on the majorization–minimization algorithm (MM), the non-convex LPC problem is effectively solved by a sequence of convex optimizations. Experimental results on both point cloud fusion and MVS reconstructions verify the effectiveness of the proposed framework and the LPC algorithm.


title: Service Process Innovation: A Case Study of BPMN in Practice

abstract: We describe the application of the Business Process Modeling Notation to the redesign of a service management process in a truck dealership in the N.E. US. After the deployment of a new service management system did not lead to the expected efficiency gains the authors were asked to analyze the as-is service process, make suggestions for improvement, and simulate the financial impact of the proposed changes. The as-is and to-be process were documented in BPMN, and a BPMN-based simulation tool was used to quantify the effects of process improvement. As an outcome of this project we developed an initial subset of BPMN constructs that were a useful starting point for modeling in a process-unaware domain with members of the surveyed organization. We found that the deliberate modification of BPMN constructs allows for the highlighting of common process weaknesses, and we show how an analytical process model changes when it is modified to serve as a simulation models.


title: Predicting traffic of online advertising in real-time bidding systems from perspective of demand-side platforms

abstract: Online advertising has been all the rage these years. Budget control and traffic prediction turn out to be important issues for the demand-side platforms (DSPs). However, DSPs cannot easily grab the information of audiences and media platforms. Although DSPs might have the information immediately, it is still hard to response the request of advertisements in real-time due to the high volume of features. Therefore, we propose a method predicting traffic of requests from perspective of DSPs. The features we used are simple to be extracted from historical data. The prediction model we chose is regression model with closed-form solution. Both the features and regression model make our prediction adaptive in real-time systems. Our method can detect traffic anomalies and prevent it from overwhelming prediction. Moreover, our method can also keep pace of the trend. Experiment results show that our method's error rate of prediction is about 0.9% in total, and 10% per time unit.


title: OntoLife: an Ontology for Semantically Managing Personal Information

abstract: Personal knowledge management has been studied from various angles, one of which is the Semantic Web. Ontologies, the primary knowledge representation tool for the Semantic Web, can play a significant role in semantically managing personal knowledge. The scope of this paper focuses on addressing the issue of effective personal knowledge management, by proposing an ontology for modelling the domain of biographical events. The proposed ontology also undergoes a thorough evaluation, based on specific criteria presented in the literature.


title: Dynamic underactuated flying-walking (DUCK) robot

abstract: This paper describes the development of a flying and walking robot, called the dynamic underactuated flying-walking (DUCK) robot. The DUCK robot combines a high-mobility flying platform, such as a quadcopter (quadrotor helicopter), with passive-dynamic legs to create a versatile system that can fly and walk. One of the advantages of passive-dynamic legs for walking is that additional actuators are not needed for terrestrial locomotion. Herein, a mathematical model is presented and simulations are used to help design a prototype robot. Experimental results demonstrate the feasibility of combining an aerial platform with passive-dynamic legs to create an effective flying and walking robot. In particular, two modes of walking are demonstrated: (1) passive walking down inclined surfaces for low-energy terrestrial locomotion, and (2) active (powered) walking by leveraging the capabilities of the flying platform, where thrust from the quadcopter's rotors enables the DUCK robot to take steps and walk on flat surfaces or up inclined surfaces.


title: Selection of Security Activities for Integration with Agile Methods after Combining their Agility and Effectiveness

abstract: Nowadays security concerns are central in software engineering. Moreover, the idea of incorporating security in agile software development has always been difficult because most of the security activities conflict with the agile principles. Agile development methodologies promise simple and iterative development with minimal documentation and a solution to rapidly changing environment. However, security techniques are complex and require too much documentation thus not suited for agile methods. The goal of this paper is to select those security activities which are best to integrate with agile methods. This selection is based on two distinct measures, effectiveness and agility degree of security activities. The former indicates that the considered security activity will produce the intended result and ultimately reflects its impression in achieving security of software system. The latter refers to the flexible and low-cost ability of the security activity in providing effective responses to unpredictable changes. The challenge is to find the right mix of above said two measures according to project’s need. This has been accomplished in the proposed approach, CEASA (Combining Effectiveness and Agility of Security Activity), by using a weighted decision theory.


title: Bayesian Nonparametric Inference of Switching Linear Dynamical Systems

abstract: Many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switching linear dynamical system (SLDS) and the switching vector autoregressive (VAR) process. Our Bayesian nonparametric approach utilizes a hierarchical Dirichlet process prior to learn an unknown number of persistent, smooth dynamical modes. We additionally employ automatic relevance determination to infer a sparse set of dynamic dependencies allowing us to learn SLDS with varying state dimension or switching VAR processes with varying autoregressive order. We develop a sampling algorithm that combines a truncated approximation to the Dirichlet process with efficient joint sampling of the mode and state sequences. The utility and flexibility of our model are demonstrated on synthetic data, sequences of dancing honey bees, the IBOVESPA stock index, and a maneuvering target tracking application.


title: Fooling Neural Networks in Face Attractiveness Evaluation: Adversarial Examples with High Attractiveness Score But Low Subjective Score

abstract: People are fond of taking and sharing photos in their social life, and a large part of it is face images, especially selfies. A lot of researchers are interested in analyzing attractiveness of face images. Benefited from deep neural networks (DNNs) and training data, researchers have been developing deep learning models that can evaluate facial attractiveness of photos. However, recent development on DNNs showed that they could be easily fooled even when they are trained on a large dataset. In this paper, we used two approaches to generate adversarial examples that have high attractiveness scores but low subjective scores for face attractiveness evaluation on DNNs. In the first approach, experimental results using the SCUT-FBP dataset showed that we could increase attractiveness score of 20 test images from 2.67 to 4.99 on average (score range: [1, 5]) without noticeably changing the images. In the second approach, we could generate similar images from noise image with any target attractiveness score. Results show by using this approach, a part of attractiveness information could be manipulated artificially.


title: Perceptual Assimilation and L2 Learning: Evidence from the Perception of Southern British English Vowels by Native Speakers of Greek and Japanese

abstract: This study examined the extent to which previous experience with duration in first language (L1) vowel distinctions affects the use of duration when perceiving vowels in a second language (L2). Native


title: Traffic Accident Analysis Using Decision Trees and Neural Networks

abstract: The costs of fatalities and injuries due to traffic accident have a great impact on society. This paper presents our research to model the severity of injury resulting from traffic accidents using artificial neural networks and decision trees. We have applied them to an actual data set obtained from the National Automotive Sampling System (NASS) General Estimates System (GES). Experiment results reveal that in all the cases the decision tree outperforms the neural network. Our research analysis also shows that the three most important factors in fatal injury are: driver's seat belt usage, light condition of the roadway, and driver's alcohol usage.


title: A computation offloadingscheme on handheld devices

abstract: In this paper, we present a computation offloading scheme on handheld devices. This scheme partitions an ordinary program into a client-server distributed program, such that the client code runs on the handheld device and the server code runs on the server. Our partition analysis and program transformation guarantee correct distributed execution under all possible execution contexts. We give a polynomial time algorithm to find the optimal program partition for given program input data. We use an option-clustering approach to handle different program partitions for different program execution options. Experimental results show significant improvement of performance and energy consumption on an HP IPAQ handheld device through computation offloading.


title: A wire-grid type transparent film UWB antenna

abstract: In this paper we have introduced transparent wire-grid type conductive film antenna, made of Ag-paste. The wire-grid has wire-width of only a few microns and makes the conductive film almost invisible. The antenna is designed to cover an ultra-wide band (UWB) from 2 to 6 GHz and the radiating part is made with different mesh sizes of the conductive film and ground has been taken with continuous sheet. Performance of these mesh antennas is compared with a full Cu-sheet antenna. Input characteristics and radiation patterns are approximately the same as that of the Cu-antenna. Even the wire-grid is so sparse, the radiation efficiency is higher than 50% at most frequencies. In the later part of this paper, vertical slit and horizontal slit antennas made of Cu-sheet are investigated. The horizontal slit antenna shows deviation in the results, whereas the vertical slit and full Cu antenna are matching the results to each other for the UWB of 2–6 GHz. Vertical and horizontal slit antennas confirm the appropriate polarization.


title: SOLVATOCHROMIC STUDIES OF STATIONARY PHASES ON THIN-LAYER CHROMATOGRAPHIC PLATES

abstract: Three polarity-indicating dyes, Reichardt's betaine, N,N-diethyl-4-nitroaniline, and N-methyl-2-nitroaniline, have been used to characterize the mobile-phase-stationary-phase interface of thin-layer chromatographic plates. Diffuse reflectance spectroscopy was used to detect the shifts in the wavelength maxima of these dyes, which have been related to the π* and E T (30) solvent polarity scales


title: Obesity, Diabetes, and Gut Microbiota

abstract: The connection between gut microbiota and energy homeostasis and inflammation and its role in the pathogenesis of obesity-related disorders are increasingly recognized. Animals models of obesity connect an altered microbiota composition to the development of obesity, insulin resistance, and diabetes in the host through several mechanisms: increased energy harvest from the diet, altered fatty acid metabolism and composition in adipose tissue and liver, modulation of gut peptide YY and glucagon-like peptide (GLP)-1 secretion, activation of the lipopolysaccharide toll-like receptor-4 axis, and modulation of intestinal barrier integrity by GLP-2. Instrumental for gut microbiota manipulation is the understanding of mechanisms regulating gut microbiota composition. Several factors shape the gut microflora during infancy: mode of delivery, type of infant feeding, hospitalization, and prematurity. Furthermore, the key importance of antibiotic use and dietary nutrient composition are increasingly recognized. The role of the Western diet in promoting an obesogenic gut microbiota is being confirmation in subjects. Following encouraging results in animals, several short-term randomized controlled trials showed the benefit of prebiotics and probiotics on insulin sensitivity, inflammatory markers, postprandial incretins, and glucose tolerance. Future research is needed to unravel the hormonal, immunomodulatory, and metabolic mechanisms underlying microbe-microbe and microbiota-host interactions and the specific genes that determine the health benefit derived from probiotics. While awaiting further randomized trials assessing long-term safety and benefits on clinical end points, a healthy lifestyle—including breast lactation, appropriate antibiotic use, and the avoidance of excessive dietary fat intake—may ensure a friendly gut microbiota and positively affect prevention and treatment of metabolic disorders.


title: Image deblurring and denoising using color priors

abstract: Image blur and noise are difficult to avoid in many situations and can often ruin a photograph. We present a novel image deconvolution algorithm that deblurs and denoises an image given a known shift-invariant blur kernel. Our algorithm uses local color statistics derived from the image as a constraint in a unified framework that can be used for deblurring, denoising, and upsampling. A pixel's color is required to be a linear combination of the two most prevalent colors within a neighborhood of the pixel. This two-color prior has two major benefits: it is tuned to the content of the particular image and it serves to decouple edge sharpness from edge strength. Our unified algorithm for deblurring and denoising out-performs previous methods that are specialized for these individual applications. We demonstrate this with both qualitative results and extensive quantitative comparisons that show that we can out-perform previous methods by approximately 1 to 3 DB.


title: Prefrontal cortex and decision making in a mixed-strategy game

abstract: In a multi-agent environment, where the outcomes of one's actions change dynamically because they are related to the behavior of other beings, it becomes difficult to make an optimal decision about how to act. Although game theory provides normative solutions for decision making in groups, how such decision-making strategies are altered by experience is poorly understood. These adaptive processes might resemble reinforcement learning algorithms, which provide a general framework for finding optimal strategies in a dynamic environment. Here we investigated the role of prefrontal cortex (PFC) in dynamic decision making in monkeys. As in reinforcement learning, the animal's choice during a competitive game was biased by its choice and reward history, as well as by the strategies of its opponent. Furthermore, neurons in the dorsolateral prefrontal cortex (DLPFC) encoded the animal's past decisions and payoffs, as well as the conjunction between the two, providing signals necessary to update the estimates of expected reward. Thus, PFC might have a key role in optimizing decision-making strategies.


title: A Study of Segmentation Algorithm for Decoration of Statue Based on Curve Skeleton

abstract: This paper presents our novel algorithm for decoration points' segmentation with simple calculation in a two-dimensional domain. Our algorithm works for unorganized point clouds. It extracts the relationship between two adjacent sub-regions, which are derived from a single cross section of the point cloud. The cutting planes are defined by the Frenet frame of curve skeleton. In our method, angle-based information from the cross sections is introduced and plays an important role. The angle-based information called "angle function" indicates angle difference of the adjacent left and right sub-regions, which are determined by a line segment calculated by a linear least square of the nearest left and right points. Using the angle function, critical points such as inflection and extrinsic points can be found. Finally, the segmentation algorithm is able to detect the decoration points using the principal points.


title: Finite state machine induction using genetic algorithm based on testing and model checking

abstract: In this paper, we describe the method of finite state machine (FSM) induction using genetic algorithm with fitness function, cross-over and mutation based on testing and model checking. Input data for the genetic algorithm is a set of tests and a set of properties described using linear time logic. Each test consists of an input sequence of events and the corresponding output action sequence. In previous works testing and model checking were used separately in genetic algorithms. Usage of such an approach is limited because the behavior of system usually cannot be described by tests only. So, additional validation or verification is needed. Calculation of fitness function based only on verification do not perform well because there are very few possible values of fitness function (verification gives only "yes" or "no" answer). The approach described is tested on the problem of finite state machine induction for elevator doors controlling. Using tests only the genetic algorithm constructs the finite machine working improperly in some cases. Usage of verification allows to induct the correct finite state machine.


title: The Brain-Heart Connection: Frontal Cortex and Left Ventricle Angiotensinase Activities in Control and Captopril-Treated Hypertensive Rats—A Bilateral Study

abstract: The model of neurovisceral integration suggests that the frontal cortex (FC) and the cardiovascular function are reciprocally and asymmetrically connected. We analyzed several angiotensinase activities in the heart left ventricle (VT) of control and captopril-treated SHR, and we search for a relationship between these activities and those determined in the left and right FC. Captopril was administered in drinking water for 4 weeks. Samples from the left VT and from the left and right FC were obtained. Soluble and membrane-bound enzymatic activities were measured fluorometrically using arylamides as substrates. The weight of heart significantly decreased after treatment with captopril, mainly, due to the reduction of the left VT weight. In the VT, no differences for soluble activities were observed between control and treated SHR. In contrast, a generalized significant reduction was observed for membrane-bound activities. The most significant correlations between FC and VT were observed in the right FC of the captopril-treated group. The other correlations, right FC versus VT and left FC versus VT in controls and left FC versus VT in the captopril group, were few and low. These results confirm that the connection between FC and cardiovascular system is asymmetrically organized.


title: Output regulation of nonlinear systems with delay

abstract: Abstract Output regulation of retarded type nonlinear systems is considered. Regulator equations are derived, which generalize Francis–Byrnes–Isidori equations to the case of systems with delay. It is shown that, under standard assumptions, the regulator problem is solvable if and only if these equations are solvable. In the linear case, the solution of these equations is reduced to linear matrix equations. An example of a delayed Van der Pol equation illustrates the efficiency of the results.


title: Sound Coding in Cochlear Implants: From electric pulses to hearing

abstract: Cochlear implantation is a life-changing intervention for people with a severe hearing impairment [1]. For most cochlear implant (CI) users, speech intelligibility is satisfactory in quiet environments. Although modern CIs provide up to 22 stimulation channels, information transfer is still limited for the perception of fine spectrotemporal details in many types of sound. These details contribute to the perception of music and speech in common listening situations, such as where background noise is present. Over the past several decades, many different sound processing strategies have been developed to provide more details about acoustic signals to CI users. In this article, progress in sound coding for CIs is reviewed. Starting from a basic strategy, the current commercially most-used signal processing schemes are discussed, as well as recent developments in coding strategies that aim to improve auditory perception. This article focuses particularly on the stimulation strategies, which convert sound signals into patterns of nerve stimulation. The neurophysiological rationale behind some of these strategies is discussed and aspects of CI performance that require further improvement are identified.


title: A Real-Time Recognition System for User Characteristics Based on Deep Learning

abstract: This paper describes an implementation of a novel real-time recognition system which is capable to identify important information from a single user such as gender, age, emotions and hand gestures. The key of this recognition system is the classification process. This is carried out by using several convolutional neural networks that were designed to achieve a high accuracy rate and acceptable response time making use of low computational resources. As a result, this recognition system could be useful in numerous applications like human-computer interaction, person identification, security control and others.


title: Component Model of Addiction Treatment: A Pragmatic Transdiagnostic Treatment Model of Behavioral and Substance Addictions

abstract: Behavioral addictions such as gambling, video games, sex, and shopping share many clinical features with substance use addictions including etiology, course, and neurobiology. Yet, the treatment of behavioral and substance use addictions tends to be separated. However, we argue that a more effective and efficient treatment approach is to conceptualize behavioral and substance use addictions as different expressions of a common underlying disorder and, in treatment, to address the underlying mechanisms common to both. To this end, the article presents a developing transdiagnostic treatment model of addictions that targets underlying similarities between behavioral and substance use addictions, called the components model of addiction treatment (CMAT). The CMAT is transdiagnostic in that it can be used in the treatment of both behavioral and substance use addictions. It is pragmatic in that it targets component vulnerabilities, which are enduring, yet malleable, individual psychological, cognitive and neurobiological characteristics that are common to all addictive disorders and have been demonstrated to be modifiable. A working model of CMAT is presented, including proposed component vulnerabilities: lack of motivation, urgency, maladaptive expectancies, deficits in self-control, deficits in social support, and compulsivity, as well as their potential intervention possibilities. Future directions and potential implications of the CMAT are discussed.


title: ExIt-OOS: Towards Learning from Planning in Imperfect Information Games

abstract: The current state of the art in playing many important perfect information games, including Chess and Go, combines planning and deep reinforcement learning with self-play. We extend this approach to imperfect information games and present ExIt-OOS, a novel approach to playing imperfect information games within the Expert Iteration framework and inspired by AlphaZero. We use Online Outcome Sampling, an online search algorithm for imperfect information games in place of MCTS. While training online, our neural strategy is used to improve the accuracy of playouts in OOS, allowing a learning and planning feedback loop for imperfect information games.


title: Dataflow anomaly detection

abstract: Beginning with the work of Forrest et al, several researchers have developed intrusion detection techniques based on modeling program behaviors in terms of system calls. A weakness of these techniques is that they focus on control flows involving system calls, but not their arguments. This weakness makes them susceptible to several classes of attacks, including attacks on security-critical data, race-condition and symbolic link attacks, and mimicry attacks. To address this weakness, we develop a new approach for learning dataflow behaviors of programs. The novelty in our approach, as compared to previous system-call argument learning techniques, is that it learns temporal properties involving the arguments of different system calls, thus capturing the flow of security-sensitive data through the program. An interesting aspect of our technique is that it can be uniformly layered on top of most existing control-flow models, and can leverage control-flow contexts to significantly increase the precision of dataflows captured by the model. This contrasts with previous system-call argument learning techniques that did not leverage control-flow information, and moreover, were focused on learning statistical properties of individual system call arguments. Through experiments, we show that temporal properties enable detection of many attacks that aren't detected by previous approaches. Moreover, they support formal reasoning about security assurances that can be provided when a program follows its dataflow behavior model, e.g., tar would read only files located within a directory specified as a command-line argument.


title: Depth-of-Field Rendering by Pyramidal Image Processing

abstract: We present an image-based algorithm for interactive rendering depth-of-field effects in images with depth maps. While previously published methods for interactive depth-of-field rendering suffer from various rendering artifacts such as color bleeding and sharpened or darkened silhouettes, our algorithm achieves a significantly improved image quality by employing recently proposed GPU-based pyramid methods for image blurring and pixel disocclusion. Due to the same reason, our algorithm offers an interactive rendering performance on modern GPUs and is suitable for real-time rendering for small circles of confusion. We validate the image quality provided by our algorithm by side-by-side comparisons with results obtained by distributed ray tracing.


title: How the built environment affects physical activity

abstract: The link between the built environment and human behavior has long been of interest to the field of urban planning, but direct assessments of the links between the built environment and physical activity as it influences personal health are still rare in the field. Yet the concepts, theories, and methods used by urban planners provide a foundation for an emerging body of research on the relationship between the built environment and physical activity. Recent research efforts in urban planning have focused on the idea that land use and design policies can be used to increase transit use as well as walking and bicycling. The development of appropriate measures for the built environment and for travel behavior is an essential element of this research. The link between the built environment and travel behavior is then made using theoretical frameworks borrowed from economics, and in particular, the concept of travel as a derived demand. The available evidence lends itself to the argument that a combination of urban design, land use patterns, and transportation systems that promotes walking and bicycling will help create active, healthier, and more livable communities. To provide more conclusive evidence, however, researchers must address the following issues: An alternative to the derived-demand framework must be developed for walking, measures of the built environment must be refined, and more-complete data on walking must be developed. In addition, detailed data on the built environment must be spatially matched to detailed data on travel behavior.


title: Rotarod studies in the rat of the GABAA receptor agonist gaboxadol: lack of ethanol potentiation and benzodiazepine cross-tolerance.

abstract: All benzodiazepines and benzodiazepine site agonists impair motor performance dose-dependently and potentiate the effects of ethanol. In order to evaluate the risk of benzodiazepine and ethanol interaction with the direct acting GABA(A) receptor agonist 4,5,6,7-tetrahydroisoxazolo (5,4-c) pyridin-3-ol (gaboxadol), we studied impairment of motor coordination for combinations of gaboxadol, ethanol and a series of benzodiazepines (flunitrazepam, zolpidem and indiplon) in a rat rotarod model. All compounds produced a dose-dependent motor impairment and, in agreement with earlier data, a supra-additive effect of the benzodiazepine ligands and ethanol 1 g/kg was seen. In contrast, no significant potentiation of the effects of gaboxadol by ethanol was detected, and furthermore, no synergistic interaction between gaboxadol and any of the benzodiazepines was seen. A 30-day tolerance study was conducted with daily injections of gaboxadol (7.9 mg/kg) and zolpidem (1.25 mg/kg), respectively. A time-dependent tolerance developed to the motor impairment produced by both compounds. On day 31, cross-tolerance studies between zolpidem/gaboxadol and gaboxadol/zolpidem were conducted. No cross-tolerance was observed, indicating that the motor coordination effects observed with gaboxadol and zolpidem may arise from interaction with different receptor populations.


title: 10×10nm2 Hf/HfOx crossbar resistive RAM with excellent performance, reliability and low-energy operation

abstract: We report on world's smallest HfO 2 -based Resistive RAM (RRAM) cell to date, featuring a novel Hf/HfO x resistive element stack, with an area of less than 10×10 nm2, fast ns-range on/off switching times at low-voltages and with a switching energy per bit of <0.1pJ. With excellent endurance of more than 5.107cycles, large on/off verified-window (>50), no closure of the on/off window after 30hrs/200C and failure-free device operation after 30hrs/250C thermal stress, the major device-level nonvolatile memory requirements are met. Furthermore, we clarify the impact of film crystallinity on cell operation from a scalability viewpoint, the role of the cap layer and bring insight into the switching mechanisms.


title: Learning the Cultural Consistent Facial Aesthetics by Convolutional Neural Network

abstract: Studying facial aesthetics has stimulated great interests in psychology and computer science due to a constant debate on whether it is cross-culture coherent or culture specific. Most computational models follow the cross-culture coherence theory and quantify the facial aesthetics by handcrafted geometry and appearance features, however, which are not directly derived from the raw data. In this work, we develop an end-to-end Convolutional Neural Network (CNN) model to recognize the facial aesthetics, which is able to learn the aesthetics attributes automatically from data. By visualizing the attributes in the last fully connected layer, we find that they are largely consistent with the cross-culture coherence theory. Furthermore, the learned attributes in the sallower layer illustrate a potential correlation with the culture specific theory. This research demonstrates a case study of the complicated facial aesthetics cognition.


title: An analysis of convolutional neural networks for speech recognition

abstract: Despite the fact that several sites have reported the effectiveness of convolutional neural networks (CNNs) on some tasks, there is no deep analysis regarding why CNNs perform well and in which case we should see CNNs' advantage. In the light of this, this paper aims to provide some detailed analysis of CNNs. By visualizing the localized filters learned in the convolutional layer, we show that edge detectors in varying directions can be automatically learned. We then identify four domains we think CNNs can consistently provide advantages over fully-connected deep neural networks (DNNs): channel-mismatched training-test conditions, noise robustness, distant speech recognition, and low-footprint models. For distant speech recognition, a CNN trained on 1000 hours of Kinect distant speech data obtains relative 4% word error rate reduction (WERR) over a DNN of a similar size. To our knowledge, this is the largest corpus so far reported in the literature for CNNs to show its effectiveness. Lastly, we establish that the CNN structure combined with maxout units is the most effective model under small-sizing constraints for the purpose of deploying small-footprint models to devices. This setup gives relative 9.3% WERR from DNNs with sigmoid units.


Gen Y, not so Bleeding Edge: The use of information technology in university teaching


title: A 0.2 V, 480 kb Subthreshold SRAM With 1 k Cells Per Bitline for Ultra-Low-Voltage Computing

abstract: A 2 muW, 100 kHz, 480 kb subthreshold SRAM operating at 0.2 V is demonstrated in a 130 nm CMOS process. A 10-T SRAM cell allows 1 k cells per bitline by eliminating the data-dependent bitline leakage. A virtual ground replica scheme is proposed for logic "0" level tracking and optimal sensing margin in read buffers. Utilizing the strong reverse short channel effect in the subthreshold region improves cell writability and row decoder performance due to the increased current drivability at a longer channel length. The sizing method leads to an equivalent write wordline voltage boost of 70 mV and a delay improvement of 28% in the row decoder compared to the conventional sizing scheme at 0.2 V. A bitline writeback scheme was used to eliminate the pseudo-write problem in unselected columns.


title: Power management in dc-grid through two-input dc-dc converter

abstract: A new two-input dc-dc converter for power management in dc grid is presented in this paper. This two-input converter is evolved through the combination of Boost and SEPIC converter. Steady-state and state-space analysis is performed to understand the performance of the proposed converter and then to design appropriate digital controllers for better power management. Using relative gain array theory the interactions among the control-loop is identified and then suitable pairing of controlling and controlled variable is determined. A 48 V, 150 Watt converter performance is analyzed, compared with the simulation and measurement observations for demonstrating the power management.


title: Caveat eptor: A comparative study of secure device pairing methods

abstract: ''Secure Device Pairing'' or ''Secure First Connect'' is the process of bootstrapping a secure channel between two previously unassociated devices over some (usually wireless) human-imperceptible communication channel. Absence of prior security context and common trust infrastructure open the door for the so-called Man-in-the-Middle and Evil Twin attacks. Mitigation of these attacks requires some level of user involvement in the device pairing process. Prior research yielded a number of technically sound methods relying on various auxiliary human-perceptible out-of-band channels, e.g., visual, acoustic and tactile. Such methods engage the user in authenticating information exchanged over the human-imperceptible channel, thus defending against MiTM attacks and forming the basis for secure pairing. This paper reports on a comprehensive and comparative evaluation of notable secure device pairing methods. This evaluation was obtained via a thorough analysis of these methods, in terms of both security and usability. The results help us identify methods best-suited for specific combinations of devices and human abilities. This work is an important step in understanding usability in one of the rare settings where a very wide range of users (not just specialists) are confronted with modern security technology.