query: Higher mode SIW excitation technology and its array application

positive: A single-layer SIW slot array antenna with TE20 mode The idea of using higher order modes is demonstrated by an SIW slot array antenna with a TE20 mode to realize boresight radiation. In order to excite the TE20 mode, an SIW hybrid coupler and a compact SIW 90° phase shifter are designed to offer the two out-of-phase outputs with identical amplitude. Besides, a CPW-SIW transition is adopted for measurement. The SIW slot array antenna with the TE20 mode is designed at X-band in a single-layer PCB. The measured bandwidth is 5.5% (9.72–10.27 GHz). The measured total efficiency is 80% at 10 GHz. Compared to a conventional SIW slot array just with a power divider, using the TE20 mode provides the merits of not only reducing the number of vias but also relaxing fabrication limit and tolerance which could be the fabrication bottleneck at high frequencies.

positive: Low-Cost Wideband and High-Gain Slotted Cavity Antenna Using High-Order Modes for Millimeter-Wave Application A novel single-fed low-cost wideband and high-gain slotted cavity antenna based on substrate integrated waveguide (SIW) technology using high-order cavity modes is demonstrated in this paper. High-order resonant modes (TE130, TE310, TE330) inside the cavity are simply excited by a coaxial probe which is located at the center of the antenna. Energy is coupled out of the cavity by a 3 × 3 slot array etched on the top surface of the cavity. Two antennas with different polarizations are designed and tested. Measured results show that the linearly polarized prototype achieves an impedance bandwidth (|S11| <; -10 dB) of > 26% (28 to 36.6 GHz), and a 1-dB gain bandwidth of 14.1% (30.3 to 34.9 GHz). In addition, a measured maximum gain of 13.8 dBi and radiation efficiency of 92% are obtained. To generate circularly polarized radiation, a rotated dipole array is placed in front of the proposed linearly polarized antenna. Measured results show that the circularly polarized antenna exhibits a common bandwidth (10-dB return loss bandwidth, 3-dB axial ratio bandwidth, and 1-dB gain bandwidth) of 11%.

positive: Wideband Excitation Technology of ${\rm TE}_{20}$ Mode Substrate Integrated Waveguide (SIW) and Its Applications The higher order mode substrate integrated waveguide (SIW) has advantages in the applications, which are: simplifying the structure with reduced fabrication cost and enhancing the stability of performance by relaxed fabrication tolerance. In this paper, two wideband TE20 mode excitation structures are presented and investigated for the SIW. A slot along the mid line in the longitudinal direction of the SIW is employed to convert the electromagnetic field pattern between slotline and the TE20 mode SIW. The second structure is based on the slot aperture coupling and can realize wideband direct transition between the TE20 mode SIW and microstrip line. Both transitions have a simple and compact structure, as well as broadband characteristics. The wideband transition performance is demonstrated in this paper. As application examples of the transitions, two broadband substrate integrated baluns are presented and fabricated based on the TE20 mode excitation structures and TE20 mode characteristics. The 180 ° out-of-phase and equal magnitude of the balun outputs in a wide frequency range can be achieved inherently. The balun based on the slotline excitation has a fractional bandwidth of 50.2%, from 7.3 to 12.2 GHz, with measured return loss, amplitude imbalance, and phase imbalance better than 10 and 0.45 dB and 3.8 °. The balun based on the aperture coupling excitation shows a fractional bandwidth of 50.3%, from 7 to 11.7 GHz, with measured return loss, insertion loss, amplitude imbalance, and phase imbalance better than 10, 1, and 0.27 dB and 2.4 °, respectively. Both baluns not only show good performance, but also demonstrate the existence of the TE20 mode in the SIW.

positive: 60-GHz Circularly Polarized U-Slot Patch Antenna Array on LTCC This communication presents a 60-GHz wideband circularly polarized (CP) U-slot patch antenna array of 4 × 4 elements on low temperature cofired ceramic (LTCC). A CP U-slot patch antenna is used as the array element to enhance the impedance bandwidth and a stripline sequential rotation feeding scheme is applied to achieve wide axial ratio (AR) bandwidth. Meanwhile, a grounded coplanar waveguide (GCPW) to stripline transition is designed for probe station measurement. The fabricated antenna array has a dimension of 14 ×16 × 1.1 mm3 . The simulated and measured impedance bandwidths, AR bandwidths, and radiation patterns are investigated and compared. Measured results show that the proposed antenna array has a wide impedance bandwidth from 50.5 GHz to 67 GHz for |S11| <; -10 dB, and a wide AR bandwidth from 54 GHz to 65.5 GHz for AR <; 3 dB. In addition, it exhibits a peak gain of 16 dBi and a beam-shaped pattern with 3-dB beam width of 20°. Moreover, its AR keeps below 3 dB within the 3-dB beam width.

positive: Integrated transition of coplanar to rectangular waveguides Usual transitions between planar circuit and rectangular waveguide make use of 3-D complex mounting structures. Such an integration requires costly high precision mechanical alignment, In this paper, a new planar platform is developed in which a coplanar waveguide (CPW) and a rectangular waveguide are fully integrated on the same substrate, and they are interconnected via a simple transition. They can be built with a standard PCB process. Our experiments at 28 GHz show that an effective bandwidth of 7% at 15 dB return loss can easily be achieved. The CPW-to-waveguide transition allows for a complete integration of waveguide components on substrate with active components such as MMIC.

negative: TagAssist: Automatic Tag Suggestion for Blog Posts In this paper, we describe a system called TagAssist that provides tag suggestions for new blog posts by utilizing existing tagged posts. The system is able to increase the quality of suggested tags by performing lossless compression over existing tag data. In addition, the system employs a set of metrics to evaluate the quality of a potential tag suggestion. Coupled with the ability for users to manually add tags, TagAssist can ease the burden of tagging and increase the utility of retrieval and browsing systems built on top of tagging data.

negative: Storytelling by the StoryCake visualization On the issue of how to explore the complex relationships of entities in a story, Storyline visualization has proven to be a useful approach. The traditional Storyline visualization technology only applies to a linear continuous story and has the limitation of reflecting the development of the plots in a story. In this paper, we propose a hierarchical plot visualization method called StoryCake to improve this problem. First, according to the story elements, inter-sessions are divided into groups, whereby the layout of the entities will be optimized in individual groups. Then, the hierarchical relationships of entities in polar coordinates can be confirmed. Finally, we use several skills, e.g., interaction, labels and a fan-shaped visualization view, to enable people better understand how story evolves and track the hierarchical relationship more conveniently. Experimental results show that the proposed hierarchical plot visualization method in polar coordinates can reveal discontinuous events and nonlinear stories. The results also show that our algorithm presents the narrative structure and development of the story.

negative: The self to other model of empathy: Providing a new framework for understanding empathy impairments in psychopathy, autism, and alexithymia Despite increasing empirical and theoretical work on empathy, particularly on the content of empathic representations, there is a relative lack of consensus regarding the information processing necessary for empathy to occur. Here we attempt to delineate a mechanistic cognitive model of empathy in order to provide a framework within which neuroimaging work on empathy can be located, and which may be used in order to understand various disorders characterised by atypical levels of empathy. To this end data from individuals with psychopathy, autism, and alexithymia inform the model, and the model is used to provide a unifying framework for any empathy impairments seen in these disorders. The model adopts a developmental framework and tries to address the four difficult questions of empathy: How do we know what another is feeling? What is the role of theory of mind in empathy? How does the state of another cause a corresponding state in the self? How do we represent another's emotion once emotional contagion has taken place?

negative: Rectification of QR-code images using the parametric cylindrical surface model In recent years, QR codes have found a wide range of applications in business and industry. Moreover, their popularity and function are still growing (fast). Therefore, the processing of the images of QR codes is an important task. In this paper, we try to solve a problem encountered in QR decoding when the QR code is printed near the binding edge inside a book or on the surface of a cylindrical container. When a picture is taken on such a QR code, the picture is no longer in a rectangular shape, which is supposedly the “right” shape that any QR code should take, due to warping from the image capturing mechanism by the camera. We refer to such warping as cylinder-to-screen (C2S) warping. This warping translates into serious distortion of the original QR-code image (also referred to as QR image, for short) and thus causes failure in its decoding. In this paper, a mathematical model called the parametric cylindrical surface model (PCSM) is proposed. Then, based on it, a scheme is proposed to rectify C2S-warped QR images. Experiments show that the proposed scheme is effective, in the sense that many failed QR decodings become successful after the rectification.

negative: Image Segmentation Techniques Overview The technology of image segmentation is widely used in medical image processing, face recognition pedestrian detection, etc. The current image segmentation techniques include region-based segmentation, edge detection segmentation, segmentation based on clustering, segmentation based on weakly-supervised learning in CNN, etc. This paper analyzes and summarizes these algorithms of image segmentation, and compares the advantages and disadvantages of different algorithms. Finally, we make a prediction of the development trend of image segmentation with the combination of these algorithms.

negative: Biometrics from Brain Electrical Activity: A Machine Learning Approach The potential of brain electrical activity generated as a response to a visual stimulus is examined in the context of the identification of individuals. Specifically, a framework for the visual evoked potential (VEP)-based biometrics is established, whereby energy features of the gamma band within VEP signals were of particular interest. A rigorous analysis is conducted which unifies and extends results from our previous studies, in particular, with respect to 1) increased bandwidth, 2) spatial averaging, 3) more robust power spectrum features, and 4) improved classification accuracy. Simulation results on a large group of subject support the analysis

negative: EARTh: An Environmental Application Reference Thesaurus in the Linked Open Data cloud The paper aims at providing a description of EARTh, the Environmental Application Reference Thesaurus. It represents a general-purpose thesaurus for the environment, which has been published as a SKOS dataset in the Linked Open Data cloud. It promises to become a core tool for indexing and discovery environmental resources by refining and extending GEMET, which is considered the de facto standard when speaking of general-purpose thesaurus for the environment in Europe, besides it has been interlinked to popular LOD datasets as AGROVOC, EUROVOC, DBPEDIA and UMTHES. The paper illustrates the main characteristics of EARTh as a guide to its usage. It clarifies (i) the methodology adopted to define the EARTh content; (ii) the design and technological choices made when publishing EARTh as Linked Data; (iii) the information pertaining to its access and maintenance. Descriptions of EARTh applications and future relevance are highlighted. CNR-IIA-EKOLab Environmental Knowledge Organization Laboratory, Area della Ricerca di Roma 1, Via Salaria Km 29,300 C.P. 10,I-00016 Monterotondo stazione RM, {difranco, vds, plini} @iia.cnr.it

negative:
Nurse Managers’ Emotional Intelligence and Effective Leadership: A Review of the Current Evidence Background
Emotional Intelligence has made a significant contribution to effective leadership, becoming one of the key characteristics of leaders.Objective: The aim of the present study was to review qualitative and quantitative studies concerning Emotional Intelligence of nurse leaders and the evidence-based composition of their results.

Method
A search was performed in the electronic databases (Pubmed, Scopus and CINAHL) for articles, which were published in the period 2000-2017 in English or Greek. Eleven studies met the inclusion criteria, of which 10 were quantitative and one was qualitative.

Results
The results suggested that Emotional Intelligence is a useful tool for nurse leaders and contributes decisively to the achievement of effective management in healthcare.

Conclusion
It is necessary for nurses to improve their social and emotional skills because of the particular nature of the nursing profession, which places the healthy or weak person at its center.

negative: ProgTest: An environment for the submission and evaluation of programming assignments based on testing activities Programming foundations is not an easy subject to be taught — many students have difficulties understanding the abstract concepts of programming and have a wrong view about the programming activity. In order to address these problems, experiences have suggested the integrated teaching of programming concepts and software testing in introductory CS courses. Shortly, the idea is that testing can contribute to enhance the students' capabilities of understanding and analysis. However, such perspective requires tools to provide an adequate feedback to evaluate the students' performance concerning programming and testing activities. In this paper we describe ProgTest — a web-based tool for the submission and automatic evaluation of practical programming assignments based on testing activities. Results from a preliminary validation of ProgTest are also presented. Such results provide evidences on the practical use of ProgTest as a supporting mechanism for the integrated teaching of programming foundations and software testing.

negative: Building a HighLevel Dataflow System on top of MapReduce: The Pig Experience Increasingly, organizations capture, transform and analyze enormous data sets. Prominent examples include internet companies and e-science. The Map-Reduce scalable dataflow paradigm has become popular for these applications. Its simple, explicit dataflow programming model is favored by some over the traditional high-level declarative approach: SQL. On the other hand, the extreme simplicity of MapReduce leads to much low-level hacking to deal with the many-step, branching dataflows that arise in practice. Moreover, users must repeatedly code standard operations such as join by hand. These practices waste time, introduce bugs, harm readability, and impede optimizations. Pig is a high-level dataflow system that aims at a sweet spot between SQL and Map-Reduce. Pig offers SQL-style high-level data manipulation constructs, which can be assembled in an explicit dataflow and interleaved with custom Mapand Reduce-style functions or executables. Pig programs are compiled into sequences of Map-Reduce jobs, and executed in the Hadoop Map-Reduce environment. Both Pig and Hadoop are open-source projects administered by the Apache Software Foundation. This paper describes the challenges we faced in developing Pig, and reports performance comparisons between Pig execution and raw Map-Reduce execution.

negative: Matroids and the greedy algorithm (0) Many discrete programming algorithms are being proposed. One thing most of them have in common is that they do not work very well. Solving problems that are a priori finite, astronomically finite, is preferably a matter of finding algorithms that are s o m e h o w better than finite. These considerations prompt looking for good algorithms and trying to understand how and why at least a few combinatorial problems have them. (1) Let H be a finite (for convenience) set of real-valued vectors , x = [xj] , ] ~ E. Often all the members of H will be integer-valued; often they will all be {0, 1}-valued. The index-set, E, is any finite set of elements. Let c = [cj], ] ~ E, be any real vector on E, called the objective or weighting of E. The problem of finding a member o f H which maximizes (or minimizes) cx = iN cjx/, j ~ E, we call a loco problem or loco programming. "Loco" stands for "linear-objective combinatorial". (2) In order for a loco problem to be a completely defined problem, the way H is given must of course be specified. There are various ways to describe implicitly very large sets H so tha t it is relatively easy to determine whether or not any particular vector is a member of H. One well-known:way is in linear programming, where H is the set of extreme points of the solution-set of a given finite system, L, of linear equations and <_ type linear inequalities in the variables xj (briefly, a

negative: Bitcoin Network Measurements for Simulation Validation and Parameterization Bitcoin is gaining increasing popularity nowadays, even though the crypto-currencies field has plenty of digital currencies that have emerged before the adoption of Bitcoin idea. Bitcoin is a decentralized digital currency which relies on set of miners to maintain a distributed public ledger and peer-to-peer network to broadcast transactions. In this paper, we analyse how transaction validation is achieved by the transaction propagation round trip and how transaction dissemination throughout the network can lead to inconsistencies in the view of the current transactions ledger by different nodes. We then measure the transaction propagation delay in the real Bitcoin network and how it is affected by the number of nodes and network topology. This measurement enables a precise validation of any simulation model of the Bitcoin network. Large-scale measurements of the real Bitcoin network are performed in this paper. This will provide an opportunity to parameterise any model of the Bitcoin

negative: Haptic jamming: A deformable geometry, variable stiffness tactile display using pneumatics and particle jamming Many controllable tactile displays present the user with either variable mechanical properties or adjustable surface geometries, but controlling both simultaneously is challenging due to electromechanical complexity and the size/weight constraints of haptic applications. This paper discusses the design, manufacturing, control, and preliminary evaluation of a novel haptic display that achieves both variable stiffness and deformable geometry via air pressure and a technique called particle jamming. The surface of the device consists of a flat, deformable layer of hollow silicone cells filled with coffee grounds. It selectively solidifies in different regions when the air is vacuumed out of individual cells, jamming the coffee particles together. The silicone layer is clamped over a chamber with regulated air pressure. Different sequences of air pressure and vacuum level adjustment allow regions of the surface to display a small rigid lump, a large soft plane, and various other combinations of lump size and stiffness. Experimental data from individual cells show that surface stiffness increases with vacuum level and the elliptical shape of the cells become increasingly spherical with increased chamber pressure.

negative: Design of a Dual-Band High-Gain Antenna Array for WLAN and WiMAX Base Station A dual-band 3 ×2 antenna array with high gain is presented in this letter. A dual-band folded dipole antenna is utilized as the antenna array element. A broadband symmetric feed network is designed to maintain each array element with the identical current magnitude and phase. The antenna array has a low profile of 12 mm (about 0.1λL; λL is the wavelength in free space at the lowest operating frequency). Dual bands 2.3 ~ 2.56 and 3 ~ 4.2 GHz for return loss (RL) being higher than 10 dB can be obtained in simulation, while two 10-dB RL bands 2.31 ~ 2.51 and 3.1 ~ 4.15 GHz in measurement cover the wireless local area network (WLAN, 2.4 ~ 2.48 GHz) and Worldwide Interoperability for Microwave Access (WiMAX, 3.3 ~ 3.8 GHz). The measured results also show that this planar antenna array has a high gain 14.3 ~ 15.1 dBi (2.31 ~ 2.51 GHz), 16.2 ~ 17.3 dBi ( 3.1 ~ 4.15 GHz) in + z-direction and a low cross-polarization level <; - 26 dB.

negative: Short Group Signatures We construct a short group signature scheme. Signatures in our scheme are approximately the size of a standard RSA signature with the same security. Security of our group signature is based on the Strong Diffie-Hellman assumption and a new assumption in bilinear groups called the Decision Linear assumption. We prove security of our system, in the random oracle model, using a variant of the security definition for group signatures recently given by Bellare, Micciancio, and Warinschi.

negative: Systematic review of the research evidence examining the effectiveness of interventions using a sensory integrative approach for children. Twenty-seven studies were systematically reviewed to identify, evaluate, and synthesize the research literature on the effectiveness of sensory integration (SI) intervention on the ability of children with difficulty processing and integrating sensory information to engage in desired occupations and to apply these findings to occupational therapy practice. Results suggest the SI approach may result in positive outcomes in sensorimotor skills and motor planning; socialization, attention, and behavioral regulation; reading-related skills; participation in active play; and achievement of individualized goals. Gross motor skills, self-esteem, and reading gains may be sustained from 3 mo to 2 yr. Findings may be limited by Type II error because of small sample sizes, variable intervention dosage, lack of fidelity to intervention, and selection of outcomes that may not be meaningful to clients and families or may not change with amount of treatment provided. Replication of findings with methodologically and theoretically sound studies is needed to support current findings.

negative: Speed measurement algorithm for low speed permanent magnet synchronous motor based on overlapped measurement regions In order to improve the speed measurement performance of low speed PMSM drives equipped with absolute optical encoders, this paper proposes a speed measurement method based on overlapped measurement regions. The proposed method replaces the long measurement region of the conventional backward differentiation method with several short ones which are overlapped at most regions. In one speed measurement process, the results obtained from the short regions are averaged to improve precision. Then the relationship between precision, time delay and region length, overlap times, are analyzed using expectation and variance as performance index. Based on the analysis, an optimal design method of the measurement region is derived. Experimental researches are carried out on a low speed permanent magnet synchronous motor equipped with a 13 bit absolute encoder. The results prove the validity and effectiveness of the proposed method.

negative: Multi-task low-rank affinity pursuit for image segmentation This paper investigates how to boost region-based image segmentation by pursuing a new solution to fuse multiple types of image features. A collaborative image segmentation framework, called multi-task low-rank affinity pursuit, is presented for such a purpose. Given an image described with multiple types of features, we aim at inferring a unified affinity matrix that implicitly encodes the segmentation of the image. This is achieved by seeking the sparsity-consistent low-rank affinities from the joint decompositions of multiple feature matrices into pairs of sparse and low-rank matrices, the latter of which is expressed as the production of the image feature matrix and its corresponding image affinity matrix. The inference process is formulated as a constrained nuclear norm and ℓ2;1-norm minimization problem, which is convex and can be solved efficiently with the Augmented Lagrange Multiplier method. Compared to previous methods, which are usually based on a single type of features, the proposed method seamlessly integrates multiple types of features to jointly produce the affinity matrix within a single inference step, and produces more accurate and reliable segmentation results. Experiments on the MSRC dataset and Berkeley segmentation dataset well validate the superiority of using multiple features over single feature and also the superiority of our method over conventional methods for feature fusion. Moreover, our method is shown to be very competitive while comparing to other state-of-the-art methods.

negative: Sensors and systems for fruit detection and localization: A review This paper reviews the research and development of machine vision systems for fruit detection and localization for robotic harvesting and/or crop-load estimation of specialty tree crops including apples, pears, and citrus. Variable lighting condition, occlusions, and clustering are some of the important issues needed to be addressed for accurate detection and localization of fruit in orchard environment. To address these issues, various techniques have been investigated using different types of sensors and their combinations as well as with different image processing techniques. This paper summarizes various techniques and their advantages and disadvantages in detecting fruit in plant or tree canopies. The paper also summarizes the sensors and systems developed and used by researchers to localize fruit as well as the potential and limitations of those systems. Finally, major challenges for the successful application of machine vision system for robotic fruit harvesting and crop-load estimation, and potential future directions for research and development are discussed. 2015 Elsevier B.V. All rights reserved.

negative: Change impact analysis for Natural Language requirements: An NLP approach Requirements are subject to frequent changes as a way to ensure that they reflect the current best understanding of a system, and to respond to factors such as new and evolving needs. Changing one requirement in a requirements specification may warrant further changes to the specification, so that the overall correctness and consistency of the specification can be maintained. A manual analysis of how a change to one requirement impacts other requirements is time-consuming and presents a challenge for large requirements specifications. We propose an approach based on Natural Language Processing (NLP) for analyzing the impact of change in Natural Language (NL) requirements. Our focus on NL requirements is motivated by the prevalent use of these requirements, particularly in industry. Our approach automatically detects and takes into account the phrasal structure of requirements statements. We argue about the importance of capturing the conditions under which change should propagate to enable more accurate change impact analysis. We propose a quantitative measure for calculating how likely a requirements statement is to be impacted by a change under given conditions. We conduct an evaluation of our approach by applying it to 14 change scenarios from two industrial case studies.

negative: Privacy-Preserving Multiple Linear Regression of Vertically Partitioned Real Medical Datasets This paper studies the feasibility of privacy-preservingdata mining in epidemiological study. As for the data-miningalgorithm, we focus to a linear multiple regression thatcan be used to identify the most significant factorsamong many possible variables, such as the historyof many diseases. We try to identify the linear model to estimate a lengthof hospital stay from distributed dataset related tothe patient and the disease information. In this paper, we have done experiment usingthe real medical dataset related to stroke andattempt to apply multiple regression with sixpredictors of age, sex, the medical scales, e.g., Japan Coma Scale, and the modified Rankin Scale. Our contributions of this paper include(1) to propose a practical privacy-preserving protocols for linear multiple regressionwith vertically partitioned datasets, and(2) to show the feasibility of the proposed system usingthe real medical dataset distributed into two parties, the hospital who knows the technical details of diseasesduring the patients are in the hospital, and the local government who knows the residence even afterthe patients left hospital. (3) to show the accuracy and the performance of thePPDM system which allows us to estimate the expectedprocessing time with arbitrary number of predictors.

negative: Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene Vision-based object detection is one of the fundamental functions in numerous traffic scene applications such as self-driving vehicle systems and advance driver assistance systems (ADAS). However, it is also a challenging task due to the diversity of traffic scene and the storage, power and computing source limitations of the platforms for traffic scene applications. This paper presents a generalized Haar filter based deep network which is suitable for the object detection tasks in traffic scene. In this approach, we first decompose a object detection task into several easier local regression tasks. Then, we handle the local regression tasks by using several tiny deep networks which simultaneously output the bounding boxes, categories and confidence scores of detected objects. To reduce the consumption of storage and computing resources, the weights of the deep networks are constrained to the form of generalized Haar filter in training phase. Additionally, we introduce the strategy of sparse windows generation to improve the efficiency of the algorithm. Finally, we perform several experiments to validate the performance of our proposed approach. Experimental results demonstrate that the proposed approach is both efficient and effective in traffic scene compared with the state-of-the-art.

negative: Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition Brain electrical activity recorded via electroencephalogram (EEG) is the most convenient means for brain-computer interface (BCI), and is notoriously noisy. The information of interest is located in well defined frequency bands, and a number of standard frequency estimation algorithms have been used for feature extraction. To deal with data nonstationarity, low signal-to-noise ratio, and closely spaced frequency bands of interest, we investigate the effectiveness of recently introduced multivariate extensions of empirical mode decomposition (MEMD) in motor imagery BCI. We show that direct multichannel processing via MEMD allows for enhanced localization of the frequency information in EEG, and, in particular, its noise-assisted mode of operation (NA-MEMD) provides a highly localized time-frequency representation. Comparative analysis with other state of the art methods on both synthetic benchmark examples and a well established BCI motor imagery dataset support the analysis.

negative: Source Code Metrics for Programmable Logic Controller (PLC) Ladder Diagram (LD) Visual Programming Language The IEC 611131-3, an open international standard for Programmable Logic Controllers (PLC) defines several domain specific languages for industrial and process automation. Domain specific languages have several features, programming constructs and notations which are different than general purpose languages and hence the traditional source code metrics for general purpose programming languages cannot be directly applied to domain specific languages for writing control programs in the area of industrial automation. We propose source code level metrics to measure size, vocabulary, cognitive complexity and testing complexity of a visual Programmable Logic Controller (PLC) programming language. We present metrics for Ladder Diagram (LD) programming language which is one of the five languages defined in the IEC 61131-3 standard. The proposed metric is based on factors like number of distinct operators and operands, total number of operators and operands, number of rungs, weights of different basic control structures, structure of the program and control flow. We apply Weyukur's property to validate the metrics and evaluate the number of properties satisfied by the proposed metric.

negative: Accelerometer-based tilt estimation of a rigid body with only rotational degrees of freedom An estimation algorithm is developed for determining pitch and roll angles (tilt) of a rigid body fixed at a pivot point using multiple accelerometers. The estimate is independent of the rigid body dynamics; the method is applicable both in static conditions and for any dynamic motion of the body. No dynamic model is required for the estimator; only the mounting positions of the sensors need to be known. The proposed estimator is the optimal linear estimate in a least-squares sense if knowledge of the system dynamics is not used. The estimate may be used as a basis for further filtering and fusion techniques, such as sensor fusion with rate gyro data. The estimation algorithm is applied to the problem of state estimation for the Balancing Cube, a rigid structure that can actively balance on its corners. Experimental results are provided.

negative: A New Travelling Wave Antenna in Microstrip The radiation characteristics of the first higher order mode of microstrip lines are investigated. As a result, a simple travelling wave antenna element is scribed, having a larger bandwidth compared with resonator antennas. A method to excite the first higher order mode is shown. A single antenna element is treated theoretically and experimentally, and an array of four antenna elements is demonstrated.

negative: Wideband Millimeter-Wave Substrate Integrated Waveguide Cavity-Backed Rectangular Patch Antenna In this letter, a new type of wideband substrate integrated waveguide (SIW) cavity-backed patch antenna and array for millimeter wave (mmW) are investigated and implemented. The proposed antenna is composed of a rectangular patch with a backed SIW cavity. In order to enhance the bandwidth and radiation efficiency, the cavity is designed to resonate at its TE210 mode. Based on the proposed antenna, a 4 × 4 array is also designed. Both the proposed antenna and array are fabricated with standard printed circuit board (PCB) process, which possess the advantage of easy integration with planar circuits. The measured bandwidth (|S11| ≤ -10 dB) of the antenna element is larger than 15%, and that of the antenna array is about 8.7%. The measured peak gains are 6.5 dBi for the element and 17.8 dBi for the array, and the corresponding simulated radiation efficiencies are 83.9% and 74.9%, respectively. The proposed antenna and array are promising for millimeter-wave applications due to its merits of wide band, high efficiency, low cost, low profile, etc.

negative: Optimum Design of Traveling-Wave SIW Slot Array Antennas This paper presents an optimum design method based on the Method of Least Squares (MLS) for the traveling-wave substrate integrated waveguide (SIW) slot array antenna. Elliot's design formulas for dielectric filled waveguide including external and internal mutual coupling are used for the design by means of the equivalent waveguide. In order to achieve an efficient antenna, design formulas are extended to nonuniformly spaced slots. An error function consisting of four terms is formulated and then minimized with respect to the design parameters namely lengths, offsets, spacings and excitations of slots using combination of genetic algorithm and conjugate gradient method (GA-CG). A linear slot array at $X$ -band is designed, simulated and fabricated for verification. Results of the proposed method, simulation software and measurement are in good agreement.

negative: Substrate Integrated Waveguide (SIW) Rotman Lens and Its Ka-Band Multibeam Array Antenna Applications A new type of substrate integrated waveguide (SIW) multibeam antenna is proposed for mobile satellite communications using beam switching and diversity techniques. It employs an SIW Rotman lens as the beamforming network. The prototype of a single multibeam antenna is implemented at 28.5 GHz with seven input ports and an antenna array constructed by nine SIW linear slot arrays, which can generate a corresponding number of beams along one dimension. Several such antennas are grouped in two different ways to cover a 2-D solid angle with multiple beams. Experiment results show that the 2-D solid angle around (-40deg, 40deg) X (-35deg, 35deg) or (-25deg, 25deg) x (-35deg, 35deg) are covered with 20 or 25 beams with 5 -dB beamwidth, respectively. It is demonstrated that this type of printed multibeam antenna is a good choice for communication applications where mobility and high gain are simultaneously required.

negative: Excitation Control Method for a Low Sidelobe SIW Series Slot Array Antenna With 45$^{\circ}$  Linear Polarization A sidelobe suppression method for a series slot array antenna which radiates 45° -inclined linear polarization is proposed. Axial displacements are employed to create arbitrary excitation coefficients for individual centered-inclined radiating slots along the center line of a broad wall. To verify the proposed design method, we design two types of center-fed linear slot array antennas with a Dolph-Chebyshev distribution for -20 dB and -26 dB sidelobe levels (SLLs) in the Ka band. Furthermore, a cross-validation process involving an equivalent circuit model analysis and electromagnetic full-wave simulation using CST MWS is utilized. The entire structure of the proposed series slot array antenna is fabricated on printed circuit boards (PCBs), including drilling and chemical etching, to secure advantages of miniaturization and cost reduction. The measured realized gains are 15.17 and 15.95 dBi and SLLs are -18.7 and -22.5 dB respectively for two types of fabricated antennas. It demonstrates the validity of the proposed sidelobe suppression method.

negative: Low cost 60 GHz millimeter-wave microstrip patch antenna array using low-loss planar feeding scheme A novel low cost feeding mechanism based upon new synthesized planar waveguide substrate integrated waveguide (SIW), is presented for microstrip antenna arrays (MPAs) at millimeter-wave (mmW) band to avoid feeding loss, specially the conduction loss. As an example, SIW-fed two dimensional (2D) 2×2 MPA array @ 60 GHz is introduced. The presented antenna is simulated by HFSS to validate its performance for mmW applications. The simulated results show a broadside gain of 11.71 dB within an operating impedance bandwidth of 500 MHz around a center frequency of 59.95 GHz. Also, the simulated radiation efficiency of the developed antenna is better than 85%, which is higher than that of conventional mmW microstrip antenna array.

negative: Low-Cost High-Gain and Broadband Substrate- Integrated-Waveguide-Fed Patch Antenna Array for 60-GHz Band A low-cost high-gain and broadband substrate integrated waveguide (SIW)-fed patch antenna array is demonstrated at the 60-GHz band. A single-layered SIW feeding network with wideband T-junctions and wideband high-gain cavity-backed patch antennas are employed to achieve high gain and wideband performance simultaneously. Although the proposed antenna array has a multilayered structure, it can be fabricated by conventional low-cost single-layered printed circuit board (PCB) technology and then realized by stacking and fixing all of single layers together. The simulated and measured impedance bandwidths of a 4 × 4 antenna array are 27.5% and 22.6% for 10 dB. The discrepancy between simulation and measurement is analyzed. A gain up to 19.6 dBi, and symmetrical unidirectional radiation patterns with low cross polarization are also achieved. With advantages of low fabrication cost and good performances, the proposed antenna array is a promising candidate for millimeter-wave wireless communication systems.


query: Spatial cloaking for anonymous location-based services in mobile peer-to-peer environments

positive: Preventing Location-Based Identity Inference in Anonymous Spatial Queries The increasing trend of embedding positioning capabilities (for example, GPS) in mobile devices facilitates the widespread use of location-based services. For such applications to succeed, privacy and confidentiality are essential. Existing privacy-enhancing techniques rely on encryption to safeguard communication channels, and on pseudonyms to protect user identities. Nevertheless, the query contents may disclose the physical location of the user. In this paper, we present a framework for preventing location-based identity inference of users who issue spatial queries to location-based services. We propose transformations based on the well-established K-anonymity concept to compute exact answers for range and nearest neighbor search, without revealing the query source. Our methods optimize the entire process of anonymizing the requests and processing the transformed spatial queries. Extensive experimental studies suggest that the proposed techniques are applicable to real-life scenarios with numerous mobile users.

positive: MobiHide: A Mobilea Peer-to-Peer System for Anonymous Location-Based Queries Modern mobile phones and PDAs are equipped with positioning capabilities (e.g., GPS). Users can access public location-based services (e.g., Google Maps) and ask spatial queries. Although communication is encrypted, privacy and confidentiality remain major concerns, since the queries may disclose the location and identity of the user. Commonly, spatial K-anonymity is employed to hide the query initiator among a group of K users. However, existing work either fails to guarantee privacy, or exhibits unacceptably long response time. In this paper we propose MobiHide, a Peer-to-Peer system for anonymous location-based queries, which addresses these problems. MobiHide employs the Hilbert space-filling curve to map the 2-D locations of mobile users to 1-D space. The transformed locations are indexed by a Chord-based distributed hash table, which is formed by the mobile devices. The resulting Peer-to-Peer system is used to anonymize a query by mapping it to a random group of K users that are consecutive in the 1-D space. Compared to existing state-of-the-art, MobiHide does not provide theoretical anonymity guarantees for skewed query distributions. Nevertheless, it achieves strong anonymity in practice, and it eliminates system hotspots. Our experimental evaluation shows that MobiHide has good load balancing and fault tolerance properties, and is applicable to real-life scenarios with numerous mobile users.

positive: Location Privacy in Mobile Systems: A Personalized Anonymization Model This paper describes a personalized k-anonymity model for protecting location privacy against various privacy threats through location information sharing. Our model has two unique features. First, we provide a unified privacy personalization framework to support location k-anonymity for a wide range of users with context-sensitive personalized privacy requirements. This framework enables each mobile node to specify the minimum level of anonymity it desires as well as the maximum temporal and spatial resolutions it is willing to tolerate when requesting for k-anonymity preserving location-based services (LBSs). Second, we devise an efficient message perturbation engine which runs by the location protection broker on a trusted server and performs location anonymization on mobile users' LBS request messages, such as identity removal and spatio-temporal cloaking of location information. We develop a suite of scalable and yet efficient spatio-temporal cloaking algorithms, called CliqueCloak algorithms, to provide high quality personalized location k-anonymity, aiming at avoiding or reducing known location privacy threats before forwarding requests to LBS provider(s). The effectiveness of our CliqueCloak algorithms is studied under various conditions using realistic location data synthetically generated using real road maps and traffic volume data

positive: User needs for location-aware mobile services Mobile contexts of use vary a lot, and may even be continuously changing during use. The context is much more than location, but its other elements are still difficult to identify or measure. Location information is becoming an integral part of different mobile devices. Current mobile services can be enhanced with location-aware features, thus providing the user with a smooth transition towards context-aware services. Potential application fields can be found in areas such as travel information, shopping, entertainment, event information and different mobile professions. This paper studies location-aware mobile services from the user's point of view. The paper draws conclusions about key issues related to user needs, based on user interviews, laboratory and field evaluations with users, and expert evaluations of location-aware services. The user needs are presented under five main themes: topical and comprehensive contents, smooth user interaction, personal and user-generated contents, seamless service entities and privacy issues.

positive: Privacy-Preserving Data Mining A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Specifically, we address the following question. Since the primary task in data mining is the development of models about aggregated data, can we develop accurate models without access to precise information in individual data records? We consider the concrete case of building a decision-tree classifier from training data in which the values of individual records have been perturbed. The resulting data records look very different from the original records and the distribution of data values is also very different from the original distribution. While it is not possible to accurately estimate original values in individual data records, we propose a novel reconstruction procedure to accurately estimate the distribution of original data values. By using these reconstructed distributions, we are able to build classifiers whose accuracy is comparable to the accuracy of classifiers built with the original data.

negative: A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them The accuracy of optical flow estimation algorithms has been improving steadily as evidenced by results on the Middlebury optical flow benchmark. The typical formulation, however, has changed little since the work of Horn and Schunck. We attempt to uncover what has made recent advances possible through a thorough analysis of how the objective function, the optimization method, and modern implementation practices influence accuracy. We discover that “classical” flow formulations perform surprisingly well when combined with modern optimization and implementation techniques. One key implementation detail is the median filtering of intermediate flow fields during optimization. While this improves the robustness of classical methods it actually leads to higher energy solutions, meaning that these methods are not optimizing the original objective function. To understand the principles behind this phenomenon, we derive a new objective function that formalizes the median filtering heuristic. This objective function includes a non-local smoothness term that robustly integrates flow estimates over large spatial neighborhoods. By modifying this new term to include information about flow and image boundaries we develop a method that can better preserve motion details. To take advantage of the trend towards video in wide-screen format, we further introduce an asymmetric pyramid downsampling scheme that enables the estimation of longer range horizontal motions. The methods are evaluated on the Middlebury, MPI Sintel, and KITTI datasets using the same parameter settings.

negative: Deep Reinforcement Learning for Multi-Resource Multi-Machine Job Scheduling Minimizing job scheduling time is a fundamental issue in data center networks that has been extensively studied in recent years. The incoming jobs require different CPU and memory units, and span different number of time slots. The traditional solution is to design efficient heuristic algorithms with performance guarantee under certain assumptions. In this paper, we improve a recently proposed job scheduling algorithm using deep reinforcement learning and extend it to multiple server clusters. Our study reveals that deep reinforcement learning method has the potential to outperform traditional resource allocation algorithms in a variety of complicated environments.

negative:
Sign language recognition with recurrent neural network using human keypoint detection We study the sign language recognition problem which is to translate the meaning of signs from visual input such as videos. It is well-known that many problems in the field of computer vision require a huge amount of dataset to train deep neural network models. We introduce the KETI sign language dataset which consists of 10,480 videos of high resolution and quality. Since different sign languages are used in different countries, the KETI sign language dataset can be the starting line for further research on the Korean sign language recognition.
 Using the sign language dataset, we develop a sign language recognition system by utilizing the human keypoints extracted from face, hand, and body parts. The extracted human keypoint vector is standardized by the mean and standard deviation of the keypoints and used as input to recurrent neural network (RNN). We show that our sign recognition system is robust even when the size of training data is not sufficient. Our system shows 89.5% classification accuracy for 100 sentences that can be used in emergency situations.

negative: Time series forecasting using a deep belief network with restricted Boltzmann machines Multi-layer perceptron (MLP) and other artificial neural networks (ANNs) have been widely applied to time series forecasting since 1980s. However, for some problems such as initialization and local optima existing in applications, the improvement of ANNs is, and still will be the most interesting study for not only time series forecasting but also other intelligent computing fields. In this study, we propose a method for time series prediction using Hinton & Salakhutdinov’s deep belief nets (DBN) which are probabilistic generative neural network composed by multiple layers of restricted Boltzmann machine (RBM). We use a 3-layer deep network of RBMs to capture the feature of input space of time series data, and after pretraining of RBMs using their energy functions, gradient descent training, i.e., back-propagation learning algorithm is used for fine-tuning connection weights between “visible layers” and “hidden layers” of RBMs. To decide the sizes of neural networks and the learning rates, Kennedy & Eberhart’s particle swarm optimization (PSO) is adopted during the training processes. Furthermore, “trend removal”, a preprocessing to the original data, is also approached in the forecasting experiment using CATS benchmark data. Additionally, approximating and short-term prediction of chaotic time series such as Lorenz chaos and logistic map were also applied by the proposed method.

negative: Enhancement of Images Using Histogram Processing Techniques Image enhancement is a mean as the improvement of an image appearance by increasing dominance of some features or by decreasing ambiguity between different regions of the image. Image enhancement processes consist of a collection of techniques that seek to improve the visual appearance of an image or to convert the image to a form better suited for analysis by a human or machine. Many images such as medical images, remote sensing images, electron microscopy images and even real life photographic pictures, suffer from poor contrast. Therefore it is necessary to enhance the contrast.The purpose of image enhancement methods is to increase image visibility and details. Enhanced image provide clear image to eyes or assist feature extraction processing in computer vision system. Numerous enhancement methods have been proposed but the enhancement efficiency, computational requirements, noise amplification, user intervention, and application suitability are the common factors to be considered when choosing from these different methods for specific image processing application.

negative: Insights into Alzheimer disease pathogenesis from studies in transgenic animal models Alzheimer disease is the most common cause of dementia among the elderly, accounting for ~60-70% of all cases of dementia. The neuropathological hallmarks of Alzheimer disease are senile plaques (mainly containing p-amyloid peptide derived from amyloid precursor protein) and neurofibrillary tangles (containing hyperphosphorylated Tau protein), along with neuronal loss. At present there is no effective treatment for Alzheimer disease. Given the prevalence and poor prognosis of the disease, the development of animal models has been a research priority to understand pathogenic mechanisms and to test therapeutic strategies. Most cases of Alzheimer disease occur sporadically in people over 65 years old, and are not genetically inherited. Roughly 5% of patients with Alzheimer disease have familial Alzheimer disease--that is, related to a genetic predisposition, including mutations in the amyloid precursor protein, presenilin 1, and presenilin 2 genes. The discovery of genes for familial Alzheimer disease has allowed transgenic models to be generated through the overexpression of the amyloid precursor protein and/or presenilins harboring one or several mutations found in familial Alzheimer disease. Although none of these models fully replicates the human disease, they have provided valuable insights into disease mechanisms as well as opportunities to test therapeutic approaches. This review describes the main transgenic mouse models of Alzheimer disease which have been adopted in Alzheimer disease research, and discusses the insights into Alzheimer disease pathogenesis from studies in such models. In summary, the Alzheimer disease mouse models have been the key to understanding the roles of soluble b-amyloid oligomers in disease pathogenesis, as well as of the relationship between p-amyloid and Tau pathologies.

negative: Centrality based Document Ranking In this paper, we address the problem of ranking clinical documents using centrality based approach. We model the documents to be ranked as nodes in a graph and place edges between documents based on their similarity. Given a query, we compute similarity of the query with respect to every document in the graph. Based on these similarity values, documents are ranked for a given query. Initially, Lucene is used to retrieve top fifty documents that are relevant to the query and then our proposed approach is applied on these retrieved documents to rerank them. Experimental results show that our approach did not perform well as the documents retrieved by Lucene are not among the top 50 documents in the Gold Standard.

negative: Proceso de Valoración para la Mejora de Procesos Software en Pequeñas Organizaciones 1 Grupo IDIS Facultad de Ingeniería Electrónica y Telecomunicaciones Universidad del Cauca Calle 5 No. 4 – 70. Popayán, Cauca, Colombia. fjpino@unicauca.edu.co. Web: http://www.unicauca.edu.co/idis/ 2 Grupo Alarcos Escuela Superior de Informática Universidad Castilla-La Mancha Paseo de la Universidad 4, Ciudad Real, España. {Felix.Garcia, Mario.Piattini}@uclm.es. Web: http://alarcos.inf-cr.uclm.es/

negative: Exploiting Structure in Policy Construction Markov decision processes (MDPs) have recently been appbed to the problem of modeling decisiontheoretic planning While traditional methods for solving MDPs are often practical for small states spaces, their effectiveness for large AI planning problems is questionable We present an algorithm, called structured policy Iteration (SPI), that con structs optimal policies without explicit enumera tion of the state space The algorithm retains the fundamental computational steps of the commonly used modified policy iteration algorithm, but ex ploits the variable and prepositional independencies reflected in a temporal Bayesian network represen tation of MDPs The principles behind SPI can be applied to any structured representation of stochas tic actions, pobcies and value functions, and the algorithm itself can be used in conjunction with re cent approximation methods

negative: Machine Learning with World Knowledge: The Position and Survey Machine learning has become pervasive in multiple domains, impacting a wide variety of applications, such as knowledge discovery and data mining, natural language processing, information retrieval, computer vision, social and health informatics, ubiquitous computing, etc. Two essential problems of machine learning are how to generate features and how to acquire labels for machines to learn. Particularly, labeling large amount of data for each domain-specific problem can be very time consuming and costly. It has become a key obstacle in making learning protocols realistic in applications. In this paper, we will discuss how to use the existing general-purpose world knowledge to enhance machine learning processes, by enriching the features or reducing the labeling work. We start from the comparison of world knowledge with domain-specific knowledge, and then introduce three key problems in using world knowledge in learning processes, i.e., explicit and implicit feature representation, inference for knowledge linking and disambiguation, and learning with direct or indirect supervision. Finally we discuss the future directions of this research topic.

negative: Learning Concept Embeddings for Query Expansion by Quantum Entropy Minimization In web search, users queries are formulated using only few terms and term-matching retrieval functions could fail at retrieving relevant documents. Given a user query, the technique of query expansion (QE) consists in selecting related terms that could enhance the likelihood of retrieving relevant documents. Selecting such expansion terms is challenging and requires a computational framework capable of encoding complex semantic relationships. In this paper, we propose a novel method for learning, in a supervised way, semantic representations for words and phrases. By embedding queries and documents in special matrices, our model disposes of an increased representational power with respect to existing approaches adopting a vector representation. We show that our model produces high-quality query expansion terms. Our expansion increase IR measures beyond expansion from current word-embeddings models and well-established traditional QE methods.

negative: Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework Deep learning approaches have reached a celebrity status in artificial intelligence field, its success have mostly relied on Convolutional Networks (CNN) and Recurrent Networks. By exploiting fundamental spatial properties of images and videos, the CNN always achieves dominant performance on visual tasks. And the Recurrent Networks (RNN) especially long short-term memory methods (LSTM) can successfully characterize the temporal correlation, thus exhibits superior capability for time series tasks. Traffic flow data have plentiful characteristics on both time and space domain. However, applications of CNN and LSTM approaches on traffic flow are limited. In this paper, we propose a novel deep architecture combined CNN and LSTM to forecast future traffic flow (CLTFP). An 1-dimension CNN is exploited to capture spatial features of traffic flow, and two LSTMs are utilized to mine the short-term variability and periodicities of traffic flow. Given those meaningful features, the feature-level fusion is performed to achieve short-term traffic flow forecasting. The proposed CLTFP is compared with other popular forecasting methods on an open datasets. Experimental results indicate that the CLTFP has considerable advantages in traffic flow forecasting. in additional, the proposed CLTFP is analyzed from the view of Granger Causality, and several interesting properties of traffic flow and CLTFP are discovered and discussed . Traffic flow forecasting, Convolutional neural network, Long short-term memory, Feature-level fusion

negative: Bijective parameterization with free boundaries We present a fully automatic method for generating guaranteed bijective surface parameterizations from triangulated 3D surfaces partitioned into charts. We do so by using a distortion metric that prevents local folds of triangles in the parameterization and a barrier function that prevents intersection of the chart boundaries. In addition, we show how to modify the line search of an interior point method to directly compute the singularities of the distortion metric and barrier functions to maintain a bijective map. By using an isometric metric that is efficient to compute and a spatial hash to accelerate the evaluation and gradient of the barrier function for the boundary, we achieve fast optimization times. Unlike previous methods, we do not require the boundary be constrained by the user to a non-intersecting shape to guarantee a bijection, and the boundary of the parameterization is free to change shape during the optimization to minimize distortion.

negative: Classification of Artefacts in EEG Signal Recordings and Overview of Removing Techniques EEG is a record of brain activity from various sites of the brain. Artefacts are unwanted noise signals in an EEG record. Classification of artefacts based on source of its generation like physiological artefacts and external artefacts. Body of the subjects are main source of Physiological artifacts, while external artefacts are from outside the body due to the environment or measuring devices. Recognition, identification and elimination of artifacts is an important process to minimize the chance of misinterpretation of EEG, not only for clinical and non-clinical fields such as brain computer interface, intelligent control system robotics etc. This paper classifies the artefacts from the database collected at Dr. R. N. Cooper Mun. General Hospital Mumbai India.

negative: Autonomia: an autonomic computing environment The prolifeation of Internet technologies, services and devices, have made the current networked system designs, and management tools incapable of designing reliable, secure networked systems and services. In fact, we have reached a level of complexity, heterogeneity, and a rapid change rate that our information infiastructure is becoming unmanageable and insecure. This had led researchers to consider alternative designs and management techniques that are based on strategies used by biological systems to deal with complexity, heterogeneity and uncertainty. The approach is referred to as autonomic computing. An autonomic computing system is the system that has the capabilities of being sevdefining, self-healing, self-configwing, self-optimizing, etc. In this paper, we present our approach to implement an autonomic computing infastructure, Autonomia that provides dynamically programmable control and management services to support the development and deployment of smart (intelligent) applications. The A UTONOMIA environment provides the application developers with all the tools required to specifj, the appropriate control and management schemes to maintain any quality of service requirement or application attribute/firnctionality (e.g., perjormance, fault, security, etc.) and the core autonomic middleware services to maintain the autonomic requirements of a wide range of network applications and services. We have successfully implemented a proof-of-concept prototype system that can support the self-configuring, self-deploying and selfhealing of any networked application.

negative: SnapApp: Reducing Authentication Overhead with a Time-Constrained Fast Unlock Option We present SnapApp, a novel unlock concept for mobile devices that reduces authentication overhead with a time-constrained quick-access option. SnapApp provides two unlock methods at once: While PIN entry enables full access to the device, users can also bypass authentication with a short sliding gesture ("Snap"). This grants access for a limited amount of time (e.g. 30 seconds). The device then automatically locks itself upon expiration. Our concept further explores limiting the possible number of Snaps in a row, and configuring blacklists for app use during short access (e.g. to exclude banking apps). We discuss opportunities and challenges of this concept based on a 30-day field study with 18 participants, including data logging and experience sampling methods. Snaps significantly reduced unlock times, and our app was perceived to offer a good tradeoff. Conceptual challenges include, for example, supporting users in configuring their blacklists.

negative: Atrial Fibrillation Classification from a Short Single Lead ECG Recording Using a Deep Convolutional Neural Network In this work, a deep convolutional neural network (CNN) is proposed to detect atrial fibrillation (AF) among the normal, noisy and other categories of cardiac arrhythmias electrocardiogram (ECG) recordings. The proposed CNN is trained by stochastic gradient descent with the categorical cross-entropy loss function. The network performance is evaluated on training (75%) and validation (25%) data sets that are obtained from 2017 Physionet/CinC challenge database. The proposed CNN model respectively achieves the average accuracy and F1 score of 87% and 0.84 on validation data set. One of the main advantages of this work besides high accuracy and reliability, is to simplify the feature extraction process and to remove the need for detecting ECG signal fiducial points and extracting hand-crafted features unlike conventional methods available in the literature. Moreover, it provides an opportunity for ECG screening in a large population, especially for atrial fibrillation screening, using wearable devices such as OM apparel that records high-quality single channel ECG signal.

negative: Indexing and Query Processing Techniques in Spatio-Temporal Data Indexing and query processing is an emerging research field in spatio temporal data. Most of the real-time applications such as location based services, fleet management, traffic prediction and radio frequency identification and sensor networks are based on spatiotemporal indexing and query processing. All the indexing and query processing applications is any one of the forms, such as spatio index access and supporting queries or spatio-temporal indexing method and support query or temporal dimension, while in spatial data it is considered as the second priority. In this paper, give the survey of the various uncertain indexing and query processing techniques. Most of the existing survey works on spatio-temporal are based on indexing methods and query processing, but presented separately. Both the indexing and querying are related, hence state of art of both the indexing and query processing techniques are considered together. This paper gives the details of spatio-temporal data classification, various types of indexing methods, query processing, application areas and research direction of spatio-temporal indexing and query

negative: Towards Conversational Recommender Systems People often ask others for restaurant recommendations as a way to discover new dining experiences. This makes restaurant recommendation an exciting scenario for recommender systems and has led to substantial research in this area. However, most such systems behave very differently from a human when asked for a recommendation. The goal of this paper is to begin to reduce this gap. In particular, humans can quickly establish preferences when asked to make a recommendation for someone they do not know. We address this cold-start recommendation problem in an online learning setting. We develop a preference elicitation framework to identify which questions to ask a new user to quickly learn their preferences. Taking advantage of latent structure in the recommendation space using a probabilistic latent factor model, our experiments with both synthetic and real world data compare different types of feedback and question selection strategies. We find that our framework can make very effective use of online user feedback, improving personalized recommendations over a static model by 25% after asking only 2 questions. Our results demonstrate dramatic benefits of starting from offline embeddings, and highlight the benefit of bandit-based explore-exploit strategies in this setting.

negative: An autonomous flyer photographer In this paper we explore the combination of a latest generation mobile device and a micro quadrotor platform to perform indoor autonomous navigation for the purpose of autonomous photography. We use the Yellowstone tablet from Google's Tango project [1], equipped with onboard, fully integrated sensing platform and with significant computational capability. To the best of our knowledge we are the first to exploit the Google's Tango tablet as source of pose estimate to control the quadrotor's motion. Using the tablet's onboard camera the system is able to detect people and generate a desired pose that the quadrotor will have to reach in order to take a well framed picture of the detected subject. The experimental results and live video demonstrate the capabilities of the autonomous flying robot photographer using the system described throughout this manuscript.

negative:
Conservative surgical management of subungual (matrix derived) melanoma: report of seven cases and literature review. BACKGROUND
Subungual melanoma (SUM) is a rare entity, comprising approximately 0·7-3·5% of all melanoma subtypes. SUM histopathologically belongs to the acral lentiginous pathological subtype of malignant melanoma. Its diagnosis is helped by dermoscopy but pathological examination of doubtful cases is required. Classical management of SUM is based on radical surgery, namely distal phalanx amputation. Conservative treatment with nonamputative wide excision of the nail unit followed by a skin graft has been insufficiently reported in the medical literature even though it is performed in many centres.

OBJECTIVES
To report a series of patients with in situ or minimally invasive SUM treated by conservative surgery, to investigate the postoperative evolution and to evaluate the outcome with a review of the literature.

METHODS
We performed a retrospective extraction study from our melanoma register of all patients with in situ and minimally invasive SUM treated with conservative surgery in the University Hospital Department of Dermatology, Lyon, France from 2004 to 2009. The patient demographics, disease presentation, delay to diagnosis, histopathology and postoperative evolution were reviewed.

RESULTS
Seven cases of SUM treated as such were identified in our melanoma database. All cases had a clinical presentation of melanonychia striata. The mean delay to diagnosis was 2years. Surgical excision of the entire nail unit with a 5-10mm safety margin without bone resection followed by full-thickness skin graft taken from the arm was performed in all cases. No recurrence was observed with a mean follow-up of 45months. Functional results were found satisfactory by all patients and their referring physicians. Sixty-two other cases have been found in the literature and are also discussed.

CONCLUSIONS
Conservative surgical management in patients with in situ or minimally invasive SUM is a procedure with good cosmetic and functional outcome and, in our cases as well as in the literature, the prognosis is not changed.

negative: Anomaly Detection Algorithms in Business Process Logs In some domains of application, like software development and health care processes, a normative business process system (e.g. workflow management system) is not appropriate because a flexible support is needed to the participants. On the other hand, while it is important to support flexibility of execution in these domains, security requirements can not be met whether these systems do not offer extra control, which characterizes a trade off between flexibility and security in such domains. This work presents and assesses a set of anomaly detection algorithms in logs of Process Aware Systems (PAS). The detection of an anomalous instance is based on the “noise” which an instance makes in a process model discovered by a process mining algorithm. As a result, a trace that is an anomaly for a discovered model will require more structural changes for this model fit it than a trace that is not an anomaly. Hence, when aggregated to PAS, these methods can support the coexistence of security and flexibility.

negative: Examining The Adoption Of Human Resource Information System In The Context Of Saudi Arabia The aim of this paper is to examine the factors influencing the adoption of the human resource information system (HRIS) in the Saudi context. A few researches have looked at the adoption of HRIS, but none of these studies have examined the adoption of such a system from the users’ perspective in general and Saudi Arabia (SA) in particular. This study has developed a model that integrates service quality with perceived usefulness and perceived ease of use to investigate the factors affecting the users’ adoption of HRIS. The model was empirically tested using the structure equation modelling by employing data collected from a survey of HRIS users. The findings and discussion presented in this study will help the Saudi organisations to understand the current status of their HRIS to improve it and benefit from it. This study provides guidelines for its implementation for theory and practices, limitations and future directions.

negative: Exploring the role of robotic kissing “Kissenger” in digital communication through alan turing's imitation game With advances in robotics and artificial intelligence, and increasing prevalence of commercial social robots and chatbots, humans are communicating with artificially intelligent machines for various applications in a wide range of settings. Intimate touch is crucial to relationships forming and emotional bonding, however it remains as a missing element in digital communication between humans, as well as humans and machines. A kissing machine, Kissenger, is built to transmit haptic kissing sensations in a communication network. In order to investigate the role of robotic kissing using the Kissenger device in digital communication, we conducted a modified version of the Imitation Game described by Alan Turing by including the use of the kissing machine. The study consists of 4 rounds, in which participants communicate with two players, a male and a female, using online chatrooms with the goal to identify the female player. In the last two rounds, the male player is replaced with a chatbot with female personas. Participants are told to remotely kiss the players using the kissing device at least once during the chat in the second and fourth round, but not in the first and third round. Results show that robotic kissing has no effect on the winning rates of the male and female players during human-human communication, but it increases the winning rate of the female player when a chatbot is involved in the game.

negative: A Neuroevolution Approach to General Atari Game Playing This paper addresses the challenge of learning to play many different video games with little domain-specific knowledge. Specifically, it introduces a neuroevolution approach to general Atari 2600 game playing. Four neuroevolution algorithms were paired with three different state representations and evaluated on a set of 61 Atari games. The neuroevolution agents represent different points along the spectrum of algorithmic sophistication - including weight evolution on topologically fixed neural networks (conventional neuroevolution), covariance matrix adaptation evolution strategy (CMA-ES), neuroevolution of augmenting topologies (NEAT), and indirect network encoding (HyperNEAT). State representations include an object representation of the game screen, the raw pixels of the game screen, and seeded noise (a comparative baseline). Results indicate that direct-encoding methods work best on compact state representations while indirect-encoding methods (i.e., HyperNEAT) allow scaling to higher dimensional representations (i.e., the raw game screen). Previous approaches based on temporal-difference (TD) learning had trouble dealing with the large state spaces and sparse reward gradients often found in Atari games. Neuroevolution ameliorates these problems and evolved policies achieve state-of-the-art results, even surpassing human high scores on three games. These results suggest that neuroevolution is a promising approach to general video game playing (GVGP).

negative: SpaceTwist: Managing the Trade-Offs Among Location Privacy, Query Performance, and Query Accuracy in Mobile Services In a mobile service scenario, users query a server for nearby points of interest but they may not want to disclose their locations to the service. Intuitively, location privacy may be obtained at the cost of query performance and query accuracy. The challenge addressed is how to obtain the best possible performance, subjected to given requirements for location privacy and query accuracy. Existing privacy solutions that use spatial cloaking employ complex server query processing techniques and entail the transmission of large quantities of intermediate result. Solutions that use transformation-based matching generally fall short in offering practical query accuracy guarantees. Our proposed framework, called SpaceTwist, rectifies these shortcomings for k nearest neighbor (kNN) queries. Starting with a location different from the user's actual location, nearest neighbors are retrieved incrementally until the query is answered correctly by the mobile terminal. This approach is flexible, needs no trusted middleware, and requires only well-known incremental NN query processing on the server. The framework also includes a server-side granular search technique that exploits relaxed query accuracy guarantees for obtaining better performance. The paper reports on empirical studies that elicit key properties of SpaceTwist and suggest that the framework offers very good performance and high privacy, at low communication cost.

negative: AP3: cooperative, decentralized anonymous communication This paper describes a cooperative overlay network that provides anonymous communication services for participating users. The Anonymizing Peer-to-Peer Proxy (AP3) system provides clients with three primitives: (i) anonymous message delivery, (ii) anonymous channels, and (iii) secure pseudonyms. AP3 is designed to be lightweight, low-cost and provides "probable innocence" anonymity to participating users, even under a large-scale coordinated attack by a limited fraction of malicious overlay nodes. Additionally, we use AP3's primitives to build novel anonymous group communication facilities (multicast and anycast), which shield the identity of both publishers and subscribers.

negative: Deanonymizing mobility traces: using social network as a side-channel Location-based services, which employ data from smartphones, vehicles, etc., are growing in popularity. To reduce the threat that shared location data poses to a user's privacy, some services anonymize or obfuscate this data. In this paper, we show these methods can be effectively defeated: a set of location traces can be deanonymized given an easily obtained social network graph. The key idea of our approach is that a user may be identified by those she meets: a "contact graph" identifying meetings between anonymized users in a set of traces can be structurally correlated with a social network graph, thereby identifying anonymized users. We demonstrate the effectiveness of our approach using three real world datasets: University of St Andrews mobility trace and social network (27 nodes each), SmallBlue contact trace and Facebook social network (125 nodes), and Infocom 2006 bluetooth contact traces and conference attendees' DBLP social network (78 nodes). Our experiments show that 80% of users are identified precisely, while only 8% are identified incorrectly, with the remainder mapped to a small set of users.

negative: CLIP: Continuous Location Integrity and Provenance for Mobile Phones Many location-based services require a mobile user to continuously prove his location. In absence of a secure mechanism, malicious users may lie about their locations to get these services. Mobility trace, a sequence of past mobility points, provides evidence for the user's locations. In this paper, we propose a Continuous Location Integrity and Provenance (CLIP) Scheme to provide authentication for mobility trace, and protect users' privacy. CLIP uses low-power inertial accelerometer sensor with a light-weight entropy-based commitment mechanism and is able to authenticate the user's mobility trace without any cost of trusted hardware. CLIP maintains the user's privacy, allowing the user to submit a portion of his mobility trace with which the commitment can be also verified. Wireless Access Points (APs) or colocated mobile devices are used to generate the location proofs. We also propose a light-weight spatial-temporal trust model to detect fake location proofs from collusion attacks. The prototype implementation on Android demonstrates that CLIP requires low computational and storage resources. Our extensive simulations show that the spatial-temporal trust model can achieve high (> 0.9) detection accuracy against collusion attacks.

negative: Geographical Routing With Location Service in Intermittently Connected MANETs Combining mobile platforms such as manned or unmanned vehicles and peer-assisted wireless communication is an enabler for a vast number of applications. A key enabler for the applications is the routing protocol that directs the packets in the network. Routing packets in fully connected mobile ad hoc networks (MANETs) has been studied to a great extent, but the assumption on full connectivity is generally not valid in a real system. This case means that a practical routing protocol must handle intermittent connectivity and the absence of end-to-end connections. In this paper, we propose a geographical routing algorithm called location-aware routing for delay-tolerant networks (LAROD), enhanced with a location service, location dissemination service (LoDiS), which together are shown to suit an intermittently connected MANET (IC-MANET). Because location dissemination takes time in IC-MANETs, LAROD is designed to route packets with only partial knowledge of geographic position. To achieve low overhead, LAROD uses a beaconless strategy combined with a position-based resolution of bids when forwarding packets. LoDiS maintains a local database of node locations, which is updated using broadcast gossip combined with routing overhearing. The algorithms are evaluated under a realistic application, i.e., unmanned aerial vehicles deployed in a reconnaissance scenario, using the low-level packet simulator ns-2. The novelty of this paper is the illustration of sound design choices in a realistic application, with holistic choices in routing, location management, and the mobility model. This holistic approach justifies that the choice of maintaining a local database of node locations is both essential and feasible. The LAROD-LoDiS scheme is compared with a leading delay-tolerant routing algorithm (spray and wait) and is shown to have a competitive edge, both in terms of delivery ratio and overhead. For spray and wait, this case involved a new packet-level implementation in ns-2 as opposed to the original connection-level custom simulator.

negative: On the privacy-utility tradeoff in participatory sensing systems The ubiquity of sensors-equipped mobile devices has enabled citizens to contribute data via participatory sensing systems. This emergent paradigm comes with various applications to improve users' quality of life. However, the data collection process may compromise the participants' privacy when reporting data tagged or correlated with their sensitive information. Therefore, anonymization and location cloaking techniques have been designed to provide privacy protection, yet to some cost of data utility which is a major concern for queriers. Different from past works, we assess simultaneously the two competing goals of ensuring the queriers' required data utility and protecting the participants' privacy. First, we introduce a trust worthy entity to the participatory sensing traditional system. Also, we propose a general privacy-preserving mechanism that runs on this entity to release a distorted version of the sensed data in order to minimize the information leakage with its associated private information. We demonstrate how to identify a near-optimal solution to the privacy-utility tradeoff by maximizing a privacy score while considering a utility metric set by data queriers (service providers). Furthermore, we tackle the challenge of data with large size alphabets by investigating quantization techniques. Finally, we evaluate the proposed model on three different real datasets while varying the prior knowledge and the obfuscation type. The obtained results demonstrate that, for different applications, a limited distortion may ensure the participants' privacy while maintaining about 98% of the required data utility.

negative: An Analysis of Anonymity in the Bitcoin System Anonymity in Bit coin, a peer-to-peer electronic currency system, is a complicated issue. Within the system, users are identified by public-keys only. An attacker wishing to de-anonymize its users will attempt to construct the one to-many mapping between users and public-keys and associate information external to the system with the users. Bitcoinfrustrates this attack by storing the mapping of a user to his or her public-keys on that user's node only and by allowing each user to generate as many public-keys as required. In this paper we consider the topological structure of two networks derived from Bitcoin's public transaction history. We show that the two networks have a non-trivial topological structure, provide complementary views of the Bit coin system and have implications for anonymity. We combine these structures with external information and techniques such as context discovery and flow analysis to investigate an alleged theft of Bit coins, which, at the time of the theft, had a market value of approximately half a million U.S. dollars.