title: TagTrack: device-free localization and tracking using passive RFID tags

abstract: Device-free passive localization aims to localize or track targets without requiring them to carry any devices or to be actively involved with the localization process. This technique has received much attention recently in a wide range of applications including elderly people surveillance, intruder detection, and indoor navigation. In this paper, we propose a novel localization and tracking system based on the Received Signal Strength field formed by a set of cost-efficient passive RFID tags. We firstly formulate localization as a classification task, where we compare several state-of-the-art learning-based classification methods including k Nearest Neighbor (kNN), Multivariate Gaussian Mixture Model (GMM) and Support Vector Machine (SVM). To track a moving subject, we propose two Hidden Markov Model (HMM)-based methods, namely GMM-based HMM and kNN-based HMM. kNN-based HMM extends kNN into a probabilistic style to approximate the Emission Probability Matrix in HMM. The proposed methods can be easily applied into other fingerprint-based tracking systems regardless of their hardware platforms. We conduct extensive experiments and the results demonstrate the effectiveness and accuracy of our approaches with up to 98% localization accuracy and an average of 0.7m tracking error.


title: A 1-V CMOS Pseudo-Differential Amplifier With Multiple Common-Mode Stabilization and Frequency Compensation Loops

abstract: This paper presents an operational amplifier for a 1-V supply voltage. It comprises three gain stages with ac-boosting and buffered Miller feedback compensation circuits. The implementation uses a standard 0.35-mum CMOS process ( V and V). To accommodate maximum voltage headroom between power rails, a pseudo-differential structure is adopted in this amplifier. The large common-mode gain associated with the structure is suppressed by two common-mode stabilization loops. The amplifier driving 100-pF loads achieves a 4.3-MHz gain-bandwidth product. The settling time of a 1- input step signal is 1.1s. The amplifier consumes 249 muW and occupies 0.06-mm silicon area.


title: DRFM range false target cancellation method based on slope-varying LFM chirp signal

abstract: Range false target created by Digital Radio Frequency Memory (DRFM) repeat jammer is a very common deception jamming in Electronic Counter Measures (ECM). In this paper, a jamming cancellation method based on Slope-Varying Linear Frequency Modulation (LFM) chirp signal is proposed to suppress this kind of jamming signal. In the proposed method, the radar firstly transmits LFM chirp signal with varying slope at each pulse repletion intervals (PRIs). In the following, the jamming signal is reconstructed by using the parameters of range false target, which are estimated from marched-filter output. Finally, DRFM range false target can be cancelled out by subtracting received signal from reconstructed jamming signal. The effectiveness of the proposed method is verified by simulations.


title: Mosaicing New Views: The Crossed-Slits Projection

abstract: We introduce anew kind of mosaicing, where the position of the sampling strip varies as a function of the input camera location. The new images that are generated this way correspond to a new projection model defined by two slits, termed here the Crossed-Slits (X-Slits) projection. In this projection model, every 3D point is projected by a ray defined as the line that passes through that point and intersects the two slits. The intersection of the projection rays with the imaging surface defines the image. X-Slits mosaicing provides two benefits. First, the generated mosaics are closer to perspective images than traditional pushbroom mosaics. Second, by simple manipulations of the strip sampling function, we can change the location of one of the virtual slits, providing a virtual walkthrough of a X-Slits camera; all this can be done without recovering any 3D geometry and without calibration. A number of examples where we translate the virtual camera and change its orientation are given; the examples demonstrate realistic changes in parallax, reflections, and occlusions.


title: A MEMS accelerometer for spindle monitoring system

abstract: Vibration acceleration detection of spindle monitoring requires that the accelerometer has an adequate measurement sensitivity and response frequency above the interested range. This paper aims at design a kind of micro-electro-mechanical system (MEMS) accelerometer that can be used in the spindle component's vibration detection. To measure the vibration acceleration of the high speed spindle accurately, the accelerometer uses the cantilever beam-membrane (CBM) structure as sensing structure. In order to verify that the design of the sensor can be used in the spindle monitoring system, the vibration signal of spindle monitoring system were tested at different rotational speed. Experimental results show that the developed sensor is able to accurately identify the vibration signals in the monitoring system.


title: A Supervised Time Series Feature Extraction Technique Using DCT and DWT

abstract: The increased availability of time series datasets prompts the development of new tools and methods that allow machine learning classifiers to better cope with time series data. Time series data are usually characterized by a high space dimensionality and a very strong correlation among features. This special nature makes the development of effective time series classifiers a challenging task. This work proposes and analyzes methods combining spectral decomposition and feature selection for time series classification problems and compares them against methods that work with original time series and time-dependent features. Briefly, our approach first applies discrete cosine transform (DCT) or discrete wavelet transform (DWT) on time series data. Then, it performs supervised feature selection/reduction by selecting only the most discriminative set of coefficients to represent the data. Experimental evaluations, carried out on multiple datasets, demonstrate the benefits of our approach in learning efficient and accurate time series classifiers.


title: A multi-level marketing case study : specifying forests and trees in Z : research article

abstract: A formal specification of a multi-level marketing (MLM) business is presented. Specifying a MLM business boils down to specifying properties of and operations on mathematical forests and trees. The usefulness of the model-based specification language, Z, is investigated as a vehicle for a formal specification of these recursive structures. The specification is presented following a prescribed format, namely the Established Strategy for constructing a Z specification. The Established Strategy is augmented with the notion of proof aimed at corroborating the correctness of critical parts of a specification. We show how attempts at discharging two different proof obligations using the resolution-based, first-order theorem prover OTTER calls for the use of two automated reasoning strategies, namely avoiding equality and using resonance.


title: Curve skeleton extraction from incomplete point cloud

abstract: We present an algorithm for curve skeleton extraction from imperfect point clouds where large portions of the data may be missing. Our construction is primarily based on a novel notion of generalized rotational symmetry axis (ROSA) of an oriented point set. Specifically, given a subset S of oriented points, we introduce a variational definition for an oriented point that is most rotationally symmetric with respect to S. Our formulation effectively utilizes normal information to compensate for the missing data and leads to robust curve skeleton computation over regions of a shape that are generally cylindrical. We present an iterative algorithm via planar cuts to compute the ROSA of a point cloud. This is complemented by special handling of non-cylindrical joint regions to obtain a centered, topologically clean, and complete 1D skeleton. We demonstrate that quality curve skeletons can be extracted from a variety of shapes captured by incomplete point clouds. Finally, we show how our algorithm assists in shape completion under these challenges by developing a skeleton-driven point cloud completion scheme.


title: Optimization of Prosthetic Foot Stiffness to Reduce Metabolic Cost and Intact Knee Loading During Below-Knee Amputee Walking: A Theoretical Study

abstract: Unilateral below-knee amputees develop abnormal gait characteristics that include bilateral asymmetries and an elevated metabolic cost relative to non-amputees. In addition, long-term prosthesis use has been linked to an increased prevalence of joint pain and osteoarthritis in the intact leg knee. To improve amputee mobility, prosthetic feet that utilize elastic energy storage and return (ESAR) have been designed, which perform important biomechanical functions such as providing body support and forward propulsion. However, the prescription of appropriate design characteristics (e.g., stiffness) is not well-defined since its influence on foot function and important in vivo biomechanical quantities such as metabolic cost and joint loading remain unclear. The design of feet that improve these quantities could provide considerable advancements in amputee care. Therefore, the purpose of this study was to couple design optimization with dynamic simulations of amputee walking to identify the optimal foot stiffness that minimizes metabolic cost and intact knee joint loading. A musculoskeletal model and distributed stiffness ESAR prosthetic foot model were developed to generate muscle-actuated forward dynamics simulations of amputee walking. Dynamic optimization was used to solve for the optimal muscle excitation patterns and foot stiffness profile that produced simulations that tracked experimental amputee walking data while minimizing metabolic cost and intact leg internal knee contact forces. Muscle and foot function were evaluated by calculating their contributions to the important walking subtasks of body support, forward propulsion and leg swing. The analyses showed that altering a nominal prosthetic foot stiffness distribution by stiffening the toe and mid-foot while making the ankle and heel less stiff improved ESAR foot performance by offloading the intact knee during early to mid-stance of the intact leg and reducing metabolic cost. The optimal design also provided moderate braking and body support during the first half of residual leg stance, while increasing the prosthesis contributions to forward propulsion and body support during the second half of residual leg stance. Future work will be directed at experimentally validating these results, which have important implications for future designs of prosthetic feet that could significantly improve amputee care.


title: High-Density Integration of High-Frequency High-Current Point-of-Load (POL) Modules With Planar Inductors

abstract: Planar inductors made by mixed laminates of low-temperature sintered Ni-Cu-Zn ferrite tapes and metal-flake composite materials are used for high-density integration of point-of-load (POL) modules. Incremental permeability and core loss density were characterized on toroidal samples under high dc bias to demonstrate that both materials are suitable for application in high-frequency high-current POL converters. In order to realize a high power density POL module, a multilayer ferrite inductor laminated with alternating layers of ESL 40010 and ESL 40012 in a 1:1 ratio has been fabricated and integrated with the active layer. Meanwhile, standard printed circuit board (PCB) processing has been adopted for the POL integration with a PCB-embedded inductor using NEC-TOKIN's metal-flake composite materials. These developed 3-D integration approaches can be used to reduce the footprint and increase the power density for POL converters. It has been demonstrated that the power efficiency of both POL modules with integrated planar inductors can achieve above 87% at an operating frequency of 2 MHz and an output current of 15 A. Additionally, no obvious efficiency degradation was observed on the integrated POL modules after a certain number of thermal cycling from -40 °C to +150 °C.


title: RCS Reduction With a Dual Polarized Self-Complementary Connected Array Antenna

abstract: In this paper, a wideband dual polarized self-complementary connected array antenna with low radar cross section (RCS) under normal and oblique incidence is presented. First, an analytical model of the multilayer structure is proposed in order to obtain a fast and reliable predimensioning tool providing an optimized design of the infinite array. The accuracy of this model is demonstrated thanks to comparative simulations with a full wave analysis software. RCS reduction compared to a perfectly conducting flat plate of at least 10 dB has been obtained over an ultrawide bandwidth of nearly 7:1 at normal incidence and 5:1 (3.8 to 19 GHz) at 60° in both polarizations. These performances are confirmed by finite element tearing and interconnecting computations of finite arrays of different sizes. Finally, the realization of a 28 × 28 cell prototype and measurement results are detailed.


title: Modeling the Human-Robot Trust Phenomenon: A Conceptual Framework based on Risk

abstract: This article presents a conceptual framework for human-robot trust which uses computational representations inspired by game theory to represent a definition of trust, derived from social psychology. This conceptual framework generates several testable hypotheses related to human-robot trust. This article examines these hypotheses and a series of experiments we have conducted which both provide support for and also conflict with our framework for trust. We also discuss the methodological challenges associated with investigating trust. The article concludes with a description of the important areas for future research on the topic of human-robot trust.


title: An Efficient Approach to Informative Feature Extraction from Multimodal Data

abstract: One primary focus in multimodal feature extraction is to find the representations of individual modalities that are maximally correlated. As a well-known measure of dependence, the Hirschfeld-Gebelein-Renyi (HGR) maximal correlation be-´ comes an appealing objective because of its operational meaning and desirable properties. However, the strict whitening constraints formalized in the HGR maximal correlation limit its application. To address this problem, this paper proposes Soft-HGR, a novel framework to extract informative features from multiple data modalities. Specifically, our framework prevents the “hard” whitening constraints, while simultaneously preserving the same feature geometry as in the HGR maximal correlation. The objective of Soft-HGR is straightforward, only involving two inner products, which guarantees the efficiency and stability in optimization. We further generalize the framework to handle more than two modalities and missing modalities. When labels are partially available, we enhance the discriminative power of the feature representations by making a semi-supervised adaptation. Empirical evaluation implies that our approach learns more informative feature mappings and is more efficient to optimize.


title: Biomaterials and biomechanics of oral and maxillofacial implants: current status and future developments.

abstract: M advances have occurred over the last 3 decades in the clinical use of oral and maxillofacial implants. Statistics on the use of dental implants bear this out; about 100,000 to 300,000 dental implants are placed per year,1 which approximates the numbers of artificial hip and knee joints placed per year.2 Implants are currently used to replace missing teeth, rebuild the craniofacial skeleton, provide anchorage during orthodontic treatments, and even to help form new bone in the process of distraction osteogenesis. Despite the impressive clinical accomplishments with oral and maxillofacial implants—and the undisputed fact that implants have improved the lives of millions of patients—it is nevertheless disquieting that key information is still missing about fundamental principles underlying their design and clinical use. With some important exceptions, the design and use of oral and maxillofacial implants has often been driven by an aggressive, “copycat” marketing environment, rather than by basic advances in biomaterials, biomechanics, or bone biology. A wide variety of implants now exists for use in many clinical indications, with over 50 companies listed by the United States Food and Drug Administration (FDA) as being involved in the manufacture, marketing, and distribution of dental implants. While this situation is not necessarily a problem, in many instances new companies have entered the dental implant market by simply copying or making minor, incremental changes to the sizes, shapes, materials, and surfaces of competitors’ products, while exaggerating the new product’s effectiveness. In addition, busy clinicians, not always equipped to discern the difference between marketing hype and scientific advance, yet wanting to help their patients sooner rather than later, have often been too eager to use new implants in new clinical situations before these new indications have been fully researched from the clinical or basic science viewpoint. For better or for worse, the current state of the oral implant field is such that a myriad of different types of implants are now being used in a very wide variety of clinical indications, under largely undocumented loading conditions in different quantities and qualities of bone that has healed to varying extents. It is a fertile but complicated state of affairs. Given this situation and the many variables that can affect the performance of oral implants, it is sometimes difficult to separate fact from fiction and make reliable predictions for the future. However, a helpful starting point is to appreciate that the use of oral implants—and the key role of biomaterials and biomechanics—is an excellent example of a multifaceted design problem.


title: Un-restricted Common Due-Date Problem with Controllable Processing Times

abstract: This paper considers the un-restricted case of the Common Due-Date (CDD) problem with controllable ::: ::: processing times. The problem consists of scheduling jobs with controllable processing times on a single ::: ::: machine against a common due-date to minimize the overall earliness/tardiness and the compression penalties ::: ::: of the jobs. The objective of the problem is to find the processing sequence of jobs, the optimal reduction ::: ::: in the processing times of the jobs and their completion times. In this work, we first present and prove an ::: ::: essential property for the controllable processing time CDD problem for the un-restricted case along with an ::: ::: exact linear algorithm for optimizing a given job sequence for a single machine with a run-time ::: ::: complexity of O(n), where n is the number of jobs. Henceforth, we implement our polynomial algorithm in ::: ::: conjunction with a modified Simulated Annealing (SA) algorithm and Threshold Accepting (TA) to obtain the ::: ::: optimal/best processing sequence while comparing the two heuristic approaches, as well. The implementation ::: ::: is carried out on appended CDD benchmark instances provided in the OR-library.


title: An evolutionary approach to sudoku puzzles with filtered mutations

abstract: Sudoku puzzles are logical number placement puzzle games. They are classified as combinatorial optimization problems and are NP-complete. To solve problems in this complexity class, metaheuristic approaches, such as genetic algorithms (GAs), are often adopted. However, conventional GAs with random swap mutations suffer from slow convergence, especially in extremely difficult sudoku puzzles, in which only a few given numbers are provided. This paper proposes a GA with sophisticated genetic mutations that mitigate the worsening of fitness values. The comparisons between the conventional method and the proposed method are conducted mainly from the viewpoints of success rate.


title: MSClique: Multiple Structure Discovery through the Maximum Weighted Clique Problem

abstract: We present a novel approach for feature correspondence and multiple structure discovery in computer vision. In contrast to existing methods, we exploit the fact that point-sets on the same structure usually lie close to each other, thus forming clusters in the image. Given a pair of input images, we initially extract points of interest and extract hierarchical representations by agglomerative clustering. We use the maximum weighted clique problem to find the set of corresponding clusters with maximum number of inliers representing the multiple structures at the correct scales. Our method is parameter-free and only needs two sets of points along with their tentative correspondences, thus being extremely easy to use. We demonstrate the effectiveness of our method in multiple-structure fitting experiments in both publicly available and in-house datasets. As shown in the experiments, our approach finds a higher number of structures containing fewer outliers compared to state-of-the-art methods.


title: The Effect of Haptic Support Systems on Driver Performance: A Literature Survey

abstract: A large number of haptic driver support systems have been described in the scientific literature. However, there is little consensus regarding the design, evaluation methods, and effectiveness of these systems. This literature survey aimed to investigate: (1) what haptic systems (in terms of function, haptic signal, channel, and supported task) have been experimentally tested, (2) how these haptic systems have been evaluated, and (3) their reported effects on driver performance and behaviour. We reviewed empirical research in which participants had to drive a vehicle in a real or simulated environment, were able to control the heading and/or speed of the vehicle, and a haptic signal was provided to them. The results indicated that a clear distinction can be made between warning systems (using vibrations) and guidance systems (using continuous forces). Studies typically used reaction time measures for evaluating warning systems and vehicle-centred performance measures for evaluating guidance systems. In general, haptic warning systems reduced the reaction time of a driver compared to no warnings, although these systems may cause annoyance. Guidance systems generally improved the performance of drivers compared to non-aided driving, but these systems may suffer from after-effects. Longitudinal research is needed to investigate the transfer and retention of effects caused by haptic support systems.


title: FPGA vs. GPU for sparse matrix vector multiply

abstract: Sparse matrix-vector multiplication (SpMV) is a common operation in numerical linear algebra and is the computational kernel of many scientific applications. It is one of the original and perhaps most studied targets for FPGA acceleration. Despite this, GPUs, which have only recently gained both general-purpose programmability and native support for double precision floating-point arithmetic, are viewed by some as a more effective platform for SpMV and similar linear algebra computations. In this paper, we present an analysis comparing an existing GPU SpMV implementation to our own, novel FPGA implementation. In this analysis, we describe the challenges faced by any SpMV implementation, the unique approaches to these challenges taken by both FPGA and GPU implementations, and their relative performance for SpMV.


title: A Subjective and Objective Fuzzy-Based Analytical Hierarchy Process Model for Prioritization of Lean Product Development Practices

abstract: In this paper, a subjective and objective fuzzy-based Analytical Hierarchy Process (AHP) model is proposed. The model which is based on a newly defined evaluation matrix replaces the fuzzy comparison matrix (FCM) in the traditional fuzzy AHP model, which has been found ineffective and time-consuming when criteria/alternatives are increased. The main advantage of the new model is that it is straightforward and completely eliminates the repetitive adjustment of data that is common with the FCM in traditional AHP model. The model reduces the complete dependen-cy on human judgment in prioritization assessment since the weights values are solved automati-cally using the evaluation matrix and the modified priority weight formula in the proposed mod-el. By virtue of a numerical case study, the model is successfully applied in the determination of the implementation priorities of lean practices for a product development environment and com-pared with similar computational methods in the literature.


title: A practical approach for recognizing eating moments with wrist-mounted inertial sensing

abstract: Recognizing when eating activities take place is one of the key challenges in automated food intake monitoring. Despite progress over the years, most proposed approaches have been largely impractical for everyday usage, requiring multiple on-body sensors or specialized devices such as neck collars for swallow detection. In this paper, we describe the implementation and evaluation of an approach for inferring eating moments based on 3-axis accelerometry collected with a popular off-the-shelf smartwatch. Trained with data collected in a semi-controlled laboratory setting with 20 subjects, our system recognized eating moments in two free-living condition studies (7 participants, 1 day; 1 participant, 31 days), with F-scores of 76.1% (66.7% Precision, 88.8% Recall), and 71.3% (65.2% Precision, 78.6% Recall). This work represents a contribution towards the implementation of a practical, automated system for everyday food intake monitoring, with applicability in areas ranging from health research and food journaling.


title: Search-based requirements traceability recovery: A multi-objective approach

abstract: Software systems nowadays are complex and difficult to maintain due to the necessity of continuous change and adaptation. One of the challenges in software maintenance is keeping requirements traceability up to date automatically. The process of generating requirements traceability is time-consuming and error-prone. Currently, most available tools do not support the automated recovery of traceability links. In some situations, companies accumulate the history of changes from past maintenance experiences. In this paper, we consider requirements traceability recovery as a multi objective search problem in which we seek to assign each requirement to one or many software elements (code elements, API documentation, and comments) by taking into account the recency of change, the frequency of change, and the semantic similarity between the description of the requirement and the software element. We use the Non-dominated Sorting Genetic Algorithm (NSGA-II) to find the best compromise between these three objectives. We report the results of our experiments on three open source projects.


title: Dark Web: Exploring and Mining the Dark Side of the Web

abstract: This talk will review the emerging research in Terrorism Informatics based on a web mining perspective. Recent progress in the internationally renowned Dark Web project will be reviewed, including: deep/dark web spidering (web sites, forums, Youtube, virtual worlds), web metrics analysis, dark network analysis, web-based authorship analysis, and sentiment and affect analysis for terrorism tracking. In collaboration with selected international terrorism research centers and intelligence agencies, the Dark Web project has generated one of the largest databases in the world about extremist/ terrorist-generated Internet contents (web sites, forums, blogs, and multimedia documents). Dark Web research has received significant international press coverage, including: Associated Press, USA Today, The Economist, NSF Press, Washington Post, Fox News, BBC, PBS, Business Week, Discover magazine, WIRED magazine, Government Computing Week, Second German TV (ZDF), Toronto Star, and Arizona Daily Star, among others.


title: Fast access control technology solutions (FACTS)

abstract: Positive identification (ID) verification of a large number of people (possibly thousands) in a short time, such as when people enter the gates of a manufacturing plant in the morning, can be a daunting task for the security guards. Current methods that use a password, card swipe system, proximity card reader system, or combination are slow and vulnerable. For example a card can be stolen or a password forgotten, copied or given to an unauthorized person. Hence the need for biometric-based access control systems (ACS). Many biometric systems address the ID verification problem; however, none provide quick and convenient positive (reliable) ID. Fingerprinting is unreliable and requires too much cooperation, face recognition systems (FRS) are unacceptable in a one-to-many application, and iris scan is slow and cumbersome. In this paper, we describe a fast access control technology solution (FACTS) system we developed and demonstrated at Honeywell Labs that enables many motorists to enter a secured parking lot without coming to a complete stop. The FACTS solution combines radio frequency identification (RFID) tags with FRS, reducing the one-to-many FRS problem by using a one-to-one match for facial verification (FV). This solution is dynamic, secure, positive, and hands-free for fast gate access control. At the heart of the Honeywell FV system is a unique tri-band imaging (TBI) camera that reliably locates and records the face of an individual or driver in motion while entering a gate.


title: Edge Adaptive Seeding for Superpixel Segmentation

abstract: Finding a suitable seeding resolution when using superpixel segmentation methods is usually challenging. Different parts of the image contain different levels of clutter, resulting in an either too dense or too coarse segmentation. Since both possible solutions cause problems with respect to subsequent processing, we propose an edge adaptive seeding for superpixel segmentation methods, generating more seeds in areas with more edges and vise versa. This follows the assumption that edges distinguish objects and thus are a good indicator of the level of clutter in an image region. We show in our evaluation on five datasets by using three popular superpixel segmentation methods that using edge adaptive seeding leads to improved results compared to other priors as well as to uniform seeding.


title: Karyotypic Relationships among Species of Boykinia, Heuchera, Mitella, Sullivantia, Tiarella, and Tolmiea (Saxifragaceae)

abstract: A base chromosome number of x = 7 characterizes many genera of the Saxifraginae. A high degree of karyotypic constancy was found among species representing six of these genera (Boykinia, Heuchera, Mitella, Sullivantia, Tiarella, and Tolmiea). Boykinia aconitifolia and six species of Sullivantia are characterized by the same karyotype. Four species of Heuchera, Mitella diphylla, Tiarella cordifolia, and Tolmiea menziesii (the last at the tetraploid level) possess a second karyotype that differs from the first in the position of the centromere of only one chromosome pair. These karyological observations suggest generic relationships that agree with those based on morphology, flavonoid chemistry, and crossability.


Time-of-Flight Camera - An Introduction


title: Research on cloud computing security problem and strategy

abstract: The cloud computing is a new computing model which comes from grid computing, distributed computing, parallel computing, virtualization technology, utility computing and other computer technologies and it has more advantage characters such as large scale computation and data storage, virtualization, high expansibility, high reliability and low price service. The security problem of cloud computing is very important and it can prevent the rapid development of cloud computing. This paper introduces some cloud computing systems and analyzes cloud computing security problem and its strategy according to the cloud computing concepts and characters. The data privacy and service availability in cloud computing are the key security problem. Single security method cannot solve the cloud computing security problem and many traditional and new technologies and strategies must be used together for protecting the total cloud computing system.


title: Do physiological data relate to traditional usability indexes

abstract: Task performance data and subjective assessment data are widely used as usability measures in the human-computer interaction (HCI) field. Recently, physiology has also been explored as a metric for evaluating usability. However, it is not clear how physiological measures relate to traditional usability evaluation measures. In this paper, we investigated the relationships among three kinds of data: task performance, subjective assessment and physiological measures. We found evidence that physiological data correlate with task performance data in a video game: with a decrease of the task performance level, the normalized galvanic skin response (GSR) increases. In addition, physiological data are mirrored in subjective reports assessing stress level. The research provides an initial step toward establishing a new usability method using physiology as a complementary measure for traditional HCI evaluation.


title: I know what you did on your smartphone: Inferring app usage over encrypted data traffic

abstract: Smartphones and tablets are now ubiquitous in many people's lives and are used throughout the day in many public places. They are often connected to a wireless local area network (IEEE 802.11 WLANs) and rely on encryption protocols to maintain their security and privacy. In this paper, we show that even in presence of encryption, an attacker without access to encryption keys is able to determine the users' behavior, in particular, their app usage. We perform this attack using packet-level traffic analysis in which we use side-channel information leaks to identify specific patterns in packets regardless of whether they are encrypted or not. We show that just by collecting and analyzing small amounts of wireless traffic, one can determine what apps each individual smartphone user in the vicinity is using. Furthermore, and more worrying, we show that by using these apps the privacy of the user is more at risk compared to using online services through browsers on mobile devices. This is due to the fact that apps generate more identifiable traffic patterns. Using random forests to classify the apps we show that we are able to identify individual apps, even in presence of noise, with great accuracy. Given that most online services now provide native apps that may be identified by this method, these attacks represent a serious threat to users' privacy.


Computing machinery and intelligence. 1950 Universal Turing Machine


title: BYTEWEIGHT: learning to recognize functions in binary code

abstract: Function identification is a fundamental challenge in reverse engineering and binary program analysis. For instance, binary rewriting and control flow integrity rely on accurate function detection and identification in binaries. Although many binary program analyses assume functions can be identified a priori, identifying functions in stripped binaries remains a challenge. ::: ::: In this paper, we propose BYTEWEIGHT, a new automatic function identification algorithm. Our approach automatically learns key features for recognizing functions and can therefore easily be adapted to different platforms, new compilers, and new optimizations. We evaluated our tool against three well-known tools that feature function identification: IDA, BAP, and Dyninst. Our data set consists of 2,200 binaries created with three different compilers, with four different optimization levels, and across two different operating systems. In our experiments with 2,200 binaries, we found that BYTE-WEIGHT missed 44,621 functions in comparison with the 266,672 functions missed by the industry-leading tool IDA. Furthermore, while IDA misidentified 459,247 functions, BYTEWEIGHT misidentified only 43,992 functions.


title: An Automatic Segmentation Method of Popular Music Based on SVM and Self-similarity

abstract: Popular music segmentation plays an important role in popular music content retrieval, melody extraction and semantic understanding etc. Segmentation boundary detection is one of the key technologies in lots of conventional algorithms. In this paper, we propose an automatic segmentation approach that combines SVM classification and audio self-similarity segmentation, which firstly separate the sung clips and accompaniment clips from pop music by using SVM preliminary classification, and then heuristic rules are used to filter and merge the classification result to determine potential segment boundaries further. Finally, self-similarity detecting algorithm is introduced to refine our segmentation results in the vicinity of potential points. Experiment shows that our approach can achieve more sophisticated and accurate results by using the local change context substantially to determine the segmentation boundaries.


title: Calculation of multivariate normal probabilities by simulation, with applications to maximum simulated likelihood estimation

abstract: We discuss methods for calculating multivariate normal probabilities by simulation and two new Stata programs for this purpose: -mdraws- for deriving draws from the standard uniform density using either Halton or pseudo-random sequences, and an egen function -mvnp()- for calculating the probabilities themselves. Several illustrations show how the programs may be used for maximum simulated likelihood estimation.


title: A real time clustering method using document index graph

abstract: From a previous survey, 45% of users did not get what they are actually looking for in the web using any search engine. Suppose, you have a million of text file in your server or in your computer, then there is a need to categorize them on the basis of their content in a very efficient way. As a result, IR (Information Retrieval) tool has been developed, it provides a more effective ways for users to categorize relevant data. Most of the clustering algorithm like Vector Space Model considers only single words but it is not incremental so it can't be applied on-line and another algorithm, STC, involves ‘trie’ concept to identify shared phrases suitable to apply on-line, but the main problem is, it doesn't work for large number of data set. In this paper, we have introduced DIGE clustering algorithm which generates the clusters based on the common phrases and also on the single terms. DIGE clustering algorithm based on the DIG model for the representation of documents. The construction of DIG model is incremental, so DIGE is also capable to produce cluster using online document and also it doesn't occupy much memory, so also applicable for offline.


title: Image interpolation by joint view triangulation

abstract: Creating novel views by interpolating prestored images or view morphing has many applications in visual simulation. We present in this paper a new method of automatically interpolating two images which tackles two most difficult problems of morphing due to the lack of depth informational pixel matching and visibility handling. We first describe a quasi-dense matching algorithm based on region growing with the best first strategy for match propagation. Then, we describe a robust construction of matched planar patches using local geometric constraints encoded by a homography. After that we introduce a novel representation, joint view triangulation, for visible and half-occluded patches in two images to handle their visibility during the creation of new view. Finally we demonstrate these techniques on real image pairs.


title: The Advantages and Disadvantages of the Blockchain Technology

abstract: The Blockchain is the newest and perspective technology in modern economy. This technology can help to solve different kind of problems in the industrial sphere, such as trust, transparency, security and reliability of data processing. In theory, the use of Blockchain technology shows great and positive results, but what can say about practice? In this paper the description of the Blockchain technology, and it advantages and disadvantages are analyzed. Many already implemented applications of Blockchain technology were studied, as well as affected success or problems factors during the implementations. This paper aim is to analyze conveniences and difficulties, related to the Blockchain integration and implementation in the different fields of modern industry.


title: Compact, Low-Loss, Wideband, and High-Power Handling Phase Shifters With Piezoelectric Transducer-Controlled Metallic Perturber

abstract: Compact phase shifters with large phase shift, low loss, and high-power-handling capability are desired for a variety of applications. In this paper, a new type of compact meander line phase shifter with metallic perturber controlled by a piezoelectric transducer (PET) has been designed, fabricated, and tested. The new phase-shifter design led to a large phase shift of 360° with a control voltage of 50 V at 3.5 GHz, a low insertion loss of 1 ~ 3 dB, a wide range of operation frequency of 1 ~ 6 GHz, a high-power-handling capability beyond 30 dBm on a compact meander line of 18 mm × 18 mm, compared with similar phase shifters with a dielectric perturber which exhibited very limited phase shift at the S-band. In addition, this is a low-cost phase-shifter design that is extendable to the W -band and beyond. Combined with a low loss, large phase shift, compact size, and a high-power-handling capability, the new meander line phase shifter with PET-controlled metallic perturber showed great potential to be used in different RF/microwave systems.


title: Sentiment analysis on conversational texts

abstract: This paper describes ongoing work related to the analysis of spoken utterance transcripts and estimating the speaker’s attitude towards the whole dialogue on the basis of their opinions expressed by utterances. Using the standard technology used in sentiment analysis, we report promising results which can be linked to the conversational participants’ self-evaluation of their experience of the interaction.


title: Performance evaluation of color correction approaches for automatic multi-view image and video stitching

abstract: Many different automatic color correction approaches have been proposed by different research communities in the past decade. However, these approaches are seldom compared, so their relative performance and applicability are unclear. For multi-view image and video stitching applications, an ideal color correction approach should be effective at transferring the color palette of the source image to the target image, and meanwhile be able to extend the transferred color from the overlapped area to the full target image without creating visual artifacts. In this paper we evaluate the performance of color correction approaches for automatic multi-view image and video stitching. We consider nine color correction algorithms from the literature applied to 40 synthetic image pairs and 30 real mosaic image pairs selected from different applications. Experimental results show that both parametric and non-parametric approaches have members that are effective at transferring colors, while parametric approaches are generally better than non-parametric approaches in extendability.


title: Sensorless control of induction motor drives

abstract: Controlled induction motor drives without mechanical speed sensors at the motor shaft have the attractions of low cost and high reliability. To replace the sensor the information on the rotor speed is extracted from measured stator voltages and currents at the motor terminals. Vector-controlled drives require estimating the magnitude and spatial orientation of the fundamental magnetic flux waves in the stator or in the rotor. Open-loop estimators or closed-loop observers are used for this purpose. They differ with respect to accuracy, robustness, and sensitivity against model parameter variations. Dynamic performance and steady-state speed accuracy in the low-speed range can be achieved by exploiting parasitic effects of the machine. The overview in this paper uses signal flow graphs of complex space vector quantities to provide an insightful description of the systems used in sensorless control of induction motors.


Long-term effect of the insoluble thread-lifting technique


title: Models and issues in data stream systems

abstract: In this overview paper we motivate the need for and research issues arising from a new model of data processing. In this model, data does not take the form of persistent relations, but rather arrives in multiple, continuous, rapid, time-varying data streams. In addition to reviewing past work relevant to data stream systems and current projects in the area, the paper explores topics in stream query languages, new requirements and challenges in query processing, and algorithmic issues.


title: IL-17 and Th17 Cells

abstract: CD4+ T cells, upon activation and expansion, develop into different T helper cell subsets with different cytokine profiles and distinct effector functions. Until recently, T cells were divided into Th1 or Th2 cells, depending on the cytokines they produce. A third subset of IL-17-producing effector T helper cells, called Th17 cells, has now been discovered and characterized. Here, we summarize the current information on the differentiation and effector functions of the Th17 lineage. Th17 cells produce IL-17, IL-17F, and IL-22, thereby inducing a massive tissue reaction owing to the broad distribution of the IL-17 and IL-22 receptors. Th17 cells also secrete IL-21 to communicate with the cells of the immune system. The differentiation factors (TGF-β plus IL-6 or IL-21), the growth and stabilization factor (IL-23), and the transcription factors (STAT3, RORγt, and RORα) involved in the development of Th17 cells have just been identified. The participation of TGF-β in the differentiation of Th17 cells places ...


title: An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem

abstract: Given a large overcomplete dictionary of basis vectors, the goal is to simultaneously represent L>1 signal vectors using coefficient expansions marked by a common sparsity profile. This generalizes the standard sparse representation problem to the case where multiple responses exist that were putatively generated by the same small subset of features. Ideally, the associated sparse generating weights should be recovered, which can have physical significance in many applications (e.g., source localization). The generic solution to this problem is intractable and, therefore, approximate procedures are sought. Based on the concept of automatic relevance determination, this paper uses an empirical Bayesian prior to estimate a convenient posterior distribution over candidate basis vectors. This particular approximation enforces a common sparsity profile and consistently places its prominent posterior mass on the appropriate region of weight-space necessary for simultaneous sparse recovery. The resultant algorithm is then compared with multiple response extensions of matching pursuit, basis pursuit, FOCUSS, and Jeffreys prior-based Bayesian methods, finding that it often outperforms the others. Additional motivation for this particular choice of cost function is also provided, including the analysis of global and local minima and a variational derivation that highlights the similarities and differences between the proposed algorithm and previous approaches.


title: A noninvasive blood gluco se measurement system by arduino and near-infrared

abstract: This paper proposes a new hardware system to measure the blood glucose concentration. This system has the characteristics of noninvasive, simple and quick. Firstly, we use near-infrared light irradiation fingers. The infrared light transmittance can receive a signal about the blood glucose concentration. Then after an amplifier amplification, this signals will transmit to the Arduino. We use the Arduino Uno board as the CPU to analysis and processing the signal. Through comparison with the standards set, our system can determine blood glucose concentration of the measuring signal. Finally, the results will show on the LCD screen, and it can transmission to a phone or laptop for storage via Bluetooth at the same time. It is convenient that customers to know their changing trends in blood glucose levels. Patients will try to take measures to control the adverse effects caused by high blood glucose when they know their blood glucose levels.


title: Distinguishing Load Torque Oscillations and Eccentricity Faults in Induction Motors Using Stator Current Wigner Distributions

abstract: This paper proposes a novel diagnosis method for detection and discrimination of two typical mechanical failures in induction motors by stator current analysis: load torque oscillations and dynamic rotor eccentricity. A theoretical analysis shows that each fault modulates the stator current in a different way: torque oscillations lead to stator current phase modulation, whereas rotor eccentricities produce stator current amplitude modulation. The use of traditional current spectrum analysis involves identical frequency signatures with the two fault types. A time-frequency analysis of the stator current with the Wigner distribution leads to different fault signatures that can be used for a more accurate diagnosis. The theoretical considerations and the proposed diagnosis techniques are validated on experimental signals.


title: Classification of Attributes in a Natural Language Query into Different SQL Clauses

abstract: Attribute information in a natural language query is one of the key features for converting a natural language query into a Structured Query Language 1 (SQL) in Natural Language Interface to Database systems. In this paper, we explore the task of classifying the attributes present in a natural language query into different SQL clauses in a SQL query. In particular, we investigate the effectiveness of various features and Conditional Random Fields for this task. Our system uses a statistical classifier trained on manually prepared data. We report our results on three different domains and also show how our system can be used for generating a complete SQL query.


title: Bayesian Networks: With Examples in R

abstract: Introduction. The Discrete Case: Multinomial Bayesian Networks. The Continuous Case: Gaussian Bayesian Networks. More Complex Cases. Theory and Algorithms for Bayesian Networks. Real-World Applications of Bayesian Networks. Appendices. Bibliography.


title: A Deformed-Film UWB Antenna

abstract: Recently, microwave ultra-wideband (UWB) technology has attracted much attention in high speed wireless communications, imaging and radar applications. Antennas with very wide impedance bandwidth and stable radiations are required for such systems. Among many UWB antennas, planar types are low profile, light weight, low cost and suitable for mobile devices [1]. However, the planar antennas may not radiate omni-directionally at all operating frequencies because they generally have wide structures and are not rotationally symmetrical. A roll monopole antenna which is constructed by twisting a planar radiator into a roll shape has been proposed for improving radiation characteristics, but the bandwidth of the antenna was limited [2]. In this paper, we will propose a deformed film UWB antenna for operating at the FCC approved UWB of 3.1-10.6 GHz. The antenna is constructed by deforming a planar dipole which has a glass-shaped radiation element [3]. The antenna is optimized for the UWB operation in its planar form and can be deformed by different manners such as folding, meandering or twisting, without much influence on input characteristics. The deformation not only miniaturizes the antenna but also improves its radiation characteristics. To investigate experimentally the antenna, a prototype with a dimension of 20x33 mm is fabricated and then the antenna is deformed by rolling into a circular rod with a diameter of 6.5 mm, or meandering it into a square rod with a cross-sectional dimension of 6x5 mm. It is demonstrated that the deformed antennas still operate at the UWB and have better omni-directional radiation patterns than the antenna in its planar form.


title: Coarse-to-fine sentence-level emotion classification based on the intra-sentence features and sentential context

abstract: This paper proposes a novel approach using a coarse-to-fine analysis strategy for sentence-level emotion classification which takes into consideration of similarities to sentences in training set as well as adjacent sentences in the context. First, we use intra-sentence based features to determine the emotion label set of a target sentence coarsely through the statistical information gained from the label sets of the k most similar sentences in the training data. Then, we use the emotion transfer probabilities between neighboring sentences to refine the emotion labels of the target sentences. Such iterative refinements terminate when the emotion classification converges. The proposed algorithm is evaluated on Ren-CECps, a Chinese blog emotion corpus. Experimental results show that the coarse-to-fine emotion classification algorithm improves the sentence-level emotion classification by 19.11% on the average precision metric, which outperforms the baseline methods.


title: Data mining: machine learning, statistics, and databases

abstract: Knowledge discovery in databases and data mining aim at semiautomatic tools for the analysis of large data sets. We give an overview of the area and present some of the research issues, especially from the database angle.


title: A Review of Truck Platooning Projects for Energy Savings

abstract: Technical studies on automated driving of passenger cars were started in the 1950s, but those on heavy trucks were started in the mid-1990s, and only a few projects have dealt with truck automation, which include “Chauffeur” within the EU project T-TAP from the mid-1990s, truck automation by California PATH from around 2000, “KONVOI” in Germany from 2005, and “Energy ITS” by Japan from 2008. The objectives of truck automation are energy saving and enhanced transportation capacity by platooning, and eventually possible reduction of personnel cost by unmanned operation of following vehicles. The sensing technologies for automated vehicle control are computer vision, radar, lidar, laser scanners, localization by GNSS, and vehicle to vehicle communications. Experiments of platooning of three or four heavy trucks have shown the effectiveness of platooning in achieving energy saving due to short gaps between vehicles.


title: Taking Antonymy Mask off in Vector Space

abstract: Automatic detection of antonymy is an important task in Natural Language Processing (NLP) for Information Retrieval (IR), Ontology Learning (OL) and many other semantic applications. However, current unsupervised approaches to antonymy detection are still not fully effective because they cannot discriminate antonyms from synonyms. In this paper, we introduce APAnt, a new AveragePrecision-based measure for the unsupervised discrimination of antonymy from synonymy using Distributional Semantic Models (DSMs). APAnt makes use of Average Precision to estimate the extent and salience of the intersection among the most descriptive contexts of two target words. Evaluation shows that the proposed method is able to distinguish antonyms and synonyms with high accuracy across different parts of speech, including nouns, adjectives and verbs. APAnt outperforms the vector cosine and a baseline model implementing the cooccurrence hypothesis.


title: science data visualization tools for the Tropospheric Emission Spectrometer ground data system

abstract: The Tropospheric Emission Spectrometer (TES) instrument seeks to analyze the chemical composition of the atmosphere based on the emission, absorption, and transmission of infrared radiation. Meeting the scientific objectives requires demanding analysis of the data being collected and processed. Visualization tools will assist in the understanding of the data and of the effects of the various types of processing being performed. The TES visualization tools are designed to verify correct functioning; of the instrument, provide early detection of potential problems, and report on the quality and validity of the science data for drawing scientific conclusions. Visualization displays include the Level III tools for displaying the end result of all the processing, merged and georeferenced for display relative to maps or global images, and displays for characterizing the behavior of the science processing algorithms and exploring the effects of implementation decisions. Displays of interest include plots of spectra and profiles, animations showing variations in the data along spatial or temporal axes, and results of various operations on the data. Together, these tools provide a visualization suite for more rapidly analyzing the science results of the TES instrument and detecting and identifying problems in the instrument or processing system.


title: Blockchain-Based Database to Ensure Data Integrity in Cloud Computing Environments

abstract: Data is nowadays an invaluable resource, indeed it guides all business decisions in most of the computer-aided human activities. Threats to data integrity are thus of paramount relevance, as tampering with data may maliciously affect crucial business decisions. This issue is especially true in cloud computing environments, where data owners cannot control fundamental data aspects, like the physical storage of data and the control of its accesses. Blockchain has recently emerged as a fascinating technology which, among others, provides compelling properties about data integrity. Using the blockchain to face data integrity threats seems to be a natural choice, but its current limitations of low throughput, high latency, and weak stability hinder the practical feasibility of any blockchain-based solutions. In this paper, by focusing on a case study from the European SUNFISH project, which concerns the design of a secure by-design cloud federation platform for the public sector, we precisely delineate the actual data integrity needs of cloud computing environments and the research questions to be tackled to adopt blockchain-based databases. First, we detail the open research questions and the difficulties inherent in addressing them. Then, we outline a preliminary design of an effective blockchain-based database for cloud computing environments.


title: Radar-based perception for autonomous outdoor vehicles

abstract: Autonomous vehicle operations in outdoor environments challenge robotic perception. Construction, mining, agriculture, and planetary exploration environments are examples in which the presence of dust, fog, rain, changing illumination due to low sun angles, and lack of contrast can dramatically degrade conventional stereo and laser sensing. Nonetheless, environment perception can still succeed under compromised visibility through the use of a millimeter-wave radar. Radar also allows for multiple object detection within a single beam, whereas other range sensors are limited to one target return per emission. However, radar has shortcomings as well, such as a large footprint, specularity effects, and limited range resolution, all of which may result in poor environment survey or difficulty in interpretation. This paper presents a novel method for ground segmentation using a millimeter-wave radar mounted on a ground vehicle. Issues relevant to short-range perception in an outdoor environment are described along with field experiments and a quantitative comparison to laser data. The ability to classify the ground is successfully demonstrated in clear and low-visibility conditions, and significant improvement in range accuracy is shown. Finally, conclusions are drawn on the utility of millimeter-wave radar as a robotic sensor for persistent and accurate perception in natural scenarios. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.


title: Can Computers Overcome Humans? Consciousness Interaction and its Implications

abstract: Can computers overcome human capabilities? This is a paradoxical and controversial question, particularly because there are many hidden assumptions. This article focuses on that issue putting on evidence some misconception related with future generations of machines and the understanding of the brain. It will be discussed to what extent computers might reach human capabilities, and how it could be possible only if the computer is a conscious machine. However, it will be shown that if the computer is conscious, an interference process due to consciousness would affect the information processing of the system. Therefore, it might be possible to make conscious machines to overcome human capabilities, which will have limitations as well as humans. In other words, trying to overcome human capabilities with computers implies the paradoxical conclusion that a computer will never overcome human capabilities at all, or if the computer does, it should not be considered as a computer anymore.


title: The Verilog® Hardware Description Language

abstract: Thomas & Moorbys The Verilog Hardware Description Language has become the standard reference text for Verilog. This edition presents the new IEEE 1364-2001 standard of the language. The examples have all been updated to illustrate the new features of the language. A cross referenced guide to the new and old features is provided. Thus, designers already familiar with Verilog can quickly learn the new features. Newcomers to the language can use it as a guide for reading old specifications. The Verilog Hardware Description Language, Fifth Edition, is a valuable resource for engineers and students interested in describing, simulating, and synthesizing digital systems; the extensive number of simulatable examples and wide range of representation styles covered ensure its quick use in design. The book is also ready for use in university courses, having been used for introductory logic design and simulation through advanced VLSI design courses. An appendix with tutorial help and a work-along style is keyed into the introduction for new students. Material supporting a computer-aided design course on the inner working of simulators is also included.


title: �Human Brain Mapping 17:143–155(2002) � Fast Robust Automated Brain Extraction

abstract: An automated method for segmenting magnetic resonance head images into brain and non-brain has been developed. It is very robust and accurate and has been tested on thousands of data sets from a wide variety of scanners and taken with a wide variety of MR sequences. The method, Brain Extraction Tool (BET), uses a deformable model that evolves to fit the brain's surface by the application of a set of locally adaptive model forces. The method is very fast and requires no preregistration or other pre-processing before being applied. We describe the new method and give examples of results and the results of extensive quantitative testing against “gold-standard” hand segmentations, and two other popular automated methods. Hum. Brain Mapping 17:143–155, 2002. © 2002 Wiley-Liss, Inc.


title: Self-Oscillating Fluxgate-Based Quasi-Digital Sensor for DC High-Current Measurement

abstract: A quasi-digital dc current sensor with rated current of ±600 A while overload current of about ±750 A is proposed in this paper. The new sensor is based on the open-loop self-oscillating fluxgate technology, but its originality is using a microcontroller unit to detect the duty cycle of the exciting voltage of the fluxgate. Compared with the published similar method, the whole signal chain of the new sensor is quasi-digital and without low-pass filter and analog-to-digital converter required when connected to digital systems. A precisely theoretical equation with respect to the linear dependence between the duty cycle and the current to be measured is established. Based on the equation, factors affecting the sensor sensitivity, accuracy, and resolution are determined, which constitutes the theoretical basis on the optimization design of the new sensor. The sensor linearity is improved using the least-squares polynomial fitting method. Some key specifications including the linearity, repeatability, and power supply effect of the sensor are characterized. The measurement results show that the linearity of the new sensor with the theoretical equation is better than 1.7% in the full scale of ±600 A, and can be improved to 0.3% when the fifth-order polynomial fitting method is used.


title: Investigation of the Dynamic Stability of Microgrid

abstract: Microgrid is an aggregation of distributed generators (DGs) and energy storage systems (ESS) through corresponding power interface, such as synchronous generators, asynchronous generators and power electronic devices. Without the support from the public grid, the control and management of an autonomous microgrid is more complex due to its poor equivalent system inertia. To investigate microgrid dynamic stability, a small-signal model of a typical microgrid containing asynchronous generator based wind turbine, synchronous diesel generator, power electronic based energy storage and power network is proposed in this paper. The small-signal model of each of the subsystem is established respectively and then the global model is set up in a global reference axil frame. Eigenvalues distributions of the microgrid system under certain steady operating status are identified to indicate the damping of the oscillatory terms and its effect on system stability margin. Eigenvalues loci analysis is also presented which helps identifying the relationship among the dynamic stability, system configuration and operation status, such as the variation of intermittent generations and ESS with different control strategies. The results obtained from the model and eigenvalues analysis are verified through simulations and experiments on a study microgrid system.


title: A Note on Boosting Algorithms for Image Denoising

abstract: In recent years, non-local methods have been among most efficient tools to address the classical problem of image denoising. Recently, Romano et al. have proposed a novel algorithm aimed at “boosting” of a number of non-local denoising algorithms as a “black-box.” In this manuscript, we consider this algorithm and derive an analytical expression corresponding to successive applications of their proposed “boosting scheme.” Mathematically, we prove that such successive application does not always enhance the input image and is equivalent to a re-parameterization of the original “boosting” algorithm. We perform a set of computational experiments on test images to support this claim. Finally, we conclude that considering the blind application of such boosting methods as a general remedy for all denoising schemes is questionable.


title: The Rotation Flap for Covering Denuded Root Surfaces—A Closed Wound Technique

abstract: A rotation flap for covering denuded single root surfaces with attached gingiva has been described and illustrated. This was performed utilizing a closed wound technique. The single flap provided attached gingiva coverage for a moderate area while a double flap provided coverage for a larger area. Although more attached gingiva is required from the donor site than for the laterally positioned or sliding flap, the technique may be performed where a moderate degree of attached gingiva is available.


title: Joint Blind Source Separation by Multiset Canonical Correlation Analysis

abstract: In this work, we introduce a simple and effective scheme to achieve joint blind source separation (BSS) of multiple datasets using multi-set canonical correlation analysis (M-CCA) [1]. We first propose a generative model of joint BSS based on the correlation of latent sources within and between datasets. We specify source separability conditions, and show that, when the conditions are satisfied, the group of corresponding sources from each dataset can be jointly extracted by M-CCA through maximization of correlation among the extracted sources. We compare source separation performance of the M-CCA scheme with other joint BSS methods and demonstrate the superior performance of the M-CCA scheme in achieving joint BSS for a large number of datasets, group of corresponding sources with heterogeneous correlation values, and complex-valued sources with circular and non-circular distributions. We apply M-CCA to analysis of functional magnetic resonance imaging (fMRI) data from multiple subjects and show its utility in estimating meaningful brain activations from a visuomotor task.


title: Detecting changes in XML documents

abstract: We present a diff algorithm for XML data. This work is motivated by the support for change control in the context of the Xyleme project that is investigating dynamic warehouses capable of storing massive volumes of XML data. Because of the context, our algorithm has to be very efficient in terms of speed and memory space even at the cost of some loss of quality. Also, it considers, besides insertions, deletions and updates (standard in diffs), a move operation on subtrees that is essential in the context of XML. Intuitively, our diff algorithm uses signatures to match (large) subtrees that were left unchanged between the old and new versions. Such exact matchings are then possibly propagated to ancestors and descendants to obtain more matchings. It also uses XML specific information such as ID attributes. We provide a performance analysis of the algorithm. We show that it runs in average in linear time vs. quadratic time for previous algorithms. We present experiments on synthetic data that confirm the analysis. Since this problem is NP-hard, the linear time is obtained by trading some quality. We present experiments (again on synthetic data) that show that the output of our algorithm is reasonably close to the optimal in terms of quality. Finally we present experiments on a small sample of XML pages found on the Web.


title: Magnetic Induction for Underwater Wireless Communication Networks

abstract: Although acoustic waves are the most versatile and widely used physical layer technology for underwater wireless communication networks (UWCNs), they are adversely affected by ambient noise, multipath propagation, and fading. The large propagation delays, low bandwidth, and high bit error rates of the underwater acoustic channel hinder communication as well. These operational limits call for complementary technologies or communication alternatives when the acoustic channel is severely degraded. Magnetic induction (MI) is a promising technique for UWCNs that is not affected by large propagation delays, multipath propagation, and fading. In this paper, the MI communication channel has been modeled. Its propagation characteristics have been compared to the electromagnetic and acoustic communication systems through theoretical analysis and numerical evaluations. The results prove the feasibility of MI communication in underwater environments. The MI waveguide technique is developed to reduce path loss. The communication range between source and destination is considerably extended to hundreds of meters in fresh water due to its superior bit error rate performance.


title: Searching OCR'ed Text: An LDA Based Approach

abstract: Indexing and retrieval performance over digitized document collection significantly depends on the performance of available Optical Character Recognition (OCR). The paper presents a novel document indexing framework which attends the document digitization errors in the indexing process to improve the overall retrieval accuracy. The proposed indexing framework is based on topic modeling using Latent Dirichlet Allocation (LDA). The OCR's confidence in correctly recognizing a symbol is propagated in topic learning process such that semantic grouping of word examples carefully distinguishes between commonly confusing words. We present a novel application of Lucene with topic modeling for document indexing application. The experimental evaluation of the proposed framework is presented on document collection belonging to Devanagari script.


title: Investigating emotion with music: an fMRI study

abstract: The present study used pleasant and unpleasant music to evoke emotion and functional magnetic resonance imaging (fMRI) to determine neural correlates of emotion processing. Unpleasant (permanently dissonant) music contrasted with pleasant (consonant) music showed activations of amyg- dala, hippocampus, parahippocampal gyrus, and temporal poles. These structures have previously been implicated in the emotional processing of stimuli with (negative) emotional valence; the present data show that a cerebral network comprising these structures can be activated during the perception of auditory (musical) information. Pleasant (contrasted to unpleasant) music showed activations of the inferior frontal gyrus (IFG, inferior Brodmann's area (BA) 44, BA 45, and BA 46), the anterior superior insula, the ventral striatum, Heschl's gyrus, and the Rolandic operculum. IFG activations appear to reflect processes of music-syntactic analysis and working memory operations. Activations of Rolandic opercular areas possibly reflect the activation of mirror-function mechanisms during the perception of the pleasant tunes. Rolandic operculum, anterior superior insula, and ventral striatum may form a motor-related circuitry that serves the formation of (premotor) representations for vocal sound production during the perception of pleasant auditory information. In all of the mentioned structures, except the hippocampus, activations increased over time during the presentation of the musical stimuli, indicating that the effects of emotion processing have temporal dynamics; the temporal dynamics of emotion have so far mainly been neglected in the functional imaging literature. Hum Brain Mapp 27:239 -250, 2006. © 2005 Wiley-Liss, Inc.


title: Sad or Happy? The Effects of Emotions on Stated Preferences for Environmental Goods

abstract: A substantial literature in behavioural science and psychology shows that emotions affect human choices and values. This paper investigates whether such emotional impacts are also present in stated choice experiments for environmental goods. If this were so, it would introduce an additional element of context dependence to the welfare measures derived from such methods, and would be at odds with the rational choice model underlying welfare economics. A laboratory experiment using three different emotion treatments was combined with a stated preference choice experiment concerned with changes in coastal water quality and fish populations in New Zealand. No statistically significant effects of changes in emotional state on estimated preference parameters, willingness to pay or the randomness of choices were found. The paper concludes by questioning, why such a contrast exists with empirical findings in behavioural science.


title: Young adults' experiences of seeking online information about diabetes and mental health in the age of social media

abstract: Background ::: The Internet is a primary source of health information for many. Since the widespread adoption of social media, user-generated health-related content has proliferated, particularly around long-term health issues such as diabetes and common mental health disorders (CMHDs). ::: ::: Objective ::: To explore perceptions and experiences of engaging with health information online in a sample of young adults familiar with social media environments and variously engaged in consuming user-generated content. ::: ::: Methods ::: Forty semi-structured interviews were conducted with young adults, aged 18–30, with experience of diabetes or CMHDs. Data were analysed following a thematic networks approach to explore key themes around online information-seeking and content consumption practices. ::: ::: Results ::: Although participants primarily discussed well-rehearsed approaches to health information-seeking online, particularly reliance on search engines, their accounts also reflected active engagement with health-related content on social media sites. Navigating between professionally produced websites and user-generated content, many of the young adults seemed to appreciate different forms of health knowledge emanating from varied sources. Participants described negotiating health content based on social media practices and features and assessing content heuristically. Some also discussed habitual consumption of content related to their condition as integrated into their everyday social media use. ::: ::: Conclusion ::: Technologies such as Facebook, Twitter and YouTube offer opportunities to consume and assess content which users deem relevant and useful. As users and organizations continue to colonize social media platforms, opportunities are increasing for health communication and intervention. However, how such innovations are adopted is dependent on their alignment with users' expectations and consumption practices.


title: Mining public transport user behaviour from smart card data

abstract: Abstract In urban public transport, smart card data is made of millions of observations of users boarding vehicles over the network across several days. The issue addresses whether data mining techniques can be used to study user behaviour from these observations. This must be done with the help of transportation planning knowledge. Hence, this paper presents a common “transportation planning/data mining” methodology for user behaviour analysis. Experiments were conducted on data from a Canadian transit authority. This experience demonstrates that a combination of planning knowledge and data mining tool allows producing travel behaviours indicators, mainly regarding regularity and daily patterns, from data issued from operational and management system. Results show that the public transport users of this study can rapidly be divided in four major behavioural groups, whatever type of ticket they use..


title: Rethinking stress: the role of mindsets in determining the stress response.

abstract: This article describes 3 studies that explore the role of mindsets in the context of stress. In Study 1, we present data supporting the reliability and validity of an 8-item instrument, the Stress Mindset Measure (SMM), designed to assess the extent to which an individual believes that the effects of stress are either enhancing or debilitating. In Study 2, we demonstrate that stress mindsets can be altered by watching short, multimedia film clips presenting factual information biased toward defining the nature of stress in 1 of 2 ways (stress-is-enhancing vs. stress-is-debilitating). In Study 3, we demonstrate the effect of stress mindset on physiological and behavioral outcomes, showing that a stress-is-enhancing mindset is associated with moderate cortisol reactivity and high desire for feedback under stress. Together, these 3 studies suggest that stress mindset is a distinct and meaningful variable in determining the stress response.


title: Vertical cell array using TCAT(Terabit Cell Array Transistor) technology for ultra high density NAND flash memory

abstract: Vertical NAND flash memory cell array by TCAT (terabit cell array transistor) technology is proposed. Damascened metal gate SONOS type cell in the vertical NAND flash string is realized by a unique dasiagate replacementpsila process. Also, conventional bulk erase operation of the cell is successfully demonstrated. All advantages of TCAT flash is achieved without any sacrifice of bit cost scalability.


title: The Multilayer Firewall

abstract: We present a new security technology called the Multilayer Firewall. We argue that it is useful in some situations for which other approaches, such as cryptographically protected communications, present operational or economic difficulties. In other circumstances a Multilayer Firewall can compliment such security technology by providing additional protection against intruder attacks. We first present the operational theory behind the Multilayer Firewall and then describe a prototype that we designed and


title: From Cybersecurity to Collaborative Resiliency

abstract: This paper presents the holistic approach to cyber resilience as a means of preparing for the "unknown unknowns". Principles of augmented cyber risks management and resilience management model at national level are presented, with elaboration on multi-stakeholder engagement and partnership for the implementation of national cyber resilience collaborative framework. The complementarity of governance, law, and business/industry initiatives is outlined, with examples of the collaborative resilience model for the Bulgarian national strategy and its multi-national engagements.


title: Scalable Distributed Stream Processing

abstract: Stream processing fits a large class of new applications for which conventional DBMSs fall short. Because many stream-oriented systems are inherently geographically distributed and because distribution offers scalable load management and higher availability, future stream processing systems will operate in a distributed fashion. They will run across the Internet on computers typically owned by multiple cooperating administrative domains. This paper describes the architectural challenges facing the design of large-scale distributed stream processing systems, and discusses novel approaches for addressing load management, high availability, and federated operation issues. We describe two stream processing systems, Aurora* and Medusa, which are being designed to explore complementary solutions to these challenges. This paper discusses the architectural issues facing the design of large-scale distributed stream processing systems. We begin in Section 2 with a brief description of our centralized stream processing system, Aurora [4]. We then discuss two complementary efforts to extend Aurora to a distributed environment: Aurora* and Medusa. Aurora* assumes an environment in which all nodes fall under a single administrative domain. Medusa provides the infrastructure to support federated operation of nodes across administrative boundaries. After describing the architectures of these two systems in Section 3, we consider three design challenges common to both: infrastructures and protocols supporting communication amongst nodes (Section 4), load sharing in response to variable network conditions (Section 5), and high availability in the presence of failures (Section 6). We also discuss high-level policy specifications employed by the two systems in Section 7. For all of these issues, we believe that the push-based nature of stream-based applications not only raises new challenges but also offers the possibility of new domain-specific solutions.


title: Learning to generate images with perceptual similarity metrics

abstract: Deep networks are increasingly being applied to problems involving image synthesis, e.g., generating images from textual descriptions and reconstructing an input image from a compact representation. Supervised training of image-synthesis networks typically uses a pixel-wise loss (PL) to indicate the mismatch between a generated image and its corresponding target image. We propose instead to use a loss function that is better calibrated to human perceptual judgments of image quality: the multiscale structural-similarity score (MS-SSIM). Because MS-SSIM is differentiable, it is easily incorporated into gradient-descent learning. We compare the consequences of using MS-SSIM versus PL loss on training deterministic and stochastic autoencoders. For three different architectures, we collected human judgments of the quality of image reconstructions. Observers reliably prefer images synthesized by MS-SSIM-optimized models over those synthesized by PL-optimized models, for two distinct PL measures ($\ell_1$ and $\ell_2$ distances). We also explore the effect of training objective on image encoding and analyze conditions under which perceptually-optimized representations yield better performance on image classification. Finally, we demonstrate the superiority of perceptually-optimized networks for super-resolution imaging. Just as computer vision has advanced through the use of convolutional architectures that mimic the structure of the mammalian visual system, we argue that significant additional advances can be made in modeling images through the use of training objectives that are well aligned to characteristics of human perception.


title: Automatic clustering of malware variants

abstract: The emergence of malware creation tools in recent years has facilitated the creation of new variations of existing malware instances. Typically, Anti-Virus companies process new malware instances manually to determine their maliciousness and generate their signatures. However, with the overwhelming number of new malware variants that are created automatically to evade pattern based detection, manual analysis is becoming a bottleneck that hinders the process of responding to new threats. This paper proposes a novel method to automatically cluster malware variants into malware families based on the structured control flow graphs of the malware instances. Our final results demonstrate high effectiveness in terms of accuracy, an average of %94 accuracy, and speed in clustering malware variants.


title: Model predictive quadrotor control : attitude, altitude, and position experimental studies

abstract: This article addresses the control problem of an unmanned quadrotor in an indoor environment where there is lack of absolute localization data. Based on an attached Inertia Measurement Unit, a sona ...


title: State-of-Charge Determination From EMF Voltage Estimation: Using Impedance, Terminal Voltage, and Current for Lead-Acid and Lithium-Ion Batteries

abstract: State-of-charge (SOC) determination is an increasingly important issue in battery technology. In addition to the immediate display of the remaining battery capacity to the user, precise knowledge of SOC exerts additional control over the charging/discharging process, which can be employed to increase battery life. This reduces the risk of overvoltage and gassing, which degrade the chemical composition of the electrolyte and plates. The proposed model in this paper determines the SOC by incorporating the changes occurring due to terminal voltage, current load, and internal resistance, which mitigate the disadvantages of using impedance only. Electromotive force (EMF) voltage is predicted while the battery is under load conditions; from the estimated EMF voltage, the SOC is then determined. The method divides the battery voltage curve into two regions: 1) the linear region for full to partial SOC and 2) the hyperbolic region from partial to low SOC. Algorithms are developed to correspond to the different characteristic changes occurring within each region. In the hyperbolic region, the rate of change in impedance and terminal voltage is greater than that in the linear region. The magnitude of current discharge causes varying rates of change to the terminal voltage and impedance. Experimental tests and results are presented to validate the new models.


title: Parallel culling and sorting based on adaptive static balancing

abstract: This article presents a new and effective method for parallel octree culling and sorting for multicore systems, using counting sort and based on a new balancing algorithm, called adaptive delayed static balancing. The adaptive nature of the method is governed by a dynamic split level that can adjust the algorithm to new camera positions, keeping a well-balanced workload among the processors. This article also introduces the concept of n-dimensional resource space as a discrete Euclidean space. This work presents a simple and effective thread management system, called MinTMS.


A Model for Anomalies Detection in Internet of Things (IoT) Using Inverse Weight Clustering and Decision Tree


title: Retrieving Similar Styles to Parse Clothing

abstract: Clothing recognition is a societally and commercially important yet extremely challenging problem due to large variations in clothing appearance, layering, style, and body shape and pose. In this paper, we tackle the clothing parsing problem using a retrieval-based approach. For a query image, we find similar styles from a large database of tagged fashion images and use these examples to recognize clothing items in the query. Our approach combines parsing from: pre-trained global clothing models, local clothing models learned on the fly from retrieved examples, and transferred parse-masks (Paper Doll item transfer) from retrieved examples. We evaluate our approach extensively and show significant improvements over previous state-of-the-art for both localization (clothing parsing given weak supervision in the form of tags) and detection (general clothing parsing). Our experimental results also indicate that the general pose estimation problem can benefit from clothing parsing.


title: Coding gestural behavior with the NEUROGES-ELAN system

abstract: Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands We present a coding system combined with an annotation tool for the analysis of gestural behavior. The NEUROGES coding system consists of three modules that progress from gesture kinetics to gesture function. Grounded on empirical neuropsychological and psychological studies, the theoretical assumption behind NEUROGES is that its main kinetic and functional movement categories are differentially associated with specific cognitive, emotional, and interactive functions. ELAN is a free, multimodal annotation tool for digital audio and video media. It supports multileveled transcription and complies with such standards as XML and Unicode. ELAN allows gesture categories to be stored with associated vocabularies that are reusable by means of template files. The combination of the NEUROGES coding system and the annotation tool ELAN creates an effective tool for empirical research on gestural behavior.


title: User Acceptance of Hedonic Information Technologies: A Perceived Value Perspective

abstract: Hedonic information technologies do not focus on productivity-oriented applications, but rather, on lifestyle-augmentation and entertainment. As such users of hedonic IT are provided with benefits related to general enjoyment as opposed to gains in organizational efficiency. The main purpose of this study is to investigate this burgeoning phenomenon by measuring several value drivers for downloading mobile phone ringtones. Hypothesis testing was performed with PLS techniques applied to data collected from 119 ringtone users. Results confirmed that the overall value of hedonic IT is a third-order construct, which successfully predicted behavioral usage intentions and positive word of mouth.


title: Pruning an ensemble of classifiers via reinforcement learning

abstract: This paper studies the problem of pruning an ensemble of classifiers from a reinforcement learning perspective. It contributes a new pruning approach that uses the Q-learning algorithm in order to approximate an optimal policy of choosing whether to include or exclude each classifier from the ensemble. Extensive experimental comparisons of the proposed approach against state-of-the-art pruning and combination methods show very promising results. Additionally, we present an extension that allows the improvement of the solutions returned by the proposed approach over time, which is very useful in certain performance-critical domains.


title: An Alpha-wave-based binaural beat sound control system using fuzzy logic and autoregressive forecasting model

abstract: We are developing a new real-time control system for customizing auditory stimulus (the binaural beat sound) by judging user alpha waves to entrain a userpsilas feeling in the most relaxed way. Since brainwave activity provides the necessary predictive information for arousal states, we use an autoregressive forecasting model to estimate the frequency response series of the alpha frequency bands and the inverted-U concept to determine the userpsilas arousal state. A fuzzy logic controller is also employed to regulate the binaural beat control signal on a forecasting error signal. Our system allows comfortable user self-relaxation. The results of experiments confirm the constructed systempsilas effectiveness and necessity.


title: Analysis and Characterization of a Multipole Reconfigurable Transmitarray Element

abstract: A reconfigurable transmitarray element is presented that operates at 5.5 GHz and is low-profile, low-cost and easy to fabricate. The design consists of a three-layer structure implementing two slot-coupled patch antennas. Varactor diodes are used to load the patches and slot to create a third-order resonant structure. A simple circuit-based analysis approach is used to predict the response of the unit cell based on the location of its poles and zeros. Simulation and experimental results are presented, with experimental results achieving 245° of phase agility with less than 3 dB of variation in transmission magnitude throughout the tuning range. The effects of diode loss, angle of incidence, and mutual coupling are investigated. The element is also capable of functioning as a low-loss reconfigurable reflectarray element, making it particularly versatile for different scanning configurations.


title: Brain volumes differ between diagnostic groups of violent criminal offenders

abstract: Studies on structural abnormalities in antisocial individuals have reported inconsistent results, possibly due to inhomogeneous samples, calling for an investigation of brain alterations in psychopathologically stratified subgroups. We explored structural differences between antisocial offenders with either borderline personality disorder (ASPD-BPD) or high psychopathic traits (ASPD-PP) and healthy controls (CON) using region-of-interest-based and voxel-based morphometry approaches. Besides common distinct clusters of reduced gray matter volumes within the frontal pole and occipital cortex, there was remarkably little overlap in the regional distribution of brain abnormalities in ASPD-BPD and ASPD-PP, when compared to CON. Specific alterations of ASPD-BPD were detected in orbitofrontal and ventromedial prefrontal cortex regions subserving emotion regulation and reactive aggression and the temporal pole, which is involved in the interpretation of other peoples’ motives. Volumetric reductions in ASPD-PP were most significant in midline cortical areas involved in the processing of self-referential information and self-reflection (i.e., dorsomedial prefrontal cortex, posterior cingulate/precuneus) and recognizing emotions of others (postcentral gyrus) and could reflect neural correlates of the psychopathic core features of callousness and poor moral judgment. The findings of this first exploratory study therefore may reflect correlates of prominent psychopathological differences between the two criminal offender groups, which have to be replicated in larger samples.


A Revisit to Support Vector Data Description


title: ECG Biometric Recognition: A Comparative Analysis

abstract: The electrocardiogram (ECG) is an emerging biometric modality that has seen about 13 years of development in peer-reviewed literature, and as such deserves a systematic review and discussion of the associated methods and findings. In this paper, we review most of the techniques that have been applied to the use of the electrocardiogram for biometric recognition. In particular, we categorize the methodologies based on the features and the classification schemes. Finally, a comparative analysis of the authentication performance of a few of the ECG biometric systems is presented, using our inhouse database. The comparative study includes the cases where training and testing data come from the same and different sessions (days). The authentication results show that most of the algorithms that have been proposed for ECG-based biometrics perform well when the training and testing data come from the same session. However, when training and testing data come from different sessions, a performance degradation occurs. Multiple training sessions were incorporated to diminish the loss in performance. That notwithstanding, only a few of the proposed ECG recognition algorithms appear to be able to support performance improvement due to multiple training sessions. Only three of these algorithms produced equal error rates (EERs) in the single digits, including an EER of 5.5% using a method proposed by us.


title: Wavelet networks

abstract: A wavelet network concept, which is based on wavelet transform theory, is proposed as an alternative to feedforward neural networks for approximating arbitrary nonlinear functions. The basic idea is to replace the neurons by 'wavelons', i.e., computing units obtained by cascading an affine transform and a multidimensional wavelet. Then these affine transforms and the synaptic weights must be identified from possibly noise corrupted input/output data. An algorithm of backpropagation type is proposed for wavelet network training, and experimental results are reported. >


title: Hidden from History: Reclaiming the Gay and Lesbian Past.

abstract: A collection of 30 essays which explore homosexuality in various cultures, and different eras, from late imperial China and Renaissance Italy to "Jazz-Age" America and from London to Harlem and Japan. Other chapters look at male prostitutes, cross-dressing and schoolgirl crushes in public schools.