title: ANN for FOREX Forecasting and Trading

abstract: Modern approach to the FOREX currency exchange market requires support from the computer algorithms to manage huge volumes of the transactions and to find opportunities in a vast number of currency pairs traded daily. There are many well known techniques used by market participants on both FOREX and stock-exchange markets (i.e. Fundamental and technical analysis) but nowadays AI based techniques seem to play key role in the automated transaction and decision supporting systems. This paper presents the comprehensive analysis over Feed Forward Multilayer Perceptron (ANN) parameters and their impact to accurately forecast FOREX trend of the selected currency pair. The goal of this paper is to provide information on how to construct an ANN with particular respect to its parameters and training method to obtain the best possible forecasting capabilities. The ANN parameters investigated in this paper include: number of hidden layers, number of neurons in hidden layers, use of constant/bias neurons, activation functions, but also reviews the impact of the training methods in the process of the creating reliable and valuable ANN, useful to predict the market trends. The experimental part has been performed on the historical data of the EUR/USD pair.


title: Energy Efficient Resource Management in Virtualized Cloud Data Centers

abstract: Rapid growth of the demand for computational power by scientific, business and web-applications has led to the creation of large-scale data centers consuming enormous amounts of electrical power. We propose an energy efficient resource management system for virtualized Cloud data centers that reduces operational costs and provides required Quality of Service (QoS). Energy savings are achieved by continuous consolidation of VMs according to current utilization of resources, virtual network topologies established between VMs and thermal state of computing nodes. We present first results of simulation-driven evaluation of heuristics for dynamic reallocation of VMs using live migration according to current requirements for CPU performance. The results show that the proposed technique brings substantial energy savings, while ensuring reliable QoS. This justifies further investigation and development of the proposed resource management system.


title: Evaluation of Linux Security Frameworks

abstract: The number of threats to computers attached to networks continually increases. The focus of preventing security exploits has been on the network, while local exploits has been mostly overlooked ...


title: Secure QR code system

abstract: Quick Response (QR) codes are two dimensional barcodes that can be used to efficiently store small amount of data. They are increasingly used in all life fields, especially with the wide spread of smart phones which are used as QR code scanners. While QR codes have many advantages that make them very popular, there are several security issues and risks that are associated with them. Running malicious code, stealing users' sensitive information and violating their privacy and identity theft are some typical security risks that a user might be subject to in the background while he/she is just reading the QR code in the foreground. In this paper, a security system for QR codes that guarantees both users and generators security concerns is implemented. The system is backward compatible with current standard used for encoding QR codes. The system is implemented and tested using an Android-based smartphone application. It was found that the system introduces a little overhead in terms of the delay required for integrity verification and content validation.


title: Preference Learning and Ranking by Pairwise Comparison

abstract: This chapter provides an overview of recent work on preference learning and ranking via pairwise classification. The learning by pairwise comparison (LPC) paradigm is the natural machine learning counterpart to the relational approach to preference modeling and decision making. From a machine learning point of view, LPC is especially appealing as it decomposes a possibly complex prediction problem into a certain number of learning problems of the simplest type, namely binary classification. We explain how to approach different preference learning problems, such as label and instance ranking, within the framework of LPC. We primarily focus on methodological aspects, but also address theoretical questions as well as algorithmic and complexity issues.


title: Personalized search result diversification via structured learning

abstract: This paper is concerned with the problem of personalized diversification of search results, with the goal of enhancing the performance of both plain diversification and plain personalization algorithms. In previous work, the problem has mainly been tackled by means of unsupervised learning. To further enhance the performance, we propose a supervised learning strategy. Specifically, we set up a structured learning framework for conducting supervised personalized diversification, in which we add features extracted directly from the tokens of documents and those utilized by unsupervised personalized diversification algorithms, and, importantly, those generated from our proposed user-interest latent Dirichlet topic model. Based on our proposed topic model whether a document can cater to a user's interest can be estimated in our learning strategy. We also define two constraints in our structured learning framework to ensure that search results are both diversified and consistent with a user's interest. We conduct experiments on an open personalized diversification dataset and find that our supervised learning strategy outperforms unsupervised personalized diversification methods as well as other plain personalization and plain diversification methods.


title: Using Deep Learning to Identify Utility Poles with Crossarms and Estimate Their Locations from Google Street View Images

abstract: Traditional methods of detecting and mapping utility poles are inefficient and costly because of the demand for visual interpretation with quality data sources or intense field inspection. The advent of deep learning for object detection provides an opportunity for detecting utility poles from side-view optical images. In this study, we proposed using a deep learning-based method for automatically mapping roadside utility poles with crossarms (UPCs) from Google Street View (GSV) images. The method combines the state-of-the-art DL object detection algorithm (i.e., the RetinaNet object detection algorithm) and a modified brute-force-based line-of-bearing (LOB, a LOB stands for the ray towards the location of the target [UPC at here] from the original location of the sensor [GSV mobile platform]) measurement method to estimate the locations of detected roadside UPCs from GSV. Experimental results indicate that: (1) both the average precision (AP) and the overall accuracy (OA) are around 0.78 when the intersection-over-union (IoU) threshold is greater than 0.3, based on the testing of 500 GSV images with a total number of 937 objects; and (2) around 2.6%, 47%, and 79% of estimated locations of utility poles are within 1 m, 5 m, and 10 m buffer zones, respectively, around the referenced locations of utility poles. In general, this study indicates that even in a complex background, most utility poles can be detected with the use of DL, and the LOB measurement method can estimate the locations of most UPCs.


title: Baby‐led complementary feeding: Randomized controlled study

abstract: BACKGROUND ::: Baby-led weaning (BLW) is an approach to introducing solid foods to infants that gives control of the feeding process to the infant. Anecdotal evidence suggests that BLW is becoming popular with parents, but scientific research is limited to a few publications. This study assessed growth, hematological parameters and iron intake in 6-12-month-old infants fed by traditional or baby-led complementary feeding. ::: ::: ::: METHODS ::: We recruited 280 healthy 5-6-month-old infants allocated to a control (traditional spoon feeding; TSF) group or an intervention (BLW) group in a randomized controlled trial. Infant growth, hematologic parameters and iron intake were evaluated at age 12 months. ::: ::: ::: RESULTS ::: Infants in the TSF were significantly heavier than those in the BLW group. Mean weight in the BLW group was 10.4 ± 0.9 kg compared with 11.1 ± 0.5 kg in the TSF group. There was no statistically significant difference in the iron intake from complementary foods between the BLW (7.97 ± 1.37 mg/day) and TSF (7.90 ± 1.68 mg/day) participants who completed the diet records. Hematologic parameters were similar at 12 months. The incidence of choking reported in the weekly interviews was not different between the groups. ::: ::: ::: CONCLUSIONS ::: To the best of our knowledge, this is the first randomized -controlled study to have examined the impact of weaning method on iron intake, hematological parameters and growth in breast-fed infants. BLW can be an alternative complementary feeding type without increasing the risk of iron deficiency, choking or growth impairment.


title: Face-to-BMI: Using Computer Vision to Infer Body Mass Index on Social Media

abstract: A person's weight status can have profound implications on their life, ranging from mental health, to longevity, to financial income. At the societal level, "fat shaming" and other forms of "sizeism" are a growing concern, while increasing obesity rates are linked to ever raising healthcare costs. For these reasons, researchers from a variety of backgrounds are interested in studying obesity from all angles. To obtain data, traditionally, a person would have to accurately self-report their body-mass index (BMI) or would have to see a doctor to have it measured. In this paper, we show how computer vision can be used to infer a person's BMI from social media images. We hope that our tool, which we release, helps to advance the study of social aspects related to body weight.


title: A Criticism to Society (As Seen by Twitter Analytics)

abstract: Analytic tools are beginning to be largely employed, given their ability to rank, e.g., the visibility of social media users. Visibility that, in turns, can have a monetary value, since social media popular people usually either anticipate or establish trends that could impact the real world (at least, from a consumer point of view). The above rationale has fostered the flourishing of private companies providing statistical results for social media analysis. These results have been accepted, and largely diffused, by media without any apparent scrutiny, while Academia has moderately focused its attention on this phenomenon. In this paper, we provide evidence that analytic results provided by field-flagship companies are questionable (at least). In particular, we focus on Twitter and its "fake followers". We survey popular Twitter analytics that count the fake followers of some target account. We perform a series of experiments aimed at verifying the trustworthiness of their results. We compare the results of such tools with a machine-learning classifier whose methodology bases on scientific basis and on a sound sampling scheme. The findings of this work call for a serious re-thinking of the methodology currently used by companies providing analytic results, whose present deliveries seem to lack on any reliability.


title: A sentiment analysis model for hotel reviews based on supervised learning

abstract: As the widespread use of computers and the high-speed development of the Internet, E-Commerce has already penetrated as a part of our daily life. For a popular product, there are a large number of reviews. This makes it difficult for a potential customer to make an informed decision on purchasing the product, as well as for the manufacturer of the product to keep track and to manage customer opinions. In this paper, we pay attention to online hotel reviews, and propose a supervised machine learning approach using unigram feature with two types of information (frequency and TF-IDF) to realize polarity classification of documents. As shown in our experimental results, the information of TF-IDF is more effective than frequency.


title: EMC and switching loss improvement for fast switching power stages by di/dt, dv/dt optimization with 10ns variable current source gate driver

abstract: There is a growing need for motor drives with improved EMC in various automotive and industrial applications. An often referenced approach to reduce EME is to change the shape of the switching signal to reduce the EMI caused by the voltage and current transitions. This requires very precise gate control of the power MOSFET to achieve better switching behaviour and lower EME without a major increase in switching losses. In order to find an optimal trade-off, this work utilizes a monolithic current mode gate driver with a variable output current that can be changed within 10ns. With this driver, measurements with different gate current profiles were taken. The di/dt transition was confirmed to be as important as the dv/dt transition in the power MOSFET. As a result of the improved switching behavior the emissions were reduced by up to 20dB between 7MHz and 60MHz with a switching loss that is 52% lower than with a constantly low gate current.


Genetic Algorithms and Machine Learning


title: Fast Pedestrian Detection for Mobile Devices

abstract: In this paper we present a fast and robust solution for pedestrian detection that can run in real time conditions even on mobile devices with limited computational power. An optimization of the channel features based multiscale detection schemes is proposed by using 8 detection models for each half octave scales. The image features have to be computed only once each half octave and there is no need for feature approximation. We use multiscale square features for training the multiresolution pedestrian classifiers. The proposed solution achieves state of art detection results on Caltech pedestrian benchmark at over 100 FPS using a CPU implementation, being the fastest detection approach on the benchmark. The solution is fast enough to perform under real time conditions on mobile platforms, yet preserving its robustness. The full detection process can run at over 20 FPS on a quad-core ARM CPU based smartphone or tablet, being a suitable solution for limited computational power mobile devices or embedded platforms.


title: 3D printing in neonatal care

abstract: In recent years additive manufacturing, or three-dimensional (3D) printing, it is becoming increasingly widespread and used also in the medical and biomedical field [1]. ::: ::: 3D printing is a technology that allows to print, in plastic or other material, solid objects of any shape from its digital model. The printing process takes place by overlapping layers of material corresponding to cross sections of the final product. The 3D models can be created de novo, with a 3D modeling software, or it is possible to replicate an existing object with the use of a 3D scanner. In the past years, the development of appropriate software packages allowed to generate 3D printable anatomical models from computerized tomography, magnetic resonance imaging and ultrasound scans [2,3]. ::: ::: Up to now there have been 3D printed objects of nearly any size (from nanostructures to buildings) and material. Plastics, metals, ceramics, graphene and even derivatives of human tissues. The so-called “bio-printers”, in fact, allow to print one above the other thin layers of cells immersed in a gelatinous matrix. Recent advances of 3D bioprinting enabled researchers to print biocompatible scaffolds and human tissues such as skin, bone, cartilage, vessels and are driving to the design and 3D printing of artificial organs like liver and kidney [4]. ::: ::: Dentistry, prosthetics, craniofacial reconstructive surgery, neurosurgery and orthopedic surgery are among the disciplines that have already shown versatility and possible applications of 3D printing in adults and children [2,5]. Only a few experiences have instead been reported in newborn and infants. 3D printed individualized bioresorbable airway splints have been used for the treatment of three infants with severe tracheobronchomalacia, ensuring resolution of pulmonary and extrapulmonary symptoms [6,7]. A 3D model of a complex congenital heart defects have been used for preoperative planning of intraoperative procedures, allowing surgeons to repair a complex defect in a single intervention [8]. ::: ::: As already shown for children with obstructive sleep apnea and craniofacial anomalies [9]. personalized 3D printed masks could improve CPAP effectiveness and comfort also in term and preterm neonates. ::: ::: Neonatal emergency transport services and rural hospitals could also benefit from this technology, making possible to print medical devices spare parts, surgical and medical instruments wherever not readily available. ::: ::: It is envisaged that 3D printing, in the next future, will give its contribute toward the individualization of neonatal care, although further multidisciplinary studies are still needed to evaluate safety, possible applications and realize its full potential.


title: When Richer is Poorer: Understanding the Influence of Channel Richness and Presence on the Introduction of a Mission Statement

abstract: Developing and implementing mission statements continues to be a widely used managerial strategy. This study tests a model incorporating the immersion and transportation dimensions of presence and media richness for evaluating the effectiveness of two commonly used strategies (paper versus video) for introducing a mission statement to members of an organization. Outcomes include participants‘ recall of the statement, involvement with the statement, and perceived importance of the statement. Results suggest: (a) channel richness does not directly impact the three outcomes, (b) the richer video channel resulted in less presence, and (c) greater presence positively impacted all three outcomes. The implications of these results are discussed for both future presence research and organizational practitioners.


title: Design and analysis of BLDC motor for aerospace application using FEM

abstract: In this paper, a 3 phase 16 pole, 1500 rpm surface mounted inner rotor radial BLDC motor for aerospace industry is designed and analysed using the finite element method (FEM). BLDC motors are still classified as special motors. Analysis of these motors are a tedious task due to lack of analysing tools and the opportunities available for the developed motor. Analytical calculations to obtain the motor parameters are verified using Opera 18 R2 simulations and ensures a high value of accuracy and efficiency.


title: Structural Attention Neural Networks for improved sentiment analysis

abstract: We introduce a tree-structured attention neural network for sentences and small phrases and apply it to the problem of sentiment classification. Our model expands the current recursive models by incorporating structural information around a node of a syntactic tree using both bottom-up and top-down information propagation. Also, the model utilizes structural attention to identify the most salient representations during the construction of the syntactic tree. To our knowledge, the proposed models achieve state of the art performance on the Stanford Sentiment Treebank dataset.


title: A fuzzy multi-criteria decision making model for menu engineering

abstract: Menu remains a key element in influencing the success of restaurants which is very dynamic and highly competitive with a high failure rate within the first three years of operation. Menu engineering refers to the specific techniques used to evaluate performance of individual menu items leading to strategic decision. Many influencing elements are consolidated to two elements of popularity index and contribution margin providing four different combinations that can be used to choose decision options. Decision makers carry out menu engineering against manually set target values which are imprecise and choice of strategic options becomes erroneous and tedious. As a step toward providing a more powerful decision making tool this study presents a fuzzy multi-criteria decision making model to choose strategy decision options that extends the set of combinations from four to nine. The model uses trapezoidal fuzzy numbers for normalization and linguistic variables for fuzzification. The applicability of the model is tested using thirty menu items in four categories.


Collaborative Consumption On Mobile Applications: A Study Of Multi-sided Digital Platform GoCatch


title: An HOG-LBP human detector with partial occlusion handling

abstract: By combining Histograms of Oriented Gradients (HOG) and Local Binary Pattern (LBP) as the feature set, we propose a novel human detection approach capable of handling partial occlusion. Two kinds of detectors, i.e., global detector for whole scanning windows and part detectors for local regions, are learned from the training data using linear SVM. For each ambiguous scanning window, we construct an occlusion likelihood map by using the response of each block of the HOG feature to the global detector. The occlusion likelihood map is then segmented by Mean-shift approach. The segmented portion of the window with a majority of negative response is inferred as an occluded region. If partial occlusion is indicated with high likelihood in a certain scanning window, part detectors are applied on the unoccluded regions to achieve the final classification on the current scanning window. With the help of the augmented HOG-LBP feature and the global-part occlusion handling method, we achieve a detection rate of 91.3% with FPPW= 10−6, 94.7% with FPPW= 10−5, and 97.9% with FPPW= 10−4 on the INRIA dataset, which, to our best knowledge, is the best human detection performance on the INRIA dataset. The global-part occlusion handling method is further validated using synthesized occlusion data constructed from the INRIA and Pascal dataset.


title: Progressive lossless compression of arbitrary simplicial complexes

abstract: Efficient algorithms for compressing geometric data have been widely developed in the recent years, but they are mainly designed for closed polyhedral surfaces which are manifold or "nearly manifold". We propose here a progressive geometry compression scheme which can handle manifold models as well as "triangle soups" and 3D tetrahedral meshes. The method is lossless when the decompression is complete which is extremely important in some domains such as medical or finite element.While most existing methods enumerate the vertices of the mesh in an order depending on the connectivity, we use a kd-tree technique [Devillers and Gandoin 2000] which does not depend on the connectivity. Then we compute a compatible sequence of meshes which can be encoded using edge expansion [Hoppe et al. 1993] and vertex split [Popovic and Hoppe 1997].The main contributions of this paper are: the idea of using the kd-tree encoding of the geometry to drive the construction of a sequence of meshes, an improved coding of the edge expansion and vertex split since the vertices to split are implicitly defined, a prediction scheme which reduces the code for simplices incident to the split vertex, and a new generalization of the edge expansion operation to tetrahedral meshes.


title: Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition

abstract: Temporal information has useful features for recognizing facial expressions. However, to manually design useful features requires a lot of effort. In this paper, to reduce this effort, a deep learning technique, which is regarded as a tool to automatically extract useful features from raw data, is adopted. Our deep network is based on two different models. The first deep network extracts temporal appearance features from image sequences, while the other deep network extracts temporal geometry features from temporal facial landmark points. These two models are combined using a new integration method in order to boost the performance of the facial expression recognition. Through several experiments, we show that the two models cooperate with each other. As a result, we achieve superior performance to other state-of-the-art methods in the CK+ and Oulu-CASIA databases. Furthermore, we show that our new integration method gives more accurate results than traditional methods, such as a weighted summation and a feature concatenation method.


title: Wide Cell Pitch LPT(II)-CSTBT™(III) technology rating up to 6500 V for low loss

abstract: In this paper, an advanced High Voltage (HV) IGBT technology, which is focused on low loss and is the ultimate device concept for HV IGBT, is presented. CSTBTTM technology utilizing “ULSI technology” and “Light Punch-Through (LPT) II technology” (i.e. narrow Wide Cell Pitch LPT(II)-CSTBT(III)) for the first time demonstrates breaking through the limitation of HV IGBT's characteristics with voltage ratings ranging from 2500 V up to 6500 V. The improved significant trade-off characteristic between on-state voltage (V CE (sat)) and turn-off loss (E OFF ) is achieved by means of a “narrow Wide Cell Pitch CSTBT(III) cell”. In addition, this device achieves a wide operating junction temperature (@218 ∼ 448K) and excellent short circuit behavior with the new cell and vertical designs. The LPT(II) concept is utilized for ensuring controllable IGBT characteristics and achieving a thin N− drift layer. Our results cover design of the Wide Cell Pitch LPT(II)-CSTBT(III) technology and demonstrate high total performance with a great improvement potential.


title: A Highly Efficient Reconfigurable Charge Pump Energy Harvester With Wide Harvesting Range and Two-Dimensional MPPT for Internet of Things

abstract: A monolithic microwatt-level charge pump energy harvester is proposed for smart nodes of Internet of Things (IOT) networks. Due to the variation of the available voltage and power in IOT scenarios, the charge pump was optimized by the proposed architecture and circuit level innovations. First, a reconfigurable charge pump is introduced to provide the hybrid conversion ratios (CRs) as $1\frac {1}{3} \times$ up to $8 \times$ for minimizing the charge redistribution loss. Second, the reconfigurable feature also dynamically tunes to maximum power point tracking (MPPT) with the frequency modulation, resulting in a two-dimensional (2-D) MPPT. Therefore, the voltage conversion efficiency (VCE) and the power conversion efficiency (PCE) are enhanced and flattened. Third, the constant-on-time (COT) scheme from the regulation part was reused with the proposed MPPT arbiter as a sensing approach, which eliminates the conventional power hungry current sensor. The proposed energy harvester is capable of harvesting various energy sources, such as a photovoltaic (PV) cell and a thermoelectric generator (TEG), with a wide input range from 0.45 to 3 V and a regulated 3.3 V output voltage for the IOT smart nodes. Measured results showed that the harvester achieved a flattened and improved PCE as high as 89% for ultra-low power operation capability below $50\,\upmu{\text {W}}$ .


Fetal heart rate and motor activity associations with maternal organochlorine levels: results of an exploratory study


title: Low Power High-Efficiency Shift Register Using Implicit Pulse-Triggered Flip-Flop in 130 nm CMOS Process for a Cryptographic RFID Tag

abstract: The shift register is a type of sequential logic circuit which is mostly used for storing digital data or the transferring of data in the form of binary numbers in radio frequency identification (RFID) applications to improve the security of the system. A power-efficient shift register utilizing a new flip-flop with an implicit pulse-triggered structure is presented in this article. The proposed flip-flop has features of high performance and low power. It is composed of a sampling circuit implemented by five transistors, a C-element for rise and fall paths, and a keeper stage. The speed is enhanced by executing four clocked transistors together with a transition condition technique. The simulation result confirms that the proposed topology consumes the lowest amounts of power of 30.1997 and 22.7071 nW for parallel in –parallel out (PIPO) and serial in –serial out (SISO) shift register respectively covering 22 µm2 chip area. The overall design consist of only 16 transistors and is simulated in 130 nm complementary-metal-oxide-semiconductor (CMOS) technology with a 1.2 V power supply.


title: Color, change, and control of quantitative data display

abstract: Color is used widely and reliably to display the value of a single scalar variable. It is more rarely, and far less reliably, used to display multivariate data. Calico, a dynamic tool for the creation and manipulation of color mappings, adds the element of dynamic control over the color mapping to that of color itself for the more effective display and exploration of multivariate spatial data. Using Calico, a one-or two variable color mapping can be created using parametric equations and a variety of color models. This mapping can be manipulated by moving input devices referenced in the parametric expressions, by applying affine transforms, or by performing free-form deformations. As the user changes the mapping, an image showing the data displayed using the current mapping is updated in real time, along with the images describing the mapping. This paper presents an empirical study investigating the effects of user control and smooth change in the display of quantitative data on user accuracy, confidence, and preference. While the user control did not quite produce a significant effect on accuracy, it did produce significant increases in user preference and confidence.


title: KOKON - automotive high frequency technology at 77/79 GHz

abstract: In future all automotive long and short range radar systems in Europe have to operate in the frequency range 76-81 GHz due to the European radar frequency regulation. For the development of future low-cost, high-performance and highly reliable radar systems the joint project KOKON was initiated. This contribution introduces the objectives of KOKON and presents current results achieved including sensor specification with respect to automotive applications, chip technology development, and realization of first sensor prototypes.


title: Dedicated Short-Range Communications (DSRC) Standards in the United States

abstract: Wireless vehicular communication has the potential to enable a host of new applications, the most important of which are a class of safety applications that can prevent collisions and save thousands of lives. The automotive industry is working to develop the dedicated short-range communication (DSRC) technology, for use in vehicle-to-vehicle and vehicle-to-roadside communication. The effectiveness of this technology is highly dependent on cooperative standards for interoperability. This paper explains the content and status of the DSRC standards being developed for deployment in the United States. Included in the discussion are the IEEE 802.11p amendment for wireless access in vehicular environments (WAVE), the IEEE 1609.2, 1609.3, and 1609.4 standards for Security, Network Services and Multi-Channel Operation, the SAE J2735 Message Set Dictionary, and the emerging SAE J2945.1 Communication Minimum Performance Requirements standard. The paper shows how these standards fit together to provide a comprehensive solution for DSRC. Most of the key standards are either recently published or expected to be completed in the coming year. A reader will gain a thorough understanding of DSRC technology for vehicular communication, including insights into why specific technical solutions are being adopted, and key challenges remaining for successful DSRC deployment. The U.S. Department of Transportation is planning to decide in 2013 whether to require DSRC equipment in new vehicles.


title: SMART ANTENNA ARRAY PATTERNS SYNTHESIS: NULL STEERING AND MULTI-USER BEAMFORMING BY PHASE CONTROL

abstract: In this article, an efficient method for the pattern synthesis of the linear antenna arrays with the prescribed null and multi-lobe Beamforming is presented. Multi-lobe pattern and adaptive nulling of the pattern is achieved by controlling only the phase of each array element. The proposed method is based on the Sequential Quadratic Programming (SQP) algorithm and the linear antenna array synthesis was modelled as a multi-objective optimization problem. Multi-objective optimization is concerned with the maximization (or minimization) of a vector of objectives functions in the directions of desired signal that can be subject of a number of constraints (in our case, the constraints can be imposed as the null in the direction of interfering signal). To verify the validity of the technique, several illustrative examples of uniform excited array patterns with the main beam is placed in the direction of the useful signal and null is placed in the direction of potential interferers, and multi-beam patterns are demonstrated.


title: A Multilingual Datasets Repository of the Hadith Content

abstract: Knowledge extraction from unstructured data is a challenging research problem in research domain of Natural Language Processing (NLP). It requires complex NLP tasks like entity extraction and Information Extraction (IE), but one of the most challenging tasks is to extract all the required entities of data in the form of structured format so that data analysis can be applied. Our focus is to explain how the data is extracted in the form of datasets or conventional database so that further text and data analysis can be carried out. This paper presents a framework for Hadith data extraction from the Hadith authentic sources. Hadith is the collection of sayings of Holy Prophet Muhammad, who is the last holy prophet according to Islamic teachings. This paper discusses the preparation of the dataset repository and highlights issues in the relevant research domain. The research problem and their solutions of data extraction, pre-processing and data analysis are elaborated. The results have been evaluated using the standard performance evaluation measures. The dataset is available in multiple languages, multiple formats and is available free of cost for research purposes.


title: A new perspective on computational thinking

abstract: Addressing its cognitive essence, universal value, and curricular practices.


title: Addendum to “Relationships between Authentic Leadership, Moral Courage, and Ethical and Pro-Social Behaviors”

abstract: The authors provide this addendum to the following article to provide corrections to the results reported and further explanation of the structural equation modeling techniques utilized: Sean T. Hannah, Bruce J. Avolio, and Fred O. Walumbwa, "The Relationships between Authentic Leadership, Moral Courage, and Ethical and Pro-Social Behaviors,"Business Ethics Quarterly 21:4 The authors provide this addendum to the following article to provide corrections to the results reported and further explanation of the structural equation modeling techniques utilized: Sean T. Hannah, Bruce J. Avolio, and Fred O. Walumbwa, "The Relationships between Authentic Leadership, Moral Courage, and Ethical and Pro-Social Behaviors,"


title: Region-enhanced imaging for sparse-aperture passive radar

abstract: We present the application of a recently-developed region-enhanced synthetic aperture radar (SAR) image reconstruction technique to the problem of passive radar imaging. One goal in passive radar imaging is to form images of aircraft using signals transmitted by commercial radio and television stations, which then get reflected from the objects of interest. This involves reconstructing an image from sparse samples of its Fourier transform. Due to the sparse nature of the aperture, a conventional image formation approach based on direct Fourier transformation results in quite dramatic artifacts in the image, as compared to the case of active SAR imaging. The region-enhanced image formation method we consider appears to significantly reduce such artifacts, and preserve the features of the imaged object. Furthermore, this approach exhibits robustness to measurement noise. We demonstrate our results using data based on electromagnetic simulations.© (2004) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.


title: Lithium-ion battery thermal-electrochemical model-based state estimation using orthogonal collocation and a modified extended Kalman filter

abstract: Abstract This paper investigates the state estimation of a high-fidelity spatially resolved thermal-electrochemical lithium-ion battery model commonly referred to as the pseudo two-dimensional model. The partial-differential algebraic equations (PDAEs) constituting the model are spatially discretised using Chebyshev orthogonal collocation enabling fast and accurate simulations up to high C-rates. This implementation of the pseudo-2D model is then used in combination with an extended Kalman filter algorithm for differential-algebraic equations to estimate the states of the model. The state estimation algorithm is able to rapidly recover the model states from current, voltage and temperature measurements. Results show that the error on the state estimate falls below 1% in less than 200 s despite a 30% error on battery initial state-of-charge and additive measurement noise with 10 mV and 0.5 K standard deviations.


title: Magnetometer calibration using inertial sensors

abstract: In this work we present a practical algorithm for calibrating a magnetometer for the presence of magnetic disturbances and for magnetometer sensor errors. To allow for combining the magnetometer measurements with inertial measurements for orientation estimation, the algorithm also corrects for misalignment between the magnetometer and the inertial sensor axes. The calibration algorithm is formulated as the solution to a maximum likelihood problem and the computations are performed offline. The algorithm is shown to give good results using data from two different commercially available sensor units. Using the calibrated magnetometer measurements in combination with the inertial sensors to determine the sensor's orientation is shown to lead to significantly improved heading estimates.


title: Multi-output power supply with series voltage compensation capability for Magnetic Resonance Imaging system

abstract: In Magnetic Resonance Imaging system, the most powerful device is gradient driver, which is also one of the key components to realize fast imaging with high quality. To meet the requirements of high-end MRI products, the power rating of the gradient driver increase from several hundred-kVA to 1MVA and even higher in the future. Commonly the gradient driver applies full bridge cascade technology and needs a lot isolated DC source with high power capability. To provide solution to this requirement, several technology platforms have been developed. This paper presents one of the most attractive options. In the proposed power supply structure, an AC pre-regulator and a multi-winding transformer are combined together with diode rectifiers to provide multiple isolated DC outputs. The AC pre-regulator compensates the utility voltage variation by injecting voltage to the input line through a series transformer, and that make the multi-winding transformer get a stable input. With this solution, the DC outputs voltage is only determined by the transformer leakage inductance and load current. Compared with high frequency DC-DC conversion methods, this scheme does not need high frequency magnetic devices and capacitors, which are difficult to build and high cost for high power application. The switching devices (IGBTs) in this option can also operate at a much lower frequency (several kHz) to reduce the switching loss, and lower switching frequency also benefit the device selection. Beside that the rectifiers applied at the secondaries of the multi-winding transformer operate at line frequency and that benefits the device selection and cost too. Here in this paper, the control algorithm of the power supply is presented and discussed. Both its self-performance and co-operation with the amplifier are evaluated with Saber simulation. The results indicate the attractiveness of the proposed scheme.


title: Progger: An Efficient, Tamper-Evident Kernel-Space Logger for Cloud Data Provenance Tracking

abstract: Cloud data provenance, or "what has happened to my data in the cloud", is a critical data security component which addresses pressing data accountability and data governance issues in cloud computing systems. In this paper, we present Progger (Provenance Logger), a kernel-space logger which potentially empowers all cloud stakeholders to trace their data. Logging from the kernel space empowers security analysts to collect provenance from the lowest possible atomic data actions, and enables several higher-level tools to be built for effective end-to-end tracking of data provenance. Within the last few years, there has been an increasing number of proposed kernel space provenance tools but they faced several critical data security and integrity problems. Some of these prior tools' limitations include (1) the inability to provide log tamper-evidence and prevention of fake/manual entries, (2) accurate and granular timestamp synchronisation across several machines, (3) log space requirements and growth, and (4) the efficient logging of root usage of the system. Progger has resolved all these critical issues, and as such, provides high assurance of data security and data activity audit. With this in mind, the paper will discuss these elements of high-assurance cloud data provenance, describe the design of Progger and its efficiency, and present compelling results which paves the way for Progger being a foundation tool used for data activity tracking across all cloud systems.


title: Nonlinear common source MESFET behaviour and model validation

abstract: In this paper we investigate the effect of bias conditions and load resistance on the nonlinear behaviour of a MESFET Common Source (CS) amplifier at medium frequencies. The distortion nulling effects observed in measured results provide a good criteria for assessing large signal model performance over a wide range of bias conditions. The Parker-Skellern model of the MESFET is used to simulate the MESFET circuit configuration for which the distortion measurements were performed. The model predicts the observed distortion structure.


Building Security In


title: Massively parallel Monte Carlo for many-particle simulations on GPUs

abstract: Current trends in parallel processors call for the design of efficient massively parallel algorithms for scientific computing. Parallel algorithms for Monte Carlo simulations of thermodynamic ensembles of particles have received little attention because of the inherent serial nature of the statistical sampling. In this paper, we present a massively parallel method that obeys detailed balance and implement it for a system of hard disks on the GPU. We reproduce results of serial high-precision Monte Carlo runs to verify the method. This is a good test case because the hard disk equation of state over the range where the liquid transforms into the solid is particularly sensitive to small deviations away from the balance conditions. On a Tesla K20, our GPU implementation executes over one billion trial moves per second, which is 148 times faster than on a single Intel Xeon E5540 CPU core, enables 27 times better performance per dollar, and cuts energy usage by a factor of 13. With this improved performance we are able to calculate the equation of state for systems of up to one million hard disks. These large system sizes are required in order to probe the nature of the melting transition, which has been debated for the last forty years. In this paper we present the details of our computational method, and discuss the thermodynamics of hard disks separately in a companion paper.


title: Enabling information extraction by inference of regular expressions from sample entities

abstract: Regular expressions are the dominant technique to extract business relevant entities (e.g., invoice numbers or product names) from text data (e.g., invoices), since these entity types often follow a strict underlying syntactical pattern. However, the manual construction of regular expressions that guarantee a high recall and precision is a tedious manual task and requires expert knowledge. In this paper, we propose an approach that automatically infers regular expressions from a set of (positive) sample entities, which in turn can be derived either from enterprise databases (e.g., a product catalog) or annotated documents (e.g., historical invoices). The main innovation of our approach is that it learns effective regular expressions that can be easily interpreted and modified by a user. The effectiveness is obtained by a novel method that weights dependent entity features of different granularity (i.e. on character and token level) against each other and selects the most suitable ones to form a regular expression.


title: ADOLESCENT RESILIENCE: AFramework for Understanding Healthy Development in the Face of Risk

abstract: ▪ Abstract Adolescent resilience research differs from risk research by focusing on the assets and resources that enable some adolescents to overcome the negative effects of risk exposure. We discuss three models of resilience—the compensatory, protective, and challenge models—and describe how resilience differs from related concepts. We describe issues and limitations related to resilience and provide an overview of recent resilience research related to adolescent substance use, violent behavior, and sexual risk behavior. We then discuss implications that resilience research has for intervention and describe some resilience-based interventions.


title: Slant: A Blackboard System to Generate Plot, Figuration, and Narrative Discourse Aspects of Stories

abstract: We introduce Slant, a system that integrates more than a decade of research into computational creativity, and specifically story generation, by connecting subsystems that deal with plot, figuration, and the narrative discourse using a blackboard. The process of integrating these systems highlights differences in the representation of story and has led to a better understanding of how story can be usefully abstracted. The plot generator MEXICA and a component of Curveship are used with little modification in Slant, while the figuration subsystem Fig-S and the template generator GRIOT-Gen, inspired by GRIOT, are also components. The development of the new subsystem Verso, which deals with genre, shows how different genres can be computationally modeled and applied to in-development stories to generate results that are surprising in terms of their connections and valuable in terms of their relationship to cultural questions. Example stories are discussed, as is the potential of the system to allow for broader collaboration, the empirical testing of how subsystems interrelate, and possible contributions in literary and artistic contexts.


title: The Brain’s Default Network and Its Adaptive Role in Internal Mentation

abstract: During the many idle moments that comprise daily life, the human brain increases its activity across a set of midline and lateral cortical brain regions known as the “default network.” Despite the robustness with which the brain defaults to this pattern of activity, surprisingly little is known about the network’s precise anatomical organization and adaptive functions. To provide insight into these questions, this article synthesizes recent literature from structural and functional imaging with a growing behavioral literature on mind wandering. Results characterize the default network as a set of interacting hubs and subsystems that play an important role in “internal mentation”—the introspective and adaptive mental activities in which humans spontaneously and deliberately engage in every day.


title: Nano- and micro-structured assemblies for encapsulation of food ingredients.

abstract: This tutorial review provides an overview of the science of food materials and encapsulation techniques that underpin the development of delivery vehicles for functional food components, nutrients and bioactives. Examples of how the choice of materials, formulation and process affect the structure of micro- and nano-encapsulated ingredients and the release of the core are provided. The review is of relevance to chemists, material scientists, food scientists, engineers and nutritionists who are interested in addressing delivery challenges in the food and health industries.


FEATURE SELECTION BY USING CLASSIFICATION AND REGRESSION TREES ( CART )


title: Expected similarity estimation for large-scale batch and streaming anomaly detection

abstract: We present a novel algorithm for anomaly detection on very large datasets and data streams. The method, named EXPected Similarity Estimation (expose), is kernel-based and able to efficiently compute the similarity between new data points and the distribution of regular data. The estimator is formulated as an inner product with a reproducing kernel Hilbert space embedding and makes no assumption about the type or shape of the underlying data distribution. We show that offline (batch) learning with exposecan be done in linear time and online (incremental) learning takes constant time per instance and model update. Furthermore, exposecan make predictions in constant time, while it requires only constant memory. In addition, we propose different methodologies for concept drift adaptation on evolving data streams. On several real datasets we demonstrate that our approach can compete with state of the art algorithms for anomaly detection while being an order of magnitude faster than most other approaches.


title: Methodological alignment in design-based research

abstract: Empirical research is all about trying to model and predict the world. In this article, I discuss how design-based research methods can help do this effectively. In particular, design-based research methods can help with the problem of methodological alignment: ensuring that the research methods we use actually test what we think they are testing. I argue that our current notions of rigor overemphasize certain types of rigor at the expense of others and that design-based research provides an opportunity to select different inferential trade-offs. I describe how 1 design-based research trajectory evolved over time in a way that helped ensure that the learning theories being studied were well represented by the planned interventions and that the interpretation of outcomes was grounded in an understanding of not only the research design, but how the research played out in practice when enacted in real classrooms.


Gradient-Based Learning Algorithms for Recurrent Networks and Their Computational Complexity


The kNN-TD Reinforcement Learning Algorithm


title: Beyond Triplet Loss: A Deep Quadruplet Network for Person Re-identification

abstract: Person re-identification (ReID) is an important task in wide area video surveillance which focuses on identifying people across different cameras. Recently, deep learning networks with a triplet loss become a common framework for person ReID. However, the triplet loss pays main attentions on obtaining correct orders on the training set. It still suffers from a weaker generalization capability from the training set to the testing set, thus resulting in inferior performance. In this paper, we design a quadruplet loss, which can lead to the model output with a larger inter-class variation and a smaller intra-class variation compared to the triplet loss. As a result, our model has a better generalization ability and can achieve a higher performance on the testing set. In particular, a quadruplet deep network using a margin-based online hard negative mining is proposed based on the quadruplet loss for the person ReID. In extensive experiments, the proposed network outperforms most of the state-of-the-art algorithms on representative datasets which clearly demonstrates the effectiveness of our proposed method.


title: Ain't Nobody Got Time For Coding: Structure-Aware Program Synthesis From Natural Language

abstract: Program synthesis from natural language (NL) is practical for humans and, once technically feasible, would significantly facilitate software development and revolutionize end-user programming. We present SAPS, an end-to-end neural network capable of mapping relatively complex, multi-sentence NL specifications to snippets of executable code. The proposed architecture relies exclusively on neural components, and is trained on abstract syntax trees, combined with a pretrained word embedding and a bi-directional multi-layer LSTM for processing of word sequences. The decoder features a doubly-recurrent LSTM, for which we propose novel signal propagation schemes and soft attention mechanism. When applied to a large dataset of problems proposed in a previous study, SAPS performs on par with or better than the method proposed there, producing correct programs in over 92% of cases. In contrast to other methods, it does not require post-processing of the resulting programs, and uses a fixed-dimensional latent representation as the only interface between the NL analyzer and the source code generator.


title: Performance enhancement of a Malware Detection System using score based prioritization of snort rules

abstract: Snort is an open source Intrusion Detection System (IDS) that uses a rule-based approach to detect different kinds of malware, online attacks, vulnerabilities, etc. The performance of a Malware Detection System (MDS) deployed in a large network depends on the nature and type of rules stored in its database. As the number and type of attacks are increasing, more number of rules are appended in the MDS database. This increase in the size of rule database itself becomes the bottleneck in the performance of the MDS. This paper proposes a rule scoring based mechanism for prioritizing the snort rules so as to optimize the number of rules in the MDS database. Only those rules are retained in the database whose total score is greater than the computed threshold value. The results show that the performance of MDS has enhanced remarkably.


title: A 24-GHz CMOS Butler Matrix MMIC for multi-beam smart antenna systems

abstract: A 24-GHz 4-way Butler matrix MMIC in 0.18-mum CMOS technology is presented. The multi-layer structure of CMOS process is utilized to monolithically realize the bulky Butler matrix on silicon substrate. Particularly, the multi-layer bifilar transformer is introduced to miniaturize the circuit and reduce the signal loss. The implemented CMOS Butler matrix MMIC only occupies a chip area of 0.41 mm2 (excluding I/O pads). The experimental results show that insertion losses are 2.2plusmn0.6 dB from 23 to 25 GHz and the phase errors are within 6deg. Therefore, by connecting this Butler matrix to a linear array antenna, four orthogonal beams, pointing to -49deg, -15deg, 15deg, and 49deg, respectively, are generated within 0.3deg error.


title: Automatic Music Genre Classification Based on Modulation Spectral Analysis of Spectral and Cepstral Features

abstract: In this paper, we will propose an automatic music genre classification approach based on long-term modulation spectral analysis of spectral (OSC and MPEG-7 NASE) as well as cepstral (MFCC) features. Modulation spectral analysis of every feature value will generate a corresponding modulation spectrum and all the modulation spectra can be collected to form a modulation spectrogram which exhibits the time-varying or rhythmic information of music signals. Each modulation spectrum is then decomposed into several logarithmically-spaced modulation subbands. The modulation spectral contrast (MSC) and modulation spectral valley (MSV) are then computed from each modulation subband. Effective and compact features are generated from statistical aggregations of the MSCs and MSVs of all modulation subbands. An information fusion approach which integrates both feature level fusion method and decision level combination method is employed to improve the classification accuracy. Experiments conducted on two different music datasets have shown that our proposed approach can achieve higher classification accuracy than other approaches with the same experimental setup.


title: Fixed-rank representation for unsupervised visual learning

abstract: Subspace clustering and feature extraction are two of the most commonly used unsupervised learning techniques in computer vision and pattern recognition. State-of-the-art techniques for subspace clustering make use of recent advances in sparsity and rank minimization. However, existing techniques are computationally expensive and may result in degenerate solutions that degrade clustering performance in the case of insufficient data sampling. To partially solve these problems, and inspired by existing work on matrix factorization, this paper proposes fixed-rank representation (FRR) as a unified framework for unsupervised visual learning. FRR is able to reveal the structure of multiple subspaces in closed-form when the data is noiseless. Furthermore, we prove that under some suitable conditions, even with insufficient observations, FRR can still reveal the true subspace memberships. To achieve robustness to outliers and noise, a sparse regularizer is introduced into the FRR framework. Beyond subspace clustering, FRR can be used for unsupervised feature extraction. As a non-trivial byproduct, a fast numerical solver is developed for FRR. Experimental results on both synthetic data and real applications validate our theoretical analysis and demonstrate the benefits of FRR for unsupervised visual learning.


title: Text clustering with important words using normalization

abstract: Important words, which usually exist in part of Title, Subject and Keywords, can briefly reflect the main topic of a document. In recent years, it is a common practice to exploit the semantic topic of documents and utilize important words to achieve document clustering, especially for short texts such as news articles. This paper proposes a novel method to extract important words from Subject and Keywords of articles, and then partition documents only with those important words. Considering the fact that frequencies of important words are usually low and the scale matrix dataset for important words is small, a normalization method is then proposed to normalize the scale dataset so that more accurate results can be achieved by sufficiently exploiting the limited information. The experiments validate the effectiveness of our method.


The story of the therac-25 in lotos


title: LIP-READING VIA DEEP NEURAL NETWORKS USING HYBRID VISUAL FEATURES

abstract: Lip-reading is typically known as visually interpreting the speaker's lip movements during speaking. Experiments over many years have revealed that speech intelligibility increases if visual facial information becomes available. This effect becomes more apparent in noisy environments. Taking steps toward automating this process, some challenges will be raised such as coarticulation phenomenon, visual units' type, features diversity and their inter-speaker dependency. While efforts have been made to overcome these challenges, presentation of a flawless lip-reading system is still under the investigations. This paper searches for a lipreading model with an efficiently developed incorporation and arrangement of processing blocks to extract highly discriminative visual features. Here, application of a properly structured Deep Belief Network (DBN)- based recognizer is highlighted. Multi-speaker (MS) and speaker-independent (SI) tasks are performed over CUAVE database, and phone recognition rates (PRRs) of 77.65% and 73.40% are achieved, respectively. The best word recognition rates (WRRs) achieved in the tasks of MS and SI are 80.25% and 76.91%, respectively. Resulted accuracies demonstrate that the proposed method outperforms the conventional Hidden Markov Model (HMM) and competes well with the state-of-the-art visual speech recognition works.


Data Mining: Practical Machine Learning Algorithms with Java Implementations


title: Beamspace MIMO for Millimeter-Wave Communications: System Architecture, Modeling, Analysis, and Measurements

abstract: Millimeter-wave wireless systems are emerging as a promising technology for meeting the exploding capacity requirements of wireless communication networks. Besides large bandwidths, small wavelengths at mm-wave lead to a high-dimensional spatial signal space, that can be exploited for significant capacity gains through high-dimensional multiple-input multiple-output (MIMO) techniques. In conventional MIMO approaches, optimal performance requires prohibitively high transceiver complexity. By combining the concept of beamspace MIMO communication with a hybrid analog-digital transceiver, continuous aperture phased (CAP) MIMO achieves near-optimal performance with dramatically lower complexity. This paper presents a framework for physically-accurate computational modeling and analysis of CAP-MIMO, and reports measurement results on a DLA-based prototype for multimode line-of-sight communication. The model, based on a critically sampled system representation, is used to demonstrate the performance gains of CAP-MIMO over state-of-the-art designs at mm-wave. For example, a CAP-MIMO system can achieve a spectral efficiency of 10-20 bits/s/Hz with a 17-31 dB power advantage over state-of-the-art, corresponding to a data rate of 10-200 Gbps with 1-10 GHz system bandwidth. The model is refined to analyze critical sources of power loss in an actual multimode system. The prototype-based measurement results closely follow the theoretical predictions, validating CAP-MIMO theory, and illustrating the utility of the model.


title: Unsupervised classification and clustering of image features for vehicle detection in large scale aerial images

abstract: This paper presents a set of algorithms for vehicle detection in large scale aerial images. Vehicles are detected based on geometric and radiometric features, extracted within a multiresolution linear Gaussian scale-space. The image features, described by their local structures, are classified using support vector machines. Classified features are then clustered by an unsupervised affine propagation clustering algorithm, within a feature-level fusion scheme. Subcomponent of vehicles' body parts are aggregate together with respect to shared spatial relations and based on constraints on the orientation of detected vehicles. Experimental results using large scale aerial imagery demonstrate the efficient and robustness of the proposed algorithms for the detection of vehicles in an urban environment.


RLS relieved by tobacco chewing: paradoxical role of nicotine


3 D Indoor Object Recognition by Holistic Scene Understanding


DiffLDA: Topic Evolution in Software Projects


MT/IE: Cross-lingual Open Information Extraction with Neural Sequence-to-Sequence Models


title: Optimal path planning and coverage control for multi-robot persistent coverage in environments with obstacles

abstract: Persistent coverage aims to maintain a certain coverage level over time in an environment where such level deteriorates. This level can be associated to temperature, dust or sensor information. We propose an algorithmic solution in which each robot locally finds the best paths and coverage actions to keep the desired coverage level over the whole environment. Using Fast Marching Methods, optimal paths are computed in terms of coverage quality, while keeping a safety distance to obstacles. Additionally, our solution enables a computationally efficient evaluation of a list of potential trajectories, allowing us to choose the one that mostly improves the coverage along the whole path. The combination of this algorithm with a Dynamic Window navigation makes our approach competitive in terms of flexibility and robustness in changing environments with existing solutions. Finally, we also propose a coverage action controller, locally computed and optimal, that makes the robots maintain the coverage level of the environment significantly close to the objective. Simulations and real experiments validate the whole approach.


title: Towards ubiquitous human gestures recognition using wireless networks

abstract: Purpose ::: ::: ::: ::: ::: Recently, many researches have been devoted to studying the possibility of using wireless signals of the Wi-Fi networks in human-gesture recognition. They focus on classifying gestures despite who is performing them, and only a few of the previous work make use of the wireless channel state information in identifying humans. This paper aims to recognize different humans and their multiple gestures in an indoor environment. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: The authors designed a gesture recognition system that consists of channel state information data collection, preprocessing, features extraction and classification to guess the human and the gesture in the vicinity of a Wi-Fi-enabled device with modified Wi-Fi-device driver to collect the channel state information, and process it in real time. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The proposed system proved to work well for different humans and different gestures with an accuracy that ranges from 87 per cent for multiple humans and multiple gestures to 98 per cent for individual humans’ gesture recognition. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: This paper used new preprocessing and filtering techniques, proposed new features to be extracted from the data and new classification method that have not been used in this field before.


title: ‘Reading NGOs visually’—Implications of visual images for NGO management

abstract: This paper explores the ways in which the visual images of international non-governmental organisations (INGOs) inform their key management areas of internal organisation & policies, roles and relationships. Drawing upon some unexpected findings based on the analysis of regular fundraising imagery of a UK-based development INGO and brief comparisons with relevant media imagery, it challenges the predominantly 'negative' view of pre-Ethiopian fundraising imagery. The paper carves out several implications of visual images for INGOs including the complex issue of internal coherence, their advocacy roles, and their relationships with Southern clients, media, Northern donors and public. Copyright © 2006 John Wiley & Sons, Ltd.


title: Dynamic heart rate monitors algorithm for reflection green light wearable device

abstract: The paper proposed dynamic direct heart rate algorithms in wearable device. Green light LED to direct heart rate is very popular in recent years which used analogy signal feedback to microcontroller calculation. However, the different person physical signals are independent that including body fat, physical color, blood concentrations and oxygen concentration to influence observer data. Moreover, the body shock influence reflection feedback signal. This paper proposed automatic initial detect process and dynamic direct heart rate filter algorithms to process feedback analogy error noise. We presented a low cost solution for Texas Instruments AFE4403 heart rate oximeters with MSP430G2553 microcontroller to monitors body heart rate in wearable device. MSP430G2553 only have 512 byte RAM and 8k flash memory, so that the digital process calculation is an important topic. Finally, the experimental results show the dynamic algorithm has satisfactory performance.


title: Deep Learning of Turbulent Scalar Mixing

abstract: Based on recent developments in physics-informed deep learning and deep hidden physics models, we put forth a framework for discovering turbulence models from scattered and potentially noisy spatio-temporal measurements of the probability density function (PDF). The models are for the conditional expected diffusion and the conditional expected dissipation of a Fickian scalar described by its transported single-point PDF equation. The discovered model are appraised against exact solution derived by the amplitude mapping closure (AMC)/ Johnsohn-Edgeworth translation (JET) model of binary scalar mixing in homogeneous turbulence.


title: Trashing IMSI catchers in mobile networks

abstract: We address the decades-old privacy problem of disclosure of the permanent subscriber identity (IMSI) that makes IMSI catchers a real threat to all generations of mobile networks. A number of possible modifications to existing protocols have been proposed to address the problem; however, most require significant changes to existing deployed infrastructures. We propose a novel authentication approach for 3G and 4G systems that does not affect intermediate entities, notably the serving network and mobile equipment. It prevents disclosure of the subscriber's IMSI by using a dynamic pseudo-IMSI that is only identifiable by the home network for the USIM. A major challenge in using dynamic pseudo-IMSIs is possible loss of identity synchronisation between USIM and home network, an issue that has not been adequately addressed in previous work. We present an approach for identity recovery to be used in the event of pseudo-IMSI desynchronisation. The scheme requires changes to the home network and the USIM, but not to the serving network, mobile phone or other internal network protocols, enabling simple, transparent and evolutionary migration. We provide a detailed analysis of the scheme, and verify its correctness and security properties using ProVerif.


title: A general-purpose graph dynamical system modeling framework

abstract: We describe InterSim, a general purpose flexible framework for simulating graph dynamical systems (GDS) and their generalizations. GDS provide a powerful formalism to model and analyze agent-based systems (ABS) because there is a direct mapping between nodes and edges (which denote interactions) in a GDS and agents and interactions in an ABS, thereby providing InterSim with great expressive power. We describe the design, implementation, capabilities, and features of InterSim; e.g., it enables users to quickly produce simulations of ABS in many application domains. We present illustrative case studies that focus on the simulation of social phenomena. InterSim has been used to simulate networks with 4 million agents and to execute large parametric simulation studies.


title: Prediction of protein – protein interactions using random decision forest framework

abstract: Motivation: Protein interactions are of biological interest because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. Domains are the building blocks of proteins; therefore, proteins are assumed to interact as a result of their interacting domains. Many domain-based models for protein interaction prediction have been developed, and preliminary results have demonstrated their feasibility. Most of the existing domain-based methods, however, consider only single-domain pairs (one domain from one protein) and assume independence between domain--domain interactions. ::: ::: Results: In this paper, we introduce a domain-based random forest of decision trees to infer protein interactions. Our proposed method is capable of exploring all possible domain interactions and making predictions based on all the protein domains. Experimental results on Saccharomyces cerevisiae dataset demonstrate that our approach can predict protein--protein interactions with higher sensitivity (79.78%) and specificity (64.38%) compared with that of the maximum likelihood approach. Furthermore, our model can be used to infer interactions not only for single-domain pairs but also for multiple domain pairs. ::: ::: Contact: xwchen@ku.edu ::: ::: Availability: Source code is written in Java and is available upon request from the authors. ::: ::: Supplementary information: http://www.ittc.ku.edu/~xwchen/PPI/random_forest_PPI


title: A Survey of Multi-Agent Organizational Paradigms

abstract: Many researchers have demonstrated that the organizational design employed by an agent system can have a significant, quantitative effect on its performance characteristics. A range of organizational strategies have emerged from this line of research, each with different strengths and weaknesses. In this article we present a survey of the major organizational paradigms used in multi-agent systems. These include hierarchies, holarchies, coalitions, teams, congregations, societies, federations, markets, and matrix organizations. We will provide a description of each, discuss their advantages and disadvantages, and provide examples of how they may be instantiated and maintained. This summary will facilitate the comparative evaluation of organizational styles, allowing designers to first recognize the spectrum of possibilities, and then guiding the selection of an appropriate organizational design for a particular domain and environment.


8.01 3 A NOVEL PROCESS TO REMOVE AND RECOVER CFCs FROM GISCHARGE GAS STREAMS


title: Achieving Secure Role-Based Access Control on Encrypted Data in Cloud Storage

abstract: With the rapid developments occurring in cloud computing and services, there has been a growing trend to use the cloud for large-scale data storage. This has raised the important security issue of how to control and prevent unauthorized access to data stored in the cloud. One well known access control model is the role-based access control (RBAC), which provides flexible controls and management by having two mappings, users to roles and roles to privileges on data objects. In this paper, we propose a role-based encryption (RBE) scheme that integrates the cryptographic techniques with RBAC. Our RBE scheme allows RBAC policies to be enforced for the encrypted data stored in public clouds. Based on the proposed scheme, we present a secure RBE-based hybrid cloud storage architecture that allows an organization to store data securely in a public cloud, while maintaining the sensitive information related to the organization's structure in a private cloud. We describe a practical implementation of the proposed RBE-based architecture and discuss the performance results. We demonstrate that users only need to keep a single key for decryption, and system operations are efficient regardless of the complexity of the role hierarchy and user membership in the system.


title: A Domain Partition-Based Trust Model for Unreliable Clouds

abstract: Cloud computing has become an important scientific computing and commercial application paradigm. Many computing resources and data exist in clouds, and face various trust issues. Malicious providers may provide poor services to users, while malicious users may give good providers unfaithful trust evaluations. Hence, it is important to detect these malicious nodes that can be providers and users. Current studies on trust management do not sufficiently address the issues of minimizing trust management overhead and maximizing the ability to detect malicious nodes. This paper proposes a new trust model and related algorithm to decrease trust management overhead and improve malicious node detection ability based on domain partition. Partitioning nodes into domains is helpful for decreasing the overhead of trust management in terms of trust storage and computation. Domain and cross-domain sliding-windows are proposed and utilized to store the most recent trust values. Then, an algorithm is designed to compute domain and cross-domain trust values for nodes, and a filter procedure is adopted to remove malicious trust evaluations and malicious nodes from a domain. Simulation results show that the proposed model and algorithm outperform two updated methods, i.e., one based on fuzzy mathematics and another based on authenticated trust and reputation calculation and management, in terms of speed and accuracy of trust computation and malicious node detection.


title: Odontoblast processes in human dentin revealed by fluorescence labeling and transmission electron microscopy

abstract: In the present undertaking, the distribution of odontoblast processes in human dentin was determined through the DiI carbocyanine dye fluorescent staining of the cell membrane, while F-actin was identified by rhodamine-phalloidin. Confocal laser scanning microscopy revealed intense labeling for both agents in inner dentin, while transmission electron microscopy (TEM) identified dentinal tubules including odontoblast processes in this area, each process being surrounded by a cell membrane and containing an abundance of filamentous structures. Electron-dense "lamina limitans" lined the dentinal tubules. Individual cell processes became narrower toward the middle area, and their overall numbers decreased as well under TEM. Labeling for F-actin was absent in both middle and outer dentin, while faint labeling for DiI was visible along the dentinal tubules as far as the dentino-enamel junction (DEJ), where it was also recognized within the tubules themselves. Under TEM, the dentinal tubules lined with electron-dense structures were, in fact, empty in the middle and outer dentin. Immediately below the DEJ, however, the tubules manifested dense concentrations of fine granular material. Our study, therefore, appears to suggest that odontoblast processes do not extend beyond the inner dentin of fully erupted human premolars.


title: Heterosexual and Nonheterosexual Young University Students' Involvement in Traditional and Cyber Forms of Bullying

abstract: Abstract Research has consistently found that school students who do not identify as self-declared completely heterosexual are at increased risk of victimization by bullying from peers. This study examined heterosexual and nonheterosexual university students' involvement in both traditional and cyber forms of bullying, as either bullies or victims. Five hundred twenty-eight first-year university students (M=19.52 years old) were surveyed about their sexual orientation and their bullying experiences over the previous 12 months. The results showed that nonheterosexual young people reported higher levels of involvement in traditional bullying, both as victims and perpetrators, in comparison to heterosexual students. In contrast, cyberbullying trends were generally found to be similar for heterosexual and nonheterosexual young people. Gender differences were also found. The implications of these results are discussed in terms of intervention and prevention of the victimization of nonheterosexual university st...


title: Robot-aided neurorehabilitation

abstract: The authors' goal is to apply robotics and automation technology to assist, enhance, quantify, and document neurorehabilitation. This paper reviews a clinical trial involving 20 stroke patients with a prototype robot-aided rehabilitation facility developed at the Massachusetts Institute of Technology, Cambridge, (MIT) and tested at Burke Rehabilitation Hospital, White Plains, NY. It also presents the authors' approach to analyze kinematic data collected in the robot-aided assessment procedure. In particular, they present evidence (1) that robot-aided therapy does not have adverse effects, (2) that patients tolerate the procedure, and (3) that peripheral manipulation of the impaired limb may influence brain recovery. These results are based on standard clinical assessment procedures. The authors also present one approach using kinematic data in a robot-aided assessment procedure.


Adversarial Inversion: Inverse Graphics with Adversarial Priors


title: mine. Corporate Incentives and Nuclear Safety

abstract: Following electricity market restructuring, approximately half of all commercial US nuclear power reactors were sold by price-regulated public utilities to independent power producers. At the time of the sales, some policymakers raised concerns that these corporations would ignore safety. Others claimed that the sales would bring improved reactor management, with positive effects on safety. Using data on various safety measures and a difference-in-differences estimation strategy, I find that safety improved following ownership transfers and the removal of price regulations. Generation increased, and this does not appear to have come at the cost of public safety.


title: Optimization of Hybrid PV/Wind Power System for Remote Telecom Station

abstract: The rapid depletion of fossil fuel resources and environmental concerns has given awareness on generation of renewable energy resources. Among the various renewable resources, hybrid solar and wind energy seems to be promising solutions to provide reliable power supply with improved system efficiency and reduced storage requirements for stand-alone applications. This paper presents a feasibility assessment and optimum size of photovoltaic (PV) array, wind turbine and battery bank for a standalone hybrid Solar/Wind Power system (HSWPS) at remote telecom station of Nepal at Latitude (27{\deg}23'50") and Longitude (86{\deg}44'23") consisting a telecommunication load of Very Small Aperture Terminal (VSAT), Repeater station and Code Division Multiple Access Base Transceiver Station (CDMA 2C10 BTS). In any RES based system, the feasibility assessment is considered as the first step analysis. In this work, feasibility analysis is carried through hybrid optimization model for electric renewables (HOMER) and mathematical models were implemented in the MATLAB environment to perform the optimal configuration for a given load and a desired loss of power supply probability (LPSP) from a set of systems components with the lowest value of cost function defined in terms of reliability and levelized unit electricity cost (LUCE). The simulation results for the existing and the proposed models are compared. The simulation results shows that existing architecture consisting of 6.12 kW KC85T photovoltaic modules, 1kW H3.1 wind turbine and 1600 Ah GFM-800 battery bank have a 36.6% of unmet load during a year. On the other hand, the proposed system includes 1kW *2 H3.1 Wind turbine, 8.05 kW TSM-175DA01 photovoltaic modules and 1125 Ah T-105 battery bank with system reliability of 99.99% with a significant cost reduction as well as reliable energy production.


Heliomote: enabling long-lived sensor networks through solar energy harvesting


title: Optimization of Robotic Arm Trajectory Using Genetic Algorithm

abstract: Abstract The paper presents a genetic algorithm - based design approach of the robotic arm trajectory control with the optimization of various criterions. The described methodology is based on the inverse kinematics problem and it additionally considers the minimization of the operating-time, and/or the minimization of energy consumption as well as the minimization of the sum of all rotation changes during the operation cycle. Each criterion evaluation includes the computationally demanding simulation of the arm movement. The proposed approach was verified and all the proposed criterions have been compared on the trajectory optimization of the industrial robot ABB IRB 6400FHD, which has six degrees of freedom.


title: SubsMatch: scanpath similarity in dynamic scenes based on subsequence frequencies

abstract: The analysis of visual scanpaths, i.e., series of fixations and saccades, in complex dynamic scenarios is highly challenging and usually performed manually. We propose SubsMatch, a scanpath comparison algorithm for dynamic, interactive scenarios based on the frequency of repeated gaze patterns. Instead of measuring the gaze duration towards a semantic target object (which would be hard to label in dynamic scenes), we examine the frequency of attention shifts and exploratory eye movements. SubsMatch was evaluated on highly dynamic data from a driving experiment to identify differences between scanpaths of subjects who failed a driving test and subjects who passed.


title: The Life Satisfaction Advantage of Being Married and Gender Specialization

abstract: A large body of literature shows that married people are happier and more satisfied with their lives than unmarried persons (see, e.g., Gove, Style, & Hughes, 1990; Mastekaasa, 1994; Stack & Eshleman, 1998; Verbakel, 2012), yet growing divorce and cohabitation rates and falling marriage and fertility rates suggest a "retreat from marriage" (see, e.g., Adams, 2004; Cherlin, 2004; Huston & Melz, 2004; Popenoe, 1993). Research shows that the life satisfaction advantage of being married (defined as the difference between the population-based averages of the life satisfaction of married and unmarried persons) has decreased over time in the United States (Glenn & Weaver, 1988). This suggests that marriages in contemporary societies have become less advantageous than they were in the past.Over recent decades, another change has occurred: Men and women now allocate their time in a more similar way than they did in the past (Bianchi, Milkie, Sayer, & Robinson, 2000). The employment of women, even those who are married and have children, is now accepted in most developed countries (Brewster & Rindfuss, 2000; Sayer & Bianchi, 2000). The general trend is of a decline in specialization, defined as gendered divisions of tasks within married couples between the labor market (typically assigned to men) and the household work (typically performed by women). Although theoretical approaches in both economics and sociology postulate that married couples benefit from gender specialization within marriages (e.g., Becker, 1981; Parsons, 1949; Stevenson & Wolfers, 2007), the relationship between specialization and the life satisfaction advantage of being married has so far received little attention.The contribution of this article is twofold. First, I describe how the relationship between marriage and life satisfaction has changed over time and across countries. The second contribution is to verify whether macro-level characteristics-in particular, the contextual gender specialization-account for the changes to the life satisfaction advantage of being married.CHANGES OVER TIME TO THE LIFE SATISFACTION ADVANTAGE OF BEING MARRIEDPrevious research has investigated changes to the life satisfaction advantage of being married over recent decades, providing mixed results. Waite (2000) compared the population averages of the well-being of married persons versus never-married and previously married persons over the period 1972-1996 in the United States and found no significant shift in the life satisfaction advantage of being married. In contrast, Glenn and Weaver (1988) showed a decline in the cross-sectional relationship between marital status and declared happiness over the period 1972-1986, which mainly was due to the negative trend of life satisfaction among married women and a positive trend among never-married men. Both Waite (2000) and Glenn and Weaver focused on population averages; moreover, both examined the case of the United States using data from the General Social Survey.If the life satisfaction advantage of being married decreases, it may be due to a lowered satisfaction of married persons or to increased life satisfaction of unmarried persons. The former has been examined by studies that have investigated changes in marital quality, marital interaction, and marital conflict. For instance, Waite (2000) and Glenn (1991) showed that the percentage of married men and women who declared that their marriage was very happy has been declining slightly in the United States since the 1970s. Similarly, Amato, Johnson, Booth, and Rogers (2003) showed that in the United States during the years 1980-2000 marital interactions declined, even though marital quality and divorce proneness changed little. In a similar vein, Rogers and Amato (2000) provided evidence that the cohort married between 1981 and 1997 reported less interaction and more marital conflict than the cohort married between 1964 and 1980. …


title: Structural Edge Detection for Cardiovascular Modeling

abstract: Computational simulations provide detailed hemodynamics and physiological data that can assist in clinical decision-making. However, accurate cardiovascular simulations require complete 3D models constructed from image data. Though edge localization is a key aspect in pinpointing vessel walls in many segmentation tools, the edge detection algorithms widely utilized by the medical imaging community have remained static. In this paper, we propose a novel approach to medical image edge detection by adopting the powerful structured forest detector and extending its application to the medical imaging domain. First, we specify an effective set of medical imaging driven features. Second, we directly incorporate an adaptive prior to create a robust three-dimensional edge classifier. Last, we boost our accuracy through an intelligent sampling scheme that only samples areas of importance to edge fidelity. Through experimentation, we demonstrate that the proposed method outperforms widely used edge detectors and probabilistic boosting tree edge classifiers and is robust to error in a prori information.


title: Certificateless Public Key Cryptography

abstract: This paper introduces and makes concrete the concept of certificateless public key cryptography (CL-PKC), a model for the use of public key cryptography which avoids the inherent escrow of identity-based cryptography and yet which does not require certificates to guarantee the authenticity of public keys. The lack of certificates and the presence of an adversary who has access to a master key necessitates the careful development of a new security model. We focus on certificateless public key encryption (CL-PKE), showing that a concrete pairing-based CL-PKE scheme is secure provided that an underlying problem closely related to the Bilinear Diffie-Hellman Problem is hard.


title: Comparing activity theory with distributed cognition for video analysis: beyond "kicking the tires"

abstract: The field of HCI is growing, not only in the variety of application areas or the volume of research conducted, but also in the number of analytical approaches for use in the evaluation and design of interactive systems. However, despite the abundance of theoretical frameworks available, relatively little work has directly compared the application of these frameworks. This paper compares video analysis methods based on two analytic frameworks - activity theory (AT) and distributed cognition (DCog) - by performing an analysis of the same system from each of the two different theoretical perspectives. The results presented here provide a better understanding of how such theoretically informed methods in practice both resemble and differ from one another. Furthermore, this comparison enables specific insights about each of the theories themselves, as well as more general discussion about the role of theory in HCI.


title: Competitive Two-Level Adaptive Scheduling Using Resource Augmentation

abstract: As multi-core processors proliferate, it has become more important than ever to ensure efficient execution of parallel jobs on multiprocessor systems. In this paper, we study the problem of scheduling parallel jobs with arbitrary release time on multiprocessors while minimizing the jobs' mean response time. We focus on non-clairvoyant scheduling schemes that adaptively reallocate processors based on periodic feedbacks from the individual jobs. Since it is known that no deterministic non-clairvoyant algorithm is competitive for this problem, we focus on resource augmentation analysis, and show that two adaptive algorithms, Agdeq and Abgdeq, achieve competitive performance using O(1) times faster processors than the adversary. These results are obtained through a general framework for analyzing the mean response time of any two-level adaptive scheduler. Our simulation results verify the effectiveness of Agdeq and Abgdeq by evaluating their performances over a wide range of workloads consisting of synthetic parallel jobs with different parallelism characteristics.


title: Gamification for enforcing coding conventions

abstract: Software is a knowledge intensive product, which can only evolve if there is effective and efficient information exchange between developers. Complying to coding conventions improves information exchange by improving the readability of source code. However, without some form of enforcement, compliance to coding conventions is limited. We look at the problem of information exchange in code and propose gamification as a way to motivate developers to invest in compliance. Our concept consists of a technical prototype and its integration into a Scrum environment. By means of two experiments with agile software teams and subsequent surveys, we show that gamification can effectively improve adherence to coding conventions.


title: Drug-drug Interaction Extraction via Recurrent Neural Network with Multiple Attention Layers

abstract: Drug-drug interaction (DDI) is a vital information when physicians and pharmacists intend to co-administer two or more drugs. Thus, several DDI databases are constructed to avoid mistakenly combined use. In recent years, automatically extracting DDIs from biomedical text has drawn researchers' attention. However, the existing work utilize either complex feature engineering or NLP tools, both of which are insufficient for sentence comprehension. Inspired by the deep learning approaches in natural language processing, we propose a recur- rent neural network model with multiple attention layers for DDI classification. We evaluate our model on 2013 SemEval DDIExtraction dataset. The experiments show that our model classifies most of the drug pairs into correct DDI categories, which outperforms the existing NLP or deep learning methods.


title: GPS-less Low Cost Outdoor Localization For Very Small Devices

abstract: Instrumenting the physical world through large networks of wireless sensor nodes, particularly for applications like environmental monitoring of water and soil, requires that these nodes be very small, lightweight, untethered, and unobtrusive. The problem of localization, that is, determining where a given node is physically located in a network, is a challenging one, and yet extremely crucial for many of these applications. Practical considerations such as the small size, form factor, cost and power constraints of nodes preclude the reliance on GPS of all nodes in these networks. We review localization techniques and evaluate the effectiveness of a very simple connectivity metric method for localization in outdoor environments that makes use of the inherent RF communications capabilities of these devices. A fixed number of reference points in the network with overlapping regions of coverage transmit periodic beacon signals. Nodes use a simple connectivity metric, which is more robust to environmental vagaries, to infer proximity to a given subset of these reference points. Nodes localize themselves to the centroid of their proximate reference points. The accuracy of localization is then dependent on the separation distance between two-adjacent reference points and the transmission range of these reference points. Initial experimental results show that the accuracy for 90 percent of our data points is within one-third of the separation distance. However, future work is needed to extend the technique to more cluttered environments.


title: "Brace Technology" Thematic Series - The ScoliOlogiC® Chêneau light™ brace in the treatment of scoliosis

abstract: Background ::: Bracing concepts in use today for the treatment of scoliosis include symmetric and asymmetric hard braces usually made of polyethylene (PE) and soft braces. The plaster cast method worldwide seems to be the most practiced technique for the construction of hard braces at the moment. CAD (Computer Aided Design) systems are available which allow brace adjustments without plaster. Another possibility is the use of the ScoliOlogiC™ off the shelf system enabling the Certified Prosthetist and Orthotist (CPO) to construct a light brace for scoliosis correction from a variety of pattern specific shells to be connected to an anterior and a posterior upright. This Cheneau light™ brace, developed according to the Cheneau principles, promises a reduced impediment of quality of life in the brace. The correction effects of the first 81 patients (main diagnosis Adolescent Idiopathic Scoliosis (AIS) [n = 64] or Early Onset Scoliosis (EOS) [n = 15]), treated according to the principles of the Cheneau light™ brace have shown a satisfactory in-brace correction exceeding 50% of the initial Cobb angle.


title: Confirmatory Factor Analysis of the End‐User Computing Satisfaction Instrument: Replication within an ERP Domain*

abstract: Over the past decade, organizations have made significant investments in enterprise resource planning (ERP) systems. The realization of benefits from these investments depends on supporting effective use of information technology (IT) and satisfying IT users. User satisfaction with information systems is one of the most important determinants of the success of those systems. Drawing upon a sample of 407 end users of ERP systems and working within the framework of confirmatory factor analysis (CFA), this study examines the structure and dimensionality, and reliability and validity of the end-user computing satisfaction (EUCS) instrument posited by Doll and Torkzadeh (1988). In response to Klenke's (1992) motion to cross-validate management information system (MIS) instruments and to retest the end user computing satisfaction instrument using new data, this study's results, consistent with previous findings, confirm that the EUCS instrument maintains its psychometric stability when applied to users of enterprise resource planning application software. Implications of these results for practice and research are provided.


DRAFT — — — An introduction to particle filters