title: The effect of path loss variations on the capacity of MIMO systems

abstract: Flat fading MIMO channels are often modelled by a transfer matrix with independent but identically distributed (i.i.d) complex random numbers. Measurements indicate that the assumption of an identical distribution of all matrix entries is not fulfilled, at least for some antenna configurations. We adapt the channel model to include this observation and study the influence on the capacity and performance of MIMO systems. The results are compared with measurements.


title: Result Analysis of the NIPS 2003 Feature Selection Challenge

abstract: The NIPS 2003 workshops included a feature selection competition organized by the authors. We provided participants with five datasets from different application domains and called for classification results using a minimal number of features. The competition took place over a period of 13 weeks and attracted 78 research groups. Participants were asked to make on-line submissions on the validation and test sets, with performance on the validation set being presented immediately to the participant and performance on the test set presented to the participants at the workshop. In total 1863 entries were made on the validation sets during the development period and 135 entries on all test sets for the final competition. The winners used a combination of Bayesian neural networks with ARD priors and Dirichlet diffusion trees. Other top entries used a variety of methods for feature selection, which combined filters and/or wrapper or embedded methods using Random Forests, kernel methods, or neural networks as a classification engine. The results of the benchmark (including the predictions made by the participants and the features they selected) and the scoring software are publicly available. The benchmark is available at www.nipsfsc.ecs.soton.ac.uk for post-challenge submissions to stimulate further research.


title: RNASEL Arg462Gln variant is implicated in up to 13% of prostate cancer cases

abstract: RNASEL (encoding ribonuclease L) has recently been proposed as a candidate for the hereditary prostate cancer (HPC1) gene. We determined that the RNASEL variant Arg462Gln has three times less enzymatic activity than the wildtype and is significantly associated with prostate cancer risk (P = 0.007). At least one copy of the mutated allele that causes this substitution is carried by nearly 60% of the men in our study. Men that are heterozygous with respect to the mutated allele have 50% greater risk of prostate cancer than non-carriers, and homozygotes have more than double the risk.


title: A Hybrid Forecasting Model for Foreign Exchange Rate Based on a Multi-neural Network

abstract: In this work, a multi-neural network model consisting of three sub-networks and one master network is proposed to combine the fundamental theorem and technical analysis in TWD/USD exchange rate forecasting. The long-term, mid-term, and short-term tendencies of exchange rate are forecasted separately by different sub-networks. Five macro economics factors of price level, interest rates, money supply, imports/exports, and productivity, and seven practical technical indicators of fifteen-day and one-day intervals are selected as the input variables of the three sub-networks. The master network then provides the integrated forecasting according to the three sub-networks. To increase forecasting accuracy, a threshold filtering mechanism was applied in this work. The experiment result shows that the multi-neural network is more effective than the random walk model and single-neural network model, and with the threshold filtering, can achieve high accuracy.


title: Trust and Acceptance of Cloud Computing: A Revised UTAUT Model

abstract: Despite the fact that benefits of adopting cloud computing services and applications have become well known within the communities of information technology, adoption of such technology still facing various challenges. Currently, establishing trust is considered as the main barrier preventing individuals and enterprises adopting cloud solutions. Though, to best of our knowledge it has not been considered as a main factor when investigating cloud computing acceptance whereas the majority of studies in the literature focused mainly on finding out ways and approaches increasing trust of cloud computing solutions. Therefore, we present a revised Unified Theory of Acceptance and Use of Technology (UTAUT) where trust is considered as a main construct beside its original constructs.


title: Rapid prototyping of an EEG-based brain-computer interface (BCI)

abstract: The electroencephalogram (EEG) is modified by motor imagery and can be used by patients with severe motor impairments (e.g., late stage of amyotrophic lateral sclerosis) to communicate with their environment. Such a direct connection between the brain and the computer is known as an EEG-based brain-computer interface (BCI). This paper describes a new type of BCI system that uses rapid prototyping to enable a fast transition of various types of parameter estimation and classification algorithms to real-time implementation and testing. Rapid prototyping is possible by using Matlab, Simulink, and the Real-Time Workshop. It is shown how to automate real-time experiments and perform the interplay between on-line experiments and offline analysis. The system is able to process multiple EEG channels on-line and operates under Windows 95 in real-time on a standard PC without an additional DSP board. The BCI can be controlled over the Internet, LAN or modem. This BCI was tested on 3 subjects whose task it was to imagine either left or right hand movement. A classification accuracy between 70% and 95% could be achieved with two EEG channels after some sessions with feedback using an adaptive autoregressive model and linear discriminant analysis.


title: Do the Advantages of Incumbency Advantage Incumbents

abstract: We develop a model that calls into question whether some key sources of incumbency advantage frequently cited in the empirical and theoretical literature are, in fact, beneficial to incumbents. Our results show that increases in ostensible benefits of incumbency associated with these sources make it difficult for voters to differentiate incumbents of higher and lower quality. While this leads to an improvement in the electoral prospects of lower-quality incumbents, it is harmful to those of higher quality. Whether the net electoral consequence for high-quality incumbents is positive or negative depends on whether an ostensible source of incumbency advantage affects candidate entry and exit decisions directly or indirectly, as mediated through voters' choices. Our findings suggest, further, that fundamental tensions may exist between different sources of incumbency advantage, and point to obstacles to disaggregating the sources of incumbency advantage empirically.


Classification in Data Mining


title: Development and analysis of U-core switched reluctance machine

abstract: Switched reluctance machines (SRMs) have seen a lot of interest due to their rugged and fault tolerant construction as well as their high efficiency over a wide speed range. The technology however suffers from torque ripple, acoustic noise and low torque density. Many concepts to address these disadvantages have been presented, but not all of them have been demonstrated practically. This paper presents a practical demonstration and assessment of a segmented U-core SRM, which copes with some of the disadvantages of the regular SRM. The U-core SRM has a segmented stator, with a short flux path and reduced flux reversal, reducing core losses. Due to an increased number of poles, torque density is increased and torque ripple reduced. A prototype is built and through a number of tests, the machine is mapped and all loss components are analysed. As a result of the analysis, an assessment is presented, which addresses the shortcomings of the U-core technology, as well as general considerations for SRMs. This allows for further development and improvement of the technology, and also contributes to improve the development and modelling of conventional SRMs.


title: Virtual Adversarial Training: a Regularization Method for Supervised and Semi-supervised Learning

abstract: We propose a new regularization method based on virtual adversarial loss: a new measure of local smoothness of the conditional label distribution given input. Virtual adversarial loss is defined as the robustness of the conditional label distribution around each input data point against local perturbation. Unlike adversarial training, our method defines the adversarial direction without label information and is hence applicable to semi-supervised learning. Because the directions in which we smooth the model are only “virtually” adversarial, we call our method virtual adversarial training (VAT). The computational cost of VAT is relatively low. For neural networks, the approximated gradient of virtual adversarial loss can be computed with no more than two pairs of forward- and back-propagations. In our experiments, we applied VAT to supervised and semi-supervised learning tasks on multiple benchmark datasets. With a simple enhancement of the algorithm based on the entropy minimization principle, our VAT achieves state-of-the-art performance for semi-supervised learning tasks on SVHN and CIFAR-10.


title: Impact of Data Locality on Garbage Collection in SSDs: A General Analytical Study

abstract: Solid-state drives (SSDs) necessitate garbage collection (GC) to erase data blocks and reclaim the space of invalidated data, and GC inevitably introduces additional writes due to data relocation. The performance of GC, which is quantified by cleaning cost or write amplification, is critical to the overall performance of SSDs. However, characterizing GC performance is complicated by the general implementations of GC algorithms and the complex data locality characteristics of real-world workloads. This paper presents a general analytical study to characterize the performance impact of data locality on a general family of GC algorithms. We develop probabilistic models to address two fundamental issues: (1) What is the impact of data locality on the performance of locality-oblivious GC? (2) How can data locality be leveraged to improve the performance in locality-aware GC? We further conduct extensive trace-driven simulations on real-world workloads to validate the findings of our models.


title: Moore's Law: The First Ending and a New Beginning

abstract: Moore's law has accurately predicted roughly biennial doubling of component capacity at minimal cost for almost 50 years. Recent flash memory scaling exhibits increased density, but reduced write and read lifetimes effectively constitute an ending of Moore's law. However, new resilience techniques, including adaptive management algorithms, and storage technologies based on information theory could maintain progress for a decade or more.


title: Aspects of favorable propagation in Massive MIMO

abstract: Favorable propagation, defined as mutual orthogonality among the vector-valued channels to the terminals, is one of the key properties of the radio channel that is exploited in Massive MIMO. However, there has been little work that studies this topic in detail. In this paper, we first show that favorable propagation offers the most desirable scenario in terms of maximizing the sum-capacity. One useful proxy for whether propagation is favorable or not is the channel condition number. However, this proxy is not good for the case where the norms of the channel vectors may not be equal. For this case, to evaluate how favorable the propagation offered by the channel is, we propose a ``distance from favorable propagation'' measure, which is the gap between the sum-capacity and the maximum capacity obtained under favorable propagation. Secondly, we examine how favorable the channels can be for two extreme scenarios: i.i.d. Rayleigh fading and uniform random line-of-sight (UR-LoS). Both environments offer (nearly) favorable propagation. Furthermore, to analyze the UR-LoS model, we propose an urns-and-balls model. This model is simple and explains the singular value spread characteristic of the UR-LoS model well.


title: A static hand gesture recognition algorithm using k-mean based radial basis function neural network

abstract: The accurate classification of static hand gestures is a vital role to develop a hand gesture recognition system which is used for human-computer interaction (HCI) and for human alternative and augmentative communication (HAAC) application. A vision-based static hand gesture recognition algorithm consists of three stages: preprocessing, feature extraction and classification. The preprocessing stage involves following three sub-stages: segmentation which segments hand region from its background images using a histogram based thresholding algorithm and transforms into binary silhouette; rotation that rotates segmented gesture to make the algorithm, rotation invariant; filtering that effectively removes background noise and object noise from binary image by morphological filtering technique. To obtain a rotation invariant gesture image, a novel technique is proposed in this paper by coinciding the 1st principal component of the segmented hand gestures with vertical axes. A localized contour sequence (LCS) based feature is used here to classify the hand gestures. A k-mean based radial basis function neural network (RBFNN) is also proposed here for classification of hand gestures from LCS based feature set. The experiment is conducted on 500 train images and 500 test images of 25 class grayscale static hand gesture image dataset of Danish/international sign language hand alphabet. The proposed method performs with 99.6% classification accuracy which is better than earlier reported technique.


Debugging Machine Learning Models


title: Predicting customer churn from valuable B2B customers in the logistics industry: a case study

abstract: This study uncovers the effect of the length, recency, frequency, monetary, and profit (LRFMP) customer value model in a logistics company to predict customer churn. This unique context has useful business implications compared to the main stream customer churn studies where individual customers (rather than business customers) are the main focus. Our results show the five LRFMP variables had a varying effect on customer churn. Specifically length, recency and monetary variables had a significant effect on churn, while the frequency variable only became a top predictor when the variability of the first three variables was limited. The profit variable had never become a significant predictor. Certain other behavioral variables (such as time between transactions) also had an effect on churn. The resulting set of predictors of churn expands the original LRFMP and RFM models with additional insights. Managerial implications were provided.


title: High efficiency Ka-band Gallium Nitride power amplifier MMICs

abstract: The design and performance of two high efficiency Ka-band power amplifier MMICs utilizing a 0.15μm GaN HEMT process technology is presented. Measured in-fixture continuous wave (CW) results for the 3-stage balanced amplifier demonstrates up to 11W of output power and 30% power added efficiency (PAE) at 30GHz. The 3-stage single-ended design produced over 6W of output power and up to 34% PAE. The die size for the balanced and single-ended MMICs are 3.24×3.60mm2 and 1.74×3.24mm2 respectively.


title: Wind Speed and Rotor Position Sensorless Control for Direct-Drive PMG Wind Turbines

abstract: Wind turbine generators (WTGs) are usually equipped with mechanical sensors to measure wind speed and rotor position for system control, monitoring, and protection. The use of mechanical sensors increases the cost and hardware complexity and reduces the reliability of the WTG system. This paper proposes a wind speed and rotor position sensorless control for wind turbines directly driving permanent magnetic generators (PMGs). A sliding mode observer is designed to estimate the rotor position of the PMG, which is then used to calculate the shaft rotating speed. Based on the measured output electrical power and estimated rotor speed of the PMG, the mechanical power of the turbine is estimated by taking account into the power losses of the WTG system. A back propagation artificial neural network (BPANN) is then designed to estimate the wind speed in real time by using the estimated turbine shaft speed and mechanical power. The estimated wind speed is used to determine the optimal shaft speed or power reference for the PMG control system. Finally, a sensorless control is developed for the PMG wind turbines to continuously generate the maximum electrical power without using any wind speed or rotor position sensors. The validity of the proposed estimation and control algorithms are shown by simulation studies on a 3-kW PMG wind turbine and are further demonstrated by experimental results on a 300-W practical PMG wind turbine.


title: Clusters of male and female Alzheimer’s disease patients in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database

abstract: This paper presents homogeneous clusters of patients, identified in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data population of 317 females and 342 males, described by a total of 243 biological and clinical descriptors. Clustering was performed with a novel methodology, which supports identification of patient subpopulations that are homogeneous regarding both clinical and biological descriptors. Properties of the constructed clusters clearly demonstrate the differences between female and male Alzheimer’s disease patient groups. The major difference is the existence of two male subpopulations with unexpected values of intracerebral and whole brain volumes.


title: On Linear Genetic Programming

abstract: Linear Genetic Programming presents a variant of Genetic Programming that evolves imperative computer programs as linear sequences of instructions, in contrast to the more traditional functional expressions or syntax trees. Typical GP phenomena, such as non-effective code, neutral variations, and code growth are investigated from the perspective of linear GP. This book serves as a reference for researchers; it includes sufficient introductory material for students and newcomers to the field.


title: Design of Three-Stage Class-AB 16 $\Omega$ Headphone Driver Capable of Handling Wide Range of Load Capacitance

abstract: In this paper, the effect of load capacitance variation on the location of the closed loop poles of three-stage amplifiers is analyzed and a frequency compensation scheme that automatically adjusts the damping factor according to the load capacitance is proposed. A class-AB 16 Omega headphone driver designed using the proposed scheme in 0.13 mum technology can handle 1 pF to 22 nF capacitive load while consuming as low as 1.2 mW of quiescent power. It can deliver a peak power of 40 mW (1.6 Vpp swing) to the load with - 84.8 dB THD and 92 dB peak SNR. It occupies 0.1 mm2 area.


title: Geographic routing without location information

abstract: For many years, scalable routing for wireless communication systems was a compelling but elusive goal. Recently, several routing algorithms that exploit geographic information (e.g. GPSR) have been proposed to achieve this goal. These algorithms refer to nodes by their location, not address, and use those coordinates to route greedily, when possible, towards the destination. However, there are many situations where location information is not available at the nodes, and so geographic methods cannot be used. In this paper we define a scalable coordinate-based routing algorithm that does not rely on location information, and thus can be used in a wide variety of ad hoc and sensornet environments.


title: Filler Rhinoplasty Evaluated by Anthropometric Analysis.

abstract: BACKGROUND ::: There are no reports of objectively evaluating the efficacy of filler rhinoplasty by anthropometric techniques. ::: ::: ::: OBJECTIVE ::: To objectively demonstrate the effectiveness of filler rhinoplasty by anthropometric analysis. ::: ::: ::: MATERIALS AND METHODS ::: A total of 242 patients who revisited the clinic within 2 months of undergoing hyaluronic acid filler rhinoplasty were analyzed based on the injection site, injected volume, and the change in anthropometry. ::: ::: ::: RESULTS ::: Among the 242 patients, 112 (46.3%) were in the nasal dorsum augmentation group, 8 (3.3%) were in the tip rotation group, and 122 (50.4%) were in the whole nose augmentation group. Average injection volume was 1 ± 0.4 mL for nasal dorsum and 0.9 ± 0.3 mL for tip rotation, whereas 1.6 ± 0.5 mL was used for whole nose augmentation. On follow-up, the radix height, nasofrontal angle, and nasolabial angle (NLA) had increased by 78.3%, 5.7 ± 4.1°, and 9.4 ± 4.5°, respectively, whereas the modified nasofacial angle had decreased by 1.9 ± 2.9°. Three cases (1.2%) of vascular complications were encountered. ::: ::: ::: CONCLUSION ::: Filler rhinoplasty is a simple and effective treatment modality producing outcomes comparable with surgical augmentation rhinoplasty. Among various anthropometric measurements, the nasal radix height was the most useful for evaluating dorsum augmentation, whereas the NLA was the best for nasal tip rotation.


title: A Current Control Scheme With Back EMF Cancellation and Tracking Error Adapted Commutation Shift for Switched-Reluctance Motor Drive

abstract: In this paper, an improved winding current control scheme for switched-reluctance motor drive is developed and evaluated. The current feedback controller (CFBC) is augmented with an observed back electromotive force (EMF) current feedforward controller (CFFC) and a current tracking error adapted dynamic commutation shift controller (DCSC). In the proposed DCSC, the rising winding current tracking errors of all phases are extracted, synthesized, and processed to generate the suitable advanced commutation shift angle. The effects of motor back EMF and nonlinear winding inductance can be reduced to yield improved winding current tracking waveforms under a wide operation range. The gradual improvements in current responses and efficiencies by augmenting the CFBC with the CFFC and then the DCSC are verified experimentally. Then, the robust speed control scheme is properly designed to achieve good acceleration/deceleration, reversible running, and dynamic characteristics.


title: Tomato Productivity and Quality in Aquaponics: Comparison of Three Hydroponic Methods

abstract: Aquaponics (AP) is a food production system that combines hydroponic (HP) crop production with recirculating aquaculture. Different types of hydroponic systems have been used for growing crops in aquaponics. However, very few studies have compared their suitability and efficiency in an aquaponic context. The study presented here compares tomato yield, morphological (external) and biochemical (internal) fruit quality, and overall tomato plant vitality from three different HP systems (nutrient film technique, drip irrigation system, and floating raft culture) and examines the distribution of nutrients in different parts of the tomato plant. Three replicate AP systems were set up, each incorporating the three different HP systems coupled with a separate recirculating aquaculture unit growing Nile tilapia. The results showed that the choice of the cultivation system had little influence on most of the above-mentioned properties. Tomato fruit mineral content was found to be in similar range for N, P, K, Ca, Mg, Fe, and Zn as reported in the literature. Yield and fruit quality were similar in all three systems. However, the drip irrigation system did perform slightly better. The slightly higher oxygen radical absorbance capacity (ORAC) of the fruits grown in AP in comparison to commercially produced and supermarket derived tomatoes might indicate a potential for producing fruits with higher health value for humans.


title: Web-Based Learning Environment: A Theory-Based Design Process for Development and Evaluation

abstract: Introduction As an increasingly powerful, interactive, and dynamic medium for delivering information, the World Wide Web (Web) in combination with information technology (e.g., LAN, WAN, Internet, etc.) has found many applications. One popular application has been for educational use, such as Web-based, distance, distributed or online learning. The use of the Web as an educational tool has provided learners and educators with a wider range of new and interesting learning experiences and teaching environments, not possible in traditional in-class education (Khan, 1997). Web-based learning environments have been developed mainly by instructional designers using traditional instructional design models such as the instructional systems design (Dick & Carey, 1996), cognitive flexibility theory (Spiro, Feltovich, Jacobson, & Coulson, 1991), and constructivist learning environment (Jonassen, 1999). However, many of these approaches still lack two important considerations needed for implementing learning applications based on the Web: (1) integration of the user interface design with instructional design, and (2) development of the evaluation framework to improve the overall quality of Web-based learning environments. First, little attention has been paid to design issues of the human-computer interface, which are critical factors to the success of Web-based instruction (Henke, 1997; Plass, 1998). Learners must be able to easily focus on learning materials without having to make an effort to figure out how to access them (Lohr, 2000). However, current instructional design principles and models do not explicitly address usability issues of the human-computer interface. Second, the rapid growth of Web-based learning applications has generated a need for methods to systematically collect continuous feedback from users to improve learning environments. Unfortunately, few attempts have been made to develop such formative evaluation frameworks for Web-based learning environments whose foci are both the instructional system and user interface system. In addition, few approaches take user interface design issues into account in their evaluation processes. A number of evaluation frameworks that can be used to evaluate the user interfaces have been proposed (e.g., Nielsen, 1993; Rubin, 1994). But, these models are intended for software environments rather than for Web-based learning environments in which user interface systems should be developed to support users' learning activities. This study addressed these weaknesses while developing a user-centered, Web-based learning support environment for Global Positioning System (GPS) education: a Web-based distance and distributed learning ([WD.sup.2]L) environment. More specifically, there are two main research goals addressed in this study, and these goals aimed to improve the design process and usability of the [WD.sup.2]L environment. First, this study offered a systematic approach to the design, development, and evaluation of a user-centered, [WD.sup.2]L environment for supporting engineering courses. Second, this study evaluated the design process model by assessing the overall quality of the [WD.sup.2]L environment prototype in terms of 1) students' learning performance and 2) the quality of resources implemented in the [WD.sup.2]L environment. We first give an overview of relevant literature that guided the design, development, and evaluation of the [WD.sup.2]L environment supporting GPS education. The development process will then be briefly summarized. In addition, evaluation processes through the proposed formative evaluation framework will be outlined. Finally, relationships between the design process framework and the effectiveness of the [WD.sup.2]L environment will be discussed. Background Overview of GPS Education To understand the application domain, a GPS course was analyzed or used as the testbed. As shown in Table 1, there is the educational demand for a new learning environment to effectively support the course while meeting the societal demands on engineers educated in GPS fundamentals. …


title: Emotion control of unstructured dance movements

abstract: Motion capture technology has enabled the acquisition of high quality human motions for animating digital characters with extremely high fidelity. However, despite all the advances in motion editing and synthesis, it remains an open problem to modify pre-captured motions that are highly expressive, such as contemporary dances, for stylization and emotionalization. In this work, we present a novel approach for stylizing such motions by using emotion coordinates defined by the Russell's Circumplex Model (RCM). We extract and analyze a large set of body and motion features, based on the Laban Movement Analysis (LMA), and choose the effective and consistent features for characterizing emotions of motions. These features provide a mechanism not only for deriving the emotion coordinates of a newly input motion, but also for stylizing the motion to express a different emotion without having to reference the training data. Such decoupling of the training data and new input motions eliminates the necessity of manual processing and motion registration. We implement the two-way mapping between the motion features and emotion coordinates through Radial Basis Function (RBF) regression and interpolation, which can stylize free-style highly dynamic dance movements at interactive rates. Our results and user studies demonstrate the effectiveness of the stylization framework with a variety of dance movements exhibiting a diverse set of emotions.


title: The Complex Molecular Biology of Amyotrophic Lateral Sclerosis ( ALS )

abstract: Amyotrophic lateral sclerosis (ALS) is an adult-onset neurodegenerative disorder that causes selective death of motor neurons followed by paralysis and death. A subset of ALS cases is caused by mutations in the gene for Cu, Zn superoxide dismutase (SOD1), which impart a toxic gain of function to this antioxidant enzyme. This neurotoxic property is widely believed to stem from an increased propensity to misfold and aggregate caused by decreased stability of the native homodimer or a tendency to lose stabilizing posttranslational modifications. Study of the molecular mechanisms of SOD1-related ALS has revealed a complex array of interconnected pathological processes, including glutamate excitotoxicity, dysregulation of neurotrophic factors and axon guidance proteins, axonal transport defects, mitochondrial dysfunction, deficient protein quality control, and aberrant RNA processing. Many of these pathologies are directly exacerbated by misfolded and aggregated SOD1 and/or cytosolic calcium overload, suggesting the primacy of these events in disease etiology and their potential as targets for therapeutic intervention.


title: Static analysis tools as early indicators of pre-release defect density

abstract: During software development it is helpful to obtain early estimates of the defect density of software components. Such estimates identify fault-prone areas of code requiring further testing. We present an empirical approach for the early prediction of pre-release defect density based on the defects found using static analysis tools. The defects identified by two different static analysis tools are used to fit and predict the actual pre-release defect density for Windows Server 2003. We show that there exists a strong positive correlation between the static analysis defect density and the pre-release defect density determined by testing. Further, the predicted pre-release defect density and the actual pre-release defect density are strongly correlated at a high degree of statistical significance. Discriminant analysis shows that the results of static analysis tools can be used to separate high and low quality components with an overall classification rate of 82.91%.


Learning the Structure of Probabilistic Sentential Decision Diagrams


The Successful Practice of Evidence-based Medicine May Be Contingent Upon the Methods We Use to Measure Our Interventions.


title: Hierarchical Temporal Memory Method for Time-Series-Based Anomaly Detection

abstract: Abstract The time-series-based anomaly detection is a well-studied subject, and it is well-documented in the literature. Theories and techniques have been proposed and applied successfully for domain-specific applications. However, this subject has received renewed interest motivated by the increasing importance of continuously learning, tolerance to noise and generalization. This paper tackles these problems by applying Hierarchical Temporal Memory (HTM), a novel biological neural network. HTM is more suitable for dealing with the changing pattern of data since it is capable of incorporating contextual information from the past to make more accurate prediction. Both artificial and real datasets are tested with HTM for the time-series-based anomaly detection. The experiment results show that HTM can efficiently detect the anomalies in time series data.


title: Personalized Recommendation Combining User Interest and Social Circle

abstract: With the advent and popularity of social network, more and more users like to share their experiences, such as ratings, reviews, and blogs. The new factors of social network like interpersonal influence and interest based on circles of friends bring opportunities and challenges for recommender system (RS) to solve the cold start and sparsity problem of datasets. Some of the social factors have been used in RS, but have not been fully considered. In this paper, three social factors, personal interest, interpersonal interest similarity, and interpersonal influence, fuse into a unified personalized recommendation model based on probabilistic matrix factorization. The factor of personal interest can make the RS recommend items to meet users' individualities, especially for experienced users. Moreover, for cold start users, the interpersonal interest similarity and interpersonal influence can enhance the intrinsic link among features in the latent space. We conduct a series of experiments on three rating datasets: Yelp, MovieLens, and Douban Movie. Experimental results show the proposed approach outperforms the existing RS approaches. Index Terms—Interpersonal influence, personal interest, recommender system, social networks —————————— ——————————


Accept or appropriate ? A design-oriented critique on technology acceptance models


title: Is emotional contagion special? An fMRI study on neural systems for affective and cognitive empathy

abstract: article Empathy allows us to simulate others' affective and cognitive mental states internally, and it has been proposed that the mirroring or motor representation systems play a key role in such simulation. As emotions are related to important adaptive events linked with benefit or danger, simulating others' emotional states might constitute of a special case of empathy. In this functional magnetic resonance imaging (fMRI) study we tested if emotional versus cognitive empathy would facilitate the recruitment of brain networks involved in motor representation and imitation in healthy volunteers. Participants were presented with photographs depicting people in neutral everyday situations (cognitive empathy blocks), or suffering serious threat or harm (emotional empathy blocks). Participants were instructed to empathize with specified persons depicted in the scenes. Emotional versus cognitive empathy resulted in increased activity in limbic areas involved in emotion processing (thalamus), and also in cortical areas involved in face (fusiform gyrus) and body perception, as well as in networks associated with mirroring of others' actions (inferior parietal lobule). When brain activation resulting from viewing the scenes was controlled, emotional empathy still engaged the mirror neuron system (premotor cortex) more than cognitive empathy. Further, thalamus and primary somatosensory and motor cortices showed increased functional coupling during emotional versus cognitive empathy. The results suggest that emotional empathy is special. Emotional empathy facilitates somatic, sensory, and motor representation of other peoples' mental states, and results in more vigorous mirroring of the observed mental and bodily states than cognitive empathy.


title: Design and Coordination Kinematics of an Insertable Robotic Effectors Platform for Single-Port Access Surgery

abstract: Single port access surgery (SPAS) presents surgeons with added challenges that require new surgical tools and surgical assistance systems with unique capabilities. To address these challenges, we designed and constructed a new insertable robotic end-effectors platform (IREP) for SPAS. The IREP can be inserted through a O15 mm trocar into the abdomen and it uses 21 actuated joints for controlling two dexterous arms and a stereo-vision module. Each dexterous arm has a hybrid mechanical architecture comprised of a two-segment continuum robot, a parallelogram mechanism for improved dual-arm triangulation, and a distal wrist for improved dexterity during suturing. The IREP is unique because of the combination of continuum arms with active and passive segments with rigid parallel kinematics mechanisms. This paper presents the clinical motivation, design considerations, kinematics, statics, and mechanical design of the IREP. The kinematics of coordination between the parallelogram mechanisms and the continuum arms is presented using the pseudo-rigid-body model of the beam representing the passive segment of each snake arm. Kinematic and static simulations and preliminary experiment results are presented in support of our design choices.


title: A practical evaluation of radio signal strength for ranging-based localization

abstract: Radio signal strength (RSS) is notorious for being a noisy signal that is difficult to use for ranging-based localization. In this study, we demonstrate that RSS can be used to localize a multi-hop sensor network, and we quantify the effects of various environmental factors on the resulting localization error. We achieve 4.1m error in a 49 node network deployed in a half-football field sized area, demonstrating that RSS localization can be a feasible alternative to solutions like GPS given the right conditions. However, we also show that this result is highly sensitive to subtle environmental factors such as the grass height, radio enclosure, and elevation of the nodes from the ground.


title: Cooperation of autonomous vehicles using a hierarchy of auction-based and model-predictive control

abstract: This paper proposes an approach to establish cooperative behavior within traffic scenarios involving only autonomously driving vehicles. The main idea is to employ principles of auction-based control to determine driving strategies by which the vehicles reach their driving goals, while adjusting their paths to each other and adhering to imposed constraints like traffic rules. Driving plans (bids) are repetitively negotiated among the control units of the vehicles (the auction) to obtain a compromise between separate (local) vehicle goals and the global objective to resolve the considered traffic scenario. The agreed driving plans serve as reference trajectories for local model-predictive controllers of the vehicles to realize the driving behavior. The approach is illustrated for a cooperative overtaking scenario comprising three vehicles.


title: A combination of VLC and WiFi based indoor wireless access network and its handover strategy

abstract: Visible Light Communication (VLC) has many advantages such as high-speed data transmission and non-frequency authorization, which provided a good solution for indoor access environment with an effective and energy-saving way. This paper proposes a combination VLC + WiFi based indoor wireless access network structure. Different network architectures are analyzed and optimal scheme is given. Based on the numerical calculation, the handover model is introduced. Finally, a demo system is designed and implemented.


title: Fault Tolerance in Cluster Computing System

abstract: With advancement in technology, the needs for high performance computing are increasing tremendously. Cluster computing has developed due to the availability of high performance cost effective processors and high speed networks. The long-term trend in High performance computing requires increasing number of nodes in parallel computing platforms. This however entails a higher failure probability. The Message Passing Paradigm (MPI) is currently the programming paradigm and communication library most commonly used on parallel computing platforms. MPI applications may get stopped at any time due to unpredictable failures during execution. In our paper we propose an efficient fault tolerant approach for MPI system in an asymmetric cluster computing environment. In this paper, we use centralized logging process. In the approach proposed, we use message logging for message losses. The process has three main parts failure detection, failure recovery and overload detection. Our System maintains monitor nodes for all nodes in cluster, the difference being all monitor nodes can work as a cluster node even when the system is functioning properly and not just at the time of node failure.


title: User-driven Intelligent Interface on the Basis of Multimodal Augmented Reality and Brain-Computer Interaction for People with Functional Disabilities

abstract: The analysis of the current integration attempts of some modes and use cases of human-to-machine interaction is presented. The new concept of the user-driven intelligent interface for accessibility is proposed on the basis of multimodal augmented reality and brain-computer interaction for various applications: in disabilities studies, education, home care, health care, eHealth, etc. The several use cases of multimodal augmentation are presented. The perspectives of the better human comprehension by the immediate feedback through neurophysical channels by means of brain-computer interaction are outlined. It is shown that brain–computer interface (BCI) technology provides new strategies to overcome limits of the currently available user interfaces, especially for people with functional disabilities. The results of the previous studies of the low end consumer and open-source BCI-devices allow us to conclude that combination of machine learning (ML), multimodal interactions (visual, sound, tactile) with BCI will profit from the immediate feedback from the actual neurophysical reactions classified by ML methods. In general, BCI in combination with other modes of AR interaction can deliver much more information than these types of interaction themselves. Even in the current state the combined AR-BCI interfaces could provide the highly adaptable and personal services, especially for people with functional disabilities.


title: Selective Laser Melting Manufacturing of Microwave Waveguide Devices

abstract: Additive manufacturing technologies are currently envisaged to boost the development of a next generation of microwave and millimeter-wave devices intended for, among others, satellite telecommunications, navigation, imaging, radio-astronomy, and cosmology. Due to their excellent electromagnetic and mechanical properties, all-metal waveguide components are key building blocks of several radio frequency (RF) systems used in these application domains. This article reports on the prospects originating from the application of all-metal 3D printing to the manufacturing of high-performance microwave waveguide devices. The technology investigated is the selective laser melting process, where a laser beam is used to fuse metal powder particles spread over a building platform. The complete parts are built by overlapping several constant-thickness layers. An overview on process parameters, material properties, and design rules is reported for this technology. The electromagnetic properties of test samples built in Al and Ti alloys have been experimentally characterized. A robust design of Ku/K-band filters aimed at satellite telecommunications has been implemented in several prototypes manufactured in Al. The corresponding measured performance confirm the applicability of the laser selective melting process to the intended applications.


title: Real-Time Obstacles Detection and Status Classification for Collision Warning in a Vehicle Active Safety System

abstract: This paper presents real-time obstacles detection and their status classification method for collision warning in the vehicle active safety system. Specifically, stereo cameras and millimeter wave (mmw)-radar are fused to help the driving ego-vehicle to find “Danger” or “Potential Danger” in a timely way through combining with the vehicle kinematic model. The proposed method makes full use of the unique advantages of stereo cameras and mmw-radar to sense the environment through several modules. Cameras are mainly used to detect the near or lateral dynamic objects and to obtain the obstacles region of interest (ROI) considering its rich information and high sensitivity to the lateral displacement, while far or longitudinal relative dynamic objects are detected by mmw-radar according to its observational ability to make up for the disadvantage of cameras. In detail, a cameras detector utilizes ”error vectors” rather than the optical flow to obtain dynamic classes through two times clustering. Mmw-radar mainly detects relative dynamic objects, whose absolute speed can be computed according to the ego-vehicle’s state. Then, the detected objects of these two detectors are integrated in an obstacles ROI map, which is obtained through an UV-disparity obstacles detection algorithm to get the final dynamic and relative dynamic objects. Finally, they are classified by comparing them with a dangerous area that is acquired according to the vehicle kinematic model in a special vehicle coordinate system, which is fixed to the ground temporarily. This method is tested on our mobile platforms and the results prove that it can work effectively even though the ego-vehicle drives quickly.


title: Multistage and Multilevel Power Electronic Converter-Based Power Supply for Plasma DBD Devices

abstract: This paper presents the development and performance evaluation of a compact-converter-based power supply for plasma dielectric barrier discharge (DBD) devices. The developed power supply is designed to meet weight and size requirements for applications in aeronautical systems. Multistage and multilevel switch-mode converters are employed to construct the power supply. The multistage part of the power supply is constructed from multiple dc–dc boost converters that have their inputs fed by rechargeable batteries. The outputs of the dc–dc converters are used to feed different levels of a single phase cascaded $H$ -bridge (CHB) multilevel dc–ac converter. The switching signals for the CHB dc–ac converter are generated to facilitate the adjustments of the magnitude and/or frequency of the output voltage. Such adjustments are set to allow manipulating the generated plasma body force. A prototype for the multistage multilevel power supply is constructed for performance evaluation using a fiberglass DBD device. Performance results show an effective generation and control of plasma body force, which can be achieved by a modular, lightweight, and compact size power supply.


title: Making middleboxes someone else's problem: network processing as a cloud service

abstract: Modern enterprises almost ubiquitously deploy middlebox processing services to improve security and performance in their networks. Despite this, we find that today's middlebox infrastructure is expensive, complex to manage, and creates new failure modes for the networks that use them. Given the promise of cloud computing to decrease costs, ease management, and provide elasticity and fault-tolerance, we argue that middlebox processing can benefit from outsourcing the cloud. Arriving at a feasible implementation, however, is challenging due to the need to achieve functional equivalence with traditional middlebox deployments without sacrificing performance or increasing network complexity. In this paper, we motivate, design, and implement APLOMB, a practical service for outsourcing enterprise middlebox processing to the cloud. Our discussion of APLOMB is data-driven, guided by a survey of 57 enterprise networks, the first large-scale academic study of middlebox deployment. We show that APLOMB solves real problems faced by network administrators, can outsource over 90% of middlebox hardware in a typical large enterprise network, and, in a case study of a real enterprise, imposes an average latency penalty of 1.1ms and median bandwidth inflation of 3.8%.


title: Uninvited Connections: A Study of Vulnerable Devices on the Internet of Things (IoT)

abstract: The Internet of Things (IoT) continues to grow as uniquely identifiable objects are added to the internet. The addition of these devices, and their remote connectivity, has brought a new level of efficiency into our lives. However, the security of these devices has come into question. While many may be secure, the sheer number creates an environment where even a small percentage of insecure devices may create significant vulnerabilities. This paper evaluates some of the emerging vulnerabilities that exist and puts some figures to the scale of the threat.


title: A Deep Learning Approach for Joint Video Frame and Reward Prediction in Atari Games

abstract: Reinforcement learning is concerned with identifying reward-maximizing behaviour policies in environments that are initially unknown. State-of-the-art reinforcement learning approaches, such as deep Q-networks, are model-free and learn to act effectively across a wide range of environments such as Atari games, but require huge amounts of data. Model-based techniques are more data-efficient, but need to acquire explicit knowledge about the environment. ::: In this paper, we take a step towards using model-based techniques in environments with a high-dimensional visual state space by demonstrating that it is possible to learn system dynamics and the reward structure jointly. Our contribution is to extend a recently developed deep neural network for video frame prediction in Atari games to enable reward prediction as well. To this end, we phrase a joint optimization problem for minimizing both video frame and reward reconstruction loss, and adapt network parameters accordingly. Empirical evaluations on five Atari games demonstrate accurate cumulative reward prediction of up to 200 frames. We consider these results as opening up important directions for model-based reinforcement learning in complex, initially unknown environments.


title: Analysis of multimedia workloads with implications for internet streaming

abstract: In this paper, we study the media workload collected from a large number of commercial Web sites hosted by a major ISP and that collected from a large group of home users connected to the Internet via a well-known cable company. Some of our key findings are: (1) Surprisingly, the majority of media contents are still delivered via downloading from Web servers. (2) A substantial percentage of media downloading connections are aborted before completion due to the long waiting time. (3) A hybrid approach, pseudo streaming, is used by clients to imitate real streaming. (4) The mismatch between the downloading rate and the client playback speed in pseudo streaming is common, which either causes frequent playback delays to the clients, or unnecessary traffic to the Internet. (5) Compared with streaming, downloading and pseudo streaming are neither bandwidth efficient nor performance effective. To address this problem, we propose the design of AutoStream, an innovative system that can provide additional previewing and streaming services automatically for media objects hosted on standard Web sites in server farms at the client's will.


title: Diversification as a Corporate Strategy and Its Effect on Firm Performance: A Study of Zimbabwean Listed Conglomerates in the Food and Beverages Sector

abstract: Normal 0 false false false EN-ZW X-NONE X-NONE Portfolio diversification in capital markets is an accepted investment strategy. On the other hand corporate diversification has drawn many opponents especially the agency theorists who argue that executives must not diversify on behalf of share holders. Diversification is a strategic option used by many managers to improve their firm’s performance. While extensive literature investigates the diversification performance linkage, little agreements exist concerning the nature of this relationship. Both theoretical and empirical disagreements abound as the extensive research has neither reached a consensus nor any interpretable and acceptable findings. This paper looked at diversification as a corporate strategy and its effect on firm performance using Conglomerates in the Food and Beverages Sector listed on the ZSE. The study used a combination of primary and secondary data. Primary data was collected through interviews while secondary data were gathered from financial statements and management accounts. Data was analyzed using SPSS computer package. Three competing models were derived from literature (the linear model, Inverted U model and Intermediate model) and these were empirically assessed and tested.


title: Real-time neuroimaging and cognitive monitoring using wearable dry EEG

abstract: Goal: We present and evaluate a wearable high-density dry-electrode EEG system and an open-source software framework for online neuroimaging and state classification. Methods: The system integrates a 64-channel dry EEG form factor with wireless data streaming for online analysis. A real-time software framework is applied, including adaptive artifact rejection, cortical source localization, multivariate effective connectivity inference, data visualization, and cognitive state classification from connectivity features using a constrained logistic regression approach (ProxConn). We evaluate the system identification methods on simulated 64-channel EEG data. Then, we evaluate system performance, using ProxConn and a benchmark ERP method, in classifying response errors in nine subjects using the dry EEG system. Results: Simulations yielded high accuracy (AUC = 0.97 ± 0.021) for real-time cortical connectivity estimation. Response error classification using cortical effective connectivity [short-time direct-directed transfer function (sdDTF)] was significantly above chance with similar performance (AUC) for cLORETA $(0.74 \pm 0.09)$ and LCMV $(0.72 \pm 0.08)$ source localization. Cortical ERP-based classification was equivalent to ProxConn for cLORETA $(0.74 \pm 0.16)$ but significantly better for LCMV $(0.82 \pm 0.12)$ . Conclusion: We demonstrated the feasibility for real-time cortical connectivity analysis and cognitive state classification from high-density wearable dry EEG. Significance: This paper is the first validated application of these methods to 64-channel dry EEG. This study addresses a need for robust real-time measurement and interpretation of complex brain activity in the dynamic environment of the wearable setting. Such advances can have broad impact in research, medicine, and brain–computer interfaces. The pipelines are made freely available in the open-source SIFT and BCILAB toolboxes.


Home Automation System via Internet using Android Phone


title: Severe Equinus Deformity After Radiofrequency-Induced Calf Muscle Reduction

abstract: Introduction Radiofrequency-induced calf muscle volume reduction is a commonly used method for cosmetic shaping of the lower leg contour. Functional disabilities associated with the use of the radiofrequency (RF) technique, with this procedure targeting the normal gastrocnemius muscle, still have not been reported. However, the authors have experienced several severe ankle equinus cases after RF-induced calf muscle volume reduction. Material and Method This study retrospectively reviewed 19 calves of 12 patients who showed more than 20 of fixed equinus even though they underwent physical therapy for more than 6 months. All were women with a mean age of 32 years (range, 23‐41 years). Of the 12 patients, 7 were bilateral. All the patients received surgical Achilles lengthening for deformity correction. To evaluate the clinical outcome, serial ankle dorsiflexion was measured, and the American Orthopedic Foot and Ankle Society (AOFAS) score was evaluated at the latest follow-up visit. The presence of soleus muscle involvement and an ongoing lesion that might affect the postoperative results of preoperative magnetic resonance imaging (MRI) were investigated. Statistical analysis was conducted to analyze preoperative factors strongly associated with patient clinical outcomes.


title: Audio-video steganography

abstract: Steganography is the art and science of writing messages which is to be hide behind original cover file which may be audio, video or image. In this paper we are working on audio-video steganography which is the combination of Audio steganography and Image steganography, in this we are using computer forensics technique for authentication purpose. In this paper our aim is to hide secret information behind audio and image of video file. As we know that video is the combination of many still frames of images and audio. We can select any frame of video and audio for hiding our secret data. This paper proposed an algorithm for hiding image in selected video sequence is an image-hiding technique based on Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) and random LSB (Least Significant Bit) audio steganography method for hiding secret text information inside audio of the audio-video file, it reduce embedding distortion of the host audio. This paper focuses the idea of computer forensics technique which is use as a tool for authentication and data security purpose and its use in video steganography in security manner.


Endocrine Spotlights on Brain and Behaviour *


title: Mathematical modeling and control of a tilt-rotor aircraft

abstract: Abstract This paper presents a novel model of large-size tilt-rotor aircraft, which can operate as a helicopter as well as being capable of transition to fixed-wing flight. Aerodynamics of the dynamic large-size tilt-rotors based on blade element method is analyzed during mode transition. For the large-size aircraft with turboshaft engines, the blade pitch angles of the rotors are regulated to vary according to the desired level of thrust, and the following expressions are formulated explicitly: rotor thrust and blade pitch angle, drag torque and blade pitch angle. A finite-time convergent observer based on Lyapunov function is developed to reconstruct the unknown variables and uncertainties during mode transitions. The merits of this design include the modeling of dynamic large-size tilt-rotor, ease of the uncertainties estimation during the tilting and the widely applications. Moreover, a switched logic controller based on the finite-time convergent observer is proposed to drive the aircraft to implement the mode transition with invariant flying height.


title: Rethinking Sentiment Analysis in the News: from Theory to Practice and back

abstract: Most sentiment analysis work has been carried out on highly subjective text types where the target is clearly defined and unique across the text (movie or product reviews). However, when applying sentiment analysis to the news domain, it is necessary to clearly define the scope of the task, in a more specific manner than it has been done in the field so far. The main tasks we identified for news opinion mining are: definition of the target; separation of the good and bad news content from the good and bad sentiment expressed on the target; and analysis of clearly marked opinion that is expressed explicitly, not needing interpretation or the use of world knowledge. It is furthermore important to distinguish three different possible views on newspaper articles - author, reader and text. These have to be addressed differently at the time of analysing sentiment, especially the case of author intention and reader interpretation, where specific profiles must be defined if the proper sentiment is to be extracted.


A Pairwise Document Analysis Approach for Monolingual Plagiarism Detection


title: Subspace distance analysis with application to adaptive Bayesian algorithm for face recognition

abstract: We propose subspace distance measures to analyze the similarity between intrapersonal face subspaces, which characterize the variations between face images of the same individual. We call the conventional intrapersonal subspace average intrapersonal subspace (AIS) because the image differences often come from a large number of persons. An intrapersonal subspace is referred to as specific intrapersonal subspace (SIS) if the image differences are from just one person. We demonstrate that SIS varies significantly from person to person, and most SISs are not similar to AIS. Based on these observations, we introduce the maximum a posteriori (MAP) adaptation to the problem of SIS estimation, and apply it to the Bayesian face recognition algorithm. Experimental results show that the adaptive Bayesian algorithm outperforms the non-adaptive Bayesian algorithm as well as Eigenface and Fisherface methods if a small number of adaptation images are available.


title: Towards generic framework for tabular data extraction and management in documents

abstract: Tables are one of the common data presentation structures in documents. However, the task of automatic recognition and extraction of tables embedded in documents is still a significant challenge, and data contained within tables still remains under-utilised. Although some common steps can be defined for table extraction, there is no generic approach for table extraction tasks which can be applied to different sources and provide an end-to-end repeatable work-flow. This paper looks at the table extraction problem from the process point of view and proposes a table extraction workflow, which can be considered as a plug-and-play architecture for table extraction. Also, we present an overview of our complete system where the extracted tables are stored and managed. Table extraction is considered in the context of financial statements in this work, but the methods apply generally.


title: Directed evolution of estrogen receptor proteins with altered ligand-binding specificities

abstract: Transcriptional activators that respond to ligands with no cellular targets are powerful tools that can confer regulated expression of a transgene in almost all biological systems. In this study, we altered the ligand-binding specificity of the human estrogen receptor alpha (hER alpha) so that it would recognize a non-steroidal synthetic compound with structural similarities to the phytoestrogen resveratrol. For this purpose, we performed iterative rounds of site-specific saturation mutagenesis of a fixed set of ligand-contacting residues and subsequent random mutagenesis of the entire ligand-binding domain. Selection of the receptor mutants and quantification of the interaction were carried out by exploiting a yeast two-hybrid system that reports the ligand-dependent interaction between hER alpha and steroid receptor coactivator-1 (SRC-1). The screen was performed with a synthetic ligand (CV3320) that promoted growth of the reporter yeast strain to half maximal levels at a concentration of 3.7 microM. The optimized receptor mutant (L384F/L387M/Y537S) showed a 67-fold increased activity to the synthetic ligand CV3320 (half maximal yeast growth at 0.055 microM) and a 10-fold decreased activity to 17beta-estradiol (E2; half maximal yeast growth at 4 nM). The novel receptor-ligand pair partially fulfills the requirements for a specific 'gene switch' as it responds to concentrations of the synthetic ligand which do not activate the wildtype receptor. Due to its residual responsiveness to E2 at concentrations (4 nM) that might occur in vivo, further improvements have to be performed to render the system applicable in organisms with endogenous E2 synthesis.


title: Real-Time Visual Tracking: Promoting the Robustness of Correlation Filter Learning

abstract: Correlation filtering based tracking model has received lots of attention and achieved great success in real-time tracking, however, the lost function in current correlation filtering paradigm could not reliably response to the appearance changes caused by occlusion and illumination variations. This study intends to promote the robustness of the correlation filter learning. By exploiting the anisotropy of the filter response, three sparsity related loss functions are proposed to alleviate the overfitting issue of previous methods and improve the overall tracking performance. As a result, three real-time trackers are implemented. Extensive experiments in various challenging situations demonstrate that the robustness of the learned correlation filter has been greatly improved via the designed loss functions. In addition, the study reveals, from an experimental perspective, how different loss functions essentially influence the tracking performance. An important conclusion is that the sensitivity of the peak values of the filter in successive frames is consistent with the tracking performance. This is a useful reference criterion in designing a robust correlation filter for visual tracking.


title: A Concise Overview of Applications of Ant Colony Optimization

abstract: Ant colony optimization (ACO) [1–3] is a metaheuristic for solving hard combinatorial optimization problems inspired by the indirect communication of real ants. In ACO algorithms, (artificial) ants construct candidate solutions to the problem being tackled, making decisions that are stochastically biased by numerical information based on (artificial) pheromone trails and available heuristic information. The pheromone trails are updated during algorithm execution to bias the ants search toward promising decisions previously found. The article titled Ant Colony Optimization gives a detailed overview of the main concepts of ACO. Despite being one of the youngest metaheuristics, the number of applications of ACO algorithms is very large. In principle, ACO can be applied to any combinatorial optimization problem for which some iterative solution construction mechanism can be conceived. Most applications of ACO deal with NP-hard combinatorial optimization problems, that is, with problems for which no polynomial time algorithms are known. ACO algorithms have also been extended to handle problems with multiple objectives, stochastic data, and dynamically changing problem information. There are extensions of the ACO metaheuristic for dealing with problems with continuous decision variables, as well. This article provides a concise overview of several noteworthy applications of ACO algorithms. This overview is necessarily incomplete because the number of currently available ACO applications goes into the hundreds. Our description of the applications


title: Analysis of facial features in identical twins

abstract: A study of the distinctiveness of different facial features (MLBP, SIFT, and facial marks) with respect to distinguishing identical twins is presented. The accuracy of distinguishing between identical twin pairs is measured using the entire face, as well as each facial component (eyes, eyebrows, nose, and mouth). The impact of discriminant learning methods on twin face recognition is investigated. Experimental results indicate that features that perform well in distinguishing identical twins are not always consistent with the features that best distinguish two non-twin faces.


title: Can machine learning explain human learning

abstract: Learning Analytics (LA) has a major interest in exploring and understanding the learning process of humans and, for this purpose, benefits from both Cognitive Science, which studies how humans learn, and Machine Learning, which studies how algorithms learn from data. Usually, Machine Learning is exploited as a tool for analyzing data coming from experimental studies, but it has been recently applied to humans as if they were algorithms that learn from data. One example is the application of Rademacher Complexity, which measures the capacity of a learning machine, to human learning, which led to the formulation of Human Rademacher Complexity (HRC). In this line of research, we propose here a more powerful measure of complexity, the Human Algorithmic Stability (HAS), as a tool to better understand the learning process of humans. The experimental results from three different empirical studies, on more than 600 engineering students from the University of Genoa, showed that HAS (i) can be measured without the assumptions required by HRC, (ii) depends not only on the knowledge domain, as HRC, but also on the complexity of the problem, and (iii) can be exploited for better understanding of the human learning process.


Multi-view Indoor Spatial Layout Estimation Yuanfang ( Yolanda )


title: A performance evaluation of spatial join processing strategies

abstract: We provide an evaluation of query execution plans (QEP) in the case of queries with one or two spatial joins. The QEPs assume R*-tree indexed relations and use a common set of spatial joins algorithms, among which one is a novel extension of a strategy based on an on-the-fly index creation prior to the join with another indexed relation. A common platform is used on which a set of spatial access methods and join algorithms are available. The QEPs are implemented with a general iterator-based spatial query processor, allowing for pipelined QEP execution, thus minimizing memory space required for intermediate results.


title: HUNTS: A Trajectory Recommendation System for Effective and Efficient Hunting of Taxi Passengers

abstract: Nowadays, there are many taxis traversing around the city searching for available passengers, but their hunts of passengers are not always efficient. To the dynamics of traffic and biased passenger distributions, current offline recommendations based on place of interests may not work well. In this paper, we define a new problem, global-optimal trajectory retrieving (GOTR), as finding a connected trajectory of high profit and high probability to pick up a passenger within a given time period in real-time. To tackle this challenging problem, we present a system, called HUNTS, based on the knowledge from both historical and online GPS data and business data. To achieve above objectives, first, we propose a dynamic scoring system to evaluate each road segment in different time periods by considering both picking-up rate and profit factors. Second, we introduce a novel method, called trajectory sewing, based on a heuristic method and the Skyline technique, to produce an approximate optimal trajectory in real-time. Our method produces a connected trajectory rather than several place of interests to avoid frequent next-hop queries. Third, to avoid congestion and other real-time traffic situations, we update the score of each road segment constantly via an online handler. Finally, we validate our system using a large-scale data of around 15,000 taxis in a large city in China, and compare the results with regular taxis' hunts and the state-of-the-art.


title: Sperm recovery and ICSI outcomes in Klinefelter syndrome: a systematic review and meta-analysis

abstract: BACKGROUND ::: Specific factors underlying successful surgical sperm retrieval rates (SRR) or pregnancy rates (PR) after testicular sperm extraction (TESE) in adult patients with Klinefelter syndrome (KS) have not been completely clarified. ::: ::: ::: OBJECTIVE AND RATIONALE ::: The aim of this review was to meta-analyse the currently available data from subjects with KS regarding SRRs as the primary outcome. In addition, when available, PRs and live birth rates (LBRs) after the ICSI technique were also investigated as secondary outcomes. ::: ::: ::: SEARCH METHODS ::: An extensive Medline, Embase and Cochrane search was performed. All trials reporting SRR for conventional-TESE (cTESE) or micro-TESE (mTESE) and its specific determinants without any arbitrary restriction were included. ::: ::: ::: OUTCOMES ::: Out of 139 studies, 37 trials were included in the study, enrolling a total of 1248 patients with a mean age of 30.9 ± 5.6 years. The majority of the studies (n = 18) applied mTESE, 13 applied cTESE and in one case testicular sperm aspiration (TESA) was used. Additionally, four studies used a mixed approach and in one study, the method applied for sperm retrieval was not specified. Overall, a SRR per TESE cycle of 44[39;48]% was detected. Similar results were observed when mTESE was compared to cTESE (SRR 43[35;50]% vs 45[38;52]% for cTESE vs micro-TESE, respectively; Q = 0.20, P = 0.65). Meta-regression analysis showed that none of the parameters tested, including age, testis volume and FSH, LH and testosterone (T) levels at enrollment, affected the final SRR. Similarly, no difference was observed when a bilateral procedure was compared to a unilateral approach. No sufficient data were available to evaluate the effect of previous T treatment on SRR. Information on fertility outcome after ICSI was available for 29 studies. Overall a total of 218 biochemical pregnancies after 410 ICSI cycles were observed (PR = 43[36;50]%). Similar results were observed when LBR was analyzed (LBR = 43[34;53]%). Similar to what was observed for SRR, no influence of KS age, mean testis volume, LH, FSH or total T levels on either PR and LBR was observed. No sufficient data were available to test the effect of the women's age or other female fertility problems on PR and LBR. Finally, no difference in PR or LBR was observed when the use of fresh sperm was compared to the utilization of cryopreserved sperm. ::: ::: ::: WIDER IMPLICATIONS ::: The present data suggest that performing TESE/micro-TESE in subjects with KS results in SRRs of close to 50%, and then PRs and LBRs of close to 50%, with the results being independent of any clinical or biochemical parameters tested.


title: Finding local communities in protein networks

abstract: BACKGROUND ::: Protein-protein interactions (PPIs) play fundamental roles in nearly all biological processes, and provide major insights into the inner workings of cells. A vast amount of PPI data for various organisms is available from BioGRID and other sources. The identification of communities in PPI networks is of great interest because they often reveal previously unknown functional ties between proteins. A large number of global clustering algorithms have been applied to protein networks, where the entire network is partitioned into clusters. Here we take a different approach by looking for local communities in PPI networks. ::: ::: ::: RESULTS ::: We develop a tool, named Local Protein Community Finder, which quickly finds a community close to a queried protein in any network available from BioGRID or specified by the user. Our tool uses two new local clustering algorithms Nibble and PageRank-Nibble, which look for a good cluster among the most popular destinations of a short random walk from the queried vertex. The quality of a cluster is determined by proportion of outgoing edges, known as conductance, which is a relative measure particularly useful in undersampled networks. We show that the two local clustering algorithms find communities that not only form excellent clusters, but are also likely to be biologically relevant functional components. We compare the performance of Nibble and PageRank-Nibble to other popular and effective graph partitioning algorithms, and show that they find better clusters in the graph. Moreover, Nibble and PageRank-Nibble find communities that are more functionally coherent. ::: ::: ::: CONCLUSION ::: The Local Protein Community Finder, accessible at http://xialab.bu.edu/resources/lpcf, allows the user to quickly find a high-quality community close to a queried protein in any network available from BioGRID or specified by the user. We show that the communities found by our tool form good clusters and are functionally coherent, making our application useful for biologists who wish to investigate functional modules that a particular protein is a part of.


title: High-gain observers in nonlinear feedback control

abstract: SUMMARY ::: ::: In this document, we present the main ideas and results concerning high-gain observers and some of their applications in control. The introduction gives a brief history of the topic. Then, a motivating second-order example is used to illustrate the key features of high-gain observers and their use in feedback control. This is followed by a general presentation of high-gain-observer theory in a unified framework that accounts for modeling uncertainty, as well as measurement noise. The paper concludes by discussing the use of high-gain observers in the robust control of minimum-phase nonlinear systems. Copyright © 2013 John Wiley & Sons, Ltd.


title: 53BP1 A guardian for centrosomal integrity

abstract: 53BP1 is known as a mediator in DNA damage response and a regulator of DNA double-stranded breaks (DSBs) repair. 53BP1 was recently reported to be a centrosomal protein and a binding partner of mitotic polo-like kinase 1 (Plk1). The stability of 53BP1, in response to DSBs, is regulated by its phosphorylation, deubiquitination, and ubiquitination. During mitosis, 53BP1 is stabilized by phosphorylation at S380, a putative binding region with polo-box domain of Plk1, and deubiquitination by ubiquitin-specific protease 7 (USP7). In the absence of DSBs, 53BP1 is abundant in the nucleoplasm; DSB formation results in its rapid localization to the damaged chromatin. Mitotic 53BP1 is also localized at the centrosome and spindle pole. 53BP1 depletion induces mitotic defects such as disorientation of spindle poles attributed to extra centrosomes or mispositioning of centrosomes, leading to phenotypes similar to those in USP7-deficient cells. Here, we discuss how 53BP1 controls the centrosomal integrity through its interaction with USP7 and centromere protein F by regulation of its stability and its physiology in response to DNA damage.


title: Dynamic Target Tracking and Obstacle Avoidance using a Drone

abstract: This paper focuses on tracking dynamic targets using a low cost, commercially available drone. The approach presented utilizes a computationally simple potential field controller expanded to operate not only on relative positions, but also relative velocities. A brief background on potential field methods is given, and the design and implementation of the proposed controller is presented. Experimental results using an external motion capture system for localization demonstrate the ability of the drone to track a dynamic target in real time as well as avoid obstacles in its way.


title: Performance comparison of state synchronization techniques in a distributed LTE EPC

abstract: With Network Function Virtualization (NFV) generating significant interest in the network operator community, many network functions, including the LTE EPC, are being built as virtualized software appliances running on commodity hardware, as opposed to custom hardware. To provide fault tolerance and scalable performance, the virtualized network functions are typically built in a clustered architecture, with a front-end load balancer distributing incoming traffic across multiple replicas, and the replicas synchronizing shared state with each other for resilience and consistency. Our work compares several distributed designs of the LTE EPC along the axis of the frequency of state synchronization across the replicas, and quantifies the performance overhead of state synchronization for control plane and data plane operations. Using experiments with our distributed EPC prototypes, we show that synchronizing state for every message incurs a prohibitive performance penalty (over 70% reduction in throughput as compared to the case of no synchronization). On the other hand, synchronizing state at session boundaries, while providing lower fault tolerance guarantees, imposes a smaller performance penalty. We believe that our open-source prototypes and experimental results will guide future distributed EPC designs, and help operators pick the right design that is appropriate for their fault tolerance and performance requirements.


Extraction of text regions in natural images


title: Oral health status in adult patients with newly diagnosed acute leukemia

abstract: BACKGROUND ::: The aim of this cross-sectional study was to evaluate the oral health of adult patients with newly diagnosed acute leukemia. ::: ::: ::: METHODS ::: Patients with initially diagnosed acute myeloid (AML) or lymphocytic (ALL) leukemia and a matched healthy control (HC) group were included. The oral investigation comprised inspection of the oral mucosa; the decayed (D), missing (M), and filled (F) teeth (DMF-T) index; and a detailed periodontal status. Subgingival biofilm samples were analyzed (polymerase chain reaction) for the presence of selected potentially periodontal pathogenic bacteria. Statistical analysis was performed using Fisher's exact test, chi-squared test, and Mann-Whitney U test (significance level α = 5%). ::: ::: ::: RESULTS ::: Thirty-nine patients with leukemia (AML 26, ALL 13) and 38 HCs were included. Oral mucosal findings were present in 62% of L compared to 0% of HC patients, whereby gingival hyperplasia was the most detected finding. Furthermore, a higher caries prevalence in leukemia patients was shown (D value 3.64 ± 3.98 vs. 0.72 ± 1.72, p < 0.01). The periodontal parameters were poorer in leukemia patients. No substantial differences in microbiological findings of selected bacteria were detected within L group and between L and HC patients. ::: ::: ::: CONCLUSION ::: The high prevalence of oral diseases supports the demand of an early and consequent dental treatment of leukemia patients, especially considering subsequent therapy.


title: A triple active bridge DC-DC converter capable of achieving full-range ZVS

abstract: In this paper, a triple active bridge converter is proposed. The topology is capable of achieving ZVS across the full load range with wide input voltage while minimizing heavy load conduction losses to increase overall efficiency. This topology comprises three full bridges coupled by a three-winding transformer. At light load, by adjusting the phase shift between two input bridges, all switching devices can maintain ZVS due to a controlled circulating current. At heavy load, the two input bridges work in parallel to reduce conduction loss. The operation principles of this topology are introduced and the ZVS boundaries are derived. Based on analytical models of power loss, a 200W laboratory prototype has been built to verify theoretical considerations.


Corpas na Gaeilge ( 1882-1926 ) : Integrating Historical and Modern Irish Texts


title: Chinese NER Using Lattice LSTM

abstract: We investigate a lattice-structured LSTM model for Chinese NER, which encodes a sequence of input characters as well as all potential words that match a lexicon. Compared with character-based methods, our model explicitly leverages word and word sequence information. Compared with word-based methods, lattice LSTM does not suffer from segmentation errors. Gated recurrent cells allow our model to choose the most relevant characters and words from a sentence for better NER results. Experiments on various datasets show that lattice LSTM outperforms both word-based and character-based LSTM baselines, achieving the best results.


Analysis of Privacy and Security Affecting the Intention of Use in Personal Data Collection in an IoT Environment


title: Management of risk in the information age

abstract: Linked together, organisations can exchange information and engage in transactions in ways unanticipated before, the emphasis being on information, which became core to most business activities and without which business will fail to operate [Owens S. Information security management :an introduction. London: British Standards Institution; 1998. pp. 1-2]. Consequently, to contribute to ensuring business continuity, the protection of information resources had to be pursued. Risk analysis was traditionally used to analyse risks posing a threat to mostly IT assets [Jung C, Han I, Suh B. Risk analysis for electronic commerce using case-based reasoning. International Journal of Intelligent Systems in Accounting, Finance & Management 1999;8:61-73. John Wiley & Sons, Ltd., p. 62]. Resulting in recommendations for the implementation of appropriate security measures, to reduce those identified high priority risks to an acceptable level. However, Bandyopadhyay et al. [Bandyopadhyay K, Mykytyn PP, Mykytyn K. A framework for integrated risk management in information technology. Management Decision 1999;37(5):437-44. MCB Press, p. 440] state that the evaluation of risk related to IT alone is unrealistic. A holistic view of assessing risks should instead be adopted, moving away from the isolated and partial view of today's ''closed world assumption'' of searching only within a specific domain to evaluate the risks associated to IT, to consider the entire spectrum related to the IT environment. Thus an alternative approach to risk analysis might have to be developed, to assist in analysing risks to information-specific resources.


title: A compact LTE multi-band loop antenna with additional 3400–3800 MHz coverage for mobile phone application

abstract: In this paper a multi-band loop antenna for 4G-LTE mobile phone application was proposed. The antenna has a compact volume of 60×6×5mm3 and covering 824–960, 1710–2530, 3400–3800 MHz with over 60% simulated efficiency.


title: Incentivizing Reliable Demand Response with Customers' Uncertainties and Capacity Planning

abstract: One of the major issues with the integration of renewable energy sources into the power grid is the increased uncertainty and variability that they bring. If this uncertainty is not sufficiently addressed, it will limit the further penetration of renewables into the grid and even result in blackouts. Compared to energy storage, Demand Response (DR) has advantages to provide reserves to the load serving entities (LSEs) in a cost-effective and environmentally friendly way. DR programs work by changing customers' loads when the power grid experiences a contingency such as a mismatch between supply and demand. Uncertainties from both the customer-side and LSE-side make designing algorithms for DR a major challenge. This paper makes the following main contributions: (i) We propose DR control policies based on the optimal structures of the offline solution. (ii) A distributed algorithm is developed for implementing the control policies without efficiency loss. (iii) We further offer an enhanced policy design by allowing flexibilities into the commitment level. (iv) We perform real world trace based numerical simulations which demonstrate that the proposed algorithms can achieve near optimal social cost. Details can be found in our extended version.


title: Integrating art historical, psychological, and neuroscientific explanations of artists' advantages in drawing and perception.

abstract: Art historians, artists, psychologists, and neuroscientists have long asserted that artists perceive the world differently than nonartists. Although empirical research on the nature and correlates of skilled drawing is limited, the available evidence supports this view: artists outperform nonartists on visual analysis and form recognition tasks and their perceptual advantages are correlated with and can be largely accounted for by drawing skill. The authors propose an integrative model to explain these results, derived from research in psychology and cognitive neuroscience on how category knowledge, attention, and motor plans influence visual perception. The authors claim that (a) artists' specialized, declarative knowledge of the structure of objects' appearances and (b) motor priming achieved via proceduralization and practice in an artistic medium both contribute to attention-shifting mechanisms that enhance the encoding of expected features in the visual field and account for artists' advantages in drawing and visual analysis. Suggestions for testing the model are discussed. Skilled visual artists are able to create convincing representa- tional drawings and paintings; in contrast, realistic rendering is extraordinarily difficult for most nonartists. How can we under- stand this profound disparity? One long-standing explanation is that artists perceive the world differently than nonartists. This view has been espoused by artists (Goldwater & Treves, 1972; Schlewitt-Haynes, Earthman, & Burns, 2002), art instructors (Dodson, 1985; Hale, 1964), art critics and historians (Fry, 1919/ 1981; Gombrich, 1960; Ruskin, 1857/1971), psychologists (Arnheim, 1954; Cohen & Bennett, 1997; Kozbelt, 2001; Mitchell, Ropar, Ackroyd, & Rajendran, 2005), and neuroscientists (Chatterjee, 2004; Livingstone, 2002; Ramachandran & Hirstein, 1999; Zeki & Lamb, 1994). However, far from resolving the issue, this claim raises a host of additional questions. If the assertion is true, what is the nature of artists' perceptual differences? What mechanisms are involved? How are such differences related to drawing skill? In this article, we attempt to integrate these perspectives into a unified account and offer a provisional explanation of psycholog- ical mechanisms underlying artists' drawing and perceptual abili- ties. Although the psychological basis of skilled drawing remains a relatively neglected area of investigation, the available evidence supports the claim that artists do perceive the world differently, and in some respects better, than nonartists. By better, we mean objectively measurable superior performance on tasks relevant to meaningful computational goals, such as recognizing objects in blurry or otherwise degraded images. We review theoretical per- spectives on artists' perceptual abilities from several disciplines and examine supporting empirical evidence. We then propose an integrative, interdisciplinary model to account for these results. The model is derived from recent research in psychology and cognitive neuroscience on the roles played by category knowledge and motor planning in visual attention and perception. We argue that artists develop attentional strategies that enhance the encoding of expected features in the visual field. These strategies operate in two ways: (a) via specialized, declarative knowledge of the "struc- ture of appearances" of objects and scenes and (b) via motor priming achieved through proceduralization and practice of pro- ductive techniques in artistic media. Both of these focus attention on stimulus features relevant for adequate depiction. Because these features are also diagnostic for identifying objects, we argue that artists' attentional strategies account for their perceptual advan- tages as well. We conclude by proposing ways to test the model.


title: AlGaN/GaN-Based HEMTs Failure Physics and Reliability: Mechanisms Affecting Gate Edge and Schottky Junction

abstract: This paper presents a comprehensive review of AlGaN/GaN high electron mobility transistor failure physics and reliability, focusing on mechanisms affecting the gate-drain edge, where maximum electric field and peak temperatures are reached. Physical effects at the origin of device degradation (inverse piezoelectric effect, time-dependent trap formation and percolative conductive paths formation, and electrochemical AlGaN and GaN degradation) are discussed on the basis of literature data and unpublished results. Thermally activated mechanisms involving metal-metal and metal-semiconductor interdiffusion at the gate Schottky junction are also discussed.


Epistemic Network Analysis and Topic Modeling for Chat Data from Collaborative Learning Environment


title: Coffee Ingestion Enhances One-Mile Running Race Performance

abstract: Purpose:Caffeine, often in the form of coffee, is frequently supplemented by athletes in an attempt to facilitate improved performance during exercise. The aim of the present study was to investigate the effectiveness of coffee ingestion as an ergogenic aid prior to a one-mile (1609 m) race. Methods:In a double-blind, randomised, crossover, placebo-controlled design 13 trained male runners completed a one-mile race 60 minutes following the ingestion of 0.09 g·kg-1 coffee (COF), 0.09 g·kg-1 decaffeinated coffee (DEC), or a placebo (PLA). All trials were dissolved in 300 ml of hot water. Results:The race completion time was 1.3% faster following the ingestion of COF (04:35:37 ± 00:10:51 mm·ss) compared with DEC (04:39:14 ± 00:11:21 mm·ss; P=0.018; 95%CI: -0.11, -0.01; d=0.32) and 1.9% faster compared with PLA (04:41:00 ± 00:09:57 mm:ss; P=0.006; 95%CI: -0.15, -0.03; d=0.51). A large trial and time interaction for salivary caffeine concentration was observed (P<0.001; η2P=0.69) with a very large increase (6....


title: Detecting and Resolving Firewall Policy Anomalies

abstract: The advent of emerging computing technologies such as service-oriented architecture and cloud computing has enabled us to perform business services more efficiently and effectively. However, we still suffer from unintended security leakages by unauthorized actions in business services. Firewalls are the most widely deployed security mechanism to ensure the security of private networks in most businesses and institutions. The effectiveness of security protection provided by a firewall mainly depends on the quality of policy configured in the firewall. Unfortunately, designing and managing firewall policies are often error prone due to the complex nature of firewall configurations as well as the lack of systematic analysis mechanisms and tools. In this paper, we represent an innovative policy anomaly management framework for firewalls, adopting a rule-based segmentation technique to identify policy anomalies and derive effective anomaly resolutions. In particular, we articulate a grid-based representation technique, providing an intuitive cognitive sense about policy anomaly. We also discuss a proof-of-concept implementation of a visualization-based firewall policy analysis tool called Firewall Anomaly Management Environment (FAME). In addition, we demonstrate how efficiently our approach can discover and resolve anomalies in firewall policies through rigorous experiments.


title: Eunomos, a legal document and knowledge management system for the Web to provide relevant, reliable and up-to-date information on the law

abstract: This paper describes the Eunomos software, an advanced legal document and knowledge management system, based on legislative XML and ontologies. We describe the challenges of legal research in an increasingly complex, multi-level and multi-lingual world and how the Eunomos software helps users cut through the information overload to get the legal information they need in an organized and structured way and keep track of the state of the relevant law on any given topic. Using NLP tools to semi-automate the lower-skill tasks makes this ambitious project a realistic commercial prospect as it helps keep costs down while at the same time allowing greater coverage. We describe the core system from workflow and technical perspectives, and discuss applications of the system for various user groups.


title: A real-time privacy-sensitive data hiding approach based on chaos cryptography

abstract: A multimedia surveillance system aims to provide security and safety of people in a monitored space. However, due to the nature of surveillance, privacy-sensitive information, such as face, gait and other physical parameters based on the captured media from multiple sensors, can be revealed without the concern of the people. This is a major concern in recent days. Therefore, it is desirable to have such mechanism that can hide privacy-sensitive information as much as possible, yet supporting effective surveillance tasks. In this paper, we propose a chaos cryptography based data hiding approach that can be applied on selected regions of interest (ROIs) in video camera footage, which contains privacy-sensitive data. Our approach also supports multiple levels of abstraction of data hiding depending on the role of the authorized user. In order to evaluate the suitability of this approach, we applied our algorithm on some video camera footage and observed that our approach is computationally efficient and applicable for real-time video surveillance tasks.


title: Commerce, e-commerce, and m-commerce: what comes next?

abstract: In ancient times, people exchanged their goods and services to obtain what they needed (such as clothes and tools) from other people. This system of bartering compensated for the lack of currency. People offered goods/services and received in kind other goods/services. Now, despite the existence of multiple currencies and the progress of humanity from the Stone Age to the Byte Age, people still barter but in a different way. Mainly, people use money to pay for the goods they purchase and the services they obtain. Commerce Notwithstanding the technologies that are involved, undertaking commerce can be associated with one of the four types of exchange: bargaining, bidding, auctioning, and clearing [3]. The first two types of exchange are bilateral and the last two types of exchange are trilateral (that is, a third party intervenes). • Bargaining involves one user that negotiates with a provider until an agreement between both is reached. First, the user looks for a provider, browses their products, and then negotiates with the provider for an agreement. If the negotiation fails, the user continues searching for other providers until an agreement with one of them is reached. • Bidding involves one user and several providers. First, the user calls for bids. Next, the user compares the offers that providers have submitted after receiving the call for bids. Finally, the user selects the provider that has made the lowest offer (that is, the offer that minimizes the user’s expense). • Auctioning (English scenario) involves one provider, several potential users, and one broker. First, the provider fixes the lowest price of the product. Through the broker, the provider advertises their products and calls for auctions. Next, the different users respond to the call for auctions by making offers to the broker. Acting on the provider’s behalf, the broker selects the user who has made the highest offer regarding the first offer of the provider (that is, the user’s offer maximizes