A comparative analysis of biclustering algorithms for gene expression data

The need to analyze high-dimension biological data is driving the development of new data mining methods. Biclustering algorithms have been successfully applied to gene expression data to discover local patterns, in which a subset of genes exhibit similar expression levels over a subset of conditions. However, it is not clear which algorithms are best suited for this task. Many algorithms have been published in the past decade, most of which have been compared only to a small number of algorithms. Surveys and comparisons exist in the literature, but because of the large number and variety of biclustering algorithms, they are quickly outdated. In this article we partially address this problem of evaluating the strengths and weaknesses of existing biclustering methods. We used the BiBench package to compare 12 algorithms, many of which were recently published or have not been extensively studied. The algorithms were tested on a suite of synthetic data sets to measure their performance on data with varying conditions, such as different bicluster models, varying noise, varying numbers of biclusters and overlapping biclusters. The algorithms were also tested on eight large gene expression data sets obtained from the Gene Expression Omnibus. Gene Ontology enrichment analysis was performed on the resulting biclusters, and the best enrichment terms are reported. Our analyses show that the biclustering method and its parameters should be selected based on the desired model, whether that model allows overlapping biclusters, and its robustness to noise. In addition, we observe that the biclustering algorithms capable of finding more than one model are more successful at capturing biologically relevant clusters.


Digital signature based on PlayGamal algorithm

To keep the security and integrity of digital messages, can be applied cryptography method. Digital Signature is one of the vital digital data, so the secrecy must be maintained at the time of distribution and storage of data. This study aims to combine modern cryptographic methods with ElGamal traditional algorithm method is called PlayGamal Cipher Algorithm. Moreover. the method will be implement as application for digital signature. The proposed method will be compare with the existing El-Gamal to know the performance during encryption and decryption process.


Chiron: a parallel engine for algebraic scientific workflows

Large-scale scientific experiments based on computer simulations are typically modeled as scientific workflows, which eases the chaining of different programs. These scientific workflows are defined, executed, and monitored by scientific workflowmanagement systems (SWfMS). As these experiments manage large amounts of data, it becomes critical to execute them in high-performance computing environments, such as clusters, grids, and clouds. However, few SWfMS provide parallel support. The ones that do so are usually labor-intensive for workflow developers and have limited primitives to optimize workflow execution. To address these issues, we developed workflow algebra to specify and enable the optimization of parallel execution of scientific workflows. In this paper, we show how the workflow algebra is efficiently implemented in Chiron, an algebraic based parallel scientific workflow engine. Chiron has a unique native distributed provenance mechanism that enables runtime queries in a relational database. We developed two studies to evaluate the performance of our algebraic approach implemented in Chiron; the first study compares Chiron with different approaches, whereas the second one evaluates the scalability of Chiron. By analyzing the results, we conclude that Chiron is efficient in executing scientific workflows, with the benefits of declarative specification and runtime provenance support. Copyright © 2013 John Wiley & Sons, Ltd.


Change from baseline and analysis of covariance revisited.

The case for preferring analysis of covariance (ANCOVA) to the simple analysis of change scores (SACS) has often been made. Nevertheless, claims continue to be made that analysis of covariance is biased if the groups are not equal at baseline. If the required equality were in expectation only, this would permit the use of ANCOVA in randomized clinical trials but not in observational studies. The discussion is related to Lord's paradox. In this note, it is shown, however that it is not a necessary condition for groups to be equal at baseline, not even in expectation, for ANCOVA to provide unbiased estimates of treatment effects. It is also shown that although many situations can be envisaged where ANCOVA is biased it is very difficult to imagine circumstances under which SACS would then be unbiased and a causal interpretation could be made.


Determinants of Behavioral Intention to Mobile Banking Case From Yemen

Nowadays, new tools and technologies are emerging rapidly. They are often used cross-culturally before being tested for suitability and validity. However, they must be validated to ensure that they work with all users, not just part of them. Mobile banking (as a new technology tool) has been introduced assuming that it performs well concerning authentication, among all members of the society. Our research aimed to evaluate authentication mobile banking user acceptance, through Technology Acceptance Model (TAM), in Arabic countries, namely Yemen. The results confirm the previous studies that have shown the importance of perceived ease of use and perceived usefulness. Furthermore, perceived ease of use plays a determinant role. KeywordsTechnology acceptance models; Mobile Banking;


Reliability and validity of the Evaluation Tool of Children's Handwriting-Cursive (ETCH-C) using the general scoring criteria.

OBJECTIVES
To determine the reliability and aspects of validity of the Evaluation Tool of Children's Handwriting-Cursive (ETCH-C; Amundson, 1995), using the general scoring criteria, when assessing children who use alternative writing scripts.

METHOD
Children in Years 5 and 6 with handwriting problems and a group of matched control participants from their respective classrooms were assessed with the ETCH-C twice, 4 weeks apart.

RESULTS
Total Letter scores were most reliable; more variability should be expected for Total Word scores. Total Numeral scores showed unacceptable reliability levels and are not recommended. We found good discriminant validity for Letter and Word scores and established cutoff scores to distinguish children with and without handwriting dysfunction (Total Letter <90%, Total Word <85%).

CONCLUSION
The ETCH-C, using the general scoring criteria, is a reliable and valid test of handwriting for children using alternative scripts.


Design, Analysis, and Optimization of Ironless Stator Permanent Magnet Machines

This paper presents a methodology for the design, analysis, and graphical optimization of ironless brushless permanent magnet machines primarily for generator applications. Magnetic flux in this class of electromagnetic machine tends to be 3-D due to the lack of conventional iron structures and the absence of a constrained magnetic flux path. The proposed methodology includes comprehensive geometric, magnetic and electrical dimensioning followed by detailed 3-D finite element (FE) modeling of a base machine for which parameters are determined. These parameters are then graphically optimized within sensible volumetric and electromagnetic constraints to arrive at improved design solutions. This paper considers an ironless machine design to validate the 3-D FE model to optimize power conversion for the case of a low-speed, ironless stator generator. The machine configuration investigated in this paper has concentric arrangement of the rotor and the stator, solenoid-shaped coils, and a simple mechanical design considered for ease of manufacture and maintenance. Using performance and material effectiveness as the overriding optimization criteria, this paper suggests optimal designs configurations featuring two different winding arrangements, i.e., radial and circumferentially mounted. Performance and material effectiveness of the studied ironless stator designs are compared to published ironless machine configurations.


Radiation Efficiency Measurement Method for Passive UHF RFID Dipole Tag Antennas

Concurrently with the continuously developing radio frequency identification (RFID) technology, new types of tag antenna materials and structures are emerging to fulfill the requirements encountered within the new application areas. In this work, a radiation efficiency measurement method is developed and verified for passive ultra-high frequency (UHF) RFID dipole tag antennas. In addition, the measurement method is applied to measure the radiation efficiency of sewed dipole tag antennas for wearable body-centric wireless communication applications. The acquired information from measurements can be used to characterize tag antenna material structures losses and to further both improve and optimize tag antenna performance and reliability.


Failure Trends in a Large Disk Drive Population

It is estimated that over 90% of all new information produced in the world is being stored on magnetic media, most of it on hard disk drives. Despite their importance, there is relatively little published work on the failure patterns of disk drives, and the key factors that affect their lifetime. Most available data are either based on extrapolation from accelerated aging experiments or from relatively modest sized field studies. Moreover, larger population studies rarely have the infrastructure in place to collect health signals from components in operation, which is critical information for detailed failure analysis. We present data collected from detailed observations of a large disk drive population in a production Internet services deployment. The population observed is many times larger than that of previous studies. In addition to presenting failure statistics, we analyze the correlation between failures and several parameters generally believed to impact longevity. Our analysis identifies several parameters from the drive’s self monitoring facility (SMART) that correlate highly with failures. Despite this high correlation, we conclude that models based on SMART parameters alone are unlikely to be useful for predicting individual drive failures. Surprisingly, we found that temperature and activity levels were much less correlated with drive failures than previously reported.


DRAM errors in the wild: a large-scale field study

Errors in dynamic random access memory (DRAM) are a common form of hardware failure in modern compute clusters. Failures are costly both in terms of hardware replacement costs and service disruption. While a large body of work exists on DRAM in laboratory conditions, little has been reported on real DRAM failures in large production clusters. In this paper, we analyze measurements of memory errors in a large fleet of commodity servers over a period of 2.5 years. The collected data covers multiple vendors, DRAM capacities and technologies, and comprises many millions of DIMM days.
 The goal of this paper is to answer questions such as the following: How common are memory errors in practice? What are their statistical properties? How are they affected by external factors, such as temperature and utilization, and by chip-specific factors, such as chip density, memory technology and DIMM age?
 We find that DRAM error behavior in the field differs in many key aspects from commonly held assumptions. For example, we observe DRAM error rates that are orders of magnitude higher than previously reported, with 25,000 to 70,000 errors per billion device hours per Mbit and more than 8% of DIMMs affected by errors per year. We provide strong evidence that memory errors are dominated by hard errors, rather than soft errors, which previous work suspects to be the dominant error mode. We find that temperature, known to strongly impact DIMM error rates in lab conditions, has a surprisingly small effect on error behavior in the field, when taking all other factors into account. Finally, unlike commonly feared, we don't observe any indication that newer generations of DIMMs have worse error behavior.


A scalable, commodity data center network architecture

Today's data centers may contain tens of thousands of computers with significant aggregate bandwidth requirements. The network architecture typically consists of a tree of routing and switching elements with progressively more specialized and expensive equipment moving up the network hierarchy. Unfortunately, even when deploying the highest-end IP switches/routers, resulting topologies may only support 50% of the aggregate bandwidth available at the edge of the network, while still incurring tremendous cost. Non-uniform bandwidth among data center nodes complicates application design and limits overall system performance.
 In this paper, we show how to leverage largely commodity Ethernet switches to support the full aggregate bandwidth of clusters consisting of tens of thousands of elements. Similar to how clusters of commodity computers have largely replaced more specialized SMPs and MPPs, we argue that appropriately architected and interconnected commodity switches may deliver more performance at less cost than available from today's higher-end solutions. Our approach requires no modifications to the end host network interface, operating system, or applications; critically, it is fully backward compatible with Ethernet, IP, and TCP.


The case for RAMClouds: scalable high-performance storage entirely in DRAM

Disk-oriented approaches to online storage are becoming increasingly problematic: they do not scale gracefully to meet the needs of large-scale Web applications, and improvements in disk capacity have far outstripped improvements in access latency and bandwidth. This paper argues for a new approach to datacenter storage called RAMCloud, where information is kept entirely in DRAM and large-scale systems are created by aggregating the main memories of thousands of commodity servers. We believe that RAMClouds can provide durable and available storage with 100-1000x the throughput of disk-based systems and 100-1000x lower access latency. The combination of low latency and large scale will enable a new breed of dataintensive applications.


Sketching in circuits: designing and building electronics on paper

The field of new methods and techniques for building electronics is quickly growing - from research in new materials for circuit building, to modular toolkits, and more recently to untoolkits, which aim to incorporate more off-the-shelf parts. However, the standard mediums for circuit design and construction remain the breadboard, protoboard, and printed circuit board (PCB). As an alternative, we introduce a method in which circuits are hand-made on ordinary paper substrates, connected with conductive foil tape and off-the-shelf circuit components with the aim of supporting the durability, scalability, and accessibility needs of novice and expert circuit builders alike. We also used electrified notebooks to investigate how the circuit design and build process would be affected by the constraints and affordances of the bound book. Our ideas and techniques were evaluated through a series of workshops, through which we found our methods supported a wide variety of approaches and results - both technical and expressive - to electronics design and construction.


Entailment-based Fully Automatic Technique for Evaluation of Summaries

We propose a fully automatic technique for evaluating text summaries without the need to prepare the gold standard summaries manually. A standard and popular summary evaluation techniques or tools are not fully automatic; they all need some manual process or manual reference summary. Using recognizing textual entailment (TE), automatically generated summaries can be evaluated completely automatically without any manual preparation process. We use a TE system based on a combination of lexical entailment module, lexical distance module, Chunk module, Named Entity module and syntactic text entailment (TE) module. The documents are used as text (T) and summary of these documents are taken as hypothesis (H). Therefore, the more information of the document is entailed by its summary the better the summary. Comparing with the ROUGE 1.5.5 evaluation scores over TAC 2008 (formerly DUC, conducted by NIST) dataset, the proposed evaluation technique predicts the ROUGE scores with a accuracy of 98.25% with respect to ROUGE-2 and 95.65% with respect to ROUGE-SU4.


Hemispheric asymmetry reduction in older adults: the HAROLD model.

A model of the effects of aging on brain activity during cognitive performance is introduced. The model is called HAROLD (hemispheric asymmetry reduction in older adults), and it states that, under similar circumstances, prefrontal activity during cognitive performances tends to be less lateralized in older adults than in younger adults. The model is supported by functional neuroimaging and other evidence in the domains of episodic memory, semantic memory, working memory, perception, and inhibitory control. Age-related hemispheric asymmetry reductions may have a compensatory function or they may reflect a dedifferentiation process. They may have a cognitive or neural origin, and they may reflect regional or network mechanisms. The HAROLD model is a cognitive neuroscience model that integrates ideas and findings from psychology and neuroscience of aging.


Salient Object Detection: A Survey

Detecting and segmenting salient objects in natural scenes, often referred to as salient object detection, has attracted a lot of interest in computer vision. While many models have been proposed and several applications have emerged, yet a deep understanding of achievements and issues is lacking. We aim to provide a comprehensive review of the recent progress in salient object detection and situate this field among other closely related areas such as generic scene segmentation, object proposal generation, and saliency for fixation prediction. Covering 228 publications, we survey i) roots, key concepts, and tasks, ii) core techniques and main modeling trends, and iii) datasets and evaluation metrics in salient object detection. We also discuss open problems such as evaluation metrics and dataset bias in model performance and suggest future research directions.


How to stop spread of misinformation on social media: Facebook plans vs. right-click authenticate approach

One of the key features of social networks is that users are able to share information, and through cascades of sharing information, this information may reach a large number of individuals. The high availability of user-provided contents on online social media facilitates people aggregation around shared beliefs, interests, worldviews and narratives. With lack of means to verify information, social media has been accused of becoming a hot bed for sharing of misinformation. Facebook, as one of the largest social networking services, has been facing widespread criticism on how its newsfeed algorithm is designed thus amplifying dissemination of misinformation. In late 2016, Facebook revealed plans to address fake news on Facebook newsfeeds. In this work, we study the methods Facebook has proposed to combat the spread of misinformation and compare it with our previously proposed approach called ‘Right-click Authenticate’. By analyzing the Business Process Modeling and Notation of both approaches, this paper suggests some key weaknesses and improvements social media companies need to consider when tackling the spread of misinformation online.


Instantaneous ego-motion estimation using multiple Doppler radars

The estimation of the ego-vehicle's motion is a key capability for advanced driving assistant systems and mobile robot localization. The following paper presents a robust algorithm using radar sensors to instantly determine the complete 2D motion state of the ego-vehicle (longitudinal, lateral velocity and yaw rate). It evaluates the relative motion between at least two Doppler radar sensors and their received stationary reflections (targets). Based on the distribution of their radial velocities across the azimuth angle, non-stationary targets and clutter are excluded. The ego-motion and its corresponding covariance matrix are estimated. The algorithm does not require any preprocessing steps such as clustering or clutter suppression and does not contain any model assumptions. The sensors can be mounted at any position on the vehicle. A common field of view is not required, avoiding target association in space. As an additional benefit, all targets are instantly labeled as stationary or non-stationary.


The capacity of low-density parity-check codes under message-passing decoding

In this paper we present a general method for determining the capacity of low-density paritycheck codes under message-passing decoding when used over any binary-input memoryless channel with discrete or continuous output alphabets. Transmitting at rates below this capacity, a randomly chosen element of the given ensemble will achieve an arbitrarily small target probability of error with a probability that approaches one exponentially fast in the length of the code. (By concatenating with an appropriate outer code one can achieve a probability of error that approaches zero exponentially fast in the length of the code with arbitrarily small loss in rate.) Conversely, transmitting at rates above this capacity the probability of error is bounded away from zero by a strictly positive constant which is independent of the length of the code and of the number of iterations performed. Our results are based on the observation that the concentration of the performance of the decoder around its average performance, as observed by Luby et. al. [1] in the case of a binary symmetric channel and a binary message passing algorithm, is a general phenomenon. For the particularly important case of belief-propagation decoders, we provide an effective algorithm to determine the corresponding capacity to any desired degree of accuracy. The ideas presented in this paper are broadly applicable and extensions of the general method to low-density parity-check codes over larger alphabets, turbo codes, and other concatenated coding schemes are outlined. Index Terms – low-density parity-check codes, turbo codes, message-passing decoders, iterative decoding, belief-propagation, turbo decoding


Model-Based Wheel Slip Detection for Outdoor Mobile Robots

This paper introduces a model-based approach to estimating longitudinal wheel slip and detecting immobilized conditions of autonomous mobile robots operating on outdoor terrain. A novel tire traction/braking model is presented and used to calculate vehicle dynamic forces in an extended Kalman filter framework. Estimates of external forces and robot velocity are derived using measurements from wheel encoders, IMU, and GPS. Weak constraints are used to constrain the evolution of the resistive force estimate based upon physical reasoning. Experimental results show the technique accurately and rapidly detects robot immobilization conditions while providing estimates of the robot's velocity during normal driving. Immobilization detection is shown to be robust to uncertainty in tire model parameters. Accurate immobilization detection is demonstrated in the absence of GPS, indicating the algorithm is applicable for both terrestrial applications and space robotics.


Robot Modeling and Control [Book Review]

The field of robotics began in the 1960s and 1970s with the design and control of general purpose robotic manipulators. During the 1980s the expense of integrating robotics into manufacturing lines and the difficulty of performing seemingly easy tasks, such as manipulating flexible objects or washing a window, led to disenchantment among many researchers and funding agencies. Since the 1990s the popularity of robotics has resurged with many new exciting areas of application particularly in the medical, defense, and service sectors. Robotics has grown from a field that is primarily concerned with problems such as the manipulation of rigid bodies in well-structured environments to a field that has tackled challenging problems such as extraterrestrial exploration and remote minimally invasive surgery. I began my involvement in the field of robotics as a graduate student studying control theory. My project was to design a control algorithm to induce stable walking in a biped robot. As with many control problems, before formulating a solution I had to determine a suitable model for the plant. Since I was an electrical engineering student with little background in the kinematics and dynamics of rigid-body mechanical systems, this task was daunting. With some guidance from a fellow student, I fumbled my way through Lagrange’s method to derive the robot’s equations of motion. I had little idea at the time why forming the Lagrangian and taking certain derivatives resulted in the right equations. For insight I was pointed to various texts on classical mechanics. While these texts provided the desired insight, I was still left with questions: Given an arbitrary system, how do I systematically construct the Lagrangian so that I can derive the system’s equation of motion? What are the most effective techniques for robot control? What special aspects of the robot’s dynamics should be considered in developing controllers? Five years after my introduction to the field of robotics, I now know that research in the field goes well beyond the dynamics and control of traditional manipulators. The concepts required to analyze and control manipulators, however, are still needed in modern robotics research. These fundamental concepts, which are addressed by many introductory texts on robotics, are the following: 1) Forward kinematics: Given joint displacements, determine the position and orientation of the endeffector with respect to a specified reference frame. 2) Inverse kinematics: Given the position and orientation of the end-effector frame relative to a specified reference frame, determine the joint variables that give rise to this configuration. 3) Differential kinematics: Given the manipulator’s joint rates, determine the linear and angular velocities of the end-effector frame relative to a fixed reference frame. 4) Statics: Given the forces and moments applied to the manipulator, determine the corresponding joint forces and torques required to maintain equilibrium in the presence of applied forces and moments. 5) Dynamics: Determine the equations of motion of the system. Specifically, determine the differential equations relating the applied forces and torques to the joint variables, joint rates, and joint accelerations. 6) Trajectory generation: Given initial and final configurations of the manipulator, find a trajectory of the joint variables connecting these two configurations. 7) Control: Given desired joint trajectories or forces between the end-effector and the environment determine joint torques and forces that effect these joint trajectories and forces.


K-modestream algorithm for clustering categorical data streams

Clustering categorical data streams is a challenging problem because new data points are continuously adding to the already existing database at rapid pace and there exists no natural order among the categorical values. Recently, some algorithms have been discussed to tackle the problem of clustering the categorical data streams. However, in all these schemes the user needs to pre-specify the number of clusters, which is not trivial, and it renders to inefficient in the data stream environment. In this paper, we propose a new clustering algorithm, named it as k-modestream, which follows the k-modes algorithm paradigm to dynamically cluster the categorical data streams. It automatically computes the number of clusters and their initial modes simultaneously at regular time intervals. We analyse the time complexity of our scheme and perform various experiments using the synthetic and real world datasets to evaluate its efficacy.


A 3.5–6.8GHz wide-bandwidth DTC-assisted fractional-N all-digital PLL with a MASH ΔΣ TDC for low in-band phase noise

We present a digital-to-time converter (DTC)-assisted fractional-N wide-bandwidth all-digital PLL (ADPLL). It employs a MASH ΔΣ time-to-digital converter (TDC) to achieve low in-band phase noise, and a wide-tuning range digitally-controlled oscillator (DCO). Fabricated in 40nm CMOS, the ADPLL consumes 10.7 mW while outputting 1.73 to 3.38 GHz (after a ÷2 division) and achieves better than -109 dBc/Hz in-band phase noise and 420fsrms integrated jitter.


Measuring Semantic Similarity in the Taxonomy of WordNet

This paper presents a new model to measure semantic similarity in the taxonomy of WordNet, using edgecounting techniques. We weigh up our model against a benchmark set by human similarity judgment, and achieve a much improved result compared with other methods: the correlation with average human judgment on a standard 28 word pair dataset is 0.921, which is better than anything reported in the literature and also significantly better than average individual human judgments. As this set has been effectively used for algorithm selection and tuning, we also cross-validate an independent 37 word pair test set (0.876) and present results for the full 65 word pair superset (0.897).


A Hardware-Assisted Realtime Attack on A5/2 Without Precomputations

A5/2 is a synchronous stream cipher that is used for protecting GSM communication. Recently, some powerful attacks [2,10] on A5/2 have been proposed. In this contribution we enhance the ciphertext-only attack [2] by Barkan, Biham, and Keller by designing special-purpose hardware for generating and solving the required systems of linear equations. For realizing the LSE solver component, we use an approach recently introduced in [5,6] describing a parallelized hardware implementation of the Gauss-Jordan algorithm. Our hardware-only attacker immediately recovers the initial secret state of A5/2 which is sufficient for decrypting all frames of a session using a few ciphertext frames without any precomputations and memory. More precisely, in contrast to [2] our hardware architecture directly attacks the GSM speech channel (TCH/FS and TCH/EFS). It requires 16 ciphertext frames and completes the attack in about 1 second. With minor changes also input from other GSM channels (e.g., SDCCH/8) can be used to mount the attack.


Patterns of Internet and Traditional News Media Use in a Networked Community

This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. The growing popularity of the World Wide Web as a source of news raises questions about the future of traditional news media. Is the Web likely to become a supplement to newspapers and television news, or a substitute for these media? Among people who have access to newspapers, television, and the World Wide Web, why do some prefer to use the Web as a source of news, while others prefer traditional news media? Drawing from a survey of 520 undergraduate students at a large public university where Internet use is woven into the fabric of daily life, this study suggests that use of the Web as a news source is positively related with reading newspapers but has no relationship with viewing television news. Members of this community use the Web mainly as a source of entertainment. Patterns of Web and traditional media exposure are examined in light of computer anxiety, desire for control, and political knowledge. This study suggests that even when computer skills and Internet access become more widespread in the general population, use of the World Wide Web as a news source seems unlikely to diminish substantially use of traditional news media. Will the World Wide Web become a supplement or substitute for traditional news media? As the World Wide Web became popularly accessible only with the advent of Mosaic browser software in 1993, it is still unclear at this early stage of the Web's development how dominant it is likely to become as a provider of daily news to the general public. Yet the rapid spread of on-line news outlets raises profound questions about the future of traditional newspapers and television news programs. The number of newspapers around the world being published …


Smart home automation system using Bluetooth technology

In this paper a low cost and user friendly remote controlled home automation system is presented using Arduino board, Bluetooth module, smartphone, ultrasonic sensor and moisture sensor. A smartphone application is used in the suggested system which allows the users to control up to 18 devices including home appliances and sensors using Bluetooth technology. Nowadays, most of conventional home automation systems are designed for special purposes while proposed system is a general purpose home automation system. Which can easily be implement in existing home. The suggested system has more features than conventional home automation systems such as an ultrasonic sensor is used for water level detection and soil moisture sensor is use for automatic plant irrigation system. This paper also describes the hardware and software architecture of system, future work and scope. The proposed prototype of home automation system is implemented and tested on hardware and it gave the exact and expected results.


A two-stage training deep neural network for small pedestrian detection

In the present paper, we propose a deep network architecture in order to improve the accuracy of pedestrian detection. The proposed method contains a proposal network and a classification network that are trained separately. We use a single shot multibox detector (SSD) as a proposal network to generate the set of pedestrian proposals. The proposal network is fine-tuned from a pre-trained network by several pedestrian data sets of large input size (512 × 512 pixels) in order to improve detection accuracy of small pedestrians. Then, we use a classification network to classify pedestrian proposals. We then combine the scores from the proposal network and the classification network to obtain better final detection scores. Experiments were evaluated using the Caltech test set, and, compared to other state-of-the-art methods of pedestrian detection task, the proposed method obtains better results for small pedestrians (30 to 50 pixels in height) with an average miss rate of 42%.


Why Have Child Maltreatment and Child Victimization Declined ?

Various forms of child maltreatment and child victimization declined as much as 40–70% from 1993 until 2004, including sexual abuse, physical abuse, sexual assault, homicide, aggravated assault, robbery, and larceny. Other child welfare indicators also improved during the same period, including teen pregnancy, teen suicide, and children living in poverty. This article reviews a wide variety of possible explanations for these changes: demography, fertility and abortion legalization, economic prosperity, increased incarceration of offenders, increased agents of social intervention, changing social norms and practices, the dissipation of the social changes from the 1960s, and psychiatric pharmacology. Multiple factors probably contributed. In particular, economic prosperity, increasing agents of social intervention, and psychiatric pharmacology have advantages over some of the other explanations in accounting for the breadth and timing of the improvements.


Development Process for AUTOSAR-based Embedded System

Recently automotive embedded system has developed highly since the advent of smart car, electric card and so on. They have various value-added system for example IPA (Intelligent Parking Assistance), BSW (Blind Spot Warning), LDWS (Lane Departure warning System), LKS(Lane Keeping System)-these are ADAS (Advanced Driver Assistance Systems). AUTOSAR (AUTomotive Open System Architecture) is the most notable industrial standard for developing automotive embedded software. AUTOSAR is a partnership of automotive manufacturers and suppliers working together to develop and establish an open industry standard for automotive E/E architectures. In this paper, we will introduce AUTOSAR briefly and demonstrate the result of automotive software LDWS (Lane Detection & Warning System) development.


Physical and psychological factors predict outcome following whiplash injury

Predictors of outcome following whiplash injury are limited to socio-demographic and symptomatic factors, which are not readily amenable to secondary and tertiary intervention. This prospective study investigated the predictive capacity of early measures of physical and psychological impairment on pain and disability 6 months following whiplash injury. Motor function (ROM; kinaesthetic sense; activity of the superficial neck flexors (EMG) during cranio-cervical flexion), quantitative sensory testing (pressure, thermal pain thresholds, brachial plexus provocation test), sympathetic vasoconstrictor responses and psychological distress (GHQ-28, TSK, IES) were measured in 76 acute whiplash participants. The outcome measure was Neck Disability Index scores at 6 months. Stepwise regression analysis was used to predict the final NDI score. Logistic regression analyses predicted membership to one of the three groups based on final NDI scores (<8 recovered, 10-28 mild pain and disability, >30 moderate/severe pain and disability). Higher initial NDI score (1.007-1.12), older age (1.03-1.23), cold hyperalgesia (1.05-1.58), and acute post-traumatic stress (1.03-1.2) predicted membership to the moderate/severe group. Additional variables associated with higher NDI scores at 6 months on stepwise regression analysis were: ROM loss and diminished sympathetic reactivity. Higher initial NDI score (1.03-1.28), greater psychological distress (GHQ-28) (1.04-1.28) and decreased ROM (1.03-1.25) predicted subjects with persistent milder symptoms from those who fully recovered. These results demonstrate that both physical and psychological factors play a role in recovery or non-recovery from whiplash injury. This may assist in the development of more relevant treatment methods for acute whiplash.


Differential privacy for location pattern mining

One main concern for individuals to participate in the data collection of personal location history records is the disclosure of their location and related information when a user queries for statistical or pattern mining results derived from these records. In this paper, we investigate how the privacy goal that the inclusion of one's location history in a statistical database with location pattern mining capabilities does not substantially increase one's privacy risk. In particular, we propose a differentially private pattern mining algorithm for interesting geographic location discovery using a region quadtree spatial decomposition to preprocess the location points followed by applying a density-based clustering algorithm. A differentially private region quadtree is used for both de-noising the spatial domain and identifying the likely geographic regions containing the interesting locations. Then, a differential privacy mechanism is applied to the algorithm outputs, namely: the interesting regions and their corresponding stay point counts. The quadtree spatial decomposition enables one to obtain a localized reduced sensitivity to achieve the differential privacy goal and accurate outputs. Experimental results on synthetic datasets are used to show the feasibility of the proposed privacy preserving location pattern mining algorithm.


From categories to subcategories: Large-scale image classification with partial class label refinement

The number of digital images is growing extremely rapidly, and so is the need for their classification. But, as more images of pre-defined categories become available, they also become more diverse and cover finer semantic differences. Ultimately, the categories themselves need to be divided into subcategories to account for that semantic refinement. Image classification in general has improved significantly over the last few years, but it still requires a massive amount of manually annotated data. Subdividing categories into subcategories multiples the number of labels, aggravating the annotation problem. Hence, we can expect the annotations to be refined only for a subset of the already labeled data, and exploit coarser labeled data to improve classification. In this work, we investigate how coarse category labels can be used to improve the classification of subcategories. To this end, we adopt the framework of Random Forests and propose a regularized objective function that takes into account relations between categories and subcategories. Compared to approaches that disregard the extra coarse labeled data, we achieve a relative improvement in subcategory classification accuracy of up to 22% in our large-scale image classification experiments.


Hemisphere lens-loaded Vivaldi antenna for time domain microwave imaging of concealed objects

The hemisphere lens-loaded Vivaldi antenna for the microwave imaging applications is designed and tested in this paper. The proposed antenna is designed to work in the wide frequency band of 1–14 GHz, and is fabricated on the FR-4 substrate. The directivity of the proposed Vivaldi antenna is enhanced using a hemispherical shape dielectric lens, which is fixed on the end-fire direction of the antenna. The proposed antenna is well suited for the microwave imaging applications because of the wide frequency range and high directivity. The design of the antenna is carried out using the CST microwave studio, and various parameters such as the return loss, the radiation pattern, the directivity, and input impedance are optimized. The maximum improvement of 4.19 dB in the directivity is observed with the designed hemisphere lens. The antenna design is validated by fabricating and testing it in an anechoic environment. Finally, the designed antenna is utilized to establish a setup for measuring the scattering coefficients of various objects and structures in the frequency band of 1–14 GHz. The two-dimensional (2D) microwave images of these objects are successfully obtained in terms of the measured wide band scattering data using a novel time domain inverse scattering approach, which shows the applicability of the proposed antenna.


ELM-ART: An Adaptive Versatile System for Web-based Instruction

This paper discusses the problems of developing versatile adaptive and intelligent learning systems that can be used in the context of practical Web-based education. We argue that versatility is an important feature of successful Web-based education systems. We introduce ELM-ART, an intelligent interactive educational system to support learning programming in LISP. ELM-ART provides all learning material online in the form of an adaptive interactive textbook. Using a combination of an overlay model and an episodic student model, ELM-ART provides adaptive navigation support, course sequencing, individualized diagnosis of student solutions, and example-based problem-solving support. Results of an empirical study show different effects of these techniques on different types of users during the first lessons of the programming course. ELM-ART demonstrates how some interactive and adaptive educational components can be implemented in WWW context and how multiple components can be naturally integrated together in a single system.


A probabilistic model for retrospective news event detection

Retrospective news event detection (RED) is defined as the discovery of previously unidentified events in historical news corpus. Although both the contents and time information of news articles are helpful to RED, most researches focus on the utilization of the contents of news articles. Few research works have been carried out on finding better usages of time information. In this paper, we do some explorations on both directions based on the following two characteristics of news articles. On the one hand, news articles are always aroused by events; on the other hand, similar articles reporting the same event often redundantly appear on many news sources. The former hints a generative model of news articles, and the latter provides data enriched environments to perform RED. With consideration of these characteristics, we propose a probabilistic model to incorporate both content and time information in a unified framework. This model gives new representations of both news articles and news events. Furthermore, based on this approach, we build an interactive RED system, HISCOVERY, which provides additional functions to present events, Photo Story and Chronicle.


A cross-collection mixture model for comparative text mining

In this paper, we define and study a novel text mining problem, which we refer to as Comparative Text Mining (CTM). Given a set of comparable text collections, the task of comparative text mining is to discover any latent common themes across all collections as well as summarize the similarity and differences of these collections along each common theme. This general problem subsumes many interesting applications, including business intelligence and opinion summarization. We propose a generative probabilistic mixture model for comparative text mining. The model simultaneously performs cross-collection clustering and within-collection clustering, and can be applied to an arbitrary set of comparable text collections. The model can be estimated efficiently using the Expectation-Maximization (EM) algorithm. We evaluate the model on two different text data sets (i.e., a news article data set and a laptop review data set), and compare it with a baseline clustering method also based on a mixture model. Experiment results show that the model is quite effective in discovering the latent common themes across collections and performs significantly better than our baseline mixture model.


Analysis of first-order anti-aliasing integration sampler

Performance of the first-order anti-aliasing integration sampler used in software-defined radio (SDR) receivers is analyzed versus all practical nonidealities. The nonidealities that are considered in this paper are transconductor finite output resistance, switch resistance, nonzero rise and fall times of the sampling clock, charge injection, clock jitter, and noise. It is proved that the filter is quite robust to all of these nonidealities except for transconductor finite output resistance. Furthermore, linearity and noise performances are all limited to design of a low-noise and highly linear transconductor.


If You Measure It, Can You Improve It?: Exploring The Value of Energy Disaggregation

Over the past few years, dozens of new techniques have been proposed for more accurate energy disaggregation, but the jury is still out on whether these techniques can actually save energy and, if so, whether higher accuracy translates into higher energy savings. In this paper, we explore both of these questions. First, we develop new techniques that use disaggregated power data to provide actionable feedback to residential users. We evaluate these techniques using power traces from 240 homes and find that they can detect homes that need feedback with as much as 84% accuracy. Second, we evaluate whether existing energy disaggregation techniques provide power traces with sufficient fidelity to support the feedback techniques that we created and whether more accurate disaggregation results translate into more energy savings for the users. Results show that feedback accuracy is very low even while disaggregation accuracy is high. These results indicate a need to revisit the metrics by which disaggregation is evaluated.


Supervised Word Sense Disambiguation using Python

In this paper, we discuss the problem of Word Sense Disambigu ation (WSD) and one approach to solving the lexical sample problem. We use training and te st data fromSENSEVAL-3 and implement methods based on Naı̈ve Bayes calculations, cosi ne comparison of word-frequency vectors, decision lists, and Latent Semantic Analysis. We a lso implement a simple classifier combination system that combines these classifiers into one WSD module. We then prove the effectiveness of our WSD module by participating in the M ultilingual Chinese-English Lexical Sample Task from SemEval-2007.


Replicability Analysis for Natural Language Processing: Testing Significance with Multiple Datasets

With the ever growing amount of textual data from a large variety of languages, domains, and genres, it has become standard to evaluate NLP algorithms on multiple datasets in order to ensure a consistent performance across heterogeneous setups. However, such multiple comparisons pose significant challenges to traditional statistical analysis methods in NLP and can lead to erroneous conclusions. In this paper we propose a Replicability Analysis framework for a statistically sound analysis of multiple comparisons between algorithms for NLP tasks. We discuss the theoretical advantages of this framework over the current, statistically unjustified, practice in the NLP literature, and demonstrate its empirical value across four applications: multi-domain dependency parsing, multilingual POS tagging, cross-domain sentiment classification and word similarity prediction.


Speech Recognition Engineering Issues in Speech to Speech Translation System Design for Low Resource Languages and Domains

Engineering automatic speech recognition (ASR) for speech to speech (S2S) translation systems, especially targeting languages and domains that do not have readily available spoken language resources, is immensely challenging due to a number of reasons. In addition to contending with the conventional data-hungry speech acoustic and language modeling needs, these designs have to accommodate varying requirements imposed by the domain needs and characteristics, target device and usage modality (such as phrase-based, or spontaneous free form interactions, with or without visual feedback) and huge spoken language variability arising due to socio-linguistic and cultural differences of the users. This paper, using case studies of creating speech translation systems between English and languages such as Pashto and Farsi, describes some of the practical issues and the solutions that were developed for multilingual ASR development. These include novel acoustic and language modeling strategies such as language adaptive recognition, active-learning based language modeling, class-based language models that can better exploit resource poor language data, efficient search strategies, including N-best and confidence generation to aid multiple hypotheses translation, use of dialog information and clever interface choices to facilitate ASR, and audio interface design for meeting both usability and robustness requirements


Effects of prior hamstring strain injury on strength, flexibility, and running mechanics.

BACKGROUND
Previous studies have shown evidence of residual scar tissue at the musculotendon junction following a hamstring strain injury, which could influence re-injury risk. The purpose of this study was to investigate whether bilateral differences in strength, neuromuscular patterns, and musculotendon kinematics during sprinting are present in individuals with a history of unilateral hamstring injury, and whether such differences are linked to the presence of scar tissue.

METHODS
Eighteen subjects with a previous hamstring injury (>5 months prior) participated in a magnetic resonance (MR) imaging exam, isokinetic strength testing, and a biomechanical assessment of treadmill sprinting. Bilateral comparisons were made for peak knee flexion torque, angle of peak torque, and the hamstrings:quadriceps strength ratio, as well as muscle activations and peak hamstring stretch during sprinting. MR images were used to measure the volumes of the proximal tendon/aponeurosis of the biceps femoris, with asymmetries considered indicative of scar tissue.

FINDINGS
A significantly enlarged proximal biceps femoris tendon volume was measured on the side of prior injury. However, no significant differences between the previously injured and uninjured limbs were found in strength measures, peak hamstring stretch, or muscle activation patterns. Further, the degree of asymmetry in tendon volume was not correlated to any of the functional measures.

INTERPRETATION
Injury-induced changes in morphology do not seem discernable from strength measures, running kinematics, or muscle activation patterns. Further research is warranted to ascertain whether residual scarring alters localized musculotendon mechanics in a way that may contribute to the high rates of muscle re-injury that are observed clinically.


Gray-level grouping (GLG): an automatic method for optimized image contrast enhancement - part II: the variations

This is Part II of the paper, "Gray-Level Grouping (GLG): an Automatic Method for Optimized Image Contrast Enhancement". Part I of this paper introduced a new automatic contrast enhancement technique: gray-level grouping (GLG). GLG is a general and powerful technique, which can be conveniently applied to a broad variety of low-contrast images and outperforms conventional contrast enhancement techniques. However, the basic GLG method still has limitations and cannot enhance certain classes of low-contrast images well, e.g., images with a noisy background. The basic GLG also cannot fulfill certain special application purposes, e.g., enhancing only part of an image which corresponds to a certain segment of the image histogram. In order to break through these limitations, this paper introduces an extension of the basic GLG algorithm, selective gray-level grouping (SGLG), which groups the histogram components in different segments of the grayscale using different criteria and, hence, is able to enhance different parts of the histogram to various extents. This paper also introduces two new preprocessing methods to eliminate background noise in noisy low-contrast images so that such images can be properly enhanced by the (S)GLG technique. The extension of (S)GLG to color images is also discussed in this paper. SGLG and its variations extend the capability of the basic GLG to a larger variety of low-contrast images, and can fulfill special application requirements. SGLG and its variations not only produce results superior to conventional contrast enhancement techniques, but are also fully automatic under most circumstances, and are applicable to a broad variety of images.


Affective Computing: A Review

Affective computing is currently one of the most active research topics, furthermore, having increasingly intensive attention. This strong interest is driven by a wide spectrum of promising applications in many areas such as virtual reality, smart surveillance, perceptual interface, etc. Affective computing concerns multidisciplinary knowledge background such as psychology, cognitive, physiology and computer sciences. The paper is emphasized on the several issues involved implicitly in the whole interactive feedback loop. Various methods for each issue are discussed in order to examine the state of the art. Finally, some research challenges and future directions are also discussed.


Advances in Instance Selection for Instance-Based Learning Algorithms

The basic nearest neighbour classifier suffers from the indiscriminate storage of all presented training instances. With a large database of instances classification response time can be slow. When noisy instances are present classification accuracy can suffer. Drawing on the large body of relevant work carried out in the past 30 years, we review the principle approaches to solving these problems. By deleting instances, both problems can be alleviated, but the criterion used is typically assumed to be all encompassing and effective over many domains. We argue against this position and introduce an algorithm that rivals the most successful existing algorithm. When evaluated on 30 different problems, neither algorithm consistently outperforms the other: consistency is very hard. To achieve the best results, we need to develop mechanisms that provide insights into the structure of class definitions. We discuss the possibility of these mechanisms and propose some initial measures that could be useful for the data miner.


Effects of sleep deprivation on performance: a meta-analysis.

To quantitatively describe the effects of sleep loss, we used meta-analysis, a technique relatively new to the sleep research field, to mathematically summarize data from 19 original research studies. Results of our analysis of 143 study coefficients and a total sample size of 1.932 suggest that overall sleep deprivation strongly impairs human functioning. Moreover, we found that mood is more affected by sleep deprivation than either cognitive or motor performance and that partial sleep deprivation has a more profound effect on functioning than either long-term or short-term sleep deprivation. In general, these results indicate that the effects of sleep deprivation may be underestimated in some narrative reviews, particularly those concerning the effects of partial sleep deprivation.


An investigation of the impacts of some external contextual factors on ERP systems success assessment: a case of firms in Baltic-Nordic region

Enterprise Resource Planning (ERP) systems are among the largest information systems (IS) investments made by firms, and the use of such systems is spreading globally. Many researchers have discussed their adoption and implementation, few have investigated the impact of external contextual factors on the success of such technologies in adopting firms. This study aims to fill this gap in research by examining the effects of three external contextual factors, i.e., industry type, industry climate, and national economic climate on ERP success assessment. We obtained data from Estonia and Finland and our analysis shows that industry and national economic climates have significant relationships with ERP success.


An Open Smart City IoT Test Bed: Street Light Poles as Smart City Spines: Poster Abstract

Street light poles will be a key enabler for a smart city's hardware infrastructure, thanks to their ubiquity throughout the city as well as access to power. We propose an IoT test bed around light poles for the city, with a modular hardware and software architecture to enable experimentation with various technologies.


RFBoost: An improved multi-label boosting algorithm and its application to text categorisation

The AdaBoost.MH boosting algorithm is considered to be one of the most accurate algorithms for multilabel classification. AdaBoost.MH works by iteratively building a committee of weak hypotheses of decision stumps. In each round of AdaBoost.MH learning, all features are examined, but only one feature is used to build a new weak hypothesis. This learning mechanism may entail a high degree of computational time complexity, particularly in the case of a large-scale dataset. This paper describes a way to manage the learning complexity and improve the classification performance of AdaBoost.MH. We propose an improved version of AdaBoost.MH, called RFBoost . The weak learning in RFBoost is based on filtering a small fixed number of ranked features in each boosting round rather than using all features, as AdaBoost.MH does. We propose two methods for ranking the features: One Boosting Round and Labeled Latent Dirichlet Allocation (LLDA), a supervised topic model based on Gibbs sampling. Additionally, we investigate the use of LLDA as a feature selection method for reducing the feature space based on the maximal conditional probabilities of words across labels. Our experimental results on eight well-known benchmarks for multi-label text categorisation show that RFBoost is significantly more efficient and effective than the baseline algorithms. Moreover, the LLDA-based feature ranking yields the best performance for RFBoost. © 2016 Elsevier B.V. All rights reserved.


Novel use of electronic whiteboard in the operating room increases surgical team compliance with pre-incision safety practices.

BACKGROUND
Despite evidence that use of a checklist during the pre-incision time out improves patient morbidity and mortality, compliance with performing the required elements of the checklist has been low. In an effort to improve compliance, a standardized time out interactive Electronic Checklist System [iECS] was implemented in all hospital operating room (OR) suites at 1 institution. The purpose of this 12-month prospective observational study was to assess whether an iECS in the OR improves and sustains improved surgical team compliance with the pre-incision time out.

METHODS
Direct observational analyses of preprocedural time outs were performed on 80 cases 1 month before, and 1 and 9 months after implementation of the iECS, for a total of 240 observed cases. Three observers, who achieved high interrater reliability (kappa = 0.83), recorded a compliance score (yes, 1; no, 0) on each element of the time out. An element was scored as compliant if it was clearly verbalized by the surgical team.

RESULTS
Pre-intervention observations indicated that surgical staff verbally communicated the core elements of the time out procedure 49.7 ± 12.9% of the time. After implementation of the iECS, direct observation of 80 surgical cases at 1 and 9 months indicated that surgical staff verbally communicated the core elements of the time out procedure 81.6 ± 11.4% and 85.8 ± 6.8% of the time, respectively, resulting in a statistically significant (P < .0001) increase in time out procedural compliance.

CONCLUSION
Implementation of a standardized, iECS can dramatically increase compliance with preprocedural time outs in the OR, an important and necessary step in improving patient outcomes and reducing preventable complications and deaths.


Self-Adaptive Skin Segmentation in Color Images

In this paper, we present a new method for skin detection and segmentation, relying on spatial analysis of skin-tone pixels. Our contribution lies in introducing self-adaptive seeds, from which the skin probability is propagated using the distance transform. The seeds are determined from a local skin color model that is learned on-line from a presented image, without requiring any additional information. This is in contrast to the existing methods that need a skin sample for the adaptation, e.g., acquired using a face detector. In our experimental study, we obtained F-score of over 0.85 for the ECU benchmark, and this is highly competitive compared with several state-of-the-art methods.


Mobile Robot Control on a Reference Path

In this paper a control design of a nonholonomic mobile robot with a differential drive is presented. On the basis of robot kinematics equations a robot control is designed where the robot is controlled to follow the arbitrary path reference with a predefined velocity profile. The designed control algorithm proved stable and robust to the errors in robot initial positions, to input and output noises and to other disturbances. The obtained control law is demonstrated on a simple trajectory example, however, for a more general applicability a time-optimal motion planning algorithm considering acceleration constraints is presented as well


An investigation of factors associated with the health and well-being of HIV-infected or HIV-affected older people in rural South Africa

BACKGROUND
Despite the severe impact of HIV in sub-Saharan Africa, the health of older people aged 50+ is often overlooked owing to the dearth of data on the direct and indirect effects of HIV on older people's health status and well-being. The aim of this study was to examine correlates of health and well-being of HIV-infected older people relative to HIV-affected people in rural South Africa, defined as participants with an HIV-infected or death of an adult child due to HIV-related cause.

METHODS
Data were collected within the Africa Centre surveillance area using instruments adapted from the World Health Organization (WHO) Study on global AGEing and adult health (SAGE). A stratified random sample of 422 people aged 50+ participated. We compared the health correlates of HIV-infected to HIV-affected participants using ordered logistic regressions. Health status was measured using three instruments: disability index, quality of life and composite health score.

RESULTS
Median age of the sample was 60 years (range 50-94). Women HIV-infected (aOR 0.15, 95% confidence interval (CI) 0.08-0.29) and HIV-affected (aOR 0.20, 95% CI 0.08-0.50), were significantly less likely than men to be in good functional ability. Women's adjusted odds of being in good overall health state were similarly lower than men's; while income and household wealth status were stronger correlates of quality of life. HIV-infected participants reported better functional ability, quality of life and overall health state than HIV-affected participants.

DISCUSSION AND CONCLUSIONS
The enhanced healthcare received as part of anti-retroviral treatment as well as the considerable resources devoted to HIV care appear to benefit the overall well-being of HIV-infected older people; whereas similar resources have not been devoted to the general health needs of HIV uninfected older people. Given increasing numbers of older people, policy and programme interventions are urgently needed to holistically meet the health and well-being needs of older people beyond the HIV-related care system.


A Machine Learning Approach to Anomaly Detection

Much of the intrusion detection research focuses on signature (misuse) detection, where models are built to recognize known attacks. However, signature detection, by its nature, cannot detect novel attacks. Anomaly detection focuses on modeling the normal behavior and identifying significant deviations, which could be novel attacks. In this paper we explore two machine learning methods that can construct anomaly detection models from past behavior. The first method is a rule learning algorithm that characterizes normal behavior in the absence of labeled attack data. The second method uses a clustering algorithm to identify outliers.


Construction and Evaluation of a User Experience Questionnaire

An end-user questionnaire to measure user experience quickly in a simple and immediate way while covering a preferably comprehensive impression of the product user experience was the goal of the reported construction process. An empirical approach for the item selection was used to ensure practical relevance of items. Usability experts collected terms and statements on user experience and usability, including ‘hard’ as well as ‘soft’ aspects. These statements were consolidated and transformed into a first questionnaire version containing 80 bipolar items. It was used to measure the user experience of software products in several empirical studies. Data were subjected to a factor analysis which resulted in the construction of a 26 item questionnaire including the six factors Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, and Novelty. Studies conducted for the original German questionnaire and an English version indicate a satisfactory level of reliability and construct validity.


Transformational change and business process reengineering (BPR): Lessons from the British and Dutch public sector

Available online 18 May 2011


Clustering of multivariate time series data using particle swarm optimization

Particle swarm optimization (PSO) is a practical and effective optimization approach that has been applied recently for data clustering in many applications. While various non-evolutionary optimization and clustering algorithms have been applied for clustering multivariate time series in some applications such as customer segmentation, they usually provide poor results due to their dependency on the initial values and their poor performance in manipulating multiple objectives. In this paper, a particle swarm optimization algorithm is proposed for clustering multivariate time series data. Since the time series data sometimes do not have the same length and they usually have missing data, the regular Euclidean distance and dynamic time warping can not be applied for such data to measure the similarity. Therefore, a hybrid similarity measure based on principal component analysis and Mahalanobis distance is applied in order to handle such limitations. The comparison between the results of the proposed method with the similar ones in the literature shows the superiority of the proposed method.


Nonlinear Dynamics of Spring Softening and Hardening in Folded-MEMS Comb Drive Resonators

This paper studies analytically and numerically the spring softening and hardening phenomena that occur in electrostatically actuated microelectromechanical systems comb drive resonators utilizing folded suspension beams. An analytical expression for the electrostatic force generated between the combs of the rotor and the stator is derived and takes into account both the transverse and longitudinal capacitances present. After formulating the problem, the resulting stiff differential equations are solved analytically using the method of multiple scales, and a closed-form solution is obtained. Furthermore, the nonlinear boundary value problem that describes the dynamics of inextensional spring beams is solved using straightforward perturbation to obtain the linear and nonlinear spring constants of the beam. The analytical solution is verified numerically using a Matlab/Simulink environment, and the results from both analyses exhibit excellent agreement. Stability analysis based on phase plane trajectory is also presented and fully explains previously reported empirical results that lacked sufficient theoretical description. Finally, the proposed solutions are, once again, verified with previously published measurement results. The closed-form solutions provided are easy to apply and enable predicting the actual behavior of resonators and gyroscopes with similar structures.


Automatic Detection and Classification of Brain Hemorrhages

Computer-aided diagnosis systems have been the focus of many research endeavors. They are based on the idea of processing and analyzing images of different parts of the human body for a quick and accurate diagnosis. In this paper, the aforementioned approach is followed to detect whether a brain hemorrhage exists or not in a Computed Topography (CT) scans of the brain. Moreover, the type of the hemorrhage is identified. The implemented system consists of several stages that include image preprocessing, image segmentation, feature extraction, and classification. The results of the conducted experiments are very promising. A recognition rate of 100% is attained for detecting whether a brain hemorrhage exists or not. For the hemorrhage type classification, more than 92% accuracy is achieved. Key–Words:brain hemorrhage, brain ct scans, machine learning, image processing, image segmentation


Analysis of a linear series-fed rectangular microstrip antenna array

A new method is proposed to analyze a linear series-fed microstrip antenna array which maintains accuracy while not requiring a full-wave analysis of the entire structure. The method accounts for mutual coupling between patches as well as the patch excitations by the feed lines.


Direct least squares fitting of ellipses

This work presents a new efficient method for fitting ellipses to scattered data. Previous algorithms either fitted general conics or were computationally expensive. By minimizing the algebraic distance subject to the constraint 4 2 1 the new method incorporates the ellipticity constraint into the normalization factor. The new method combines several advantages: (i) It is ellipse-specific so that even bad data will always return an ellipse; (ii) It can be solved naturally by a generalized eigensystem and (iii) it is extremely robust, efficient and easy to implement. We compare the proposed method to other approaches and show its robustness on several examples in which other non-ellipse-specific approaches would fail or require computationally expensive iterative refinements. Source code for the algorithm is supplied and a demonstration is available on ! " ! $#% '& () "*) & +, ./10 0 32, . 4) "*) "*) % 5* 0


SUITOR: an attentive information system

Attentive systems pay attention to what users do so that they can attend to what users need. Such systems track user behavior, model user interests, and anticipate user desires and actions. Because the general class of attentive systems is broad — ranging from human butlers to web sites that profile users — we have focused specifically on attentive information systems, which observe user actions with information resources, model user information states, and suggest information that might be helpful to users. In particular, we describe an implemented system, Simple User Interest Tracker (Suitor), that tracks computer users through multiple channels — gaze, web browsing, application focus — to determine their interests and to satisfy their information needs. By observing behavior and modeling users, Suitor finds and displays potentially relevant information that is both timely and non-disruptive to the users' ongoing activities.


A real-time face tracker

We present a real-time face tracker in this paper The system has achieved a rate of 30% frameshecond using an HP-9000 workstation with a framegrabber and a Canon VC-CI camera. It can track a person 'sface while the person moves freely (e.g., walks, jumps, sits down and stands up) in a room. Three types of models have been employed in developing the system. First, we present a stochastic model to characterize skin-color distributions of human faces. The information provided by the model is sufJicient for tracking a human face in various poses and views. This model is adaptable to different people and different lighting conditions in real-time. Second, a motion model e's used to estimate image motion and to predict search window. Third, a camera model is used toprediet and to compensate for camera motion. The system can be applied to tele-conferencing and many HCI applications including lip-reading and gaze tracking. The principle in developing this system can be extended to other tracking problems such as tracking the human hand.


Single Image Super-Resolution via Multiple Mixture Prior Models

Example learning-based single image super-resolution (SR) is a promising method for reconstructing a high-resolution (HR) image from a single-input low-resolution (LR) image. Lots of popular SR approaches are more likely either time-or space-intensive, which limit their practical applications. Hence, some research has focused on a subspace view and delivered state-of-the-art results. In this paper, we utilize an effective way with mixture prior models to transform the large nonlinear feature space of LR images into a group of linear subspaces in the training phase. In particular, we first partition image patches into several groups by a novel selective patch processing method based on difference curvature of LR patches, and then learning the mixture prior models in each group. Moreover, different prior distributions have various effectiveness in SR, and in this case, we find that student-t prior shows stronger performance than the well-known Gaussian prior. In the testing phase, we adopt the learned multiple mixture prior models to map the input LR features into the appropriate subspace, and finally reconstruct the corresponding HR image in a novel mixed matching way. Experimental results indicate that the proposed approach is both quantitatively and qualitatively superior to some state-of-the-art SR methods.


Pharmacogenetics and adverse drug reactions

Polymorphisms in the genes that code for drug-metabolising enzymes, drug transporters, drug receptors, and ion channels can affect an individual's risk of having an adverse drug reaction, or can alter the efficacy of drug treatment in that individual. Mutant alleles at a single gene locus are the best studied individual risk factors for adverse drug reactions, and include many genes coding for drug-metabolising enzymes. These genetic polymorphisms of drug metabolism produce the phenotypes of "poor metabolisers" or "ultrarapid metabolisers" of numerous drugs. Together, such phenotypes make up a substantial proportion of the population. Pharmacogenomic techniques allow efficient analysis of these risk factors, and genotyping tests have the potential to optimise drug therapy in the future.


High performance work systems: the gap between policy and practice in health care reform.

PURPOSE
Studies of high-performing organisations have consistently reported a positive relationship between high performance work systems (HPWS) and performance outcomes. Although many of these studies have been conducted in manufacturing, similar findings of a positive correlation between aspects of HPWS and improved care delivery and patient outcomes have been reported in international health care studies. The purpose of this paper is to bring together the results from a series of studies conducted within Australian health care organisations. First, the authors seek to demonstrate the link found between high performance work systems and organisational performance, including the perceived quality of patient care. Second, the paper aims to show that the hospitals studied do not have the necessary aspects of HPWS in place and that there has been little consideration of HPWS in health system reform.

DESIGN/METHODOLOGY/APPROACH
The paper draws on a series of correlation studies using survey data from hospitals in Australia, supplemented by qualitative data collection and analysis. To demonstrate the link between HPWS and perceived quality of care delivery the authors conducted regression analysis with tests of mediation and moderation to analyse survey responses of 201 nurses in a large regional Australian health service and explored HRM and HPWS in detail in three casestudy organisations. To achieve the second aim, the authors surveyed human resource and other senior managers in all Victorian health sector organisations and reviewed policy documents related to health system reform planned for Australia.

FINDINGS
The findings suggest that there is a relationship between HPWS and the perceived quality of care that is mediated by human resource management (HRM) outcomes, such as psychological empowerment. It is also found that health care organisations in Australia generally do not have the necessary aspects of HPWS in place, creating a policy and practice gap. Although the chief executive officers of health service organisations reported high levels of strategic HRM, the human resource and other managers reported a distinct lack of HPWS from their perspectives. The authors discuss why health care organisations may have difficulty in achieving HPWS.

ORIGINALITY/VALUE
Leaders in health care organisations should focus on ensuring human resource management systems, structures and processes that support HPWS. Policy makers need to consider HPWS as a necessary component of health system reform. There is a strong need to reorient organisational human resource management policies and procedures in public health care organisations towards high performing work systems.


Analysis and design of HBT Cherry-Hooper amplifiers with emitter-follower feedback for optical communications

In this article, the large-signal, small-signal, and noise performance of the Cherry-Hooper amplifier with emitter-follower feedback are analyzed from a design perspective. A method for choosing the component values to obtain a low group delay distortion or Bessel transfer function is given. The design theory is illustrated with an implementation of the circuit in a 47-GHz SiGe process. The amplifier has 19.7-dB gain, 13.7-GHz bandwidth, and /spl plusmn/10-ps group delay distortion. The amplifier core consumes 34 mW from a -3.3-V supply.


A Deep Learning Framework for Smart Street Cleaning

Conventional street cleaning methods include street sweepers going to various spots in the city and manually verifying if the street needs cleaning and taking action if required. However, this method is not optimized and demands a huge investment in terms of time and money. This paper introduces an automated framework which addresses street cleaning problem in a better way by making use of modern equipment with cameras and computational techniques to analyze, find and efficiently schedule clean-up crews for the areas requiring more attention. Deep learning-based neural network techniques can be used to achieve better accuracy and performance for object detection and classification than conventional machine learning algorithms for large volume of images. The proposed framework for street cleaning leverages the deep learning algorithm pipeline to analyze the street photographs and determines if the streets are dirty by detecting litter objects. The pipeline further determines the the degree to which the streets are littered by classifying the litter objects detected in earlier stages. The framework also provides information on the cleanliness status of the streets on a dashboard updated in real-time. Such framework can prove effective in reducing resource consumption and overall operational cost involved in street cleaning.


A New Paradigm for Collision-free Hashing: Incrementality at Reduced Cost

We present a simple, new paradigm for the design of collision-free hash functions. Any function emanating from this paradigm is incremental. (This means that if a message x which I have previously hashed is modi ed to x0 then rather than having to re-compute the hash of x 0 from scratch, I can quickly \update" the old hash value to the new one, in time proportional to the amount of modi cation made in x to get x.) Also any function emanating from this paradigm is parallelizable, useful for hardware implementation. We derive several speci c functions from our paradigm. All use a standard hash function, assumed ideal, and some algebraic operations. The rst function, MuHASH, uses one modular multiplication per block of the message, making it reasonably e cient, and signi cantly faster than previous incremental hash functions. Its security is proven, based on the hardness of the discrete logarithm problem. A second function, AdHASH, is even faster, using additions instead of multiplications, with security proven given either that approximation of the length of shortest lattice vectors is hard or that the weighted subset sum problem is hard. A third function, LtHASH, is a practical variant of recent lattice based functions, with security proven based, again on the hardness of shortest lattice vector approximation. Dept. of Computer Science & Engineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA. E-Mail: mihir@cs.ucsd.edu. URL: http://www-cse.ucsd.edu/users/mihir. Supported in part by NSF CAREER Award CCR-9624439 and a Packard Foundation Fellowship in Science and Engineering. yMIT Laboratory for Computer Science, 545 Technology Square, Cambridge, MA 02139, USA. E-Mail: miccianc@theory.lcs.mit.edu. Supported in part by DARPA contract DABT63-96-C-0018.


Bayesian Rose Trees

Hierarchical structure is ubiquitous in data across many domains. There are many hierarchical clustering methods, frequently used by domain experts, which strive to discover this structure. However, most of these methods limit discoverable hierarchies to those with binary branching structure. This limitation, while computationally convenient, is often undesirable. In this paper we explore a Bayesian hierarchical clustering algorithm that can produce trees with arbitrary branching structure at each node, known as rose trees. We interpret these trees as mixtures over partitions of a data set, and use a computationally efficient, greedy agglomerative algorithm to find the rose trees which have high marginal likelihood given the data. Lastly, we perform experiments which demonstrate that rose trees are better models of data than the typical binary trees returned by other hierarchical clustering algorithms.


Digital Business Strategy and Value Creation: Framing the Dynamic Cycle of Control Points

Within changing value networks, the profits and competitive advantages of participation reside dynamically at control points that are the positions of greatest value and/or power. The enterprises that hold these positions have a great deal of control over how the network operates, how the benefits are redistributed, and how this influences the execution of a digital business strategy. This article is based on a field study that provides preliminary, yet promising, empirical evidence that sheds light on the dynamic cycle of value creation and value capture points in digitally enabled networks in response to triggers related to technology and business strategy. We use the context of the European and U.S. broadcasting industry. Specifically, we illustrate how incremental innovations may shift value networks from static, vertically integrated networks to more loosely coupled networks, and how cross-boundary industry disruptions may then, in turn, shift those to two-sided markets. Based on our analysis we provide insights and implications for digital business strategy research and practice.


Attitude Detection for One-Round Conversation: Jointly Extracting Target-Polarity Pairs

We tackle Attitude Detection, which we define as the task of extracting the replier's attitude, i.e., a target-polarity pair, from a given one-round conversation. While previous studies considered Target Extraction and Polarity Classification separately, we regard them as subtasks of Attitude Detection. Our experimental results show that treating the two subtasks independently is not the optimal solution for Attitude Detection, as achieving high performance in each subtask is not sufficient for obtaining correct target-polarity pairs. Our jointly trained model AD-NET substantially outperforms the separately trained models by alleviating the target-polarity mismatch problem. Moreover, we proposed a method utilising the attitude detection model to improve retrieval-based chatbots by re-ranking the response candidates with attitude features. Human evaluation indicates that with attitude detection integrated, the new responses to the sampled queries from are statistically significantly more consistent, coherent, engaging and informative than the original ones obtained from a commercial chatbot.


Robust misinterpretation of confidence intervals.

Null hypothesis significance testing (NHST) is undoubtedly the most common inferential technique used to justify claims in the social sciences. However, even staunch defenders of NHST agree that its outcomes are often misinterpreted. Confidence intervals (CIs) have frequently been proposed as a more useful alternative to NHST, and their use is strongly encouraged in the APA Manual. Nevertheless, little is known about how researchers interpret CIs. In this study, 120 researchers and 442 students-all in the field of psychology-were asked to assess the truth value of six particular statements involving different interpretations of a CI. Although all six statements were false, both researchers and students endorsed, on average, more than three statements, indicating a gross misunderstanding of CIs. Self-declared experience with statistics was not related to researchers' performance, and, even more surprisingly, researchers hardly outperformed the students, even though the students had not received any education on statistical inference whatsoever. Our findings suggest that many researchers do not know the correct interpretation of a CI. The misunderstandings surrounding p-values and CIs are particularly unfortunate because they constitute the main tools by which psychologists draw conclusions from data.


Low-complexity image denoising based on statistical modeling of wavelet coefficients

We introduce a simple spatially adaptive statistical model for wavelet image coefficients and apply it to image denoising. Our model is inspired by a recent wavelet image compression algorithm, the estimation-quantization (EQ) coder. We model wavelet image coefficients as zero-mean Gaussian random variables with high local correlation. We assume a marginal prior distribution on wavelet coefficients variances and estimate them using an approximate maximum a posteriori probability rule. Then we apply an approximate minimum mean squared error estimation procedure to restore the noisy wavelet image coefficients. Despite the simplicity of our method, both in its concept and implementation, our denoising results are among the best reported in the literature.


Crowdlet: Optimal worker recruitment for self-organized mobile crowdsourcing

In this paper, we advocate Crowdlet, a novel self-organized mobile crowdsourcing paradigm, in which a mobile task requester can proactively exploit a massive crowd of encountered mobile workers at real-time for quick and high-quality results. We present a comprehensive system model of Crowdlet that defines task, worker arrival and worker ability models. Further, we introduce a service quality concept to indicate the expected service gain that a requester can enjoy when he recruits an encountered worker, by jointly taking into account worker ability, real-timeness and task reward. Based on the models, we formulate an online worker recruitment problem to maximize the expected sum of service quality. We derive an optimal worker recruitment policy through the dynamic programming principle, and show that it exhibits a nice threshold based structure. We conduct extensive performance evaluation based on real traces, and numerical results demonstrate that our policy can achieve superior performance and improve more than 30% performance gain over classic policies. Besides, our Android prototype shows that Crowdlet is cost-efficient, requiring less than 7 seconds and 6 Joule in terms of time and energy cost for policy computation in most cases.


The population genetics of the Jewish people

Adherents to the Jewish faith have resided in numerous geographic locations over the course of three millennia. Progressively more detailed population genetic analysis carried out independently by multiple research groups over the past two decades has revealed a pattern for the population genetic architecture of contemporary Jews descendant from globally dispersed Diaspora communities. This pattern is consistent with a major, but variable component of shared Near East ancestry, together with variable degrees of admixture and introgression from the corresponding host Diaspora populations. By combining analysis of monoallelic markers with recent genome-wide variation analysis of simple tandem repeats, copy number variations, and single-nucleotide polymorphisms at high density, it has been possible to determine the relative contribution of sex-specific migration and introgression to map founder events and to suggest demographic histories corresponding to western and eastern Diaspora migrations, as well as subsequent microevolutionary events. These patterns have been congruous with the inferences of many, but not of all historians using more traditional tools such as archeology, archival records, linguistics, comparative analysis of religious narrative, liturgy and practices. Importantly, the population genetic architecture of Jews helps to explain the observed patterns of health and disease-relevant mutations and phenotypes which continue to be carefully studied and catalogued, and represent an important resource for human medical genetics research. The current review attempts to provide a succinct update of the more recent developments in a historical and human health context.


Learning deep physiological models of affect

More than 15 years after the early studies in Affective Computing (AC), [1] the problem of detecting and modeling emotions in the context of human-computer interaction (HCI) remains complex and largely unexplored. The detection and modeling of emotion is, primarily, the study and use of artificial intelligence (AI) techniques for the construction of computational models of emotion. The key challenges one faces when attempting to model emotion [2] are inherent in the vague definitions and fuzzy boundaries of emotion, and in the modeling methodology followed. In this context, open research questions are still present in all key components of the modeling process. These include, first, the appropriateness of the modeling tool employed to map emotional manifestations and responses to annotated affective states; second, the processing of signals that express these manifestations (i.e., model input); and third, the way affective annotation (i.e., model output) is handled. This paper touches upon all three key components of an affective model (i.e., input, model, output) and introduces the use of deep learning (DL) [3], [4], [5] methodologies for affective modeling from multiple physiological signals.


Three Implementations of SquishQL, a Simple RDF Query Language

RDF provides a basic way to represent data for the Semantic Web. We have been experimenting with the query paradigm for working with RDF data in semantic web applications. Query of RDF data provides a declarative access mechanism that is suitable for application usage and remote access. We describe work on a conceptual model for querying RDF data that refines ideas first presented in at the W3C workshop on Query Languages [14] and the design of one possible syntax, derived from [7], that is suitable for application programmers. Further, we present experience gained in three implementations of the query language.


Introduction to Statistical Learning Theory

The goal of statistical learning theory is to study, in a statistical framework, the properties of learning algorithms. In particular, most results take the form of so-called error bounds. This tutorial introduces the techniques that are used to obtain such results.


Fast Texture Transfer


Social Media: The Good, the Bad, and the Ugly

Companies when strategizing are looking for innovative ways to have a competitive advantage over their competitors. One way in which they compete is by the adoption of social media. Social media has evolved over the years and as a result, new concepts and applications are developed which promises to provide business value to a company. However, despite the usefulness of social media, many businesses fail to reap its full benefits. The current literature shows evidence of lack of strategically designed process for companies to successfully implement social media. The purpose of this study is to suggest a framework which provides the necessary alignment between social media goals with business objectives. From the literature review, a social media strategy framework was derived to offer an effective step by step approach to the development and imple‐ mentation of social media goals aligned with a company’s business objectives. The contribution to this study is the development of a social media strategy framework that can be used by organisations for business value.


Web Mining for Information Retrieval

The present era is engulfed in data and it is quite difficult to churn the emergence of unstructured data in order to mine relevant information. In this paper we present a basic outline of web mining techniques which changed the scenario of today world and help in retrieval of information. Web Mining actually referred as mining of interesting pattern by using set of tools and techniques from the vast pool of web. It focuses on different aspects of web mining referred as Web Content Mining, Web Structure Mining and Web Usage Mining. The overview of these mining techniques which help in retrieving desired information are covered along with the approaches used and algorithm employed.


Argumentation Mining: Where are we now, where do we want to be and how do we get there?

This paper gives a short overview of the state-of-the-art and goals of argumentation mining and it provides ideas for further research. Its content is based on two invited lectures on argumentation mining respectively at the FIRE 2013 conference at the India International Center in New Delhi, India and a lecture given as SICSA distinguished visitor at the University of Dundee, UK in the summer of 2014.


1 Unsupervised Feature Learning in Time Series 2 Prediction Using Continuous Deep Belief Network 3

A continuous Deep Belief Network (cDBN) with two hidden layers is proposed in this 18 paper, focusing on the problem of weak feature learning ability when dealing with continuous data. 19 In cDBN, the input data is trained in an unsupervised way by using continuous version of transfer 20 functions, the contrastive divergence is designed in hidden layer training process to raise 21 convergence speed, an improved dropout strategy is then implemented in unsupervised training to 22 realize features learning by de-cooperating between the units, and then the network is fine-tuned 23 using back propagation algorithm. Besides, hyper-parameters are analysed through stability 24 analysis to assure the network can find the optimal. Finally, the experiments on Lorenz chaos series, 25 CATS benchmark and other real world like CO2 and waste water parameters forecasting show that 26 cDBN has the advantage of higher accuracy, simpler structure and faster convergence speed than 27 other methods. 28