title: Space and Time in the Child’s Mind: Evidence for a Cross-Dimensional Asymmetry.

abstract: What is the relationship between space and time in the human mind? Studies in adults show an asymmetric relationship between mental representations of these basic dimensions of experience: Representations of time depend on space more than representations of space depend on time. Here we investigated the relationship between space and time in the developing mind. Native Greekspeaking children watched movies of two animals traveling along parallel paths for different distances or durations and judged the spatial and temporal aspects of these events (e.g., Which animal went for a longer distance, or a longer time?). Results showed a reliable cross-dimensional asymmetry. For the same stimuli, spatial information influenced temporal judgments more than temporal information influenced spatial judgments. This pattern was robust to variations in the age of the participants and the type of linguistic framing used to elicit responses. This finding demonstrates a continuity between space-time representations in children and adults, and informs theories of analog magnitude representation.


title: Communication intervention for individuals with Down syndrome: Systematic review and meta-analysis

abstract: ABSTRACTObjective: A systematic review was conducted to identify effective intervention strategies for communication in individuals with Down syndrome. Methods: We updated and extended previous reviews by examining: (1) participant characteristics; (2) study characteristics; (3) characteristics of effective interventions (e.g., strategies and intensity); (4) whether interventions are tailored to the Down syndrome behavior phenotype; and (5) the effectiveness (i.e., percentage nonoverlapping data and Cohen’s d) of interventions. Results: Thirty-seven studies met inclusion criteria. The majority of studies used behavior analytic strategies and produced moderate gains in communication targets. Few interventions were tailored to the needs of the Down syndrome behavior phenotype. Conclusion: The results suggest that behavior analytic strategies are a promising approach, and future research should focus on replicating the effects of these interventions with greater methodological rigor.


title: Uniplanar Printed Coupled-Fed PIFA With a Band-Notching Slit for WLAN/WiMAX Operation in the Laptop Computer

abstract: A small-size uniplanar printed antenna suitable for 2.4/5.2/5.8 GHz WLAN and 2.5/3.5/5.5 GHz WiMAX operations is presented. The antenna is a coupled-fed PIFA and shows a height of 9 mm and a width of 13 mm only, when mounted at the top edge of the display panel in the laptop computer. The multiband operation is achieved by using the coupling feed and further embedding a band-notching slit in the radiating arm of the PIFA. With the coupling feed, two wide operating bands can be generated at about 2.5 and 5.5 GHz for the PIFA. By further embedding the slit of length about a quarter-wavelength at about 4 GHz, a band-notching characteristic at about 4 GHz is obtained, which leads to an additional resonance at about 3.5 GHz and shows very small effects on the desired 2.5 and 5.5 GHz bands. This behavior results in successful excitation of an additional resonant mode for 3.5 GHz WiMAX operation. Hence, with no increase in the antenna size, three wide operating bands for covering all the desired operating bands of WLAN/WiMAX systems are achieved for the proposed PIFA. Details of the design considerations are presented, and results of the fabricated prototype are discussed.


title: Concurrent engineering: Supporting subsystems

abstract: Abstract Case studies of worldwide winners: Ford Motor Company's Team Ranger and mid-sized, US$10 million, engineer-to-order Airolite Company, ventilator louver manufacturer, authenticate benefits of applying concurrent Engineering to design processes. They confront and need to surmount: global competition, increased labor costs, rising customer expectations, shorter product life cycles and government regulation. This paper clearly illustrates how Concurrent Engineering in meeting these demands embraces supporting subsystems that include Computer Aided Drafting & Design, Quality Function Deployment and Design for Manufacture & Assembly.


title: Side channel analysis of AVR XMEGA crypto engine

abstract: AVR XMEGA is the recent general-purpose 8-bit microcontroller from Atmel featuring symmetric crypto engines. We analyze the resistance of XMEGA crypto engines to side channel attacks. We reveal the relatively strong side channel leakage of the AES engine that enables full 128-bit AES secret key recovery in a matter of several minutes with a measurement setup cost about 1000 USD. 3000 power consumption traces are sufficient for the successful attack. Our analysis was performed without knowing the details of the crypto engine internals; quite the contrary, it reveals some details about the implementation. We sketch other feasible side channel attacks on XMEGA and suggest the counter-measures that can raise the complexity of the attacks but not fully prevent them.


title: An Efficient Architecture and Algorithm for Resource Provisioning in Fog Computing

abstract: Cloud computing is a model of sharing computing resources over any communication network by using virtualization. Virtualization allows a server to be sliced in virtual machines. Each virtual machine has its own operating system/applications that rapidly adjust resource allocation. Cloud computing offers many benefits, one of them is elastic resource allocation. To fulfill the requirements of clients, cloud environment should be flexible in nature and can be achieve by efficient resource allocation. Resource allocation is the process of assigning available resources to clients over the internet and plays vital role in Infrastructure-as-aService (IaaS) model of cloud computing. Elastic resource allocation is required to optimize the allocation of resources, minimizing the response time and maximizing the throughput to improve the performance of cloud computing. Sufficient solutions have been proposed for cloud computing to improve the performance but for fog computing still efficient solution have to be found. Fog computing is the virtualized intermediate layer between clients and cloud. It is a highly virtualized technology which is similar to cloud and provide data, computation, storage, and networking services between end users and cloud servers. This paper presents an efficient architecture and algorithm for resources provisioning in fog computing environment by using virtualization technique.


title: Dynamic Mobile Edge Caching with Location Differentiation

abstract: Mobile edge caching enables content delivery directly within the radio access network, which effectively alleviates the backhaul burden and reduces round-trip latency. To fully exploit the edge resources, the most popular contents should be identified and cached. Observing that content popularity varies greatly at different locations, to maximize local hit rate, this paper proposes an online learning algorithm that dynamically predicts content hit rate, and makes location-differentiated caching decisions. Specifically, a linear model is used to estimate the future hit rate. Considering the variations in user demand, a perturbation is added to the estimation to account for uncertainty. The proposed learning algorithm requires no training phase, and hence is adaptive to the time-varying content popularity profile. Theoretical analysis indicates that the proposed algorithm asymptotically approaches the optimal policy in the long term. Extensive simulations based on real world traces show that, the proposed algorithm achieves higher hit rate and better adaptiveness to content popularity fluctuation, compared with other schemes.


title: Active Learning for Multivariate Time Series Classification with Positive Unlabeled Data

abstract: Traditional time series classification problem with supervised learning algorithm needs a large set of labeled training data. In reality, the number of labeled data is often smaller and there is huge number of unlabeled data. However, manually labeling these unlabeled examples is time-consuming and expensive, and sometimes it is even impossible. Although some semi-supervised and active learning methods were proposed to handle univariate time series data, few work have touched positive and unlabeled data for multivariate time series (MTS) classification due to the data being more complex. In this paper we focus on active learning for multivariate time series classification with positive unlabeled data. First, we propose a sample selection strategy to find the most informative unlabeled examples for manual labeling. Second, we introduce two active learning approaches to obtain a high-confident training dataset for classification. Experiments on real datasets demonstrate the validity of our proposed approaches.


title: Improving SAR estimations in MRI using subject-specific models.

abstract: To monitor and strategically control energy deposition in magnetic resonance imaging (MRI), measured as a specific absorption rate (SAR), numerical methods using generic human models have been employed to estimate worst-case values. Radiofrequency (RF) sequences are therefore often designed conservatively with large safety margins, potentially hindering the full potential of high-field systems. To more accurately predict the patient SAR values, we propose the use of image registration techniques, in conjunction with high-resolution image and tissue libraries, to create patient-specific voxel models. To test this, a matching model from the archives was first selected. Its tissue information was then warped to the patient's coordinates by registering the high-resolution library image to the pilot scan of the patient. Results from studying the models' 1 g SAR distribution suggest that the developed patient model can predict regions of elevated SAR within the patient with remarkable accuracy. Additionally, this work also proposes a voxel analytical metric that can assist in the construction of a patient library and the selection of the matching model from the library for a patient. It is hoped that, by developing voxel models with high accuracy in patient-specific anatomy and positioning, the proposed method can accurately predict the safety margins for high-field human applications and, therefore maximize the safe use of RF sequence power in high-field MRI systems.


title: Psychological profiling of offender characteristics from crime behaviors in serial rape offences

abstract: Criminal psychological profiling has progressively been incorporated into police procedures despite a dearth of empirical research. Indeed, in the study of serial violent crimes for the purpose of psychological profiling, very few original, quantitative, academically reviewed studies actually exist. This article reports on the analysis of 62 incidents of serial sexual assault. The statistical procedure of multidimensional scaling was employed in the analysis of this data, which in turn produced a five-cluster model of serial rapist behavior. First, a central cluster of behaviors were identified that represent common behaviors to all patterns of serial rape. Second, four distinct outlying patterns were identified as demonstrating distinct offence styles, these being assigned the following descriptive labels brutality, intercourse, chaotic, and ritual. Furthermore, analysis of these patterns also identified distinct offender characteristics that allow for the use of empirically robust offender profiles in f...


title: Teenagers and cosmetic surgery: focus on breast augmentation and liposuction.

abstract: Two of the most popular and controversial cosmetic procedures for adolescents are liposuction and breast implants. In this review article, the procedures are discussed. In addition, the physiological and psychological reasons to delay these procedures, including concerns about body dysmorphic disorder and research findings regarding changes in teenagers' body image as they mature, are described. The lack of persuasive empirical research on the mental health benefits of plastic surgery for teenagers is highlighted. Finally, the long-term financial and health implications of implanted medical devices with a limited lifespan are presented. Adolescent medicine providers need to be involved in improving informed decision making for these procedures, aware of the absence of data on the health and mental health risks and benefits of these surgeries for teenagers, and understand the limitations on teenagers' abilities to evaluate risks.


title: Phi.sh/$oCiaL: the phishing landscape through short URLs

abstract: Size, accessibility, and rate of growth of Online Social Media (OSM) has attracted cyber crimes through them. One form of cyber crime that has been increasing steadily is phishing, where the goal (for the phishers) is to steal personal information from users which can be used for fraudulent purposes. Although the research community and industry has been developing techniques to identify phishing attacks through emails and instant messaging (IM), there is very little research done, that provides a deeper understanding of phishing in online social media. Due to constraints of limited text space in social systems like Twitter, phishers have begun to use URL shortener services. In this study, we provide an overview of phishing attacks for this new scenario. One of our main conclusions is that phishers are using URL shorteners not only for reducing space but also to hide their identity. We observe that social media websites like Facebook, Habbo, Orkut are competing with e-commerce services like PayPal, eBay in terms of traffic and focus of phishers. Orkut, Habbo, and Facebook are amongst the top 5 brands targeted by phishers. We study the referrals from Twitter to understand the evolving phishing strategy. A staggering 89% of references from Twitter (users) are inorganic accounts which are sparsely connected amongst themselves, but have large number of followers and followees. We observe that most of the phishing tweets spread by extensive use of attractive words and multiple hashtags. To the best of our knowledge, this is the first study to connect the phishing landscape using blacklisted phishing URLs from PhishTank, URL statistics from bit.ly and cues from Twitter to track the impact of phishing in online social media.


title: A robust multi-constrained model for fingerprint orientation field construction

abstract: This paper proposes a robust multi-constrained model for fingerprint orientation field construction. The orientation field construction problem is formulated as an overdetermined regularization system, in which three constraints are incorporated to ensure the accuracy. Constraints required in the model are: 1) a least square data term aiming to maintain consistency between the constructed and original orientation field; 2) a total variation regularization aiming to smooth the orientation field and eliminate the global noise; and 3) a nuclear norm regularization aiming to eliminate sparse noise while preserve the structure of the orientation field. According to experiments on both high-quality fingerprint image and low-quality fingerprint image, the proposed model shows high performance in achieving accurate orientation field. Thanks to the short running time, the proposed model is applicable to applications such as fingerprint indexing.


title: Effects of Teachers’ Mathematical Knowledge for Teaching on Student Achievement:

abstract: This study explored whether and how teachers’ mathematical knowledge for teaching contributes to gains in students’ mathematics achievement. The authors used a linear mixed-model methodology in which first and third graders’ mathematical achievement gains over a year were nested within teachers, who in turn were nested within schools. They found that teachers’ mathematical knowledge was significantly related to student achievement gains in both first and third grades after controlling for key student- and teacher-level covariates. This result, while consonant with findings from the educational production function literature, was obtained via a measure focusing on the specialized mathematical knowledge and skills used in teaching mathematics. This finding provides support for policy initiatives designed to improve students’ mathematics achievement by improving teachers’ mathematical knowledge.


title: Learning robot motions with stable dynamical systems under diffeomorphic transformations

abstract: Accuracy and stability have in recent studies been emphasized as the two major ingredients to learn robot motions from demonstrations with dynamical systems. Several approaches yield stable dynamical systems but are also limited to specific dynamics that can potentially result in a poor reproduction performance. The current work addresses this accuracy-stability dilemma through a new diffeomorphic transformation approach that serves as a framework generalizing the class of demonstrations that are learnable by means of provably stable dynamical systems. We apply the proposed framework to extend the application domain of the stable estimator of dynamical systems (SEDS) by generalizing the class of learnable demonstrations by means of diffeomorphic transformations ? . The resulting approach is named ? -SEDS and analyzed with rigorous theoretical investigations and robot experiments. A novel method named tau-SEDS that learns stable robot motions from demonstrations on the basis of nonlinear dynamical systems is proposed.The method allows learning dynamical systems that are stable and accurate by construction without much tuning effort.The approach is rigorously analyzed within a substantial theoretical framework.Several robotics experiments underline the applicability of the method.


The Associations among Poor Single Mothers’ Economic Hardship, Depressive Symptoms, Parenting Stress, Child Abuse and Neglect, and their Children’s Development


title: A Comparison of Physical Therapy Modalities Versus Acupuncture in the Treatment of Fibromyalgia Syndrome: A Pilot Study

abstract: Abstract Objectives: Physical therapy (PT) modalities are used in the treatment of fibromyalgia syndrome (FMS). Acupuncture is a treatment option often sought after by FMS patients. The aim of this...


Predicting Stock Prices Using LSTM


title: Conditional Generation and Snapshot Learning in Neural Dialogue Systems

abstract: Recently a variety of LSTM-based conditional language models (LM) have been applied across a range of language generation tasks. In this work we study various model architectures and different ways to represent and aggregate the source information in an end-to-end neural dialogue system framework. A method called snapshot learning is also proposed to facilitate learning from supervised sequential signals by applying a companion cross-entropy objective function to the conditioning vector. The experimental and analytical results demonstrate firstly that competition occurs between the conditioning vector and the LM, and the differing architectures provide different trade-offs between the two. Secondly, the discriminative power and transparency of the conditioning vector is key to providing both model interpretability and better performance. Thirdly, snapshot learning leads to consistent performance improvements independent of which architecture is used.


title: Sleep deprivation reduces default mode network connectivity and anti-correlation during rest and task performance

abstract: article i nfo Sleep deprivation (SD) can alter extrinsic, task-related fMRI signal involved in attention, memory and executive function. However, its effects on intrinsic low-frequency connectivity within the Default Mode Network (DMN) and its related anti-correlated network (ACN) have not been well characterized. We investigated the effect of SD on functional connectivity within the DMN, and on DMN-ACN anti-correlation, both during the resting state and during performance of a visual attention task (VAT). 26 healthy participants underwent fMRI twice: once after a normal night of sleep in rested wakefulness (RW) and once following approximately 24 h of total SD. A seed-based approach was used to examine pairwise correlations of low- frequency fMRI signal across different nodes in each state. SD was associated with significant selective reductions in DMN functional connectivity and DMN-ACN anti-correlation. This was congruent across resting state and VAT analyses, suggesting that SD induces a robust alteration in the intrinsic connectivity within and between these networks.


title: A serializability violation detector for shared-memory server programs

abstract: We aim to improve reliability of multithreaded programs by proposing a dynamic detector that detects potentially erroneous program executions and their causes. We design and evaluate a Serializability Violation Detector (SVD) that has two unique goals: (I) triggering automatic recovery from erroneous executions using backward error recovery (BER), or simply alerting users that a software error may have occurred; and (II) helping debug programs by revealing causes of error symptoms.Two properties of SVD help in achieving these goals. First, to detect only erroneous executions, SVD checks serializability of atomic regions, which are code regions that need to be executed atomically. Second, to improve usability, SVD does not require a priori annotations of atomic regions; instead, SVD approximates them using a heuristic. Experimental results on three widely-used multithreaded server programs show that SVD finds real bugs and reports modest false positives. The goal of this paper is to develop a detector suitable for (I) BER-based avoidance of erroneous program executions; and (II) alerting users as software errors occur. We argue that such a detector should have the following two properties.


title: The genetic architecture of Alzheimer's disease: beyond APP, PSENs and APOE

abstract: Alzheimer's disease (AD) is a complex disorder with a clear genetic component. Three genes have been identified as the cause of early onset familial AD (EOAD). The most common form of the disease, late onset Alzheimer's disease (LOAD), is, however, a sporadic one presenting itself in later stages of life. The genetic component of this late onset form of AD has been the target of a large number of studies, because only one genetic risk factor (APOE4) has been consistently associated with the disease. However, technological advances allow new approaches in the study of complex disorders. In this review, we discuss the new results produced by genome wide association studies, in light of the current knowledge of the complexity of AD genetics.


title: Mathematical Model of Pneumatic Artificial Muscle Reinforced by Straight Fibers

abstract: This article describes a mathematical model of a pneumatic artificial muscle reinforced by straight fibers. The pneumatic artificial muscle is lightweight and high power. In addition, it is possible for it to emit exhaust into the atmosphere because the transmission medium of its energy is air, and it needs neither a tank nor maintenance like hydraulic equipment. In addition, safety to a person and the environment is high because the base of the actuator is a soft polymeric material even if damage to the artificial muscle is caused. On the other hand, because the device is composed of a thin film cylinder, and is easily influenced by outside power for the conversion of the volume change in rubber into physical contraction axially, and its passive character is strong, precise positional control is difficult. However, the living thing that has a verbose degree of freedom enables minute motion and walking, etc. by skillfully adjusting to avoid impedance, because it understands its own muscular characteristic beforehand. Similarly, it is thought that the precise positional control is possible by understanding the characteristics of an artificial muscle beforehand. In this research, it is assumed that the expansion shape of the pneumatic artificial muscle that we developed from the pressure distribution is equivalent to the centenary curve, and its model is based on dynamic balance. The result shows the effectiveness of control of an artificial muscle that uses highly accurate calculations and models.


title: Twitter Sentiment Analysis for Security-Related Information Gathering

abstract: Analysing public sentiment about future events, such as demonstration or parades, may provide valuable information while estimating the level of disruption and disorder during these events. Social media, such as Twitter or Facebook, provides views and opinions of users related to any public topics. Consequently, sentiment analysis of social media content may be of interest to different public sector organisations, especially in the security and law enforcement sector. In this paper we present a lexicon-based approach to sentiment analysis of Twitter content. The algorithm performs normalisation of the sentiment in an effort to provide intensity of the sentiment rather than positive/negative label. Following this, we evaluate an evidence-based combining function that supports the classification process in cases when positive and negative words co-occur in a tweet. Finally, we illustrate a case study examining the relation between sentiment of twitter posts related to English Defence League and the level of disorder during the EDL related events.


title: Antimicrobial activity of extracts of local cough mixtures on upper respiratory tract bacterial pathogens.

abstract: The punched-hole and the paper disc diffusion methods were used in screening for the antimicrobial activity of six common ingredients used locally in cough mixtures, against the following bacteria: Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus. Enterococcus faecalis, Escherichia coli, Salmonella spp, Salmonella paratyphi, Shigella dysenteria, Shigella sonnei and Candida albicans. The results, evaluated as the diameter of zone of inhibition of microbial growth, showed that lime, garlic onion, onion and honey were active against Staphylococcus aureus, Streptococcus faecalis, Candida albicans, Klebsiella pneumoniae, Pseudomonas aeruginosa, Escherichia coli, Salmonella spp and Shigella dysenteriae. Bitter-kola nut extract and palm kernel oil showed no antimicrobial activities against any of the tested organisms. None of the extracts inhibited the growth of Salmonella paratyphi and Shigella sonnei and the most susceptible organisms were Escherichia coli and Salmonella spp.


title: Center feed single layer slotted waveguide array

abstract: A center feed configuration is newly introduced to enhance the bandwidth as well as to suppress the frequency dependent beam squinting in single layer slotted waveguide arrays. A multiple-way power divider consisting of cross-junctions is installed at the center of the aperture in an alternating-phase fed waveguide slot array to reduce the long line effect in the radiating waveguide. Each radiating slot is equipped with an inductive wall for reflection canceling, which dispenses with the beam tilting technique for suppressing cumulative reflection and assures the boresight beam. All these are equipped in single layer and are potential for low cost mass-production as is usual the case with alternating phase-fed arrays. The test antenna is fabricated at 26GHz band for FWA system. A 30.5 dBi gain with 46% efficiency with the main beam staying in the boresight is obtained. The sidelobes of -9.5 dB associated with the aperture blockage for the center feed is suppressed to below -14.7 dB at the design frequency, by applying a genetic algorithm for controlling the slot excitation


title: Hierarchical Text Generation and Planning for Strategic Dialogue

abstract: End-to-end models for strategic dialogue are challenging to train, because linguistic and strategic aspects are entangled in latent state vectors. We introduce an approach to generating latent representations of dialogue moves, by inducing sentence representations to maximize the likelihood of subsequent sentences and actions. The effect is to decouple much of the semantics of the utterance from its linguistic realisation. We then use these latent sentence representations for hierarchical language generation, planning and reinforcement learning. Experiments show that using our message representations increases the reward achieved by the model, improves the effectiveness of long-term planning using rollouts, and allows self-play reinforcement learning to improve decision making without diverging from human language. Our hierarchical latent-variable model outperforms previous work both linguistically and strategically.


title: Fluorographene based Ultrasensitive Ammonia Sensor

abstract: Single molecule detection using graphene can be brought by tuning the interactions via specific dopants. Electrostatic interaction between the most electronegative element fluorine (F) and hydrogen (H) is one of the strong interactions in hydrogen bonding, and here we report the selective binding of ammonia/ammonium with F in fluorographene (FG) resulting to a change in the impedance of the system. Very low limit of detection value of ~0.44 pM with linearity over wide range of concentrations (1 pM–0.1 μM) is achieved using the FG based impedance sensor, andthisscreen printed FG sensor works in both ionized (ammonium) and un-ionized ammonia sensing platforms. The interaction energies of FG and NH3/NH4+ are evaluated using density functional theory calculations and the interactions are mapped. Here FGs with two different amounts of fluorinecontents −~5 atomic% (C39H16F2) and ~24 atomic% (C39H16F12) - are theoretically and experimentally studied for selective, high sensitive and ultra-low level detection of ammonia. Fast responding, high sensitive, large area patternable FG based sensor platform demonstrated here can open new avenues for the development of point-of-care devices and clinical sensors.


title: Automatic mood detection and tracking of music audio signals

abstract: Music mood describes the inherent emotional expression of a music clip. It is helpful in music understanding, music retrieval, and some other music-related applications. In this paper, a hierarchical framework is presented to automate the task of mood detection from acoustic music data, by following some music psychological theories in western cultures. The hierarchical framework has the advantage of emphasizing the most suitable features in different detection tasks. Three feature sets, including intensity, timbre, and rhythm are extracted to represent the characteristics of a music clip. The intensity feature set is represented by the energy in each subband, the timbre feature set is composed of the spectral shape features and spectral contrast features, and the rhythm feature set indicates three aspects that are closely related with an individual's mood response, including rhythm strength, rhythm regularity, and tempo. Furthermore, since mood is usually changeable in an entire piece of classical music, the approach to mood detection is extended to mood tracking for a music piece, by dividing the music into several independent segments, each of which contains a homogeneous emotional expression. Preliminary evaluations indicate that the proposed algorithms produce satisfactory results. On our testing database composed of 800 representative music clips, the average accuracy of mood detection achieves up to 86.3%. We can also on average recall 84.1% of the mood boundaries from nine testing music pieces.


title: Analysis of OPC unified architecture for healthcare applications

abstract: This study presents an assessment of the OPC Unified Architecture as an integration framework for heterogeneous healthcare systems enabling compliance with the Industry 4.0 paradigm. The contributions of this work are: 1) a conceptual architecture with heterogeneous networks for enabling Internet of Things healthcare applications, 2) OPC UA data models based on the HL7 Reference Information Model for information exchange and storage, and 3) an analysis of how OPC UA can enable the healthcare sector to be part of the Industry 4.0.


title: An Artificial Agent for Anatomical Landmark Detection in Medical Images

abstract: Fast and robust detection of anatomical structures or pathologies represents a fundamental task in medical image analysis. Most of the current solutions are however suboptimal and unconstrained by learning an appearance model and exhaustively scanning the space of parameters to detect a specific anatomical structure. In addition, typical feature computation or estimation of meta-parameters related to the appearance model or the search strategy, is based on local criteria or predefined approximation schemes. We propose a new learning method following a fundamentally different paradigm by simultaneously modeling both the object appearance and the parameter search strategy as a unified behavioral task for an artificial agent. The method combines the advantages of behavior learning achieved through reinforcement learning with effective hierarchical feature extraction achieved through deep learning. We show that given only a sequence of annotated images, the agent can automatically and strategically learn optimal paths that converge to the sought anatomical landmark location as opposed to exhaustively scanning the entire solution space. The method significantly outperforms state-of-the-art machine learning and deep learning approaches both in terms of accuracy and speed on 2D magnetic resonance images, 2D ultrasound and 3D CT images, achieving average detection errors of 1-2 pixels, while also recognizing the absence of an object from the image.


title: Model Driven Requirements Engineering: Mapping the field and beyond

abstract: Model Driven Engineering (MDE) holds the promise of raising the level of abstraction when designing systems by promoting domain specific modeling languages, model transformation techniques and code generation engines. Although requirements engineering (RE) relies on models and on modeling, RE and MDE have evolved separately and in distinct communities. The goal of our ongoing work is to analyze the state of the art regarding the convergence and evolution of MDE and RE. This paper is a preliminary study in which we review all papers published at MoDRE workshops and map them according to three perspectives: research issue, research contribution and evaluation method. Our analysis indicates clear predominance of proposals of new language for requirement representation and the derivation of system specifications. Other facets such as requirements elicitation and requirements validation methods are much less tackled, and traceability is seldom discussed.


title: Part of Speech Tagging for Hindi Corpus

abstract: The wide utilization of internet for making search of information has got emerging use of computational linguistics as most of the search systems uses bag of words mode which causes problem in retrieval due to polysemy, homonymy, synonymy[9][3]. This has lead to shift in the accepted boundary between what kinds of query information are submitted by humans and what kinds further intreprations in form of annotation of query information can be done so as to get better results[1][4]. In this regards the process of annotating the words in a text in accordance to a particular part of speech is the objective of this paper. further POS tagging is much tougher than making a list of words and their parts of speech, as most words tend to have more than one part of speech in different scenarios and some parts of speech of these words are rather complex or unspoken[5] [6]. There are large numbers of POS tagger available for english language which has got satisfactory performance but cannot be applied to hindi language due to structural differences[8]. This paper aims at part of speech tagging for hindi corpus as large no of hindi documents are growing on internet.


title: Modal-Based Design of a Wideband Quadruple-Ridged Flared Horn Antenna

abstract: In this paper, a systematic design technique for quadruple-ridged flared horn antennas is presented, to enhance the radiation properties through the profiling of the ridge taper. The technique relies on control of the cutoff frequencies of specific modes inside the horn, instead of brute-force optimization. This is used to design a prototype antenna as a feed for an offset Gregorian reflector system, such as considered for the Square Kilometer Array (SKA) radio telescope, to achieve an optimized aperture efficiency from 2 to 12 GHz. The antenna is employed with a quadraxial feeding network that allows the excitation of the fundamental $TE_{11}$ mode, while suppressing all other modes that causes phase errors in the aperture. Measured results confirm the validity of this approach, where good agreement is found with the simulated results.


title: A 1W Class-D Audio Power Amplifier in a 0.35μm CMOS Process

abstract: A fully integrated class-D audio power amplifier for mobile applications is presented. The amplifier is realized in a 0.35 μm CMOS process featuring 3.3 V and 5 V transistors. To reduce the out-of-band noise generation, possibly eliminating the need of an output filter, the double sided natural sampling (DSNS) pulse width modulation (PWM) scheme is used at a designed carrier frequency of 384 kHz. By using a 5 V full bridge, a maximum output power of 1 W (rms) was measured in a 7.5Ω load at 1% THD+N and an efficiency of 80%. The THD+N was 0.24% at 500 mW output, and 0.15% in minimum. The total chip area is 2.55 mm2 including pads. This chip was designed and simulated with Cadence IC design tools as a student project in the course IC Project and Verification at Lund University.


title: Base-delta-immediate compression: practical data compression for on-chip caches

abstract: Cache compression is a promising technique to increase on-chip cache capacity and to decrease on-chip and off-chip bandwidth usage. Unfortunately, directly applying well-known compression algorithms (usually implemented in software) leads to high hardware complexity and unacceptable decompression/compression latencies, which in turn can negatively affect performance. Hence, there is a need for a simple yet efficient compression technique that can effectively compress common in-cache data patterns, and has minimal effect on cache access latency.


A Mathematics Teacher's Identity Study through Their Teaching Practices in a Postgraduate Training Course


title: Rapid information architecture prototyping

abstract: To create user-centered information architectures (IA), designers need a structured methodology that allows them to move rapidly from initial exploration of domain, to designing and testing information architecture. Additionally, in rapidly changing domains, design needs to flexibly incorporate future additions and evolutions. Finally, IA design should also take into account business concerns and goals. This paper describes Rapid Information Architecture Prototyping, a three-stage methodology for creating and testing IA based on user and business requirements. First, stakeholder analysis is used to understand business and organizational context, while free-listing exercises are used to explore the domain. Next, results of free-listing are used in an open card-sorting to understand user mental models and generate prototype IAs. Finally, closed card-sorting is used to evaluate and choose between candidate structures. The last two stages can be used in an iterative manner to design and test prototype IAs. The results yield a future-oriented IA that can flexibly incorporate future changes to site content and functionality, and provide design direction for years to come.


title: Multivariate Poisson regression with covariance structure

abstract: In recent years the applications of multivariate Poisson models have increased, mainly because of the gradual increase in computer performance. The multivariate Poisson model used in practice is based on a common covariance term for all the pairs of variables. This is rather restrictive and does not allow for modelling the covariance structure of the data in a flexible way. In this paper we propose inference for a multivariate Poisson model with larger structure, i.e. different covariance for each pair of variables. Maximum likelihood estimation, as well as Bayesian estimation methods are proposed. Both are based on a data augmentation scheme that reflects the multivariate reduction derivation of the joint probability function. In order to enlarge the applicability of the model we allow for covariates in the specification of both the mean and the covariance parameters. Extension to models with complete structure with many multi-way covariance terms is discussed. The method is demonstrated by analyzing a real life data set.


title: Alive human body detection system using an autonomous mobile rescue robot

abstract: In this paper, a new approach for detecting alive humans in destructed environments using an autonomous robot is proposed. Human detection in an unmanned area can be done only by an automated system. Alive human body detection system proposed a monitoring system using ultrasonic sensors and camera to record, transmit and analyze conditions of human body. The task of identify human being in rescue operations is difficult for the robotic agent but it is simple for the human agent. In order to detect a human body, an autonomous robot must be equipped with a specific set of sensors that provide information about the presence of a person in the environment around. This work describes an autonomous robot for rescue operations. The proposed system uses an ultrasonic sensor in order to detect the existence of living humans and a low-cost camera in order to acquire a video of the scene as needed. Additional, other sensors include temperature, fire and metal detector works as bomb sensor to detect the presence of bomb in Warfield and in rescue operations. Having detected a sign of a living human, the ultrasonic sensor Triggers the camera to show live scene. The video is then displayed on the screen. This approach requires a relatively small number of data to be acquired and processed during the rescue operation. This way, the real-time cost of processing and data transmission is considerably reduced. This system has the potential to achieve high performance in detecting alive humans in devastated environments relatively quickly and cost-effectively. The detection depending on a number of factors such as the body position and the light intensity of the scene. Results show that the system provides an efficient way to track human motion. The aim of this article is to present our experience with various sensors designed and developed.


title: Enabling High-Level Application Development for the Internet of Things

abstract: Application development in the Internet of Things (IoT) is challenging because it involves dealing with a wide range of related issues such as lack of separation of concerns, and lack of high-level of abstractions to address both the large scale and heterogeneity. Moreover, stakeholders involved in the application development have to address issues that can be attributed to different life-cycles phases. when developing applications. First, the application logic has to be analyzed and then separated into a set of distributed tasks for an underlying network. Then, the tasks have to be implemented for the specific hardware. Apart from handling these issues, they have to deal with other aspects of life-cycle such as changes in application requirements and deployed devices.Several approaches have been proposed in the closely related fields of wireless sensor network, ubiquitous and pervasive computing, and software engineering in general to address the above challenges. However, existing approaches only cover limited subsets of the above mentioned challenges when applied to the IoT. This paper proposes an integrated approach for addressing the above mentioned challenges. The main contributions of this paper are: (1) a development methodology that separates IoT application development into different concerns and provides a conceptual framework to develop an application, (2) a development framework that implements the development methodology to support actions of stakeholders. The development framework provides a set of modeling languages to specify each development concern and abstracts the scale and heterogeneity related complexity. It integrates code generation, task-mapping, and linking techniques to provide automation. Code generation supports the application development phase by producing a programming framework that allows stakeholders to focus on the application logic, while our mapping and linking techniques together support the deployment phase by producing device-specific code to result in a distributed system collaboratively hosted by individual devices. Our evaluation based on two realistic scenarios shows that the use of our approach improves the productivity of stakeholders involved in the application development.


title: An Image Processing Approach to Solve Labyrinth Discovery Robotics Problem

abstract: Maze solving using multiple algorithms is one of the important problems in the last years, maze solving problem is to determine the path of a mobile robot from its source position to a destination position through a workspace populated with obstacles, in addition to finding the shortest path among the solutions. Autonomous robotics is a field with wide-reaching applications, from bomb-sniffing robots to autonomous devices for finding humans in wreckage to home automation; many people are interested in low-power, high speed, reliable solutions. we will introduce an algorithm to solve mazes and avoid robots to go through the long process, this algorithm is based on image processing and shortest path algorithm, the algorithm will work efficiently because of the preprocessing on maze's image data rather than going through the maze cell by cell. Therefore, it focuses on the entire maze rather than the current part that an agent is in it. This approach will give the robot preplanning time before falling in mistakes like loops and being trapped in a local minimum of dead ends of labyrinth besides saving time, because the entire maze map will be known before the navigation starts.


title: A SURVEY OF CONTEXT-AWARE MOBILE RECOMMENDATIONS

abstract: Mobile recommender systems target on recommending the right product or information to the right mobile users at anytime and anywhere. It is well known that the contextual information is often the key for the performances of mobile recommendations. Therefore, in this paper, we provide a focused survey of the recent development of context-aware mobile recommendations. After briefly reviewing the state-of-the-art of recommender systems, we first discuss the general notion of mobile context and how the contextual information is collected. Then, we introduce the existing approaches to exploit contextual information for modeling mobile recommendations. Furthermore, we summarize several existing recommendation tasks in the mobile scenarios, such as the recommendations in the tourism domain. Finally, we discuss some key issues that are still critical in the field of context-aware mobile recommendations, including the privacy problem, the energy efficiency issues, and the design of user interfaces.


title: MOA Concept Drift Active Learning Strategies for Streaming Data

abstract: We present a framework for active learning on evolving data streams, as an extension to the MOA system. In learning to classify streaming data, obtaining the true labels may require major eort and may incur excessive cost. Active learning focuses on learning an accurate model with as few labels as possible. Streaming data poses additional challenges for active learning, since the data distribution may change over time (concept drift) and classiers need to adapt. Conventional active learning strategies concentrate on querying the most uncertain instances, which are typically concentrated around the decision boundary. If changes do not occur close to the boundary, they will be missed and classiers will fail to adapt. We propose a software system that implements active learning strategies, extending the MOA framework. This software is released under the GNU GPL license.


title: Enhancing Sparsity by Reweighted L1 Minimization

abstract: It is now well understood that (1) it is possible to reconstruct sparse signals exactly from what appear to be highly incomplete sets of linear measurements and (2) that this can be done by constrained L1 minimization. In this paper, we study a novel method for sparse signal recovery that in many situations outperforms L1 minimization in the sense that substantially fewer measurements are needed for exact recovery. The algorithm consists of solving a sequence of weighted L1-minimization problems where the weights used for the next iteration are computed from the value of the current solution. We present a series of experiments demonstrating the remarkable performance and broad applicability of this algorithm in the areas of sparse signal recovery, statistical estimation, error correction and image processing. Interestingly, superior gains are also achieved when our method is applied to recover signals with assumed near-sparsity in overcomplete representations--not by reweighting the L1 norm of the coefficient sequence as is common, but by reweighting the L1 norm of the transformed object. An immediate consequence is the possibility of highly efficient data acquisition protocols by improving on a technique known as compressed sensing.


title: A Parallel DBSCAN Algorithm Based on Spark

abstract: With the explosive growth of data, we have entered the era of big data. In order to sift through masses of information, many data mining algorithms using parallelization are being implemented. Cluster analysis occupies a pivotal position in data mining, and the DBSCAN algorithm is one of the most widely used algorithms for clustering. However, when the existing parallel DBSCAN algorithms create data partitions, the original database is usually divided into several disjoint partitions, with the increase in data dimension, the splitting and consolidation of high-dimensional space will consume a lot of time. To solve the problem, this paper proposes a parallel DBSCAN algorithm (S_DBSCAN) based on Spark, which can quickly realize the partition of the original data and the combination of the clustering results. It is divided into the following steps: 1) partitioning the raw data based on a random sample, 2) computing local DBSCAN algorithms in parallel, 3) merging the data partitions based on the centroid. Compared with the traditional DBSCAN algorithm, the experimental result shows the proposed S_DBSCAN algorithm provides better operating efficiency and scalability.


title: Patterns of Sexual Arousal in Homosexual , Bisexual , and Heterosexual Men

abstract: The purpose of this study was to determine if self-identified bisexual, heterosexual, and homosexual men show differential genital and subjective arousal patterns to video presentations of bisexual, heterosexual, male homosexual, and lesbian sexual interactions. It was predicted that, relative to heterosexual and homosexual stimuli, bisexual men would show the highest levels of sexual arousal to bisexual erotic material, while this stimulus would induce relatively low levels of response in heterosexual and homosexual men. A sample of 59 men (19 homosexual, 13 bisexual, and 27 heterosexual) were presented with a series of 4-min sexual videos while their genital and subjective sexual responses were measured continuously. Bisexual men did not differ significantly in their responses to male homosexual stimuli (depicting men engaging in sex) from homosexual men, and they did not differ significantly in their responses to heterosexual (depicting two women, without same-sex contact, engaged in sex with a man) and lesbian (depicting women engaging in sex) stimuli from heterosexual men. However, bisexual men displayed significantly higher levels of both genital and subjective sexual arousal to a bisexual stimulus (depicting a man engaged in sex with both a man and a woman) than either homosexual or heterosexual men. The findings of this study indicate that bisexuality in men is associated with a unique and specific pattern of sexual arousal.


title: In Vivo Assessment of Trabecular Bone Microarchitecture by High-Resolution Peripheral Quantitative Computed Tomography

abstract: Context: Assessment of trabecular microarchitecture may enhance the prediction of fracture risk and improve monitoring of treatment response. A new high-resolution peripheral quantitative computed tomography (HR-pQCT) system permits in vivo assessment of trabecular architecture and volumetric bone mineral density (BMD) at the distal radius and tibia with a voxel size of 82 μm3. Objective and Patients: We determined the short-term reproducibility of this device by measuring 15 healthy volunteers three times each. We compared HR-pQCT measurements in 108 healthy premenopausal, 113 postmenopausal osteopenic, and 35 postmenopausal osteoporotic women. Furthermore, we compared values in postmenopausal osteopenic women with (n = 35) and without previous fracture history (n = 78). Design and Setting: We conducted a cross-sectional study in a private clinical research center. Intervention and Main Outcome Measure: We took HR-pQCT measurements of the radius and tibia. Femoral neck and spine BMD were measured in post...


title: Learning of Automata Models Extended with Data

abstract: One of the challenges in the Connect project is to develop techniques for learning models of networked components from exploratory interaction with the component, based on analyzing messages exchanged between the component and its environment. Many approaches to this problem employ regular inference (aka. automata learning) techniques which generate modest-size finite-state models. Most communication with real-life systems involves data values being relevant to the communication context and thus influencing the observable behavior of the communication endpoints. When applying methods from the realm of automata learning, it is desirable to handle such data-occurrences. It is therefore important to extend inference techniques to handle message alphabets and state-spaces with structures containing data parameters, often with large domains. After very briefly mentioning several approaches to the problem, we give a longer account of an approach proposed by Aarts et al, which adapts ideas from of predicate abstraction, successfully used in formal verification. We illustrate the techniques by application to a simple running example, which models a simple booking service.


title: A dynamic processor allocation policy for multiprogrammed shared-memory multiprocessors

abstract: We propose and evaluate empirically the performance of a dynamic processor-scheduling policy for multiprogrammed shared-memory multiprocessors. The policy is dynamic in that it reallocates processors from one parallel job to another based on the currently realized parallelism of those jobs. The policy is suitable for implementation in production systems in that: —It interacts well with very efficient user-level thread packages, leaving to them many low-level thread operations that do not require kernel intervention. —It deals with thread blocking due to user I/O and page faults. —It ensures fairness in delivering resources to jobs. —Its performance, measured in terms of average job response time, is superior to that of previously proposed schedulers, including those implemented in existing systems. It provides good performance to very short, sequential (e.g., interactive) requests. We have evaluated our scheduler and compared it to alternatives using a set of prototype implementations running on a Sequent Symmetry multiprocessor. Using a number of parallel applications with distinct qualitative behaviors, we have both evaluated the policies according to the major criterion of overall performance and examined a number of more general policy issues, including the advantage of “space sharing” over “time sharing” the processors of a multiprocessor, and the importance of cooperation between the kernel and the application in reallocating processors between jobs. We have also compared the policies according to other criteia important in real implementations, in particular, fairness and respone time to short, sequential requests. We conclude that a combination of performance and implementation considerations makes a compelling case for our dynamic scheduling policy.


Roughly Balanced Bagging for Imbalanced Data


title: Image analysis and machine learning for detecting malaria

abstract: Malaria remains a major burden on global health, with roughly 200 million cases worldwide and more than 400,000 deaths per year. Besides biomedical research and political efforts, modern information technology is playing a key role in many attempts at fighting the disease. One of the barriers toward a successful mortality reduction has been inadequate malaria diagnosis in particular. To improve diagnosis, image analysis software and machine learning methods have been used to quantify parasitemia in microscopic blood slides. This article gives an overview of these techniques and discusses the current developments in image analysis and machine learning for microscopic malaria diagnosis. We organize the different approaches published in the literature according to the techniques used for imaging, image preprocessing, parasite detection and cell segmentation, feature computation, and automatic cell classification. Readers will find the different techniques listed in tables, with the relevant articles cited next to them, for both thin and thick blood smear images. We also discussed the latest developments in sections devoted to deep learning and smartphone technology for future malaria diagnosis.


title: First Step into Visceral Interaction with Autonomous Vehicles

abstract: Autonomous vehicles (AVs) seem to be the next big step into the future of mobility solutions. Various research institutions in the automobile industry and academic sectors are striving to come out with the most futuristic concept. This paper focuses on the interaction between AVs and pedestrians. Furthermore, the paper aims to explore if complex communication needs can be signaled to pedestrians through motion behavior of the vehicle. In order to learn how other road users feel surrounded by self-driving cars, participants were confronted with different drivability of a self-programmed vehicle during our evaluation. As the results show, the vehicle's motion behavior is able to evoke certain emotions, which in turn lead to rational decisions within the participants. This can be seen as a form of mutual communication.


title: Addressing the Raven's Progressive Matrices Test of "General" Intelligence

abstract: TheRaven'sProgressiveMatrices� (RPM)� testisa� commonlyusedtestofgeneralhumanintelligence.�� The� RPMissomewhatuniqueasageneralintelligencete stin� thatitfocusesonvisualproblemsolving,�andinp articular,� onvisualsimilarityandanalogy.��Wearedevelopin gasmall� setofmethodsforproblemsolvingintheRPMwhich �use� propositional,� imagistic,� andmultimodalrepresenta tions,� respectively,� toinvestigatehowdifferentrepresen tations� cancontributetovisualproblemsolvingandhowth eeffects� oftheirusemightemergeinbehavior.�


title: Using grounded theory to study the experience of software development

abstract: Grounded Theory is a research method that generates theory from data and is useful for understanding how people resolve problems that are of concern to them. Although the method looks deceptively simple in concept, implementing Grounded Theory research can often be confusing in practice. Furthermore, despite many papers in the social science disciplines and nursing describing the use of Grounded Theory, there are very few examples and relevant guides for the software engineering researcher. This paper describes our experience using classical (i.e., Glaserian) Grounded Theory in a software engineering context and attempts to interpret the canons of classical Grounded Theory in a manner that is relevant to software engineers. We provide model to help the software engineering researchers interpret the often fuzzy definitions found in Grounded Theory texts and share our experience and lessons learned during our research. We summarize these lessons learned in a set of fifteen guidelines.


title: Coplanar Inverted-F Antenna With Open-End Ground Slots for Multiband Operation

abstract: This letter reports on a new idea to achieve multiband operation of a coplanar inverted-F antenna (CIFA). In the proposed approach, a microstripline is used to feed the primary CIFA radiator. This feeding arrangement allows for a flexible coupling of the feed to open-end slots in the finite-size ground to launch new resonant frequencies. Two configurations of meandered-tail CIFA with ground slots for WLAN and WiMAX applications are designed, fabricated, and experimentally tested to validate the proposed technique. Good agreement between the simulated and experimental results confirms the practical feasibility of the proposed multiband antenna design.


title: Ring chromosome 16

abstract: Ring chromosome 16 was found in a 33-year-old woman with mental, motor, and growth defects. Apart from a low percentage of monosomy 16 cell lines, the patient appears to have virtually all of the normal chromosome 16 genetic material at the microscopic level. Her impressive problems highlight the limitation of our ability to detect small deletions and the profound importance of the integrity of chromosome 16 in normal human development.


title: Clamp-and-Forget: A self-sustainable non-invasive wireless sensor node for smart metering applications

abstract: Measuring and managing the power consumption of household appliances, as well as that of industrial machineries, is becoming more and more important to improve the distribution and usage of the electrical energy and to reduce the energy bill. We present the design of a non-invasive wireless current meter which can measure AC current up to 60 A RMS exploiting a small clamp-on inductive sensor. The novelty of the design is a subsystem consisting of a harvesting circuit designed to extract energy from the same current transducer used for measurements. Experiments have been conducted to validate the approach, to assess the accuracy of the sensing system and deviations due to the energy harvester, and to determine the condition which permits us to achieve the energy neutrality and thus, a self-sustainable smart meter.


title: Optical dissection of brain circuits with patterned illumination through the phase modulation of light

abstract: Brain function relies on electrical signaling among ensembles of neurons. These signals are encoded in space - neurons are organized in complex three-dimensional networks - and in time-cells generate electrical signals on a millisecond scale. How the spatial and temporal structure of these signals controls higher brain functions is largely unknown. The recent advent of novel molecules that manipulate and monitor electrical activity in genetically identified cells provides, for the first time, the ability to causally test the contribution of specific cell subpopulations in these complex brain phenomena. However, most of the commonly used approaches are limited in their ability to illuminate brain tissue with high spatial and temporal precision. In this review article, we focus on one technique, patterned illumination through the phase modulation of light using liquid crystal spatial light modulators (LC-SLMs), which has the potential to overcome some of the major limitations of current experimental approaches.


title: On the Kinematic Analysis of Robotic Mechanisms

abstract: The kinematic analyses, of manipulators and other robotic devices composed of mechanical links, usually depend on the solution of sets of nonlinear equations. There are a variety of both numerical and algebraic techniques available to solve such systems of equations and to give bounds on the number of solutions. These solution methods have also led to an understanding of how special choices of the various structural parameters of a mechanism influence the number of solutions inherent to the kinematic geometry of a given structure. In this paper, results from studying the kinematic geometry of such systems are reviewed, and the three most useful solution techniques are summarized. The solution techniques are polynomial continuation, Grobner bases, and elimination. We then discuss the results that have been obtained with these techniques in the solution of two basic problems, namely, the inverse kinematics for serial-chain manipulators, and the direct kinematics of in-parallel platform devices.


title: Optimal aggregation of uncertain Preferences

abstract: A paradigmatic problem in social choice theory deals with the aggregation of subjective preferences of individuals — represented as rankings of alternatives — into a social ranking. We are interested in settings where individuals are uncertain about their own preferences, and represent their uncertainty as distributions over rankings. Under the classic objective of minimizing the (expected) sum of Kendall tau distances between the input rankings and the output ranking, we establish that preference elicitation is surprisingly straightforward and near-optimal solutions can be obtained in polynomial time. We show, both in theory and using real data, that ignoring uncertainty altogether can lead to suboptimal outcomes.


title: Using Soft Similarity in Multi-label Classification for Reuters-21578 Corpus

abstract: In classification tasks one of the main problems is to choose which features provide best results, i.e., Construct a vector space model. In this paper, we show how to complement traditional vector space model with the concept of soft similarity. We use the combination of the traditional tf-idf model with latent Dirichlet allocation applied in multi-label classification. We considered multi-label files of the Reuters-21578 corpus as study case. The methodology is evaluated using the multi-label algorithm Rakel1. We used the traditional tf-idf model as the baseline. We present the F1 measures for both models for various feature sets, preprocessing techniques and vector sizes. The new model obtains better results than the base line model.


title: Abstract Distinguishing paintings from photographs

abstract: We addressed the problem of automatically differentiating photographs of real scenes from photographs of paintings. We found that photographs differ from paintings in their color, edge, and texture properties. Based on these features, we trained and tested a classifier on a database of 6000 paintings and 6000 photographs. Using single features results in ~70-80% correct discrimination performance, whereas a classifier using multiple features exceeds 90% correct discrimination.


title: Semantic Motion Segmentation Using Dense CRF Formulation

abstract: While the literature has been fairly dense in the areas of scene understanding and semantic labeling there have been few works that make use of motion cues to embellish semantic performance and vice versa. In this paper, we address the problem of semantic motion segmentation, and show how semantic and motion priors augments performance. We propose an algorithm that jointly infers the semantic class and motion labels of an object. Integrating semantic, geometric and optical flow based constraints into a dense CRF-model we infer both the object class as well as motion class, for each pixel. We found improvement in performance using a fully connected CRF as compared to a standard clique-based CRFs. For inference, we use a Mean Field approximation based algorithm. Our method outperforms recently proposed motion detection algorithms and also improves the semantic labeling compared to the state-of-the-art Automatic Labeling Environment algorithm on the challenging KITTI dataset especially for object classes such as pedestrians and cars that are critical to an outdoor robotic navigation scenario.


title: More than meets the eye: The influence of implicit and explicit self-esteem on materialism

abstract: Abstract Prior research has found a link between self-esteem and materialism. However, these studies examine explicit self-esteem without considering implicit self-esteem. We examine if these two forms of self-esteem jointly influence materialism. Specifically, we propose that discrepancies between implicit and explicit self-esteem are an important driver of materialism. Support for our view is presented in a series of four studies. Study 1 illustrates the link between self-esteem discrepancy and materialism. Studies 2 and 3 show that increases (decreases) in self-esteem discrepancies cause increases (decreases) in materialism. Study 4 confirms the link between self-esteem discrepancy and the desire to self-enhance through material possessions.


title: Frequency domain techniques for void spectrum detection in cognitive radio network for emulation attack prevention

abstract: Cognitive Radio is relatively new standard for mobile communication, especially designed to meet the challenges of high spectrum demand for packet data. As spectrum is limited and expensive, unutilized spectrum is an economic constraint for the service provider. Cognitive radio provides a solution for this by providing means of reallocating the unused spectrum to secondary user (SU) when part of spectrum is unused by primary users (PU). Several past works have addressed this issue using various algorithm which ranges from statistical analysis to predictive analysis. In this paper we propose a novel solution for spectrum sensing using frequency domain analysis of the transmitted data. However one of the lesser addressed issues in this direction has been the consideration of PU emulation attack. If a SU has sufficient means of estimating void spectrum, then it can emulate a PU spectrum which then results is low accuracy in void spectrum detection. In this work we primarily focus on frequency domain analysis for void spectrum detection such that free spectrum can be padded up to prevent emulation attacks. We consider a cognitive radio network and adopt BPSK transmission. We assume that the access points are time synchronized and have preliminary knowledge of beginning of a transmission cycle. FFT of signal data in a time slot reveals the energy in the bands. By adaptive thresholding the energy of distinct bands we determine the free spectrum. Further we also demonstrate the means of reallocating this band by injecting secondary user data in the spectrum. We analyze the solution under AWGN channel. Comparison with QR based spectrum sensing technique reveals that the proposed work provides better sensing under high noise by triggering fewer false alarms and through more accurate prediction of the unused spectrum.


title: Trendelenburg Versus PASG Application-Hemodynamic Response in Man

abstract: Diminished venous return is the primary determinant of reduced cardiac output in hemorrhagic hypoperfusion. In this study the hemodynamic response of two therapies commonly employed to increase venous return in hemorrhagic hypoperfusion--pneumatic antishock garment (PASG) application and Trendelenburg (TREND) positioning--were compared in normovolemic man. Five patients had PASG pressure of 20 mm Hg compared with 10 degrees Trendelenburg, eight patients had 20 and 40 mm Hg PASG application compared with 10 degrees Trendelenburg. PASG application at both 20 and 40 mm Hg resulted in a significant increase in CVP (11.1 +/- 1.9 baseline to 16.0 +/- 2.7 PASG 40; p less than 0.01) left atrial pressure (LAP) (10.1 +/- 1.3 baseline to 14.4 +/- 1.8 PASG 20; p less than 0.01) pulmonary capillary wedge pressure (PCWP) (11.6 +/- 2.0 baseline to 16.8 +/- 3.4 PASG 40; p less than 0.01) and esophageal pressure (Pes) (5.0 +/- 0.8 baseline to 8.6 +/- 0.9 PASG 40; p less than 0.01). However, transmural right and left atrial pressure (RATP, LATP) and cardiac index (CI) were unchanged. Ten degrees of Trendelenburg resulted in no increase in CVP, PCWP, RATP, or LATP, but CI (2.67 +/- 0.07 baseline to 2.82 +/- 0.1 TREND; p less than 0.01) was significantly increased. Systemic vascular resistance index (570 +/- 46 TREND vs. 668 +/- 53 PASG 40; p less than 0.01) was significantly less in Trendelenburg compared to PASG at 40 mm Hg. The data demonstrate that elevation in CVP, LAP, and PCWP following PASG application is secondary to an increase in intrathoracic pressure (as measured by Pes).(ABSTRACT TRUNCATED AT 250 WORDS)


title: Recognizing Explicit and Implicit Hate Speech Using a Weakly Supervised Two-path Bootstrapping Approach

abstract: In the wake of a polarizing election, social media is laden with hateful content. To address various limitations of supervised hate speech classification methods including corpus bias and huge cost of annotation, we propose a weakly supervised two-path bootstrapping approach for an online hate speech detection model leveraging large-scale unlabeled data. This system significantly outperforms hate speech detection systems that are trained in a supervised manner using manually annotated data. Applying this model on a large quantity of tweets collected before, after, and on election day reveals motivations and patterns of inflammatory language.


title: Electric Vehicle Charging Station With an Energy Storage Stage for Split-DC Bus Voltage Balancing

abstract: This paper proposes a novel balancing approach for an electric vehicle bipolar dc charging station at the megawatt level, enabled by a grid-tied neutral-point-clamped converter. The study uses the presence of an energy storage stage with access to both of the dc buses to perform the complementary balance. It proposes a generic balancing structure that can achieve balance regardless the kind of energy storage system (ESS) employed. This is aiming to reduce the hardware requirements of the system and maximize the usage of the ESS, whose main function is to perform the energy management related tasks. To meet this purpose, a three-level dc-dc interface is employed, allowing to compensate the dc currents with a single ESS. Furthermore, in order to prevent the appearance of even-order harmonics in the input current during asymmetrical operation, an alternative switching sequence for the central converter is proposed. Results indicate that, without altering dramatically the charging process of the ESS, it is possible to cover the whole load scenario without the need of a balancing circuit. This allows the use of off-the-shelf products both for the rectifier and the fast chargers. In this paper, simulation and experimental results are presented to validate the proposed balancing strategy.


title: Skin and Platysma Muscle Anchoring

abstract: Anatomically, the platysma muscle is composed of two parts: a facial part and a cervical part. This observation allows a better understanding of the modifications due to aging. The correction of platysma bands needs nearly vertical directional vectors at the facial level. The periosteum of the malar bone and the anterior border of the parotid fascia are two solid structures that are not modified during aging. They allow the anchoring of the ptotic tissues including premalar fat pads, jowls, and platysma bands. The connections between the skin and the platysma muscle are not affected, although most of the described techniques need a detachment of the platysma muscle from the skin and a suture of the anterior border via a submental approach. Furthermore, the posterior border of the platysma muscle is not modified by aging. This explains why it is useless to detach this structure. The principles of the proposed technique are fundamentally different. Lore’s fascia is used as a guide for approaching the trunk of the facial nerve. Labbe and colleagues have done an anatomic study of this procedure. It allows a solid anchoring of the platysma muscle, which is not detached from the skin. This is the key to the operation because the skin and the muscle are elevated en bloc. The author demonstrates that the absence of separation between skin and muscle, particularly at the cervical level, is a very efficient means for correcting platysma bands and for obtaining good definition of the cervicomental angle.


title: A procedural approach to authoring solid models

abstract: We present a procedural approach to authoring layered, solid models. Using a simple scripting language, we define the internal structure of a volume from one or more input meshes. Sculpting and simulation operators are applied within the context of the language to shape and modify the model. Our framework treats simulation as a modeling operator rather than simply as a tool for animation, thereby suggesting a new paradigm for modeling as well as a new level of abstraction for interacting with simulation environments.Capturing real-world effects with standard modeling techniques is extremely challenging. Our key contribution is a concise procedural approach for seamlessly building and modifying complex solid geometry. We present an implementation of our language using a flexible tetrahedral representation. We show a variety of complex objects modeled in our system using tools that interface with finite element method and particle system simulations.


title: Improved QRS detector using hybrid MAMEMI filter

abstract: QRS detection is becoming more popular in detecting the heart beat rate. The improvement is done by using the new filter. This research work focus on providing better performance in heart beat detection algorithm by using hybrid filter (improving MaMeMi filter using the first order derivative filter). An enhanced algorithm has been proposed to enhance the performance of QRS detection. Different parameters are used for the performance analysis. Specificity and Sensitivity are the parameters which are used to evaluate the performance of heart beat algorithm. The results of proposed algorithm are compared with existing heart beat detection algorithm for performance comparison. The Mean improvements in sensitivity and specificity are 0.028 and 0.74 respectively.


title: Multi-armed bandit problems with dependent arms

abstract: We provide a framework to exploit dependencies among arms in multi-armed bandit problems, when the dependencies are in the form of a generative model on clusters of arms. We find an optimal MDP-based policy for the discounted reward case, and also give an approximation of it with formal error guarantee. We discuss lower bounds on regret in the undiscounted reward scenario, and propose a general two-level bandit policy for it. We propose three different instantiations of our general policy and provide theoretical justifications of how the regret of the instantiated policies depend on the characteristics of the clusters. Finally, we empirically demonstrate the efficacy of our policies on large-scale real-world and synthetic data, and show that they significantly outperform classical policies designed for bandits with independent arms.


title: When Will AI Exceed Human Performance? Evidence from AI Experts

abstract: Advances in artificial intelligence (AI) will transform modern life by reshaping transportation, health, science, finance, and the military. To adapt public policy, we need to better anticipate these advances. Here we report the results from a large survey of machine learning researchers on their beliefs about progress in AI. Researchers predict AI will outperform humans in many activities in the next ten years, such as translating languages (by 2024), writing high-school essays (by 2026), driving a truck (by 2027), working in retail (by 2031), writing a bestselling book (by 2049), and working as a surgeon (by 2053). Researchers believe there is a 50% chance of AI outperforming humans in all tasks in 45 years and of automating all human jobs in 120 years, with Asian respondents expecting these dates much sooner than North Americans. These results will inform discussion amongst researchers and policymakers about anticipating and managing trends in AI.


title: Reviewing Grasha Teaching Methods among Faculty Members of Shiraz Medical School

abstract: Introduction: Teaching and training are the basic responsibly of a faculty member. One of the fundamental problems of education in the universities is not having a criterion to identify the effective teaching styles. The aim of this study was to determine Grasha teaching method among the faculty Members in Shiraz Medical School. Methods: this descriptive, cross- sectional study was done on 100 faculty members who were selected by census sampling method. Data collection method was Grasha questionnaire which contains 40 questions in 5 sections. Data were analyzed by SPSS 18. Results: All questionnaires were completed. The age range was from 32 to 65 and the mean age was 46. 57% were male. There were 27 PhD, 35 specialists and 38 subspecialists. The highest average score belonged to “Expert” method (2/66±0/55) and the lowest to “Personal” (2± 0/76). 96% of the academic staffs were inclined to “Expert“ method and %97, %83, %78, %80 to “Formal", “Personal", “Delegator“ and “Facilitating“ methods, respectively. There was no significant difference between male and female, but in “Expert” method, the average of females was superior. %77 was under 50 years and %23 over 50. There was no significant difference between elder and younger academic members. No significant difference was found in terms of university degree. Conclusion: This study suggested academic members are inclined to use "Expert” and Delegator methods. Therefore, it is necessary for the academic members to choose a method which creates intellectual excitement among the students through the clarity of teaching content and understanding among individuals that increase the efficiency of their methods.


title: Strong, Weak, and Latent Ties and the Impact of New Media

abstract: This article argues that consideration of the strength of ties between communicators can help reconcile disparate results on the impact of new media on social relations. It is argued from the research literature and studies by the author that where ties are strong, communicators can influence each other to adapt and expand their use of media to support the exchanges important to their tie, but where ties are weak, communicators are dependent on common, organizationally established means of communication and protocols established by others. Due to this differential use of media, a new medium that adds means and opportunities for previously unconnected others to communicate will have positive effects on weak ties and weak-tie networks, in particular by laying an infrastructure of latent ties (ones that exist technically but have not yet been activated), and providing an opportunity for weak ties to develop and strengthen. A new medium may also have positive effects on strongly tied pairs where it adds anoth...


Chapter 17 Introducing Machine Learning Concepts with WEKA


title: A Channel Access Scheme to Compromise Throughput and Fairness in IEEE 802.11p Multi-Rate/Multi-Channel Wireless Vehicular Networks

abstract: Wireless access in vehicular environment (WAVE) architecture of intelligent transportation system has been standardized in the IEEE 802.11p specification. The WAVE network supports the features of multi-rate and multi-channel and it is going to be widely deployed in realistic roadway environments in order to provide traffic information and convenient services. However, the adopted contention-based medium access control protocol, which confronts the performance anomaly problem, would severely downgrade transmission efficiency between road-side-unit (RSU) and on-board-unit (OBU) because they may use diverse data transmission rates to access channel. As a solution, this paper proposes the vehicular channel access scheme (VCAS) to group a number of OBUs with similar transmission rates into one channel to optimize the channel throughput, while the group sizes of channels are controlled in order to fulfill the fairness requirement. To flexibly compromise the tradeoff between throughput and fairness, a marginal utility model is proposed in this paper. Simulation results demonstrate that the proposed VCAS with marginal utility provides a flexible way to handle versatile vehicular scenarios.


title: 360° Video Viewing Dataset in Head-Mounted Virtual Reality

abstract: 360° videos and Head-Mounted Displays (HMDs) are getting increasingly popular. However, streaming 360° videos to HMDs is challenging. This is because only video content in viewers' Field-of-Views (FoVs) is rendered, and thus sending complete 360° videos wastes resources, including network bandwidth, storage space, and processing power. Optimizing the 360° video streaming to HMDs is, however, highly data and viewer dependent, and thus dictates real datasets. However, to our best knowledge, such datasets are not available in the literature. In this paper, we present our datasets of both content data (such as image saliency maps and motion maps derived from 360° videos) and sensor data (such as viewer head positions and orientations derived from HMD sensors). We put extra efforts to align the content and sensor data using the timestamps in the raw log files. The resulting datasets can be used by researchers, engineers, and hobbyists to either optimize existing 360° video streaming applications (like rate-distortion optimization) and novel applications (like crowd-driven camera movements). We believe that our dataset will stimulate more research activities along this exciting new research direction.


title: Translingual Document Representations from Discriminative Projections

abstract: Representing documents by vectors that are independent of language enhances machine translation and multilingual text categorization. We use discriminative training to create a projection of documents from multiple languages into a single translingual vector space. We explore two variants to create these projections: Oriented Principal Component Analysis (OPCA) and Coupled Probabilistic Latent Semantic Analysis (CPLSA). Both of these variants start with a basic model of documents (PCA and PLSA). Each model is then made discriminative by encouraging comparable document pairs to have similar vector representations. We evaluate these algorithms on two tasks: parallel document retrieval for Wikipedia and Europarl documents, and cross-lingual text classification on Reuters. The two discriminative variants, OPCA and CPLSA, significantly outperform their corresponding baselines. The largest differences in performance are observed on the task of retrieval when the documents are only comparable and not parallel. The OPCA method is shown to perform best.


Strip mining on SIMD architectures


title: A novel sentiment analysis of social networks using supervised learning

abstract: Online microblog-based social networks have been used for expressing public opinions through short messages. Among popular microblogs, Twitter has attracted the attention of several researchers in areas like predicting the consumer brands, democratic electoral events, movie box office, popularity of celebrities, the stock market, etc. Sentiment analysis over a Twitter-based social network offers a fast and efficient way of monitoring the public sentiment. This paper studies the sentiment prediction task over Twitter using machine-learning techniques, with the consideration of Twitter-specific social network structure such as retweet. We also concentrate on finding both direct and extended terms related to the event and thereby understanding its effect. We employed supervised machine-learning techniques such as support vector machines (SVM), Naive Bayes, maximum entropy and artificial neural networks to classify the Twitter data using unigram, bigram and unigram + bigram (hybrid) feature extraction model for the case study of US Presidential Elections 2012 and Karnataka State Assembly Elections (India) 2013. Further, we combined the results of sentiment analysis with the influence factor generated from the retweet count to improve the prediction accuracy of the task. Experimental results demonstrate that SVM outperforms all other classifiers with maximum accuracy of 88 % in predicting the outcome of US Elections 2012, and 68 % for Indian State Assembly Elections 2013.


title: Investigating Jordanian nurses' handwashing beliefs, attitudes, and compliance.

abstract: BACKGROUND ::: Low rates of handwashing compliance among nurses are still reported in literature. Handwashing beliefs and attitudes were found to correlate and predict handwashing practices. However, such an important field is not fully explored in Jordan. ::: ::: ::: OBJECTIVES ::: This study aims at exploring Jordanian nurses' handwashing beliefs, attitudes, and compliance and examining the predictors of their handwashing compliance. ::: ::: ::: METHODS ::: A cross-sectional multicenter survey design was used to collect data from registered nurses and nursing assistants (N = 198) who were providing care to patients in governmental hospitals in Jordan. Data collection took place over 3 months during the period of February 2011 to April 2011 using the Handwashing Assessment Inventory. ::: ::: ::: RESULTS ::: Participants' mean score of handwashing compliance was 74.29%. They showed positive attitudes but seemed to lack knowledge concerning handwashing. Analysis revealed a 5-predictor model, which accounted for 37.5% of the variance in nurses' handwashing compliance. Nurses' beliefs relatively had the highest prediction effects (β = .309, P < .01), followed by skin assessment (β = .290, P < .01). ::: ::: ::: CONCLUSION ::: Jordanian nurses reported moderate handwashing compliance and were found to lack knowledge concerning handwashing protocols, for which education programs are recommended. This study raised the awareness regarding the importance of complying with handwashing protocols.


title: Approximating Explicit Model Predictive Control Using Constrained Neural Networks

abstract: This paper presents a method to compute an approximate explicit model predictive control (MPC) law using neural networks. The optimal MPC control law for constrained linear quadratic regulator (LQR) systems is piecewise affine on polytopes. However, computing this optimal control law becomes computationally intractable for large problems, and motivates the application of reinforcement learning techniques using neural networks with rectified linear units. We introduce a modified reinforcement learning policy gradient algorithm that utilizes knowledge of the system model to efficiently train the neural network. We guarantee that the network generates feasible control inputs by projecting onto polytope regions derived from the maximal control invariant set of the system. Finally, we present numerical examples that demonstrate the characteristics and performance of our algorithm.


title: Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition

abstract: Here, we develop an audiovisual deep residual network for multimodal apparent personality trait recognition. The network is trained end-to-end for predicting the Big Five personality traits of people from their videos. That is, the network does not require any feature engineering or visual analysis such as face detection, face landmark alignment or facial expression recognition. Recently, the network won the third place in the ChaLearn First Impressions Challenge with a test accuracy of 0.9109.


title: Direct Torque Control of Brushless DC Motor with Non-sinusoidal Back-EMF

abstract: In this paper, a direct torque control (DTC) technique for brushless dc (BLDC) motors with non-sinusoidal back-EMF operating in the constant torque region is presented. This approach introduces a two-phase conduction mode as opposed to the conventional three-phase DTC drives. In this control scheme, only two phases conduct at any instant of time. Unlike conventional six-step PWM current control, by properly selecting the inverter voltage space vectors of the two-phase conduction mode from a simple look-up table at a predefined sampling time, the desired quasi-square wave current is obtained. Therefore, a much faster torque response is achieved compared to conventional PWM current control. In this paper, it is also shown that in the constant torque region under the two-phase conduction DTC scheme, the amplitude of the stator flux linkage cannot easily be controlled due to the sharp changes and the curved shape of the flux vector between two consecutive commutation points in the stator flux linkage locus. Furthermore, to eliminate the low- frequency torque oscillations caused by the non-ideal trapezoidal shape of the actual back-EMF waveform of the BLDC motor, a pre-stored back-EMF versus position look-up table is designed. As a result, it is possible to achieve DTC of a BLDC motor drive with faster torque response due to the fact that the voltage space vectors are directly controlled while the stator flux linkage amplitude is deliberately kept almost constant by ignoring the flux control in the constant torque region. Since the flux control along with PWM generation is removed, fewer algorithms are required for the proposed control scheme. A theoretical concept is developed and the validity and effectiveness of the proposed DTC scheme are verified through the simulations and experimental results.


title: Severe postoperative nausea and vomiting in a parkinsonian patient.

abstract: A 74-year-old woman was reviewed 6 hours postoperatively following a mastectomy and axillary clearance as she was vomiting and feeling nauseous. She had experienced a severe episode of emesis after surgery more than 10 years previously, which, she recalled, had lasted several days.


title: State of the Art: Bipedal Robots for Lower Limb Rehabilitation

abstract: The bipedal robot is one of the most attractive robots types given its similarity to the locomotion of human beings and its ability to assist people to walk during rehabilitation. This review summarizes the chronological historical development of bipedal robots and introduces some current popular bipedal robots age. Then, the basic theory-stability control and key technology-motion planning of bipedal robots are introduced and analyzed. Bipedal robots have a wide range of applications in the service, education, entertainment, and other industries. After that, we specifically discuss the applications of bipedal robots in lower limb rehabilitation, including wearable exoskeleton robots, rehabilitation equipment, soft exoskeleton robots, and unpowered exoskeleton robots, and their control methods. Lastly, the future development and the challenges in this field are discussed.


title: Why Do White Americans Support the Death Penalty ?

abstract: This article explores the roots of white support for capital punishment in the United States. Our analysis addresses individual-level and contextual factors, paying particular attention to how racial attitudes and racial composition influence white support for capital punishment. Our findings suggest that white support hinges on a range of attitudes wider than prior research has indicated, including social and governmental trust and individualist and authoritarian values. Extending individual-level analyses, we also find that white responses to capital punishment are sensitive to local context. Perhaps most important, our results clarify the impact of race in two ways. First, racial prejudice emerges here as a comparatively strong predictor of white support for the death penalty. Second, black residential proximity functions to polarize white opinion along lines of racial attitude. As the black percentage of county residents rises, so too does the impact of racial prejudice on white support for capital pu...


title: Analysis and reduction of common mode EMI noise for resonant converters

abstract: In this paper, the common mode (CM) EMI noise characteristic of three popular topologies of resonant converter (LLC, CLL and LCL) is analyzed. The comparison of their EMI performance is provided. A state-of-art LLC resonant converter with matrix transformer is used as an example to further illustrate the CM noise problem of resonant converters. The CM noise model of LLC resonant converter is provided. A novel method of shielding is provided for matrix transformer to reduce common mode noise. The CM noise of LLC converter has a significantly reduction with shielding. The loss of shielding is analyzed by finite element analysis (FEA) tool. Then the method to reduce the loss of shielding is discussed. There is very little efficiency sacrifice for LLC converter with shielding according to the experiment result.


title: Efficient Licence Plate Detection By Unique Edge Detection Algorithm and Smarter Interpretation Through IoT

abstract: Vehicles play a vital role in modern day transportation systems. Number plate provides a standard means of identification for any vehicle. To serve this purpose, automatic licence plate recognition system was developed. This consisted of four major steps: Pre-processing of the obtained image, extraction of licence plate region, segmentation and character recognition. In earlier research, direct application of Sobel edge detection algorithm or applying threshold were used as key steps to extract the licence plate region, which does not produce effective results when the captured image is subjected to the high intensity of light. The use of morphological operations causes deformity in the characters during segmentation. We propose a novel algorithm to tackle the mentioned issues through a unique edge detection algorithm. It is also a tedious task to create and update the database of required vehicles frequently. This problem is solved by the use of Internet of things(IOT) where an online database can be created and updated from any module instantly. Also, through IoT, we connect all the cameras in a geographical area to one server to create a universal eye which drastically increases the probability of tracing a vehicle over having manual database attached to each camera for identification purpose.