title: Detection of Frequent Alarm Patterns in Industrial Alarm Floods Using Itemset Mining Methods

abstract: The presence of alarm floods is identified as the main reason for low efficiency of alarm systems and the leading cause of many industrial accidents. In practice, a commonly used technique to deal with alarm floods is dynamic alarm suppression, which temporally suppresses predefined groups of alarms following unplanned events that are not relevant or meaningful to the operator. However, determining what alarms to suppress from a pool of thousands of configured alarm variables remains a challenging problem. This paper proposes a data-driven method to find such alarm groups by detecting frequent patterns in alarm floods from historical alarm data. Main contributions of this study are: 1) the identification and extraction of alarm floods are formulated; 2) frequent alarm patterns are defined and itemset mining methods are adapted to discover meaningful patterns in alarm floods; and 3) new visualization techniques are proposed based on exiting plots to show alarm floods and alarm patterns. The effectiveness of the proposed method is demonstrated by application to real industrial data.


title: Anxiety, attention, and decision making: The moderating role of heart rate variability

abstract: The current exploratory research examined whether high frequency heart rate variability (HF-HRV) modulates the association between anxiety and (1) executive attentional control during situations involving neutral stimuli, in which the distractor stimuli are in conflict with the target stimulus, and (2) risk aversion in decision making. Forty-five participants (21 with low and 24 with high trait-anxiety) performed a modified version of the Attention Network Test to measure attentional control, and the Balloon Analog Risk Task to measure risk aversion. HF-HRV was recorded during a rest period before completion of the tasks. Results showed that individuals with high anxiety and low HF-HRV have worse attentional control in the face of conflicting information as well as greater risk aversion, in comparison with individuals with both high anxiety and high HF-HRV or low anxiety (regardless of HF-HRV). HF-HRV was positively associated with attentional control and negatively associated with risk aversion. Furthermore, a strong negative association was observed between attentional control and risk aversion. These results suggest that HF-HRV modulates the influence of anxiety on both attentional control to neutral stimuli, and risk aversion in decision making. Greater HF-HRV appears to fulfill a protective role in highly anxious individuals. The associations observed also suggest that executive control of attention plays a relevant role in decision making. These results support the relevance of the autonomic nervous system in sustained cognition and are in accordance with theories in which vagal-mediated heart rate variability is taken as an indicator of prefrontal cortex inhibitory influences.


title: Application of the Naive Bayes Method to a Decision Support System to Provide Discounts (Case Study: PT. Bina Usaha Teknik)

abstract: Researchers have designed a decision support system for granting discounts by using the Naive Bayes method. Naive Bayes can be used for decisions to grant discounts within a multi-criteria system. The criteria are determined by the company, the purchase of some items, the status of the product, the big day, price ranges. The management system helps the company in providing discounts accordingly. The result from this study is an application that can simplify the analysis of large amounts of data, in order to help provide the information as the result of corporate decisions. The programming language used was PHP and MySQL as a database. The system development method used was Rapid Application Development (RAD) with Unified Modelling Language (UML) tools and Microsoft Visio as a system design editor. Hopefully, by use of the decision support system for granting discounts by employing the Naive Bayes method, the process of granting rebates can be more effective and efficient.


title: Robust parameter estimation in computer vision

abstract: Estimation techniques in computer vision applications must estimate accurate model parameters despite small-scale noise in the data, occasional large-scale measurement errors (outliers), and measurements from multiple populations in the same data set. Increasingly, robust estimation techniques, some borrowed from the statistics literature and others described in the computer vision literature, have been used in solving these parameter estimation problems. Ideally, these techniques should effectively ignore the outliers and measurements from other populations, treating them as outliers, when estimating the parameters of a single population. Two frequently used techniques are least-median of squares (LMS) [P. J. Rousseeuw, {J. Amer. Statist. Assoc., 79 (1984), pp. 871--880] and M-estimators [Robust Statistics: The Approach Based on Influence Functions, F. R. Hampel et al., John Wiley, 1986; Robust Statistics, P. J. Huber, John Wiley, 1981]. LMS handles large fractions of outliers, up to the theoretical limit of 50% for estimators invariant to affine changes to the data, but has low statistical efficiency. M-estimators have higher statistical efficiency but tolerate much lower percentages of outliers unless properly initialized. ::: While robust estimators have been used in a variety of computer vision applications, three are considered here. In analysis of range images---images containing depth or X, Y, Z measurements at each pixel instead of intensity measurements---robust estimators have been used successfully to estimate surface model parameters in small image regions. In stereo and motion analysis, they have been used to estimate parameters of what is called the ''fundamental matrix,'' which characterizes the relative imaging geometry of two cameras imaging the same scene. Recently, robust estimators have been applied to estimating a quadratic image-to-image transformation model necessary to create a composite, ''mosaic image'' from a series of images of the human retina. In each case, a straightforward application of standard robust estimators is insufficient, and carefully developed extensions are used to solve the problem.


title: Modeling Surround Suppression in V1 Neurons with a Statistically Derived Normalization Model

abstract: We examine the statistics of natural monochromatic images decomposed using a multi-scale wavelet basis. Although the coefficients of this representation are nearly decorrelated, they exhibit important higher-order statistical dependencies that cannot be eliminated with purely linear procssing. In particular, rectified coefficients corresponding to basis functions at neighboring spatial positions, orientations and scales are highly correlated. A method of removing these dependencies is to divide each coefficient by a weighted combination of its rectified neighbors. Several successful models of the steady -state behavior of neurons in primary visual cortex are based on such "divisive normalization" computations, and thus our analysis provides a theoretical justification for these models. Perhaps more importantly, the statistical measurements explicitly specify the weights that should be used in computing the normalization signal. We demonstrate that this weighting is qualitatively consistent with recent physiological experiments that characterize the suppressive effect of stimuli presented outside of the classical receptive field. Our observations thus provide evidence for the hypothesis that early visual neural processing is well matched to these statistical properties of images.


title: Terra: a virtual machine-based platform for trusted computing

abstract: We present a flexible architecture for trusted computing, called Terra, that allows applications with a wide range of security requirements to run simultaneously on commodity hardware. Applications on Terra enjoy the semantics of running on a separate, dedicated, tamper-resistant hardware platform, while retaining the ability to run side-by-side with normal applications on a general-purpose computing platform. Terra achieves this synthesis by use of a trusted virtual machine monitor (TVMM) that partitions a tamper-resistant hardware platform into multiple, isolated virtual machines (VM), providing the appearance of multiple boxes on a single, general-purpose platform. To each VM, the TVMM provides the semantics of either an "open box," i.e. a general-purpose hardware platform like today's PCs and workstations, or a "closed box," an opaque special-purpose platform that protects the privacy and integrity of its contents like today's game consoles and cellular phones. The software stack in each VM can be tailored from the hardware interface up to meet the security requirements of its application(s). The hardware and TVMM can act as a trusted party to allow closed-box VMs to cryptographically identify the software they run, i.e. what is in the box, to remote parties. We explore the strengths and limitations of this architecture by describing our prototype implementation and several applications that we developed for it.


title: Game-theoretic integration for image segmentation

abstract: Robust segmentation of structures from an image is essential for a variety of image analysis problems. However, the conventional methods of region-based segmentation and gradient-based boundary finding are often frustrated by poor image quality. Here we propose a method to integrate the two approaches using game theory in an effort to form a unified approach that is robust to noise and poor initialization. This combines the perceptual notions of complete boundary information using edge data and shape priors with gray-level homogeneity using two computational modules. The novelty of the method is that this is a bidirectional framework, whereby both computational modules improve their results through mutual information sharing. A number of experiments were performed both on synthetic datasets and datasets of real images to evaluate the new approach and it is shown that the integrated method typically performs better than conventional gradient-based boundary finding.


title: On the Optimality of the Simple Bayesian Classifier under Zero-One Loss

abstract: The simple Bayesian classifier is known to be optimal when attributes are independent given the class, but the question of whether other sufficient conditions for its optimality exist has so far not been explored. Empirical results showing that it performs surprisingly well in many domains containing clear attribute dependences suggest that the answer to this question may be positive. This article shows that, although the Bayesian classifier‘s probability estimates are only optimal under quadratic loss if the independence assumption holds, the classifier itself can be optimal under zero-one loss (misclassification rate) even when this assumption is violated by a wide margin. The region of quadratic-loss optimality of the Bayesian classifier is in fact a second-order infinitesimal fraction of the region of zero-one optimality. This implies that the Bayesian classifier has a much greater range of applicability than previously thought. For example, in this article it is shown to be optimal for learning conjunctions and disjunctions, even though they violate the independence assumption. Further, studies in artificial domains show that it will often outperform more powerful classifiers for common training set sizes and numbers of attributes, even if its bias is a priori much less appropriate to the domain. This article‘s results also imply that detecting attribute dependence is not necessarily the best way to extend the Bayesian classifier, and this is also verified empirically.


title: A Comparison Between Single-Phase Quasi- $Z$-Source and Quasi-Switched Boost Inverters

abstract: The properties of a single-phase quasi- $Z$ -source inverter (qZSI) and a single-phase quasi-switched boost inverter (qSBI), both of which are single-stage buck-boost inverters, are investigated and compared. For the same operating conditions, qSBI has the following advantages over qZSI: 1) Three capacitors are saved; 2) the current rating on both of its switches and diodes is lower; 3) its boost factor is higher with an equivalent parasitic effect; and 4) its efficiency is higher. However, qSBI has one more active switch and one more diode than $Z$ -source/qZSIs. In additi on, the capacitor voltage stress of qSBI is higher than that of qZSI. The dc and ac component circuit analysis, impedance design with low-frequency and high-frequency ripples, component stresses, and power loss calculation are presented. A prototype based on a TMS320F28335 DSP is built in order to compare the operating principle of qSBI and qZSI.


title: Breast cancer classification using deep belief networks

abstract: We present a CAD scheme using DBN unsupervised path followed by NN supervised path.Our two-phase method 'DBN-NN' classification accuracy is higher than using one phase.Overall accuracy of DBN-NN reaches 99.68% with 100% sensitivity & 99.47% specificity.DBN-NN was tested on the Wisconsin Breast Cancer Dataset (WBCD).DBN-NN results show classifier performance improvements over previous studies. Over the last decade, the ever increasing world-wide demand for early detection of breast cancer at many screening sites and hospitals has resulted in the need of new research avenues. According to the World Health Organization (WHO), an early detection of cancer greatly increases the chances of taking the right decision on a successful treatment plan. The Computer-Aided Diagnosis (CAD) systems are applied widely in the detection and differential diagnosis of many different kinds of abnormalities. Therefore, improving the accuracy of a CAD system has become one of the major research areas. In this paper, a CAD scheme for detection of breast cancer has been developed using deep belief network unsupervised path followed by back propagation supervised path. The construction is back-propagation neural network with Liebenberg Marquardt learning function while weights are initialized from the deep belief network path (DBN-NN). Our technique was tested on the Wisconsin Breast Cancer Dataset (WBCD). The classifier complex gives an accuracy of 99.68% indicating promising results over previously-published studies. The proposed system provides an effective classification model for breast cancer. In addition, we examined the architecture at several train-test partitions.


title: Light Field Multi-View Video Coding With Two-Directional Parallel Inter-View Prediction

abstract: Light field (LF) technology has been popularly adopted by a wide range of conventional industries. However, one problem when dealing with LFs is the sheer size of data volume. There have been many multi-view video coding (MVC)-based LF video coding methods reported in the literature, aiming at finding the best prediction structure for LF video coding. It is clear that the number of possible prediction structures is unlimited, and it is also observed that the coding bit-rate can be reduced by increasing the number of bi-directionally encoded views in the prediction structure. However, none work has been conducted to analyze the relationship of the prediction structure with its coding performance. In light of this observation, we first design a new LF-MVC prediction structure by extending the inter-view prediction into a two-directional parallel structure. Analytical models for source coding rate and encoding time are developed to analyze their relationships with the prediction structure, and are proven to be well-matched to our experimental results. Experimental evaluation of two LF video sequences demonstrates that the proposed LF-MVC prediction structure can achieve a factor of 26% bit-rate reduction against the conventional MVC prediction structure for an LF video with $5\times 5$ views, and a further 34% bit-rate reduction for an LF video with a larger $10\times 10$ views. Compared with the state-of-the-art MVC-based LF video coding prediction structures in the literature, LF-MVC can achieve the best coding performance, and with its high encoding efficiency, is well suited for deployment in practical LF-based 3D systems.


title: A Deeper Look at 3D Shape Classifiers

abstract: We investigate the role of representations and architectures for classifying 3D shapes in terms of their computational efficiency, generalization, and robustness to adversarial transformations. By varying the number of training examples and employing cross-modal transfer learning we study the role of initialization of existing deep architectures for 3D shape classification. Our analysis shows that multiview methods continue to offer the best generalization even without pretraining on large labeled image datasets, and even when trained on simplified inputs such as binary silhouettes. Furthermore, the performance of voxel-based 3D convolutional networks and point-based architectures can be improved via cross-modal transfer from image representations. Finally, we analyze the robustness of 3D shape classifiers to adversarial transformations and present a novel approach for generating adversarial perturbations of a 3D shape for multiview classifiers using a differentiable renderer. We find that point-based networks are more robust to point position perturbations while voxel-based and multiview networks are easily fooled with the addition of imperceptible noise to the input.


title: Periplasmic expression in and release of Fab fragments fromEscherichia coliusing stress minimization

abstract: BACKGROUND ::: The bacterium Escherichia coli is a commonly used host for the production of recombinant protein biopharmaceutical products. One class of such molecules is antibody fragments, typified by the Crohn's disease and rheumatoid arthritis therapy Certolizumab pegol (Cimzia®). Antibody fragments generated in E. coli are often directed to the periplasm, so that disulphide bonding can occur and release can be simplified. However, many recombinant protein products are prone to misfolding and mislocalization. Here, we optimized the production of a Fab fragment, D1.3, and its release from the periplasm of E. coli using osmotic shock. ::: ::: RESULTS ::: By minimizing stress imposed on the bacterial hosts and monitoring Fab, total protein and DNA concentrations of fractions isolated following osmotic release, we successfully targeted the majority of recombinant Fab to the periplasm and were able to rapidly define optimal harvest points. Coupled optimization of fermentation and release increased the Fab concentration of the periplasmic extract by more than 20-fold. ::: ::: CONCLUSION ::: Simultaneous optimization of fermentation and periplasmic release allowed rapid definition of operational space and generation of recombinant protein in a form compatible with downstream processing steps. This methodology could be used for optimization of the production of a range of periplasmically-targeted recombinant proteins. © 2015 Society of Chemical Industry


title: Design of very-high-frequency synchronous resonant DC-DC converter for variable load operation

abstract: Applying RF circuit design techniques to dc-dc power conversion has pushed the switching frequency of power converters using discrete components beyond 100 MHz. This paper proposes a simple solution to implement synchronous rectification for resonant dc-dc power converters operating in the Very-High-Frequency (VHF) range, by using a symmetric bandpass matching network between the inverter and rectifier. A bandpass matching network maintains a close-to-zero phase shift between the inverter and rectifier voltage, enabling the synchronous switch to be driven by the same clock signal as the inverter switch. Furthermore, this type of VHF converter is able to maintain a near-constant voltage conversion ratio over a wide load range. Experimental results of a 64 MHz 36 V-12 V converter are provided as proof of concept.


title: The effect of malate and other dicarboxylic acids on mitochondrial isocitrate metabolism.

abstract: Abstract The increased isocitrate consumption of isolated rat liver mitochondria brought about by the addition of L-malate is quantitatively accounted for by increases in the production of both α-ketoglutarate and citrate. The effect on citrate production is still observed under conditions such that the oxidation of isocitrate to α-ketoglutarate is completely inhibited. The specificity of the effect has been investigated, and it is shown that a number of dicarboxylic acids can stimulate isocitrate metabolism in a similar fashion. Marked stereospecificity is indicated by the fact that among the tartrate stereoisomers only the meso form is active.


title: An ultrawide-band double discone antenna with the tapered cylindrical wires

abstract: A novel double discone antenna is composed of the asymmetric biconical antenna and upside-down discone antenna. The asymmetric biconical antenna has all-wire structure like octopus legs for covering at lower operating frequency, and the upside-down discone antenna has conical shape playing a role as covering at higher operating frequency. An elaborate assembly of two antenna exhibits a 1:100 impedance bandwidth (180 MHz-18 GHz) with VSWR below 2.5 while maintaining an acceptable omnidirectional radiation pattern except at about 12 GHz region. The simulated performances of the proposed antenna are verified by the measured results of the fabricated antenna. There seems to be good agreement with each other. These features make such antennas highly suitable for application to UWB system.


title: "Was It Something I Said?" "No, It Was Something You Posted!" A Study of the Spiral of Silence Theory in Social Media Contexts

abstract: Abstract New media technologies make it necessary for scholars to reassess mass communication theories developed among legacy media. One such theory is the spiral of silence theory originally proposed by Noelle-Neumann in the 1970s. Increasing diversity of media content, selectivity, social networking site (SNS) interactivity, and the potential for anonymity have posed various challenges to its theoretical assumptions. While application of the spiral of silence in SNS contexts has been theorized, its empirical testing is scarce. To fill this void, the Pew 2012 Search, Social Networks, and Politics survey is used to test the theory. Results reveal that encountering agreeable political content predicts speaking out, while encountering disagreeable postings stifles opinion expression, supporting the spiral of silence theory in the SNS environment. However, certain uses of SNSs and psychological factors demonstrate a liberating effect on opinion expression.


title: Psychopathic predators? Getting specific about the relation between psychopathy and violence.

abstract: Objective ::: The Psychopathy Checklist-Revised (PCL-R; Hare, 1991, 2003) is often used to assess risk of violence, perhaps based on the assumption that it captures emotionally detached individuals who are driven to prey upon others. This study is designed to assess the relation between (a) core interpersonal and affective traits of psychopathy and impulsive antisociality on the one hand, and (b) the risk of future violence, and patterns of motivation for past violence, on the other.


title: Image Processing and Recognition - Mixture of Experts for Classification of Gender, Ethnic Origin, and Pose of Human Faces

abstract: We describe the application of mixtures of experts on gender and ethnic classification of human faces, and pose classification, and show their feasibility on the FERET database of facial images. The mixture of experts is implemented using the "divide and conquer" modularity principle with respect to the granularity and/or the locality of information. The mixture of experts consists of ensembles of radial basis functions (RBFs). Inductive decision trees (DTs) and support vector machines (SVMs) implement the "gating network" components for deciding which of the experts should be used to determine the classification output and to restrict the support of the input space. Both the ensemble of RBF's (ERBF) and SVM use the RBF kernel ("expert") for gating the inputs. Our experimental results yield an average accuracy rate of 96% on gender classification and 92% on ethnic classification using the ERBF/DT approach from frontal face images, while the SVM yield 100% on pose classification.


title: A survey on sentiment analysis methods and approach

abstract: Data Analytics is widely used in many industries and organization to make a better Business decision. By applying analytics to the structured and unstructured data the enterprises brings a great change in their way of planning and decision making. Sentiment analysis (or) opinion mining plays a significant role in our daily decision making process. These decisions may range from purchasing a product such as mobile phone to reviewing the movie to making investments — all the decisions will have a huge impact on the daily life. Sentiment Analysis is dealing with various issues such as Polarity Shift, accuracy related issues, Binary Classification problem and Data sparsity problem. However various methods were introduced for performing sentiment analysis, still that are not efficient in extracting the sentiment features from the given content of text. Naive Bayes, Support Vector Machine, Maximum Entropy are the machine learning algorithms used for sentiment analysis which has only a limited sentiment classification category ranging between positive and negative. Especially supervised and unsupervised algorithms have only limited accuracy in handling polarity shift and binary classification problem. Even though the advancement in sentiment Analysis technique there are various issues still to be noticed and make the analysis not accurately and efficiently. So this paper presents the survey on various sentiment Analysis methodologies and approaches in detailed. This will be helpful to earn clear knowledge about sentiment analysis methodologies. At last the comparison is made between various paper's approach and issues addressed along with the metrics used.


title: Microautologous Fat Transplantation for Primary Augmentation Rhinoplasty: Long-Term Monitoring of 198 Asian Patients

abstract: Background Numerous techniques and materials are available for increasing the dorsal height and length of the nose. Microautologous fat transplantation (MAFT) may be an appropriate strategy for augmentation rhinoplasty. ::: ::: Objectives The authors sought to determine the long-term results of MAFT with the so-called one-third maneuver in Asian patients who underwent augmentation rhinoplasty. ::: ::: Methods A total of 198 patients who underwent primary augmentation rhinoplasty with MAFT were evaluated in a retrospective study. Fat was harvested by liposuction and was processed and refined by centrifugation. Minute parcels of purified fat were transplanted to the nasal dorsum with a MAFT-Gun. Patient satisfaction was scored with a 5-point Likert scale, and aesthetic outcomes were validated with pre- and postoperative photographs. ::: ::: Results The mean age of the patients was 45.5 years. The mean operating time for MAFT was 25 minutes, and patients underwent 1-3 MAFT sessions. The mean volume of fat delivered per session was 3.4 mL (range, 2.0-5.5 mL). Patients received follow-up for an average of 19 months (range, 6-42 months). Overall, 125 of 198 patients (63.1%) indicated that they were satisfied with the results of 1-3 sessions of MAFT. There were no major complications. ::: ::: Conclusions The results of this study support MAFT as an appropriate fat-transfer strategy for Asian patients undergoing primary augmentation rhinoplasty. ::: ::: Level of Evidence 4![Graphic][1] Therapeutic ::: ::: [1]: /embed/inline-graphic-1.gif


title: Multimodal augmented reality: the norm rather than the exception

abstract: Augmented reality (AR) is commonly seen as a technology that overlays virtual imagery onto a participant's view of the world. In line with this, most AR research is focused on what we see. In this paper, we challenge this focus on vision and make a case for an experience-focused and modalities-encompassing understanding of AR. We argue that multi-modality in AR is the norm rather than the exception, as AR environments consist of both virtual content and our real, physical, multimodal world. We explore the role multi-modal and non-visual aspects of our physical reality can play when creating AR scenarios and the possibilities and challenges that emerge when approaching AR from a modalities-encompassing perspective.


title: Probabilistic sonar scan matching for an AUV

abstract: This paper proposes MSISpIC, a probabilistic sonar scan matching algorithm for the localization of an Autonomous Underwater Vehicle (AUV). The technique uses range scans gathered with a Mechanical Scanning Imaging Sonar (MSIS), the robot displacement estimated through dead-reckoning using a Doppler Velocity Log (DVL) and a Motion Reference Unit (MRU). The proposed method is an extension of the pIC algorithm. An Extended Kalman Filter (EKF) is used to estimate the robot-path during the scan in order to reference all the range and bearing measurements as well as their uncertainty to a scan fixed frame before registering. The major contribution consists of experimentally proving that probabilistic sonar scan matching techniques have the potential to improve the DVL-based navigation. The algorithm has been tested on an AUV guided along a 600m path within an abandoned marina underwater environment with satisfactory results.


title: Attachment style as a predictor of adult romantic relationships.

abstract: Questionnaire measures of attachment style, attachment history, beliefs about relationships, self-esteem, limerence, loving, love addiction, and love styles were administered to 374 undergraduates. Attachment style was related in theoretically expected ways to attachment history and to beliefs about relationships. Securely attached Ss reported relatively positive perceptions of their early family relationships. Avoidant Ss were most likely to report childhood separation from their mother and to express mistrust of others. Anxious-ambivalent subjects were less likely than avoidant Ss to see their father as supportive, and they reported a lack of independence and a desire for deep commitment in relationships. The self-esteem measure and each of the scales measuring forms of love were factor analyzed separately. Analyses based on scale scores derived from the resulting factors indicated that attachment style was also strongly related to self-esteem and to the various forms of love discussed in other theoretical frameworks. The results suggest that attachment theory offers a useful perspective on adult love relationships.


title: Spatial indexing and analytics on Hadoop

abstract: Effective processing of extremely large volumes of spatial data has led to many organizations employing distributed processing frameworks. Hadoop is one such open-source framework that is enjoying widespread adoption. In this paper, we detail an approach to indexing and performing key analytics on spatial data that is persisted in HDFS. Our technique differs from other approaches in that it combines spatial indexing, data load balancing, and data clustering in order to optimize performance across the cluster. In addition, our index supports efficient, random-access queries without requiring a MapReduce job; neither a full table scan, nor any MapReduce overhead is incurred when searching. This facilitates large numbers of concurrent query executions. We will also demonstrate how indexing and clustering positively impacts the performance of range and k-NN queries on large real-world datasets. The performance analysis will enable a number of interesting observations to be made on the behavior of spatial indexes and spatial queries in this distributed processing environment.


title: A Review on Home Control Automation Using GSM and Bluetooth

abstract: Home automation in essence plays a very vital role in modern era because of its flexibility in using at different places with high precision which will intern save money and time by decreasing human hard work. General idea of home automation shows the quality of human being at house. Prime focus of this technology is to control the household equipment's like light, fan, door, AC etc. automatically. In hazardous condition, it is useful for old aged and handicapped persons. In this technology, remote, wireless controlled switches, PC, Laptop or smartphone are used for operating. In this paper we detailed a survey on home control automation using GSM and Bluetooth by considering the parameters like efficiency of working, controllers used, type of communication, the apps developed etc. and at last a comparative discussion is given which summarizes the previous literature work.


title: Convergence rates for persistence diagram estimation in Topological Data Analysis

abstract: Computational topology has recently seen an important development toward data analysis, giving birth to Topological Data Analysis. Persistent homology appears as a fundamental tool in this field. We show that the use of persistent homology can be naturally considered in general statistical frameworks . We establish convergence rates of persistence diagrams associated to data randomly sampled from any compact metric space to a well defined limit diagram encoding the topological features of the support of the measure from which the data have been sampled. Our approach relies on a recent and deep stability result for persistence that allows to relate our problem to support estimation problems (with respect to the Gromov-Hausdorff distance). Some numerical experiments are performed in various contexts to illustrate our results.


title: Detection of Plagiarism in University Projects Using Metrics-based Spectral Similarity

abstract: An original method of spectral similarity analysis for plagia- rism detection in university project is presented. The approach is based on a clone detection tool called CLAN that performs metrics based sim- ilarity analysis of source code fragments. Denitions and algorithms for spectral similarity analysis are presented and discussed. Experiments performed on university projects are presented. Experimental results include the distribution of similarity in C and C++ projects. Analy- sis of spectral similarity distribution identies the most similar pairs of projects that can be considered as candidates for plagiarism.


title: Beyond Monte Carlo Tree Search: Playing Go with Deep Alternative Neural Network and Long-Term Evaluation

abstract: Monte Carlo tree search (MCTS) is extremely popular in computer Go which determines each action by enormous simulations in a broad and deep search tree. However, human experts select most actions by pattern analysis and careful evaluation rather than brute search of millions of future nteractions. In this paper, we propose a computer Go system that follows experts way of thinking and playing. Our system consists of two parts. The first part is a novel deep alternative neural network (DANN) used to generate candidates of next move. Compared with existing deep convolutional neural network (DCNN), DANN inserts recurrent layer after each convolutional layer and stacks them in an alternative manner. We show such setting can preserve more contexts of local features and its evolutions which are beneficial for move prediction. The second part is a long-term evaluation (LTE) module used to provide a reliable evaluation of candidates rather than a single probability from move predictor. This is consistent with human experts nature of playing since they can foresee tens of steps to give an accurate estimation of candidates. In our system, for each candidate, LTE calculates a cumulative reward after several future interactions when local variations are settled. Combining criteria from the two parts, our system determines the optimal choice of next move. For more comprehensive experiments, we introduce a new professional Go dataset (PGD), consisting of 253233 professional records. Experiments on GoGoD and PGD datasets show the DANN can substantially improve performance of move prediction over pure DCNN. When combining LTE, our system outperforms most relevant approaches and open engines based on MCTS.


title: Determination of Base-Flipping Free-Energy Landscapes from Nonequilibrium Stratification

abstract: Correct calculation of the variation of free energy upon base flipping is crucial in understanding the dynamics of DNA systems. The free-energy landscape along the flipping pathway gives the thermodynamic stability and the flexibility of base-paired states. Although numerous free-energy simulations are performed in the base flipping cases, no theoretically rigorous nonequilibrium techniques are devised and employed to investigate the thermodynamics of base flipping. In the current work, we report a general nonequilibrium stratification scheme for the efficient calculation of the free-energy landscape of base flipping in DNA duplex. We carefully monitor the convergence behavior of the equilibrium sampling based free-energy simulation and the nonequilibrium stratification and determine the empirical length of time blocks required for converged sampling. Comparison between the performances of the equilibrium umbrella sampling and the nonequilibrium stratification is given. The results show that nonequilibriu...


title: ISO 9000 certification benefits, reality or myth?

abstract: Given the rapid recent growth in ISO 9000 applications and the business performance benefits being claimed for it by National Accreditation Registrars, it is timely to review the research in this area to see if any substantial proof exists for these claims. The paper explores the literature and finds that there is no proven link between quality certification (ISO 9000) and improved business performance. However, it is clear from the research reviewed on business performance factors, that better quality does have a consistent, positive relationship with business performance. Combining these findings leads to the inference that quality certification to ISO 9000 standards is not consistently associated with having a quality assurance system that delivers improved process control, or better quality. We conclude that the National Accreditation Registrars need to reflect on the standards of proof that they currently use to support claims for business performance improvement from the application of the ISO 9000 standards.


title: Class based dynamic priority scheduling for uplink to support M2M communications in LTE

abstract: Machine-to-Machine (M2M) communication has emerged as a key technology with huge market potential for cellular service providers deploying LTE networks. Addition of enormous number of M2M devices into the cellular networks poses a heavy competition to existing Human-to-Human (H2H) devices for getting radio resources, thereby affecting the performance of the H2H communications. But, one can not treat all M2M flows as low priority and schedule them after H2H flows, as there are many M2M applications like healthcare and tracking which are of high importance and delay-intolerant. Hence, there is a need for class based priority scheduling of the traffic of M2M and H2H sessions in the network. In this paper, we propose a class based dynamic priority scheduling algorithm for uplink transmission of M2M and H2H traffic in LTE. The performance of the algorithm is evaluated by various metrics such as H2H throughput and system throughput and also compared with existing schedulers.


title: Context-Specific Independence in Bayesian Networks

abstract: Bayesian networks provide a language for qualitatively representing the conditional independence properties of a distribution, This allows a natural and compact representation of the distribution, eases knowledge acquisition, and supports effective inference algorithms. It is well-known, however, that there are certain independencies that we cannot capture qualitatively within the Bayesian network structure: independencies that hold only in certain contexts, i.e., given a specific assignment of values to certain variables, In this paper, we propose a formal notion of context-specific independence (CSI), based on regularities in the conditional probability tables (CPTs) at a node. We present a technique, analogous to (and based on) d-separation, for determining when such independence holds in a given network. We then focus on a particular qualitative representation scheme--tree-structured CPTs-- for capturing CSI. We suggest ways in which this representation can be used to support effective inference algorithms, in particular, we present a structural decomposition of the resulting network which can improve the performance of clustering algorithms, and an alternative algorithm based on outset conditioning.


title: Molar distalization with a pendulum appliance K-loop combination

abstract: The aim of this study was to evaluate the dentoalveolar effects of a pendulum appliance supported buccally by a K-loop, and to compare these with a cervical headgear (CHG) group. The records of 30 patients with skeletal Class I and dental Class II malocclusions were divided in to two groups: Patients in group 1 (seven females and eight males; mean age, 15.0 ± 3.4 years) were treated with a pendulum appliance supported with a K-loop buccally, while in group 2 (10 females and 5 males; mean age, 14.2 ± 2.9 years), the patients were treated with CHG. Standardized lateral cephalograms and study models were taken at the beginning of treatment (T0) and at the end of distal molar movement (T1). T0–T1 changes within the groups were analysed with a paired t -test, and between the groups with a t -test. ::: ::: The mean amount of distalization was 4.53 ± 1.46 mm in group 1 and 2.23 ± 1.68 mm in group 2. The mean amount of distal tipping for group 1 was 5.13 ± 4.90 degrees; the mean amount of mesial tipping for group 2 was 0.80 ± 2.27 degrees. Intrusion and mesiobuccal rotation of the maxillary molars were achieved in both groups. In group 1, the amount of labial protrusion and tipping of the maxillary incisors was not statistically significant. In group 2, palatoversion and retrusion of the maxillary incisors was statistically significant ( P < 0.01 and P < 0.001, respectively). ::: ::: The two major disadvantages of intraoral appliances, which are distal tipping of molars and loss of anchorage at the anterior teeth, were significantly decreased with the use of a pendulum appliance K-loop combination.


title: Pudendal nerve function during pregnancy and after delivery

abstract: The aim of the study was to assess pudendal nerve function serially during pregnancy and after delivery. Twenty-eight women participated at 14, 30 and 36 weeks of pregnancy and 12 weeks postpartum. A prospective study of pudendal nerve terminal motor latency during pregnancy and after delivery was carried out. Results showed that pudendal nerve terminal motor latency did not increase significantly during pregnancy but increased significantly after delivery.


Purchase Decision Making : the Effects of Unconscious Thought , Information Quality , and Information Quantity


1 IEEE 802.11p Transmission Using GNURadio


title: An offset cancellation technique for comparators using body-voltage trimming

abstract: In this paper an offset cancellation technique based on body voltage trimming is presented to be used in the comparators employed in Flash or Successive-Approximation analog-to-digital converters. The proposed offset cancellation is achieved by body voltage adjustment using low-power simple analog control feedback circuit without any additional capacitive loading at the comparator output. The accuracy of the proposed technique is higher than its digital calibration counterparts due to its analog nature. The technique is employed in the design of a 6-bit 1-GSps Flash ADC in 0.18μm CMOS technology. Simulation results show that using the proposed technique the standard deviation of the comparator offset is significantly reduced from 28mV to 750μV operating at 1-GHz with only 25μW power in offset cancellation. The cancellation scheme generally improves the ENOB by approximately 0.5 bit after cancellation.


title: Effect of Submucosal Injection of Dexamethasone on Postoperative Discomfort after Third Molar Surgery: A Prospective Study

abstract: AIM ::: To evaluate the relative ability of 4 mg dose of intraoperative dexamethasone, administered submucosally, to reduce the postoperative discomfort after third molar surgery. ::: ::: ::: MATERIALS AND METHODS ::: A total of 100 patients requiring surgical removal of a single mandibular third molar were included. The experimental group (50) received dexamethasone 4 mg as submucosal injection and control group (50) received no drugs. The maximum interincisal distance and facial contours were measured at baseline and at postsurgery days 2 and 7. The measurement of pain was done using visual analog scale (VAS). ::: ::: ::: RESULTS ::: None of the patients developed wound infection or any serious postoperative complications. Postoperative edema tended to be less severe on the second postoperative day in the experimental group and the result was statistically significant. There were no significant differences in the reduction of pain and trismus between the two groups studied. ::: ::: ::: CONCLUSION ::: Submucosal administration of dexamethasone sodium phosphate (4 mg) results in reduction of postoperative edema, comparable with or greater than other routes of administration. Presumably, a higher effective drug concentration at the site of injury without loss due to distribution to other compartments may be achieved, and the submucosal route is convenient for both the surgeon and the patient. ::: ::: ::: CLINICAL SIGNIFICANCE ::: Submucosal route of administration of dexamethasone (4 mg) is effective in reducing postoperative discomfort after third molar surgeries.


title: Bayesian probabilistic matrix factorization using Markov chain Monte Carlo

abstract: Low-rank matrix approximation methods provide one of the simplest and most effective approaches to collaborative filtering. Such models are usually fitted to data by finding a MAP estimate of the model parameters, a procedure that can be performed efficiently even on very large datasets. However, unless the regularization parameters are tuned carefully, this approach is prone to overfitting because it finds a single point estimate of the parameters. In this paper we present a fully Bayesian treatment of the Probabilistic Matrix Factorization (PMF) model in which model capacity is controlled automatically by integrating over all model parameters and hyperparameters. We show that Bayesian PMF models can be efficiently trained using Markov chain Monte Carlo methods by applying them to the Netflix dataset, which consists of over 100 million movie ratings. The resulting models achieve significantly higher prediction accuracy than PMF models trained using MAP estimation.


Progressive Web Apps: The Possible Web-native Unifier for Mobile Development


title: Laser Dicing and Subsequent Die Strength Enhancement Technologies for Ultra-thin Wafer

abstract: Current mechanical wafer dicing process adopting diamond grit shows advantages of low cost and high productivity. However, mechanical process for ultra-thin wafers would induce residual stress or mechanical damage, which can lead to wafer broken and die cracking. With the development of laser technology, laser precision micromachining has been employed for thin semiconductor wafer singulation, which shows advantages of no chipping, small kerf width, and high throughput over mechanical blade dicing. However, thermal damage to the chip induced by laser ablation results in die strength degradation. For ultra thin chip, low die strength tends to induce die crack in packaging process. Thus, thermal damage to the chip needs to be studied. In this study, first we made a comparison between mechanical blade sawing and laser ablation processes. Die strength and microstructure changes were studied by means of bending test and transmission electron microscope (TEM) analysis, respectively. Die strength results showed that the die strength obtained by laser dicing was far lower than that obtained by blade sawing. TEM analysis demonstrated that formation of microcracks and porosities in laser diced face, caused the die strength degradation. In addition, significant deviation between frontside and backside die strength was found in the laser micromachinned dies. The reason for this deviation was clarified as the defects density difference existing in top and bottom layer of the chip sidewalk Experiments results showed that the die strength obtained by laser dicing can not meet the demand of the packaging process. It tends to crack or fracture in the die attach or wire bonding process. Thus, it is essential to improve the die strength. Thus, in this investigation, etching processes including wet-etch and dry-etch were attempted to recover the die strength by removing the chip side wall damage. SEM and TEM images indicated that, before etching, the laser diced side walls were with rough surfaces, voids and microcracks. After etching, the surfaces got smooth and most of the voids and microcracks were removed. Chip strength measurement also verified the partial die strength recovery after etching process.


title: A one-step process for oil-in-water-in-oil double emulsion formation using a single surfactant.

abstract: A one-step double emulsification protocol using one surfactant was developed for oil-in-water-in-oil (O(1)/W/O(2)) double emulsions. Two n-alkane oils and three different surfactants were studied, with focus placed on a formulation containing mineral oil, glycerol monoleate (GMO) and deionized water. Phenomenologically, double emulsion formation and stability originate from the combined actions of phase inversion and interfacial charging of the oil/water interface during high shear homogenization. Based on the extent of double emulsion formation and stability, a critical emulsification zone dependent on the weight ratios of GMO to water was identified. Within this critical zone, enhanced O(1)/W/O(2) emulsion formation occurred at higher pH and lower salt concentrations, demonstrating the key role of interfacial charging on double emulsification. Overall, this novel approach provides a novel platform for the development of double emulsions with simple compositions and processing requirements.


title: Opioid and orexin hedonic hotspots in rat orbitofrontal cortex and insula

abstract: Hedonic hotspots are brain sites where particular neurochemical stimulations causally amplify the hedonic impact of sensory rewards, such as “liking” for sweetness. Here, we report the mapping of two hedonic hotspots in cortex, where mu opioid or orexin stimulations enhance the hedonic impact of sucrose taste. One hedonic hotspot was found in anterior orbitofrontal cortex (OFC), and another was found in posterior insula. A suppressive hedonic coldspot was also found in the form of an intervening strip stretching from the posterior OFC through the anterior and middle insula, bracketed by the two cortical hotspots. Opioid/orexin stimulations in either cortical hotspot activated Fos throughout a distributed “hedonic circuit” involving cortical and subcortical structures. Conversely, cortical coldspot stimulation activated circuitry for “hedonic suppression.” Finally, food intake was increased by stimulations at several prefrontal cortical sites, indicating that the anatomical substrates in cortex for enhancing the motivation to eat are discriminable from those for hedonic impact.


Automatic Accident Detection via Embedded GSM message interface with Sensor Technology


title: Utilization of Data Mining Techniques for Prediction and Diagnosis of Tuberculosis Disease Survivability

abstract: The prediction and diagnosis of Tuberculosis survivability has been a challenging research problem for many researchers. Since the early dates of the related research, much advancement has been recorded in several related fields. For instance, thanks to innovative biomedical technologies, better explanatory prognostic factors are being measured and recorded; thanks to low cost computer hardware and software technologies, high volume better quality data is being collected and stored automatically; and finally thanks to better analytical methods, those voluminous data is being processed effectively and efficiently. Tuberculosis is one of the leading diseases for all people in developed countries including India. It is the most common cause of death in human being. The high incidence of Tuberculosis in all people has increased significantly in the last years. In this paper we have discussed various data mining approaches that have been utilized for Tuberculosis diagnosis and prognosis. This study paper summarizes various review and technical articles on Tuberculosis diagnosis and prognosis also we focus on current research being carried out using the data mining techniques to enhance the Tuberculosis diagnosis and prognosis. Here, we took advantage of those available technological advancements to develop the best prediction model for Tuberculosis survivability.


Sampling imbalance dataset for software defect prediction using hybrid neuro-fuzzy systems with Naive Bayes classifier


STAR-Net: A SpaTial Attention Residue Network for Scene Text Recognition


title: Effect of RESET Voltage on Distribution of SET Switching Time of Bipolar Resistive Switching in a Tantalum Oxide Thin Film

abstract: The distribution of SET switching time of bipolar switching tantalum oxide thin films is studied using pulse measurement techniques. SET switching times are measured by repeating SET and RESET operations in a single cell. It is found that the distribution measured with a high RESET voltage can be well described by a steep Weibull distribution with a shape parameter >1, but lowering the RESET voltage results in a broad distribution at high cumulative frequencies. Statistical analysis shows that this broadening of the distribution can be attributed to the variation of initial conditions for SET, whereas a steep Weibull distribution points to an aging process leading to SET under a voltage stress. It is also shown that although the power of the leakage current before SET determines the fastest limit of the SET switching speed, those SET in the broadened distributions are delayed due to gradual current increase prior to the SET. The waiting time for the start of the gradual current increase has a correlation with the power of the leakage current, showing that Joule heating effect is still a significant factor in the SET mechanism even if the SET time distribution is broadened due to the variation of initial states programmed by low RESET voltages.


title: Advancing the discovery of unique column combinations

abstract: Unique column combinations of a relational database table are sets of columns that contain only unique values. Discovering such combinations is a fundamental research problem and has many different data management and knowledge discovery applications. Existing discovery algorithms are either brute force or have a high memory load and can thus be applied only to small datasets or samples. In this paper, the well-known Gordian algorithm [9] and "Apriori-based" algorithms [4] are compared and analyzed for further optimization. We greatly improve the Apriori algorithms through efficient candidate generation and statistics-based pruning methods. A hybrid solution HCA-Gordian combines the advantages of Gordian and our new algorithm HCA, and it outperforms all previous work in many situations.


title: Resiliency of Mobility-as-a-Service Systems to Denial-of-Service Attacks

abstract: Mobility-as-a-Service (MaaS) systems, such as ride-sharing services, have expanded very quickly over the past years. However, the popularity of MaaS systems make them increasingly vulnerable to denial-of-service (DOS) attacks, in which attackers attempt to disrupt the system to make it unavailable to the customers. Expanding on an established queuing-theoretical model for MaaS systems, attacks are modeled as a malicious control of a fraction of vehicles in the network. We then formulate a stochastic control problem that maximizes the passenger loss in the network in steady state, and solve it as a sequence of linear and quadratic programs. Combined with a Jackson network simulation and an economic model of supply and demand for attacks, we quantify how raising the cost of attacks (via cancellation fees and higher level of security) removes economical incentives for DoS attacks. Calibrating the model on 1B taxi rides, we dynamically simulate a system under attack and estimate the passenger loss under different scenarios, such as arbitrarily depleting taxis or maximizing the passenger loss. Cost of attacks of U.S.$ 15 protects the MaaS system against DoS attacks. The contributions are thus a theoretical framework for the analysis of the network, and practical conclusions in terms of financial countermeasures to the attacks.


title: A fast multi-scale method for drawing large graphs

abstract: We present a multi-scale layout algorithm for the aesthetic drawing of undirected graphs with straight-line edges. The algorithm is extremely fast, and is capable of drawing graphs of substantially larger size than any other algorithm we are awars of. For example, the algorithm achieves optimal drawings of 1000 vertex graphs in less than 3 seconds. The paper contains graphs with over 6000 nodes. The proposed algorithm embodies a new multi-scale scheme for drawing graphs, which can significantly improve the speed of essentially any force-directed method. Graphs have become an important part of recently proposed user interfaces, hence the relevance of this paper to work on interface issues.


ROBUST MOTION PLANNING IN PRESENCE OF UNCERTAINTIES USING A MANEUVER AUTOMATON


title: A Comparison of Word Embeddings for English and Cross-Lingual Chinese Word Sense Disambiguation

abstract: Word embeddings are now ubiquitous forms of word representation in natural language processing. There have been applications of word embeddings for monolingual word sense disambiguation (WSD) in English, but few comparisons have been done. This paper attempts to bridge that gap by examining popular embeddings for the task of monolingual English WSD. Our simplified method leads to comparable state-of-the-art performance without expensive retraining. Cross-Lingual WSD - where the word senses of a word in a source language e come from a separate target translation language f - can also assist in language learning; for example, when providing translations of target vocabulary for learners. Thus we have also applied word embeddings to the novel task of cross-lingual WSD for Chinese and provide a public dataset for further benchmarking. We have also experimented with using word embeddings for LSTM networks and found surprisingly that a basic LSTM network does not work well. We discuss the ramifications of this outcome.


title: The Psychological Health Benefits of Accepting Negative Emotions and Thoughts: Laboratory, Diary, and Longitudinal Evidence

abstract: Author(s): Ford, BQ; Lam, P; John, OP; Mauss, IB | Abstract: © 2018 American Psychological Association. Individuals differ in the degree to which they tend to habitually accept their emotions and thoughts without judging them-a process here referred to as habitual acceptance. Acceptance has been linked with greater psychological health, which we propose may be due to the role acceptance plays in negative emotional responses to stressors: acceptance helps keep individuals from reacting to-and thus exacerbating-their negative mental experiences. Over time, experiencing lower negative emotion should promote psychological health. To test these hypotheses, Study 1 (N = 1,003) verified that habitually accepting mental experiences broadly predicted psychological health (psychological well-being, life satisfaction, and depressive and anxiety symptoms), even when controlling for potentially related constructs (reappraisal, rumination, and other mindfulness facets including observing, describing, acting with awareness, and nonreactivity). Next, in a laboratory study (Study 2, N = 156), habitual acceptance predicted lower negative (but not positive) emotional responses to a standardized stressor. Finally, in a longitudinal design (Study 3, N = 222), acceptance predicted lower negative (but not positive) emotion experienced during daily stressors that, in turn, accounted for the link between acceptance and psychological health 6 months later. This link between acceptance and psychological health was unique to accepting mental experiences and was not observed for accepting situations. Additionally, we ruled out potential confounding effects of gender, ethnicity, socioeconomic status, and life stress severity. Overall, these results suggest that individuals who accept rather than judge their mental experiences may attain better psychological health, in part because acceptance helps them experience less negative emotion in response to stressors.


title: Unsupervised Classification of Dialogue Acts using a Dirichlet Process Mixture Model

abstract: In recent years Dialogue Acts have become a popular means of modelling the communicative intentions of human and machine utterances in many modern dialogue systems. Many of these systems rely heavily on the availability of dialogue corpora that have been annotated with Dialogue Act labels. The manual annotation of dialogue corpora is both tedious and expensive. Consequently, there is a growing interest in unsupervised systems that are capable of automating the annotation process. This paper investigates the use of a Dirichlet Process Mixture Model as a means of clustering dialogue utterances in an unsupervised manner. These clusters can then be analysed in terms of the possible Dialogue Acts that they might represent. The results presented here are from the application of the Dirichlet Process Mixture Model to the Dihana corpus.


Book Review: Amazing Ourselves to Death: Neil Postman’s Brave New World Revisited


title: Children Seen But Not Heard: When Parents Compromise Children's Online Privacy

abstract: Children's online privacy has garnered much attention in media, legislation, and industry. Adults are concerned that children may not adequately protect themselves online. However, relatively little discussion has focused on the privacy breaches that may occur to children at the hands of others, namely, their parents and relatives. When adults post information online, they may reveal personal information about their children to other people, online services, data brokers, or surveillant authorities. This information can be gathered in an automated fashion and then linked with other online and offline sources, creating detailed profiles which can be continually enhanced throughout the children's lives. In this paper, we conduct a study to see how widespread these behaviors are among adults on Facebook and Instagram. We use a number of methods. Firstly, we automate a process to examine 2,383 adult users on Facebook for evidence of children in their public photo albums. Using the associated comments in combination with publicly available voter registration records, we are able to infer children's names, faces, birth dates, and addresses. Secondly, in order to understand what additional information is available to Facebook and the users' friends, we survey 357 adult Facebook users about their behaviors and attitudes with regard to posting their children's information online. Thirdly, we analyze 1,089 users on Instagram to infer facts about their children. Finally, we make recommendations for privacy-conscious parents and suggest an interface change through which Facebook can nudge parents towards better stewardship of their children's privacy.


title: Implications of Internet architecture on net neutrality

abstract: Net neutrality represents the idea that Internet users are entitled to service that does not discriminate on the basis of source, destination, or ownership of Internet traffic. The United States Congress is considering legislation on net neutrality, and debate over the issue has generated intense lobbying. Congressional action will substantially affect the evolution of the Internet and of future Internet research. In this article, we argue that neither the pro nor anti net neutrality positions are consistent with the philosophy of Internet architecture. We develop a net neutrality policy founded on a segmentation of Internet services into infrastructure services and application services, based on the Internet's layered architecture. Our net neutrality policy restricts an Internet service Provider's ability to engage in anticompetitive behavior while simultaneously ensuring that it can use desirable forms of network management. We illustrate the effect of this policy by discussing acceptable and unacceptable uses of network management.


title: Transition-based Dependency Parsing with Selectional Branching

abstract: We present a novel approach, called selectional branching, which uses confidence estimates to decide when to employ a beam, providing the accuracy of beam search at speeds close to a greedy transition-based dependency parsing approach. Selectional branching is guaranteed to perform a fewer number of transitions than beam search yet performs as accurately. We also present a new transition-based dependency parsing algorithm that gives a complexity ofO(n) for projective parsing and an expected linear time speed for non-projective parsing. With the standard setup, our parser shows an unlabeled attachment score of 92.96% and a parsing speed of 9 milliseconds per sentence, which is faster and more accurate than the current state-of-the-art transitionbased parser that uses beam search.


title: Dermoscopy of pigmented skin lesions.

abstract: This paper describes the basic concepts of dermoscopy, the various dermoscopic equipments and the standard criteria for diagnosing pigmented skin lesions. In assessing dermoscopic images, both global and local features can be recognized. These features will be systematically described and illustrated in Part I of this article. First, we will focus on 8 morphologically rather distinctive global features that allow a quick, preliminary categorization of a given pigmented skin lesion. Second, we will describe various local features representing the letters of the dermoscopic alphabet. The local features permit a more detailed assessment of pigmented skin lesions.


title: Data analytics on network traffic flows for botnet behaviour detection

abstract: Botnets represent one of the most destructive cybersecurity threats. Given the evolution of the structures and protocols botnets use, many machine learning approaches have been proposed for botnet analysis and detection. In the literature, intrusion and anomaly detection systems based on unsupervised learning techniques showed promising performances. In this paper, we investigate the capability of employing the Self-Organizing Map (SOM), an unsupervised learning technique as a data analytics system. In doing so, our aim is to understand how far such an approach could be pushed to analyze unknown traffic to detect botnets. To this end, we employed three different unsupervised training schemes using publicly available botnet data sets. Our results show that SOMs possess high potential as a data analytics tool on unknown traffic. They can identify the botnet and normal flows with high confidence approximately 99% of the time on the data sets employed in this work.


title: Impact of disk corruption on open-source DBMS

abstract: Despite the best intentions of disk and RAID manufacturers, on-disk data can still become corrupted. In this paper, we examine the effects of corruption on database management systems. Through injecting faults into the MySQL DBMS, we find that in certain cases, corruption can greatly harm the system, leading to untimely crashes, data loss, or even incorrect results. Overall, of 145 injected faults, 110 lead to serious problems. More detailed observations point us to three deficiencies: MySQL does not have the capability to detect some corruptions due to lack of redundant information, does not isolate corrupted data from valid data, and has inconsistent reactions to similar corruption scenarios. To detect and repair corruption, a DBMS is typically equipped with an offline checker. Unfortunately, the MySQL offline checker is not comprehensive in the checks it performs, misdiagnosing many corruption scenarios and missing others. Sometimes the checker itself crashes; more ominously, its incorrect checking can lead to incorrect repairs. Overall, we find that the checker does not behave correctly in 18 of 145 injected corruptions, and thus can leave the DBMS vulnerable to the problems described above.


title: Topology comparison for 6.6kW On board charger: Performance, efficiency, and selection guideline

abstract: This paper compares various topologies for 6.6kW on-board charger (OBC) to find out suitable topology. In general, OBC consists of 2-stage; power factor correction (PFC) stage and DC-DC converter stage. Conventional boost PFC, interleaved boost PFC, and semi bridgeless PFC are considered as PFC circuit, and full-bridge converter, phase shift full-bridge converter, and series resonant converter are taken into account for DC-DC converter circuit. The design process of each topology is presented. Then, loss analysis is implemented in order to calculate the efficiency of each topology for PFC circuit and DC-DC converter circuit. In addition, the volume of magnetic components and number of semi-conductor elements are considered. Based on these results, topology selection guideline according to the system specification of 6.6kW OBC is proposed.


title: Behavior of MVC (Model View Controller) based Web Application developed in PHP and .NET framework

abstract: As the people are shifting from offline to online in every perspective of life, the dependency on Web Applications is becoming rampant. Web Applications serve the people by easing their tasks. There are ample web applications distributed over the internet. But what matters a lot is the performance shown by them. Performance means the request response time, page load time, etc. In this paper we have discussed the behavior of MVC architecture based on PHP and .NET framework. The comparison helps us to distinguish among them that which technology performs better. We have used one Web Application testing tool. This tool helps us to evaluate the web application based on various metrics.


title: XML with incomplete information

abstract: We study models of incomplete information for XML, their computational properties, and query answering. While our approach is motivated by the study of relational incompleteness, incomplete information in XML documents may appear not only as null values but also as missing structural information. Our goal is to provide a classification of incomplete descriptions of XML documents, and separate features—or groups of features—that lead to hard computational problems from those that admit efficient algorithms. Our classification of incomplete information is based on the combination of null values with partial structural descriptions of documents. The key computational problems we consider are consistency of partial descriptions, representability of complete documents by incomplete ones, and query answering. We show how factors such as schema information, the presence of node ids, and missing structural information affect the complexity of these main computational problems, and find robust classes of incomplete XML descriptions that permit tractable query evaluation.


title: Understanding Social Network Usage: Impact of Co- Presence, Intimacy, and Immediacy

abstract: This study examines individuals' intentions and behaviour on Social Networking Sites (SNSs). The study proposed model asserts that "Co-presence", "Intimacy", "Immediacy", "Perceived Enjoyment", and "Perceived ease of use" formed individuals' "Attitude" towards "behavioral intention" to use SNSs. The results support all formulated hypotheses. The proposed model in this study explains 69% of individual's attitude or feelings towards adoption the SNSs, 59% of the variance in "Behavioral Intention" and 54% of the variance in "Usage Behaviour". The present study has shown the importance of social presence's factors, namely co-presence, intimacy, and immediacy, in explaining individuals' intentions and behavior. The study findings confirmed that social presence positively influence SNS usage indirectly through user attitude, and as aforementioned that three factors, namely co-presence, intimacy, and immediacy are framing the construct of social presence. Thus, this study is the first empirical effort to examine the impact of co-presence, intimacy, and immediacy in determining intention or behaviour.


title: Lock n' LoL: Group-based Limiting Assistance App to Mitigate Smartphone Distractions in Group Activities

abstract: Prior studies have addressed many negative aspects of mobile distractions in group activities. In this paper, we present Lock n' LoL. This is an application designed to help users focus on their group activities by allowing group members to limit their smartphone usage together. In particular, it provides synchronous social awareness of each other's limiting behavior. This synchronous social awareness can arouse feelings of connectedness among group members and can mitigate social vulnerability due to smartphone distraction (e.g., social exclusion) that often results in poor social experiences. After following an iterative prototyping process, we conducted a large-scale user study (n = 976) via real field deployment. The study results revealed how the participants used Lock n' LoL in their diverse contexts and how Lock n' LoL helped them to mitigate smartphone distractions.


title: The Stigma Complex

abstract: Since the beginning of the twenty-first century, research on stigma has continued. Building on conceptual and empirical work, the recent period clarifies new types of stigmas, expansion of measures, identification of new directions, and increasingly complex levels. Standard beliefs have been challenged, the relationship between stigma research and public debates reconsidered, and new scientific foundations for policy and programs suggested. We begin with a summary of the most recent Annual Review articles on stigma, which reminded sociologists of conceptual tools, informed them of developments from academic neighbors, and claimed findings from the early period of “resurgence.” Continued (even accelerated) progress has also revealed a central problem. Terms and measures are often used interchangeably, leading to confusion and decreasing accumulated knowledge. Drawing from this work but focusing on the past 14 years of stigma research (including mental illness, sexual orientation, HIV/AIDS, and race/ethnici...


title: Assessing Competition Policy Performance Metrics: Concerns About Cross-Country Generalisability

abstract: Recent interest in competition policy performance has typically relied on subjective performance metrics that have undergone little direct scrutiny by users. We examine the quality of the popular World Economic Forum's antitrust performance metric and assess whether it is immune from perception-bias. A bias-free metric is required to ensure cross-country consistency in its intended performance assessment. We note various instances where the WEF's competition policy performance survey was completed but where there existed neither competition legislation nor an associated enforcement agency at the time. This seeming inconsistency is neither amenable to traditional econometric heterogeneity treatment nor instrumentable; importantly, it is likely to embed non-random error onto the WEF antitrust survey. We test and discuss some possible explanations for the observed bias: we find that both halo effects and a nation's modest experience with market institutions may be responsible for the bias. Underscoring these results may be the fact that survey respondents may not share a common understanding of competition policy. We offer some discussion supporting this latter point. The presence of these biases may invalidate the usefulness of cross-nationally valid rankings of competition policy performance. On the bright side, however, the results suggest that efforts aimed at enhancing stakeholders' understanding of the objectives and limitations of competition policy might in turn enhance competition policy's impact as well as perceptions of performance.


title: Active Contour-Based Visual Tracking by Integrating Colors, Shapes, and Motions

abstract: In this paper, we present a framework for active contour-based visual tracking using level sets. The main components of our framework include contour-based tracking initialization, color-based contour evolution, adaptive shape-based contour evolution for non-periodic motions, dynamic shape-based contour evolution for periodic motions, and the handling of abrupt motions. For the initialization of contour-based tracking, we develop an optical flow-based algorithm for automatically initializing contours at the first frame. For the color-based contour evolution, Markov random field theory is used to measure correlations between values of neighboring pixels for posterior probability estimation. For adaptive shape-based contour evolution, the global shape information and the local color information are combined to hierarchically evolve the contour, and a flexible shape updating model is constructed. For the dynamic shape-based contour evolution, a shape mode transition matrix is learnt to characterize the temporal correlations of object shapes. For the handling of abrupt motions, particle swarm optimization is adopted to capture the global motion which is applied to the contour in the current frame to produce an initial contour in the next frame.


Generating Rental Data for Car Sharing Relocation Simulations on the Example of Station-Based One-Way Car Sharing


title: Tracking the Untrackable: Learning to Track Multiple Cues with Long-Term Dependencies

abstract: The majority of existing solutions to the Multi-Target Tracking (MTT) problem do not combine cues over a long period of time in a coherent fashion. In this paper, we present an online method that encodes long-term temporal dependencies across multiple cues. One key challenge of tracking methods is to accurately track occluded targets or those which share similar appearance properties with surrounding objects. To address this challenge, we present a structure of Recurrent Neural Networks (RNN) that jointly reasons on multiple cues over a temporal window. Our method allows to correct data association errors and recover observations from occluded states. We demonstrate the robustness of our data-driven approach by tracking multiple targets using their appearance, motion, and even interactions. Our method outperforms previous works on multiple publicly available datasets including the challenging MOT benchmark.


title: Industrial IoT lifecycle via digital twins

abstract: Currently, the IoT discussion is focused primarily on the operational phase. This includes how a IoT device behaves, operates, communicates, and interacts with other IoT devices during operation. However, IoT devices and systems have other lifecycle phases before and after operation. This extended abstract provides an overview of how other IoT lifecycle phases (e.g., design and service) can be improved with information feedback and feedforward flows between them. Digital Twins are a new mechanism to manage IoT devices and IoT systems-of-systems throughout their lifecycle. We present our vision on the industrial IoT lifecycle managed and optimized at scale via Digital Twins.


title: Registration of 3D Point Cloud of Human Body Based on the Range Images and RGB Images

abstract: The existing 3D reconstruction techniques rarely can be easily used in people's daily life, and the traditional registration algorithms have the drawback of massive calculation. In this paper it presented a registration algorithm of body point cloud based on RGB images and Range images. First, it used kinect to obtain the RGB images and Range images from different perspectives. Then it extracted the pairs of 2D feature points on RGB images using scale-invariant feature transform (SIFT) and random sample consensus (RANSAC) algorithm application for the coarse registration and used the improved iterative closest point (ICP) algorithm for the fine registration. Second, it eliminated the background information and the noise points of the model edges. Finally it completed the registration process. Experimental results show that the algorithm can accurately accomplish the body point clouds registration using the low-cost instrument and has a relatively high efficiency.


title: There is a blind spot in AI research

abstract: Fears about the future impacts of artificial intelligence are distracting researchers from the real risks of deployed systems, argue Kate Crawford and Ryan Calo.


title: Conversational Semantic Search: Looking Beyond Web Search, Q&A and Dialog Systems

abstract: User expectations of web search are changing. They are expecting search engines to answer questions, to be more conversational, and to offer means to complete tasks on their behalf. At the same time, to increase the breadth of tasks that personal digital assistants (PDAs), such as Microsoft»s Cortana or Amazon»s Alexa, are capable of, PDAs need to better utilize information about the world, a significant amount of which is available in the knowledge bases and answers built for search engines. It thus seems likely that the underlying systems that power web search and PDAs will converge. This demonstration presents a system that merges elements of traditional multi-turn dialog systems with web based question answering. This demo focuses on the automatic composition of semantic functional units, Botlets, to generate responses to user»s natural language (NL) queries. We show that such a system can be trained to combine information from search engine answers with PDA tasks to enable new user experiences.


title: Negative Emotion, Positive Experience?: Emotionally Moving Moments in Digital Games

abstract: Emotions are key to the player experience (PX) and interest in the potential of games to provide unique emotional, sometimes uncomfortable experiences is growing. Yet there has been little empirical investigation of what game experiences players consider emotionally moving, their causes and effects, and whether players find these experiences rewarding at all. We analyzed 121 players' accounts of emotionally moving game experiences in terms of the feelings and thoughts they evoked, different PX constructs, as well as game-related and personal factors contributing to these. We found that most players enjoyed and appreciated experiencing negatively valenced emotions, such as sadness. Emotions were evoked by a variety of interactive and non-interactive game aspects, such as in-game loss, character attachment and (lack of) agency, but also personal memories, and were often accompanied by (self-)reflection. Our findings highlight the potential of games to provide emotionally rewarding and thought-provoking experiences, as well as outline opportunities for future research and design of such experiences. They also showcase that negative affect may contribute to enjoyment, thereby extending our notion of positive player experience.


title: A modular correctness proof of IEEE 802.11i and TLS

abstract: The IEEE 802.11i wireless networking protocol provides mutual authentication between a network access point and user devices prior to user connectivity. The protocol consists of several parts, including an 802.1X authentication phase using TLS over EAP, the 4-Way Handshake to establish a fresh session key, and an optional Group Key Handshake for group communications. Motivated by previous vulnerabilities in related wireless protocols and changes in 802.11i to provide better security, we carry out a formal proof of correctness using a Protocol Composition Logic previously used for other protocols. The proof is modular, comprising a separate proof for each protocol section and providing insight into the networking environment in which each section can be reliably used. Further, the proof holds for a variety of failure recovery strategies and other implementation and configuration options. Since SSL/TLS is widely used apart from 802.11i, the security proof for SSL/TLS has independent interest.


title: A Fuzzy Neural Network Model with Application to Telecommunications Business

abstract: There are many factors involved in a revenue-forecasting problem of telecommunications business (TB), which are not independent and conflicted with each other. The traditional methods of forecasting TB revenues are based on regression analysis, but the precisions of the results are usually not satisfied in practice. In this study, statistical analysis is first used to evaluate factors and then the main factors are selected as inputs to construct a neural network model combined with fuzzy rules. Furthermore, the fuzzy neural network (FNN) model is applied to predict the revenue of a specific telecommunications business. The results indicate that the developed FNN model satisfies practical requirements and is possible to expand to other business applications, such as customers? evaluation and financial risk pre-warning. To make this clearer, an illustrative example is given for demonstration.


title: Saliency Unified: A Deep Architecture for simultaneous Eye Fixation Prediction and Salient Object Segmentation

abstract: Human eye fixations often correlate with locations of salient objects in the scene. However, only a handful of approaches have attempted to simultaneously address the related aspects of eye fixations and object saliency. In this work, we propose a deep convolutional neural network (CNN) capable of predicting eye fixations and segmenting salient objects in a unified framework. We design the initial network layers, shared between both the tasks, such that they capture the object level semantics and the global contextual aspects of saliency, while the deeper layers of the network address task specific aspects. In addition, our network captures saliency at multiple scales via inceptionstyle convolution blocks. Our network shows a significant improvement over the current state-of-the-art for both eye fixation prediction and salient object segmentation across a number of challenging datasets.


title: CD30-positive cutaneous lymphoma: report of four cases with an emphasis on clinicopathological correlations*

abstract: Collision or contiguous tumors, defined as two or more distinct tumors occurring at one site, are often an unexpected finding and may represent a diagnostic challenge, as clinical and histological presentations do not always coincide. Various combinations of collision tumors have been described with respect to melanocytic lesions, with the most frequently reported being the combination of nevus and basal cell carcinoma. We present an unusual case on the nose involving a melanoma in situ and a clinically-inapparent syringoma, which, to the best of our knowledge, is the first report of this combination


title: Deep Burst Denoising

abstract: Noise is an inherent issue of low-light image capture, one which is exacerbated on mobile devices due to their narrow apertures and small sensors. One strategy for mitigating noise in a low-light situation is to increase the shutter time of the camera, thus allowing each photosite to integrate more light and decrease noise variance. However, there are two downsides of long exposures: (a) bright regions can exceed the sensor range, and (b) camera and scene motion will result in blurred images. Another way of gathering more light is to capture multiple short (thus noisy) frames in a "burst" and intelligently integrate the content, thus avoiding the above downsides. In this paper, we use the burst-capture strategy and implement the intelligent integration via a recurrent fully convolutional deep neural net (CNN). We build our novel, multiframe architecture to be a simple addition to any single frame denoising model, and design to handle an arbitrary number of noisy input frames. We show that it achieves state of the art denoising results on our burst dataset, improving on the best published multi-frame techniques, such as VBM4D and FlexISP. Finally, we explore other applications of image enhancement by integrating content from multiple frames and demonstrate that our DNN architecture generalizes well to image super-resolution.


Management information systems


title: OWL-Q for Semantic QoS-based Web Service Description and Discovery

abstract: Semantic Web Services are emerging for their promise to produce a more accurate and precise Web Service discovery process. However, most of research approaches focus only on the functional part of semantic Web Service description. The above fact along with the proliferation of Web Services is highly probable to lead to a situation where Web Service registries will return many functionally-equivalent Web Service advertisements for each user request. This problem can be solved with the semantic description of QoS for Web Services. QoS is a set of non-functional properties encompassing performance and network-related characteristics of resources. So it can be used for distinguishing between functionally-equivalent Web Services. Current research approaches for QoS-based Web Service description are either syntactic or poor or non-extensible. To solve this problem, we have developed a rich and extensible ontological specification called OWL-Q for semantic QoS-based Web Service description. We analyze all OWL-Q parts and reason that rules should be added in order to support property inferencing and constraint enforcement. Finally, we line out our under-development semantic framework for QoS-based Web Service description and discovery.


title: Bioconductor: open software development for computational biology and bioinformatics

abstract: The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methods, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples.


title: SiC-SIT Circuit Breakers With Controllable Interruption Voltage for 400-V DC Distribution Systems

abstract: 400-V direct current (400-V dc) distribution systems are promising candidate of highly efficient and reliable distribution systems for data centers. To realize the 400-V dc distribution systems, development of methods for high-speed overcurrent protection is one of the important issues. In this paper, as a solution to this problem, a semiconductor dc circuit breaker using SiC static induction transistors (SiC-SIT's) is investigated. The SiC-SIT's has extremely low on-state resistance and very large safe operating area (SOA). These properties are attractive in the application of the circuit breakers for 400-V dc distribution systems. A novel control method of the gate voltage waveform to reduce the transient overvoltage and resonance during the interruption process is proposed. The effectiveness of the proposed method is confirmed by actual operation tests employing an experimental prototype of the dc distribution system. All of the result is confirmed by the fabricated SiC-SIT circuit breaker prototype.


title: New Approach for Fast Color Image Encryption Using Chaotic Map

abstract: Image encryption using chaotic maps has been established a great way. The study shows that a number of functional architecture has already been proposed that utilize the process of diffusion and confusion. However, permutation and diffusion are considered as two separate stages, both requiring image-scanning to obtain pixel values. If these two stages are mutual, the duplicate scanning effort can be minimized and the encryption can be accelerated. This paper presents a technique which replaces the traditional preprocessing complex system and utilizes the basic operations like confusion, diffusion which provide same or better encryption using cascading of 3D standard and 3D cat map. We generate diffusion template using 3D standard map and rotate image by using vertically and horizontally red and green plane of the input image. We then shuffle the red, green, and blue plane by using 3D Cat map and standard map. Finally the Image is encrypted by performing XOR operation on the shuffled image and diffusion template. Theoretical analyses and computer simulations on the basis of Key space Analysis, statistical analysis, histogram analysis, Information entropy analysis, Correlation Analysis and Differential Analysis confirm that the new algorithm that minimizes the possibility of brute force attack for decryption and very fast for practical image encryption


title: Detecting malicious tweets in trending topics using clustering and classification

abstract: Detection of spam twitter social networks is one of the significant research areas to discover unauthorized user accounts. A number of research works have been carried out to solve these issues but most of the existing techniqueshad not focused on various features and doesn't group similar user trending topics which become their major limitation. Trending topics collects the current Internet trends and topics of argument ofeach and every user. In order to overcome the problem of feature extraction,this work initially extracts many features such as user profile features, user activity features, location based features and text and content features. Then the extracted text features use Jenson-Shannon Divergence (JSD) measure to characterize each labeled tweet using natural language models. Different features are extracted from collected trending topics data in twitter. After features are extracted, clusters are formed to group similar trending topics of tweet user profile. Fuzzy K-means (FKM) algorithm primarily cluster the similar user profiles with same trending topics of tweet and centers are determined to similar user profiles with same trending topics of tweet from fuzzy membership function. Moreover, Extreme learning machine (ELM) algorithm is appliedtoanalyze the growing characteristics of spam with similar topics in twitter from clustering result and acquire necessary knowledge in the detection of spam. The results are evaluated with F-measure, True Positive Rate (TPR), False Positive Rate (FPR) and Classification Accuracy with improved detection results.


title: PhraseRNN: Phrase Recursive Neural Network for Aspect-based Sentiment Analysis

abstract: This paper presents a new method to identify sentiment of an aspect of an entity. It is an extension of RNN (Recursive Neural Network) that takes both dependency and constituent trees of a sentence into account. Results of an experiment show that our method significantly outperforms previous methods.


title: Heuristics for Internet map discovery

abstract: Mercator is a program that uses hop-limited probes-the same primitive used in traceroute-to infer an Internet map. It uses informed random address probing to carefully exploring the IP address space when determining router adjacencies, uses source-route capable routers wherever possible to enhance the fidelity of the resulting map, and employs novel mechanisms for resolving aliases (interfaces belonging to the same router). This paper describes the design of these heuristics and our experiences with Mercator, and presents some preliminary analysis of the resulting Internet map.


title: Despeckle Filtering for Multiscale Amplitude-Modulation Frequency-Modulation (AM-FM) Texture Analysis of Ultrasound Images of the Intima-Media Complex

abstract: The intima-media thickness (IMT) of the common carotid artery (CCA) is widely used as an early indicator of cardiovascular disease (CVD). Typically, the IMT grows with age and this is used as a sign of increased risk of CVD. Beyond thickness, there is also clinical interest in identifying how the composition and texture of the intima-media complex (IMC) changed and how these textural changes grow into atherosclerotic plaques that can cause stroke. Clearly though texture analysis of ultrasound images can be greatly affected by speckle noise, our goal here is to develop effective despeckle noise methods that can recover image texture associated with increased rates of atherosclerosis disease. In this study, we perform a comparative evaluation of several despeckle filtering methods, on 100 ultrasound images of the CCA, based on the extracted multiscale Amplitude-Modulation Frequency-Modulation (AM-FM) texture features and visual image quality assessment by two clinical experts. Texture features were extracted from the automatically segmented IMC for three different age groups. The despeckle filters hybrid median and the homogeneous mask area filter showed the best performance by improving the class separation between the three age groups and also yielded significantly improved image quality.


title: What sentence are you referring to and why? Identifying cited sentences in scientific literature

abstract: Comunicacio presentada a la International Conference Recent Advances in Natural Language Processing (RANLP 2017), celebrada els dies 2 a 8 de setembre de 2017 a Varna, Bulgaria.


title: From today's INTRAnet of things to a future INTERnet of things: a wireless- and mobility-related view

abstract: In this article, we present the current status of the Internet of Things, and discuss how the current situation of many "Intranets" of Things should evolve into a much more integrated and heterogeneous system. We also summarize what in our opinion are the main wirelessand mobility-related technical challenges that lie ahead, and outline some initial ideas on how such challenges can be addressed in order to facilitate the IoT's development and acceptance in the next few years. We also describe a case study on the IoT protocol architecture.