title: Polygon-Invariant Generalized Hough Transform for High-Speed Vision-Based Positioning

abstract: The generalized Hough transform (GHT) is widely used for detecting or locating objects under similarity transformation. However, a weakness of the traditional GHT is its large storage requirement and time-consuming computational complexity due to the 4-D parameter space voting strategy. In this paper, a polygon-invariant GHT (PI-GHT) algorithm, as a novel scale- and rotation-invariant template matching method, is presented for high-speed object vision-based positioning. To demonstrate the performance of PI-GHT, several experiments were carried out to compare this novel algorithm with the other five popular matching methods. Experimental results show that the computational effort required by PI-GHT is smaller than that of the common methods due to the similarity transformations applied to the scale- and rotation-invariant triangle features. Moreover, the proposed PI-GHT maintains inherent robustness against partial occlusion, noise, and nonlinear illumination changes, because the local triangle features are based on the gradient directions of edge points. Consequently, PI-GHT is implemented in packaging equipment for radio frequency identification devices at an average time of 4.13 ms and 97.06% matching rate, to solder paste printing at average time nearly 5 ms with 99.87%. PI-GHT is applied to LED manufacturing equipment to locate multiobjects at least five times improvement in speed with a 96% matching rate.


title: Design of Voice Coil Motor With the Forward Winding for Linear Vibro-Impact Systems

abstract: A linear voice coil motor (VCM) with forward winding for linear vibro-impact systems is proposed in this paper. Generally, only increasing the air-gap flux density of VCM, which ignores its bad thrust characteristic condition, is limited to obtain a higher thrust density, due to its intermittent duty and reciprocating linear motion. Thus, the analysis on the thrust characteristic is of momentous significance for VCM design. Considering this fact, we try to adjust the thrust characteristic to obtain the maximum thrust density. Magnetic-circuit analysis method is utilized to analyze the thrust characteristic along with a given total working stroke. Besides, finite-element analysis is also used in the selection of parameters. A series of prototype VCMs for experiments is made. The results from both theoretical simulations and practical experiments show increase of thrust density and decrease of thrust ripple compared with the original design, and prove the superiority of the proposed structure.


A Speaker Adaptive DNN Training Approach for Speaker-Independent Acoustic Inversion


Dynamic system crisis’s detecting using big data flow


title: A parallel one dimensional resampling algorithm

abstract: Separable algorithms are useful in casting image warping into a framework that is amenable to hardware implementation. Growing interest in this area has gained impetus from the widespread proliferation of advanced workstations and digital signal processors. Examples include realtime hardware for video effects among others. The central benefit of separable algorithms is the reduction in complexity of one dimensional resampling algorithms. When the input is restricted to be one-dimensional, efficient solutions are made possible for the image reconstruction and antialiasing components of resampling. Fant (1986) presented such a solution that is well suited for hardware implementation. Fant's algorithm, however, is inherently serial. We present an alternative but equivalent (in the sense of producing exactly the same output scan lines) algorithm, that is parallel. The new algorithm was inspired by Wolberg's algorithm (1990). The new algorithm adds some overhead to the hardware implementation of Fant's algorithm to be able to achieve parallelism.


title: Summarizability in OLAP and statistical data bases

abstract: The summarizability of OLAP (online analytical processing) and statistical databases is an a extremely important property, because violating this condition can lead to erroneous conclusions and decisions. In this paper, we explore the conditions for summarizability. We introduce a framework for precisely specifying the context in which statistical objects are defined. We use a three-step process to define normalized statistical objects. Using this framework, we identify three necessary conditions for summarizability. We provide specific tests for each of the conditions that can be verified either from semantic knowledge or by checking the statistical database itself. We also provide the reasoning for our belief that these three summarizability conditions are sufficient as well.


title: The Role of Genital Nerve Afferents in the Physiology of the Sexual Response and Pelvic Floor Function

abstract: INTRODUCTION ::: Our understanding of genital and pelvic floor physiology is rapidly expanding. Penile erection is a neurovascular event controlled by spinal autonomic centers, the activity of which is dependent on input from supraspinal centers and the genitalia. Genital afferent stimulation excites spinal autonomic nuclei and supraspinal sexual centers of both genders. ::: ::: ::: AIM ::: To present a detailed understanding of the functional importance of genital afferent neuroanatomy and neurophysiology. ::: ::: ::: METHODS ::: English-written articles of diverse disciplines from 1980 to 2010 that contained information on genital anatomy, pudendal/dorsal/perineal/cavernous nerves, vibratory stimulation, reflexogenic erection, peripheral/central nervous system-mediated erectile and micturition pathways, and sexual arousal in animals and humans were reviewed. ::: ::: ::: MAIN OUTCOME MEASURES ::: Analysis of supporting evidence for the role of genital afferents in the physiology of erectile response and pelvic floor function. ::: ::: ::: RESULTS ::: Basic science and clinical studies support the concept that pudendal nerve circuitry serves an essential purpose for sexual behavior, erectile function, penile rigidity, ejaculation, and micturition. Males and females share a comparable pattern of genital afferent neuroanatomy and neurophysiology, and sexual and micturition reflexes are similar in both genders. Pudendal nerve branches communicate with the cavernous nerves and are nitric oxide synthase positive. Genital afferents activate multiple spinal reflexes that modulate erection and micturition. Genital sensory information is transmitted to supraspinal centers important for sexual function. ::: ::: ::: CONCLUSIONS ::: There is expanding support for the critical role of genital afferent neurophysiology in the mechanisms of erectile function and micturition. Genital afferent stimulation is a safe and natural modality that can be harnessed to amplify autonomic and somatic activity within the penis, female genitalia, spinal cord, and higher centers via established neurological principles. Such physiological adaptive processes may be beneficial in improving sexual response, erectile function, and micturition in many disease states, including in men after radical pelvic surgery. Well-designed and -executed studies in each specific population are needed to authenticate such prospects.


title: Asking the right question in collaborative q&a systems

abstract: Collaborative Question Answering (cQA) platforms are a very popular repository of crowd-generated knowledge. By formulating questions, users express needs that other members of the cQA community try to collaboratively satisfy. Poorly formulated questions are less likely to receive useful responses, thus hindering the overall knowledge generation process. Users are often asked to reformulate their needs, adding specific details, providing examples, or simply clarifying the context of their requests. Formulating a good question is a task that might require several interactions between the asker and other community members, thus delaying the actual answering and, possibly, decreasing the interest of the community in the issue. This paper contributes new insights to the study of cQA platforms by investigating the editing behaviour of users. We identify a number of editing actions, and provide a two-step approach for the automatic suggestion of the most likely editing actions to be performed for a newly created question. We evaluated our approach in the context of the Stack Overflow cQA , demonstrating how, for given types of editing actions, it is possible to provide accurate reformulation suggestions.


title: FCCF: forecasting citywide crowd flows based on big data

abstract: Predicting the movement of crowds in a city is strategically important for traffic management, risk assessment, and public safety. In this paper, we propose predicting two types of flows of crowds in every region of a city based on big data, including human mobility data, weather conditions, and road network data. To develop a practical solution for citywide traffic prediction, we first partition the map of a city into regions using both its road network and historical records of human mobility. Our problem is different than the predictions of each individual's movements and each road segment's traffic conditions, which are computationally costly and not necessary from the perspective of public safety on a citywide scale. To model the multiple complex factors affecting crowd flows, we decompose flows into three components: seasonal (periodic patterns), trend (changes in periodic patterns), and residual flows (instantaneous changes). The seasonal and trend models are built as intrinsic Gaussian Markov random fields which can cope with noisy and missing data, whereas a residual model exploits the spatio-temporal dependence among different flows and regions, as well as the effect of weather. Experiment results on three real-world datasets show that our method is scalable and outperforms all baselines significantly in terms of accuracy.


title: RavenClaw: Dialog Management Using Hierarchical Task Decomposition and an Expectation Agenda

abstract: We describe RavenClaw, a new dialog management framework developed as a successor to the Agenda [1] architecture used in the CMU Communicator. RavenClaw introduces a clear separation between task and discourse behavior specification, and allows rapid development of dialog management components for spoken dialog systems operating in complex, goal-oriented domains. The system development effort is focused entirely on the specification of the dialog task, while a rich set of domain-independent conversational behaviors are transparently generated by the dialog engine. To date, RavenClaw has been applied to five different domains allowing us to draw some preliminary conclusions as to the generality of the approach. We briefly describe our experience in developing these systems.


title: Open-source, affordable, modular, light-weight, underactuated robot hands

abstract: In this paper we present a series of design directions for the development of affordable, modular, light-weight, intrinsically-compliant, underactuated robot hands, that can be easily reproduced using off-the-shelf materials. The proposed robot hands, efficiently grasp a series of everyday life objects and are considered to be general purpose, as they can be used for various applications. The efficiency of the proposed robot hands has been experimentally validated through a series of experimental paradigms, involving: grasping of multiple everyday life objects with different geometries, myoelectric (EMG) control of the robot hands in grasping tasks, preliminary results on a grasping capable quadrotor and autonomous grasp planning under object position and shape uncertainties.


title: Mindfulness-Based Interventions in Context: Past, Present, and Future

abstract: studies from the Center for Mindfulness in Medicine, Health Care, and Society not reviewed by Baer but which raise a number of key questions about clinical applicability, study design, and mechanism of action, and (7) current opportunities for professional training and development in mindfulness and its clinical applications.


title: HCIC: Hardware-assisted Control-flow Integrity Checking

abstract: Recently, code reuse attacks (CRAs), such as return-oriented programming (ROP) and jump-oriented programming (JOP), have emerged as a new class of ingenious security threats. Attackers can utilize CRAs to hijack the control flow of programs to perform malicious actions without injecting any codes. Many defenses, classed into software-based and hardware-based, have been proposed. However, software-based methods are difficult to be deployed in practical systems due to high performance overhead. Hardware-based methods can reduce performance overhead but may require extending instruction set architectures (ISAs) and modifying the compiler or suffer the vulnerability of key leakage. To tackle these issues, this paper proposes a new hardware-assisted control flow checking method to resist CRAs with negligible performance overhead without extending ISAs, modifying the compiler or leaking the encryption/decryption key. The key technique involves two control flow checking mechanisms. The first one is the encrypted Hamming distances matching between the physical unclonable function (PUF) response and the return addresses, which prevents attackers from returning between gadgets so long as the PUF response is secret, thus resisting ROP attacks. The second one is the linear encryption/decryption operation (XOR) between the PUF response and the instructions at target addresses of call and jmp instructions to defeat JOP attacks. Advanced return-based full-function reuse attacks will be prevented with the dynamic key-updating method. Experimental evaluations on benchmarks demonstrate that the proposed method introduces negligible 0.95% runtime overhead and 0.78% binary size overhead on average.


title: Various Approaches to Aspect-based Sentiment Analysis

abstract: The problem of aspect-based sentiment analysis deals with classifying sentiments (negative, neutral, positive) for a given aspect in a sentence. A traditional sentiment classification task involves treating the entire sentence as a text document and classifying sentiments based on all the words. Let us assume, we have a sentence such as"the acceleration of this car is fast, but the reliability is horrible". This can be a difficult sentence because it has two aspects with conflicting sentiments about the same entity. Considering machine learning techniques (or deep learning), how do we encode the information that we are interested in one aspect and its sentiment but not the other? Let us explore various pre-processing steps, features, and methods used to facilitate in solving this task.


title: Why We Share: A Uses and Gratifications Approach to Privacy Regulation in Social Media Use

abstract: The contradiction between the stated preferences of social media users toward privacy and actual privacy behaviors has suggested a willingness to trade privacy regulation for social goals. This study employs data from a survey of 361 social media users, which collected data on privacy attitudes, online privacy strategies and behaviors, and the uses and gratifications that social media experiences bring. Using canonical correlation, it examines in detail how underlying dimensions of privacy concern relate to specific contexts of social media use, and how these contexts relate to various domains of privacy-protecting behaviors. In addition, this research identifies how specific areas of privacy concern relate to levels of privacy regulation, offering new insight into the privacy paradox. In doing so, this study lends greater nuance to how the dynamic of privacy and sociality is understood and enacted by users, and how privacy management and the motivations underlying media use intersect.


title: The essential role of ritual in the transmission and reinforcement of social norms.

abstract: Social norms are communally agreed upon, morally significant behavioral standards that are, at least in part, responsible for uniquely human forms of cooperation and social organization. This article summarizes evidence demonstrating that ritual and ritualized behaviors are essential to the transmission and reinforcement of social norms. Ritualized behaviors reliably signal an intentional mental state giving credibility to verbal expressions while emotionally binding people to each other and group-based values. Early ritualized infant-caregiver interactions and the family routines and rituals that emerge from them are primary mechanisms for transmitting social norms vertically from parent to offspring, while adult community rituals are a primary mechanism by which norms are reinforced horizontally within the community.


title: Essential features of telepresence robots

abstract: Telepresence robots are mobile robot platforms capable of providing two way audio and video communication. Recently there has been a surge in companies designing telepresence robots. We conducted a series of user studies at Google in Mountain View with two different commercially available telepresence robots. Based on the data collected from these user studies, we present a set of guidelines for designing telepresence robots. These essential guidelines pertain to video, audio, user interface, physical features, and autonomous behaviors.


title: Data-Driven Agent-based Modeling of Innovation Diffusion

abstract: We present a novel data-driven agent-based modeling framework to study innovation diffusion. Our first step is to learn a model of individual agent behavior from individual adoption characteristics. We then construct an agent-based simulation with the learned model embedded in artificial agents, and proceed to validate it using a holdout sequence of collective adoption decisions. Finally, we exemplify the proposed method can be used to explore and analyze a broad class of policies aimed at spurring innovation adoption.


title: Towards Detecting Compromised Accounts on Social Networks

abstract: Compromising social network accounts has become a profitable course of action for cybercriminals. By hijacking control of a popular media or business account, attackers can distribute their malicious messages or disseminate fake information to a large user base. The impacts of these incidents range from a tarnished reputation to multi-billion dollar monetary losses on financial markets. In our previous work, we demonstrated how we can detect large-scale compromises (i.e., so-called campaigns) of regular online social network users. In this work, we show how we can use similar techniques to identify compromises of individual high-profile accounts. High-profile accounts frequently have one characteristic that makes this detection reliable -- they show consistent behavior over time. We show that our system, were it deployed, would have been able to detect and prevent three real-world attacks against popular companies and news agencies. Furthermore, our system, in contrast to popular media, would not have fallen for a staged compromise instigated by a US restaurant chain for publicity reasons.


title: The Correlated Correspondence Algorithm for Unsupervised Registration of Nonrigid Surfaces

abstract: We present an unsupervised algorithm for registering 3D surface scans of an object undergoing significant deformations. Our algorithm does not need markers, nor does it assume prior knowledge about object shape, the dynamics of its deformation, or scan alignment. The algorithm registers two meshes by optimizing a joint probabilistic model over all point-to-point correspondences between them. This model enforces preservation of local mesh geometry, as well as more global constraints that capture the preservation of geodesic distance between corresponding point pairs. The algorithm applies even when one of the meshes is an incomplete range scan; thus, it can be used to automatically fill in the remaining surfaces for this partial scan, even if those surfaces were previously only seen in a different configuration. We evaluate the algorithm on several real-world datasets, where we demonstrate good results in the presence of significant movement of articulated parts and non-rigid surface deformation. Finally, we show that the output of the algorithm can be used for compelling computer graphics tasks such as interpolation between two scans of a non-rigid object and automatic recovery of articulated object models.


title: Technical Section: Using augmented and virtual reality for the development of acrophobic scenarios. Comparison of the levels of presence and anxiety

abstract: Acrophobia has been treated using exposure in imagination, exposure ''in vivo'' and Virtual Reality (VR). This paper presents the development of an Augmented Reality (AR) system and a VR system that includes acrophobic scenarios. A study involving both these systems and non-phobic users has been carried out in order to compare the levels of presence and anxiety. In the acrophobic scenario, the floor fell away and the walls rose up. 20 participants took part in this study. After using each system (AR or VR), the participants were asked to fill out an adapted SUS questionnaire [26], and paired t-tests and ANOVA analyses were applied to the data obtained. For the sense of presence and anxiety levels, we did not find differences between the systems using an experiment with enough sensitivity to detect differences as large as d=0.66. For the anxiety level, the results show that there is a significant difference between the level of anxiety felt at the moment before starting the experiment and the level felt during the different stages of the experiment. For the correlation between anxiety and presence, the results show very low correlation between anxiety and presence. These results suggest that AR is likely to be as effective as VR in treating acrophobia.


title: A latent topic model for mining heterogenous non-randomly missing electronic health records data

abstract: Electronic health records (EHR) are rich heterogeneous collection of patient health information, whose broad adoption provides great opportunities for systematic health data mining. However, heterogeneous EHR data types and biased ascertainment impose computational challenges. Here, we present mixEHR, an unsupervised generative model integrating collaborative filtering and latent topic models, which jointly models the discrete distributions of data observation bias and actual data using latent disease-topic distributions. We apply mixEHR on 12.8 million phenotypic observations from the MIMIC dataset, and use it to reveal latent disease topics, interpret EHR results, impute missing data, and predict mortality in intensive care units. Using both simulation and real data, we show that mixEHR outperforms previous methods and reveals meaningful multi-disease insights.


title: Swing-Pay: One Card Meets All User Payment and Identity Needs: A Digital Card Module using NFC and Biometric Authentication for Peer-to-Peer Payment

abstract: Advancement in payment technologies has an important impact on one's quality of life. Emerging payment technologies create both opportunities and challenges for the future. Being a quick and convenient process, contactless payment gained momentum, especially with merchants, with throughput being the main parameter. However, it poses risks to issuers, as no robust customer verification method is available. Thus, efforts have been underway to evolve and sustain a well-organized, efficient, reliable, and secure unified payment system, which may contribute to the smooth functioning of the market by eliminating obstacles in business.


title: Design and Optimization of Circular Magnetic Structures for Lumped Inductive Power Transfer Systems

abstract: A solution that enables safe, efficient, and convenient overnight recharging of electric vehicles is needed. Inductive power transfer (IPT) is capable of meeting these needs, however, the main limiting factor is the performance of the magnetic structures (termed power pads) that help transfer power efficiently. These should transfer 2-5 kW with a large air gap and have good tolerance to misalignment. Durability, low weight, and cost efficiency are also critical. 3-D finite-element analysis modeling is used to optimize circular power pads. This technique is viable, since measured and simulated results differ by 10% at most. A sample of power pads was considered in this work, and key design parameters were investigated to determine their influence on coupled power and operation. A final 2 kW 700-mm-diameter pad was constructed and tested having a horizontal radial tolerance of 130 mm (equivalent to a circular charging zone of diameter 260 mm) with a 200 mm air gap. The leakage magnetic flux of a charging system was investigated via simulation and measurement. The proposed pads meet human exposure regulations with measurement techniques specific by the Australian Radiation Protection and Nuclear Safety Agency (ARPANSA) which uses the International Commission on Non-Ionizing Radiation Protection (ICNIRP) guidelines as a foundation.


title: Mathematical modeling of the initiation and spread of peat fires

abstract: We propose a new statement and a numerical solution of the problem on ignition of a peat bed as a result of the action of the nucleation site of a low fire on the basis of the mathematical model of a porous reactive medium. It has been obtained that at moderate temperatures (T2 ≤ 750 K) the smoldering of the original reactant is determined by the processes of heat and mass exchange with the nucleation site of the forest fire, drying, and pyrolysis of peat, as well as by the oxidation reaction of carbon oxide, the peat height, and the thickness of the water stratum under its bed.


title: Community-acquired pneumonia in the elderly

abstract: Background: Community-acquired pneumonia (CAP) is a frequent cause of hospitalization and death among the elderly. ::: ::: Objective: This article reviews information on CAP among the elderly, including age-related changes, predisposing risk factors, causes, treatment strategies, and prevention. ::: ::: Methods: Searches of MEDLINE (January 1990-November 2009), International Pharmaceutical Abstracts (January 1990-November 2009), and Google Scholar were conducted using the terms community-acquired pneumonia, pneumonia, treatment guidelines, and elderly. Additional publications were found by searching the reference lists of the identified articles. Studies that reported diagnostic criteria as well as the treatment outcomes achieved in adult patients with CAP were selected for this review. ::: ::: Results: Three practice guidelines, 5 reviews, and 43 studies on CAP in the elderly were identified in the literature search. Based on those publications, risk factors that predispose the elderly to pneumonia include comorbid conditions, poor functional and nutritional status, consumption of alcohol, and smoking. The clinical presentation of pneumonia in the elderly (≥65 years of age) may be subtle, lacking the typical acute symptoms (fever, cough, dyspnea, and purulent sputum) observed in younger adults. Pneumonia should be suspected in all elderly patients who have fever, altered mental status, or a sudden decline in functional status, with or without lower respiratory tract symptoms such as cough, purulent sputum, and dyspnea. Treatment of CAP in the elderly should be guided by the latest recommendations of the Infectious Diseases Society of America and the American Thoracic Society (IDSA/ATS), along with consideration of local rates and patterns of antimicrobial resistance, as well as individual patient risk factors for acquiring less common or more resistant pathogens. Recommended empiric antimicrobial regimens generally consist of either a β-lactam plus a macrolide or a respiratory fluoroquinolone alone. Adherence to the IDSA/ATS guidelines has been found to improve in-hospital mortality (adherence vs nonadherence, 8%; 95% CI, 7%–10% vs 17%; 95% CI, 14%–20%; P< 0.01), length of hospital stay (8 days; interquartile range [IQR], 5–15 vs 10 days; IQR, 6–24 days, respectively; P < 0.01), and time to clinical stability in elderly patients with CAP (percentage of stable patients by day 7, 71%; 95% CI, 68%–74% vs 57%; 95% CI, 53%–61%, respectively; P < 0.01). All elderly patients should be vaccinated against pneumococcal disease and influenza based on recommendations from the Centers for Disease Control and Prevention. Lifestyle modifications and nutritional support are also important elements in the prevention of pneumonia in the elderly. ::: ::: Conclusion: Adherence to established guidelines, along with customization of antimicrobial therapy based on local rates and patterns of resistance and patient-specific risk factors, likely will improve the treatment outcome of elderly patients with CAP.


title: Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices.

abstract: Attention can be focused volitionally by “top-down” signals derived from task demands and automatically by “bottom-up” signals from salient stimuli. The frontal and parietal cortices are involved, but their neural activity has not been directly compared. Therefore, we recorded from them simultaneously in monkeys. Prefrontal neurons reflected the target location first during top-down attention, whereas parietal neurons signaled it earlier during bottom-up attention. Synchrony between frontal and parietal areas was stronger in lower frequencies during top-down attention and in higher frequencies during bottom-up attention. This result indicates that top-down and bottom-up signals arise from the frontal and sensory cortex, respectively, and different modes of attention may emphasize synchrony at different frequencies.


title: Mindfulness for Long-Distance Runners: An Open Trial Using Mindful Sport Performance Enhancement (MSPE)

abstract: The present study sought to determine the effects of Mindful Sport Performance Enhancement (MSPE) on runners. Participants were 25 recreational long-distance runners openly assigned to either the 4-week intervention or to a waiting-list control group, which later received the same program. Results indicate that the MSPE group showed significantly more improvement in organizational demands (an aspect of perfectionism) compared with controls. Analyses of pre- to postworkshop change found a significant increase in state mindfulness and trait awareness and decreases in sport-related worries, personal standards perfectionism, and parental criticism. No improvements in actual running performance were found. Regression analyses revealed that higher ratings of expectations and credibility of the workshop were associated with lower postworkshop perfectionism, more years running predicted higher ratings of perfectionism, and more life stressors predicted lower levels of worry. Findings suggest that MSPE may be a us...


title: p130Cas, Crk-Associated Substrate, Plays Important Roles in Osteoclastic Bone Resorption

abstract: p130Cas, Crk-associated substrate (Cas), is an adaptor/scaffold protein that plays a central role in actin cytoskeletal reorganization. We previously reported that p130Cas is not tyrosine-phosphorylated in osteoclasts derived from Src-deficient mice, which are congenitally osteopetrotic, suggesting that p130Cas serves as a downstream molecule of c-Src and is involved in osteoclastic bone resorption. However, the physiological role of p130Cas in osteoclasts has not yet been confirmed because the p130Cas-deficient mice displayed embryonic lethality. Osteoclast-specific p130Cas conditional knockout (p130CasΔOCL–) mice exhibit a high bone mass phenotype caused by defect in multinucleation and cytoskeleton organization causing bone resorption deficiency. Bone marrow cells from p130CasΔOCL– mice were able to differentiate into osteoclasts and wild-type cells in vitro. However, osteoclasts from p130CasΔOCL– mice failed to form actin rings and resorb pits on dentine slices. Although the initial events of osteoclast attachment, such as β3-integrin or Src phosphorylation, were intact, the Rac1 activity that organizes the actin cytoskeleton was reduced, and its distribution was disrupted in p130CasΔOCL– osteoclasts. Dedicator of cytokinesis 5 (Dock5), a Rho family guanine nucleotide exchanger, failed to associate with Src or Pyk2 in osteoclasts in the absence of p130Cas. These results strongly indicate that p130Cas plays pivotal roles in osteoclastic bone resorption. © 2013 American Society for Bone and Mineral Research.


title: The nature of phonological processing and its causal role in the acquisition of reading skills.

abstract: Three bodies of research that have developed in relative isolation center on each of three kinds of phonological processing: phonological awareness, awareness of the sound structure of language; phonological receding in lexical access, receding written symbols into a sound-based representational system to get from the written word to its lexical referent; and phonetic receding in working memory, recoding written symbols into a sound-based representational system to maintain them efficiently in working memory. In this review we integrate these bodies of research and address the interdependent issues of the nature of phonological abilities and their causal roles in the acquisition of reading skills. Phonological ability seems to be general across tasks that purport to measure the three kinds of phonological processing, and this generality apparently is independent of general cognitive ability. However, the generality of phonological ability is not complete, and there is an empirical basis for distinguishing phonological awareness and phonetic recoding in working memory. Our review supports a causal role for phonological awareness in learning to read, and suggests the possibility of similar causal roles for phonological recoding in lexical access and phonetic recoding in working memory. Most researchers have neglected the probable causal role of learning to read in the development of phonological skills. It is no longer enough to ask whether phonological skills play a causal role in the acquisition of reading skills. The question now is which aspects of phonological processing (e.g., awareness, recoding in lexical access, recoding in working memory) are causally related to which aspects of reading (e.g., word recognition, word analysis, sentence comprehension), at which point in their codevelopment, and what are the directions of these causal relations?


title: Wetlands of the Lowland Amazon Basin: Extent, Vegetative Cover, and Dual-season Inundated Area as Mapped with JERS-1 Synthetic Aperture Radar

abstract: Wetland extent, vegetation cover, and inundation state were mapped for the first time at moderately high (100 m) resolution for the entire lowland Amazon basin, using mosaics of Japanese Earth Resources Satellite (JERS-1) imagery acquired during low- and high-water seasons in 1995–1996. A wetlands mask was created by segmentation of the mosaics and clustering of the resulting polygons; a rules set was then applied to classify wetland areas into five land cover classes and two flooding classes using dual-season backscattering values. The mapped wetland area of 8.4 × 105 km2 is equivalent to 14 % of the total basin area (5.83 × 106 km2) and 17 % of the lowland basin (5.06 × 106 km2). During high-water season, open water surfaces accounted for 9 % of the wetland area, woody vegetation 77 %, and aquatic macrophytes 14 %. Producer’s accuracy as assessed using high-resolution digital videography was better than 85 % for wetland extent. The mapped flooding extent is representative of average high- and low-flood conditions for latitudes north of 6° S; flooding conditions were less well captured for the southern part of the basin. Global data sets derived from lower-resolution optical sensors capture less than 25 % of the wetland area mapped here.


title: Rapidly moving new bacteria to model-organism status

abstract: The paradigm of large research communities collectively working on a small number of model bacteria such as Escherichia coli and Bacillus subtilis is changing. While these classic model bacteria will continue to be important for advanced systems biology and new technology development, we envision that increasingly small research teams will be deeply investigating their own favorite strains, for example as new hosts for metabolic engineering or as key members of a complex microbiome. Given the lack of a research community and the sheer number of possible bacteria to interrogate, the development and application of technologies to rapidly and inexpensively advance these unstudied strains to 'model-organism' status is imperative. Here, we discuss the minimal information and tools necessary to develop a new model bacterium and how existing approaches can bring this power into the hands of a single investigator.


of attention-deficit / hyperactivity disorder : the search for endophenotypes . Nat Rev Neurosci


title: Migrating from COBOL to Java

abstract: This paper is an industrial report on a project for migrating an airport management system from a Bull mainframe using COBOL as a programming language and IDS as a database system to a distributed UNIX platform using Java and Oracle. The focus here is on the automated language transformation, performed in three phases - reengineering, conversion and refinement. The tools used are COBRedo for reengineering the COBOL code, COB2Java for converting COBOL to Java and JavRedoc for documenting the converted Java code. The paper describes the migration process and the tools used in it and the reviews the current state of the project. Keywords: Migration, Reengineering, Legacy systems, COBOL, Java, Object-oriented transformation.


title: Bangla handwritten character recognition using deep belief network

abstract: Recognition of Bangla handwritten characters is a difficult but important task for various emerging applications. For better recognition performance, good feature representation of the character images is a primary requirement. In this study, we investigate a recently proposed machine learning approach called deep learning [1] for Bangla hand written character recognition, with a focus on automatic learning of good representations. This approach differs from the traditional methods of preprocessing the characters for constructing the handcrafted features such as loops and strokes. Among different deep learning structures, we employ the deep belief network (DBN) that takes the raw character images as input and learning proceeds in two steps - an unsupervised feature learning followed by a supervised fine tuning of the network parameters. Unlike traditional neural networks, the DBN is a probabilistic generative model, i.e., we can generate samples from the model and it can fit both the semi-supervised and supervised learning settings. We demonstrate the advantages of unsupervised feature learning through the experimental studies carried on the Bangla basic characters and numerals dataset collected from the Indian Statistical Institute.


title: Ventilation measurements at model scale in a turbulent flow

abstract: Abstract Ventilation rates have been measured in a model building in a wind tunnel. Two types of opening, circular holes and model windows, have been tested under two wind conditions. One wind condition was selected to give the maximum flow through the model, while for the other condition the ventilation was due mainly to turbulent pressure fluctuations. The different characteristics of the two types of opening are illustrated. Comparisons are made between the measurements and theoretical predictions. The use of wind tunnels for ventilation studies is discussed.


An Overview of Modern Speech Recognition


abstract: ABSTRACT Introduction Disturbances in intimate relationships are among the risk factors for female sexual dysfunction. Insecure styles of anxious attachment (preoccupations about abandonment) and avoidant attachment (avoidance of closeness in relationships) are robustly associated with sexual problems, relationship difficulties, and several indices of poorer physical and mental health. Similar indices of poorer sexual, relationship, and health functioning are associated with impairment of orgasm triggered by penile‐vaginal stimulation (vaginal orgasm), but unrelated or related to greater frequency of other sexual behaviors. However, research examining the differential association of sexual activities with insecure attachment styles has been lacking. Aims The aim of this study was to test the hypotheses that insecure attachment styles are associated with lesser vaginal orgasm consistency, and are unrelated or directly related to greater frequency of other sexual behaviors. Method Seventy coitally experienced women recruited at a Scottish university completed the Revised Experience in Close Relationships scale, and reported their frequency of various sexual behaviors (and corresponding orgasms) in a recent representative month. Main Outcome Measures The main outcome measures for this study are multivariate correlations of various sexual activities with insecure attachment styles, age, and social desirability response bias. Results Anxious attachment was associated with lesser vaginal orgasm consistency, but with higher frequency of vibrator and anal sex orgasms. Avoidant attachment was associated with higher frequency of vibrator orgasms. Neither anxious nor avoidant attachment was associated with lifetime number of penile‐vaginal intercourse partners. Conclusions The results provide evidence that inability to attain a vaginal orgasm is associated with anxious attachment, among other indices of poorer mental health and relatedness. Vaginal orgasm might be the relevant sexual activity for the maintenance of a secure attachment style with a sexual partner and/or more insecurely attached women might have difficulties in attaining vaginal orgasms and be more inclined to sexual activities characterized by more emotional and physical detachment, as part of a characterological discomfort with intimacy. Costa RM and Brody S. Anxious and avoidant attachment, vibrator use, anal sex, and impaired vaginal orgasm. J Sex Med 2011;8:2493–2500.


title: Joint User Activity and Data Detection Based on Structured Compressive Sensing for NOMA

abstract: Non-orthogonal multiple access (NOMA) has been regarded as one of the promising key technologies for future 5G systems. In the uplink grant-free NOMA schemes, dynamic scheduling is not required, which can significantly reduce the signaling overhead and transmission latency. However, user activity has to be detected in grant-free NOMA systems, which is challenging in practice. In this letter, by exploiting the inherent structured sparsity of user activity naturally existing in NOMA systems, we propose a low-complexity multi-user detector based on structured compressive sensing to realize joint user activity and data detection. In particular, we propose a structured iterative support detection algorithm by exploiting such structured sparsity, which is able to jointly detect user activity and transmitted data in several continuous time slots. Simulation results show that the proposed scheme can achieve better performance than conventional solutions.


Deobfuscating Android Applications through Deep Learning


title: Stochastic Optimization Using a Trust-Region Method and Random Models

abstract: In this paper, we propose and analyze a trust-region model-based algorithm for solving unconstrained stochastic optimization problems. Our framework utilizes random models of an objective function f(x), obtained from stochastic observations of the function or its gradient. Our method also utilizes estimates of function values to gauge progress that is being made. The convergence analysis relies on requirements that these models and these estimates are sufficiently accurate with high enough, but fixed, probability. Beyond these conditions, no assumptions are made on how these models and estimates are generated. Under these general conditions we show an almost sure global convergence of the method to a first order stationary point. In the second part of the paper, we present examples of generating sufficiently accurate random models under biased or unbiased noise assumptions. Lastly, we present some computational results showing the benefits of the proposed method compared to existing approaches that are based on sample averaging or stochastic gradients.


title: An Efficient Analytical Method for Vibration Analysis of a Beam on Elastic Foundation with Elastically Restrained Ends

abstract: An efficient analytical method for vibration analysis of a Euler-Bernoulli beam on elastic foundation with elastically restrained ends has been reported. A Fourier sine series with Stoke’s transformation is used to obtain the vibration response. The general frequency determinant is developed on the basis of the analytical solution of the governing differential equation for all potential solution cases with rigid or restrained boundary conditions. Numerical analyses are performed to investigate the effects of various parameters, such as the springs at the boundaries to examine how the elastic foundation parameters affect the vibration frequencies.


Cobb Angle Reduction in a Nearly Skeletally Mature Adolescent (Risser 4) After Pattern-Specific Scoliosis Rehabilitation (PSSR)


title: Deep Semantic Instance Segmentation of Tree-Like Structures Using Synthetic Data

abstract: Tree-like structures, such as blood vessels, often express complexity at very fine scales, requiring high-resolution grids to adequately describe their shape. Such sparse morphology can alternately be represented by locations of centreline points, but learning from this type of data with deep learning is challenging due to it being unordered, and permutation invariant. In this work, we propose a deep neural network that directly consumes unordered points along the centreline of a branching structure, to identify the topology of the represented structure in a single-shot. Key to our approach is the use of a novel multi-task loss function, enabling instance segmentation of arbitrarily complex branching structures. We train the network solely using synthetically generated data, utilizing domain randomization to facilitate the transfer to real 2D and 3D data. Results show that our network can reliably extract meaningful information about branch locations, bifurcations and endpoints, and sets a new benchmark for semantic instance segmentation in branching structures.


title: Link Prediction in Criminal Networks: A Tool for Criminal Intelligence Analysis

abstract: The problem of link prediction has recently received increasing attention from scholars in network science. In social network analysis, one of its aims is to recover missing links, namely connections among actors which are likely to exist but have not been reported because data are incomplete or subject to various types of uncertainty. In the field of criminal investigations, problems of incomplete information are encountered almost by definition, given the obvious anti-detection strategies set up by criminals and the limited investigative resources. In this paper, we work on a specific dataset obtained from a real investigation, and we propose a strategy to identify missing links in a criminal network on the basis of the topological analysis of the links classified as marginal, i.e. removed during the investigation procedure. The main assumption is that missing links should have opposite features with respect to marginal ones. Measures of node similarity turn out to provide the best characterization in this sense. The inspection of the judicial source documents confirms that the predicted links, in most instances, do relate actors with large likelihood of co-participation in illicit activities.


A psychosocial care model for men with prostate cancer


title: A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring

abstract: Characteristics of physical activity are indicative of one’s mobility level, latent chronic diseases and aging process. Accelerometers have been widely accepted as useful and practical sensors for wearable devices to measure and assess physical activity. This paper reviews the development of wearable accelerometry-based motion detectors. The principle of accelerometry measurement, sensor properties and sensor placements are first introduced. Various research using accelerometry-based wearable motion detectors for physical activity monitoring and assessment, including posture and movement classification, estimation of energy expenditure, fall detection and balance control evaluation, are also reviewed. Finally this paper reviews and compares existing commercial products to provide a comprehensive outlook of current development status and possible emerging technologies.


title: A Predictive Light Transport Model for the Human Iris

abstract: Recently, light interactions with organic matter have become the object of detailed investigations by image synthesis researchers. Besides allowing these materials to be rendered in a more intuitive manner, these efforts aim to extend the scope of computer graphics applications to areas such as applied optics and biomedical imaging. There are, however, organic materials that still lack predictive simulation solutions. Among these, the ocular tissues, especially those forming the human iris, pose the most challenging modeling problems which are often associated with data scarcity. In this paper, we describe the first biophysically-based light transport model for the human iris ever presented in the scientific literature. The proposed model algorithmically simulates the light scattering and absorption processes occurring within the iridal tissues, and computes the spectral radiometric responses of these tissues. Its design is based on the current scientific understanding of the iridal morphological and optical characteristics, and it is controlled by parameters directly related to these biophysical attributes. The accuracy and predictability of the spectral results provided by the model are evaluated through comparisons with actual measured iridal data, and its integration into rendering frameworks is illustrated through the generation of images depicting iridal chromatic variations. ::: ::: ::: ::: Categories and Subject Descriptors (according to ACM CCS): I.3.8 [Computer Graphics]: Applications


title: Behavioral model composition in simulation-based design

abstract: We present a simulation and design framework for simultaneously designing and modeling electromechanical systems. By instantiating component objects and connecting them to each other via ports, a designer can configure complex systems. This configuration information is then used to automatically generate a corresponding system-level simulation model. The building block of our framework is the component object. It encapsulates design data and behavioral models and their inter-relationships. Component objects are composed into systems by connecting their ports. However, when converting a system configuration into a corresponding simulation model, the corresponding models for the component objects do not capture the physical phenomena at the component interfaces the interactions. To obtain an accurate composition, the interaction dynamics must also be captured in behavioral models. In this paper, we introduce the concept of an interaction model that captures the dynamics of the interaction. When two ports are connected, there is an intended interaction between the two components. For composition of component objects to work, an interaction model must be introduced between each pair of connected behavioral models. We illustrate these ideas using an example.


title: Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora

abstract: A significant portion of the world's text is tagged by readers on social bookmarking websites. Credit attribution is an inherent problem in these corpora because most pages have multiple tags, but the tags do not always apply with equal specificity across the whole document. Solving the credit attribution problem requires associating each word in a document with the most appropriate tags and vice versa. This paper introduces Labeled LDA, a topic model that constrains Latent Dirichlet Allocation by defining a one-to-one correspondence between LDA's latent topics and user tags. This allows Labeled LDA to directly learn word-tag correspondences. We demonstrate Labeled LDA's improved expressiveness over traditional LDA with visualizations of a corpus of tagged web pages from del.icio.us. Labeled LDA outperforms SVMs by more than 3 to 1 when extracting tag-specific document snippets. As a multi-label text classifier, our model is competitive with a discriminative baseline on a variety of datasets.


title: Continuous extrinsic online calibration for stereo cameras

abstract: Accurate stereo camera calibration is crucial for 3D reconstruction from stereo images. In this paper, we propose an algorithm for continuous online recalibration of all extrinsic parameters of a stereo camera, which is rigidly mounted on an autonomous vehicle. The algorithm estimates the six degrees-of-freedom (6-DoF) of the transformation from the vehicle coordinate system to the coordinate system of the stereo camera and at the same time the relative 6-DoF transformation between the two camera sensors. Salient points in the environment that are observed by both cameras are tracked over time in 3D space. An Unscented Kalman Filter (UKF) is applied to recursively estimate the extrinsic stereo camera calibration and the 3D position of all observed points. The projections of the points and the measured vehicle motion, which is estimated using an inertial measurement unit (IMU), are given as input. The observability of the stereo camera calibration states is analyzed to identify critical vehicle motion sequences. Results with real world data show that the algorithm is capable of continuously estimating the stereo camera calibrations in spite of large initial errors and varying extrinsic parameters.


title: xDGP: A Dynamic Graph Processing System with Adaptive Partitioning

abstract: Many real-world systems, such as social networks, rely on mining efficiently large graphs, with hundreds of millions of vertices and edges. This volume of information requires partitioning the graph across multiple nodes in a distributed system. This has a deep effect on performance, as traversing edges cut between partitions incurs a significant performance penalty due to the cost of communication. Thus, several systems in the literature have attempted to improve computational performance by enhancing graph partitioning, but they do not support another characteristic of real-world graphs: graphs are inherently dynamic, their topology evolves continuously, and subsequently the optimum partitioning also changes over time. ::: In this work, we present the first system that dynamically repartitions massive graphs to adapt to structural changes. The system optimises graph partitioning to prevent performance degradation without using data replication. The system adopts an iterative vertex migration algorithm that relies on local information only, making complex coordination unnecessary. We show how the improvement in graph partitioning reduces execution time by over 50%, while adapting the partitioning to a large number of changes to the graph in three real-world scenarios.


title: Analysis of human behavior recognition algorithms based on acceleration data

abstract: The automatic assessment of the level of independence of a person, based on the recognition of a set of Activities of Daily Living, is among the most challenging research fields in Ambient Intelligence. The article proposes a framework for the recognition of motion primitives, relying on Gaussian Mixture Modeling and Gaussian Mixture Regression for the creation of activity models. A recognition procedure based on Dynamic Time Warping and Mahalanobis distance is found to: (i) ensure good classification results; (ii) exploit the properties of GMM and GMR modeling to allow for an easy run-time recognition; (iii) enhance the consistency of the recognition via the use of a classifier allowing unknown as an answer.


title: Music Emotion Classification: A Regression Approach

abstract: Typical music emotion classification (MEC) approaches categorize emotions and apply pattern recognition methods to train a classifier. However, categorized emotions are too ambiguous for efficient music retrieval. In this paper, we model emotions as continuous variables composed of arousal and valence values (AV values), and formulate MEC as a regression problem. The multiple linear regression, support vector regression, and AdaBoost.RT are adopted to evaluate the prediction accuracy. Since the regression approach is inherently continuous, it is free of the ambiguity problem existing in its categorical counterparts.


title: Face of a fighter: Bizygomatic width as a cue of formidability

abstract: Humans can accurately extract information about men's formidability from their faces; however, the actual facial cues that inform these judgments have not been established. Here, through three studies, we test the hypothesis that bizygomatic width (i.e. facial width-to-height ratio, fWHR) covaries with actual physical formidability (hypothesis #1) and that humans use this cue when making assessments of formidability (hypothesis #2). Our data confirm that fWHR is predictive of actual fighting ability among professional combatants (study 1). We further show that subjects' assessments of formidability covary with the target's fWHR on natural faces (study 2), computer-generated images of strong and weak faces (study 2), and experimentally manipulated computer-generated faces (study 3). These results support the hypothesis that bizygomatic width is a cue of formidability that is assessed during agonistic encounters. Aggr. Behav. 9999:XX-XX, 2014. © 2014 Wiley Periodicals, Inc. Language: en


title: Permanent-Magnet Shape Optimization Effects on Synchronous Motor Performance

abstract: The magnet shape in permanent-magnet (PM) synchronous motors substantially affects the back-electromotive-force (EMF) waveform and the stator iron losses, which are of particular importance in traction applications, where the energy available in the battery box is limited. This paper presents a methodology based on geometry optimization, providing sinusoidal back-EMF waveform. The method has been applied in a surface PM motor case for electric vehicle, and its validity has been checked by measurements on two prototypes, the first one with constant magnet width and the second one with optimized magnet shape.


title: Thinking About Meaning and Learning in a World of Instability and Multiplicity

abstract: This article sketches the contemporary environments of education in “developed societies” where many of the metaphors and myths still governing educational thinking and policy derive from a past that is socially and economically organised differently. It mentions the specific issue of educating the “transitional generation”; straddling shifts in power and authority; and vast social, economic, political, and technological changes. It discusses the environments of learning, questions what is to be learned in conditions of profound challenges to authority, and sketches a theory of learning that is apt not just for the present conditions, but also as a plausible account of learning in general.


title: A Bridgeless BHB ZVS-PWM AC–AC Converter for High-Frequency Induction Heating Applications

abstract: A new prototype of a zero voltage soft-switching (ZVS) utility frequency ac to high-frequency ac resonant power converter for induction heating (IH) applications is presented in this paper. The series resonant ac–ac converter proposed herein can process the frequency conversion without any diode bridge rectifier, thereby reducing the relevant conduction power losses. In addition, power factor correction (PFC) can be naturally achieved by the inductor-based boost half-bridge circuit with the nonsmoothed dc-link. The operation principle together with an IH load power regulation scheme is described, and the converter performances including ZVS operations and PFC are demonstrated in an experiment with a 3.0-kW–30-kHz prototype by comparing it with the previously developed converter. Finally, the feasibility of the proposed ac–ac converter is evaluated from a practical point of view.


title: Interviewing intoxicated witnesses: Memory performance in theory and practice

abstract: Memory encoding and recall involving complex, effortful cognitive processes are impaired by alcohol primarily due to impairment of a select few, but crucial, cortical areas. This review shows how alcohol affects some, but not all, aspects of eyewitnesses' oral free recall performance. The principal results, so far, are that: a) free recall reports by intoxicated witnesses (at the investigated BAC-levels) may contain less, but as accurate, information as reports by sober witnesses; b) immediate reports given by intoxicated witnesses may yield more information compared to reports by sober witnesses given after a one week delay; c) an immediate interview may enhance both intoxicated and sober witnesses' ability to report information in a later interview; and d) reminiscence seems to occur over repeated interviews and the new information seems to be as accurate as the previously reported information. Based on this, recommendations are given for future research to enhance understanding of the multifaceted impact of alcohol on witnesses' oral free recall of violent crimes.


title: Pythagorean Membership Grades in Multicriteria Decision Making

abstract: We first look at some nonstandard fuzzy sets, intuitionistic, and interval-valued fuzzy sets. We note both these allow a degree of commitment of less then one in assigning membership. We look at the formulation of the negation for these sets and show its expression in terms of the standard complement with respect to the degree of commitment. We then consider the complement operation. We describe its properties and look at alternative definitions of complement operations. We then focus on the Pythagorean complement. Using this complement, we introduce a class of nonstandard Pythagorean fuzzy subsets whose membership grades are pairs, (a, b) satisfying the requirement a ::: 2 ::: + b ::: 2 ::: ≤ 1. We introduce a variety of aggregation operations for these Pythagorean fuzzy subsets. We then look at multicriteria decision making in the case where the criteria satisfaction are expressed using Pythagorean membership grades. The issue of having to choose a best alternative in multicriteria decision making leads us to consider the problem of comparing Pythagorean membership grades.


title: Linear scan register allocation

abstract: We describe a new algorithm for fast global register allocation called linear scan . This algorithm is not based on graph coloring, but allocates registers to variables in a single linear-time scan of the variables' live ranges. The linear scan algorithm is considerably faster than algorithms based on graph coloring, is simple to implement, and results in code that is almost as efficient as that obtained using more complex and time-consuming register allocators based on graph coloring. The algorithm is of interest in applications where compile time is a concern, such as dynamic compilation systems, “just-in-time” compilers, and interactive development environments.


title: NLKBIDB - Natural language and keyword based interface to database

abstract: One of the major source of information is database which plays an important role in computer field. Almost all IT applications are storing and retrieving information from databases. There are various interfaces available to retrieve data such as form based, natural language and keyword based. Data retrieval from the database requires knowledge of database language like SQL [1]. The need of natural language arises from the fact that common people (other than SQL experts) are not used to writing SQL query. Therefore researchers have identified a new system called Natural Language Interface to Database (NLIDB) and Keyword Based Interface to Database (KBIDB). In this paper we have proposed architecture of a Natural Language and Keyword Based Interface for Database (NLKBIDB) which provides solution for syntactically correct and incorrect natural language input query. Our partial experiment of Lexical Analyzer and Keyword based interface on agriculture survey database solves 53% of syntactically incorrect query which will not be solved by natural language interface resulting in increase of rate of SQL query conversion.


title: A Security Model for Preserving the Privacy of Medical Big Data in a Healthcare Cloud Using a Fog Computing Facility With Pairing-Based Cryptography

abstract: Nowadays, telemedicine is an emerging healthcare service where the healthcare professionals can diagnose, evaluate, and treat a patient using telecommunication technology. To diagnose and evaluate a patient, the healthcare professionals need to access the electronic medical record (EMR) of the patient, which might contain huge multimedia big data including X-rays, ultrasounds, CT scans, and MRI reports. For efficient access and supporting mobility for both the healthcare professionals as well as the patients, the EMR needs to be kept in big data storage in the healthcare cloud. In spite of the popularity of the healthcare cloud, it faces different security issues; for instance, data theft attacks are considered to be one of the most serious security breaches of healthcare data in the cloud. In this paper, the main focus has been given to secure healthcare private data in the cloud using a fog computing facility. To this end, a tri-party one-round authenticated key agreement protocol has been proposed based on the bilinear pairing cryptography that can generate a session key among the participants and communicate among them securely. Finally, the private healthcare data are accessed and stored securely by implementing a decoy technique.


title: C-language based PV array simulation technique considering effects of partial shading

abstract: The performance of photovoltaic (PV) array is strongly dependent on the operating conditions especially the solar irradiation. The shading on PV array by the passing clouds or neighboring buildings causes not only power losses, but also non-linearity of V-I characteristics of PV array. Under partially shaded conditions, the characteristics have more non-linearity with multiple local maxima. In spite of several researchers have studied the PV characteristics under the partial shading condition, it is difficult to simulate the partial shading condition on the simulation tool for power electronics. The C-language based PV array simulation strategy proposed in here is to simulate the effects of partial shading condition for developing more advanced maximum power point tracking algorithms or power conversion systems. Proposed modeling strategy is easily connected to simulation tools. In this paper, the modeling of photovoltaic array is based on the numerical analysis, and the verification is performed using PSIM.


title: 10.6 THz figure-of-merit phase-change RF switches with embedded micro-heater

abstract: We report on GeTe-based phase-change RF switches with embedded micro-heater for thermal switching. With heater parasitics reduced, GeTe RF switches show onstate resistance of 0.05 ohm*mm and off-state capacitance of 0.3 pF/mm. The RF switch figure-of-merit is estimated to be 10.6 THz, which is about 15 times better than state-of-the-art silicon-on-insulator switches. With on-state resistance of 1 ohm and off-state capacitance of 15 fF, RF insertion loss was measured at 25 dB at 20 GHz, respectively. RF power handling was >5.6 W for both onand off-state of GeTe.


Earnings management through real activities manipulation $ Sugata Roychowdhury


title: Automatic melody composition based on a probabilistic model of music style and harmonic rules

abstract: Abstract The aim of the present work is to perform a step towards the design of specific algorithms and methods for automatic music generation. A novel probabilistic model for the characterization of music learned from music samples is designed. This model makes use of automatically extracted music parameters, namely tempo, time signature, rhythmic patterns and pitch contours, to characterize music. Specifically, learned rhythmic patterns and pitch contours are employed to characterize music styles. Then, a novel autonomous music compositor that generates new melodies using the model developed will be presented. The methods proposed in this paper take into consideration different aspects related to the traditional way in which music is composed by humans such as harmony evolution and structure repetitions and apply them together with the probabilistic reutilization of rhythm patterns and pitch contours learned beforehand to compose music pieces.


title: Cross-coupling in coaxial cavity filters - a tutorial overview

abstract: This paper presents a tutorial overview of the use of coupling between nonadjacent resonators to produce transmission zeros at real frequencies in microwave filters. Multipath coupling diagrams are constructed and the relative phase shifts of multiple paths are observed to produce the known responses of the cascaded triplet and quadruplet sections. The same technique is also used to explore less common nested cross-coupling structures and to predict their behavior. A discussion of the effects of nonzero electrical length coupling elements is presented. Finally, a brief categorization of the various synthesis and implementation techniques available for these types of filters is given.


title: A 60 GHz Horizontally Polarized Magnetoelectric Dipole Antenna Array With 2-D Multibeam Endfire Radiation

abstract: A novel substrate integrated waveguide (SIW) fed horizontally polarized endfire magnetoelectric (ME) dipole antenna composed of an open-ended SIW with broad walls vertical to substrates and a pair of electric dipoles realized by four metallic patches is proposed. Simple configuration and excellent performance including an impedance bandwidth of 46.5%, stable gain of around 6 dBi, and symmetrical cardioid radiation patterns with low backward radiation and low cross polarizations are achieved. An SIW 90° twist integrated in three-layered substrate is implemented in order to connect the ME-dipole antenna conveniently to the SIW beam-forming network with broad walls parallel to substrates. A $4 \times 4$ SIW Butler matrix with a three-layered zigzag topology is then designed, which enables a size reduction of 45% for the matrix compared with conventional single-layered configuration but not affecting its operating characteristics. By employing a $2 \times 4$ ME-dipole array with 90° twists, two-folded Butler matrices and four SIW 3 dB E-plane couplers, a multibeam endfire array that can radiate eight beams scanning in two dimensions is designed at the 60 GHz band. The fabricated prototype verifies that a wide impedance bandwidth of 22.1%, gain varying from 10 to 13 dBi and stable radiation beams can be obtained. Due to good performance and the compact structure with low fabrication costs, the proposed design would be attractive for future millimeter-wave wireless applications including 5G communications and the WiGig system.


title: Resource Containers: A New Facility for Resource Management in Server Systems

abstract: General-purpose operating systems provide inadequate support for resource management in large-scale servers. Applications lack sufficient control over scheduling and management of machine resources, which makes it difficult to enforce priority policies, and to provide robust and controlled service. There is a fundamental mismatch between the original design assumptions underlying the resource management mechanisms of current general-purpose operating systems, and the behavior of modern server applications. In particular, the operating system's notions of protection domain and resource principal coincide in the process abstraction. This coincidence prevents a process that manages large numbers of network connections, for example, from properly allocating system resources among those connections. We propose and evaluate a new operating system abstraction called a resource container, which separates the notion of a protection domain from that of a resource principal. Resource containers enable fine-grained resource management in server systems and allow the development of robust servers, with simple and firm control over priority policies.


title: A Bayesian Data Augmentation Approach for Learning Deep Models

abstract: Data augmentation is an essential part of the training process applied to deep learning models. The motivation is that a robust training process for deep learning models depends on large annotated datasets, which are expensive to be acquired, stored and processed. Therefore a reasonable alternative is to be able to automatically generate new annotated training samples using a process known as data augmentation. The dominant data augmentation approach in the field assumes that new training samples can be obtained via random geometric or appearance transformations applied to annotated training samples, but this is a strong assumption because it is unclear if this is a reliable generative model for producing new training samples. In this paper, we provide a novel Bayesian formulation to data augmentation, where new annotated training points are treated as missing variables and generated based on the distribution learned from the training set. For learning, we introduce a theoretically sound algorithm --- generalised Monte Carlo expectation maximisation, and demonstrate one possible implementation via an extension of the Generative Adversarial Network (GAN). Classification results on MNIST, CIFAR-10 and CIFAR-100 show the better performance of our proposed method compared to the current dominant data augmentation approach mentioned above --- the results also show that our approach produces better classification results than similar GAN models.


Towards a Convex HMM Surrogate for Word Alignment


title: Applying the Roofline Performance Model to the Intel Xeon Phi Knights Landing Processor

abstract: The Roofline Performance Model is a visually intuitive method used to bound the sustained peak floating-point performance of any given arithmetic kernel on any given processor architecture. In the Roofline, performance is nominally measured in floating-point operations per second as a function of arithmetic intensity (operations per byte of data). In this study we determine the Roofline for the Intel Knights Landing (KNL) processor, determining the sustained peak memory bandwidth and floating-point performance for all levels of the memory hierarchy, in all the different KNL cluster modes. We then determine arithmetic intensity and performance for a suite of application kernels being targeted for the KNL based supercomputer Cori, and make comparisons to current Intel Xeon processors. Cori is the National Energy Research Scientific Computing Center’s (NERSC) next generation supercomputer. Scheduled for deployment mid-2016, it will be one of the earliest and largest KNL deployments in the world.


title: A Low-Power High-Precision Comparator With Time-Domain Bulk-Tuned Offset Cancellation

abstract: A novel time-domain bulk-tuned offset cancellation technique is applied to a low-power high-precision dynamic comparator to reduce its input-referred offset with minimal additional power consumption and delay. The design has been fabricated in a commercially available 0.5-μm process. Measurement results of 10 circuits show a reduction of offset standard deviation from 5.415 mV to 50.57 μV, improved by a factor of 107.1. The offset cancellation scheme does not introduce observable offset or noise, and can achieve fast and robust convergence with a wide range of common mode input. Operating at a supply of 5 V and clock frequency of 200 kHz, the comparator together with the OC circuitry consumes 4.65 μW of power, or 23 pJ of energy per comparison.


title: Unit selection in a concatenative speech synthesis system using a large speech database

abstract: One approach to the generation of natural-sounding synthesized speech waveforms is to select and concatenate units from a large speech database. Units (in the current work, phonemes) are selected to produce a natural realisation of a target phoneme sequence predicted from text which is annotated with prosodic and phonetic context information. We propose that the units in a synthesis database can be considered as a state transition network in which the state occupancy cost is the distance between a database unit and a target, and the transition cost is an estimate of the quality of concatenation of two consecutive units. This framework has many similarities to HMM-based speech recognition. A pruned Viterbi search is used to select the best units for synthesis from the database. This approach to waveform synthesis permits training from natural speech: two methods for training from speech are presented which provide weights which produce more natural speech than can be obtained by hand-tuning.


title: Can you read my mindprint?: Automatically identifying mental states from language text using deeper linguistic features

abstract: Humans routinely transmit and interpret subtle information about their mental states through the language they use, even when only the language text is available. This suggests humans can utilize the linguistic signature of a mental state (its mindprint), comprised of features in the text. Once the relevant features are identified, mindprints can be used to automatically identify mental states communicated via language. We focus on the mindprints of eight mental states resulting from intentions, attitudes, and emotions, and present a mindprint-based machine learning technique to automatically identify these mental states in realistic language data. By using linguistic features that leverage available semantic, syntactic, and valence information, our approach achieves near-human performance on average and even exceeds human performance on occasion. Given this, we believe mindprints could be very valuable for intelligent systems interacting linguistically with humans. Keywords: mental state; linguistic features; mindprint; natural language processing; information extraction


title: Reflection Symmetry Axes Detection Using Multiple Model Fitting

abstract: We propose an energy minimization approach to detect multiple reflection symmetry axes present in a given image representing fronto-parallel view of a scene. We perform local feature matching to detect the pairs of mirror symmetric points, and in order to formulate an energy function, we use the geometric characteristics of the symmetry axis. That is, it passes through the midpoint of line segment joining the two mirror symmetric points and is perpendicular to the vector joining two mirror symmetric points. We propose a novel $k$ - symmetry clustering algorithm to minimize this energy function in order to efficiently find all the symmetry axes present in the given image. We evaluate the proposed method on the standard datasets and show that we get comparable and better results than that of the state-of-the-art reflection symmetry detection methods.


title: A circuit-level substrate current model for smart-power ICs

abstract: This paper presents a new modeling methodology accounting for generation and propagation of minority carriers that can be used directly in circuit-level simulators in order to estimate coupled parasitic currents. The model is based on a new compact model of basic components (PN junction and resistance) and takes into account minority carriers at the boundary. An equivalent circuit schematic of the substrate is built by identifying these basic elements in the substrate and interconnecting them. Parasitic effects such as bipolar or latchup result from the continuity of minority carriers guaranteed by the components' model. A structure similar to a half-bridge perturbing sensitive N-well has been simulated. It is composed by four PN junctions connected together by their common P-doped sides. The results are in good agreement with those obtained from physical device simulations.


Matching in the Large: An Experimental Study


title: Identification and Recognition of Rice Diseases and Pests Using Convolutional Neural Networks

abstract: An accurate and timely detection of diseases and pests in rice plants can help farmers in applying timely treatment on the plants and thereby can reduce the economic losses substantially. Recent developments in deep learning based convolutional neural networks (CNN) have greatly improved the image classification accuracy. Being motivated by the success of CNNs in image classification, deep learning based approaches have been developed in this paper for detecting diseases and pests from rice plant images. The contribution of this paper is two fold: (i) State-of-the-art large scale architectures such as VGG16 and InceptionV3 have been adopted and fine tuned for detecting and recognizing rice diseases and pests. Experimental results show the effectiveness of these models with real datasets. (ii) Since large scale architectures are not suitable for mobile devices, a two-stage small CNN architecture has been proposed, and compared with the state-of-the-art memory efficient CNN architectures such as MobileNet, NasNet Mobile and SqueezeNet. Experimental results show that the proposed architecture can achieve the desired accuracy of 93.3\% with a significantly reduced model size (e.g., 99\% less size compared to that of VGG16).


title: Segmentation-Driven 6D Object Pose Estimation

abstract: The most recent trend in estimating the 6D pose of rigid objects has been to train deep networks to either directly regress the pose from the image or to predict the 2D locations of 3D keypoints, from which the pose can be obtained using a PnP algorithm. In both cases, the object is treated as a global entity, and a single pose estimate is computed. As a consequence, the resulting techniques can be vulnerable to large occlusions. In this paper, we introduce a segmentation-driven 6D pose estimation framework where each visible part of the objects contributes a local pose prediction in the form of 2D keypoint locations. We then use a predicted measure of confidence to combine these pose candidates into a robust set of 3D-to-2D correspondences, from which a reliable pose estimate can be obtained. We outperform the state-of-the-art on the challenging Occluded-LINEMOD and YCB-Video datasets, which is evidence that our approach deals well with multiple poorly-textured objects occluding each other. Furthermore, it relies on a simple enough architecture to achieve real-time performance.


title: Monitoring plastic ingestion by the northern fulmar Fulmarus glacialis in the North Sea

abstract: The abundance of plastics in stomachs of northern fulmars from the North Sea is used in the OSPAR Ecological Quality Objective (EcoQO) for marine litter. The preliminary EcoQO defines acceptable ecological quality as the situation where no more than 10% of fulmars exceed a critical level of 0.1 g of plastic in the stomach. During 2003e2007, 95% of 1295 fulmars sampled in the North Sea had plastic in the stomach (on average 35 pieces weighing 0.31 g) and the critical level of 0.1 g of plastic was exceeded by 58% of birds, with regional variations ranging from 48 to 78%. Long term data for the Netherlands since the 1980s show a decrease of industrial, but an increase of user plastics, with shipping and fisheries as the main sources. The EcoQO is now also used as an indicator for Good Environmental Status in the European Marine Strategy Framework Directive.


title: The effects of value co-creation practices on building harmonious brand community and achieving brand loyalty on social media in China

abstract: Value co-creation practices influence consumer-brand-consumer triad relationships.Harmonious brand communities enhance community commitment and brand loyalty.Community commitment has positive effects on brand loyalty. Social media activities generated by consumers in brand communities are claimed to have a potentially game-changing impact on interaction among brand entities and brand building. In this article, we explore whether value co-creation practices in brand community have positive effects on the consumer-brand and consumer-other consumers' relationships, as well as, community commitment and brand loyalty. A survey based empirical study with 286 respondents was conducted. The results of structural equation modeling show that four value co-creation practices positively influence consumer-other consumers' relationships and only two of the four positively influence consumer-brand relationships. The consumer-brand-consumer triad relationships could significantly enhance brand loyalty directly, as well as indirectly through community commitment. Finally, theoretical and managerial implications are discussed.


title: Cancer survivability with logistic regression

abstract: In this paper, we report on our experiments with SEER data and the problem of five-year survivability prediction using logistic regression. This work provides a detailed process for data cleaning and binary classification of 338,596 breast cancer patients. We achieve a high level of precision and recall for automatic binary classification of this data with logistic regression. This paper also gives a detailed analysis of our statistical test of significance for feature selection.


title: An In-Phase Output $K\!a$-Band Traveling-Wave Power Divider/Combiner Using Double Ridge-Waveguide Couplers

abstract: A Ka-band traveling-wave power divider/combiner, which is based on double ridge-waveguide couplers, is presented. The in-phase output, which is a challenge of the waveguide-based traveling-wave power divider, is achieved by optimizing the equivalent circuit of the proposed structure. The novel ridge-waveguide coupler has advantages of low loss, high power capability, and easy assembly. Finally, the proposed power divider/combiner is simulated, fabricated, and measured. A 15-dB measured return-loss bandwidth at the center frequency of 35 GHz is over 28%, a maximum transmission coefficients amplitude imbalance of ±1 dB is achieved, and the phase deviation is less than ± 12° from 32 to 39 GHz.


title: The Theory and Use of the Quaternion Wavelet Transform

abstract: This paper presents the theory and practicalities of the quaternion wavelet transform (QWT). The major contribution of this work is that it generalizes the real and complex wavelet transforms and derives a quaternionic wavelet pyramid for multi-resolution analysis using the quaternionic phase concept. As a illustration we present an application of the discrete QWT for optical flow estimation. For the estimation of motion through different resolution levels we use a similarity distance evaluated by means of the quaternionic phase concept and a confidence mask. We show that this linear approach is amenable to be extended to a kind of quadratic interpolation.


title: Mechanisms of Action Involved in Ozone Therapy: Is healing induced via a mild oxidative stress?

abstract: The potential mechanisms of action of ozone therapy are reviewed in this paper. The therapeutic efficacy of ozone therapy may be partly due the controlled and moderate oxidative stress produced by the reactions of ozone with several biological components. The line between effectiveness and toxicity of ozone may be dependent on the strength of the oxidative stress. As with exercise, it is well known that moderate exercise is good for health, whereas excessive exercise is not.Severe oxidative stress activates nuclear transcriptional factor kappa B (NFκB), resulting in an inflammatory response and tissue injury via the production of COX2, PGE2, and cytokines. However, moderate oxidative stress activates another nuclear transcriptional factor, nuclear factor-erythroid 2-related factor 2 (Nrf2). Nrf2 then induces the transcription of antioxidant response elements (ARE). Transcription of ARE results in the production of numerous antioxidant enzymes, such as SOD, GPx, glutathione-s-transferase(GSTr), catalase (CAT), heme-oxygenase-1 (HO-1), NADPH-quinone-oxidoreductase (NQO-1), phase II enzymes of drug metabolism and heat shock proteins (HSP). Both free antioxidants and anti-oxidative enzymes not only protect cells from oxidation and inflammation but they may be able to reverse the chronic oxidative stress. Based on these observations, ozone therapy may also activate Nrf2 via moderate oxidative stress, and suppress NFκB and inflammatory responses. Furthermore, activation of Nrf2 results in protection against neurodegenerative diseases, such as Alzheimer's and Parkinson's diseases. Mild immune responses are induced via other nuclear transcriptional factors, such as nuclear factor of activated T-cells (NFAT) and activated protein-1 (AP-1).Additionally, the effectiveness of ozone therapy in vascular diseases may also be explained by the activation of another nuclear transcriptional factor, hypoxia inducible factor-1α (HIF-1a), which is also induced via moderate oxidative stress. Recently these concepts have become widely accepted. The versatility of ozone in treating vascular and degenerative diseases as well as skin lesions, hernial disc and primary root carious lesions in children is emphasized. Further researches able to elucidate whether the mechanisms of action of ozone therapy involve nuclear transcription factors, such as Nrf2, NFAT, AP-1, and HIF-1α are warranted.


title: Energy-Aware Dynamic VM Consolidation in Cloud Data Centers Using Ant Colony System

abstract: As the scale of a cloud data center becomes larger and larger, the energy consumption of the data center also grows rapidly. Dynamic consolidation of Virtual Machines (VMs) presents a significant opportunity to save energy by turning off unused Physical Machines (PMs) in data centers. In this paper, we present a distributed controller to perform dynamic VM consolidation to improve the resource utilizations of PMs and to reduce their energy consumption. Moreover, we use the ant colony system to find a near-optimal VM placement solution based on the specified objective function. Experimental results on the real workload traces from more than a thousand PlanetLab VMs show that the proposed approach reduces energy consumption and maintains required performance levels in a large-scale data center.


title: A rule-based recommendation system for selection of software development life cycle models

abstract: The selection of a software development life cycle (SDLC) model for a software project is highly dependent upon the characteristics of the software product to be developed. We classified software products according to characteristics that matter for SDLC selection. We surveyed literature to elicit recommendations for SDLC selection. We formalized our findings to present a rule based recommendation system that can be helpful to software developers in selecting the most appropriate SDLC model to be used for the development of a software product. We conducted an initial evaluation of our system. We believe our SDLC recommendation system provides useful hints for selecting an SDLC, and provides a base for validating and refining SDLC recommendation rules.


title: Quantitative Evaluation of DC Microgrids Availability: Effects of System Architecture and Converter Topology Design Choices

abstract: This paper presents a quantitative method to evaluate dc microgrids availability by identifying and calculating minimum cut sets occurrence probability for different microgrid architectures and converter topologies. Hence, it provides planners with an essential tool to evaluate downtime costs and decide technology deployments based on quantitative risk assessments by allowing to compare the effect that converter topologies and microgrid architecture choices have on availability. Conventional architectures with single-input converters and alternative configurations with multiple-input converters (MICs) are considered. Calculations yield that all microgrid configurations except those utilizing center converters achieve similar availability of 6-nines. Three converter topologies are used as representatives of many other circuits. These three benchmark circuits are the boost, the isolated SEPIC (ISEPIC), and the current-source half-bridge. Marginal availability differences are observed for different circuit topology choices, although architectures with MICs are more sensitive to this choice. MICs and, in particular, the ISEPIC, are identified as good compromise options for dc microgrids source interfaces. The analysis also models availability influence of local energy storage, both in batteries and generators' fuel. These models provide a quantitative way of comparing dc microgrids with conventional backup energy systems. Calculations based on widely accepted data in industry supports the analysis.


title: Two plus blue equals green: Grapheme-color synesthesia allows cognitive access to numerical information via color

abstract: Abstract In grapheme-color synesthesia, graphemes (e.g., numbers or letters) evoke color experiences. It is generally reported that the opposite is not true: colors will not generate experiences of graphemes or their associated information. However, recent research has provided evidence that colors can implicitly elicit symbolic representations of associated graphemes. Here, we examine if these representations can be cognitively accessed. Using a mathematical verification task replacing graphemes with color patches, we find that synesthetes can verify such problems with colors as accurately as with graphemes. Doing so, however, takes time: ∼250 ms per color. Moreover, we find minimal reaction time switch-costs for switching between computing with graphemes and colors. This demonstrates that given specific task demands, synesthetes can cognitively access numerical information elicited by physical colors, and they do so as accurately as with graphemes. We discuss these results in the context of possible cognitive strategies used to access the information.


title: Extended free-form deformation: a sculpturing tool for 3D geometric modeling

abstract: Current research efforts focus on providing more efficient and effective design methods for 3D modeling systems. In this paper a new deformation technique is presented. Among other things, arbitrarily shaped bumps can be designed and surfaces can be bent along arbitrarily shaped curves.The purpose of this research is to define a highly interactive and intuitive modeling technique for designers and stylists. A natural way of thinking is to mimic traditional trades, such as sculpturing and moulding.Furthermore, with this deformation technique, the modeling tool paradigm is introduced. The object is deformed with a user-defined deformation tool.This method is an extension of the Free-Form Deformation (FFD) technique proposed by Sederberg and Parry [17].


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title: On the coverage of LPWANs: range evaluation and channel attenuation model for LoRa technology

abstract: In addition to long battery life and low cost, coverage is one of the most critical performance metrics for the low power wide area networks (LPWAN). In this work we study the coverage of the recently developed LoRa LPWAN technology via real-life measurements. The experiments were conducted in the city of Oulu, Finland, using the commercially available equipment. The measurements were executed for cases when a node located on ground (attached on the roof rack of a car) or on water (attached to the radio mast of a boat) reporting their data to a base station. For a node operating in the 868 MHz ISM band using 14 dBm transmit power and the maximum spreading factor, we have observed the maximum communication range of over 15 km on ground and close to 30 km on water. Besides the actual measurements, in the paper we also present a channel attenuation model derived from the measurement data. The model can be used to estimate the path loss in 868 MHz ISM band in an area similar to Oulu, Finland.


title: Detecting code smells in spreadsheet formulas

abstract: Spreadsheets are used extensively in business processes around the world and just like software, spreadsheets are changed throughout their lifetime causing maintainability issues. This paper adapts known code smells to spreadsheet formulas. To that end we present a list of metrics by which we can detect smelly formulas and a visualization technique to highlight these formulas in spreadsheets. We implemented the metrics and visualization technique in a prototype tool to evaluate our approach in two ways. Firstly, we analyze the EUSES spreadsheet corpus, to study the occurrence of the formula smells. Secondly, we analyze ten real life spreadsheets, and interview the spreadsheet owners about the identified smells. The results of these evaluations indicate that formula smells are common and that they can reveal real errors and weaknesses in spreadsheet formulas.


A Novel Time-Varying Spectral Filtering Algorithm for Reconstruction of Motion Artifact Corrupted Heart Rate Signals During Intense Physical Activities Using a Wearable Photoplethysmogram Sensor


title: Multiobjective Optimization for Brokering of Multicloud Service Composition

abstract: The choice of cloud providers whose offers best fit the requirements of a particular application is a complex issue due to the heterogeneity of the services in terms of resources, costs, technology, and service levels that providers ensure. This article investigates the effectiveness of multiobjective genetic algorithms to resolve a multicloud brokering problem. Experimental results provide clear evidence about how such a solution improves the choice made manually by users returning in real time optimal alternatives. It also investigates how the optimality depends on different genetic algorithms and parameters, problem type, and time constraints.