title: Service models and pricing schemes for cloud computing

abstract: Cloud computing is a model for enabling conve- nient, on-demand network access to a shared pool of config- urable computing resources. To ensure large-scale adoption of cloud computing by businesses, several challenges in- cluding security, privacy, trust, performance and other qual- ity attributes need to be addressed. Also, as with other utility services, adequate selection of service models and pricing schemes by service providers plays a pivotal role in market expansion. This paper investigates optimal pricing schemes for cloud services. In particular, this paper analyzes two pricing schemes: subscription and pay-per-use pricing mod- els from a provider's standpoint. Using a theoretical analysis of a simple economic model this paper analyzes the opti- mal choices of providers and discusses implications on con- sumer surplus and social welfare.


title: Lateral epicondylitis of the elbow

abstract: Lateral epicondylitis, also known as 'tennis elbow', is a very common condition affecting mainly middle-aged patients.The pathogenesis remains unknown but there appears to be a combination of local tendon pathology, alteration in pain perception and motor impairment.The diagnosis is usually clinical but some patients may benefit from additional imaging for a specific differential diagnosis.The disease has a self-limiting course of between 12 and 18 months, but in some patients, symptoms can be persistent and refractory to treatment.Most patients are well-managed with non-operative treatment and activity modification. Many surgical techniques have been proposed for patients with refractory symptoms.New non-operative treatment alternatives with promising results have been developed in recent years. Cite this article: Vaquero-Picado A, Barco R, Antuña SA. Lateral epicondylitis of the elbow. EFORT Open Rev 2016;1:391-397. DOI: 10.1302/2058-5241.1.000049.


title: Adherence to Antihypertensive Medications and Cardiovascular Morbidity Among Newly Diagnosed Hypertensive Patients

abstract: BACKGROUND ::: Nonadherence to antihypertensive treatment is a common problem in cardiovascular prevention and may influence prognosis. We explored predictors of adherence to antihypertensive treatment and the association of adherence with acute cardiovascular events. ::: ::: ::: METHODS AND RESULTS ::: Using data obtained from 400 Italian primary care physicians providing information to the Health Search/Thales Database, we selected 18,806 newly diagnosed hypertensive patients >or=35 years of age during the years 2000 to 2001. Subjects included were newly treated for hypertension and initially free of cardiovascular diseases. Patient adherence was subdivided a priori into 3 categories-high (proportion of days covered, >or=80%), intermediate (proportion of days covered, 40% to 79%), and low (proportion of days covered, <or=40%)-and compared with the long-term occurrence of acute cardiovascular events through the use of multivariable models adjusted for demographic factors, comorbidities, and concomitant drug use. At baseline (ie, 6 months after index diagnosis), 8.1%, 40.5%, and 51.4% of patients were classified as having high, intermediate, and low adherence levels, respectively. Multiple drug treatment (odds ratio, 1.62; 95% CI, 1.43 to 1.83), dyslipidemia (odds ratio, 1.52; 95% CI, 1.24 to 1.87), diabetes mellitus (odds ratio, 1.40; 95% CI, 1.15 to 1.71), obesity (odds ratio, 1.50; 95% CI, 1.26 to 1.78), and antihypertensive combination therapy (odds ratio, 1.29; 95% CI, 1.15 to 1.45) were significantly (P<0.001) associated with high adherence to antihypertensive treatment. Compared with their low-adherence counterparts, only high adherers reported a significantly decreased risk of acute cardiovascular events (hazard ratio, 0.62; 95% CI, 0.40 to 0.96; P=0.032). ::: ::: ::: CONCLUSIONS ::: The long-term reduction of acute cardiovascular events associated with high adherence to antihypertensive treatment underscores its importance in assessments of the beneficial effects of evidence-based therapies in the population. An effort focused on early antihypertensive treatment initiation and adherence is likely to provide major benefits.


title: Reflective Informatics Through Family Storytelling: Self-discovering Physical Activity Predictors

abstract: HCI research has increasingly examined how sensing technologies can help people capture and visualize data about their health-related behaviors. Yet, few systems help people reflect more fundamentally on the factors that influence behaviors such as physical activity (PA). To address this research gap, we take a novel approach, examining how such reflections can be stimulated through a medium that generations of families have used for reflection and teaching: storytelling. Through observations and interviews, we studied how 13 families interacted with a low-fidelity prototype, and their attitudes towards this tool. Our prototype used storytelling and interactive prompts to scaffold reflection on factors that impact children's PA. We contribute to HCI research by characterizing how families interacted with a story-driven reflection tool, and how such a tool can encourage critical processes for behavior change. Informed by the Transtheoretical Model, we present design implications for reflective informatics systems.


title: Detecting Group Activities With Multi-Camera Context

abstract: Human group activities detection in multi-camera CCTV surveillance videos is a pressing demand on smart surveillance. Previous works on this topic are mainly based on camera topology inference that is hard to apply to real-world unconstrained surveillance videos. In this paper, we propose a new approach for multi-camera group activities detection. Our approach simultaneously exploits intra-camera and inter-camera contexts without topology inference. Specifically, a discriminative graphical model with hidden variables is developed. The intra-camera and inter-camera contexts are characterized by the structure of hidden variables. By automatically optimizing the structure, the contexts are effectively explored. Furthermore, we propose a new spatiotemporal feature, named vigilant area (VA), to characterize the quantity and appearance of the motion in an area. This feature is effective for group activity representation and is easy to extract from a dynamic and crowded scene. We evaluate the proposed VA feature and discriminative graphical model extensively on two real-world multi-camera surveillance video data sets, including a public corpus consisting of 2.5 h of videos and a 468-h video collection, which, to the best of our knowledge, is the largest video collection ever used in human activity detection. The experimental results demonstrate the effectiveness of our approach.


Attraction - A Global Affinity Measure for Database Vertical Partitioning


title: Multiple classifier architectures and their application to credit risk assessment

abstract: Multiple classifier systems combine several individual classifiers to deliver a final classification decision. In this paper the performance of several multiple classifier systems are evaluated in terms of their ability to correctly classify consumers as good or bad credit risks. Empirical results suggest that some multiple classifier systems deliver significantly better performance than the single best classifier, but many do not. Overall, bagging and boosting outperform other multi-classifier systems, and a new boosting algorithm, Error Trimmed Boosting, outperforms bagging and AdaBoost by a significant margin.


title: Sparse nonnegative matrix approximation : new formulations and algorithms

abstract: We introduce several new formulations for sparse nonnegative matrix approximation. Subsequently, we solve these formulations by developing generic algorithms. Further, to help selecting a particular sparse formulation, we briefly discuss the interpretation of each formulation. Finally, preliminary experiments are presented to illustrate the behavior of our formulations and algorithms.


Integration of CityGML and Air Quality Spatio-Temporal Data Series via OGC SOS


title: Design optimization of 12Slot-10Pole hybrid excitation flux switching synchronous machine with 0.4kg permanent magnet for hybrid electric vehicles

abstract: Hybrid excitation flux switching synchronous machine (HEFSSM) has several attractive features compared to interior permanent magnet synchronous machines (IPMSM) conventionally employed in hybrid electric vehicles (HEV). Among various types of HEFSSM, the machine with both permanent magnet (PM) and field excitation coil (FEC) on the stator has the advantages of robust rotor structure together with variable flux control capabilities that make this machine becoming more attractive to apply for high-speed motor drive systems. This paper presents an investigation into the design possibility and parameter optimization study of 12Slot-10Pole HEFSSM for traction drives in HEV. The research target is to design a machine with reduced amount of rare-earth magnet, a maximum torque of 210Nm, a maximum power of more than 123kW, and a maximum power density of more than 3.5kW/kg. Extensions in speed and torque ranges are chosen for optimization aims. The deterministic design approach is used to treat ten design parameters in an effort to achieve the target performances. As a result, the designed HEFSSM with 0.4kg PM is able to get much higher power density compared to existing IPMSM installed on a commercial SUV-HEV.


title: More holes than cheese. What prevents the delivery of effective, high quality and safe health care in England?

abstract: What prevents the delivery of effective, high quality and safe health care in the National Health Service (NHS) in England? This paper presents 760 challenges which 330 NHS staff reported as preventing the delivery of effective, high quality and safe care. Some problems have been known for over 25 years (staff shortages, finance and patient complexity) but other challenges raise questions about the commitment of the NHS to patient and staff safety. For example, Organisational Culture leading to 'stifling bureaucracy', 'odds stacked against smooth […] working' and Workload resulting in 'firefighting daily' and 'perpetual crisis mode'. The role of Human Factors/Ergonomics professional input (engagement with safety scientists) is discussed in the context of success stories and examples of Human Factors Integration from other safety critical industries (Defence, Nuclear and Rail). Practitioner Summary: 760 challenges to the quality, effectiveness and safety of health care were identified at Human Factors/Ergonomics taster workshops in England. These are used to challenge health care providers to think about a Human Factors Integration (HFI systems) approach for safety, well-being and performance for all people involved in providing and receiving health care.


title: Beam dump window design for the Spallation Neutron Source

abstract: The Spallation Neutron Source accelerator systems will provide a 1 GeV, 1.44 MW proton beam to a liquid mercury target for neutron production. Beam tuning dumps are provided at the end of the linac (the Linac Dump) and in the Ring-to-Target transport line (the Extraction Dump). Thin windows are required to separate the accelerator vacuum from the poor vacuum upstream of the beam dump. There are several challenging engineering issues that have been addressed in the window design. Namely, handling of the high local power density deposited by the stripped electrons from the H-beam accelerated in the linac, and the need for low-exposure removal and replacement of an activated window. The thermal design of the linac dump window is presented, as is the design of a vacuum clamp and mechanism that allows remote removal and replacement of the window.


title: Glass transition behaviors of epoxy-based shape memory polymer containing linear monomer

abstract: As a novel smart material, shape memory polymer possesses the special thermo-mechanical property of shape memory ::: effect. Its shape memory effect is closely related to the glass transitions between the glass state and rubber state induced ::: by temperature changing. It is of engineering and theoretical meaning to investigate and describe the glass transition ::: behaviors of shape memory polymer. In this study the glass transition behaviors of an epoxy-based shape memory ::: polymer containing linear epoxy monomer are investigated using the tests of dynamic mechanical analysis. Results show ::: both glass transition critical temperatures and storage modulus at rubber state of the epoxy-base shape memory polymer ::: decrease as the content of linear monomer in such material increases. However the transition temperature width increases ::: as the linear monomer content increases. A new glass transition model is supposed to describe the glass transition ::: behaviors of epoxy-based shape memory polymer based on the experimental results. Numerical simulations show that ::: the new glass transition model well predicts the glass transition behaviors of the epoxy-based shape memory polymer ::: containing linear epoxy monomer.


title: Deep highway unit network for land cover type classification with GF-3 SAR imagery

abstract: The fully polarized synthetic aperture radar (SAR) is an advanced earth observation system with day and night imaging capability, which can obtain rich information of terrain and has a wide range of applications in environmental protection, urban planning and resource investigation. As the first self-developed C-band multi-polarized SAR image, the acquisition of massive data and operational operation of Chinese SAR remote sensing has entered the era of big data. Under the era of remote sensing large data, however, SAR image interpretation is a great challenge for scientific applications. At present, big data-based intelligent methods such as computer vision technology have achieved great success. Deep learning such as deep highway unit networks has revolutionized the computer vision area. However, due to the characteristics of SAR microwave band imaging and phase coherence processing, SAR images are very different from ordinary optical images in terms of band, projection direction, data composition and so on. Therefore, deep learning can not be directly used for quad-pol SAR image classification. In this paper, deep learning is applied to land cover type classification with GF-3 quad-pol SAR imagery. A deep highway unit network is employed to automatically extract a hierarchic feature representation from the data, based on which the land cover type classification can be conducted. Our classification model is trained on limited training data from forest resource inventory and planning data, and tested on a Radarsat-2 quad-pol images, which is the image of the same area acquired at different times. We also employ the machine learning such as SVM, Random Forest on the same samples for comparison. The deep highway unit network trained by the GF-3 images, which can reduce speckle, fully excavate the regularity of SAR images in time and space.


title: A 2-MHZ, process and voltage compensated clock oscillator for biomedical implantable SoC in 0.18-μm CMOS

abstract: We present the design and performance of a power and area efficient, process and voltage compensated, 2-MHz clock oscillator for state-of-the art wireless biomedical implantable systems-on-chip. The design presented in this paper is based on a fully differential three-stage ring oscillator with replica feedback bias, a novel process detection circuit, and a novel differential comparator to save power and area. The design of the comparator ensures the rail-to-rail swing and further improves power-supply-rejection-ratio (PSRR). The process corner sensing scheme is based on the leakage current of the device which generates control voltage for the replica feedback bias circuit. A total of 66 chip samples were collected from various locations on multiple full wafers and average variation of ±2.81% with process corner was measured at room temperature. The variation in clock frequency with supply was 0.11% for the voltage range of 1.9V-3V. The design of oscillator is intended for the RF powering scheme and it occupies 0.018 μm2 in 0.18-μm CMOS. The clock oscillator consumes 12μW from a 1.8 V regulated supply.


title: Adaptive Rao–Blackwellized Particle Filter and Its Evaluation for Tracking in Surveillance

abstract: Particle filters can become quite inefficient when being applied to a high-dimensional state space since a prohibitively large number of samples may be required to approximate the underlying density functions with desired accuracy. In this paper, by proposing an adaptive Rao-Blackwellized particle filter for tracking in surveillance, we show how to exploit the analytical relationship among state variables to improve the efficiency and accuracy of a regular particle filter. Essentially, the distributions of the linear variables are updated analytically using a Kalman filter which is associated with each particle in a particle filtering framework. Experiments and detailed performance analysis using both simulated data and real video sequences reveal that the proposed method results in more accurate tracking than a regular particle filter


title: Prognosis Essay Scoring and Article Relevancy Using Multi-Text Features and Machine Learning

abstract: This study develops a model for essay scoring and article relevancy. Essay scoring is a costly process when we consider the time spent by an evaluator. It may lead to inequalities of the effort by various evaluators to apply the same evaluation criteria. Bibliometric research uses the evaluation criteria to find relevancy of articles instead. Researchers mostly face relevancy issues while searching articles. Therefore, they classify the articles manually. However, manual classification is burdensome due to time needed for evaluation. The proposed model performs automatic essay evaluation using multi-text features and ensemble machine learning. The proposed method is implemented in two data sets: a Kaggle short answer data set for essay scoring that includes four ranges of disciplines (Science, Biology, English, and English language Arts), and a bibliometric data set having IoT (Internet of Things) and non-IoT classes. The efficacy of the model is measured against the Tandalla and AutoP approach using Cohen’s kappa. The model achieves kappa values of 0.80 and 0.83 for the first and second data sets, respectively. Kappa values show that the proposed model has better performance than those of earlier approaches.


title: Specific cerebellar reductions in children with chromosome 22q11.2 deletion syndrome

abstract: Abstract Children with chromosome 22q11.2 deletion syndrome commonly are found to have morphological brain changes, cognitive impairments, and elevated rates of psychopathology. One of the most commonly and consistently reported brain changes is reduced cerebellar volume. Here, we demonstrate that, in addition to the global cerebellum reductions previously reported, volumetric reductions of the anterior lobule and the vermal region of the neo-cerebellum in the mid-sagittal plane best differentiate children with the deletion from typically developing children. These results suggest that the morphological changes of specific portions of the cerebellum may be an important underlying substrate of cognitive impairments and increased incidence of psychopathology in this group.


NooShare : A decentralized ledger of shared computational resources


Lensless Imaging and Sensing Aydogan Ozcan


title: Current status of virtual reality exposure therapy in anxiety disorders: editorial review.

abstract: In recent years, a considerable number of publications have appeared on different aspects of virtual reality exposure therapy in the treatment of anxiety disorders. The purpose of the present article is to review the evidence that has emerged to support the potential of this therapy in the treatment of anxiety disorders. ::: Case histories and open comparative studies, as well as randomized controlled studies, have been published on the use of virtual reality exposure therapy in the treatment of anxiety disorders. Most studies in the field have been on specific phobias, in particular on fear of flying, acrophobia, fear of driving, claustrophobia and fear of spiders. In addition, several studies have been published on the use of virtual reality exposure therapy for social anxiety disorders, posttraumatic stress disorder and panic disorder with or without agoraphobia. ::: Recent studies provide evidence that virtual reality exposure therapy is a promising tool for the treatment of several anxiety disorders.


title: Design of a low parasitic inductance SiC power module with double-sided cooling

abstract: In this paper, a low parasitic inductance SiC power module with double-sided cooling is designed and compared with a baseline double-sided cooled module. With the unique 3D layout utilizing vertical interconnection, the power loop inductance is effectively reduced without sacrificing the thermal performance. Both simulations and experiments are carried out to validate the design. Q3D simulation results show a power loop inductance of 1.63 nH, verified by the experiment, indicating more than 60% reduction of power loop inductance compared with the baseline module. With 0Ω external gate resistance turn-off at 600V, the voltage overshoot is less than 9% of the bus voltage at a load of 44.6A.


title: Use of antennas as sensors to discover signals to form mobile broadband networks

abstract: Antennas are not generally thought of as a sensor in a classic sense. An antenna used in combination with a radio form a technology capable of being classified as a sensor; specifically, when the system is used in the process of discovery or other similar systems in an uncertain task environment. In this research, antennas are used with radios and other mechanical devices and software to create nodes which can autonomously form mobile broadband networks. Each node of the network must sense and discover other nodes via a process of search, identification and connection. As each of the nodes connects to others, the end result will be a broadband network of potentially many nodes covering a great physical distance. This self-configuring network can be used in defense, homeland security, emergency response, agriculture and many other specific areas where communication is of paramount importance.


title: A Critical Analysis on the Security Concerns of Internet of Things (IoT)

abstract: Internet of Things (IoT) has been a major research topic for almost a decade now, where physical objects would be interconnected as a result of convergence of various existing technologies. IoT is rapidly developing; however there are uncertainties about its se- curity and privacy which could affect its sustainable development. This paper analyzes the security issues and challenges and provides a well defined security architecture as a confidentiality of the user's privacy and security which could result in its wider adoption by masses.


title: Sentiment analysis on Twitter through topic-based lexicon expansion

abstract: Supervised learning approaches are domain-dependent and it is costly to obtain labeled training data from different domains. Lexicon-based approaches enjoy stable performance across domains, but often cannot capture domain-dependent features. It is also hard for lexicon-based classifiers to identify the polarities of abbreviations and misspellings, which are common in short informal social text but usually not found in general sentiment lexicons. We propose to overcome this limitation by expanding a general lexicon with domain-dependent opinion words as well as abbreviations and informal opinion expressions. The expanded terms are automatically selected based on their mutual information with emoticons. As there is an abundant amount of emoticon-bearing tweets on Twitter, our approach provides a way to do domain-dependent sentiment analysis without the cost of data annotation. We show that our technique leads to statistically significant improvements in classification accuracies across 56 topics with a state-of-the-art lexicon-based classifier. We also present the expanded terms, and show the most representative opinion expressions obtained from co-occurrence with emoticons.


title: Unsupervised recognition of interleaved activities of daily living through ontological and probabilistic reasoning

abstract: Recognition of activities of daily living (ADLs) is an enabling technology for several ubiquitous computing applications. In this field, most activity recognition systems rely on supervised learning methods to extract activity models from labeled datasets. An inherent problem of that approach consists in the acquisition of comprehensive activity datasets, which is expensive and may violate individuals' privacy. The problem is particularly challenging when focusing on complex ADLs, which are characterized by large intra- and inter-personal variability of execution. In this paper, we propose an unsupervised method to recognize complex ADLs exploiting the semantics of activities, context data, and sensing devices. Through ontological reasoning, we derive semantic correlations among activities and sensor events. By matching observed sensor events with semantic correlations, a statistical reasoner formulates initial hypotheses about the occurred activities. Those hypotheses are refined through probabilistic reasoning, exploiting semantic constraints derived from the ontology. Extensive experiments with real-world datasets show that the accuracy of our unsupervised method is comparable to the one of state of the art supervised approaches.


title: Abstract interpretation: a unified lattice model for static analysis of programs by construction or approximation of fixpoints

abstract: A program denotes computations in some universe of objects. Abstract interpretation of programs consists in using that denotation to describe computations in another universe of abstract objects, so that the results of abstract execution give some information on the actual computations. An intuitive example (which we borrow from Sintzoff [72]) is the rule of signs. The text -1515 * 17 may be understood to denote computations on the abstract universe {(+), (-), (±)} where the semantics of arithmetic operators is defined by the rule of signs. The abstract execution -1515 * 17 → -(+) * (+) → (-) * (+) → (-), proves that -1515 * 17 is a negative number. Abstract interpretation is concerned by a particular underlying structure of the usual universe of computations (the sign, in our example). It gives a summary of some facets of the actual executions of a program. In general this summary is simple to obtain but inaccurate (e.g. -1515 + 17 → -(+) + (+) → (-) + (+) → (±)). Despite its fundamentally incomplete results abstract interpretation allows the programmer or the compiler to answer questions which do not need full knowledge of program executions or which tolerate an imprecise answer, (e.g. partial correctness proofs of programs ignoring the termination problems, type checking, program optimizations which are not carried in the absence of certainty about their feasibility, …).


title: Asphyxia in newborn--risk, prevention and identification of a hypoxic event.

abstract: The aim of this review is to present the summarisation of the knowledge about biochemical and clinical changes that occur under the influence of asphyxia as well as about the treatment. Results of experimental works in animal models bring us the explanation about many postasphyxiated changes and help us to understand the pathophysiological changes and consequences of asphyxia. The authors present the most prominent consequences of neonatal asphyxia in clinical and experimental conditions. Asphyxia significantly contributes to neonatal morbidity and mortality and determines the prognosis of future development. New insights into the pathophysiology of birth asphyxia provide the opportunity how to prevent permanent damage by the activation of the fundamental molecular processes. Care of newborn asphyxia requires teamwork. Prognosis of the asphyxiated newborn is still dependent on developmental stage (gestational age), the length and intensity of asphyxia, on the level of medical care.


title: Use of active video gaming in children with neuromotor dysfunction: a systematic review

abstract: AIM ::: To examine current evidence on use of active video gaming (AVG) to improve motor function in children with movement disorders including cerebral palsy, developmental coordination disorder, and Down syndrome. ::: ::: ::: METHOD ::: Scopus, MEDLINE, Cochrane Library, EMBASE, and CINAHL were searched. Included papers studied the use of AVG for improving movement-related outcomes in these populations. Parameters studied included health condition, strength of evidence, AVG delivery methods, capacity for individualizing play, outcomes addressed, effectiveness for achieving outcomes, and challenges/limitations. ::: ::: ::: RESULTS ::: The 20 extracted articles varied in quality. Studies involved children with six different conditions using AVG in clinical, home, or school settings for 49 different motor outcomes. Dosage varied in frequency and duration. Choice of games played and difficulty level were therapist determined (n=6) or child controlled (n=14). The most common study limitations were small sample sizes and difficulty individualizing treatment. All articles showed improvement in outcomes with AVG, although differences were not consistently significant compared with conventional therapy. ::: ::: ::: INTERPRETATION ::: Heterogeneity of measurement tools and target outcomes prevented meta-analysis or development of formal recommendations. However, AVG is feasible and shows potential for improving outcomes in this population. Additional investigations of dosing variables, utility as a home supplement to clinical care, and outcomes with larger sample sizes are merited.


title: Security and Privacy Issues of Fog Computing: A Survey

abstract: Fog computing is a promising computing paradigm that extends cloud computing to the edge of networks. Similar to cloud computing but with distinct characteristics, fog computing faces new security and privacy challenges besides those inherited from cloud computing. In this paper, we have surveyed these challenges and corresponding solutions in a brief manner.


title: Rebalancing the rebalancers: optimally routing vehicles and drivers in mobility-on-demand systems

abstract: In this paper we study rebalancing strategies for a mobility-on-demand urban transportation system blending customer-driven vehicles with a taxi service. In our system, a customer arrives at one of many designated stations and is transported to any other designated station, either by driving themselves, or by being driven by an employed driver. When some origins and destinations are more popular than others, vehicles will become unbalanced, accumulating at some stations and becoming depleted at others. This problem is addressed by employing rebalancing drivers to drive vehicles from the popular destinations to the unpopular destinations. However, with this approach the rebalancing drivers themselves become unbalanced, and we need to “rebalance the rebalancers” by letting them travel back to the popular destinations with a customer. In this paper we study how to optimally route the rebalancing vehicles and drivers so that the number of waiting customers remains bounded while minimizing the number of rebalancing vehicles traveling in the network and the number of rebalancing drivers needed; surprisingly, these two objectives are aligned, and one can find the optimal rebalancing strategy by solving two decoupled linear programs. We determine the minimum number of drivers and minimum number of vehicles needed to ensure stability in the system. Our simulations suggest that, in Euclidean network topologies, one would need between 1/3 and 1/4 as many drivers as vehicles.


title: Smartphone based automatic abnormality detection of kidney in ultrasound images

abstract: Telesonography suffers from inherent limitations due to the need of all time availability of experts in cloud and data connectivity to the device. Computer-aided diagnosis (CAD) used for automatic detection of abnormalities without manual intervention can overcome these limitations. Commercially available ultrasound scanners restrict the installation of new softwares and hence CAD algorithms cannot be integrated into the existing ultrasound scanners. There is a need for an external computing device, which can acquire image data from ultrasound scanners, perform CAD and generate result. Smart-phones are now widely used in personalized healthcare due to its ubiquitous computing capability. Smartphones with embedded CAD can be used as a computing device for automated diagnosis. In this paper, we have developed an Application (APP) for a smartphone to automatically diagnose the kidney in the ultrasound image. With the developed APP, the smartphone can acquire images from any ultrasound scanner, process it and give the diagnostic result. Automatic abnormality detection of kidney is based on Viola Jones algorithm, texture feature extraction followed by SVM classifier. Stones and cysts are the abnormalities detected using the algorithm. The developed APP resulted with an accuracy of 90.91% in detecting the abnormalities.


title: Convolutional Neural Networks-based continuous speech recognition using raw speech signal

abstract: State-of-the-art automatic speech recognition systems model the relationship between acoustic speech signal and phone classes in two stages, namely, extraction of spectral-based features based on prior knowledge followed by training of acoustic model, typically an artificial neural network (ANN). In our recent work, it was shown that Convolutional Neural Networks (CNNs) can model phone classes from raw acoustic speech signal, reaching performance on par with other existing feature-based approaches. This paper extends the CNN-based approach to large vocabulary speech recognition task. More precisely, we compare the CNN-based approach against the conventional ANN-based approach on Wall Street Journal corpus. Our studies show that the CNN-based approach achieves better performance than the conventional ANN-based approach with as many parameters. We also show that the features learned from raw speech by the CNN-based approach could generalize across different databases.


title: Eudaimonic well-being, inequality, and health: Recent findings and future directions

abstract: The theoretical and philosophical underpinnings of a eudaimonic model of well-being are examined, and its empirical translation into distinct dimensions of well-being is described. Empirical findings have documented aging declines in eudaimonic well-being, but there is considerable variability within age groups. Among older adults who remain purposefully engaged, health benefits (reduced morbidity, extended longevity) have been documented. Eudaimonic well-being also appears to offer a protective buffer against increased health risks among the educationally disadvantaged. Neural and genetic mechanisms that may underlie eudaimonic influences on health are briefly noted, and interventions designed to promote eudaimonic well-being are sketched. Needed future research directions include addressing problems of unjust societies wherein greed among privileged elites may be a force compromising the eudaimonic well-being of those less privileged. Alternatively, and more positive in focus, is the need to better understand the role of the arts, broadly defined, in promoting eudaimonic well-being across all segments of society.


title: Workflow Verification: Finding Control-Flow Errors Using Petri-Net-Based Techniques

abstract: Workflow management systems facilitate the everyday operation of business processes by taking care of the logistic control of work. In contrast to traditional information systems, they attempt to support frequent changes of the workflows at hand. Therefore, the need for analysis methods to verify the correctness of workflows is becoming more prominent. In this chapter we present a method based on Petri nets. This analysis method exploits the structure of the Petri net to find potential errors in the design of the workflow. Moreover, the analysis method allows for the compositional verification of workflows.


title: Measure it? Manage it? Ignore it? software practitioners and technical debt

abstract: The technical debt metaphor is widely used to encapsulate numerous software quality problems. The metaphor is attractive to practitioners as it communicates to both technical and nontechnical audiences that if quality problems are not addressed, things may get worse. However, it is unclear whether there are practices that move this metaphor beyond a mere communication mechanism. Existing studies of technical debt have largely focused on code metrics and small surveys of developers. In this paper, we report on our survey of 1,831 participants, primarily software engineers and architects working in long-lived, software-intensive projects from three large organizations, and follow-up interviews of seven software engineers. We analyzed our data using both nonparametric statistics and qualitative text analysis. We found that architectural decisions are the most important source of technical debt. Furthermore, while respondents believe the metaphor is itself important for communication, existing tools are not currently helpful in managing the details. We use our results to motivate a technical debt timeline to focus management and tooling approaches.


title: Single image super resolution in spatial and wavelet domain

abstract: Recently single image super resolution is very important research area to generate high resolution image from given low resolution image. Algorithms of single image resolution are mainly based on wavelet domain and spatial domain. Filters support to model the regularity of natural images is exploited in wavelet domain while edges of images get sharp during up sampling in spatial domain. Here single image super resolution algorithm is presented which based on both spatial and wavelet domain and take the advantage of both. Algorithm is iterative and use back projection to minimize reconstruction error. Wavelet based denoising method is also introduced to remove noise.


title: Tell me a story--a conceptual exploration of storytelling in healthcare education.

abstract: The importance of storytelling as the foundation of human experiences cannot be overestimated. The oral traditions focus upon educating and transmitting knowledge and skills and also evolved into one of the earliest methods of communicating scientific discoveries and developments. A wide ranging search of the storytelling, education and health-related literature encompassing the years 1975-2007 was performed. Evidence from disparate elements of education and healthcare were used to inform an exploration of storytelling. This conceptual paper explores the principles of storytelling, evaluates the use of storytelling techniques in education in general, acknowledges the role of storytelling in healthcare delivery, identifies some of the skills learned and benefits derived from storytelling, and speculates upon the use of storytelling strategies in nurse education. Such stories have, until recently been harvested from the experiences of students and of educators, however, there is a growing realization that patients and service users are a rich source of healthcare-related stories that can affect, change and benefit clinical practice. The use of technology such as the Internet discussion boards or digitally-facilitated storytelling has an evolving role in ensuring that patient-generated and experiential stories have a future within nurse education.


title: Disseminating Multilayer Multimedia Content Over Challenged Networks

abstract: Mobile devices are getting increasingly popular all over the world. Mobile users in developing countries however rarely have Internet access which puts them at economic and social disadvantages compared to their counterparts in developed countries. We propose mBridge: A distributed system to disseminate multimedia content to mobile users with intermittent Internet access and opportunistic ad hoc connectivity. By disseminating various multimedia content such as news reports notification messages targeted advertisements movie trailers and TV shows mBridge aims to eliminate the digital divide. We formulate an optimization problem to compute personalized distribution plans for individual mobile users to maximize the overall user experience under various resource constraints. Our formulation jointly considers the characteristics of multimedia content mobile users and intermittent networks. We present an efficient distribution planning algorithm to solve our problem and we develop several online heuristics to adapt to the system and network dynamics. We implement a prototype system and demonstrate that our algorithm outperforms the existing algorithms by up to 206% 472% and 188% in terms of user experience disk efficiency and energy efficiency respectively. In addition we conduct trace-driven simulations to rigorously evaluate the proposed system in different environments and for large-scale deployments. Our simulation results demonstrate that the proposed algorithm substantially outperforms the closest ones in the literature in all performance measures. We believe that mBridge can allow multimedia content providers to reach out to more mobile users and mobile users to access multimedia content without always-on Internet access.


Avatar2: A Multi-Target Orchestration Platform


title: Quantum Computation and Quantum Information

abstract: Preface Acknowledgement Nomenclature and notation Part I. Fundamental Concepts: 1. Introduction and overview 2. Introduction to quantum mechanics 3. Introduction to computer science Part II. Quantum Computation: 4. Quantum circuits 5. The quantum Fourier transform and its applications 6. Quantum search algorithms 7. Quantum computers: physical realisation Part III. Quantum Information: 8. Quantum noise, open quantum systems, and quantum operations 9. Distance measurement for quantum information 10. Quantum error-correction 11. Entropy and information 12. Quantum information theory Appendix A. Notes on basic probability theory Appendix B. Group theory Appendix C. Approximating quantum gates: the Solvay-Kitaev theorem Appendix D. Number theory Appendix E. Public-key cryptography and the RSA cryptosystem Appendix F. Proof of Lieb's theorem References Index.


title: Multi-chamber arrester study at tropical area for 20 kV lines lightning protection system

abstract: This paper issues on 20kV overhead lines related to lightning strike to the medium voltage line and the consequences of lightning overvoltage at the tropical Area like Serpong area near Jakarta. Few standard lightning protection solutions are discussed in terms of expected performance and limitations. Implementation of Multi-Chamber Arrester (MCA) and the research results after 1 year of operation in the tropical area with high lightning density (>15 flashes per km2 per year) in Jakarta region are presented.


title: Long-Term Temporal Convolutions for Action Recognition

abstract: Typical human actions last several seconds and exhibit characteristic spatio-temporal structure. Recent methods attempt to capture this structure and learn action representations with convolutional neural networks. Such representations, however, are typically learned at the level of a few video frames failing to model actions at their full temporal extent. In this work we learn video representations using neural networks with long-term temporal convolutions (LTC). We demonstrate that LTC-CNN models with increased temporal extents improve the accuracy of action recognition. We also study the impact of different low-level representations, such as raw values of video pixels and optical flow vector fields and demonstrate the importance of high-quality optical flow estimation for learning accurate action models. We report state-of-the-art results on two challenging benchmarks for human action recognition UCF101 (92.7%) and HMDB51 (67.2%).


title: Approaches to uncertainty visualization

abstract: Visualized data often have dubious origins and quality. Different forms of uncertainty and errors are also introduced as the data are derived, transformed, interpolated, and finally rendered. In the absence of integrated presentation of data and uncertainty, the analysis of the visualization is incomplete at best and often leads to inaccurate or incorrect conclusions. This paper surveys techniques for presenting data together with uncertainty. These uncertainty visualization techniques present data in such a manner that users are made aware of the locations and degree of uncertainties in their data so as to make more informed analyses and decisions. The techniques include adding glyphs, adding geometry, modifying geometry, modifying attributes, animation, sonification, and psycho-visual approaches. We present our results in uncertainty visualization for environmental visualization, surface interpolation, global illumination with radiosity, flow visualization, and figure animation. We also present a classification of the possibilities in uncertainty visualization, and locate our contributions within this classification.


title: Combining Data Mining and Machine Learning for Effective User Profiling

abstract: This paper describes the automatic design of methods for detecting fraudulent behavior. Much of the design is accomplished using a series of machine learning methods. In particular, we combine data mining and constructive induction with more standard machine learning techniques to design methods for detecting fraudulent usage of cellular telephones based on profiling customer behavior. Specifically, we use a rule-learning program to uncover indicators of fraudulent behavior from a large database of cellular calls. These indicators are used to create profilers, which then serve as features to a system that combines evidence from multiple profilers to generate high-confidence alarms. Experiments indicate that this automatic approach performs nearly as well as the best hand-tuned methods for detecting fraud.


title: Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles

abstract: We propose a novel unsupervised learning approach to build features suitable for object detection and classification. The features are pre-trained on a large dataset without human annotation and later transferred via fine-tuning on a different, smaller and labeled dataset. The pre-training consists of solving jigsaw puzzles of natural images. To facilitate the transfer of features to other tasks, we introduce the context-free network (CFN), a siamese-ennead convolutional neural network. The features correspond to the columns of the CFN and they process image tiles independently (i.e., free of context). The later layers of the CFN then use the features to identify their geometric arrangement. Our experimental evaluations show that the learned features capture semantically relevant content. We pre-train the CFN on the training set of the ILSVRC2012 dataset and transfer the features on the combined training and validation set of Pascal VOC 2007 for object detection (via fast RCNN) and classification. These features outperform all current unsupervised features with \(51.8\,\%\) for detection and \(68.6\,\%\) for classification, and reduce the gap with supervised learning (\(56.5\,\%\) and \(78.2\,\%\) respectively).


title: Unobservable communication over fully untrusted infrastructure

abstract: Keeping communication private has become increasingly important in an era of mass surveillance and state-sponsored attacks. While hiding the contents of a conversation has well-known solutions, hiding the associated metadata (participants, duration, etc.) remains a challenge, especially if one cannot trust ISPs or proxy servers. This paper describes a communication system called Pung that provably hides all content and metadata while withstanding global adversaries. Pung is a key-value store where clients deposit and retrieve messages without anyone-- including Pung's servers--learning of the existence of a conversation. Pung is based on private information retrieval, which we make more practical for our setting with new techniques. These include a private multiretrieval scheme, an application of the power of two choices, and batch codes. These extensions allow Pung to handle 103× more users than prior systems with a similar threat model.


title: A Deeply-Initialized Coarse-to-fine Ensemble of Regression Trees for Face Alignment

abstract: In this paper we present DCFE, a real-time facial landmark regression method based on a coarse-to-fine Ensemble of Regression Trees (ERT). We use a simple Convolutional Neural Network (CNN) to generate probability maps of landmarks location. These are further refined with the ERT regressor, which is initialized by fitting a 3D face model to the landmark maps. The coarse-to-fine structure of the ERT lets us address the combinatorial explosion of parts deformation. With the 3D model we also tackle other key problems such as robust regressor initialization, self occlusions, and simultaneous frontal and profile face analysis. In the experiments DCFE achieves the best reported result in AFLW, COFW, and 300 W private and common public data sets.


title: Towards quality gates in continuous delivery and deployment

abstract: Quality gates, steps required to ensure the reliability of code changes, are supposed to increase the confidence stakeholders have in a release. In today's fast paced environments, we have less time to perform the necessary precautions to minimize the risk of a faulty release. This leads to an inherent trade-off between risk of lower release quality and time to market. We provide a model for this trade-off of release “confidence” and “velocity” that led to the formulation of 4 categories (cautious, balanced, problematic, madness), in which companies can be classified in. We showcase real examples of these categories as case studies based on previous empirical studies. We close by presenting possible transitions between categories that guide future research.


title: A moving target detection algorithm based on BING objectness and background estimation

abstract: This paper deals with an intelligent image processing method for the video surveillance systems. We propose a fast and robust algorithm for moving target detection which combines Binarized Normed Gradients (BING) Objectness and background estimation. A real-time surveillance system needs to detect moving objects robustly against noises and environment. So the proposed method uses a simple background estimation model with a sensitivity parameter to extract a set of rough moving foreground in image. We also use objectness detection within the foreground set to estimate another set of candidate object windows. Then the target (pedestrians/vehicles) region by the intersection of areas derives from the former two steps. To track moving objects fast, the proposed method predicts the velocity and the direction of the groups formed by moving objects. Besides, time cost is reduced by the decrease of estimation regions. Experiments on the outdoor datasets show that the combined method can not only achieve a high detection ratio (DR) but also decrease false alarm ratio (FAR), as well as time cost. Finally, the experiments show that the proposed method has the robustness against the environmental influences and the speed, which are suitable for the real-time surveillance system.


title: On the Security of Supersingular Isogeny Cryptosystems

abstract: We study cryptosystems based on supersingular isogenies. This is an active area of research in post-quantum cryptography. Our first contribution is to give a very powerful active attack on the supersingular isogeny encryption scheme. This attack can only be prevented by using a (relatively expensive) countermeasure. Our second contribution is to show that the security of all schemes of this type depends on the difficulty of computing the endomorphism ring of a supersingular elliptic curve. This result gives significant insight into the difficulty of the isogeny problem that underlies the security of these schemes. Our third contribution is to give a reduction that uses partial knowledge of shared keys to determine an entire shared key. This can be used to retrieve the secret key, given information leaked from a side-channel attack on the key exchange protocol. A corollary of this work is the first bit security result for the supersingular isogeny key exchange: Computing any component of the j-invariant is as hard as computing the whole j-invariant.


title: High-performance neuroprosthetic control by an individual with tetraplegia

abstract: Summary Background Paralysis or amputation of an arm results in the loss of the ability to orient the hand and grasp, manipulate, and carry objects, functions that are essential for activities of daily living. Brain–machine interfaces could provide a solution to restoring many of these lost functions. We therefore tested whether an individual with tetraplegia could rapidly achieve neurological control of a high-performance prosthetic limb using this type of an interface. Methods We implanted two 96-channel intracortical microelectrodes in the motor cortex of a 52-year-old individual with tetraplegia. Brain–machine-interface training was done for 13 weeks with the goal of controlling an anthropomorphic prosthetic limb with seven degrees of freedom (three-dimensional translation, three-dimensional orientation, one-dimensional grasping). The participant's ability to control the prosthetic limb was assessed with clinical measures of upper limb function. This study is registered with ClinicalTrials.gov, NCT01364480. Findings The participant was able to move the prosthetic limb freely in the three-dimensional workspace on the second day of training. After 13 weeks, robust seven-dimensional movements were performed routinely. Mean success rate on target-based reaching tasks was 91·6% (SD 4·4) versus median chance level 6·2% (95% CI 2·0–15·3). Improvements were seen in completion time (decreased from a mean of 148 s [SD 60] to 112 s [6]) and path efficiency (increased from 0·30 [0·04] to 0·38 [0·02]). The participant was also able to use the prosthetic limb to do skilful and coordinated reach and grasp movements that resulted in clinically significant gains in tests of upper limb function. No adverse events were reported. Interpretation With continued development of neuroprosthetic limbs, individuals with long-term paralysis could recover the natural and intuitive command signals for hand placement, orientation, and reaching, allowing them to perform activities of daily living. Funding Defense Advanced Research Projects Agency, National Institutes of Health, Department of Veterans Affairs, and UPMC Rehabilitation Institute.


Impact Of Blockchain Technology In Enhancing Customer Loyalty Programs In Airline Business


Graph Clustering Via Intra-Cluster Density Maximization


title: Size and burden of social phobia in Europe

abstract: This paper provides a critical review of the prevalence of social phobia in European countries, a description of associated disability and burden and of clinical correlates and risk factors associated with social phobia. On the basis of a comprehensive literature search we identified 21 community studies and two primary care studies. The median lifetime and 12-month prevalence rates of social phobia in community samples referring to DSM-III-R and DSM-IV criteria were 6.65% and 2.0%, respectively. Younger individuals showed the highest rates, and women were more frequently affected than men. Social phobia was shown to be a persistent condition with a remarkably high degree of comorbid conditions, associated impairment and disability. Research deficits lie in a lack of data for most EU countries and in a lack of studies in children and the elderly. No data are available addressing met and unmet needs for intervention and costs, and data for vulnerability and risk factors of malignant course are scarce. D 2005 Elsevier B.V. and ECNP. All rights reserved.


title: Shape Reconstruction from Raw Point Clouds using Depth Carving

abstract: Shape reconstruction from raw point sets is a hot research topic. Point sets are increasingly available as primary input source, since low-cost acquisition methods are largely accessible nowadays, and these sets are more noisy than used to be. Standard reconstruction methods rely on normals or signed distance functions, and thus many methods aim at estimating these features. Human vision can however easily discern between the inside and the outside of a dense cloud even without the support of fancy measures. We propose, here, a perceptual method for estimating an indicator function for the shape, inspired from image-based methods. The resulting function nicely approximates the shape, is robust to noise, and can be used for direct isosurface extraction or as an input for other accurate reconstruction methods.


title: Me, Myself and My Killfie: Characterizing and Preventing Selfie Deaths

abstract: Over the past couple of years, clicking and posting selfies has become a popular trend. However, since March 2014, 127 people have died and many have been injured while trying to click a selfie. Researchers have studied selfies for understanding the psychology of the authors, and understanding its effect on social media platforms. In this work, we perform a comprehensive analysis of the selfie-related casualties and infer various reasons behind these deaths. We use inferences from incidents and from our understanding of the features, we create a system to make people more aware of the dangerous situations in which these selfies are taken. We use a combination of text-based, image-based and location-based features to classify a particular selfie as dangerous or not. Our method ran on 3,155 annotated selfies collected on Twitter gave 73% accuracy. Individually the image-based features were the most informative for the prediction task. The combination of image-based and location-based features resulted in the best accuracy. We have made our code and dataset available at this http URL.


title: Adaptive PID Control Strategy Based on RBF Neural Network Identification

abstract: Radial basis function (RBF) neural network (NN) is powerful computational tools that have been used extensively in the areas of pattern recognition, systems modeling and identification. This paper proposes an adaptive PID control method based on RBF neural network identification. This approach can on-line identify the controlled plant with the RBF neural network identifier and the weights of the adaptive PID controller are adjusted timely based-on the identification of the plant and self-learning capability of RBFNN. Simulation result shows that the proposed controller has the adaptability, strong robustness and satisfactory control performance in the nonlinear and time varying system


title: The Problem of Curcumin and Its Bioavailability: Could Its Gastrointestinal Influence Contribute to Its Overall Health-Enhancing Effects?

abstract: Curcumin, from the spice turmeric, exhibits anti-inflammatory, antioxidant, anticancer, antiviral, and neurotrophic activity and therefore holds promise as a therapeutic agent to prevent and treat several disorders. However, a major barrier to curcumin's clinical efficacy is its poor bioavailability. Efforts have therefore been dedicated to developing curcumin formulations with greater bioavailability and systemic tissue distribution. However, it is proposed in this review that curcumin's potential as a therapeutic agent may not solely rely on its bioavailability, but rather its medicinal benefits may also arise from its positive influence on gastrointestinal health and function. In this review, in vitro, animal, and human studies investigating the effects of curcumin on intestinal microbiota, intestinal permeability, gut inflammation and oxidative stress, anaphylactic response, and bacterial, parasitic, and fungal infections are summarized. It is argued that positive changes in these areas can have wide-ranging influences on both intestinal and extraintestinal diseases, and therefore presents as a possible mechanism behind curcumin's therapeutic efficacy.


title: Male-Centric and Female-Centric Pornography Consumption: Relationship With Sex Life and Attitudes in Young Adults

abstract: ABSTRACTPornography availability has increased in recent years, and while there is plenty of speculation about its effects, empirical investigation about how porn influences our lives has yielded mixed results. Additionally, few studies have addressed the effects of specific pornography characteristics. Past research has shown that male-centric pornography can be related to negative outcomes, while female-centric pornography is related to more positive outcomes, particularly in women. The present survey study examined the relationship between pornography characteristics (male- and female-centric) and the self-reported attitudes and sexual experiences of men and women. Participants in the full sample were 195 men and 310 women who completed an online questionnaire. Although effect sizes were small, women who reported viewing pornography with more female-centric features also reported more positive effects of pornography on sex life and perceptions of the other gender. While men were more likely than women ...


title: Emotion Detection Using Noninvasive Low Cost Sensors

abstract: Emotion recognition from biometrics is relevant to a wide range of application domains, including healthcare. Existing approaches usually adopt multi-electrodes sensors that could be expensive or uncomfortable to be used in real-life situations. In this study, we investigate whether we can reliably recognize high vs. low emotional valence and arousal by relying on noninvasive low cost EEG, EMG, and GSR sensors. We report the results of an empirical study involving 19 subjects. We achieve state-of-the- art classification performance for both valence and arousal even in a cross-subject classification setting, which eliminates the need for individual training and tuning of classification models.


title: Building Adaptive Tutoring Model Using Artificial Neural Networks and Reinforcement Learning

abstract: With the emergence of new technology-supported learning environments (e.g., MOOCs, mobile edu games), efficient and effective tutoring mechanisms remain relevant beyond traditional intelligent tutoring systems. This paper provides an approach to build and adapt a tutoring model by using both artificial neural networks and reinforcement learning. The underlying idea is that tutoring rules can be, firstly, learned by observing human tutors' behavior and, then, adapted, at run-time, by observing how each learner reacts within a learning environment at different states of the learning process. The Zone of Proximal Development has been adopted as the underlying theory to evaluate efficacy and efficiency of the learning experience.


title: A Pneumatic Glove and Immersive Virtual Reality Environment for Hand Rehabilitative Training After Stroke

abstract: While a number of devices have recently been developed to facilitate hand rehabilitation after stroke, most place some restrictions on movement of the digits or arm. Thus, a novel glove was developed which can provide independent extension assistance to each digit while still allowing full arm movement. This pneumatic glove, the PneuGlove, can be used for training grasp-and-release movements either with real objects or with virtual objects in a virtual reality environment. Two groups of stroke survivors, with seven subjects in each group, completed a six-week rehabilitation training protocol, consisting of three 1-h sessions held each week. One group wore the PneuGlove during training, performed both within a novel virtual reality environment and outside of it with physical objects, while the other group completed the same training without the device. Across subjects, significant improvements were observed in the Fugl-Meyer Assessment for the upper extremity ( p<;;0.001), the hand/wrist portion of the Fugl-Meyer Assessment ( p<;;0.001), the Box and Blocks test ( p<;;0.05), and palmar pinch strength ( p<;;0.05). While changes in the two groups were not statistically different, the group using the PneuGlove did show greater mean improvement on each of these measures, such as gains of 3.7 versus 2.4 points on the hand/wrist portion of the Fugl-Meyer Assessment and 14 N versus 5 N in palmar pinch.


title: Adaptive Trajectory Control for Autonomous Helicopters

abstract: For autonomous helicopter flight, it is common to separate the flight control problem into an inner loop that controls attitude and an outer loop that controls the translational trajectory of the helicopter. In previous work, dynamic inversion and neural-network-based adaptation was used to increase performance of the attitude control system and the method of pseudocontrol hedging (PCH) was used to protect the adaptation process from actuator limits and dynamics. Adaptation to uncertainty in the attitude, as well as the translational dynamics, is introduced, thus, minimizing the effects of model error in all six degrees of freedom and leading to more accurate position tracking. The PCH method is used in a novel way that enables adaptation to occur in the outer loop without interacting with the attitude dynamics. A pole-placement approach is used that alleviates timescale separation requirements, allowing the outer-loop bandwidth to be closer to that of the inner loop, thus, increasing position tracking performance. A poor model of the attitude dynamics and a basic kinematics model is shown to be sufficient for accurate position tracking. The theory and implementation of such an approach, with a summary of flight-test results, are described.


title: Tangible Industry 4.0: A Scenario-Based Approach to Learning for the Future of Production

abstract: Abstract Industry is currently undergoing a transformation towards full digitalization and intelligentization of manufacturing processes. Visionary but quite realistic concepts such as the Internet of Things, Industrial Internet, Cloud-based Manufacturing and Smart Manufacturing are drivers of the so called Fourth Industrial Revolution which is commonly referred to as Industry 4.0. Although a common agreement exists on the necessity for technological advancement of production technologies and business models in the sense of Industry 4.0, a major obstacle lies in the perceived complexity and abstractness which partly hinders its quick transformation into industrial practice. To overcome these burdens, we suggest a Scenario-based Industry 4.0 Learning Factory concept that we are currently planning to implement in Austria's first Industry 4.0 Pilot Factory. The concept is built upon a tentative competency model for Industry 4.0 and the use of scenarios for problem-oriented learning of future production engineering.


title: Ontology visualization methods—a survey

abstract: Ontologies, as sets of concepts and their interrelations in a specific domain, have proven to be a useful tool in the areas of digital libraries, the semantic web, and personalized information management. As a result, there is a growing need for effective ontology visualization for design, management and browsing. There exist several ontology visualization methods and also a number of techniques used in other contexts that could be adapted for ontology representation. The purpose of this article is to present these techniques and categorize their characteristics and features in order to assist method selection and promote future research in the area of ontology visualization.


title: The genetics of arsenate tolerance in Yorkshire fog, Holcus lanatus L.

abstract: The genetics of arsenate tolerance in Holcus lanatus is investigated. Three tolerant plants from an abandoned arsenic mine, and three non-tolerant plants and one less tolerant plant (C9) from an uncontaminated site were crossed. Five polycrosses between plants from F1 crosses between mine and non-tolerants were set up. Four polycrosses between tolerant F2 progeny, and four polycrosses between non-tolerant F2 progeny, were established. A polycross involving the progeny of a single tolerant plant allowed to cross at random with a normal population was also established. The results are broadly compatible with a single-gene model for tolerance, with tolerance being dominant. The majority of F2 crosses segregated in to 3:1 ratios, and backcrosses gave 1:1 ratios. The crosses between C9 and non-tolerants gave 1:1 ratios, which suggests that the less tolerant C9 was heterozygous for tolerance. All crosses between non-tolerants gave all non-tolerant offspring. In one cross a major gene for albinism also segregated, and linkage of the tolerance gene to this gene (r.f. = 35%) was demonstrated. A number of families produced progeny ratios incompatible with the simple major gene model. Possible causes of these anomalous crosses are discussed and it is suggested that the tolerance gene may show variable penetrance depending on the genetic background.


title: Homomorphic Lower Digits Removal and Improved FHE Bootstrapping.

abstract: Bootstrapping is a crucial operation in Gentry’s breakthrough work on fully homomorphic encryption (FHE), where a homomorphic encryption scheme evaluates its own decryption algorithm. There has been a couple of implementations of bootstrapping, among which HElib arguably marks the state-of-the-art in terms of throughput, ciphertext/message size ratio and support for large plaintext moduli.


title: AMERICAN ASSOCIATION OF CLINICAL ENDOCRINOLOGISTS AND AMERICAN COLLEGE OF ENDOCRINOLOGY COMPREHENSIVE CLINICAL PRACTICE GUIDELINES FOR MEDICAL CARE OF PATIENTS WITH OBESITY

abstract: OBJECTIVE ::: Development of these guidelines is mandated by the American Association of Clinical Endocrinologists (AACE) Board of Directors and the American College of Endocrinology (ACE) Board of Trustees and adheres to published AACE protocols for the standardized production of clinical practice guidelines (CPGs). ::: ::: ::: METHODS ::: Recommendations are based on diligent review of clinical evidence with transparent incorporation of subjective factors. ::: ::: ::: RESULTS ::: There are 9 broad clinical questions with 123 recommendation numbers that include 160 specific statements (85 [53.1%] strong [Grade A]; 48 [30.0%] intermediate [Grade B], and 11 [6.9%] weak [Grade C], with 16 [10.0%] based on expert opinion [Grade D]) that build a comprehensive medical care plan for obesity. There were 133 (83.1%) statements based on strong (best evidence level [BEL] 1 = 79 [49.4%]) or intermediate (BEL 2 = 54 [33.7%]) levels of scientific substantiation. There were 34 (23.6%) evidence-based recommendation grades (Grades A-C = 144) that were adjusted based on subjective factors. Among the 1,790 reference citations used in this CPG, 524 (29.3%) were based on strong (evidence level [EL] 1), 605 (33.8%) were based on intermediate (EL 2), and 308 (17.2%) were based on weak (EL 3) scientific studies, with 353 (19.7%) based on reviews and opinions (EL 4). ::: ::: ::: CONCLUSION ::: The final recommendations recognize that obesity is a complex, adiposity-based chronic disease, where management targets both weight-related complications and adiposity to improve overall health and quality of life. The detailed evidence-based recommendations allow for nuanced clinical decision-making that addresses real-world medical care of patients with obesity, including screening, diagnosis, evaluation, selection of therapy, treatment goals, and individualization of care. The goal is to facilitate high-quality care of patients with obesity and provide a rational, scientific approach to management that optimizes health outcomes and safety. ::: ::: ::: ABBREVIATIONS ::: A1C = hemoglobin A1c AACE = American Association of Clinical Endocrinologists ACE = American College of Endocrinology ACSM = American College of Sports Medicine ADA = American Diabetes Association ADAPT = Arthritis, Diet, and Activity Promotion Trial ADHD = attention-deficit hyperactivity disorder AHA = American Heart Association AHEAD = Action for Health in Diabetes AHI = apnea-hypopnea index ALT = alanine aminotransferase AMA = American Medical Association ARB = angiotensin receptor blocker ART = assisted reproductive technology AUC = area under the curve BDI = Beck Depression Inventory BED = binge eating disorder BEL = best evidence level BLOOM = Behavioral Modification and Lorcaserin for Overweight and Obesity Management BLOSSOM = Behavioral Modification and Lorcaserin Second Study for Obesity Management BMI = body mass index BP = blood pressure C-SSRS = Columbia Suicidality Severity Rating Scale CAD = coronary artery disease CARDIA = Coronary Artery Risk Development in Young Adults CBT = cognitive behavioral therapy CCO = Consensus Conference on Obesity CHF = congestive heart failure CHO = carbohydrate CI = confidence interval COR-I = Contrave Obesity Research I CPG = clinical practice guideline CV = cardiovascular CVD = cardiovascular disease DASH = Dietary Approaches to Stop Hypertension DBP = diastolic blood pressure DEXA = dual-energy X-ray absorptiometry DPP = Diabetes Prevention Program DSE = diabetes support and education EL = evidence level ED = erectile dysfunction ER = extended release EWL = excess weight loss FDA = Food and Drug Administration FDG = 18F-fluorodeoxyglucose GABA = gamma-aminobutyric acid GERD = gastroesophageal reflux disease GI = gastrointestinal GLP-1 = glucagon-like peptide 1 HADS = Hospital Anxiety and Depression Scale HDL-c = high-density lipoprotein cholesterol HR = hazard ratio HTN = hypertension HUNT = Nord-Trøndelag Health Study ICSI = intracytoplasmic sperm injection IFG = impaired fasting glucose IGT = impaired glucose tolerance ILI = intensive lifestyle intervention IVF = in vitro fertilization LAGB = laparoscopic adjustable gastric banding LDL-c = low-density lipoprotein cholesterol LES = lower esophageal sphincter LSG = laparoscopic sleeve gastrectomy LV = left ventricle LVH = left ventricular hypertrophy LVBG = laparoscopic vertical banded gastroplasty MACE = major adverse cardiovascular events MAOI = monoamine oxidase inhibitor MI = myocardial infarction MNRCT = meta-analysis of non-randomized prospective or case-controlled trials MRI = magnetic resonance imaging MUFA = monounsaturated fatty acid NAFLD = nonalcoholic fatty liver disease NASH = nonalcoholic steatohepatitis NES = night eating syndrome NHANES = National Health and Nutrition Examination Surveys NHLBI = National Heart, Lung, and Blood Institute NHS = Nurses' Health Study NICE = National Institute for Health and Care Excellence OA = osteoarthritis OGTT = oral glucose tolerance test OR = odds ratio OSA = obstructive sleep apnea PHQ-9 = Patient Health Questionnaire PCOS = polycystic ovary syndrome PCP = primary care physician POMC = pro-opiomelanocortin POWER = Practice-Based Opportunities for Weight Reduction PPI = proton pump inhibitor PRIDE = Program to Reduce Incontinence by Diet and Exercise PSA = prostate specific antigen QOL = quality of life RA = receptor agonist RCT = randomized controlled trial ROC = receiver operator characteristic RR = relative risk RYGB = Roux-en-Y gastric bypass SAD = sagittal abdominal diameter SBP = systolic blood pressure SCOUT = Sibutramine Cardiovascular Outcome Trial SG = sleeve gastrectomy SHBG = sex hormonebinding globulin SIEDY = Structured Interview on Erectile Dysfunction SNRI = serotonin-norepinephrine reuptake inhibitors SOS = Swedish Obese Subjects SS = surveillance study SSRI = selective serotonin reuptake inhibitors STORM = Sibutramine Trial on Obesity Reduction and Maintenance TCA = tricyclic antidepressant TONE = Trial of Nonpharmacologic Intervention in the Elderly TOS = The Obesity Society T2DM = type 2 diabetes mellitus UKPDS = United Kingdom Prospective Diabetes Study U.S = United States VAT = visceral adipose tissue VLDL = very low-density lipoprotein WC = waist circumference WHO = World Health Organization WHR = waist-hip ratio WHtR = waist-to-height ratio WMD = weighted mean difference WOMAC = Western Ontario and McMaster Universities osteoarthritis index XENDOS = XEnical in the Prevention of Diabetes in Obese Subjects.


title: An Efficient Method for Lane-Mark Extraction in Complex Conditions

abstract: Lane-mark detection is one of the most important parts in intelligent transportation systems (ITS). In this paper, we propose a lane-mark detection system which can overcome a lot of difficult situations, such as bad weather conditions, shadow effect, or road sign on the road. After the region of interest (ROI) of a road image is determined, we apply the Canny edge detector to investigate boundaries. In order to remove the noise edges, we divide the boundary image into sub-images to calculate local edge-orientation of each block and remove the edge with abnormal orientation. In this step, we produce a table to store the blocks which satisfy the assumption of lane-mark edge-orientation and use this information as an adaptive ROI of lane-marks. We propose the edge-pair scanning method to verify the edges which belong to lane-marks by using the relationship of adjacent edges of lane-marks and the width between these two edges. In the local adaptive threshold finding method, we also divide the image into sub-images and apply the feature that road lane-marks are always painted with high contrast colors with the road surface. Then, we use multi-adaptive thresholding method for each block. The system can work robustly under the situation that different parts of the image have different contrast for lane-marks. After eliminate the noise edges, we apply Hough Transform to fit the lane-marks as straight line models. The experiment results show that the proposed method can detect the lane-marks in real-time for various different environments.


title: Openness to Accept Medical Technology - A Cultural View

abstract: Technology acceptance is a widely acknowledged key player in explaining technology adoption. However, there is a notable knowledge gap concerning the impact of cultural factors on technology acceptance, especially in the medical sector. It is evident though that countries differ greatly regarding their technical proneness, development and usage habits what should have considerable impact on acceptance. This study compares the openness to accept medical technology in Germany, Poland and Turkey. 300 respondents (19-85 years, 56% women, 38% chronically ill) participated in a survey, in which the pros and cons for using medical technologies were examined as well as the underlying acceptance motives and utilization barriers. The effects of different cultures, but also of age, gender and health status were analyzed regarding their impact on acceptance patterns. Results reveal both, culturally insensitive as well culturally sensitive acceptance, with strong effects of gender and exercising frequency. Overall, the study corroborates the importance of cultural views on technology acceptance.


title: Robust Light Field Depth Estimation for Noisy Scene with Occlusion

abstract: Light field depth estimation is an essential part of many light field applications. Numerous algorithms have been developed using various light field characteristics. However, conventional methods fail when handling noisy scene with occlusion. To remedy this problem, we present a light field depth estimation method which is more robust to occlusion and less sensitive to noise. Novel data costs using angular entropy metric and adaptive defocus response are introduced. Integration of both data costs improves the occlusion and noise invariant capability significantly. Cost volume filtering and graph cut optimization are utilized to improve the accuracy of the depth map. Experimental results confirm that the proposed method is robust and achieves high quality depth maps in various scenes. The proposed method outperforms the state-of-the-art light field depth estimation methods in qualitative and quantitative evaluation.


title: Exploring Multiple Execution Paths for Malware Analysis

abstract: Malicious code (or Malware) is defined as software that fulfills the deliberately harmful intent of an attacker. Malware analysis is the process of determining the behavior and purpose of a given Malware sample (such as a virus, worm, or Trojan horse). This process is a necessary step to be able to develop effective detection techniques and removal tools. Currently, Malware analysis is mostly a manual process that is tedious and time-intensive. To mitigate this problem, a number of analysis tools have been proposed that automatically extract the behavior of an unknown program by executing it in a restricted environment and recording the operating system calls that are invoked. The problem of dynamic analysis tools is that only a single program execution is observed. Unfortunately, however, it is possible that certain malicious actions are only triggered under specific circumstances (e.g., on a particular day, when a certain file is present, or when a certain command is received). In this paper, we propose a system that allows us to explore multiple execution paths and identify malicious actions that are executed only when certain conditions are met. This enables us to automatically extract a more complete view of the program under analysis and identify under which circumstances suspicious actions are carried out. Our experimental results demonstrate that many Malware samples show different behavior depending on input read from the environment. Thus, by exploring multiple execution paths, we can obtain a more complete picture of their actions.


title: Conditional Similarity Networks

abstract: What makes images similar? To measure the similarity between images, they are typically embedded in a feature-vector space, in which their distance preserve the relative dissimilarity. However, when learning such similarity embeddings the simplifying assumption is commonly made that images are only compared to one unique measure of similarity. A main reason for this is that contradicting notions of similarities cannot be captured in a single space. To address this shortcoming, we propose Conditional Similarity Networks (CSNs) that learn embeddings differentiated into semantically distinct subspaces that capture the different notions of similarities. CSNs jointly learn a disentangled embedding where features for different similarities are encoded in separate dimensions as well as masks that select and reweight relevant dimensions to induce a subspace that encodes a specific similarity notion. We show that our approach learns interpretable image representations with visually relevant semantic subspaces. Further, when evaluating on triplet questions from multiple similarity notions our model even outperforms the accuracy obtained by training individual specialized networks for each notion separately.


title: Isolation and purification of 7S and 11S globulins from broad beans and peas.

abstract: A simple and effective preparation technique for isolation of 7S and 11S globulins from broad beans and pea seeds has been developed. It is based on differential solubility of 7S and 11S globulins in sodium chloride solution at pH 4.8 and at 5 and 20 degrees C. The method makes it possible to isolate 6 g of 11S and 1 g of 7S globulins from 100 g of broad bean meal or 1.5 g of 11S and 2 g of 7S globulins from 100 g of pea meal, with only low centrifugation. If necessary, the admixture of 15S component in 11S globulin preparations can be removed by gel chromatography on Sepharose CL-6B.


Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies


A Study of Non-Abelian Public Key


title: How limited is dispersal in the rare beetle, Cryptocephalus decemmaculatus (Chrysomelidae, Cryptocephalinae)?

abstract: As suitable habitat becomes increasingly fragmented the ability of a species to reach new areas may often dictate whether populations persist or perish. An understanding of dispersal ability is a prerequisite for informed management decisions. This is particularly true for species that have become restricted to one or a small number of sites, as is the case for several species of Cryptocephalus beetle in the UK. Using mark–release–recapture, host-plant marking and direct observations we investigated population size, movement through suitable habitat and the flight behaviour of Cryptocephalus decemmaculatus. In the UK this species is known from only two sites and it is listed as a priority species on the UK Biodiversity Action Plan. At the time of the study, the one known English site for this species supported a population of ~500 individuals. The adult beetles move through their habitat using suitable host-plants as ‘stepping stones’. There appears to be no interchange of adults between sub-populations that are separated by small areas of unsuitable habitat (e.g. small tracts of woodland or areas devoid of scrub), a result reinforced by studies of genetic differentiation between sub-populations. The small population size of this beetle and its association with, mid-successional habitats makes it vulnerable to local extinction. Furthermore, its limited dispersal ability means that other habitat patches are unlikely ever to be colonised naturally.


title: Leave or Remain? Deciphering Brexit Deliberations on Twitter

abstract: Twitter is becoming a popular microblogging platform for exploring various socio-political movements. We extract 2.7 million Brexit related tweets to decipher the European Union (EU) referendum deliberations. Our volumetric analysis correctly predicts the outcome of 2016 Brexit referendum. We also investigate whether Twitter discussion adequately reflects the socio-economic concerns related to the UK's decision to leave or remain in the EU. We consider hierarchical clustering analysis (HCA) to explore various underlying themes of this political discourse. To tackle a large portion of retweets in our corpus, we employ tanglegram framework to graphically compare HCA of unique tweets (after removing the duplicates tweets) and the entire corpus. We note finer variances between these two HCA.


Effect of Temperature and Aphid-Host Plant Variety on Performance and Thermal Requirments of Coccinella undecimpunctata L. and Cheilomenes propinqua isis (mulsant)


title: Development of heuristics for usability evaluation of m-commerce applications

abstract: The evaluation of M-Commerce applications is becoming a must for both business and usability professionals. Heuristics evaluation of M-Commerce application seems to be a way out, but traditional heuristics were developed without any consideration for mobile computing. Therefore, the need to have domain specific heuristics is gradually gaining popularity, which have led to the development of domain specific heuristics for M-Health, M-Games and M-Museums, among others. In the light of this, there is a literature gap for the development of domain specific heuristics for M-Commerce applications, which this study seeks to address. The study presents the set of heuristics for M-Commerce applications in a pattern that is similar to traditional heuristics, but with much emphasis on the context of use. The study serves as initial work on the development of domain specific heuristics for the evaluation of M-Commerce applications. The proposed heuristics could be of benefit to usability professionals, significantly reducing the cost and enhancing the outcome of usability evaluations in the context of


title: Simple Evolutionary Optimization Can Rival Stochastic Gradient Descent in Neural Networks

abstract: While evolutionary algorithms (EAs) have long offered an alternative approach to optimization, in recent years backpropagation through stochastic gradient descent (SGD) has come to dominate the fields of neural network optimization and deep learning. One hypothesis for the absence of EAs in deep learning is that modern neural networks have become so high dimensional that evolution with its inexact gradient cannot match the exact gradient calculations of backpropagation. Furthermore, the evaluation of a single individual in evolution on the big data sets now prevalent in deep learning would present a prohibitive obstacle towards efficient optimization. This paper challenges these views, suggesting that EAs can be made to run significantly faster than previously thought by evaluating individuals only on a small number of training examples per generation. Surprisingly, using this approach with only a simple EA (called the limited evaluation EA or LEEA) is competitive with the performance of the state-of-the-art SGD variant RMSProp on several benchmarks with neural networks with over 1,000 weights. More investigation is warranted, but these initial results suggest the possibility that EAs could be the first viable training alternative for deep learning outside of SGD, thereby opening up deep learning to all the tools of evolutionary computation.


title: Efficient Temporal Reasoning on Streams of Events with DOTR

abstract: Many ICT applications need to make sense of large volumes of streaming data to detect situations of interest and enable timely reactions. Stream Reasoning (SR) aims to combine the performance of stream/event processing and the reasoning expressiveness of knowledge representation systems by adopting Semantic Web standards to encode streaming elements. We argue that the mainstream SR model is not flexible enough to properly express the temporal relations common in many applications. We show that the model can miss relevant information and lead to inconsistent derivations. Moving from these premises, we introduce a novel SR model that provides expressive ontological and temporal reasoning by neatly decoupling their scope to avoid losses and inconsistencies. We implement the model in the DOTR system that defines ontological reasoning using Datalog rules and temporal reasoning using a Complex Event Processing language that builds on metric temporal logic. We demonstrate the expressiveness of our model through examples and benchmarks, and we show that DOTR outperforms state-of-the-art SR tools, processing data with millisecond latency.


A fast garment fitting algorithm using skeleton-based error metric


title: Motion Generation of the Capsubot Using Internal Force and Static Friction

abstract: In this paper, a capsule type robot is designed. The motion is described. The robot has no moving part outside its body, no legs, no wheel. Its motion is purely based on its internal force and friction with the environments. A four step motion pattern is proposed. A minimal energy solution is derived. A prototype capsule robot, consisting a plastic tube with a copper coil and a NiFeB magnet rod which can slide inside the tube, is built. It is essentially a motion magnet linear motor. The motion generation results are verified experimentally.


title: Psychological Disorder in Adolescents and Adults with Asperger Syndrome

abstract: The distress and disability caused by Asperger syndrome are a consequence of the core syndrome itself, and any neurological or physical disorders which may be associated with it. Distress and disability are also influenced, to a much greater degree than has previously been recognized, by the personal reactions of the sufferer and the social reactions of those around him or her. One important type of personal reaction is the development of a psychological disorder secondary to Asperger syndrome. There is growing evidence that these reactions are commoner and more severe than has been thought previously. They include affective disorders, anxiety-related disorders and conduct disorders. Their manifestations may be altered by the presence of Asperger syndrome. Treatment usually involves a combination of psycho- education, social change, psychotropic medication and counselling.


title: Comprehensive performance analysis of IEEE 802.15.7 CSMA/CA mechanism for saturated traffic

abstract: IEEE 802.15.7 is a physical and MAC layer standard for visible-light communication. This paper thoroughly analyzes the carrier sensing multiple access with collision avoidance (CSMA/CA) mechanism of the MAC protocol of this standard. The nodal behavior of the CSMA/CA mechanism is modeled using a Markov chain. Performance parameters are derived for saturated traffic in a star topology network. The derived performance metrics of the network include throughput, transmission probability, collision probability, packet discard probability, delay, and power consumption. Furthermore in order to improve the obtained analytic results, a semi-analytic approach is developed. To verify the obtained results, CSMA/CA is completely simulated in a MATLAB environment, and network parameters are derived and compared with analytic models. Simulation results match the analytic ones very closely, which proves the validity of the proposed model.


title: Mucinous adenocarcinoma with lepidic pattern and with K‐RAS mutation in a newborn with antenatal diagnosis of congenital pulmonary airway malformation

abstract: Olivier Stephanov: substantial contributions to conception and design, acquisition of data, and analysis and interpretation of data; drafting the article and revising it critically for important intellectual content; final approval of the version to be published. ::: ::: Yohan Robert : substantial contributions to acquisition of data and analysis and interpretation of data; final approval of the version to be published. ::: ::: Florence De Fraipont substantial contributions to acquisition of data, and analysis and interpretation of data; revising it critically for important intellectual content; final approval of the version to be published. ::: ::: This article is protected by copyright. All rights reserved.


title: Web-STAR: Towards a Visual Web-Based IDE for a Story Comprehension System

abstract: In this work, we present Web-STAR, an online platform for story understanding built on top of the STAR (STory comprehension through ARgumentation) reasoning engine. This platform includes a web-based IDE, integration with the STAR system and a web service infrastructure to support integration with other systems that rely on story understanding functionality to complete their tasks. The platform also delivers a number of"social"features like public story sharing with a built-in commenting system, a public repository for sharing stories with the community and collaboration tools that can be used from both project team members for development and educators for teaching. Moreover, we discuss the ongoing work on adding new features and functionality to this platform.


title: How talkative is your mobile device?: an experimental study of Wi-Fi probe requests

abstract: The IEEE 802.11 standard defines Wi-Fi probe requests as a active mechanism with which mobile devices can request information from access points and accelerate the Wi-Fi connection process. Researchers in previous work have identified privacy hazards associated with Wi-Fi probe requests, such as leaking past access points identifiers and user mobility. Besides several efforts to develop privacy-preserving alternatives, modern mobile devices continue to use Wi-Fi probe requests. In this work, we quantify Wi-Fi probe requests' threat to privacy by conducting an experimental study of the most popular smartphones in different settings. Our objective is to identify how different factors influence the probing frequency and the average number of broadcasted probes. Our conclusions are worrisome: On average, some mobile devices send probe requests as often as 55 times per hour, thus revealing their unique MAC address at high frequency. Even if a mobile device is not charging and in sleep mode, it might broadcast about 2000 probes per hour. We also evaluate a commercially deployed MAC address randomization mechanism, and demonstrate a simple method to re-identify anonymized probes.


title: CIVSched: A Communication-Aware Inter-VM Scheduling Technique for Decreased Network Latency between Co-Located VMs

abstract: Server consolidation in cloud computing environments makes it possible for multiple servers or desktops to run on a single physical server for high resource utilization, low cost, and reduced energy consumption. However, the scheduler in the virtual machine monitor (VMM), such as Xen credit scheduler, is agnostic about the communication behavior between the guest operating systems (OS). The aforementioned behavior leads to increased network communication latency in consolidated environments. In particular, the CPU resources management has a critical impact on the network latency between co-located virtual machines (VMs) when there are CPU- and I/O-intensive workloads running simultaneously. This paper presents the design and implementation of a communication-aware inter-VM scheduling (CIVSched) technique that takes into account the communication behavior betweeninter-VMs running on the same virtualization platform. The CIVSched technique inspects the network packets transmitted between local co-resident domains to identify the target VM and process that will receive the packets. Thereafter, the target VM and process are preferentially scheduled by the VMM and the guest OS. The cooperation of these two schedulers makes the network packets to be timely received by the target application. Experimental results on the Xen virtualization platform depict that the CIVSched technique can reduce the average response time of network traffic by approximately 19 percent for the highly consolidated environment, while keeping the inherent fairness of the VMM scheduler.