title: New brooms sweep clean - an autonomous robotic cleaning assistant for professional office cleaning

abstract: Millions of office workplaces are cleaned by a surprisingly small group of cleaning workers every day, however, cleaning companies struggle to recruit enough personnel these days. One solution to this challenge is to schedule available professionals for demanding tasks while relieving them from simpler activities which are transferred to a robotic cleaning assistant. Two of such tasks are floor cleaning and waste disposal which account for 70% of the daily cleaning efforts. This paper presents the world's first autonomous cleaning robot prototype that masters both of these tasks and whose development was accompanied by the advice of a large cleaning company. Besides a detailed description of the overall system and its individual components an evaluation is provided based on real world experiments. The results indicate that both cleaning tasks can be solved at high quality but with potential for increased efficiency to meet the required performance. Hence, the paper concludes with a discussion on measures necessary for the development of a commercial prototype.


title: T.: Modeling Individualization in a Bayesian Networks Implementation of Knowledge Tracing

abstract: The field of intelligent tutoring systems has been using the well known knowledge tracing model, popularized by Corbett and Anderson (1995), to track student knowledge for over a decade Surprisingly, models currently in use do not allow for individual learning rates nor individualized estimates of student initial knowledge Corbett and Anderson, in their original articles, were interested in trying to add individualization to their model which they accomplished but with mixed results Since their original work, the field has not made significant progress towards individualization of knowledge tracing models in fitting data In this work, we introduce an elegant way of formulating the individualization problem entirely within a Bayesian networks framework that fits individualized as well as skill specific parameters simultaneously, in a single step With this new individualization technique we are able to show a reliable improvement in prediction of real world data by individualizing the initial knowledge parameter We explore three difference strategies for setting the initial individualized knowledge parameters and report that the best strategy is one in which information from multiple skills is used to inform each student's prior Using this strategy we achieved lower prediction error in 33 of the 42 problem sets evaluated The implication of this work is the ability to enhance existing intelligent tutoring systems to more accurately estimate when a student has reached mastery of a skill Adaptation of instruction based on individualized knowledge and learning speed is discussed as well as open research questions facing those that wish to exploit student and skill information in their user models.


title: Learning invariance through imitation

abstract: Supervised methods for learning an embedding aim to map high-dimensional images to a space in which perceptually similar observations have high measurable similarity. Most approaches rely on binary similarity, typically defined by class membership where labels are expensive to obtain and/or difficult to define. In this paper we propose crowd-sourcing similar images by soliciting human imitations. We exploit temporal coherence in video to generate additional pairwise graded similarities between the user-contributed imitations. We introduce two methods for learning nonlinear, invariant mappings that exploit graded similarities. We learn a model that is highly effective at matching people in similar pose. It exhibits remarkable invariance to identity, clothing, background, lighting, shift and scale.


title: Developing a sustainability non-functional requirements framework

abstract: Requirements engineers are in a unique position to encourage the consideration of sustainability at a formative phase in the software development life cycle. In this paper, we look at how we can develop sustainability as a non-functional requirement (NFR). We describe an NFR framework that is informed by sustainability models and discuss how it can be used to appropriately elicit and describe sustainability related requirements of the software system to be developed. We outline a roadmap for how we may integrate sustain- ability in requirements engineering from a theoretical NFR framework to an applicable software quality and relevant software standards.


title: Machine Learning Based Cloud Integrated Farming

abstract: With the increasing population, the demand for more agricultural production is only surged. This increasing demand can only be attained with progressing technology. Internet of Things (IoT) is dramatically advancing the way we live our life by full-scale control over data with minimal human involvement. Using IoT to meet this high demand for production is achieved. In this paper we propose a bot that can be used for small scale farming in areas like gardens or backyard. A simple web application is provided for the user to decide the plants to be farmed and the bot does rest of the work. This bot can plant the seeds, water each plant at required intervals and even plot the weed to bury it. A database is provided with the information about several parameters to be taken care of for each kind of plant. Different sensors are used to sense the properties of soil and environment which can be used to anticipate the near changes and take necessary steps. Image processing is being used for detection and prevention of weed growth. We adopted Bayesian methods of machine learning to efficiently estimate the performance parameters by probability distribution.


title: Online Bayesian Moment Matching for Topic Modeling with Unknown Number of Topics

abstract: Latent Dirichlet Allocation (LDA) is a very popular model for topic modeling as well as many other problems with latent groups. It is both simple and effective. When the number of topics (or latent groups) is unknown, the Hierarchical Dirichlet Process (HDP) provides an elegant non-parametric extension; however, it is a complex model and it is difficult to incorporate prior knowledge since the distribution over topics is implicit. We propose two new models that extend LDA in a simple and intuitive fashion by directly expressing a distribution over the number of topics. We also propose a new online Bayesian moment matching technique to learn the parameters and the number of topics of those models based on streaming data. The approach achieves higher log-likelihood than batch and online HDP with fixed hyperparameters on several corpora.


title: Numerical Simulation for Operation of Flexible Thin-Film Transistors With Bending

abstract: We theoretically study the change of the performance characteristics with various mechanical bending conditions for flexible thin-film transistors (TFTs) by two-dimensional device simulation. The characteristics of newly developed flexible TFTs with high crystalline quality and high carrier mobility are more sensitive to the degree of bending. We developed a model to estimate the change in the characteristics as a function of curvature radius of the channel with a focus on scattering of carriers in a bent TFT. Field-effect mobility decreases by bending, e.g., ~11% with a radius at R = 30 mm and a knee voltage increases, while a threshold voltage remains the same. This model can be extended to other flexible TFTs with bending.


title: Cognitive modeling of polymorphic malware using fractal based semantic characterization

abstract: Polymorphic malware belong to the class of host based threats which defy signature based detection mechanisms. Threat actors use various code obfuscation methods to hide the code details of the polymorphic malware and each dynamic iteration of the malware bears different and new signatures therefore makes its detection harder by signature based antimalware programs. Sandbox based detection systems perform syntactic analysis of the binary files to find known patterns from the un-encrypted segment of the malware file. Anomaly based detection systems can detect polymorphic threats but generate enormous false alarms. In this work, authors present a novel cognitive framework using semantic features to detect the presence of polymorphic malware inside a Microsoft Windows host using a process tree based temporal directed graph. Fractal analysis is performed to find cognitively distinguishable patterns of the malicious processes containing polymorphic malware executables. The main contributions of this paper are; the presentation of a graph theoretic approach for semantic characterization of polymorphism in the operating system's process tree, and the cognitive feature extraction of the polymorphic behavior for detection over a temporal process space.


title: Deep Learning-Based Banknote Fitness Classification Using the Reflection Images by a Visible-Light One-Dimensional Line Image Sensor

abstract: In automatic paper currency sorting, fitness classification is a technique that assesses the quality of banknotes to determine whether a banknote is suitable for recirculation or should be replaced. Studies on using visible-light reflection images of banknotes for evaluating their usability have been reported. However, most of them were conducted under the assumption that the denomination and input direction of the banknote are predetermined. In other words, a pre-classification of the type of input banknote is required. To address this problem, we proposed a deep learning-based fitness-classification method that recognizes the fitness level of a banknote regardless of the denomination and input direction of the banknote to the system, using the reflection images of banknotes by visible-light one-dimensional line image sensor and a convolutional neural network (CNN). Experimental results on the banknote image databases of the Korean won (KRW) and the Indian rupee (INR) with three fitness levels, and the Unites States dollar (USD) with two fitness levels, showed that our method gives better classification accuracy than other methods.


title: Mobile exergames for preventing diseases related to childhood obesity

abstract: Childhood obesity and physical inactivity are well-known problems which are typically related with videogames. The goal of this study is to develop digital games which motivate children and adolescents to exercise more, instead. The games are realized in such way that no special game consoles are required. The games can be played with a regular computer while mobile phones and heart rate monitors are used as the game controllers. Thus the games can also be played from a public screen in schools, for example, thus offering new exercising possibilities among youngsters. In this study, two exercising games (exergames) are presented and their ability to motivate youngsters is examined by group studies. The results indicate that the developed exergames are suitable in school environment. It is concluded that the games were found engaging and they motivated players to exercise. This indicates a positive effect of exergames in prevention of diseases which are related to childhood obesity and physical inactivity.


title: Automated de-identification of free-text medical records

abstract: BACKGROUND ::: Text-based patient medical records are a vital resource in medical research. In order to preserve patient confidentiality, however, the U.S. Health Insurance Portability and Accountability Act (HIPAA) requires that protected health information (PHI) be removed from medical records before they can be disseminated. Manual de-identification of large medical record databases is prohibitively expensive, time-consuming and prone to error, necessitating automatic methods for large-scale, automated de-identification. ::: ::: ::: METHODS ::: We describe an automated Perl-based de-identification software package that is generally usable on most free-text medical records, e.g., nursing notes, discharge summaries, X-ray reports, etc. The software uses lexical look-up tables, regular expressions, and simple heuristics to locate both HIPAA PHI, and an extended PHI set that includes doctors' names and years of dates. To develop the de-identification approach, we assembled a gold standard corpus of re-identified nursing notes with real PHI replaced by realistic surrogate information. This corpus consists of 2,434 nursing notes containing 334,000 words and a total of 1,779 instances of PHI taken from 163 randomly selected patient records. This gold standard corpus was used to refine the algorithm and measure its sensitivity. To test the algorithm on data not used in its development, we constructed a second test corpus of 1,836 nursing notes containing 296,400 words. The algorithm's false negative rate was evaluated using this test corpus. ::: ::: ::: RESULTS ::: Performance evaluation of the de-identification software on the development corpus yielded an overall recall of 0.967, precision value of 0.749, and fallout value of approximately 0.002. On the test corpus, a total of 90 instances of false negatives were found, or 27 per 100,000 word count, with an estimated recall of 0.943. Only one full date and one age over 89 were missed. No patient names were missed in either corpus. ::: ::: ::: CONCLUSION ::: We have developed a pattern-matching de-identification system based on dictionary look-ups, regular expressions, and heuristics. Evaluation based on two different sets of nursing notes collected from a U.S. hospital suggests that, in terms of recall, the software out-performs a single human de-identifier (0.81) and performs at least as well as a consensus of two human de-identifiers (0.94). The system is currently tuned to de-identify PHI in nursing notes and discharge summaries but is sufficiently generalized and can be customized to handle text files of any format. Although the accuracy of the algorithm is high, it is probably insufficient to be used to publicly disseminate medical data. The open-source de-identification software and the gold standard re-identified corpus of medical records have therefore been made available to researchers via the PhysioNet website to encourage improvements in the algorithm.


title: The Usability of Open Source Software

abstract: Open source communities have successfully developed a great deal of software although most computer users only use proprietary applications. The usability of open source software is often regarded as one reason for this limited distribution. In this paper we review the existing evidence of the usability of open source software and discuss how the characteristics of open source development influence usability. We describe how existing human-computer interaction techniques can be used to leverage distributed networked communities, of developers and users, to address issues of usability.


title: Bayesian prediction of tissue-regulated splicing using RNA sequence and cellular context

abstract: Motivation: Alternative splicing is a major contributor to cellular diversity in mammalian tissues and relates to many human diseases. An important goal in understanding this phenomenon is to infer a ‘splicing code’ that predicts how splicing is regulated in different cell types by features derived from RNA, DNA and epigenetic modifiers. ::: ::: Methods: We formulate the assembly of a splicing code as a problem of statistical inference and introduce a Bayesian method that uses an adaptively selected number of hidden variables to combine subgroups of features into a network, allows different tissues to share feature subgroups and uses a Gibbs sampler to hedge predictions and ascertain the statistical significance of identified features. ::: ::: Results: Using data for 3665 cassette exons, 1014 RNA features and 4 tissue types derived from 27 mouse tissues ( http://genes.toronto.edu/wasp), we benchmarked several methods. Our method outperforms all others, and achieves relative improvements of 52% in splicing code quality and up to 22% in classification error, compared with the state of the art. Novel combinations of regulatory features and novel combinations of tissues that share feature subgroups were identified using our method. ::: ::: Contact: frey@psi.toronto.edu ::: ::: Supplementary information:Supplementary data are available at Bioinformatics online.


title: Takayasu arteritis with pyoderma gangrenosum: case reports and literature review

abstract: Background ::: Takayasu arteritis is a rare, chronic inflammatory arteriopathy affecting mainly the aorta and its branches. Many skin manifestations have been reported in association with this disease. Pyoderma gangrenosum is a skin complication that is a neutrophilic dermatosis characterized by destructive, necrotizing and noninfective skin infiltration. However, there are no related records on these conditions in Chinese patients.


title: Test creation for stop consonants perception in children

abstract: Background Learning disabled children have been shown to have difficulties with speech perception and in particular with rapid sounds such as stop consonants. Debate concerning the origin of these difficulties exists. One theory proposes that the problem is language specific (phonological) and not purely basic auditory. The other theory proposed that a group of children with learning disabilities has central auditory processing disorders, concerning in particular the temporal component. Our goal was to create a simple test to examine children with learning disabilities and investigate the incidence of occurrence of such a difficulty in Greek dyslexic children.


title: Smartphones power flying robots

abstract: Consumer grade technology seen in cameras and phones has led to the price/performance ratio of sensors and processors falling dramatically over the last decade. In particular, most devices are packaged with a camera, a gyroscope, and an accelerometer, important sensors for aerial robotics. The low mass and small form factor make them particularly well suited for autonomous flight with small flying robots, especially in GPS-denied environments. In this work, we present the first fully autonomous smartphone-based quadrotor. All the computation, sensing and control runs on an off-the-shelf smartphone, with all the software functionality in a smartphone app.We show how quadrotors can be stabilized and controlled to achieve autonomous flight in indoor buildings with application to smart homes, search and rescue, construction and architecture. The work allows any consumer with a smartphone to autonomously drive a quadrotor robot platform, even without GPS, by downloading an app, and concurrently build 3-D maps.


Ontological Approach in Knowledge Based Recommender System to Develop the Quality of E-learning System


title: Sleep and Delinquency: Does the Amount of Sleep Matter?

abstract: Sleep, a key indicator of health, has been linked to a variety of indicators of well-being such that people who get an adequate amount generally experience greater well-being. Further, a lack of sleep has been linked to a wide range of negative developmental outcomes, yet sleep has been largely overlooked among researchers interested in adolescent delinquency. The purpose of this study was to explore the relationship between hours of sleep and delinquent behavior among adolescents by using data from Wave 1 of the National Longitudinal Study of Adolescent Health (n = 14,382; 50.2% female, 63.5% white). A series of negative binomial regressions showed that youth who typically sleep seven or fewer hours per night reported significantly more property delinquency than youth who sleep the recommended 8–10 h. Further, youth who reported sleeping 5 or fewer hours per night reported significantly more violent delinquency than youth who reported sleeping the recommended number of hours per night. The findings suggest that sleep is an important, and overlooked, dimension of delinquent behavior and studies that focus on adolescent health should further investigate the effects of insufficient sleep. Finally, the authors recommend that sleep and other relevant health behaviors be considered in the context of more comprehensive approaches to delinquency prevention and intervention.


title: Gestures without libraries, toolkits or training: a $1 recognizer for user interface prototypes

abstract: Although mobile, tablet, large display, and tabletop computers increasingly present opportunities for using pen, finger, and wand gestures in user interfaces, implementing gesture recognition largely has been the privilege of pattern matching experts, not user interface prototypers. Although some user interface libraries and toolkits offer gesture recognizers, such infrastructure is often unavailable in design-oriented environments like Flash, scripting environments like JavaScript, or brand new off-desktop prototyping environments. To enable novice programmers to incorporate gestures into their UI prototypes, we present a "$1 recognizer" that is easy, cheap, and usable almost anywhere in about 100 lines of code. In a study comparing our $1 recognizer, Dynamic Time Warping, and the Rubine classifier on user-supplied gestures, we found that $1 obtains over 97% accuracy with only 1 loaded template and 99% accuracy with 3+ loaded templates. These results were nearly identical to DTW and superior to Rubine. In addition, we found that medium-speed gestures, in which users balanced speed and accuracy, were recognized better than slow or fast gestures for all three recognizers. We also discuss the effect that the number of templates or training examples has on recognition, the score falloff along recognizers' N-best lists, and results for individual gestures. We include detailed pseudocode of the $1 recognizer to aid development, inspection, extension, and testing.


title: An empirical examination of the factor structure of compassion

abstract: Compassion has long been regarded as a core part of our humanity by contemplative traditions, and in recent years, it has received growing research interest. Following a recent review of existing conceptualisations, compassion has been defined as consisting of the following five elements: 1) recognising suffering, 2) understanding the universality of suffering in human experience, 3) feeling moved by the person suffering and emotionally connecting with their distress, 4) tolerating uncomfortable feelings aroused (e.g., fear, distress) so that we remain open to and accepting of the person suffering, and 5) acting or being motivated to act to alleviate suffering. As a prerequisite to developing a high quality compassion measure and furthering research in this field, the current study empirically investigated the factor structure of the five-element definition using a combination of existing and newly generated self-report items. This study consisted of three stages: a systematic consultation with experts to review items from existing self-report measures of compassion and generate additional items (Stage 1), exploratory factor analysis of items gathered from Stage 1 to identify the underlying structure of compassion (Stage 2), and confirmatory factor analysis to validate the identified factor structure (Stage 3). Findings showed preliminary empirical support for a five-factor structure of compassion consistent with the five-element definition. However, findings indicated that the ‘tolerating’ factor may be problematic and not a core aspect of compassion. This possibility requires further empirical testing. Limitations with items from included measures lead us to recommend against using these items collectively to assess compassion. Instead, we call for the development of a new self-report measure of compassion, using the five-element definition to guide item generation. We recommend including newly generated ‘tolerating’ items in the initial item pool, to determine whether or not factor-level issues are resolved once item-level issues are addressed.


title: Malware Detection for Mobile Devices Using Software-Defined Networking

abstract: The rapid adoption of mobile devices comes with the growing prevalence of mobile malware. Mobile malware poses serious threats to personal information and creates challenges in securing network. Traditional network services provide connectivity but do not have any direct mechanism for security protection. The emergence of Software-Defined Networking (SDN) provides a unique opportunity to achieve network security in a more efficient and flexible manner. In this paper, we analyze the behaviors of mobile malware, propose several mobile malware detection algorithms, and design and implement a malware detection system using SDN. Our system detects mobile malware by identifying suspicious network activities through real-time traffic analysis, which only requires connection establishment packets. Specifically, our detection algorithms are implemented as modules inside the OpenFlow controller, and the security rules can be imposed in real time. We have tested our system prototype using both a local testbed and GENI infrastructure. Test results confirm the feasibility of our approach. In addition, the stress testing results show that even unoptimized implementations of our algorithms do not affect the performance of the OpenFlow controller significantly.


title: A capacitive level shifter for high voltage (2.5kV)

abstract: A capacitive level-shifter as a part of a high voltage halfbridge gate driver is presented in this work. The level-shifter utilizes a differential capacitor pair to transfer the information from low side to high side. A thorough evaluation of the critical parts of the level-shifter is presented with focus on low power consumption as well as low capacitive load between the floating half-bridge node and ground (output capacitance). The operation of the level-shifter is tested and verified by measurements on a prototype half-bridge gate driver. Results conclude stabile operation at 2.44kV, 50kHz with a current consumption of 0.5mA. Operation voltage was limited by test equipment. The output capacitance is 4pF@1.5kV.


title: A Densely Connected End-to-End Neural Network for Multiscale and Multiscene SAR Ship Detection

abstract: Synthetic aperture radar (SAR) images have been widely used for ship monitoring. The traditional methods of SAR ship detection are difficult to detect small scale ships and avoid the interference of inshore complex background. Deep learning detection methods have shown great performance on various object detection tasks recently but using deep learning methods for SAR ship detection does not show an excellent performance it should have. One of the important reasons is that there is no effective model to handle the detection of multiscale ships in multiresolution SAR images. Another important reason is it is difficult to handle multiscene SAR ship detection including offshore and inshore, especially it cannot effectively distinguish between inshore complex background and ships. In this paper, we propose a densely connected multiscale neural network based on faster-RCNN framework to solve multiscale and multiscene SAR ship detection. Instead of using a single feature map to generate proposals, we densely connect one feature map to every other feature maps from top to down and generate proposals from each fused feature map. In addition, we propose a training strategy to reduce the weight of easy examples in the loss function, so that the training process more focus on the hard examples to reduce false alarm. Experiments on expanded public SAR ship detection dataset, verify the proposed method can achieve an excellent performance on multiscale SAR ship detection in multiscene.


title: Effect of radio-frequency electromagnetic radiations (RF-EMR) on passive avoidance behaviour and hippocampal morphology in Wistar rats

abstract: Introduction. The interaction of mobile phone radio-frequency electromagnetic radiation (RF-EMR) with the brain is a serious concern of our society. Objective. We evaluated the effect of RF-EMR from mobile phones on passive avoidance behaviour and hippocampal morphology in rats. Materials and methods. Healthy male albino Wistar rats were exposed to RF-EMR by giving 50 missed calls (within 1 hour) per day for 4 weeks, keeping a GSM (0.9 GHz/1.8 GHz) mobile phone in vibratory mode (no ring tone) in the cage. After the experimental period, passive avoidance behaviour and hippocampal morphology were studied. Results. Passive avoidance behaviour was significantly affected in mobile phone RF-EMR-exposed rats demonstrated as shorter entrance latency to the dark compartment when compared to the control rats. Marked morphological changes were also observed in the CA3 region of the hippocampus of the mobile phone-exposed rats in comparison to the control rats. Conclusion. Mobile phone RF-EMR exposure significantly altered the passive avoidance behaviour and hippocampal morphology in rats.


title: Multipath Assisted Positioning with Simultaneous Localization and Mapping

abstract: This paper describes an algorithm that exploits multipath propagation for position estimation of mobile receivers. We apply a novel algorithm based on recursive Bayesian filtering, named Channel-SLAM. This approach treats multipath components as signals emitted from virtual transmitters, which are time synchronized to the physical transmitter and static in their positions. Contrary to other approaches, Channel-SLAM considers also paths occurring due to multiple numbers of reflections or scattering as well as the combination. Hence, each received multipath component increases the number of transmitters resulting in a more accurate position estimate or enabling positioning when the number of physical transmitters is insufficient. Channel-SLAM estimates the receiver position and the positions of the virtual transmitters simultaneously; hence, the approach does not require any prior information, such as a room-layout or a database for fingerprinting. The only prior knowledge needed is the physical transmitter position as well as the initial receiver position and moving direction. Based on simulations, the position precision of Channel-SLAM is evaluated by a comparison to simplified algorithms and to the posterior Cramer-Rao lower bound. Furthermore, this paper shows the performance of Channel-SLAM based on measurements in an indoor scenario with only a single physical transmitter.


title: SEED: a suite of instructional laboratories for computer SEcurity EDucation

abstract: To provide students with hands-on exercises in computer security education, we have developed a laboratory environment (SEED) for computer security education. It is based on VMware, Minix, and linux, all of which are free for educational uses. Based on this environment, we have developed ten labs, covering a wide range of security principles. We have used these labs in our three courses in the last four years. This paper presents our SEED lab environment, SEED labs, and our evaluation results.


title: Exploring Intra-Urban Accessibility and Impacts of Pollution Policies with an Agent-Based Simulation Platform: GaMiroD

abstract: In this work we address the issue of sustainable cities by focusing on one of their very ::: central components: daily mobility. Indeed, if cities can be interpreted as spatial organizations ::: allowing social interactions, the number of daily movements needed to reach this goal is ::: continuously increasing. Therefore, improving urban accessibility merely results in increasing ::: traffic and its negative externalities (congestion, accidents, pollution, noise, etc.), while eventually ::: reducing the quality of life of people in the city. This is why several urban-transport policies are ::: implemented in order to reduce individual mobility impacts while maintaining equitable access to ::: the city. This challenge is however non-trivial and therefore we propose to investigate this issue ::: from the complex systems point of view. The real spatial-temporal urban accessibility of citizens ::: cannot be approximated just by focusing on space and implies taking into account the space-time ::: activity patterns of individuals, in a more dynamic way. Thus, given the importance of local ::: interactions in such a perspective, an agent based approach seems to be a relevant solution. This ::: kind of individual based and “interactionist” approach allows us to explore the possible impact ::: of individual behaviors on the overall dynamics of the city but also the possible impact of global ::: measures on individual behaviors. In this paper, we give an overview of the Miro Project and ::: then focus on the GaMiroD model design from real data analysis to model exploration tuned ::: by transportation-oriented scenarios. Among them, we start with the the impact of a LEZ (Low ::: Emission Zone) in the city center.


title: Self-calibration and visual SLAM with a multi-camera system on a micro aerial vehicle

abstract: The use of a multi-camera system enables a robot to obtain a surround view, and thus, maximize its perceptual awareness of its environment. If vision-based simultaneous localization and mapping (vSLAM) is expected to provide reliable pose estimates for a micro aerial vehicle (MAV) with a multi-camera system, an accurate calibration of the multi-camera system is a necessary prerequisite. We propose a novel vSLAM-based self-calibration method for a multi-camera system that includes at least one calibrated stereo camera, and an arbitrary number of monocular cameras. We assume overlapping fields of view to only exist within stereo cameras. Our self-calibration estimates the inter-camera transforms with metric scale; metric scale is inferred from calibrated stereo. On our MAV, we set up each camera pair in a stereo configuration which facilitates the estimation of the MAV's pose with metric scale. Once the MAV is calibrated, the MAV is able to estimate its global pose via a multi-camera vSLAM implementation based on the generalized camera model. We propose a novel minimal and linear 3-point algorithm that uses relative rotation angle measurements from a 3-axis gyroscope to recover the relative motion of the MAV with metric scale and from 2D-2D feature correspondences. This relative motion estimation does not involve scene point triangulation. Our constant-time vSLAM implementation with loop closures runs on-board the MAV in real-time. To the best of our knowledge, no published work has demonstrated real-time on-board vSLAM with loop closures. We show experimental results from simulation experiments, and real-world experiments in both indoor and outdoor environments.


title: Image data compression: A review

abstract: With the continuing growth of modern communications technology, demand for image transmission and storage is increasing rapidly. Advances in computer technology for mass storage and digital processing have paved the way for implementing advanced data compression techniques to improve the efficiency of transmission and storage of images. In this paper a large variety of algorithms for image data compression are considered. Starting with simple techniques of sampling and pulse code modulation (PCM), state of the art algorithms for two-dimensional data transmission are reviewed. Topics covered include differential PCM (DPCM) and predictive coding, transform coding, hybrid coding, interframe coding, adaptive techniques, and applications. Effects of channel errors and other miscellaneous related topics are also considered. While most of the examples and image models have been specialized for visual images, the techniques discussed here could be easily adapted more generally for multidimensional data compression. Our emphasis here is on fundamentals of the various techniques. A comprehensive bibliography with comments is included for a reader interested in further details of the theoretical and experimental results discussed here.


title: Incremental Fusion of Structure-from-Motion and GPS Using Constrained Bundle Adjustments

abstract: Two problems occur when bundle adjustment (BA) is applied on long image sequences: large calculation time and drift (or error accumulation). In recent work, the calculation time is reduced by local BAs applied in an incremental scheme. The drift may be reduced by fusion of GPS and Structure-from-Motion. An existing fusion method is BA minimizing a weighted sum of image and GPS errors. This paper introduces two constrained BAs for fusion which enforce an upper bound for the reprojection error. These BAs are alternatives to the existing fusion BA which does not guarantee a small reprojection error and requires a weight as input. Then, the three fusion BAs are integrated in an incremental Structure-from-Motion method based on local BA. Last, we will compare the fusion results on long monocular image sequences and low cost GPS.


title: A Practical Framework for Privacy-Preserving NoSQL Databases

abstract: Cloud infrastructures provide database services as cost-efficient and scalable solutions for storing and processing large amounts of data. To maximize performance, these services require users to trust sensitive information to the cloud provider, which raises privacy and legal concerns. This represents a major obstacle to the adoption of the cloud computing paradigm. Recent work addressed this issue by extending databases to compute over encrypted data. However, these approaches usually support a single and strict combination of cryptographic techniques invariably making them application specific. To assess and broaden the applicability of cryptographic techniques in secure cloud storage and processing, these techniques need to be thoroughly evaluated in a modular and configurable database environment. This is even more noticeable for NoSQL data stores where data privacy is still mostly overlooked. In this paper, we present a generic NoSQL framework and a set of libraries supporting data processing cryptographic techniques that can be used with existing NoSQL engines and composed to meet the privacy and performance requirements of different applications. This is achieved through a modular and extensible design that enables data processing over multiple cryptographic techniques applied on the same database. For each technique, we provide an overview of its security model, along with an extensive set of experiments. The framework is evaluated with the YCSB benchmark, where we assess the practicality and performance tradeoffs for different combinations of cryptographic techniques. The results for a set of macro experiments show that the average overhead in NoSQL operations performance is below 15%, when comparing our system with a baseline database without privacy guarantees.


title: Collaborative recommendation with user generated content

abstract: In the age of Web 2.0, user generated content (UGC), such as user review and social tag, ubiquitously exists on the Internet. Although there exist different kinds of UGC in recommender systems, the existing works only studied a single kind of UGC in each of their papers. Thus, the previous works lose a chance to uncover the similar effects of different kinds of UGC in recommender systems. In this paper, we propose a unified way to utilize various types of UGC to enhance the recommendation accuracy. We build two novel statistical models, which are based on collaborative filtering and topic modeling. Incorporating UGC text, one model focuses on learning user preferences, and the other model aims to learn user preferences and item aspects jointly. With an effective parameter estimation algorithm, our models can not only acquire prediction values of missing ratings, but also produce interpretable topics. We conducted comprehensive experiments on three real-world datasets. The experimental results demonstrate that our proposed models can achieve large improvements compared to several well-known baseline models.


Identifying Where to Focus in Reading Comprehension for Neural Question Generation


title: A multi-task neural network for multilingual sentiment classification and language detection on Twitter

abstract: In this paper, we propose a novel approach for classifying both the sentiment and the language of tweets. Our proposed architecture comprises a convolutional neural network (ConvNet) with two distinct outputs, each of which designed to minimize the classification error of either sentiment assignment or language identification. Results show that our method outperforms both single-task and multi-task state-of-the-art approaches for classifying multilingual tweets.


title: Program Language Translation Using a Grammar-Driven Tree-to-Tree Model

abstract: The task of translating between programming languages differs from the challenge of translating natural languages in that programming languages are designed with a far more rigid set of structural and grammatical rules. Previous work has used a tree-to-tree encoder/decoder model to take advantage of the inherent tree structure of programs during translation. Neural decoders, however, by default do not exploit known grammar rules of the target language. In this paper, we describe a tree decoder that leverages knowledge of a language's grammar rules to exclusively generate syntactically correct programs. We find that this grammar-based tree-to-tree model outperforms the state of the art tree-to-tree model in translating between two programming languages on a previously used synthetic task.


title: Self-Adaptive Matrix Completion for Heart Rate Estimation from Face Videos under Realistic Conditions

abstract: Recent studies in computer vision have shown that, while practically invisible to a human observer, skin color changes due to blood flow can be captured on face videos and, surprisingly, be used to estimate the heart rate (HR). While considerable progress has been made in the last few years, still many issues remain open. In particular, state of-the-art approaches are not robust enough to operate in natural conditions (e.g. in case of spontaneous movements, facial expressions, or illumination changes). Opposite to previous approaches that estimate the HR by processing all the skin pixels inside a fixed region of interest, we introduce a strategy to dynamically select face regions useful for robust HR estimation. Our approach, inspired by recent advances on matrix completion theory, allows us to predict the HR while simultaneously discover the best regions of the face to be used for estimation. Thorough experimental evaluation conducted on public benchmarks suggests that the proposed approach significantly outperforms state-of the-art HR estimation methods in naturalistic conditions.


title: Robust multicellular computing using genetically encoded NOR gates and chemical ‘wires’

abstract: For synthetic biologists' creativity to be unleashed, basic circuits must become truly interchangeable, that is, modular and scalable. This study, one of two linked papers, has harnessed bacterial 'quorum sensing' to achieve complex computation through communication between individual cells performing simple logic functions. Such extracellular 'chemical wiring' is one promising way to get around intracellular noise when building more complex genetic circuitry.


Combination of Texture and Geometric Features for Age Estimation in Face Images


title: 2006 Society for Design and Process Science Printed in the United States of America THE BOEING 777: NO CHAINSAW MASSACRES, PLEASE!

abstract: Iterative life cycle models have become popular in software engineering, e.g. in agile development. In contrast, the waterfall model appears to prevail in manufacturing engineering disciplines. This includes aircraft engineering and Boeing's project for developing its most recent passenger aircraft, the 777. The paper walks through the phases of the 777's development and compares this process to iterative development. The comparison suggests two observations: Firstly, the over-all waterfall approach in the 777 project appears to be well-motivated by the physical, manufactured nature of aircraft components such as wings, in addition to safety concerns. Secondly, several iterative elements in the development of the 777 can also be identified. A major source of these is digitalization of development, in particular the use of CAD tools for a process called digital preassembly.


title: Acquiring Domain-Specific Dialog Information from Task-Oriented Human-Human Interaction through an Unsupervised Learning

abstract: We describe an approach for acquiring the domain-specific dialog knowledge required to configure a task-oriented dialog system that uses human-human interaction data. The key aspects of this problem are the design of a dialog information representation and a learning approach that supports capture of domain information from in-domain dialogs. To represent a dialog for a learning purpose, we based our representation, the form-based dialog structure representation, on an observable structure. We show that this representation is sufficient for modeling phenomena that occur regularly in several dissimilar task-oriented domains, including information-access and problem-solving. With the goal of ultimately reducing human annotation effort, we examine the use of unsupervised learning techniques in acquiring the components of the form-based representation (i.e. task, subtask, and concept). These techniques include statistical word clustering based on mutual information and Kullback-Liebler distance, TextTiling, HMM-based segmentation, and bisecting K-mean document clustering. With some modifications to make these algorithms more suitable for inferring the structure of a spoken dialog, the unsupervised learning algorithms show promise.


title: Ways of Conditioning Generative Adversarial Networks

abstract: The GANs are generative models whose random samples realistically reflect natural images. It also can generate samples with specific attributes by concatenating a condition vector into the input, yet research on this field is not well studied. We propose novel methods of conditioning generative adversarial networks (GANs) that achieve state-of-the-art results on MNIST and CIFAR-10. We mainly introduce two models: an information retrieving model that extracts conditional information from the samples, and a spatial bilinear pooling model that forms bilinear features derived from the spatial cross product of an image and a condition vector. These methods significantly enhance log-likelihood of test data under the conditional distributions compared to the methods of concatenation.


title: Beyond F-Formations: Determining Social Involvement in Free Standing Conversing Groups from Static Images

abstract: In this paper, we present the first attempt to analyse differing levels of social involvement in free standing conversing groups (or the so-called F-formations) from static images. In addition, we enrich state-of-the-art F-formation modelling by learning a frustum of attention that accounts for the spatial context. That is, F-formation configurations vary with respect to the arrangement of furniture and the non-uniform crowdedness in the space during mingling scenarios. The majority of prior works have considered the labelling of conversing group as an objective task, requiring only a single annotator. However, we show that by embracing the subjectivity of social involvement, we not only generate a richer model of the social interactions in a scene but also significantly improve F-formation detection. We carry out extensive experimental validation of our proposed approach by collecting a novel set of multi-annotator labels of involvement on the publicly available Idiap Poster Data, the only multi-annotator labelled database of free standing conversing groups that is currently available.


title: Designing a virtual assistant for in-car child entertainment

abstract: Driving is an attention-demanding task, especially with children in the back seat. While most recommendations prefer to reduce children's screen time in common entertainment systems, e.g. DVD players and tablets, parents often rely on these systems to entertain the children during car trips. These systems often lack key components that are important for modern parents, namely, sociability and educational content. In this contribution we introduce PANDA, a parental affective natural driving assistant. PANDA is a virtual in-car entertainment agent that can migrate around the car to interact with the parent-driver or with children in the back seat. PANDA supports the parent-driver via speech interface, helps to mediate her interaction with children in the back seat, and works to reduce distractions for the driver while also engaging, entertaining and educating children. We present the design of PANDA system and preliminary tests of the prototype system in a car setting.


title: Dependency-Based Open Information Extraction

abstract: Building shallow semantic representations from text corpora is the first step to perform more complex tasks such as text entailment, enrichment of knowledge bases, or question answering. Open Information Extraction (OIE) is a recent unsupervised strategy to extract billions of basic assertions from massive corpora, which can be considered as being a shallow semantic representation of those corpora. In this paper, we propose a new multilingual OIE system based on robust and fast rule-based dependency parsing. It permits to extract more precise assertions (verb-based triples) from text than state of the art OIE systems, keeping a crucial property of those systems: scaling to Web-size document collections.


title: Modeling the role of salience in the allocation of overt visual attention

abstract: A biologically motivated computational model of bottom-up visual selective attention was used to examine the degree to which stimulus salience guides the allocation of attention. Human eye movements were recorded while participants viewed a series of digitized images of complex natural and artificial scenes. Stimulus dependence of attention, as measured by the correlation between computed stimulus salience and fixation locations, was found to be significantly greater than that expected by chance alone and furthermore was greatest for eye movements that immediately follow stimulus onset. The ability to guide attention of three modeled stimulus features (color, intensity and orientation) was examined and found to vary with image type. Additionally, the effect of the drop in visual sensitivity as a function of eccentricity on stimulus salience was examined, modeled, and shown to be an important determiner of attentional allocation. Overall, the results indicate that stimulus-driven, bottom-up mechanisms contribute significantly to attentional guidance under natural viewing conditions.


title: Prenylated Phenolic Compounds from the Leaves of Sabia limoniacea and Their Antiviral Activities against Porcine Epidemic Diarrhea Virus

abstract: Porcine epidemic diarrhea virus (PEDV), a serious swine epidemic, has been rampant in Asia since the 1990s. Despite the widespread use of PEDV vaccines, the occurrence of PEDV variants requires the discovery of new substances that inhibit these viruses. During a search for PEDV inhibitory materials from natural sources, seven new sabphenosides (1–7) and a new flavonoid (8), as well as eight known phenolic compounds (9–16), were obtained from the leaves of Sabia limoniacea. The structural determination of the new phenolic derivatives (1–8) was accomplished using comprehensive spectroscopic methods. Their absolute configurations were assigned by a combination of the ECD exciton chirality method following selective reduction and calculation of their ECD spectra. The bioactivities of the isolated compounds were measured based on their abilities to inhibit viral replication of PEDV. Among the test compounds, 15 and 16 exhibited the most promising antiviral activities, with IC50 values of 7.5 ± 0.7 μM and 8.0 ±...


title: 3D-Printed Super-Wideband Spidron Fractal Cube Antenna with Laminated Copper

abstract: In this paper, a 3D-printed super-wideband (SWB) Spidron fractal cube antenna is proposed. The Spidron fractal configuration is utilized as a self-complementary structure on each face of a 3D frame to attain SWB characteristics. The antenna is excited through a tapered microstrip balun for both mode transforming and impedance matching. A prototype of the proposed antenna, including the 3D frame fabricated with the help of a 3D printer and Spidron fractal patches made of copper tape, is experimentally verified. The measured −10 dB reflection ratio bandwidth is 34:1 (0.44–15.38 GHz). The peak gain varies from 3.42 to 9.29 dBi within the operating frequency bandwidth. The measured radiation patterns are nearly omnidirectional at all operating frequency bands.


title: Kalman Particle Filter for lane recognition on rural roads

abstract: Despite the availability of lane departure and lane keeping systems for highway assistance, unmarked and winding rural roads still pose challenges to lane recognition systems. To detect an upcoming curve as soon as possible, the viewing range of image-based lane recognition systems has to be extended. This is done by evaluating 3D information obtained from stereo vision or imaging radar in this paper. Both sensors deliver evidence grids as the basis for road course estimation. Besides known Kalman Filter approaches, Particle Filters have recently gained interest since they offer the possibility to employ cues of a road, which can not be described as measurements needed for a Kalman Filter approach. We propose to combine both principles and their benefits in a Kalman Particle Filter. The comparison between the results gained from this recently published filter scheme and the classical approaches using real world data proves the advantages of the Kalman Particle Filter.


title: The Internet of Multimedia Nano-Things

abstract: Abstract Nanotechnology is enabling the development of novel devices which are able to generate, process and transmit multimedia content at the nanoscale. The interconnection of pervasively deployed multimedia nano-devices with existing communication networks and ultimately the Internet defines a novel communication paradigm that is further referred to as the Internet of Multimedia Nano-Things (IoMNT). The IoMNT is a truly cyber–physical system with a plethora of applications in the biomedical, security and defense, environmental and industrial fields, amongst others. This paper discusses the state of the art and major research challenges in the realization of the IoMNT. Fundamental research challenges and future research trends are outlined in terms of multimedia data and signal processing, propagation modeling for communication amongst nano-things in the terahertz band, physical layer solutions for terahertz band communication and protocols for the IoMNT. These include novel medium access control techniques, addressing schemes, neighbor discovery and routing mechanisms, a novel QoS-aware cross-layer communication module, and novel security solutions for the IoMNT.


Prioritizing Requirements


title: Multispectral palm image fusion for accurate contact-free palmprint recognition

abstract: In this paper, we propose to improve the verification performance of a contract-free palmprint recognition system by means of feature- level image registration and pixel-level fusion of multi-spectral palm images. Our method involves image acquisition via a dedicated device under contact-free and multi-spectral environment, preprocessing to locate region of interest (ROI) from each individual hand images, feature-level registration to align ROIs from different spectral images in one sequence and fusion to combine images from multiple spectra. The advantages of the proposed method include better hygiene and higher verification performance. Given a database composed of images from 330 hands, two out of four state of the art fusion strategies offer significant performance gain and the best equal error rate (EER) is 0.5%.


title: Thread Arcs: an email thread visualization

abstract: This paper describes Thread Arcs, a novel interactive visualization technique designed to help people use threads found in email. Thread Arcs combine the chronology of messages with the branching tree structure of a conversational thread in a mixed-model visualization by Venolia and Neustaedter (2003) that is stable and compact. By quickly scanning and interacting with Thread Arcs, people can see various attributes of conversations and find relevant messages in them easily. We tested this technique against other visualization techniques with users' own email in a functional prototype email client. Thread Arcs proved an excellent match for the types of threads found in users' email for the qualities users wanted in small-scale visualizations.


title: Genome-wide atlas of gene expression in the adult mouse brain

abstract: Molecular approaches to understanding the functional circuitry of the nervous system promise new insights into the relationship between genes, brain and behaviour. The cellular diversity of the brain necessitates a cellular resolution approach towards understanding the functional genomics of the nervous system. We describe here an anatomically comprehensive digital atlas containing the expression patterns of approximately 20,000 genes in the adult mouse brain. Data were generated using automated high-throughput procedures for in situ hybridization and data acquisition, and are publicly accessible online. Newly developed image-based informatics tools allow global genome-scale structural analysis and cross-correlation, as well as identification of regionally enriched genes. Unbiased fine-resolution analysis has identified highly specific cellular markers as well as extensive evidence of cellular heterogeneity not evident in classical neuroanatomical atlases. This highly standardized atlas provides an open, primary data resource for a wide variety of further studies concerning brain organization and function.


title: Usability of car dashboard displays for elder drivers

abstract: The elder population is rising worldwide; in the US, no longer being able to drive is a significant marker of loss of independence. One of the approaches to helping elders drive more safely is to investigate the use of automotive user interface technology, and specifically, to explore the instrument panel (IP) display design to help attract and manage attention and make information easier to interpret. In this paper, we explore the premise that dashboard displays can be better designed to support elder drivers, their information needs, and their cognitive capabilities. We conducted a study to understand which display design features are critically linked to issues of divided attention and driving performance. We found that contrast of size and reduced clutter are instrumental in enhancing driving performance, particularly for the elder population. Surprisingly, our results showed that color elements have a negative effect on driving performance for elders, while color elements and fills slightly improve performance. We conclude with design implications generated from this work.


Enhancing the Safety of Critically Ill Patients by Reducing Urinary and Central Venous Catheter-related Infections


title: Is imitation learning the route to humanoid robots?

abstract: Summary This review investigates two recent developments in artificial intelligence and neural computation: learning from imitation and the development of humanoid robots. It will be postulated that the study of imitation learning offers a promising route to gain new insights into mechanisms of perceptual motor control that could ultimately lead to the creation of autonomous humanoid robots. Imitation learning focuses on three important issues: efficient motor learning, the connection between action and perception, and modular motor control in form of movement primitives. It will be reviewed how research on representations of, and functional connections between action and perception have contributed to our understanding of motor acts of other beings. The recent discovery that some areas in the primate brain are active during both movement perception and execution has provided a hypothetical neural basis of imitation. Computational approaches to imitation learning will also be described, initially from the perspective of traditional AI and robotics, but also from the perspective of neural network models and statistical learning research. Parallels and differences between biological and computational approaches to imitation will be highlighted and an overview of current projects that actually employ imitation learning for humanoid robots will be given.


title: Comparison of PAPR reduction techniques in OFDM systems

abstract: Orthogonal Frequency Division Multiplexing (OFDM) is a spectrally efficient multicarrier modulation technique for high speed data transmission over multipath fading channels. OFDM modulation schemes offer many advantages for multicarrier transmission at high data rates. One of the main issues of OFDM is high Peak-to-Average Power Ratio (PAPR) of the transmitted signal which adversely affects the complexity of power amplifiers. A number of promising techniques have been proposed and implemented to reduce PAPR of OFDM signal with expense of transmitted signal power, Bit error rate (BER), complexity etc. In this paper, clipping and filtering, selective mapping (SLM), partial transmit sequence (PTS), linear block coding (LBC), peak insertion (PI) techniques are implemented for PAPR reduction of OFDM signal at transmitter. Comparison of these PAPR reduction techniques is done based on CCDF and BER performance of the system.


title: Digital electric meter reading recognition based on horizontal and vertical binary pattern

abstract: Digital meter reading recognition from a digital electric meter image is the key step in the field of digital image processing to evaluate the meter readings from different environmental conditions automatically. This work has gain important for the purpose of automatic generation of electric bill from digital electric meter image. In this regard, a digital electric meter reading recognition framework is proposed in this paper. For that initially, the input image is normalized and converted to YCbCr image. As the digital electric meter reading region is resolute with color light, especially green, the reading region is extracted from YCbCr image based on the value of the Cb and Cr. After that, Canny edge operator is employed on the extracted region to find the edge image. In this edge image, the individual digit's edge gaps are filled up through the morphological operation to find individually connected digit edge image. These individual digits are segmented from an edge image through the vertical projection. Furthermore, the individually segmented digits are filled and thinned to detect the shapes of the digits. From these segmented digit shapes the Horizontal and Vertical Binary (HVB) pattern features are extracted. This is the key contribution of this paper. Finally, based on the HVB patterns the digital meter reading sequence is recognized. The experimental result reveals that the proposed framework is practical and effective.


title: A large scale study of text-messaging use

abstract: Text messaging has become a popular form of communication with mobile phones worldwide. We present findings from a large scale text messaging study of 70 university students in the United States. We collected almost 60, 000 text messages over a period of 4 months using a custom logging tool on our participants' phones. Our re- sults suggest that students communicate with a large number of contacts for extended periods of time, engage in simultaneous conversations with as many as 9 contacts, and often use text messaging as a method to switch between a variety of communication mediums. We also explore the content of text messages, and ways text message habits have changed over the last decade as it has become more popular. Finally, we offer design suggestions for future mobile communication tools.


title: Motion Segmentation of Truncated Signed Distance Function Based Volumetric Surfaces

abstract: Truncated signed distance function (TSDF) based volumetric surface reconstructions of static environments can be readily acquired using recent RGB-D camera based mapping systems. If objects in the environment move then a previously obtained TSDF reconstruction is no longer current. Handling this problem requires segmenting moving objects from the reconstruction. To this end, we present a novel solution to the motion segmentation of TSDF volumes. The segmentation problem is cast as CRF-based MAP inference in the voxel space. We propose: a novel data term by solving sparse multi-body motion segmentation and computing likelihoods for each motion label in the RGB-D image space, and, a novel pair wise term based on gradients of the TSDF volume. Experimental evaluation shows that the proposed approach achieves successful segmentations on reconstructions acquired with Kinect Fusion. Unlike the existing solutions which only work if the objects move completely from their initially occupied spaces, the proposed method permits segmentation of objects when they start to move.


title: Anti-forensic resilient memory acquisition

abstract: Memory analysis has gained popularity in recent years proving to be an effective technique for uncovering malware in compromised computer systems. The process of memory acquisition presents unique evidentiary challenges since many acquisition techniques require code to be run on a potential compromised system, presenting an avenue for anti-forensic subversion. In this paper, we examine a number of simple anti-forensic techniques and test a representative sample of current commercial and free memory acquisition tools. We find that current tools are not resilient to very simple anti-forensic measures. We present a novel memory acquisition technique, based on direct page table manipulation and PCI hardware introspection, without relying on operating system facilities - making it more difficult to subvert. We then evaluate this technique's further vulnerability to subversion by considering more advanced anti-forensic attacks.


title: HoPE: Hot-Cacheline Prediction for Dynamic Early Decompression in Compressed LLCs

abstract: Data compression plays a pivotal role in improving system performance and reducing energy consumption, because it increases the logical effective capacity of a compressed memory system without physically increasing the memory size. However, data compression techniques incur some cost, such as non-negligible compression and decompression overhead. This overhead becomes more severe if compression is used in the cache. In this article, we aim to minimize the read-hit decompression penalty in compressed Last-Level Caches (LLCs) by speculatively decompressing frequently used cachelines. To this end, we propose a Hot-cacheline Prediction and Early decompression (HoPE) mechanism that consists of three synergistic techniques: Hot-cacheline Prediction (HP), Early Decompression (ED), and Hit-history-based Insertion (HBI). HP and HBI efficiently identify the hot compressed cachelines, while ED selectively decompresses hot cachelines, based on their size information. Unlike previous approaches, the HoPE framework considers the performance balance/tradeoff between the increased effective cache capacity and the decompression penalty. To evaluate the effectiveness of the proposed HoPE mechanism, we run extensive simulations on memory traces obtained from multi-threaded benchmarks running on a full-system simulation framework. We observe significant performance improvements over compressed cache schemes employing the conventional Least-Recently Used (LRU) replacement policy, the Dynamic Re-Reference Interval Prediction (DRRIP) scheme, and the Effective Capacity Maximizer (ECM) compressed cache management mechanism. Specifically, HoPE exhibits system performance improvements of approximately 11%, on average, over LRU, 8% over DRRIP, and 7% over ECM by reducing the read-hit decompression penalty by around 65%, over a wide range of applications.


title: Bayesian Networks for Data Mining

abstract: A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data modeling. One, because the model encodes dependencies among all variables, it readily handles situations where some data entries are missing. Two, a Bayesian network can be used to learn causal relationships, and hence can be used to gain understanding about a problem domain and to predict the consequences of intervention. Three, because the model has both a causal and probabilistic semantics, it is an ideal representation for combining prior knowledge (which often comes in causal form) and data. Four, Bayesian statistical methods in conjunction with Bayesian networks offer an efficient and principled approach for avoiding the overfitting of data. In this paper, we discuss methods for constructing Bayesian networks from prior knowledge and summarize Bayesian statistical methods for using data to improve these models. With regard to the latter task, we describe methods for learning both the parameters and structure of a Bayesian network, including techniques for learning with incomplete data. In addition, we relate Bayesian-network methods for learning to techniques for supervised and unsupervised learning. We illustrate the graphical-modeling approach using a real-world case study.


title: A 4-bit, 3.2 GSPS flash analog to digital converter with a new multiplexer based encoder

abstract: A 4-bit flash analog to digital converter with high speed, low power, and small area for system on chip(SoC) applications is presented. The proposed ADC is designed with BSIM4 90nm CMOS technology and 1.2V power supply. In comparison to the conventional analog comparators, it uses Threshold Inverter Quantization technique to generate comparators. The replacement results in a faster digital conversion and a reduction in power that make it suitable for battery powered applications. The TIQ comparator does not need a resistor ladder network because it uses built in reference voltage of the CMOS inverters for comparison. Proposed flash ADC uses a new mux based encoder which converts Thermometer code into Gray code. Gray codes are mainly used in glitches free fast circuit design and in communication for error correction in digital modulation techniques. The maximum sampling frequency of this ADC is 3.2GSPS. This ADC consumes 4.93mW power at 3.2GSPS. INL and DNL of proposed ADC are 0.775LSB and 0.15LSB respectively. The proposed A/D converter is suitable for mobile applications and other system on chip applications.


title: Mutations in SRCAP, Encoding SNF2-Related CREBBP Activator Protein, Cause Floating-Harbor Syndrome

abstract: Floating-Harbor syndrome (FHS) is a rare condition characterized by short stature, delayed osseous maturation, expressive-language deficits, and a distinctive facial appearance. Occurrence is generally sporadic, although parent-to-child transmission has been reported on occasion. Employing whole-exome sequencing, we identified heterozygous truncating mutations in SRCAP in five unrelated individuals with sporadic FHS. Sanger sequencing identified mutations in SRCAP in eight more affected persons. Mutations were de novo in all six instances in which parental DNA was available. SRCAP is an SNF2-related chromatin-remodeling factor that serves as a coactivator for CREB-binding protein (CREBBP, better known as CBP, the major cause of Rubinstein-Taybi syndrome [RTS]). Five SRCAP mutations, two of which are recurrent, were identified; all are tightly clustered within a small (111 codon) region of the final exon. These mutations are predicted to abolish three C-terminal AT-hook DNA-binding motifs while leaving the CBP-binding and ATPase domains intact. Our findings show that SRCAP mutations are the major cause of FHS and offer an explanation for the clinical overlap between FHS and RTS.


Frames Perspective on Information Technology and Organizational Change


title: www.elsevier.com/locate/learninstruc Aids to computer-based multimedia learning

abstract: Abstract Computer-based multimedia learning environments — consisting of pictures (such as animation) and words (such as narration) — offer a potentially powerful venue for improving student understanding. How can we use words and pictures to help people understand how scientific systems work, such as how a lightning storm develops, how the human respiratory system operates, or how a bicycle tire pump works? This paper presents a cognitive theory of multimedia learning which draws on dual coding theory, cognitive load theory, and constructivist learning theory. Based on the theory, principles of instructional design for fostering multimedia learning are derived and tested. The multiple representation principle states that it is better to present an explanation in words and pictures than solely in words. The contiguity principle is that it is better to present corresponding words and pictures simultaneously rather than separately when giving a multimedia explanation. The coherence principle is that multimedia explanations are better understood when they include few rather than many extraneous words and sounds. The modality principle is that it is better to present words as auditory narration than as visual on-screen text. The redundancy principle is that it is better to present animation and narration than to present animation, narration, and on-screen text. By beginning with a cognitive theory of how learners process multimedia information, we have been able to conduct focused research that yields some preliminary principles of instructional design for multimedia messages.


title: A Review and Future Perspectives of Arabic Question Answering Systems

abstract: Question Answering Systems (QASs) have emerged as a good alternative for information seekers to retrieve precise information over the Internet. A good amount of research has been done to improve the performance of QASs across several languages, including European and Asian languages. However, Arabic, a morphologically rich Semitic language spoken by over 422 million people, has not seen similar development in the field of question answering. This article reviews the developments taking place in Arabic QASs as well as the challenges faced by researchers in developing Arabic QASs. After conducting an extensive literature survey of a number of English and Arabic QASs, this article classifies them according to several criteria. The most commonly used architecture for the development of an Arabic QAS, known as pipeline architecture, has been presented. In order to encourage and support the new researchers and scholars in conducting research in Arabic QASs, a list of techniques, tools, and computational linguistic resources, required to implement the components of the presented pipelined architecture, are described in this article in a simple and persuasive manner. Finally, the gap analysis between the research in Arabic and English QASs has been performed and accordingly, some future directions for research in Arabic QASs have been proposed.


title: Robotic Surgery Improves Technical Performance and Enhances Prefrontal Activation During High Temporal Demand

abstract: Robotic surgery may improve technical performance and reduce mental demands compared to laparoscopic surgery. However, no studies have directly compared the impact of robotic and laparoscopic techniques on surgeons’ brain function. This study aimed to assess the effect of the operative platform (robotic surgery or conventional laparoscopy) on prefrontal cortical activation during a suturing task performed under temporal demand. Eight surgeons (mean age ± SD = 34.5 ± 2.9 years, male:female ratio = 7:1) performed an intracorporeal suturing task in a self-paced manner and under a 2 min time restriction using conventional laparoscopic and robotic techniques. Prefrontal activation was assessed using near-infrared spectroscopy, subjective workload was captured using SURG-TLX questionnaires, and a continuous heart rate monitor measured systemic stress responses. Task progression scores (au), error scores (au), leak volumes (mL) and knot tensile strengths (N) provided objective assessment of technical performance. Under time pressure, robotic suturing led to improved technical performance (median task progression score: laparoscopic suturing = 4.5 vs. robotic suturing = 5.0; z = − 2.107, p = 0.035; median error score: laparoscopic suturing = 3.0 mm vs. robotic suturing = 2.1 mm; z = − 2.488, p = 0.013). Compared to laparoscopic suturing, greater prefrontal activation was identified in seven channels located primarily in lateral prefrontal regions. These results suggest that robotic surgery improves performance during high workload conditions and is associated with enhanced activation in regions of attention, concentration and task engagement.


title: ALMN: Deep Embedding Learning with Geometrical Virtual Point Generating

abstract: Deep embedding learning becomes more attractive for discriminative feature learning, but many methods still require hard-class mining, which is computationally complex and performance-sensitive. To this end, we propose Adaptive Large Margin N-Pair loss (ALMN) to address the aforementioned issues. Instead of exploring hard example-mining strategy, we introduce the concept of large margin constraint. This constraint aims at encouraging local-adaptive large angular decision margin among dissimilar samples in multimodal feature space so as to significantly encourage intraclass compactness and interclass separability. And it is mainly achieved by a simple yet novel geometrical Virtual Point Generating (VPG) method, which converts artificially setting a fixed margin into automatically generating a boundary training sample in feature space and is an open question. We demonstrate the effectiveness of our method on several popular datasets for image retrieval and clustering tasks.


title: Monocular Depth Estimation with Augmented Ordinal Depth Relationships

abstract: Most existing algorithms for depth estimation from single monocular images need large quantities of metric groundtruth depths for supervised learning. We show that relative depth can be an informative cue for metric depth estimation and can be easily obtained from vast stereo videos. Acquiring metric depths from stereo videos is sometimes impracticable due to the absence of camera parameters. In this paper, we propose to improve the performance of metric depth estimation with relative depths collected from stereo movie videos using existing stereo matching algorithm.We introduce a new "Relative Depth in Stereo" (RDIS) dataset densely labelled with relative depths. We first pretrain a ResNet model on our RDIS dataset. Then we finetune the model on RGB-D datasets with metric ground-truth depths. During our finetuning, we formulate depth estimation as a classification task. This re-formulation scheme enables us to obtain the confidence of a depth prediction in the form of probability distribution. With this confidence, we propose an information gain loss to make use of the predictions that are close to ground-truth. We evaluate our approach on both indoor and outdoor benchmark RGB-D datasets and achieve state-of-the-art performance.


Geographical concepts in ancient India


title: Adult scoliosis can be reduced through specific SEAS exercises: a case report

abstract: BackgroundIt has been known since many years that scoliosis can continue to progress after skeletal maturity: the rate of progression has shown to be linear, and it can be used to establish an individual prognosis. Once there is progression there is an indication for treatment: usually it is proposed a surgical one. There are very few papers on an alternative rehabilitation approach; since many years we propose specific SEAS exercises and the aim of this study is to present one case report on this approach.Case presentationAll radiographs have been measured blindly twice using the same protractor by one expert physician whose repeatability error proved to be < 3° Cobb; the average measurement has been used. In this case a 25 years old female scoliosis patient, previously treated from 14 (Risser 1) to 19 years of age with a decrease of the curve from 46° to 37°, showed a progression of 10° Cobb in 6 years. The patient has then been treated with SEAS exercises only, and in one year progression has been reverted from 47° to 28.5°.ConclusionA scoliosis curve is made of different components: the structural bony and ligamentous components, and a postural one that counts up to 9° in children, while it has not been quantified in adults. This case shows that when adult scoliosis aggravates it is possible to intervene with specific exercises (SEAS) not just to get stability, but to recover last years collapse. The reduction of scoliotic curve through rehabilitation presumably does not indicate a reduction of the bone deformity, but rely on a recovery of the upright postural collapse. This reduction can decrease the chronic asymmetric load on the spine and, in the long run, reduce the risks of progression.


title: A novel quadratic buck-boost DC-DC converter without floating gate-driver

abstract: A novel buck-boost dc-dc converter with high voltage step-up and step-down ability has been introduced in this paper. There are two switches in this converter, of which the sources are connected together and also connected to the ground of the input voltage source. Hence, there is no need for floating gate driver. In addition, the voltage stress of one of the switches is lower than the existing quadratic buck-boost converters. Performance of the analytical results is validated using both simulation in MATLAB/SIMULINK and experimental tests where the proposed converter is evaluated in both buck and boost modes.


title: Towards a dynamic QoS management in IMS transport layer via SDN

abstract: All real-time services provided by the IP Multimedia Subsystem (IMS) require QoS mechanisms to avoid traffic congestion. Recently, the Software Defined Networking (SDN) paradigm has emerged to enable dynamic network resources, and then a way to perform QoS management. The paper presents an approach to integrate the IMS with the SDN paradigm. The work is motivated by the lack of QoS management of real-time flows in the IMS transport layer, which can be performed through implementation over an SDN controller. The architecture proposed implements new modules in the SDN controller in order to enhance QoS management in the IMS transport layer. The feasibility and the performance of the approach proposed are assessed by experiments.


Case Study of Test Case Generation Based on Metamodel for Model Transformations


title: CacheShield: Protecting Legacy Processes Against Cache Attacks

abstract: Cache attacks pose a threat to any code whose execution flow or memory accesses depend on sensitive information. Especially in public clouds, where caches are shared across several tenants, cache attacks remain an unsolved problem. Cache attacks rely on evictions by the spy process, which alter the execution behavior of the victim process. We show that hardware performance events of cryptographic routines reveal the presence of cache attacks. Based on this observation, we propose CacheShield, a tool to protect legacy code by monitoring its execution and detecting the presence of cache attacks, thus providing the opportunity to take preventative measures. CacheShield can be run by users and does not require alteration of the OS or hypervisor, while previously proposed software-based countermeasures require cooperation from the hypervisor. Unlike methods that try to detect malicious processes, our approach is lean, as only a fraction of the system needs to be monitored. It also integrates well into today's cloud infrastructure, as concerned users can opt to use CacheShield without support from the cloud service provider. Our results show that CacheShield detects cache attacks fast, with high reliability, and with few false positives, even in the presence of strong noise.


title: Satellite image segmentation using Self- Organizing Maps and Fuzzy C-Means

abstract: The quality of image interpretation depends strongly on the segmentation process which is an important step in image processing. Most of the segmentation methods and approaches are not suitable for noisy environments such as satellite images of high resolution. Sometime they require a priori knowledge, and another time they do not work on all types of images. Self-Organizing Maps (SOMs) and Fuzzy C-Means (FCM) segmentation methods are widely used to process different types of simple and complex images. These two important known methods are reviewed, and summarized. In addition, a new approach is created based on SOMs and FCM. The reason for combining both methods is to create an unsupervised parameter free approach. The new approach is applied on two different types of medium and high resolution satellite images in order to examine the accuracy of the segmentation methods and the new approach. This paper and the results of experiments provide the reader with information about the improvement obtained by this approach compared to known commercial segmentation method.


Certificate Validation Through Public Ledgers and Blockchains


title: Silicon-Filled Rectangular Waveguides and Frequency Scanning Antennas for mm-Wave Integrated Systems

abstract: We present a technology for the manufacturing of silicon-filled integrated waveguides enabling the realization of low-loss high-performance millimeter-wave passive components and high gain array antennas, thus facilitating the realization of highly integrated millimeter-wave systems. The proposed technology employs deep reactive-ion-etching (DRIE) techniques with aluminum metallization steps to integrate rectangular waveguides with high geometrical accuracy and continuous metallic side walls. Measurement results of integrated rectangular waveguides are reported exhibiting losses of 0.15 dB/ λg at 105 GHz. Moreover, ultra-wideband coplanar to waveguide transitions with 0.6 dB insertion loss at 105 GHz and return loss better than 15 dB from 80 to 110 GHz are described and characterized. The design, integration and measured performance of a frequency scanning slotted-waveguide array antenna is reported, achieving a measured beam steering capability of 82 ° within a band of 23 GHz and a half-power beam-width (HPBW) of 8.5 ° at 96 GHz. Finally, to showcase the capability of this technology to facilitate low-cost mm-wave system level integration, a frequency modulated continuous wave (FMCW) transmit-receive IC for imaging radar applications is flip-chip mounted directly on the integrated array and experimentally characterized.


title: Aspect-Oriented Software Development based Solution for Intervention Concerns Problems:Case Study

abstract: All existing methods for Developing Software Systems, most insist on a separate system to keep the components together till they have been had the least overlapping. But these methods in the management system those have some parts and are using use case, and involved in the other parts of the systems, are inefficient. With arriving the Aspect-Oriented Programming, programmers were able to Implement the overcome some of these requirements and Implement them in a separate unit, but there are still some of the analysis requirements and design, because of the wrong analysis and design they cannot be implemented as a measure. In this article we want to prominent the phase of analysis and design of this work using the Aspect-Oriented Software, in order to implement them in the Implementation phase as a cupon.


title: Hydrostatic Transmission Technique with Secondary Regulation

abstract: In this paper the definition, constitution, performance characteristics and working principle of hydrostatic transmission technique with secondary regulation are provided. Control rules and application fields of it are also introduced.


title: The SUMMA Platform Prototype

abstract: We present the first prototype of the SUMMA Platform: an integrated platform for multilingual media monitoring. The platform contains a rich suite of low-level and high-level natural language processing technologies: automatic speech recognition of broadcast media, machine translation, automated tagging and classification of named entities, semantic parsing to detect relationships between entities, and automatic construction / augmentation of factual knowledge bases. Implemented on the Docker platform, it can easily be deployed, customised, and scaled to large volumes of incoming media streams.


title: Could Harmonic Scalpel (Ultracision®) be considered the best device in surgical treatment of vulvar cancer of patients with implanted pace-maker? Proposal and rationale.

abstract: Vulvar cancer (VC) represents about 4% of gynecologic malignancies, its incidence increases with age and peak incidence is found between 70-79 years. In cases of locally advanced disease surgery is often required and radical vulvectomy, with or without mono-bilateral inguino-femoral lymphadenectomy, is standard management. Various devices have been implemented in gynecological surgery in an attempt to minimize or avoid frequent intra/postoperative complications linked to energy use, unfortunately the majority of these devices require monopolar or bipolar energy. Ultracision® represents a unique surgical device capable of performing both cutting and coagulation at different intensities without use of electric energy. The use of Ultracision® in the radical treatment of VC has advantages both in terms of intraoperative and postoperative complications responsible for the reduction of surgical time and blood loss, complete tissue removal according to oncological criteria, diminished desensitization of peripheral areas and reduction of wound complications. These advantages have been widely demonstrated and contribute to making Ultracision® a cost-effective option in the routine treatment of patients affected by vulvar cancer especially when considering its safety in cardiopathic patients with implanted pacemaker. If the impressive results achieved in radical vulvar surgery will be confirmed, scalpel use could be proposed as routine for surgery of the routinely in surgical approach of vulvar and perineal area, in both benign and malignant disease.


title: How well does a commercially available wearable device measure sleep in young athletes

abstract: ABSTRACTThe validity of a commercially available wearable device for measuring total sleep time was examined in a sample of well-trained young athletes during night-time sleep periods and daytime naps. Participants wore a FitBit HR Charge on their non-dominant wrist and had electrodes attached to their face and scalp to enable polysomnographic recordings of sleep in the laboratory. The FitBit automatically detected 24/30 night-time sleep periods but only 6/20 daytime naps. Compared with polysomnography, the FitBit overestimated total sleep time by an average of 52 ± 152 min for night-time sleep periods, and by 4 ± 8 min for daytime naps. It is important for athletes and practitioners to be aware of the limitations of wearable devices that automatically detect sleep duration.


Physics in High Electric Fields


title: On the resonance hypothesis of storm surge and surf beat run-up

abstract: Abstract. Resonance has recently been proposed as the fundamental underlying mechanism that shapes the amplification in coastal run-up for storm surges and surf beats, which are long-wavelength disturbances created by fluid velocity differences between the wave groups and the regions outside the wave groups. It is without doubt that the resonance plays a role in run-up phenomena of various kinds; however, we think that the extent to which it plays its role has not been completely understood. For incident waves, which we assume to be linear, the best approach to investigate the role played by the resonance would be to calculate the normal modes by taking radiation damping into account and then testing how those modes are excited by the incident waves. Such modes diverge offshore, but they can still be used to calculate the run-up. There are a small number of previous works that attempt to calculate the resonant frequencies, but they do not relate the amplitudes of the normal modes to those of the incident wave. This is because, by not including radiation damping, they automatically induce a resonance that leads to infinite amplitudes, thus preventing them from predicting the exact contribution of the resonance to coastal run-up. In this study we consider two different coastal geometries: an infinitely wide beach with a constant slope connecting to a flat-bottomed deep ocean and a bay with sloping bottom, again, connected to a deep ocean. For the fully 1-D problem we find significant resonance if the bathymetric discontinuity is large.The linearisation of the seaward boundary condition leads to slightly smaller run-ups. For the 2-D ocean case the analysis shows that the wave confinement is very effective when the bay is narrow. The bay aspect ratio is the determining factor for the radiation damping. One reason why we include a bathymetric discontinuity is to mimic some natural settings where bays and gulfs may lead to abrupt depth gradients such as the Tokyo Bay. The other reason is, as mentioned above, to test the role played by the depth discontinuity for resonance.


title: Self-evaluating compilation applied to loop unrolling

abstract: A device for ultrasonic broaching of a group of holes in articles made from hard and brittle materials by means of a wire tool which is fed periodically by a discrete feeding mechanism as the wires wear out, the holder of the wire tool being fashioned as plates of which one is provided with slots, each slot accomodating one wire.


title: Honeypot-Powered Malware Reverse Engineering

abstract: Honeypots, i.e. networked computer systems specially designed and crafted to mimic the normal operations of other systems while capturing and storing information about the interactions with the world outside, are a crucial technology into the study of cyber threats and attacks that propagate and occur through networks. Among them, high interaction honeypots are considered the most efficient because the attacker (whether automated or not) perceives realistic interactions with the target machine. In the case of automated attacks, propagated by malwares, currently available honeypots alone are not specialized enough to allow the analysis of their behaviors and effects on the target system. The research presented in this paper shows how high interaction honeypots can be enhanced by powering them with specific features that improve the reverse engineering activities needed to effectively analyze captured malicious entities.


title: Implementation and Experimental Results of a Quaternion-Based Kalman Filter for Human Body Motion Tracking

abstract: A human body motion tracking system based on use of the MARG (Magnetic, Angular Rate, and Gravity) sensors has been under development at the Naval Postgraduate School and Miami University. The design of a quaternion-based Kalman filter for processing the MARG sensor data was described in [1]. This paper presents the real-time implementation and testing results of the quaternion-based Kalman filter. Experimental results validate the Kalman filter design, and show the feasibility of the MARG sensors for real-time human body motion tracking.


Adult Content Recognition from Images Using a Mixture of Convolutional Neural Networks


title: Discriminative Optimization: Theory and Applications to Point Cloud Registration

abstract: Many computer vision problems are formulated as the optimization of a cost function. This approach faces two main challenges: (1) designing a cost function with a local optimum at an acceptable solution, and (2) developing an efficient numerical method to search for one (or multiple) of these local optima. While designing such functions is feasible in the noiseless case, the stability and location of local optima are mostly unknown under noise, occlusion, or missing data. In practice, this can result in undesirable local optima or not having a local optimum in the expected place. On the other hand, numerical optimization algorithms in high-dimensional spaces are typically local and often rely on expensive first or second order information to guide the search. To overcome these limitations, this paper proposes Discriminative Optimization (DO), a method that learns search directions from data without the need of a cost function. Specifically, DO explicitly learns a sequence of updates in the search space that leads to stationary points that correspond to desired solutions. We provide a formal analysis of DO and illustrate its benefits in the problem of 2D and 3D point cloud registration both in synthetic and range-scan data. We show that DO outperforms state-of-the-art algorithms by a large margin in terms of accuracy, robustness to perturbations, and computational efficiency.