title: IsoRankN: spectral methods for global alignment of multiple protein networks

abstract: Motivation: With the increasing availability of large protein–protein interaction networks, the question of protein network alignment is becoming central to systems biology. Network alignment is further delineated into two sub-problems: local alignment, to find small conserved motifs across networks, and global alignment, which attempts to find a best mapping between all nodes of the two networks. In this article, our aim is to improve upon existing global alignment results. Better network alignment will enable, among other things, more accurate identification of functional orthologs across species. Results: We introduce IsoRankN (IsoRank-Nibble) a global multiplenetwork alignment tool based on spectral clustering on the induced graph of pairwise alignment scores. IsoRankN outperforms existing algorithms for global network alignment in coverage and consistency on multiple alignments of the five available eukaryotic networks. Being based on spectral methods, IsoRankN is both error tolerant and computationally efficient. Availability: Our software is available freely for non-commercial purposes on request from: http://isorank.csail.mit.edu/


title: Guidelines for conducting and reporting case study research in software engineering

abstract: Case study is a suitable research methodology for software engineering research since it studies contemporary phenomena in its natural context. However, the understanding of what constitutes a case study varies, and hence the quality of the resulting studies. This paper aims at providing an introduction to case study methodology and guidelines for researchers conducting case studies and readers studying reports of such studies. The content is based on the authors' own experience from conducting and reading case studies. The terminology and guidelines are compiled from different methodology handbooks in other research domains, in particular social science and information systems, and adapted to the needs in software engineering. We present recommended practices for software engineering case studies as well as empirically derived and evaluated checklists for researchers and readers of case study research.


title: Energy-aware Virtual Machine Migration for Cloud Computing - A Firefly Optimization Approach

abstract: Energy efficiency has grown into a latest exploration area of virtualized cloud computing paradigm. The increase in the number and the size of the cloud data centers has propagated the need for energy efficiency. An extensively practiced technology in cloud computing is live virtual machine migration and is thus focused in this work to save energy. This paper proposes an energy-aware virtual machine migration technique for cloud computing, which is based on the Firefly algorithm. The proposed technique migrates the maximally loaded virtual machine to the least loaded active node while maintaining the performance and energy efficiency of the data centers. The efficacy of the proposed technique is exhibited by comparing it with other techniques using the CloudSim simulator. An enhancement in the average energy consumption of about 44.39 % has been attained by reducing an average of 72.34 % of migrations and saving 34.36 % of hosts, thereby, making the data center more energy-aware.


title: Quit Playing Games with My Heart: Understanding Online Dating Scams

abstract: Online dating sites are experiencing a rise in popularity, with one in five relationships in the United States starting on one of these sites. Online dating sites provide a valuable platform not only for single people trying to meet a life partner, but also for cybercriminals, who see in people looking for love easy victims for scams. Such scams span from schemes similar to traditional advertisement of illicit services or goods i.e., spam to advanced schemes, in which the victim starts a long-distance relationship with the scammer and is eventually extorted money. ::: ::: In this paper we perform the first large-scale study of online dating scams. We analyze the scam accounts detected on a popular online dating site over a period of eleven months, and provide a taxonomy of the different types of scammers that are active in the online dating landscape. We show that different types of scammers target a different demographics on the site, and therefore set up accounts with different characteristics. Our results shed light on the threats associated to online dating scams, and can help researchers and practitioners in developing effective countermeasures to fight them.


title: Pragmatic randomized controlled trial of long-term psychoanalytic psychotherapy for treatment-resistant depression: the Tavistock Adult Depression Study (TADS)

abstract: This pragmatic randomized controlled trial tested the effectiveness of long-term psychoanalytic psychotherapy (LTPP) as an adjunct to treatment-as-usual according to UK national guidelines (TAU), compared to TAU alone, in patients with long-standing major depression who had failed at least two different treatments and were considered to have treatment-resistant depression. Patients (N=129) were recruited from primary care and randomly allocated to the two treatment conditions. They were assessed at 6-monthly intervals during the 18 months of treatment and at 24, 30 and 42 months during follow-up. The primary outcome measure was the 17-item version of the Hamilton Depression Rating Scale (HDRS-17), with complete remission defined as a HDRS-17 score ≤8, and partial remission defined as a HDRS-17 score ≤12. Secondary outcome measures included self-reported depression as assessed by the Beck Depression Inventory - II, social functioning as evaluated by the Global Assessment of Functioning, subjective wellbeing as rated by the Clinical Outcomes in Routine Evaluation - Outcome Measure, and satisfaction with general activities as assessed by the Quality of Life Enjoyment and Satisfaction Questionnaire. Complete remission was infrequent in both groups at the end of treatment (9.4% in the LTPP group vs. 6.5% in the control group) as well as at 42-month follow-up (14.9% vs. 4.4%). Partial remission was not significantly more likely in the LTPP than in the control group at the end of treatment (32.1% vs. 23.9%, p=0.37), but significant differences emerged during follow-up (24 months: 38.8% vs. 19.2%, p=0.03; 30 months: 34.7% vs. 12.2%, p=0.008; 42 months: 30.0% vs. 4.4%, p=0.001). Both observer-based and self-reported depression scores showed steeper declines in the LTPP group, alongside greater improvements on measures of social adjustment. These data suggest that LTPP can be useful in improving the long-term outcome of treatment-resistant depression. End-of-treatment evaluations or short follow-ups may miss the emergence of delayed therapeutic benefit.


title: Quantum Genetic Learning Control of Quantum Ensembles with Hamiltonian Uncertainties

abstract: In this paper, a new method for controlling a quantum ensemble that its members have uncertainties in Hamiltonian parameters is designed. Based on combining the sampling-based learning control (SLC) and a new quantum genetic algorithm (QGA) method, the control of an ensemble of a two-level quantum system with Hamiltonian uncertainties is achieved. To simultaneously transfer the ensemble members to a desired state, an SLC algorithm is designed. For reducing the transfer error significantly, an optimization problem is defined. Considering the advantages of QGA and the nature of the problem, the optimization problem by using the QGA method is solved. For this purpose, N samples through sampling of the uncertainty parameters via uniform distribution are generated and an augmented system is also created. By using QGA in the training step, the best control signal is obtained. To test the performance and validation of the method, the obtained control is implemented for some random selected samples. A couple of examples are simulated for investigating the proposed model. The results of the simulations indicate the effectiveness and the advantages of the proposed method.


title: Nectar: Automatic Management of Data and Computation in Datacenters

abstract: Managing data and computation is at the heart of datacenter computing. Manual management of data can lead to data loss, wasteful consumption of storage, and laborious bookkeeping. Lack of proper management of computation can result in lost opportunities to share common computations across multiple jobs or to compute results incrementally. ::: ::: Nectar is a system designed to address the aforementioned problems. It automates and unifies the management of data and computation within a datacenter. In Nectar, data and computation are treated interchangeably by associating data with its computation. Derived datasets, which are the results of computations, are uniquely identified by the programs that produce them, and together with their programs, are automatically managed by a datacenter wide caching service. Any derived dataset can be transparently regenerated by reexecuting its program, and any computation can be transparently avoided by using previously cached results. This enables us to greatly improve datacenter management and resource utilization: obsolete or infrequently used derived datasets are automatically garbage collected, and shared common computations are computed only once and reused by others. ::: ::: This paper describes the design and implementation of Nectar, and reports on our evaluation of the system using analytic studies of logs from several production clusters and an actual deployment on a 240-node cluster.


title: Multi-Resolution Language Grounding with Weak Supervision

abstract: Language is given meaning through its correspondence with a world representation. This correspondence can be at multiple levels of granularity or resolutions. In this paper, we introduce an approach to multi-resolution language grounding in the extremely challenging domain of professional soccer commentaries. We define and optimize a factored objective function that allows us to leverage discourse structure and the compositional nature of both language and game events. We show that finer resolution grounding helps coarser resolution grounding, and vice versa. Our method results in an F1 improvement of more than 48% versus the previous state of the art for fine-resolution grounding 1 .


title: A permutation test of host-parasite cospeciation.

abstract: We introduce a statistical method that explores host-parasite coevolution by testing the null hypothesis that hosts and their associated parasites evolved independently. This test is simple and intuitive and involves only suitable randomization of the observed data. It is not even necessary to construct host and parasite phylogenetic trees, as the test can be performed directly on distance matrices. Statistical power of the test was evaluated using simulated data consistent with the alternative hypothesis of cospeciation. Results were compared with the method of Mantel (1967) and the ParaFit method of Legendre et al. (2002). We observed that our method has greater power overall and thus a higher ability to detect cospeciation in closely related host-parasite systems. Our test was also successful when applied to the pocket gopher and chewing lice system.


title: Shared information and program plagiarism detection

abstract: A fundamental question in information theory and in computer science is how to measure similarity or the amount of shared information between two sequences. We have proposed a metric, based on Kolmogorov complexity, to answer this question and have proven it to be universal. We apply this metric in measuring the amount of shared information between two computer programs, to enable plagiarism detection. We have designed and implemented a practical system SID (Software Integrity Diagnosis system) that approximates this metric by a heuristic compression algorithm. Experimental results demonstrate that SID has clear advantages over other plagiarism detection systems. SID system server is online at http://software.bioinformatics.uwaterloo.ca/SID/.


title: Story-map: iPad companion for long form TV narratives

abstract: Long form TV narratives present multiple continuing characters and story arcs that last over multiple episodes and even over multiple seasons. Writers increasingly take pride in creating coherent and persistent story worlds with recurring characters and references to backstory. Since viewers may join the story at different points and different levels of commitment, they need support to orient them to the fictional world, to remind them of plot threads, and to allow them to review important story sequences across episodes. Using the affordances of the digital medium we can create navigation patterns and auxiliary information streams to minimize confusion and maximize immersion in the story world. In our application, the iPad is used as a secondary screen to create a character map synchronized with the TV content, and to support navigation of story threads across episodes.


title: Participatory design of wearable augmented reality display elements for children at play

abstract: This paper presents the findings of a low-fidelity participatory design activity for the design of wearable Augmented Reality (AR) experiences for children at play. The aims of the research were to gain insights into the different types of augmentations children find engaging and useful in different play contexts. The papers contribution is both the method used, and the insights gained from the design session. Using the method outlined the paper, young children were able to successfully understand the concept of augmented reality and communicate their ideas for the design of AR play experiences without first seeing a concrete example of wearable display technology. The children designed for three distinct types of play experience, Constructive play, Role play and Creative play. The children used the design packs assembled by the researchers to express their ideas. These were then coded by four experienced Child Computer Interaction (CCI) researchers to analyze the artifacts created by the children. We identify 10 design themes for AR artifacts in play and discuss the frequency of artifacts found within these themes across the different types of play investigated.


title: Toward the Integration of Meditation into Higher Education: A Review of Research Evidence.

abstract: A direction fixture, particularly to that attached on the socket, comprises a retaining ring having an elastic access notch for retaining the electric wires of the decorative lamp series to fix the direction of the series of socket, namely, the lamps on respective socket can be arranged in a certain direction in series.


title: BDCM sensorless control for twelve-step square-wave PWM

abstract: After studying the development of BDCM sensorless control with six-step square-wave PWM (SWPWM) and six-step back-EMF detecting scheme (BEDS), the BDCM sensorless control with twelve-step SWPWM and twelve-step BEDS had been proposed and implemented in this paper. The proposed twelve-step BEDS generates the twelve commutation instants from six back-EMF zero-crossings during each cycle where the six-step BEDS generates the six commutation instants from six back-EMF zero-crossings. Since the analysis of six-step BEDS is helpful to develop the twelve-step BEDS, this paper studies the six-step SWPWM and six-step BEDS first and explains why some specific six-step SWPWM type is always used in the six-step BDCM sensorless control. Then, we obtain the specific twelve-step SWPWM types and develop the proposed twelve-step BEDS. From the simulated and experimental results, BDCM sensorless control with twelve-step SWPWM shows less current harmonics than that with six-step SWPWM.


title: Multimodal Deep Autoencoder for Human Pose Recovery

abstract: Video-based human pose recovery is usually conducted by retrieving relevant poses using image features. In the retrieving process, the mapping between 2D images and 3D poses is assumed to be linear in most of the traditional methods. However, their relationships are inherently non-linear, which limits recovery performance of these methods. In this paper, we propose a novel pose recovery method using non-linear mapping with multi-layered deep neural network. It is based on feature extraction with multimodal fusion and back-propagation deep learning. In multimodal fusion, we construct hypergraph Laplacian with low-rank representation. In this way, we obtain a unified feature description by standard eigen-decomposition of the hypergraph Laplacian matrix. In back-propagation deep learning, we learn a non-linear mapping from 2D images to 3D poses with parameter fine-tuning. The experimental results on three data sets show that the recovery error has been reduced by 20%–25%, which demonstrates the effectiveness of the proposed method.


title: ProfilIoT: a machine learning approach for IoT device identification based on network traffic analysis

abstract: In this work we apply machine learning algorithms on network traffic data for accurate identification of IoT devices connected to a network. To train and evaluate the classifier, we collected and labeled network traffic data from nine distinct IoT devices, and PCs and smartphones. Using supervised learning, we trained a multi-stage meta classifier; in the first stage, the classifier can distinguish between traffic generated by IoT and non-IoT devices. In the second stage, each IoT device is associated a specific IoT device class. The overall IoT classification accuracy of our model is 99.281+.


title: Midface: Clinical Anatomy and Regional Approaches with Injectable Fillers.

abstract: The clinical approach towards the midface is one of the most important interventions for practitioners when treating age-related changes of the face. Currently a plethora of procedures are used and presented. However, few of these approaches have been validated or passed review board assigned evaluations. Therefore, it is the aim of this work to establish a guideline manual for practitioners for a safe and effective mid-face treatment based on the most current concepts of facial anatomy. The latter is based on the 5-layered structural arrangement and its understanding is the key towards the favoured outcome and for minimizing complications.


Multiple View Geometry in Computer Vision.


title: Spontaneous Canine Keratoconjunctivitis Sicca: A Useful Model for Human Keratoconjunctivitis Sicca: Treatment With Cyclosporine Eye Drops

abstract: • Thirty-six sequential cases of canine keratoconjunctivitis sicca (KCS) were treated with ophthalmic cyclosporine. The effects of topical cyclosporine were twofold: (1) cyclosporine increased tear production by 5 mm/min or greater in all cases of spontaneous KCS having an initial Schirmer's Tear Test value greater than 2 mm /min and in 59% of eyes with an initial Schirmer's Tear Test value of 0 to 2 mm/min, and (2) cyclosporine caused marked regression of chronic corneal neovascularization and granulation even in eyes in which lacrimation failed to improve. Additional benefits of topical cyclosporine were reduced mucopurulent conjunctivitis, rapid healing of nonhealing corneal ulcers, and reduced dependence on frequent topical treatments of KCS. Twelve normal beagles treated with topical cyclosporine also had a reversible increase in lacrimation compared with baseline or placebo control-treated dogs.


title: Document clustering: TF-IDF approach

abstract: Recent advances in computer and technology resulted into ever increasing set of documents. The need is to classify the set of documents according to the type. Laying related documents together is expedient for decision making. Researchers who perform interdisciplinary research acquire repositories on different topics. Classifying the repositories according to the topic is a real need to analyze the research papers. Experiments are tried on different real and artificial datasets such as NEWS 20, Reuters, emails, research papers on different topics. Term Frequency-Inverse Document Frequency algorithm is used along with fuzzy K-means and hierarchical algorithm. Initially experiment is being carried out on small dataset and performed cluster analysis. The best algorithm is applied on the extended dataset. Along with different clusters of the related documents the resulted silhouette coefficient, entropy and F-measure trend are presented to show algorithm behavior for each data set.


title: Intraday stock price forecasting based on variational mode decomposition

abstract: Abstract This paper presents a hybrid predictive model for forecasting intraday stock prices. The proposed model hybridizes the variational mode decomposition (VMD) which is a new multiresolution technique with backpropagation neural network (BPNN). The VMD is used to decompose price series into a sum of variational modes (VM). The extracted VM are used to train BPNN. Besides, particle swarm optimization (PSO) is employed for BPNN initial weights optimization. Experimental results from a set of six stocks show the superiority of the hybrid VMD–PSO–BPNN predictive model over the baseline predictive model which is a PSO–BPNN model trained with past prices.


Citing data sources in the social sciences: do authors do it?


title: A comparative study on face spoofing attacks

abstract: In the last 57 years, face biometrie researchers have achieved many successes. Face recognition systems have been extensively used in government as well as commercial applications such as mobile, banking and surveillance systems etc. In the last 10 years, the whole biometric community such as researchers, developers, and retailers have worked on challenging tasks to develop a more accurate protection method against spoofing threats. The face spoofing attacks affect high-security field in the companies, government sectors, rising small and medium sized endeavors. Although several face antispoofing or liveness detection methods have been proposed, the issue is still unresolved due to difficulty in finding the features and methods for spoof attacks. Recently it has been shown that the traditional face biometric techniques are more vulnerable to spoofing attacks, so entire research community required to concentrate more to resolve solutions against spoofing attacks. The goal of this paper is to provide a detailed study of antispoofing methodologies and evaluation of databases. The study concluded that there is a need to provide more generalized algorithms for detection of unpredictable spoofing attacks in order to make the system more secure, computationally efficient and reliable.


title: Statistical Machine Translation in Low Resource Settings

abstract: My thesis will explore ways to improve the performance of statistical machine translation (SMT) in low resource conditions. Specifically, it aims to reduce the dependence of modern SMT systems on expensive parallel data. We define low resource settings as having only small amounts of parallel data available, which is the case for many language pairs. All current SMT models use parallel data during training for extracting translation rules and estimating translation probabilities. The theme of our approach is the integration of information from alternate data sources, other than parallel corpora, into the statistical model. In particular, we focus on making use of large monolingual and comparable corpora. By augmenting components of the SMT framework, we hope to extend its applicability beyond the small handful of language pairs with large amounts of available parallel text.


title: Runtime countermeasures for code injection attacks against C and C++ programs

abstract: The lack of memory safety in C/C++ often leads to vulnerabilities. Code injection attacks exploit these vulnerabilities to gain control over the execution flow of applications. These attacks have played a key role in many major security incidents. Consequently, a huge body of research on countermeasures exists. We provide a comprehensive and structured survey of vulnerabilities and countermeasures that operate at runtime. These countermeasures make different trade-offs in terms of performance, effectivity, compatibility, etc., making it hard to evaluate and compare countermeasures in a given context. We define a classification and evaluation framework on the basis of which countermeasures can be assessed.


title: A dynamically stable single-wheeled mobile robot with inverse mouse-ball drive

abstract: Multi-wheel statically-stable mobile robots tall enough to interact meaningfully with people must have low centers of gravity, wide bases of support, and low accelerations to avoid tipping over. These conditions present a number of performance limitations. Accordingly, we are developing an inverse of this type of mobile robot that is the height, width, and weight of a person, having a high center of gravity, that balances dynamically on a single spherical wheel. Unlike balancing 2-wheel platforms which must turn before driving in some direction, the single-wheel robot can move directly in any direction. We present the overall design, actuator mechanism based on an inverse mouse-ball drive, control system, and initial results including dynamic balancing, station keeping, and point-to-point motion


title: A study in two-handed input

abstract: Two experiments were run to investigate two-handed input. The experimental tasks were representative of those found in CAD and office information systems. Experiment one involved the performance of a compound selection/positioning task. The two sub-tasks were performed by different hands using separate transducers. Without prompting, novice subjects adopted strategies that involved performing the two sub-tasks simultaneously. We interpret this as a demonstration that, in the appropriate context, users are capable of simultaneously providing continuous data from two hands without significant overhead. The results also show that the speed of performing the task was strongly correlated to the degree of parallelism employed. Experiment two involved the performance of a compound navigation/selection task. It compared a one-handed versus two-handed method for finding and selecting words in a document. The two-handed method significantly outperformed the commonly used one-handed method by a number of measures. Unlike experiment one, only two subjects adopted strategies that used both hands simultaneously. The benefits of the two-handed technique, therefore, are interpreted as being due to efficiency of hand motion. However, the two subjects who did use parallel strategies had the two fastest times of all subjects.


title: Mobile, dexterous, social robots for mobile manipulation and human-robot interaction

abstract: A tractor 10 is disclosed for engaging and advancing a web of paper 20 by engaging a series of holes 21 along an edge of the paper 20. The tractor 10 includes a pair of parallel side plates 12, 14 and an elongated guide member 32 defining a continuous guide track 34. The guide member 32 is positioned between the side plates 12, 14 so as to define a continuous guide channel 36 in cooperation with the guide plates 12, 14. The tractor 10 also includes a drive belt assembly 22 comprising a continuous belt 62 carrying a plurality of spaced drive members 38 adapted to engage the holes 21 along the edge of the paper 20. A drive sprocket 33 located at one end of the guide channel 36 engages the drive members 38. Each guide member 38 has a hemicylindrically shaped base 64 with a notch 66 therein, thereby defining two parallel edges 67 which ride along the guide track 34. Additionally, each drive member 38 includes a drive pin 80, the profile of the surface of the drive pin 80 has the shape of a segment of an involute.


title: A social movement perspective on demagoguery: Achieving symbolic realignment

abstract: This essay argues that negative assumptions about demagogues have precluded an adequate understanding of demagogic discourse. Scholars typically have been unwilling to acknowledge that demagogic responses may be functional and even appropriate in certain rhetorical situations. This essay analyzes and critiques the literature on demagoguery, and then turns more helpfully to a social movement perspective which employs a “social construction of reality” approach. Specifically, the essay analyzes the discourse of Louis Farrakhan using a “symbolic realignment” perspective drawn from social movement literature.


title: Multiagent-Based Distributed-Energy-Resource Management for Intelligent Microgrids

abstract: Microgrid is a combination of distributed generators, storage systems, and controllable loads connected to low-voltage network that can operate either in grid-connected or in island mode. High penetration of power at distribution level creates such multiple microgrids. This paper proposes a two-level architecture for distributed-energy-resource management for multiple microgrids using multiagent systems. In order to match the buyers and sellers in the energy market, symmetrical assignment problem based on naive auction algorithm is used. The developed mechanism allows the pool members such as generation agents, load agents, auction agents, grid agents, and storage agents to participate in market. Three different scenarios are identified based on the supply-demand mismatch among the participating microgrids. At the end of this paper, two case studies are presented with two and four interconnected microgrids participating in the market. Simulation results clearly indicate that the agent-based management is effective in resource management among multiple microgrids economically and profitably.


title: Physical model of a quarter-car active suspension system

abstract: The potential advantages of a modern active suspension system are recognized for race cars, on road cars or even for great series cars. Beside the passenger ride comfort, the control of the vehicle vertical acceleration directly affects the road holding. In the case of passive suspension models, the car structure is optimized in initial stage of design, taking into consideration the spring stiffness, damper coefficient, sprung and unsprung mass, various tires performance and different velocities, etc. The performance of the passive suspension system is limited, in the first line, by the inherent variation of these parameters. The active suspension system preserves the advantage of a close loop control system, because these actuators are controlled by devices which receive and process the direct information from proper sensors. In order to evaluate the active suspension system performance, more possibilities are known: mathematical analog or digital models, the design and implementation of test set-up versions or physical test models. Obviously, the physical test models are able to act as the common car structure, but these manufactured equipments are not available on the market. In this sense, the authors conceived, designed and built a test setup version, a “physical model”, as a robust, simplified solution, with only one acceleration/speed transducer in the control loop. The actuator is an electromagnet operating against an internal spring. The structure comprises a cam, driven by a DC motor, which simulates the road conditions, an articulated wheel with tire, a spring and damper. The car body is simulated by an articulated plate. To test the active suspension system performances are attached two position sensors, one for wheel and one for the car body. Based on this structure, various modern designed control strategies can be implemented and tested.


title: Discriminative Region Proposal Adversarial Networks for High-Quality Image-to-Image Translation

abstract: Image-to-image translation has been made much progress with embracing Generative Adversarial Networks (GANs). However, it’s still very challenging for translation tasks that require high quality, especially at high-resolution and photorealism. In this paper, we present Discriminative Region Proposal Adversarial Networks (DRPAN) for high-quality image-to-image translation. We decompose the procedure of image-to-image translation task into three iterated steps, first is to generate an image with global structure but some local artifacts (via GAN), second is using our DRPnet to propose the most fake region from the generated image, and third is to implement “image inpainting” on the most fake region for more realistic result through a reviser, so that the system (DRPAN) can be gradually optimized to synthesize images with more attention on the most artifact local part. Experiments on a variety of image-to-image translation tasks and datasets validate that our method outperforms state-of-the-arts for producing high-quality translation results in terms of both human perceptual studies and automatic quantitative measures.


title: Evaluating the descriptive power of Instagram hashtags

abstract: Abstract Image tagging is an essential step for developing Automatic Image Annotation (AIA) methods that are based on the learning by example paradigm. However, manual image annotation, even for creating training sets for machine learning algorithms, requires hard effort and contains human judgment errors and subjectivity. Thus, alternative ways for automatically creating training examples, i.e., pairs of images and tags, are pursued. In this work, we investigate whether tags accompanying photos in the Instagram can be considered as image annotation metadata. If such a claim is proved then Instagram could be used as a very rich, easy to collect automatically, source of training data for the development of AIA techniques. Our hypothesis is that Instagram hashtags, and especially those provided by the photo owner/creator, express more accurately the content of a photo compared to the tags assigned to a photo during explicit image annotation processes like crowdsourcing. In this context, we explore the descriptive power of hashtags by examining whether other users would use the same, with the owner, hashtags to annotate an image. For this purpose 1000 Instagram images were collected and one to four hashtags, considered as the most descriptive ones for the image in question, were chosen among the hashtags used by the photo owner. An online database was constructed to generate online questionnaires containing 20 images each, which were distributed to experiment participants so they can choose the best suitable hashtag for every image according to their interpretation. Results show that an average of 66% of the participants hashtag choices coincide with those suggested by the photo owners; thus, an initial evidence towards our hypothesis confirmation can be claimed.


title: Adolescents' Emotion Regulation in Daily Life: Links to Depressive Symptoms and Problem Behavior

abstract: This study examined links between emotion regulation and adjustment in a sample of 152 adolescents in Grades 7 (M age = 12) and 10 (M age = 15). Emotion regulation was assessed using the experience sampling method, in which adolescents provided multiple reports about the intensity, lability, and strategies used to regulate negative emotions across 1 week. Adolescents also completed self-report measures of adjustment. Adolescents who reported more intense and labile emotions and less effective regulation of these emotions also reported more depressive symptoms and problem behavior. Responding to negative emotions with disengagement (e.g., denial) or involuntary engagement (e.g., rumination) was less effective in regulating negative affect, and greater use of these strategies was related to higher levels of depressive symptoms and problem behavior.


title: Design and analysis of practical public-key encryption schemes secure against adaptive chosen ciphertext attack

abstract: A new public-key encryption scheme, along with several variants, is proposed and analyzed. The scheme and its variants are quite practical and are proved secure against adaptive chosen ciphertext attack under standard intractability assumptions. These appear to be the first public-key encryption schemes in the literature that are simultaneously practical and provably secure.


title: Grip and pinch strength: normative data for adults.

abstract: The primary purpose of this study was to establish clinical norms for adults aged 20 to 75+ years on four tests of hand strength. A dynamometer was used to measure grip strength and a pinch gauge to measure tip, key, and palmar pinch. A sample of 310 male and 328 female adults, ages 20 to 94, from the seven-county Milwaukee area were tested using standardized positioning and instructions. Right hand and left hand data were stratified into 12 age groups for both sexes. This stratification provides a means of comparing the score of individual patients to that of normal subjects of the same age and sex. The highest grip strength scores occurred in the 25 to 39 age groups. For tip, key, and palmar pinch the average scores were relatively stable from 20 to 59 years, with a gradual decline from 60 to 79 years. A high correlation was seen between grip strength and age, but a low to moderate correlation between pinch strength and age. The newer pinch gauge used in this study appears to read higher than that used in a previous normative study. Comparison of the average hand strength of right-handed and left-handed subjects showed only minimal differences.


title: A Compact Convolutional Neural Network for Textured Surface Anomaly Detection

abstract: Convolutional neural methods have proven to outperform other approaches in various computer vision tasks. In this paper we apply the deep learning technique to the domain of automated visual surface inspection. We design a unified CNN-based framework for segmentation and detection of surface anomalies. We investigate whether a compact CNN architecture, which exhibit fewer parameters that need to be learned, can be used, while retaining high classification accuracy. We propose and evaluate a compact CNN architecture on a dataset consisting of diverse textured surfaces with variously-shaped weakly-labeled anomalies. The proposed approach achieves state-of-the-art results in terms of anomaly segmentation as well as classification.


title: Consumers' Perception on Online Shopping

abstract: E-commerce is emerging as a great level given that organized retail is still not ubiquitous across the length and breadth of the country with large retail chains making up less than 10% of the market.E-commerce is helping people in smaller towns in India access quality products and services similar to what people in the larger cities have access to. It being forecast that close to 60% of online shoppers would come from beyond the top eight large cities by end of this year.Increasing internet penetration has helped to expand the potential customer pool. Internet penetration is only about 10% (or about 121 million users) as against about 81% in the US and 36% in China. However this number continues to rise at a consistent pace because of falling prices for broadband connections. The first World Wide Web server and browser, created by Tim Berners-Lee in 1990, opened for commercial use in 1991. Thereafter, subsequent technological innovations emerged in 1994: online banking, the opening of an online pizza shop by Pizza Hut, Netscape's SSL v2 encryption standard for secure data transfer, and inter-shop’s first online shopping system. Immediately after, Amazon.com launched its online shopping site in 1995 and eBay was introduced in 1996. In the past decade, there has been a dramatic change in the way consumers have altered their way of shopping. Although consumers continue to purchase from a physical store, consumers feel very convenient to shop online since it frees the customer from personally visiting the store. Internet shopping has its own advantages and it reduces the effort of travelling to a physical store. Decisions can be made from home at ease looking at various choices and prices can be easily compared with the competitor’s products to arrive at a decision. This study highlights student’s attitude towards online shopping and their product preference on online shopping. This enable the e-retailers to support their online customer better by developing suitable marketing strategy in order to attract and convert potential customer as an active customers by encouraging them in an efficient way to make a purchase decision. Keywords : Online Shopping, Consumer Perception, Buying Behaviour, Purchase Decision, e-Commerce


title: Optimal Polygon Decomposition for UAV Survey Coverage Path Planning in Wind

abstract: In this paper, a new method for planning coverage paths for fixed-wing Unmanned Aerial Vehicle (UAV) aerial surveys is proposed. Instead of the more generic coverage path planning techniques presented in previous literature, this method specifically concentrates on decreasing flight time of fixed-wing aircraft surveys. This is achieved threefold: by the addition of wind to the survey flight time model, accounting for the fact fixed-wing aircraft are not constrained to flight within the polygon of the region of interest, and an intelligent method for decomposing the region into convex polygons conducive to quick flight times. It is shown that wind can make a huge difference to survey time, and that flying perpendicular can confer a flight time advantage. Small UAVs, which have very slow airspeeds, can very easily be flying in wind, which is 50% of their airspeed. This is why the technique is shown to be so effective, due to the fact that ignoring wind for small, slow, fixed-wing aircraft is a considerable oversight. Comparing this method to previous techniques using a Monte Carlo simulation on randomised polygons shows a significant reduction in flight time.


Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis


title: Data collection and analysis of GitHub repositories and users

abstract: In this paper, we present the collection and mining of GitHub data, aiming to understand GitHub user behavior and project success factors. We collected information about approximately 100K projects and 10K GitHub users//owners of these projects, via GitHub API. Subsequently, we statistically analyzed such data, discretized values of features via k-means algorithm, and finally we applied apriori algorithm via weka in order to find out association rules. Having assumed that project success could be measured by the cardinality of downloads we kept only the rules which had as right par a download cardinality higher than a threshold of 1000 downloads. The results provide intersting insight in the GitHub ecosystem and seven success rules for GitHub projects.


title: Parallel Top-K Similarity Join Algorithms Using MapReduce

abstract: There is a wide range of applications that require finding the top-k most similar pairs of records in a given database. However, computing such top-k similarity joins is a challenging problem today, as there is an increasing trend of applications that expect to deal with vast amounts of data. For such data-intensive applications, parallel executions of programs on a large cluster of commodity machines using the MapReduce paradigm have recently received a lot of attention. In this paper, we investigate how the top-k similarity join algorithms can get benefits from the popular MapReduce framework. We first develop the divide-and-conquer and branch-and-bound algorithms. We next propose the all pair partitioning and essential pair partitioning methods to minimize the amount of data transfers between map and reduce functions. We finally perform the experiments with not only synthetic but also real-life data sets. Our performance study confirms the effectiveness and scalability of our MapReduce algorithms.


Health and Safety Issues associated with Virtual Reality - A Review of Current Literature


title: Dynamic trot-walking with the hydraulic quadruped robot — HyQ: Analytical trajectory generation and active compliance control

abstract: This paper presents a trajectory generator and an active compliance control scheme, unified in a framework to synthesize dynamic, feasible and compliant trot-walking locomotion cycles for a stiff-by-nature hydraulically actuated quadruped robot. At the outset, a CoP-based trajectory generator that is constructed using an analytical solution is implemented to obtain feasible and dynamically balanced motion references in a systematic manner. Initial conditions are uniquely determined for symmetrical motion patterns, enforcing that trajectories are seamlessly connected both in position, velocity and acceleration levels, regardless of the given support phase. The active compliance controller, used simultaneously, is responsible for sufficient joint position/force regulation. An admittance block is utilized to compute joint displacements that correspond to joint force errors. In addition to position feedback, these joint displacements are inserted to the position control loop as a secondary feedback term. In doing so, active compliance control is achieved, while the position/force trade-off is modulated via the virtual admittance parameters. Various trot-walking experiments are conducted with the proposed framework using HyQ, a ~ 75kg hydraulically actuated quadruped robot. We present results of repetitive, continuous, and dynamically equilibrated trot-walking locomotion cycles, both on level surface and uneven surface walking experiments.


title: Challenges of satisfying multiple stakeholders: quality of service in the internet of things

abstract: As wireless sensor networks become increasingly integrated with the Internet, the Internet of Things (IOT) is beginning to emerge. The IOT is a massive cyber-physical system that presents many software engineering challenges. One fundamental challenge is the need for multi-dimensional QoS that can satisfy the individual constraints of the many participants in the system. In this paper, we investigate the challenges of providing such a mechanism via a simple abstraction consisting of a general QoS function provided by each application. This function distills the multi-dimensional QoS specifications from each stakeholder into a single value that is used to determine the best configuration of interactions. We prototype our approach in a real wireless sensor network using a pervasive healthcare fall-detection application and highlight the many challenges it unveils.


title: Sentry-based power management in wireless sensor networks

abstract: This paper presents a sentry-based approach to power management in wireless sensor networks for applications such as intruder detection and tracking. To minimize average power consumption while maintaining sufficient node density for coarse sensing, nodes are partitioned dynamically into two sets: sentries and non-sentries. Sentry nodes provide sufficient coverage for continuous monitoring and basic communication services. Non-sentry nodes sleep for designated periods of time to conserve power, and switch to full power only when needed to provide more refined sensing for tracking. Non-sentry nodes check for beacons from sentry nodes to determine when they should remain on. Experimental results are presented demonstrating trade-offs between power savings and tracking performance for a network of seventeen nodes using the first implementation of a basic sentry-based power management scheme. The paper concludes with a brief description of a full set of power-management services being implemented as middle-ware for general wireless sensor applications


title: Human Pose Estimation in Space and Time using 3D CNN

abstract: This paper explores the capabilities of convolutional neural networks to deal with a task that is easily manageable for humans: perceiving 3D pose of a human body from varying angles. However, in our approach, we are restricted to using a monocular vision system. For this purpose, we apply a convolutional neural network approach on RGB videos and extend it to three dimensional convolutions. This is done via encoding the time dimension in videos as the 3\(^\mathrm{rd}\) dimension in convolutional space, and directly regressing to human body joint positions in 3D coordinate space. This research shows the ability of such a network to achieve state-of-the-art performance on the selected Human3.6M dataset, thus demonstrating the possibility of successfully representing temporal data with an additional dimension in the convolutional operation.


title: Game Designers Training First Person Shooter Bots

abstract: Interactive training is well suited to computer games as it allows game designers to interact with otherwise autonomous learning algorithms. This paper investigates the outcome of a group of five commercial first person shooter game designers using a custom built interactive training tool to train first person shooter bots. The designers are asked to train a bot using the tool, and then comment on their experiences. The five trained bots are then pitted against each other in a deathmatch scenario. The results show that the training tool has potential to be used in a commercial environment.


title: Typicality and Emotion in the Voice of Children with Autism Spectrum Condition: Evidence Across Three Languages

abstract: Only a few studies exist on automatic emotion analysis of speech from children with Autism Spectrum Conditions (ASC). Out of these, some preliminary studies have recently focused on comparing the relevance of selected prosodic features against large sets of acoustic, spectral, and cepstral features; however, no study so far provided a comparison of performances across different languages. The present contribution aims to fill this white spot in the literature and provide insight by extensive evaluations carried out on three databases of prompted phrases collected in English, Swedish, and Hebrew, inducing nine emotion categories embedded in short-stories. The datasets contain speech of children with ASC and typically developing children under the same conditions. We evaluate automatic diagnosis and recognition of emotions in atypical childrens voice over the nine categories including binary valence/arousal discrimination.


title: Geometry Driven Semantic Labeling of Indoor Scenes

abstract: We present a discriminative graphical model which integrates geometrical information from RGBD images in its unary, pairwise and higher order components. We propose an improved geometry estimation scheme which is robust to erroneous sensor inputs. At the unary level, we combine appearance based beliefs defined on pixels and planes using a hybrid decision fusion scheme. Our proposed location potential gives an improved representation of the planar classes. At the pairwise level, we learn a balanced combination of various boundaries to consider the spatial discontinuity. Finally, we treat planar regions as higher order cliques and use graphcuts to make efficient inference. In our model based formulation, we use structured learning to fine tune the model parameters. We test our approach on two RGBD datasets and demonstrate significant improvements over the state-of-the-art scene labeling techniques.


title: Saliency-based Sequential Image Attention with Multiset Prediction

abstract: Humans process visual scenes selectively and sequentially using attention. Central to models of human visual attention is the saliency map. We propose a hierarchical visual architecture that operates on a saliency map and uses a novel attention mechanism to sequentially focus on salient regions and take additional glimpses within those regions. The architecture is motivated by human visual attention, and is used for multi-label image classification on a novel multiset task, demonstrating that it achieves high precision and recall while localizing objects with its attention. Unlike conventional multi-label image classification models, the model supports multiset prediction due to a reinforcement-learning based training process that allows for arbitrary label permutation and multiple instances per label.


title: You Are How You Drive: Peer and Temporal-Aware Representation Learning for Driving Behavior Analysis

abstract: Driving is a complex activity that requires multi-level skilled operations (e.g., acceleration, braking, turning). Analyzing driving behavior can help us assess driver performances, improve traffic safety, and, ultimately, promote the development of intelligent and resilient transportation systems. While some efforts have been made for analyzing driving behavior, existing methods can be improved via representation learning by jointly exploring the peer and temporal dependencies of driving behavior. To that end, in this paper, we develop a Peer and Temporal-Aware Representation Learning based framework (PTARL) for driving behavior analysis with GPS trajectory data. Specifically, we first detect the driving operations and states of each driver from GPS traces. Then, we derive a sequence of multi-view driving state transition graphs from the driving state sequences, in order to characterize a driver's driving behavior that varies over time. In addition, we develop a peer and temporal-aware representation learning method to learn a sequence of time-varying yet relational vectorized representations from the driving state transition graphs. The proposed method can simultaneously model both the graph-graph peer dependency and the current-past temporal dependency in a unified optimization framework. Also, we provide effective solutions for the optimization problem. Moreover, we exploit the learned representations of driving behavior to score driving performances and detect dangerous regions. Finally, extensive experimental results with big trajectory data demonstrate the enhanced performance of the proposed method for driving behavior analysis.


Introduction to Equine Tendon Injury


Challenges of Session-Aware Recommendation in E-Commerce


title: Dynamic causal modelling for EEG and MEG

abstract: Dynamic Causal Modelling (DCM) is an approach first introduced for the analysis of functional magnetic resonance imaging (fMRI) to quantify effective connectivity between brain areas. Recently, this framework has been extended and established in the magneto/encephalography (M/EEG) domain. DCM for M/EEG entails the inversion a full spatiotemporal model of evoked responses, over multiple conditions. This model rests on a biophysical and neurobiological generative model for electrophysiological data. A generative model is a prescription of how data are generated. The inversion of a DCM provides conditional densities on the model parameters and, indeed on the model itself. These densities enable one to answer key questions about the underlying system. A DCM comprises two parts; one part describes the dynamics within and among neuronal sources, and the second describes how source dynamics generate data in the sensors, using the lead-field. The parameters of this spatiotemporal model are estimated using a single (iterative) Bayesian procedure. In this paper, we will motivate and describe the current DCM framework. Two examples show how the approach can be applied to M/EEG experiments.


title: Design of an arduino-based smart car

abstract: Remote-controlled cars are one of the most popular toy products currently on the mass market. Each series of car has a specific remote-control unit. This presents the consumer with a critical problem; obtaining a substitution controller where the original control unit has broken down. In this work a robot based on an external Arduino microcontroller, controllable by an Android application via Bluetooth, which can be recommended as a prototype for the combination of embedded systems with Android mobile devices is investigated.


title: The pharmacodynamics and efficacy of Implanon. An overview of the data.

abstract: Implanon is a long-acting reversible contraceptive method, consisting of a single rod that is applied subdermally. Ovulation inhibition was determined by serum progesterone (P) levels and ultrasound scanning (USS) of the ovaries. Ovarian function was further assessed by serum estradiol (E2) levels. The effects of Implanon on serum gonadotropin levels (follicle-stimulating hormone [FSH] and luteinizing hormone [LH]) and on cervical mucus were also investigated, by means of Insler scores and sperm penetration tests. The effect of the endometrium was assessed by endometrial biopsies and USS. The Pearl index was calculated over 13 studies performed according to Good Clinical Practice (GCP), including 1716 women using Implanon. Return of ovulation after implant removal was determined by P levels and USS of the ovaries. The contraceptive efficacy of Implanon was high, with zero pregnancies during 53,530 cycles (4103 woman-years), resulting in a Pearl index of 0.0 (95% confidence interval, 0.00-0.09). This was achieved by inhibition of ovulation, which was reflected by suppressed P levels, as the primary mode of action. Ovulation was inhibited, but otherwise ovarian activity was still present (follicle growth, E2 synthesis). The FSH serum concentrations were only slightly lower than preinsertion levels and LH surges were prevented. The viscosity of the cervical mucus was increased. The endometrium was thin but not atrophic; it showed primarily inactive or weak proliferation. Return of ovulation after removal of Implanon was rapid.


title: Vection and simulator sickness.

abstract: Simulator sickness has been identified as a form of motion sickness in which users of simulators exhibit symptoms characteristic of true motion sickness. In a fixed-base simulator, visual and vestibular sources of information specifying dynamic orientation are in conflict to the extent that the optical flow pattern viewed by the pilot creates a compelling illusion of self-motion, which is not corroborated by the inertial forces transmitted through the vestibular sense organs. Visually induced illusory self-motion is known as vection, and a strict interpretation of sensory conflict theory of motion sickness suggests that vection in a fixed-base simulator would be a necessary precondition for simulator sickness. Direct confirmation of this relation is reported in this article.


title: Animal Navigation: Path Integration, Visual Landmarks and Cognitive Maps

abstract: Animals typically have several navigational strategies available to them. Interactions between these strategies can reduce navigational errors and may lead to the emergence of new capacities.


title: Enterprise Content Management (ECM): Needs, challenges and recommendations

abstract: Availability of information, agility of business process, interoperability of diverse business functions and conformity of the legal requirements are few of the critical success factors for enterprises to survive in the modern and competing global market space. Enterprises while striving to achieve these critical success factors are further exposed to the complexity of massive volumes of variant data and information, which exists in a broad array of formats. Complex and extended business processes spanning across the business functions and partners around the globe, need for integration and interoperability, fulfillment of compliance to legal and regulatory requirements. Enterprise Content Management (ECM) answers the above questions and offers the capabilities to efficiently and effectively manage the challenges imposed by the demanding business requirements of the modern world. The study in-hand reviews and analyzes the existing literature on Enterprise Content Management domain. In order to conduct a structured literature review, three broader areas of considerations for ECM are identified i.e. Business Needs & Drivers, Challenges and Recommendations. The review inhand aims at consolidating valuable domain knowledge and research work accomplished in the recent years for the benefits of the industry and academia.


title: Anchorage capacity of osseointegrated and conventional anchorage systems : a randomized controlled trial.

abstract: Introduction: Our aim in this investigation was to evaluate and compare orthodontic anchorage capacity of 4 anchorage systems during leveling/aligning and space closure after maxillary premolar extractions. Methods: One hundred twenty patients (60 girls, 60 boys; mean age, 14.3 years; SD 1.73) were recruited and randomized into 4 anchorage systems: Onplant (Nobel Biocare, Gothenburg, Sweden), Orthosystem implant (Institut Straumann AG, Basel, Switzerland), headgear, and transpalatal bar. The main outcome measures were cephalometric analysis of maxillary first molar and incisor movement, sagittal growth changes of the maxilla, and treatment time. The results were also analyzed on an intention-to-treat basis. Results: The maxillary molars were stable during the leveling/aligning in the Onplant, Orthosystem implant, and headgear groups, but the transpalatal bar group had anchorage loss (mean, 1.0 mm; P P Conclusions: If maximum anchorage is required, the Orthosystem implant is the system of choice.


Application of a Lightweight Encryption Algorithm to a Quantized Speech Image for Secure IoT


title: A Customizable Model for Chronic Disease Coordination: Lessons Learned From the Coordinated Chronic Disease Program

abstract: In 2012, the Centers for Disease Control and Prevention provided funding and technical assistance to all states and territories to implement the Coordinated Chronic Disease Program, marking the first time that all state health departments had federal resources to coordinate chronic disease prevention and control programs. This article describes lessons learned from this initiative and identifies key elements of a coordinated approach. We analyzed 80 programmatic documents from 21 states and conducted semistructured interviews with 7 chronic disease directors. Six overarching themes emerged: 1) focused agenda, 2) identification of functions, 3) comprehensive planning, 4) collaborative leadership and expertise, 5) managed resources, and 6) relationship building. These elements supported 4 essential activities: 1) evidence-based interventions, 2) strategic use of staff, 3) consistent communication, and 4) strong program infrastructure. On the basis of these elements and activities, we propose a conceptual model that frames overarching concepts, skills, and strategies needed to coordinate state chronic disease prevention and control programs.


title: A high efficiency inverter design for Google little box challenge

abstract: This paper proposed a high efficiency standalone inverter design for Google Little Box Challenge (LBC). A full bridge inverter is designed with two high frequency legs and one low frequency leg. To improve the system efficiency, asymmetrical unipolar modulation is utilized and interleaved switching is used. The synchronization between high switching frequency leg and low switching frequency leg is studied and implemented based on a TI micro-controller, which minimized the zero-crossing distortion on the output voltage waveform. A prototype hardware utilizing GaN devices has been developed and tested. A peak efficiency with 99.30% and a CEC efficiency with 99.26% are both achieved.


title: A knowledge creation info-structure to acquire and crystallize the tacit knowledge of health-care experts

abstract: Tacit knowledge of health-care experts is an important source of experiential know-how, yet due to various operational and technical reasons, such health-care knowledge is not entirely harnessed and put into professional practice. Emerging knowledge-management (KM) solutions suggest strategies to acquire the seemingly intractable and nonarticulated tacit knowledge of health-care experts. This paper presents a KM methodology, together with its computational implementation, to 1) acquire the tacit knowledge possessed by health-care experts; 2) represent the acquired tacit health-care knowledge in a computational formalism--i.e., clinical scenarios--that allows the reuse of stored knowledge to acquire tacit knowledge; and 3) crystallize the acquired tacit knowledge so that it is validated for health-care decision-support and medical education systems.


Defining metrics for continuous delivery and deployment pipeline


title: From government to e-government: a transition model

abstract: Purpose – To develop a model that can explain the “government to e‐government” transition process.Design/methodology/approach – Reviews the literature on and practice of e‐government, as well as the related literature of strategic alignment and maturity models for technology adoption. Offers evidence for the model's validity through case‐type material from the web sites of e‐governments worldwide.Findings – Six transition paths can be identified, four of which are more likely to result in effective e‐government transition.Research limitations/implications – Further work is needed to test the validity of the model. This could involve historical and longitudinal studies of the government to e‐government transition process in different governments around the world.Practical implications – The transition model should be of value to e‐government strategic planners who are seeking possible transition paths towards the effective development of e‐government.Originality/value – The paper tackles the little investi...


title: Blended Learning Environments

abstract: In recent decades, rapid technological innovation has facilitated a convergence between traditional face-toface and distributed (or technology-mediated) learning environments. These blended learning environments try to take advantage of the strengths of both archetypal learning environments (Graham, 2006). The emergence of blended learning is highlighted in higher education and in industry training literature. The Chronicle of Charles R. Graham and Charles Dziuban 270 Higher Education reports that the President of Pennsylvania State University regards the convergence between online and residential instruction as the “single greatest unrecognized trend in higher education today” (Young, 2002, p. A33). Similarly, the American Society for Training and Development identifies blended learning as one of the top ten emergent trends in the knowledge delivery industry (Finn, 2002). Yet, surprisingly, we understand little about the nature of blended learning systems. This chapter identifies core issues and research about blended learning using the Sloan Consortium’s five pillars (learning effectiveness, student satisfaction, faculty satisfaction, cost effectiveness, and access) as an organizing framework (Lorenzo and Moore, 2002). The authors also discuss future directions in blended learning research.


An end-to-end hybrid algorithm for automated medication discrepancy detection


title: From content delivery today to information centric networking

abstract: Today, content delivery is a heterogeneous ecosystem composed by various independent infrastructures. The ever increasing growth of Internet traffic has encouraged the proliferation of different architectures to serve content provider needs and user demand. Despite the differences among the technology, their low level implementation can be characterized in a few fundamental building blocks: network storage, request routing, and data transfer. Existing solutions are inefficient because they try to build an information centric service model over a network infrastructure which was designed to support host-to-host communications. The Information-Centric Networking (ICN) paradigm has been proposed as a possible solution to this mismatch. ICN integrates content delivery as a native network feature. The rationale is to architect a network that automatically interprets, processes, and delivers content (information) independently of its location. This paper makes the following contributions: (1) it identifies a set of building blocks for content delivery, (2) it surveys the most popular approaches to realize the above building blocks, (3) it compares content delivery solutions relying on the current Internet infrastructure with novel ICN approaches.


title: KEA: Practical Automatic Keyphrase Extraction

abstract: Keyphrases provide semantic metadata that summarize and characterize documents. This paper describes Kea, an algorithm for automatically extracting keyphrases from text. Kea identifies candidate keyphrases using lexical methods, calculates feature values for each candidate, and uses a machine-learning algorithm to predict which candidates are good keyphrases. The machine learning scheme first builds a prediction model using training documents with known keyphrases, and then uses the model to find keyphrases in new documents. We use a large test corpus to evaluate Kea's effectiveness in terms of how many author-assigned keyphrases are correctly identified. The system is simple, robust, and publicly available.


title: Visual Question Answering: Datasets, Algorithms, and Future Challenges

abstract: Visual Question Answering (VQA) is a recent problem in computer vision and natural language processing that has garnered a large amount of interest from the deep learning, computer vision, and natural language processing communities. In VQA, an algorithm needs to answer text-based questions about images. Since the release of the first VQA dataset in 2014, additional datasets have been released and many algorithms have been proposed. In this review, we critically examine the current state of VQA in terms of problem formulation, existing datasets, evaluation metrics, and algorithms. In particular, we discuss the limitations of current datasets with regard to their ability to properly train and assess VQA algorithms. We then exhaustively review existing algorithms for VQA. Finally, we discuss possible future directions for VQA and image understanding research.


title: Diagnostic Classification of Cystoscopic Images Using Deep Convolutional Neural Networks

abstract: PURPOSE ::: The recognition of cystoscopic findings remains challenging for young colleagues and depends on the examiner's skills. Computer-aided diagnosis tools using feature extraction and deep learning show promise as instruments to perform diagnostic classification. ::: ::: ::: MATERIALS AND METHODS ::: Our study considered 479 patient cases that represented 44 urologic findings. Image color was linearly normalized and was equalized by applying contrast-limited adaptive histogram equalization. Because these findings can be viewed via cystoscopy from every possible angle and side, we ultimately generated images rotated in 10-degree grades and flipped them vertically or horizontally, which resulted in 18,681 images. After image preprocessing, we developed deep convolutional neural network (CNN) models (ResNet50, VGG-19, VGG-16, InceptionV3, and Xception) and evaluated these models using F1 scores. Furthermore, we proposed two CNN concepts: 90%-previous-layer filter size and harmonic-series filter size. A training set (60%), a validation set (10%), and a test set (30%) were randomly generated from the study data set. All models were trained on the training set, validated on the validation set, and evaluated on the test set. ::: ::: ::: RESULTS ::: The Xception-based model achieved the highest F1 score (99.52%), followed by models that were based on ResNet50 (99.48%) and the harmonic-series concept (99.45%). All images with cancer lesions were correctly determined by these models. When the focus was on the images misclassified by the model with the best performance, 7.86% of images that showed bladder stones with indwelling catheter and 1.43% of images that showed bladder diverticulum were falsely classified. ::: ::: ::: CONCLUSION ::: The results of this study show the potential of deep learning for the diagnostic classification of cystoscopic images. Future work will focus on integration of artificial intelligence-aided cystoscopy into clinical routines and possibly expansion to other clinical endoscopy applications.


title: Generation of circularly polarized conical beam pattern using toroidal helical knot antenna

abstract: A novel circularly polarized antenna with a conical beam radiation pattern is presented. It consists of a feeding probe and a (1, 6) helical torus knot as a polarizer. Measured results shows that, the antenna has an impedance bandwidth of 19.67% and axial ratio bandwidth of 15.7%. With the RHCP gain of about 6 dBic at 3.5 GHz. Toroidal helical knot antenna is mechanically simple to fabricate using additive manufacturing technology. The proposed prototype is suitable for mounting on vehicles to facilitate communication with geostationary satellites.


1 Using Smartphones and Wireless Mobile Sensor Networks to Detect Car Accidents and Provide Situational Awareness to Emergency Responders


title: EE-Grad: Exploration and Exploitation for Cost-Efficient Mini-Batch SGD

abstract: We present a generic framework for trading off fidelity and cost in computing stochastic gradients when the costs of acquiring stochastic gradients of different quality are not known a priori. We consider a mini-batch oracle that distributes a limited query budget over a number of stochastic gradients and aggregates them to estimate the true gradient. Since the optimal mini-batch size depends on the unknown cost-fidelity function, we propose an algorithm, {\it EE-Grad}, that sequentially explores the performance of mini-batch oracles and exploits the accumulated knowledge to estimate the one achieving the best performance in terms of cost-efficiency. We provide performance guarantees for EE-Grad with respect to the optimal mini-batch oracle, and illustrate these results in the case of strongly convex objectives. We also provide a simple numerical example that corroborates our theoretical findings.


title: A multi-objective artificial bee colony algorithm

abstract: Abstract This work presents a multi-objective optimization method based on the artificial bee colony, called the MOABC, for optimizing problems with multiple objectives. The MOABC uses a grid-based approach to adaptively assess the Pareto front maintained in an external archive. The external archive is used to control the flying behaviours of the individuals and structuring the bee colony. The employed bees adjust their trajectories based on the non-dominated solutions maintained in the external archive. On the other hand, the onlooker bees select the food sources advertised by the employed bees to update their positions. The qualities of these food sources are computed based on the Pareto dominance notion. The scout bees are used by the MOABC to get rid of food sources with poor qualities. The proposed algorithm was evaluated on a set of standard test problems in comparison with other state-of-the-art algorithms. Experimental results indicate that the proposed approach is competitive compared to other algorithms considered in this work.


title: Homicidal hanging masquerading as suicide.

abstract: Homicidal hanging is rare and presents special problems for the forensic pathologist. We report a case of homicide by hanging masquerading as suicide, in which the forensic evidence was of crucial importance. The victim was a 61 years old man, who was found in his house suspended by a rope around his neck. The autopsy showed fresh bruises due to blunt trauma. Moreover, a voluminous subdural haematoma with brain swelling was found which indicated that the victim was unconscious at the time of the hanging. An obvious vital reaction consisting of cutaneous bleeding in the ligature mark supported the opinion that the deceased was alive and not dead at the time of the hanging. The estimation of the time of death was of great importance, as only one other person could have been present at the estimated time interval. This person was later sentenced to prison for manslaughter.


title: SMITE: an SDN and MPLS integrated traceback mechanism

abstract: In this paper we present a new approach to IP packet traceback called SMITE which allows efficient traceback of the origin of a packet with incorrect or spoofed source addresses through an integration of software-defined networks (SDN) [15] and Multiprotocol Label Switching (MPLS) [13] in OpenFlow [19]. SMITE leverages the flexibility of SDN and the strength of MPLS network to achieve low false positive rate, ability to perform post-mortem traceback, reduction in storage pressure/ hardware investment and most importantly the ability to perform traceback for a single spoofed packet. SMITE also aims to overcome the difficulties and limitations of legacy traceback mechanisms.


title: High-Performance IQ Modulator-Based Phase Conjugator for Modular Retrodirective Antenna Array Implementation

abstract: In this paper, we verify a new phase conjugating architecture suitable for deployment as the core building block in retrodirective antenna arrays, which can be scaled to any number of elements in a modular way without impacting on complexity. Our solution is based on a modified in-phase and quadrature modulator architecture, which completely resolves four major shortcomings of the conventional mixer-based approach currently used for the synthesis of phase conjugated energy derived from a sampled incoming wavefront. 1) The architecture presented removes the need for a local oscillator running at twice the RF signal frequency to be conjugated. 2) It maintains a constant transmit power even if receive power goes as low as -120 dBm. 3) All unwanted re-transmit signal products are suppressed by at least 40 dB. 4) The issue of poor RF-IF leakage prevalent in mixer-based phase-conjugation solutions is completely mitigated. The circuit has also been shown to have high conjugation accuracy (better than plusmn1deg at -60-dBm input). Near theoretically perfect experimental monostatic and bistatic results are presented for a ten-element retrodirective array constructed using the new phase conjugation architecture.


Preserving privacy by de-identifying facial images


title: ANVIL - A Generic Annotation Tool for Multimodal Dialogue

abstract: Anvil is a tool for the annotation of audiovisual material containing multimodal dialogue. Annotation takes place on freely definable, multiple layers (tracks) by inserting time-anchored elements that hold a number of typed attribute-value pairs. Higher-level elements (suprasegmental) consist of a sequence of elements. Attributes contain symbols or cross-level links to arbitrary other elements. Anvil is highly generic (usable with different annotation schemes), platform-independent, XMLbased and fitted with an intuitive graphical user interface. For project integration, Anvil offers the import of speech transcription and export of text and table data for further statistical processing.


title: Architecture for supporting Hardware Collectives in Output-Queued High-Radix Routers

abstract: Collective communication performance is critical for many applications. In this paper, we present an architecture to efficiently support collective operations (like multicasts and reductions) in the switches of parallel computer interconnects. We present an output queuing switch architecture with cross-point buffering. Output queuing architectures have been less popular in the past as they require more internal speedup and buffering. However, with current technology it is straightforward to build output-queued switches. We demonstrate in this paper that output-queued architectures make multicasts and reductions fairly easy to implement efficiently. We show the scalability of our schemes to a large number of switch ports. We present performance of multicasts and reductions on individual switches and networks of switches. We assume a fat-tree topology for the networks of switches. We also present simulation results based on synthetic workloads that emulate a molecular dynamics application.


title: Finite-time Analysis of the Multiarmed Bandit Problem

abstract: Reinforcement learning policies face the exploration versus exploitation dilemma, i.e. the search for a balance between exploring the environment to find profitable actions while taking the empirically best action as often as possible. A popular measure of a policy's success in addressing this dilemma is the regret, that is the loss due to the fact that the globally optimal policy is not followed all the times. One of the simplest examples of the exploration/exploitation dilemma is the multi-armed bandit problem. Lai and Robbins were the first ones to show that the regret for this problem has to grow at least logarithmically in the number of plays. Since then, policies which asymptotically achieve this regret have been devised by Lai and Robbins and many others. In this work we show that the optimal logarithmic regret is also achievable uniformly over time, with simple and efficient policies, and for all reward distributions with bounded support.


title: Structural evolution during the reduction of chemically derived graphene oxide

abstract: Thermal reduction of graphene oxide is an attractive route towards the preparation of graphene, but complete removal of residual oxygen is problematic. Now, molecular dynamics simulations elucidate the chemical changes involved in this process.


title: Clustering categorical data: an approach based on dynamical systems

abstract: We describe a novel approach for clustering collections of sets, and its application to the analysis and mining of categorical data. By “categorical data,” we mean tables with fields that cannot be naturally ordered by a metric – e.g., the names of producers of automobiles, or the names of products offered by a manufacturer. Our approach is based on an iterative method for assigning and propagating weights on the categorical values in a table; this facilitates a type of similarity measure arising from the co-occurrence of values in the dataset. Our techniques can be studied analytically in terms of certain types of non-linear dynamical systems.


title: Outcomes and stability in patients with anterior open bite and long anterior face height treated with temporary anchorage devices and a maxillary intrusion splint.

abstract: INTRODUCTION ::: Temporary skeletal anchorage devices now offer the possibility of closing anterior open bites and decreasing anterior face height by intruding maxillary posterior teeth, but data for treatment outcomes are lacking. This article presents outcomes and posttreatment changes for consecutive patients treated with a standardized technique. ::: ::: ::: METHODS ::: The sample included 33 consecutive patients who had intrusion of maxillary posterior teeth with a maxillary occlusal splint and nickel-titanium coil springs to temporary anchorage devices in the zygomatic buttress area, buccal and apical to the maxillary molars. Of this group, 30 had adequate cephalograms available for the period of treatment, 27 had cephalograms including 1-year posttreatment, and 25 had cephalograms from 2 years or longer. ::: ::: ::: RESULTS ::: During splint therapy, the mean molar intrusion was 2.3 mm. The mean decrease in anterior face height was 1.6 mm, less than expected because of a 0.6-mm mean eruption of the mandibular molars. During the postintrusion orthodontics, the mean change in maxillary molar position was a 0.2-mm extrusion, and there was a mean 0.5-mm increase in face height. Positive overbite was maintained in all patients, with a slight elongation (<2 mm) of the incisors contributing to this. During the 1 year of posttreatment retention, the mean changes were a further eruption of 0.5 mm of the maxillary molars, whereas the mandibular molars intruded by 0.6 mm, and there was a small decrease in anterior face height. Changes beyond 1 year posttreatment were small and attributable to growth rather than relapse in tooth positions. ::: ::: ::: CONCLUSIONS ::: Intrusion of the maxillary posterior teeth can give satisfactory correction of moderately severe anterior open bites, but 0.5 to 1.5 mm of reeruption of these teeth is likely to occur. Controlling the vertical position of the mandibular molars so that they do not erupt as the maxillary teeth are intruded is important in obtaining a decrease in face height.


title: Bayesian Population Decoding of Motor Cortical Activity Using a Kalman Filter

abstract: Effective neural motor prostheses require a method for decoding neural activity representing desired movement. In particular, the accurate reconstruction of a continuous motion signal is necessary for the control of devices such as computer cursors, robots, or a patient's own paralyzed limbs. For such applications, we developed a real-time system that uses Bayesian inference techniques to estimate hand motion from the firing rates of multiple neurons. In this study, we used recordings that were previously made in the arm area of primary motor cortex in awake behaving monkeys using a chronically implanted multielectrode microarray. Bayesian inference involves computing the posterior probability of the hand motion conditioned on a sequence of observed firing rates; this is formulated in terms of the product of a likelihood and a prior. The likelihood term models the probability of firing rates given a particular hand motion. We found that a linear gaussian model could be used to approximate this likelihood and could be readily learned from a small amount of training data. The prior term defines a probabilistic model of hand kinematics and was also taken to be a linear gaussian model. Decoding was performed using a Kalman filter, which gives an efficient recursive method for Bayesian inference when the likelihood and prior are linear and gaussian. In off-line experiments, the Kalman filter reconstructions of hand trajectory were more accurate than previously reported results. The resulting decoding algorithm provides a principled probabilistic model of motor-cortical coding, decodes hand motion in real time, provides an estimate of uncertainty, and is straightforward to implement. Additionally the formulation unifies and extends previous models of neural coding while providing insights into the motor-cortical code.


title: A systematic framework for multilevel theorizing in information systems research

abstract: Information systems (IS) research usually investigates phenomena at one level of analysis at a time. However, complex IS phenomena may be difficult to address from such a single-level perspective. A multilevel perspective offers an alternative means to examine phenomena by simultaneously accounting for multiple levels of analysis. Although useful guidelines for theory development are widely available, they give little specific attention to developing theory that is conceptualized and analyzed at multiple levels. Multilevel theorizing or developing theory from a multilevel perspective is more complex and involves unique challenges. To promote multilevel theorizing in the IS discipline, we focus on addressing challenges involved in multilevel theorizing and propose a holistic framework for systematically developing theory from a multilevel perspective. Drawing from the organization science and IS literature, the proposed framework harmonizes and synthesizes previous guidelines, providing a practical basis for conceptualizing and studying multilevel phenomena.


title: AprilTag 2: Efficient and robust fiducial detection

abstract: AprilTags and other passive fiducial markers require specialized algorithms to detect markers among other features in a natural scene. The vision processing steps generally dominate the computation time of a tag detection pipeline, so even small improvements in marker detection can translate to a faster tag detection system. We incorporated lessons learned from implementing and supporting the AprilTag system into this improved system. This work describes AprilTag 2, a completely redesigned tag detector that improves robustness and efficiency compared to the original AprilTag system. The tag coding scheme is unchanged, retaining the same robustness to false positives inherent to the coding system. The new detector improves performance with higher detection rates, fewer false positives, and lower computational time. Improved performance on small images allows the use of decimated input images, resulting in dramatic gains in detection speed.


title: CFD Modelling of the wake interactions of two wind turbines on a Gaussian hill

abstract: One part of the solution to the problem of climate change is the use of wind energy for electricity generation. Onshore wind farms usually consist of a number of horizontal axis wind turbines closely spaced in clusters and they are often sited on complex terrain. Various models have been developed in the past to estimate the wake effects and optimize the siting of a wind farm, but they were mainly developed and validated for flat terrain and not for complex terrain sites. Computational Fluid Dynamics (CFD) can be a useful tool towards the understanding of wake behavior on complex terrain. The present work involves the use of FLUENT 6.3 commercial CFD code to study the wake interaction of two wind turbines closely spaced next to each other at the top of an ideal Gaussian hill. Contact person: Alexandros Makridis, Institute for Energy Systems – University of Edinburgh, Mayfield road, Kings Buildings, EH9 3JL, Edinburgh, telephone: +447833521488, FAX: +44 (0)131 6506554. E-mail: a.makridis@ed.ac.uk CFD Modeling of the wake interactions of two wind turbines on a Gaussian hill Alexandros Makridis, John Chick Institute for Energy Systems, University of Edinburgh – a.makridis@ed.ac.uk – Kings Buildings, Edinburgh – Institute for Energy Systems, University of Edinburgh – john.chick@ed.ac.uk – Kings Buildings, Edinburgh


title: Comparing Container-Based Microservices and Workspace as a Service: Which One to Choose?

abstract: The concept of microservices has gained increasing popularity since 2014. Almost during the same period, container technology keeps developing and is considered as an excellent way to build microservices-based applications. Mainstream public cloud vendors such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform all provide users with container-based solutions to implementing microservices. Workspace as a Service (WaaS) proposed by An et al. is another approach which uses containers to serve users. Both container-based microservices and WaaS are used to effectively utilize cluster resources via maintaining a number of containers. In this paper, we compare the designing ideas and supporting platforms of these two approaches, which provides a perspective for cluster administrators and users to understand the scenarios where to use them and how to make an appropriate choice to meet their needs. We find that container-based microservices are more suitable for professional IT companies while WaaS fits education and research institutions better.


title: JDeodorant: Identification and Removal of Type-Checking Bad Smells

abstract: In this demonstration, we present an Eclipse plug-in that automatically identifies type-checking bad smells in Java source code, and resolves them by applying the "replace conditional with polymorphism" or "replace type code with state/strategy " refactorings. To the best of our knowledge there is a lack of tools that identify type-checking bad smells. Moreover, none of the state-of-the-art IDEs support the refactorings that resolve such kind of bad smells.


title: Incorporating External Knowledge to Answer Open-Domain Visual Questions with Dynamic Memory Networks

abstract: Visual Question Answering (VQA) has attracted much attention since it offers insight into the relationships between the multi-modal analysis of images and natural language. Most of the current algorithms are incapable of answering open-domain questions that require to perform reasoning beyond the image contents. To address this issue, we propose a novel framework which endows the model capabilities in answering more complex questions by leveraging massive external knowledge with dynamic memory networks. Specifically, the questions along with the corresponding images trigger a process to retrieve the relevant information in external knowledge bases, which are embedded into a continuous vector space by preserving the entity-relation structures. Afterwards, we employ dynamic memory networks to attend to the large body of facts in the knowledge graph and images, and then perform reasoning over these facts to generate corresponding answers. Extensive experiments demonstrate that our model not only achieves the state-of-the-art performance in the visual question answering task, but can also answer open-domain questions effectively by leveraging the external knowledge.


title: Lumbar Stabilization Core Concepts and Current Literature , Part 1

abstract: The factors that affect lumbar stability have been an area of extensive research. The clinical application of this research in the form of lumbar stabilization exercise programs has become a common treatment of low back pain and is also increasingly used by athletes to improve performance and by the general public for health and the prevention of injury. This article includes a review of the key concepts behind lumbar stabilization. The literature regarding how those with low back pain differ in their ability to stabilize the spine from those without low back pain is discussed, and an overview of current research that assesses the benefits of a lumbar stabilization program to treat low back pain is provided.


title: A New Synonym Text Steganography

abstract: Steganography is a relatively new method for establishing hidden communication which gained attraction in recent years. Steganography is a method of hiding a secret message in a cover media such as image or text. In this paper a new method is proposed for steganography in English text by substituting the words which have different terms in British English and American English.


title: Transforming Sensor Data to RDF based on SSN Ontology

abstract: The development of the sensor network technology is playing more and more important role in our daily life. However, sensor data from different sources is hard to work together for lack of semantic. This paper proposed a solution to transforming sensor data to RDF based on SSN ontology. We design a XML-based mapping language (named SASML) which is used to annotate sensors and sources. Then, an algorithm (named SDRM) is designed to transform sensor data to RDF conforming to SSN ontology, based on the predefined SASML mapping file. Further, we have developed a prototype to demonstrate the effectiveness of our method.


In silico Analysis of 3 D-QSAR and Molecular Docking for Bcl-2 Inhibitors to Potential Anticancer Drug Development


title: Optimized Reactive Power Flow of DFIG Power Converters for Better Reliability Performance Considering Grid Codes

abstract: If there is no reactive power exchange between a doubly fed induction generator (DFIG) and a grid, the various characteristics of the power converters in a DFIG wind turbine system cause the lifetime expectancy of a rotor-side converter (RSC) to be significantly less than that of a grid-side converter (GSC). In order to fulfill modern grid codes, over-excited reactive power injection will further reduce the lifetime of the RSC. In this paper, the additional stress of a power semiconductor due to the reactive power injection is first evaluated in terms of a modulation index and the current loading. Then, an optimized reactive power flow is proposed in the case where an over-excited reactive power support is applied with the joint compensation from both the RSC and the GSC. Finally, some experimental validations are performed at a downscale DFIG prototype. It is concluded that, among the different combined reactive power support strategies, the best scheme will tradeoff the lifetime between the GSC and the RSC.


title: Imaging findings of adiposis dolorosa vs. massive localized lymphedema

abstract: PURPOSE ::: Adiposis dolorosa (Dercum's disease) is a condition of benign, painful subcutaneous lipomatous lesions associated with weakness, endocrine and lipid abnormalities, and mental disturbances. There is little information documenting the cross-sectional imaging findings that differentiate it from lipomatous and neoplastic soft tissue masses, or massive localized lymphedema. The purpose of this study was to provide a radiological case series of adiposis dolorosa. ::: ::: ::: METHODS ::: A 10-year retrospective review of the picture archiving and communications system was performed. Two musculoskeletal radiologists reviewed images to confirm and document imaging features, location, size, and patient demographics. Medical records were reviewed to characterize patients into three groups: one group met at least three of the four criteria of Dercum's syndrome, the second group met less than three criteria, and the third group had clinical diagnosis of cellulitis of the lower extremity. ::: ::: ::: RESULTS ::: Seventeen cases (25 masses) of adiposis dolorosa were found, nine cases of which met at least three criteria of Dercum's syndrome. All cases in the first two groups demonstrated skin thickening and lymphedema of subcutaneous fat, which was fluid attenuation on CT and low or intermediate T1-weighted and high STIR/T2-weighted MR signal. Two cases with pathology showed mild fatty infiltration with fibrous septa, and the third case showed massive localized lymphedema. The third group of ten cellulitis patients demonstrated non-mass-like subcutaneous edema with similar CT attenuation and MR signal characteristics to the first two groups, but differed by the presence of post-contrast enhancement and non-mass-like appearance in 90%. ::: ::: ::: CONCLUSION ::: Imaging findings of adiposis dolorosa and massive localized lymphedema overlap, as do the symptoms and pathological features. Due to the mass-like engorgement of the soft tissues and pain, patients will often undergo imaging to exclude neoplasm or infection. Knowledge of these conditions and the characteristic imaging findings is important to prevent unnecessary biopsy and misdiagnosis.


Possible biological correlates of precocious mathematical reasoning ability