title: An UML class recommender system for software design

abstract: Recommendation systems provide suggestions for items that are potentially interesting for a user in a given context. The provided recommendations are extracted generally from a huge amount of data collected from several sources of information. Thus a recommendation system requires firstly a pre-treatment step to prepare the data and secondly the application of some techniques such as data mining techniques to handle and extract the knowledge to be recommended to the user from the data. Our contribution consists on proposing a Recommendation System for Software Engineering (RSSE). This system recommends UML classes in the design phase of UML classes diagrams. Our RSSE is composed by two main phases: an off-line phase in which we use a clustering algorithm to partition UML classes collected from several UML classes diagrams based on the semantic relations existing between their characteristics. We have defined a metric that measures the similarity between UML classes. The second is an online phase in which we use the obtained clusters of UML classes to propose suggestions to the user based on elements added to his UML classes diagram under construction. The proposed system is then experimentally evaluated by using a UML classes corpus collected from several UML classes diagrams. The experimental evaluation shows very encouraging ratio of useful recommendations.


title: Contextual mapping: Visualization of high-dimensional spatial patterns in a single geo-map

abstract: In this study, we proposed a generic methodology for combining high-dimensional spatial data to identify and visualize the hidden spatial patterns in a single-layer geo-map. By using the less explored one-dimensional self-organizing maps, we showed how the high-dimensional data can be transformed into a spectrum of one-dimensional ordered numbers. These numbers (codes) can index a high-dimensional space with the important property that similar indices refer to similar high-dimensional contexts. Thus, the high-dimensional vectors will be attributed to single numbers, and this one-dimensional output can be easily rendered as a new single data layer in the original geographic map. As a result, it simultaneously identifies the main spatial clusters and visualizes the high-dimensional correlations (if any) in a single geographic map. Further, because the output of the proposed method is a set of ordered indices, there is no need to define a fixed number of clusters in advance. ::: ::: Because these composite spatial layers are identified on the basis of the selected context (i.e., the selected features or aspects of the spatial phenomena), they are called contextual maps. ::: ::: Finally, we showed the results of applying the proposed methodology to several synthetic and real-world data sets.


title: The smart city and mobility: The functional polarization of urban flow

abstract: The paper deals with the definition of urban mobility, assuming that inside the city, the flow of mobility is deeply connected with the distribution, quality and use of the urban activities that “polarize” different users (residents, commuters, tourists and city users). In this vision, ICT assume a strategic role, but the need to reconsider their role emerges in respect to the concept of a smart city. The consideration that “urban smartness” does not depend exclusively on the ICT component or on the quantitative presence of technologies in the city, in fact, represents a shared opinion within the current scientific debate on the subject of the smart city. The paper assumes that, for the present urban contexts, the smart vision has to be related to an integrated approach, which considers the city as a complex system. Inside the urban system, the networks for both material and immaterial mobility interact with the urban activities that play a supporting role and have characteristics that affect the levels of urban smartness. Changes in urban systems greatly depend on the sorts of innovation technology that have intensely modified the social component to a far greater extent than others. Big Data, for instance, can help with knowledge of urban processes, provided they have to be well-interpreted and managed, and this will be of interest within the interactions among urban systems and the functioning of the system as a whole. Town planning has to take on responsibility in regard to approaching cities according to a different vision and updating its tools in order to steer the urban system steadfastly into a smartness state. In a systemic vision, this transition must be framed within the context of a process of governmental transformation that is carefully oriented towards the individuation of interactions among the different subsystems composing the city. According to this vision, the study of urban mobility can be related to the attractiveness generated by the different urban functions. The formalization of the degree of polarization, activated by urban functions, represents the main objective of this study. Among the urban functions, the study considers tourism as one of the most significant in the formalization of urban mobility flow inside the smart city.


title: Open repair of abdominal aortic aneurysm in the elderly: Is it worthwhile?

abstract: Abstract Introduction Given the steady increase in life expectancy, an analysis of surgical outcomes in the aging population is of significant interest to vascular surgeons. The aim of our study was to assess the outcomes of abdominal aortic aneurysm repair in the elderly aged 75 plus and compare their results with people operated on at a younger age. Methods 171 patients who underwent open AAA repair in one centre in 1999–2008 were analyzed. The perioperative and postoperative complications, 30-day mortality, the length of ICU and hospital stay were compared, and the Kruskal–Wallis and the Chi-square tests were used for statistical analysis. Results There were no statistically significant differences in gender and patients‘ preoperative comorbidities except for ICHS and CHRI which were more frequent in the elderly 75 plus. The frequency of serious perioperative haemodynamic complications and postoperative (cardiac, respiratory, cerebral, bleeding, sepsis, surgical) complications, mortality rate and the length of hospital stay did not significantly differ in the elderly 75 plus. A statistically significant difference was found only in the frequency of postoperative renal damage and the length of ICU stay. The elderly 75 plus without enhanced haemodynamic monitoring had significantly longer ICU stay (5 days vs. 7 days p Conclusion We confirmed acceptable outcomes of patients aged 75 plus who underwent open AAA repair compared with younger population. Elective surgery for AAA can be safely performed in the elderly patients, however, other possibilities of treatment should also be considered.


title: Hybrid optical and wireless technology integrations for next generation broadband access networks

abstract: Hybrid optical and wireless technology integrations have been considered as one of the most promising candidates for the next generation broadband access networks for quite some time. The integration scheme provides the bandwidth advantages of the optical networks and mobility features of the wireless networks for Subscriber Stations (SSs). It also brings economic efficiency to the network providers particularly in rural area where the existing wired telecommunication infrastructures such as Digital Subscriber Line (DSL), Cable Modem (CM), T-1/E-1 networks or fibre deployments are either costly or unreachable. For successful integration of the optical and wireless technologies there are some technical issues which need to be addressed efficiently in order to provide End-to-End (ETE) and diverse Quality of Service (QoS) for various service classes. This paper investigates the possible challenging issues for the integrated structure of the Time Division Multiplexing and Wavelength Division Multiplexing Ethernet Passive Optical Networks (TDM EPON and WDM EPON ) with the Worldwide Interoperability for Microwave Access and Wireless Fidelity (WiMAX and Wi-Fi) networks. To reduce the ETE delay and provide the QoS for diverse service classes, we have compared six existing upstream scheduling mechanisms in two levels which are distributed on Access Points (APs) from Wi-Fi domain and Base Stations (BSs) from WiMAX domain. Performance evaluations of the existing scheduling techniques for three popular service classes (Quad-play) have been studied which show the strong impact of using the efficient up-link scheduler in converged scenario. We have also proposed a dynamic scheduling algorithm for optical and wireless integration scheme, which is under the implementation and evaluation process.


title: Deep learning for health informatics: Recent trends and future directions

abstract: Health informatics has emerged as a growing domain of interest among researchers world-wide owing to its major implications on society. Applications of machine learning in healthcare range from disease prediction to patient-level personalized services. The prevalence of big data in healthcare has paved the way for applications based on deep learning techniques in the past few years. This paper reviews recent trends and applications of deep learning applied to the healthcare domain. We highlight recent research work, identify challenges and suggest possible future directions that could be pursued further in this domain.


title: Workload Scheduling for Massive Storage Systems with Arbitrary Renewable Supply

abstract: As datacenters grow in scale, increasing energy costs and carbon emissions have led data centers to seek renewable energy, such as wind and solar energy. However, tackling the challenges associated with the intermittency and variability of renewable energy is difficult. This paper proposes a scheme called GreenMatch, which deploys an SSD cache to match green energy supplies with a time-shifting workload schedule while maintaining low latency for online data-intensive services. With the SSD cache, the process for a latency-sensitive request to access a disk is divided into two stages: a low-energy/low-latency online stage and a high-energy/high-latency off-line stage. As the process in the latter stage is off-line, it offers opportunities for time-shifting workload scheduling in response to variations of green energy supplies. We also allocate an HDD cache to guarantee data availability when renewable energy is inadequate. Furthermore, we design a novel replacement policy called Inactive P-disk First for the HDD cache to avoid inactive disk accesses. The experimental results show that GreenMatch can make full use of renewable energy while minimizing the negative impacts of intermittency and variability on performance and availability.


title: Multibaseline Polarimetric SAR Tomography of a Boreal Forest at P- and L-Bands

abstract: Longer wavelength synthetic aperture radars (SARs) are precious in the remote sensing of forested areas, being sensitive to contributions from the whole vegetation layer and from the ground below. The electromagnetic properties of such contributions are retrieved from multipolarimetric acquisitions, whereas their vertical structure is retrieved from multibaseline acquisitions through tomographic imaging. Combining baseline and polarization diversity provides most information, allowing the decomposition of the SAR signal into ground- and volume-only contributions. A formal treatment of this problem is provided with the algebraic synthesis technique, which extends the concepts of PolInSAR. The decomposition, however, is shown to be ambiguous in that different solutions are equally consistent with the data. The main goal of this paper is to discuss this topic in light of the experimental results from a tomographic and polarimetric analysis of the boreal forest within the Krycklan River catchment, Northern Sweden, investigated at P- and L-bands during the ESA campaign BioSAR 2008. Different solutions to the decomposition problem will be discussed by examining the corresponding vertical structures accessible through tomographic techniques. Elements are shown supporting the idea that ground-volume interactions play a nonnegligible role at P-band, and a solution is proposed to isolate contributions from direct volume backscattering. The retrieval of forest top height is discussed as well, leading to the conclusion that such parameter is robust against erroneous choices in the identification of volume-only contributions, thus corroborating the PolInSAR approach for the analysis of single-baseline data.


title: High performance database logging using storage class memory

abstract: Storage class memory (SCM), a new generation of memory technology, offers non-volatility, high-speed, and byte-addressability, which combines the best properties of current hard disk drives (HDD) and main memory. With these extraordinary features, current systems and software stacks need to be redesigned to get significantly improved performance by eliminating disk input/output (I/O) barriers; and simpler system designs by avoiding complicated data format transformations. In current DBMSs, logging and recovery are the most important components to enforce the atomicity and durability of a database. Traditionally, database systems rely on disks for logging transaction actions and log records are forced to disks when a transaction commits. Because of the slow disk I/O speed, logging becomes one of the major bottlenecks for a DBMS. Exploiting SCM as a persistent memory for transaction logging can significantly reduce logging overhead. In this paper, we present the detailed design of an SCM-based approach for DBMSs logging, which achieves high performance by simplified system design and better concurrency support. We also discuss solutions to tackle several major issues arising during system recovery, including hole detection, partial write detection, and any-point failure recovery. This new logging approach is used to replace the traditional disk based logging approach in DBMSs. To analyze the performance characteristics of our SCM-based logging approach, we implement the prototype on IBM SolidDB. In common circumstances, our experimental results show that the new SCM-based logging approach provides as much as 7 times throughput improvement over disk-based logging in the Telecommunication Application Transaction Processing (TATP) benchmark.


title: Attend, Infer, Repeat: Fast Scene Understanding with Generative Models

abstract: We present a framework for efficient inference in structured image models that explicitly reason about objects. We achieve this by performing probabilistic inference using a recurrent neural network that attends to scene elements and processes them one at a time. Crucially, the model itself learns to choose the appropriate number of inference steps. We use this scheme to learn to perform inference in partially specified 2D models (variable-sized variational auto-encoders) and fully specified 3D models (probabilistic renderers). We show that such models learn to identify multiple objects - counting, locating and classifying the elements of a scene - without any supervision, e.g., decomposing 3D images with various numbers of objects in a single forward pass of a neural network. We further show that the networks produce accurate inferences when compared to supervised counterparts, and that their structure leads to improved generalization.


title: Big Sensed Data Meets Deep Learning for Smarter Health Care in Smart Cities

abstract: With the advent of the Internet of Things (IoT) concept and its integration with the smart city sensing, smart connected health systems have appeared as integral components of the smart city services. Hard sensing-based data acquisition through wearables or invasive probes, coupled with soft sensing-based acquisition such as crowd-sensing results in hidden patterns in the aggregated sensor data. Recent research aims to address this challenge through many hidden perceptron layers in the conventional artificial neural networks, namely by deep learning. In this article, we review deep learning techniques that can be applied to sensed data to improve prediction and decision making in smart health services. Furthermore, we present a comparison and taxonomy of these methodologies based on types of sensors and sensed data. We further provide thorough discussions on the open issues and research challenges in each category.


FC-NIRS: A Functional Connectivity Analysis Tool for Near-Infrared Spectroscopy Data


Clonidine or remifentanil for adequate surgical conditions in patients undergoing endoscopic sinus surgery: a randomized study


title: Computer System Intrusion Detection : A Survey

abstract: The ability to detect intruders in computer systems increases in importance as computers are increasingly integrated into the systems that we rely on for the correct functioning of society. This paper reviews the history of research in intrusion detection as performed in software in the context of operating systems for a single computer, a distributed system, or a network of computers. There are two basic approaches: anomaly detection and misuse detection. Both have been practiced since the 1980s. Both have naturally scaled to use in distributed systems and networks.


title: Prenatal depression effects on early development: A review

abstract: This review of recent research on prenatal depression suggests that it is a strong predictor of postpartum depression and is more common than postpartum depression. Prenatal depression has been associated with excessive activity and growth delays in the fetus as well as prematurity, low birthweight, disorganized sleep and less responsiveness to stimulation in the neonate. Infants of depressed mothers have difficult temperament, and later in development attentional, emotional and behavioral problems have been noted during childhood and adolescence, as well as chronic illnesses in adulthood. Several variables have confounded the effects of prenatal depression including comorbid anxiety and anger as well as stressful life events. Potential mediating variables are low prenatal maternal dopamine and serotonin levels and elevated cortisol and norepinephrine. The associated intrauterine artery resistance may limit blood flow, oxygen and nutrients to the fetus. Some studies also suggest the heritability of developmental problems for the children of prenatally depressed mothers, including ADHD and antisocial behavior. Multivariate, longitudinal research is needed to disentangle these confounding and mediating variables.


title: Integrating User eXperience Practices into Software Development Processes: Implications of Subjectivity and Emergent Nature of UX

abstract: Many software companies face challenges in their work with User eXperience (UX) and how to integrate UX practices into existing development processes. A better understanding of these challenges can help researchers and practitioners better address them. Existing research does not analyse UX challenges in relation to other software quality characteristics including usability. In this empirical study, we have interviewed 17 practitioners from eight software development companies. Their responses are coded and analysed with thematic analysis. We report 11 challenges that practitioners face in their work with UX. Some of these challenges partly overlap with those reported in existing literature about usability or software quality characteristics. In contrast to these overlaps, the participants of our study either view many of the challenges unique to UX, or more severe than for usability or other quality characteristics. Although at a superficial level challenges with UX and other quality characteristics overlap, we differentiate these challenges at a deeper level through two main aspects of UX: subjectivity and emergent nature. In particular, we identify at least five issues that are essential to the very nature of UX, and add at least seven extra difficulties to the work of practitioners. These difficulties can explain why practitioners perceive the challenges to be more severe than for other quality characteristics. Our findings can be useful for researchers in identifying industrially relevant research areas and for practitioners to learn from empirically investigated challenges and base their improvement efforts on such knowledge. Investigating the overlaps can help finding research areas not only for enhancing practice of UX but also software quality in general. It also makes it easier for practitioners to spot, better understand as well as find mitigation strategies for UX challenges.


title: High-Performance Split-Gate-Enhanced UMOSFET With Dual Channels

abstract: This paper presents an optimized split-gate-enhanced trench MOSFET with dual channels (DSGE-UMOS). The 2-D device simulator ATLAS is used to investigate the characteristics of the proposed structure. When compared with the conventional SGE-UMOS, the optimized device shows a significant reduction in the specific on-resistance ( ${R}_{ {\mathrm{\scriptscriptstyle ON}}\text {-sp}}$ ) at a breakdown voltage of 120 V, which is due to the adoption of an additional p-type well region. Furthermore, the proposed structure can also enhance the single-event burnout (SEB) survivability. Based on the DSGE-UMOS, the hardened DSGE-UMOS (an n-type buffer layer is added between the epitaxial layer and substrate layer) is also investigated that the addition of the buffer layer can improve the SEB performance a lot.


title: A novel scheme for speaker recognition using a phonetically-aware deep neural network

abstract: We propose a novel framework for speaker recognition in which extraction of sufficient statistics for the state-of-the-art i-vector model is driven by a deep neural network (DNN) trained for automatic speech recognition (ASR). Specifically, the DNN replaces the standard Gaussian mixture model (GMM) to produce frame alignments. The use of an ASR-DNN system in the speaker recognition pipeline is attractive as it integrates the information from speech content directly into the statistics, allowing the standard backends to remain unchanged. Improvement from the proposed framework compared to a state-of-the-art system are of 30% relative at the equal error rate when evaluated on the telephone conditions from the 2012 NIST speaker recognition evaluation (SRE). The proposed framework is a successful way to efficiently leverage transcribed data for speaker recognition, thus opening up a wide spectrum of research directions.


title: Leveraging graph topology and semantic context for pharmacovigilance through twitter-streams

abstract: Background ::: Adverse drug events (ADEs) constitute one of the leading causes of post-therapeutic death and their identification constitutes an important challenge of modern precision medicine. Unfortunately, the onset and effects of ADEs are often underreported complicating timely intervention. At over 500 million posts per day, Twitter is a commonly used social media platform. The ubiquity of day-to-day personal information exchange on Twitter makes it a promising target for data mining for ADE identification and intervention. Three technical challenges are central to this problem: (1) identification of salient medical keywords in (noisy) tweets, (2) mapping drug-effect relationships, and (3) classification of such relationships as adverse or non-adverse.


An Adaptive Multi-Band System for Low Power Voice Command Recognition


title: Exploring multi-action relationship in reinforcement learning

abstract: In many real-world reinforcement learning problems, an agent needs to control multiple actions simultaneously. To learn under this circumstance, previously, each action was commonly treated independently with other. However, these multiple actions are rarely independent in applications, and it could be helpful to accelerate the learning if the underlying relationship among the actions is utilized. This paper explores multi-action relationship in reinforcement learning. We propose to learn the multi-action relationship by enforcing a regularization term capturing the relationship. We incorporate the regularization term into the least-square policy-iteration and the temporal-difference methods, which result efficiently solvable convex learning objectives. The proposed methods are validated empirically in several domains. Experiment results show that incorporating multiaction relationship can effectively improve the learning performance.


title: Regression and Neural Networks Models for Prediction of Crop Production 1

abstract: Neural networks have been gaining a great deal of importance and are used in the areas of prediction and classification; the areas where regression and other statistical models are traditionally being used. In this paper, a comprehensive review of literature comparing feedforward neural networks and traditional statistical methods viz. linear regression with respect to prediction of agricultural crop production has been carried out. This study presents a useful insight into the capabilities of neural networks and their statistical counterparts used in the area of prediction of crop yield.


title: Peppermint oil

abstract: Peppermint leaf and peppermint oil have a long history of use for digestive disorders. Recent evidence suggests that enteric-coated peppermint oil may be effective in relieving some of the symptoms of irritable bowel syndrome. A combination product including peppermint oil and caraway oil seems to be moderately effective in the treatment of non-ulcer dyspepsia. Topical application of peppermint oil may be effective in the treatment of tension headache. Because of its relaxing effects on smooth muscle, peppermint oil given via enema has been modestly effective for relief of colonic spasm in patients undergoing barium enemas. Peppermint oil is well tolerated at the commonly recommended dosage, but it may cause significant adverse effects at higher dosages.


title: Short-term load forecasting in smart grid: A combined CNN and K-means clustering approach

abstract: Although many methods are available to forecast short-term electricity load based on small scale data sets, they may not be able to accommodate large data sets as electricity load data becomes bigger and more complex in recent years. In this paper, a novel machine learning model combining convolutional neural network with K-means clustering is proposed for short-term load forecasting with improved scalability. The large data set is clustered into subsets using K-means algorithm, then the obtained subsets are used to train the convolutional neural network. A real-world power industry data set containing more than 1.4 million of load records is used in this study and the experimental results demonstrate the effectiveness of the proposed method.


title: Small Scale Mobile Hybrid Integrated Renewable Energy System (HI-RES) for Rapid Recovery and Emergency Response

abstract: The concept and inherent challenges associated with the development of a resilient, affordable, high quality integrated power generation, energy management and rapid recovery and emergency response (RRaER) capability for providing electrical power and sustained life support operations during power disruptions is the focus of this paper. Presently, small-scale emergency mobile backup HI-RES are expensive and used primarily for military operations. As recent headlines in the news point us towards potential threats on our energy infrastructure, it becomes more imperative for us to consider alternatives to sole dependence on our electrical power grid for supplying and meeting the demands of our global and rising consumer base. Distributed Resources (DR), such as the HI-RES RRaER Station, provide a viable option for reducing vulnerabilities of the electrical power grid while minimizing time of service to recovery while preserving focus on the human element.


title: Search-based software maintenance

abstract: The high cost of software maintenance could potentially be greatly reduced by the automatic refactoring of object-oriented programs to increase their understandability, adaptability and extensibility. This paper describes a novel approach in providing automated refactoring support for software maintenance; the formulation of the task as a search problem in the space of alternative designs. Such a search is guided by a quality evaluation function that must accurately reflect refactoring goals. We have constructed a search-based software maintenance tool and report here the results of experimental refactoring of two Java programs, which yielded improvements in terms of the quality functions used. We also discuss the comparative merits of the three quality functions employed and the actual effect on program design that resulted from their use.


title: PERSONALITY PROCESSES AND INDIVIDUAL DIFFERENCES Double Dissociation Between Implicit and Explicit Personality Self-Concept: The Case of Shy Behavior

abstract: Using the trait of shyness as an example, the authors showed that (a) it is possible to reliably assess individual differences in the implicitly measured self-concept of personality that (b) are not accessible through traditional explicit self-ratings and (c) increase significantly the prediction of spontaneous behavior in realistic social situations. A total of 139 participants were observed in a shyness-inducing laboratory situation, and they completed an Implicit Association Test (IAT) and explicit self-ratings of shyness. The IAT correlated moderately with the explicit self-ratings and uniquely predicted spontaneous (but not controlled) shy behavior, whereas the explicit ratings uniquely predicted controlled (but not spontaneous) shy behavior (double dissociation). The distinction between spontaneous and controlled behavior was validated in a 2nd study. The aim of the present study is to apply recent conceptual and methodological advances in social cognition research to the assessment of the self-concept of personality and the prediction of behavior. Using the trait of shyness as an example, we attempt to show that (a) it is possible to reliably assess individual differences in the implicit self-concept of personality that (b) are partly independent from traditional explicit self-ratings and (c) increase significantly the prediction of spontaneous behavior in a realistic social situation. For this purpose, we make a conceptual distinction between the explicit and the implicit self-concept of personality, propose a general hypothesis on how these two aspects of the personality self-concept are linked with spontaneous and controlled behavior, and test this hypothesis in a behavioral observation study. In a follow-up study, we experimentally test and confirm the distinction between spontaneous and controlled shy behavior.


Graph Convolutional Networks with Argument-Aware Pooling for Event Detection


title: Integration of Motor and Visual Information in the Parietal Area 5 During Locomotion

abstract: The parietal cortex receives both visual- and motor-related information and is believed to be one of the sites of visuo-motor coordination. This study for the first time characterizes integration of visual and motor information in activity of neurons of parietal area 5 during locomotion under conditions that require visuo-motor coordination. The activity of neurons was recorded in cats during walking on a flat surface—a task with no visuo-motor coordination required (flat locomotion), walking along a horizontal ladder or a series of barriers—a task requiring visuo-motor coordination for an accurate foot placement on surface that is heterogeneous along the direction of progression (ladder and barriers locomotion), and walking along a narrow pathway—a task requiring visuo-motor coordination on surface homogeneous along the direction of progression (narrow locomotion). During flat locomotion, activity of 66% of the neurons was modulated in rhythm of stepping, usually with one peak per cycle. During ladder and barrier locomotion, the proportion of rhythmically active neurons significantly increased, their modulation became stronger, and the majority of neurons had two peaks of activity per cycle. During narrow locomotion, however, the activity of neurons was similar to that during flat locomotion. We concluded that, during locomotion, parietal area 5 integrates two types of information: signals about the activity of basic locomotion mechanisms and signals about heterogeneity of the surface along the direction of progression. We describe here the modes of integration of these two types of information during locomotion.


title: Overview of the TAC2013 Knowledge Base Population Evaluation: English Slot Filling and Temporal Slot Filling

abstract: We overview two tracks of the TAC2013 Knowledge Base Population (KBP) evaluation: English slot filling (SF) and temporal slot filling (TSF). The goal of these two KBP tracks is to promote research in the extraction of relations between named entities from free text (SF), and identify the temporal intervals when these relations were valid (TSF). The main changes this year include the requirement for a stricter textual justification of the extracted relations for SF, and a simplification of the TSF task, where the relation to be temporally grounded is given as input. These two tracks attracted 43 registered teams, out of which 20 teams submitted a run in at least one of the tracks.


title: Comparative evaluation of shaping ability of two nickel-titanium rotary systems using cone beam computed tomography

abstract: BackgroundWe evaluated and compared the effects of different NiTi rotary systems – ProTaper Next and New One Shape – on the volume of dentin removed, canal transportation, and canal curvature in extracted human teeth using CBCT scanning with different voxel sizes.MethodsFifty extracted human maxillary first molars with mesiobuccal canal curvature (25-35°) were used. Specimens were instrumented with the ProTaper Next or New One Shape. Pre- and post-instrumentation scans were performed to compare transportation at the levels of 2, 5, and 8 mm and volumes with two different voxel sizes (0.125-and 0.100-mm3) using 3D CBCT images. This study evaluated and compare the volume of dentin removed, canal transportation, and canal curvature. Differences according to instrumentation and voxel sizes were assessed using the Mann–Whitney U-test and the Wilcoxon signed-rank test.ResultsSignificant differences were found between apical and coronal levels for both systems (p < 0.05) in canal transportation. In comparing the systems, similar values were found at each level, without significant difference (p > 0.05) in terms of canal curvature and volume. Voxel sizes did not affect the measurements on canal volume, curvature or transportation; no significant difference was found between the 0.100- and 0.125-mm3 voxel sizes (p > 0.05).ConclusionsBoth instrumentation systems produced similar canal transportation and volume changes. The two voxel resolutions also showed similar results, however a 0.125-mm3 voxel size can be recommend for a flat panel CBCT scanner with lower exposure dose.


title: Low-frequency meandering piezoelectric vibration energy harvester

abstract: The design, fabrication, and characterization of a novel low-frequency meandering piezoelectric vibration energy harvester is presented. The energy harvester is designed for sensor node applications where the node targets a width-to-length aspect ratio close to 1:1 while simultaneously achieving a low resonant frequency. The measured power output and normalized power density are 118 μW and 5.02 μW/mm(3)/g(2), respectively, when excited by an acceleration magnitude of 0.2 g at 49.7 Hz. The energy harvester consists of a laser-machined meandering PZT bimorph. Two methods, strain-matched electrode (SME) and strain-matched polarization (SMP), are utilized to mitigate the voltage cancellation caused by having both positive and negative strains in the piezoelectric layer during operation at the meander's first resonant frequency. We have performed finite element analysis and experimentally demonstrated a prototype harvester with a footprint of 27 x 23 mm and a height of 6.5 mm including the tip mass. The device achieves a low resonant frequency while maintaining a form factor suitable for sensor node applications. The meandering design enables energy harvesters to harvest energy from vibration sources with frequencies less than 100 Hz within a compact footprint.


title: Analysis of Italian Word Embeddings

abstract: In this work we analyze the performances of two of the most used word embeddings algorithms, skip-gram and continuous bag of words on Italian language. These algorithms have many hyper-parameter that have to be carefully tuned in order to obtain accurate word representation in vectorial space. We provide an accurate analysis and an evaluation, showing what are the best configuration of parameters for specific tasks.


title: Neuronavigation in minimally invasive spine surgery

abstract: Object Parallel advancements in image guidance technology and minimal access techniques continue to push the frontiers of minimally invasive spine surgery (MISS). While traditional intraoperative imaging remains widely used, newer platforms, such as 3D-fluoroscopy, cone-beam CT, and intraoperative CT/MRI, have enabled safer, more accurate instrumentation placement with less radiation exposure to the surgeon. The goal of this work is to provide a review of the current uses of advanced image guidance in MISS. Methods The authors searched PubMed for relevant articles concerning MISS, with particular attention to the use of image-guidance platforms. Pertinent studies published in English were further compiled and characterized into relevant analyses of MISS of the cervical, thoracic, and lumbosacral regions. Results Fifty-two studies were included for review. These describe the use of the iso-C system for 3D navigation during C1–2 transarticular screw placement, the use of endoscopic techniques in the cervica...


title: SLC2A knockout mice deficient in ascorbic acid synthesis recapitulate aspects of arterial tortuosity syndrome and display mitochondrial respiration defects

abstract: Arterial tortuosity syndrome (ATS) is a recessively inherited connective tissue disorder, mainly characterized by tortuosity and aneurysm formation of the major arteries. ATS is caused by loss-of-function mutations in SLC2A10, encoding the facilitative glucose transporter GLUT10. Former studies implicate GLUT10 in transport of dehydroascorbic acid, the oxidized form of ascorbic acid (AA). Mouse models carrying homozygous Slc2a10 missense mutations do not recapitulate the human phenotype. Since mice, in contrast to humans, are able to intracellularly synthesize AA, we generated a novel ATS mouse model, deficient for Slc2a10 as well as Gulo, which encodes for L-gulonolactone oxidase, an enzyme catalyzing the final step in AA biosynthesis in rodents. Gulo;Slc2a10 knock-out mice show mild phenotypic anomalies, which were absent in single knock-out controls. While Gulo;Slc2a10 knock-out mice do not fully phenocopy human ATS, histological and immunocytochemical analysis revealed compromised extracellular matrix formation. TGFβ signaling remained unaltered, while mitochondrial function was compromised in smooth muscle cells derived from Gulo;Slc2a10 knock-out mice. Altogether, our data add evidence that ATS is an ascorbate compartmentalization disorder, but additional factors underlying the observed phenotype in humans remain to be determined.


title: Deep Learning Approach for Secondary Structure Protein Prediction based on First Level Features Extraction using a Latent CNN Structure

abstract: In Bioinformatics, Protein Secondary Structure Prediction (PSSP) has been considered as one of the main challenging tasks in this field. Today, secondary structure protein prediction approaches have been categorized into three groups (Neighbor-based, model-based, and meta predicator-based model). The main purpose of the model-based approaches is to detect the protein sequence-structure by utilizing machine learning techniques to train and learn a predictive model for that. In this model, different supervised learning approaches have been proposed such as neural networks, hidden Markov chain, and support vector machines have been proposed. In this paper, our proposed approach which is a Latent Deep Learning approach relies on detecting the first level features based on using Stacked Sparse Autoencoder. This approach allows us to detect new features out of the set of training data using the sparse autoencoder which will have used later as convolved filters in the Convolutional Neural Network (CNN) structure. The experimental results show that the highest accuracy of the prediction is 86.719% in the testing set of our approach when the backpropagation framework has been used to pre-trained techniques by relying on the unsupervised fashion where the whole network can be fine-tuned in a supervised learning fashion.


title: All-printed diode operating at 1.6 GHz

abstract: Printed electronics are considered for wireless electronic tags and sensors within the future Internet-of-things (IoT) concept. As a consequence of the low charge carrier mobility of present printable organic and inorganic semiconductors, the operational frequency of printed rectifiers is not high enough to enable direct communication and powering between mobile phones and printed e-tags. Here, we report an all-printed diode operating up to 1.6 GHz. The device, based on two stacked layers of Si and NbSi2 particles, is manufactured on a flexible substrate at low temperature and in ambient atmosphere. The high charge carrier mobility of the Si microparticles allows device operation to occur in the charge injection-limited regime. The asymmetry of the oxide layers in the resulting device stack leads to rectification of tunneling current. Printed diodes were combined with antennas and electrochromic displays to form an all-printed e-tag. The harvested signal from a Global System for Mobile Communications mobile phone was used to update the display. Our findings demonstrate a new communication pathway for printed electronics within IoT applications.


title: Vascular Complications After Chin Augmentation Using Hyaluronic Acid

abstract: Vascular complications after hyaluronic acid (HA) filling of the chin have rarely been reported. In this report, two cases of vascular occlusion after HA augmentation of the mentum are presented. The first case involved local skin necrosis that resulted from a massive microcirculatory embolism and/or external compression of the chin skin microvasculature. The second case involved vascular compromise in the tongue that resulted from HA injection in the chin. The diagnosis was established on the basis of interventional angiography findings. Concerning the pathogenesis, we hypothesized that the filler embolus flowed into the branches of the deep lingual artery through the rich vascular anastomoses among the submental, sublingual, and deep lingual arteries, after being accidentally injected into the submental artery (or its branches). Level of Evidence V This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .


title: Studies on surface wettability of poly(dimethyl) siloxane (PDMS) and glass under oxygen-plasma treatment and correlation with bond strength

abstract: An issue in microfabrication of the fluidic channels in glass/poly (dimethyl siloxane) (PDMS) is the absence of a well-defined study of the bonding strength between the surfaces making up these channels. Although most of the research papers mention the use of oxygen plasma for developing chemical (siloxane) bonds between the participating surfaces, yet they only define a certain set of parameters, tailored to a specific setup. An important requirement of all the microfluidics/biosensors industry is the development of a general regime, which defines a systematic method of gauging the bond strength between the participating surfaces in advance by correlation to a common parameter. This enhances the reliability of the devices and also gives a structured approach to its future large-scale manufacturing. In this paper, we explore the possibility of the existence of a common scale, which can be used to gauge bond strength between various surfaces. We find that the changes in wettability of surfaces owing to various levels of plasma exposure can be a useful parameter to gauge the bond strength. We obtained a good correlation between contact angle of deionized water (a direct measure of wettability) on the PDMS and glass surfaces based on various dosages or oxygen plasma treatment. The exposure was done first in an inductively coupled high-density (ICP) plasma system and then in plasma enhanced chemical vapor deposition (PECVD) system. This was followed by the measurement of bond strength by use or the standardized blister test.


title: Electromechanical and Electronic Integrated Harvester for Shoes Application

abstract: Energy harvesting allows making sensors or transmitters electrically autonomous. Several studies have been proposed but most cases lack electrical and mechanical parts integration and practical application purpose. Here, with the aim of supplying a Bluetooth step-counter placed in the sole of a training shoe, a complete system is presented. It consists of a magnetoinductive transducer embedded with an electronic interface for power conditioning and exploits only the energy recovered by the impact on the ground. A $\phi$ 27 $\times$ 16 mm cylindrical device containing the transducer, the electronic interface, the step-counter electronics, and the protective shell is developed. Energy recovery derives from the magnet-free oscillation following the shoe impact. The proposed interface exploits pulse-width modulation to perform transducer output rectification and to emulate optimal load impedance while charging a storage capacitor. Numerical and experimental analysis show effective optimal resistive load emulation and energy recovery gain of 2 compared with the standard ac–dc interface. Prototypes have been manufactured and tested. Considering a sample footstep, energy recovery is 360 μJ against 400 μJ dissipated on optimal resistor. Mean energy recovery over a complete run is 644 μJ/footstep. In spite of variable excitation, energy recovery at each footstep is larger than the required and allows the step-counter transmitting the information.


title: Two Cases of Thrombosis of the Palmar Digital Vein

abstract: Palmar digital vein thrombosis causing one or more nodules seems to be a relatively rare condition, judging by the dearth of reports in the literature. It should always be considered in a patient who presents with a painful, firm, blue nodule located at or in close proximity to one of the flexion crease of the finger. Common lesions presenting as one or more solid digital nodules are ganglions, epidermal inclusion cysts, giant cell tumors, and lipomas. Thrombosis of the palmar digital veins should, however, be part of the differential diagnosis of palmar digital nodules because it is possible to manage it conservatively. We report two cases of thrombosis of palmar digital vein in a 33-year-old woman and a 78-year-old man.


title: Accurate Segmentation of Cervical Cytoplasm and Nuclei Based on Multiscale Convolutional Network and Graph Partitioning

abstract: In this paper, a multiscale convolutional network (MSCN) and graph-partitioning-based method is proposed for accurate segmentation of cervical cytoplasm and nuclei. Specifically, deep learning via the MSCN is explored to extract scale invariant features, and then, segment regions centered at each pixel. The coarse segmentation is refined by an automated graph partitioning method based on the pretrained feature. The texture, shape, and contextual information of the target objects are learned to localize the appearance of distinctive boundary, which is also explored to generate markers to split the touching nuclei. For further refinement of the segmentation, a coarse-to-fine nucleus segmentation framework is developed. The computational complexity of the segmentation is reduced by using superpixel instead of raw pixels. Extensive experimental results demonstrate that the proposed cervical nucleus cell segmentation delivers promising results and outperforms existing methods.


title: Developing a biodata measure and situational judgment inventory as predictors of college student performance.

abstract: This article describes the development and validation of a biographical data (biodata) measure and situational judgment inventory (SJI) as useful predictors of broadly defined college student performance outcomes. These measures provided incremental validity when considered in combination with standardized college-entrance tests (i.e., SAT/ACT) and a measure of Big Five personality constructs. Racial subgroup mean differences were much smaller on the biodata and SJI measures than on the standardized tests and college grade point average. Female students tended to outperform male students on most predictors and outcomes with the exception of the SAT/ACT. The biodata and SJI measures show promise for student development contexts and for selecting students on a wide range of outcomes with reduced adverse impact.


title: Weighted Level Set Evolution Based on Local Edge Features for Medical Image Segmentation

abstract: Level set methods have been widely used to implement active contours for image segmentation applications due to their good boundary detection accuracy. In the context of medical image segmentation, weak edges and inhomogeneities remain important issues that may hinder the accuracy of any segmentation method based on active contours implemented using level set methods. This paper proposes a method based on active contours implemented using level set methods for segmentation of such medical images. The proposed method uses a level set evolution that is based on the minimization of an objective energy functional whose energy terms are weighted according to their relative importance in detecting boundaries. This relative importance is computed based on local edge features collected from the adjacent region located inside and outside of the evolving contour. The local edge features employed are the edge intensity and the degree of alignment between the image's gradient vector flow field and the evolving contour's normal. We evaluate the proposed method for segmentation of various regions in real MRI and CT slices, X-ray images, and ultra sound images. Evaluation results confirm the advantage of weighting energy forces using local edge features to reduce leakage. These results also show that the proposed method leads to more accurate boundary detection results than state-of-the-art edge-based level set segmentation methods, particularly around weak edges.


title: Bag-of-embeddings for text classification

abstract: Words are central to text classification. It has been shown that simple Naive Bayes models with word and bigram features can give highly competitive accuracies when compared to more sophisticated models with part-of-speech, syntax and semantic features. Embeddings offer distributional features about words. We study a conceptually simple classification model by exploiting multiprototype word embeddings based on text classes. The key assumption is that words exhibit different distributional characteristics under different text classes. Based on this assumption, we train multiprototype distributional word representations for different text classes. Given a new document, its text class is predicted by maximizing the probabilities of embedding vectors of its words under the class. In two standard classification benchmark datasets, one is balance and the other is imbalance, our model outperforms state-of-the-art systems, on both accuracy and macro-average F-1 score.


title: Diet and lifestyle recommendations revision 2006: A scientific statement from the American Heart Association Nutrition Committee

abstract: Improving diet and lifestyle is a critical component of the American Heart Association's strategy for cardiovascular disease risk reduction in the general population. This document presents recommendations designed to meet this objective. Specific goals are to consume an overall healthy diet; aim for a healthy body weight; aim for recommended levels of low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglycerides; aim for normal blood pressure; aim for a normal blood glucose level; be physically active; and avoid use of and exposure to tobacco products. The recommendations are to balance caloric intake and physical activity to achieve and maintain a healthy body weight; consume a diet rich in vegetables and fruits; choose whole-grain, high-fiber foods; consume fish, especially oily fish, at least twice a week; limit intake of saturated fat to <7% of energy, trans fat to <1% of energy, and cholesterol to <300 mg/day by choosing lean meats and vegetable alternatives, fat-free (skim) or low-fat (1% fat) dairy products and minimize intake of partially hydrogenated fats; minimize intake of beverages and foods with added sugars; choose and prepare foods with little or no salt; if you consume alcohol, do so in moderation; and when you eat food prepared outside of the home, follow these Diet and Lifestyle Recommendations. By adhering to these diet and lifestyle recommendations, Americans can substantially reduce their risk of developing cardiovascular disease, which remains the leading cause of morbidity and mortality in the United States.


title: Investigating the impact of facebook use on cancer survivors' psychological well-being

abstract: Rapid growth of Social Network Sites (SNSs) use by cancer survivors makes it important to examine whether there is a relationship between the use of these online communities and cancer survivors’ psychological well-being. This article poses the question of how the Facebook use as the most popular SNS, may impact cancer survivors’ psychological well-being. To answer this question a comprehensive literature review of studies conducted in information systems and health disciplines has been undertaken and a theoretical model is proposed. This study is expected to contribute to the existing knowledge base through the development of a new theoretical model which introduces and explains the ways that SNS use may impact cancer survivors’ psychological well-being. It provides important information on the health-related SNSs use and is envisioned to assist health care organizations and cancer survivors to use SNS as an e-health application.


title: Modern Cryptography : Theory and Practice

abstract: Many cryptographic schemes and protocols, especially those based on public-keycryptography, have basic or so-called "textbook crypto" versions, as these versionsare usually the subjects for many textbooks on cryptography. This book takes adifferent approach to introducing cryptography: it pays much more attention tofit-for-application aspects of cryptography. It explains why "textbook crypto" isonly good in an ideal world where data are random and bad guys behave nicely.It reveals the general unfitness of "textbook crypto" for the real world by demonstratingnumerous attacks on such schemes, protocols and systems under variousreal-world application scenarios. This book chooses to introduce a set of practicalcryptographic schemes, protocols and systems, many of them standards or de factoones, studies them closely, explains their working principles, discusses their practicalusages, and examines their strong (i.e., fit-for-application) security properties, oftenwith security evidence formally established. The book also includes self-containedtheoretical background material that is the foundation for modern cryptography.


title: A system for monitoring hand hygiene compliance based-on Internet-of-Things

abstract: Studies show that the poor hand hygiene practices in public health institutions such as hospitals, clinics, schools and nursing homes is the most likely cause of the spread of various bacterial diseases. Proper hand hygiene is the single biggest defense against spread of diseases and therefore the caregivers' compliance with hand hygiene is one of the most effective tools in preventing Healthcare Associated Infections (HAIs) in such institutions. In this paper, we present a system prototype of interconnected hand-hygiene stations that use a combination Wireless Sensor Networks (WSN) and Internet-of-Things (IoT) technologies for monitoring the hand hygiene compliance rate. The cloud-connected hand hygiene stations can cooperate to detect hand hygiene events in real-time, and help measuring the hand hygiene compliance at large-scale health-care facilities where it is deployed. The presented work is an initial phase of an on-going research study on hand hygiene compliance. Further phases of the system are currently being developed. The proposed system is desired to be scalable, and easy to install so that it can overcome most of the problems of existing related systems.


Physical location of genes encoding small heat shock proteins in the Suidae genomes


title: Image segmentation using color and texture features

abstract: This paper describes a new color image segmentation method based on low-level features including color, texture and spatial information. The mean-shift algorithm with color and spatial information in color image segmentation is in general successful, however, in some cases, the color and spatial information are not sufficient for superior segmentation. The proposed method addresses this problem and employs texture as an additional feature. The method uses wavelet frames that provide translation invariant texture analysis. The method integrates additional texture feature to the color and spatial space of standard mean shift segmentation algorithm. The new algorithm with high dimensional extended feature space provides better results than standard mean shift segmentation algorithm as shown in experimental results.


title: Brain Oxytocin: A Key Regulator of Emotional and Social Behaviours in Both Females and Males

abstract: In addition to various reproductive stimuli, the neuropeptide oxytocin (OXT) is released both from the neurohypophysial terminal into the blood stream and within distinct brain regions in response to stressful or social stimuli. Brain OXT receptor-mediated actions were shown to be significantly involved in the regulation of a variety of behaviours. Here, complementary methodological approaches are discussed which were utilised to reveal, for example, anxiolytic and anti-stress effects of OXT, both in females and in males, effects that were localised within the central amygdala and the hypothalamic paraventricular nucleus. Also, in male rats, activation of the brain OXT system is essential for the regulation of sexual behaviour, and increased OXT system activity during mating is directly linked to an attenuated anxiety-related behaviour. Moreover, in late pregnancy and during lactation, central OXT is involved in the establishment and fine-tuned maintenance of maternal care and maternal aggression. In monogamous prairie voles, brain OXT is important for mating-induced pair bonding, especially in females. Another example of behavioural actions of intracerebral OXT is the promotion of social memory processes and recognition of con-specifics, as revealed in rats, mice, sheep and voles. Experimental evidence suggests that, in humans, brain OXT exerts similar behavioural effects. Thus, the brain OXT system seems to be a potential target for the development of therapeutics to treat anxiety- and depression-related diseases or abnormal social behaviours including autism.


title: Covert and Side Channels Due to Processor Architecture

abstract: Information leakage through covert channels and side channels is becoming a serious problem, especially when these are enhanced by modern processor architecture features. We show how processor architecture features such as simultaneous multithreading, control speculation and shared caches can inadvertently accelerate such covert channels or enable new covert channels and side channels. We first illustrate the reality and severity of this problem by describing concrete attacks. We identify two new covert channels. We show orders of magnitude increases in covert channel capacities. We then present two solutions, Selective Partitioning and the novel Random Permutation Cache (RPCache). The RPCache can thwart most cache-based software side channel attacks, with minimal hardware costs and negligible performance impact.


title: An early flood detection system using mobile networks

abstract: In this paper, an early flood warning system has been studied and developed. The system consists of an early flood warning station powered by solar energy, a monitoring center, and a notification system. The flood warning station is equipped with the water level sensor and the precipitation sensor. The data, which is received from sensors, will be stored in memory card and transmitted to monitoring center via GPRS protocol. The functions of monitoring center are to receive, analyse data from the stations and send results to notification system where warning can be generated to the considered residential areas. The warning levels of flood can be set up easily by using the buttons in the circuit board or by using the SMS message from user's mobile phone. A website was also created to display the river water level, rainfall and the predicted information for the next few hours.


title: Intense training: the key to optimal performance before and during the taper

abstract: The training load is markedly reduced during the taper so that athletes recover from intense training and feel energized before major events. Load reduction can be achieved by reducing the intensity, volume and/or frequency of training, but with reduced training load there may be a risk of detraining. Training at high intensities before the taper plays a key role in inducing maximal physiological and performance adaptations in both moderately trained subjects and highly trained athletes. High-intensity training can also maintain or further enhance training-induced adaptations while athletes reduce their training before a major competition. On the other hand, training volume can be markedly reduced without a negative impact on athletes' performance. Therefore, the training load should not be reduced at the expense of intensity during the taper. Intense exercise is often a performance-determining factor during match play in team sports, and high-intensity training can also elicit major fitness gains in team sport athletes. A tapering and peaking program before the start of a league format championship or a major tournament should be characterized by high-intensity activities.


title: Three-phase modular permanent magnet brushless Machine for torque boosting on a downsized ICE vehicle

abstract: The paper describes a relatively new topology of 3-phase permanent magnet (PM) brushless machine, which offers a number of significant advantages over conventional PM brushless machines for automotive applications, such as electrical torque boosting at low engine speeds for vehicles equipped with downsized internal combustion engine (ICEs). The relative merits of feasible slot/pole number combinations for the proposed 3-phase modular PM brushless ac machine are discussed, and an analytical method for establishing the open-circuit and armature reaction magnetic field distributions when such a machine is equipped with a surface-mounted magnet rotor is presented. The results allow the prediction of the torque, the phase emf, and the self- and mutual winding inductances in closed forms, and provide a basis for comparative studies, design optimization and machine dynamic modeling. However, a more robust machine, in terms of improved containment of the magnets, results when the magnets are buried inside the rotor, which, since it introduces a reluctance torque, also serves to reduce the back-emf, the iron loss and the inverter voltage rating. The performance of a modular PM brushless machine equipped with an interior magnet rotor is demonstrated by measurements on a 22-pole/24-slot prototype torque boosting machine.


title: Solutions to Detect and Analyze Online Radicalization : A Survey

abstract: Online Radicalization (also called Cyber-Terrorism or Extremism or Cyber-Racism or Cyber- Hate) is widespread and has become a major and growing concern to the society, governments and law enforcement agencies around the world. Research shows that various platforms on the Internet (low barrier to publish content, allows anonymity, provides exposure to millions of users and a potential of a very quick and widespread diffusion of message) such as YouTube (a popular video sharing website), Twitter (an online micro-blogging service), Facebook (a popular social networking website), online discussion forums and blogosphere are being misused for malicious intent. Such platforms are being used to form hate groups, racist communities, spread extremist agenda, incite anger or violence, promote radicalization, recruit members and create virtual organi- zations and communities. Automatic detection of online radicalization is a technically challenging problem because of the vast amount of the data, unstructured and noisy user-generated content, dynamically changing content and adversary behavior. There are several solutions proposed in the literature aiming to combat and counter cyber-hate and cyber-extremism. In this survey, we review solutions to detect and analyze online radicalization. We review 40 papers published at 12 venues from June 2003 to November 2011. We present a novel classification scheme to classify these papers. We analyze these techniques, perform trend analysis, discuss limitations of existing techniques and find out research gaps.


Multi-talker Speech Separation and Tracing with Permutation Invariant Training of Deep Recurrent Neural Networks


title: Orbit Regularization

abstract: We propose a general framework for regularization based on group-induced ma-jorization. In this framework, a group is defined to act on the parameter space and an orbit is fixed; to control complexity, the model parameters are confined to the convex hull of this orbit (the orbitope). We recover several well-known regularizes as particular cases, and reveal a connection between the hyperoctahedral group and the recently proposed sorted l1-norm. We derive the properties a group must satisfy for being amenable to optimization with conditional and projected gradient algorithms. Finally, we suggest a continuation strategy for orbit exploration, presenting simulation results for the symmetric and hyperoctahedral groups.


title: Mobile phones as medical devices in mental disorder treatment: an overview

abstract: Mental disorders can have a significant, negative impact on sufferers' lives, as well as on their friends and family, healthcare systems and other parts of society. Approximately 25 % of all people in Europe and the USA experience a mental disorder at least once in their lifetime. Currently, monitoring mental disorders relies on subjective clinical self-reporting rating scales, which were developed more than 50 years ago. In this paper, we discuss how mobile phones can support the treatment of mental disorders by (1) implementing human---computer interfaces to support therapy and (2) collecting relevant data from patients' daily lives to monitor the current state and development of their mental disorders. Concerning the first point, we review various systems that utilize mobile phones for the treatment of mental disorders. We also evaluate how their core design features and dimensions can be applied in other, similar systems. Concerning the second point, we highlight the feasibility of using mobile phones to collect comprehensive data including voice data, motion and location information. Data mining methods are also reviewed and discussed. Based on the presented studies, we summarize advantages and drawbacks of the most promising mobile phone technologies for detecting mood disorders like depression or bipolar disorder. Finally, we discuss practical implementation details, legal issues and business models for the introduction of mobile phones as medical devices.


title: Decentralized Prediction Market without Arbiters

abstract: We consider a prediction market in which all aspects are controlled by market forces, in particular the correct outcomes of events are decided by the market itself rather than by trusted arbiters. This kind of a decentralized prediction market can sustain betting on events whose outcome may remain unresolved for a long or even unlimited time period, and can facilitate trades among participants who are spread across diverse geographical locations, may wish to remain anonymous and/or avoid burdensome identification procedures, and are distrustful of each other. We describe how a cryptocurrency such as Bitcoin can be enhanced to accommodate a truly decentralized prediction market, by employing an innovative variant of the Colored Coins concept. We examine the game-theoretic properties of our design, and offer extensions that enable other financial instruments as well as real-time exchange.


title: Part-level fully convolutional networks for pedestrian detection

abstract: Since pedestrians in videos have a wide range of appearances such as body poses, occlusions, and complex backgrounds, pedestrian detection is a challengeable task. In this paper, we propose part-level fully convolutional networks (FCN) for pedestrian detection. We adopt deep learning to deal with the proposal shifting problem in pedestrian detection. First, we combine convolutional neural networks (CNN) and FCN to align bounding boxes for pedestrians. Then, we perform part-level pedestrian detection based on CNN to recall the lost body parts. Experimental results demonstrate that the proposed method achieves 6.83% performance improvement in log-average miss rate over CifarNet.


title: Onychophagia-Induced Longitudinal Melanonychia

abstract: : An otherwise-healthy 13-year-old girl with previously normal nails developed longitudinal pigmented bands on multiple fingernails. Physical examination revealed faintly pigmented bands on multiple fingernails and on the left fifth toenail. We believed that the cause of the pigmented bands was onychophagia-induced longitudinal melanonychia, a rare phenomenon, which emphasizes the need for dermatologists to question patients with melanonychia about their nail biting habits because they may not be forthcoming with this information.


title: Human Genetics Shape the Gut Microbiome

abstract: Host genetics and the gut microbiome can both influence metabolic phenotypes. However, whether host genetic variation shapes the gut microbiome and interacts with it to affect host phenotype is unclear. Here, we compared microbiotas across >1,000 fecal samples obtained from the TwinsUK population, including 416 twin pairs. We identified many microbial taxa whose abundances were influenced by host genetics. The most heritable taxon, the family Christensenellaceae, formed a co-occurrence network with other heritable Bacteria and with methanogenic Archaea. Furthermore, Christensenellaceae and its partners were enriched in individuals with low body mass index (BMI). An obese-associated microbiome was amended with Christensenella minuta, a cultured member of the Christensenellaceae, and transplanted to germ-free mice. C. minuta amendment reduced weight gain and altered the microbiome of recipient mice. Our findings indicate that host genetics influence the composition of the human gut microbiome and can do so in ways that impact host metabolism.


title: Synthetic therapeutic peptides: science and market.

abstract: The decreasing number of approved drugs produced by the pharmaceutical industry, which has been accompanied by increasing expenses for R&D, demands alternative approaches to increase pharmaceutical R&D productivity. This situation has contributed to a revival of interest in peptides as potential drug candidates. New synthetic strategies for limiting metabolism and alternative routes of administration have emerged in recent years and resulted in a large number of peptide-based drugs that are now being marketed. This review reports on the unexpected and considerable number of peptides that are currently available as drugs and the chemical strategies that were used to bring them into the market. As demonstrated here, peptide-based drug discovery could be a serious option for addressing new therapeutic challenges.


title: Novel Views of Objects from a Single Image

abstract: Taking an image of an object is at its core a lossy process. The rich information about the three-dimensional structure of the world is flattened to an image plane and decisions such as viewpoint and camera parameters are final and not easily revertible. As a consequence, possibilities of changing viewpoint are limited. Given a single image depicting an object, novel-view synthesis is the task of generating new images that render the object from a different viewpoint than the one given. The main difficulty is to synthesize the parts that are disoccluded; disocclusion occurs when parts of an object are hidden by the object itself under a specific viewpoint. In this work, we show how to improve novel-view synthesis by making use of the correlations observed in 3D models and applying them to new image instances. We propose a technique to use the structural information extracted from a 3D model that matches the image object in terms of viewpoint and shape. For the latter part, we propose an efficient 2D-to-3D alignment method that associates precisely the image appearance with the 3D model geometry with minimal user interaction. Our technique is able to simulate plausible viewpoint changes for a variety of object classes within seconds. Additionally, we show that our synthesized images can be used as additional training data that improves the performance of standard object detectors.


title: ATOM: a system for building customized program analysis tools

abstract: ATOM (Analysis Tools with OM) is a single framework for building a wide range of customized program analysis tools. It provides the common infrastructure present in all code-instrumenting tools; this is the difficult and time-consuming part. The user simply defines the tool-specific details in instrumentation and analysis routines. Building a basic block counting tool like Pixie with ATOM requires only a page of code. ATOM, using OM link-time technology, organizes the final executable such that the application program and user's analysis routines run in the same address space. Information is directly passed from the application program to the analysis routines through simple procedure calls instead of inter-process communication or files on disk. ATOM takes care that analysis routines do not interfere with the program's execution, and precise information about the program is presented to the analysis routines at all times. ATOM uses no simulation or interpretation. ATOM has been implemented on the Alpha AXP under OSF/1. It is efficient and has been used to build a diverse set of tools for basic block counting, profiling, dynamic memory recording, instruction and data cache simulation, pipeline simulation, evaluating branch prediction, and instruction scheduling.


title: WebotsTM: Professional Mobile Robot Simulation

abstract: Cyberbotics Ltd. develops WebotsTM, a mobile robotics simulation software that provides you with a rapid prototyping environment for modelling, programming and simulating mobile robots. The provided robot libraries enable you to transfer your control programs to several commercially available real mobile robots. WebotsTM lets you define and modify a complete mobile robotics setup, even several different robots sharing the same environment. For each object, you can define a number of properties, such as shape, color, texture, mass, friction, etc. You can equip each robot with a large number of available sensors and actuators. You can program these robots using your favorite development environment, simulate them and optionally transfer the resulting programs onto your real robots. WebotsTM has been developed in collaboration with the Swiss Federal Institute of Technology in Lausanne, thoroughly tested, well documented and continuously maintained for over 7 years. It is now the main commercial product available from Cyberbotics Ltd.


title: On the aggregation of red cells in Waldenström's macroglobulinaemia and multiple myeloma.

abstract: Abstract Aggregation of red cells was evaluated in 17 patients with macroglobulinaemia and in 30 patients with multiple myeloma by correcting erythrocyte sedimentation rates and velocities for plasma viscosity and adjusting to haematocrit of 30%. Aggregation of red cells is expressed in mm/hr, and values of up to 3800 mm/hr are noted. Aggreation of red cells in these diseases did not correlate with plasma fibrinogen concentration but did correlate ( P α 1 -, α 2 -, and β-globulins on the aggregation of red cells in this study. The response of the aggregation of red cells to the globulin/albumin ratio was about threefold steeper in the macroglobulinaemia series than in the multiple myeloma series (significance: P ⪡ 0.001). In analogous manner, the difference between the slopes of linear regressions between aggregation of red cells (ESR sl.pl.hc. ) and gamma + M-globulin levels in the macroglobulinaemia and in myeloma series was highly significant ( P ⪡ 0.001). Albumin/fibrinogen ratio showed a significant correlation with aggregation of red cells in the multiple myeloma series but not in the macroglobulinaemia series. Estimation of red cell aggregation from the sedimentation velocity was found superior to the estimates made from 1-hr erythrocyte sedimentation rates in the diseases studied.


title: [4] N. Chiba, T. Nishizeki, S. Abe, and T. Ozawa. A linear algorithm for embedding planar graphs using PQ-trees.

abstract: This paper presents a simple linear algorithm for embedding (or drawing) a planar graph in the plane. The algorithm is based on the “vertex-addition” algorithm of Lempel, Even, and Cederbaum (“Theory of Graphs,” Intl. Sympos. Rome, July 1966, pp. 215–232, Gordon & Breach, New York, 1967) for the planarity testing, and is a modification of Booth and Lueker's (J. Comput. System Sci. 13 (1976), 335–379) implementation of the testing algorithm using a PQ-tree. Compared with the known embedding algorithm of Hopcroft and Tarjan (J. Assoc. Comput. Mach. 21, No. 4 (1974), 549–568), this algorithm is conceptually simple and easy to understand or implement. Moreover this embedding algorithm can find all the embeddings of a planar graph.


title: A Comprehensive Light-Source Description for Computer Graphics

abstract: For realistic simulation it is necessary to thoroughly define and describe light-source characteristics?especially the light-source geometry and the luminous intensity distribution.


title: Sentence Simplification for Semantic Role Labeling

abstract: Parse-tree paths are commonly used to incorporate information from syntactic parses into NLP systems. These systems typically treat the pathsas atomic(or nearly atomic)features; these features are quite sparse due to the immense variety of syntactic expression. In this paper, we propose a general method for learning how to iteratively simplify a sentence, thus decomposing complicated syntax into small, easy-to-process pieces. Our method applies a series of hand-written transformation rules corresponding to basic syntactic patterns — for example, one rule “depassivizes” a sentence. The model is parameterized by learned weights specifying preferences for some rules over others. After applying all possible transformations to a sentence, we are left with a set of candidate simplified sentences. We apply our simplification system to semantic role labeling (SRL). As we do not have labeled examples of correct simplifications, we use labeled training data for the SRL task to jointly learn both the weights of the simplification model and of an SRL model, treating the simplification as a hidden variable. By extracting and labeling simplified sentences, this combined simplification/SRL system better generalizes across syntactic variation. It achieves a statistically significant 1.2% F1 measure increase over a strong baseline on the Conll2005 SRL task, attaining near-state-of-the-art performance.


title: Single tone parameter estimation from discrete-time observations

abstract: Estimation of the parameters of a single-frequency complex tone from a finite number of noisy discrete-time observations is discussed. The appropriate Cramer-Rao bounds and maximum-likelihood (MI.) estimation algorithms are derived. Some properties of the ML estimators are proved. The relationship of ML estimation to the discrete Fourier transform is exploited to obtain practical algorithms. The threshold effect of one algorithm is analyzed and compared to simulation results. Other simulation results verify other aspects of the analysis.


Toward a dynamic theory of emotion : The component process model of affective states


title: Entertainment, culture, and media art

abstract: This chapter is dedicated to explore the relationship between entertainment, culture, and media art. Firstly, the positioning of entertainment will be described including the historical point of view and also focusing on recent digital entertainment technologies. It will be clarified that entertainment is closely related to the mental sustainability of human and therefore has a close relationship with culture and art. Then, based on this, further investigation will be carried out to explain how culture and technology could be combined together. Firstly, the relationship between culture and technology will be discussed proposing the concept of “cultural computing.” Then three typical research examples of cultural computing, “ALICE,” “ZENetic Computer,” and “Confucius Chat” will be described emphasizing how cultural issues could be treated using computer both in the Western culture and Eastern culture. As art is the core part of culture, as a next step the role of art, especially new art called media art where technology plays an important role, will be discussed. And, as an example, media art created by an Eastern media artist will be introduced as a good example of such media art treating cultural issues.


title: Fucoxanthinol, Metabolite of Fucoxanthin, Improves Obesity-Induced Inflammation in Adipocyte Cells

abstract: Fucoxanthin (Fx) is a marine carotenoid found in edible brown seaweeds. We previously reported that dietary Fx metabolite into fucoxanthinol (FxOH), attenuates the weight gain of white adipose tissue of diabetic/obese KK-Ay mice. In this study, to evaluate anti-diabetic effects of Fx, we investigated improving the effect of insulin resistance on the diabetic model of KK-Ay mice. Furthermore, preventing the effect of FxOH on low-grade chronic inflammation related to oxidative stress was evaluated on 3T3-L1 adipocyte cells and a RAW264.7 macrophage cell co-culture system. A diet containing 0.1% Fx was fed to diabetic model KK-Ay mice for three weeks, then glucose tolerance was observed. Fx diet significantly improved glucose tolerance compared with the control diet group. In in vitro studies, FxOH showed suppressed tumor necrosis factor-α (TNF-α), and monocyte chemotactic protein-1 (MCP-1) mRNA expression and protein levels in a co-culture of adipocyte and macrophage cells. These findings suggest that Fx ameliorates glucose tolerance in the diabetic model mice. Furthermore, FxOH, a metabolite of Fx, suppresses low-grade chronic inflammation in adipocyte cells.


title: Is a Positive Review Always Effective? Advertising Appeal Effect in the Persuasion of Online Customer Reviews

abstract: Despite the expected value of online customer reviews as an emerging advertising medium, the manner of enforcing its effectiveness on customers’ purchase behavior is still a question not well answered. To address the question, we propose a new central route cue called “advertising appeal” to examine attitude and intention to purchase a product or service based on the reasoning of the elaboration likelihood model. We also propose that consumers’ consumption goal and attitude certainty separately moderate the advertising appeal effect on purchase intention through a degree of favorable attitude. We test these hypotheses by conducting a laboratory experiment with 50 participants. In this experiment, we manipulate two kinds of advertising appeals and consumption goals. This study demonstrates that attribute-based appeal reviews are more influential than emotion-based appeal reviews in the persuasion process, regardless of the individuals’ consumption goals. However, the advertising appeal effect on purchase intention is more pronounced for hedonic consumers than for utilitarian consumers.


title: Accurate Object Pose Estimation Using Depth Only

abstract: Object recognition and pose estimation is an important task in computer vision. A pose estimation algorithm using only depth information is proposed in this paper. Foreground and background points are distinguished based on their relative positions with boundaries. Model templates are selected using synthetic scenes to make up for the point pair feature algorithm. An accurate and fast pose verification method is introduced to select result poses from thousands of poses. Our algorithm is evaluated against a large number of scenes and proved to be more accurate than algorithms using both color information and depth information.


title: Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks

abstract: In this paper we present a method for learning a discriminative classifier from unlabeled or partially labeled data. Our approach is based on an objective function that trades-off mutual information between observed examples and their predicted categorical class distribution, against robustness of the classifier to an adversarial generative model. The resulting algorithm can either be interpreted as a natural generalization of the generative adversarial networks (GAN) framework or as an extension of the regularized information maximization (RIM) framework to robust classification against an optimal adversary. We empirically evaluate our method - which we dub categorical generative adversarial networks (or CatGAN) - on synthetic data as well as on challenging image classification tasks, demonstrating the robustness of the learned classifiers. We further qualitatively assess the fidelity of samples generated by the adversarial generator that is learned alongside the discriminative classifier, and identify links between the CatGAN objective and discriminative clustering algorithms (such as RIM).


title: Calibrating trajectory data for similarity-based analysis

abstract: Due to the prevalence of GPS-enabled devices and wireless communications technologies, spatial trajectories that describe the movement history of moving objects are being generated and accumulated at an unprecedented pace. Trajectory data in a database are intrinsically heterogeneous, as they represent discrete approximations of original continuous paths derived using different sampling strategies and different sampling rates. Such heterogeneity can have a negative impact on the effectiveness of trajectory similarity measures, which are the basis of many crucial trajectory processing tasks. In this paper, we pioneer a systematic approach to trajectory calibration that is a process to transform a heterogeneous trajectory dataset to one with (almost) unified sampling strategies. Specifically, we propose an anchor-based calibration system that aligns trajectories to a set of anchor points, which are fixed locations independent of trajectory data. After examining four different types of anchor points for the purpose of building a stable reference system, we propose a geometry-based calibration approach that considers the spatial relationship between anchor points and trajectories. Then a more advanced model-based calibration method is presented, which exploits the power of machine learning techniques to train inference models from historical trajectory data to improve calibration effectiveness. Finally, we conduct extensive experiments using real trajectory datasets to demonstrate the effectiveness and efficiency of the proposed calibration system.


title: Dendritic macromolecules supported Ag nanoparticles as efficient catalyst for the reduction of 4-nitrophenol

abstract: Abstract Polymer supported Ag nanoparticles, generated in situ by silver nitrate (AgNO 3 ) reduction under reaction conditions, catalyzed the hydrogenation of 4-nitrophenol with high efficiency in water at room temperature in the presence of an excess amount of NaBH 4 . Amphiphilic linear-dendritic copolymers containing a poly(ethylene glycol) (PEG) core and poly(2-ethyl-2-oxazoline)-poly(e-caprolactone) arms were able to load the Ag nanoparticles. The Ag nanoparticles with a diameter of 8–10 nm were found to show a comparable catalytic activity towards formation of the aromatic amine as single product with short reaction time.


Control of master-slave manipulators for object teleperception


title: Design of Frequency-and Polarization-Reconfigurable Antenna Based on the Polarization Conversion Metasurface

abstract: A frequency- and polarization-reconfigurable antenna composed of a metasurface, a planar slot antenna, and a metallic reflector is proposed in this letter. The metasurface is made up of 64 identical patches, and all the patches are on the top surface of the substrate. In order to extend the bandwidth of the antenna, the planar slot antenna adopts a double-slot structure. The reconfiguration of frequency and polarization can be achieved by adjusting the relative positions between the metasurface and the planar slot antenna. The antenna can be reconfigured to linear polarization, and left-hand and right-hand circular polarizations. At the same time, the gain of the antenna is improved. In order to verify this method, a frequency- and polarization-reconfigurable antenna is designed. Simulation and experimental results show that the polarization reconfiguration can be achieved from 8 to 11.2 GHz (fractional bandwidth of 33.33%) by adjusting the distance among the metasurface, source antenna, and metallic reflector. The maximum gain can achieve 16.5 dBi.


Combining advanced analytical tools with human intuition increases insight into problems


title: HFSI — A promising solution to very low speed issues in DTC-SVM induction motor drive

abstract: The stator flux and electromagnetic torque are directly controlled in a DTC system using space vector modulation technique. In DTC, torque and flux ripples increases at low speeds. A DTC-SVM induction motor drive at very low speeds is investigated here and High Frequency Signal Injection (HFSI) method is adopted to mitigate the low speed issues. In this method a sinusoidal wave of small amplitude and high frequency is injected along with the PI controller output of DTC-SVM. HFSI increases the switching frequency of SVM during low speed operation. This technique reduces torque ripple, acoustic noise, and gives excellent dynamic and steady state performance. The performance of DTC-SVM and DTC-SVM with HFSI at low speed is analyzed through simulation using matlab/simulink package. The results verify the merits of the DTC-SVM using HF injection technique with respect to DTC-SVM technique at very low speeds.


title: iSRD: Spam review detection with imbalanced data distributions

abstract: Internet is playing an essential role for modern information systems. Applications, such as e-commerce websites, are becoming popularly available for people to purchase different types of products online. During such an online shopping process, users often rely on online review reports from previous customers to make the final decision. Because online reviews are playing essential roles for the selling of online products (or services), some vendors (or customers) are providing fake/spam reviews to mislead the customers. Any false reviews of the products may result in unfair market competition and financial loss for the customers or vendors. In this research, we aim to distinguish between spam and non-spam reviews by using supervised classification methods. When training a classifier to identify spam vs. non-spam reviews, a challenging issue is that spam reviews are only a very small portion of the online review reports. This naturally leads to a data imbalance issue for training classifiers for spam review detection, where learning methods without emphasizing on minority samples (i.e., spams) may result in poor performance in detecting spam reviews (although the overall accuracy of the algorithm might be relatively high). In order to tackle the challenge, we employ a bagging based approach to build a number of balanced datasets, through which we can train a set of spam classifiers and use their ensemble to detect review spams. Experiments and comparisons demonstrate that our method, iSRD, outperforms baseline methods for review spam detection.


Green Cloud computing and Environmental Sustainability


Analysis of Association Rule Mining Algorithms to Generate Frequent Itemset


title: 1 Billion Pages = 1 Million Dollars? Mining the Web to Play"Who Wants to be a Millionaire?"

abstract: We exploit the redundancy and volume of information on the web to build a computerized player for the ABC TV game show "Who Wants To Be A Millionaire?". The player consists of a question-answering module and a decision-making module. The question-answering module utilizes question transformation techniques, natural language parsing, multiple information retrieval algorithms, and multiple search engines; results are combined in the spirit of ensemble learning using an adaptive weighting scheme. Empirically, the system correctly answers about 75% of questions from the Millionaire CD-ROM, 3rd edition--general-interest trivia questions often about popular culture and common knowledge. The decision-making module chooses from allowable actions in the game in order to maximize expected risk-adjusted winnings, where the estimated probability of answering correctly is a function of past performance and confidence in correctly answering the current question. When given a six question head start (i.e., when starting from the $2,000 level), we find that the system performs about as well on average as humans starting at the beginning. Our system demonstrates the potential of simple but well-chosen techniques for mining answers from unstructured information such as the web.


title: Adoption of e-government services in Turkey

abstract: This research aims to investigate underlying factors that play role in citizens' decision to use e-government services in Turkey. UTAUT model which was enriched by introducing Trust of internet and Trust of government factors is used in the study. The model is evaluated through a survey conducted with Turkish citizens who are from different regions of the country. A total of 529 answers collected through purposive sampling and the responses were evaluated with the SEM (Structural Equation Modeling) technique. According to the results, Performance expectancy, Social influence, Facilitating conditions and Trust of Internet were found to have a positive effect on behavioral intention to use e-government services. Additionally, both Trust factors were found to have a positive influence on Performance expectancy of e-government services, a relation which, to our best knowledge, hasn't been tested before in e-government context. Effect of Effort expectancy and Trust of government were found insignificant on behavioral intention. We believe that the findings of this study will guide professionals and policy makers in improving and popularizing e-government services by revealing the citizen's priorities regarding e-government services in Turkey. First research on e-government adoption process in Turkey.The relation between Trusting feelings and Performance expectancy is tested.The significant and insignificant factors in e-government adoption are revealed.The research may guide government to increase e-government usage in Turkey.Contributes the literature of e-government adoption in developing countries.


title: Improving Hypernymy Detection with an Integrated Path-based and Distributional Method

abstract: Detecting hypernymy relations is a key task in NLP, which is addressed in the literature using two complementary approaches. Distributional methods, whose supervised variants are the current best performers, and path-based methods, which received less research attention. We suggest an improved path-based algorithm, in which the dependency paths are encoded using a recurrent neural network, that achieves results comparable to distributional methods. We then extend the approach to integrate both path-based and distributional signals, significantly improving upon the state-of-the-art on this task.


title: Real-Time Action Recognition With Deeply Transferred Motion Vector CNNs

abstract: The two-stream CNNs prove very successful for video based action recognition. However the classical two-stream CNNs are time costly, mainly due to the bottleneck of calculating optical flows. In this paper, we propose a two-stream based real-time action recognition approach by using motion vector to replace optical flow. Motion vectors are encoded in video stream and can be extracted directly without extra calculation. However directly training CNN with motion vectors degrades accuracy severely due to the noise and the lack of fine details in motion vectors. In order to relieve this problem, we propose four training strategies which leverage the knowledge learned from optical flow CNN to enhance the accuracy of motion vector CNN. Our insight is that motion vector and optical flow share inherent similar structures which allows us to transfer knowledge from one domain to another. To fully utilize the knowledge learned in optical flow domain, we develop deeply transferred motion vector CNN. Experimental results on various datasets show the effectiveness of our training strategies. Our approach is significantly faster than optical flow based approaches and achieves processing speed of 390.7 frames per second, surpassing real-time requirement. We release our model and code to facilitate further research.