title: Pneumonia identification using statistical feature selection

abstract: Objective This paper describes a natural language processing system for the task of pneumonia identification. Based on the information extracted from the narrative reports associated with a patient, the task is to identify whether or not the patient is positive for pneumonia. Design A binary classifier was employed to identify pneumonia from a dataset of multiple types of clinical notes created for 426 patients during their stay in the intensive care unit. For this purpose, three types of features were considered: (1) word n-grams, (2) Unified Medical Language System (UMLS) concepts, and (3) assertion values associated with pneumonia expressions. System performance was greatly increased by a feature selection approach which uses statistical significance testing to rank features based on their association with the two categories of pneumonia identification. Results Besides testing our system on the entire cohort of 426 patients (unrestricted dataset), we also used a smaller subset of 236 patients (restricted dataset). The performance of the system was compared with the results of a baseline previously proposed for these two datasets. The best results achieved by the system (85.71 and 81.67 F1-measure) are significantly better than the baseline results (50.70 and 49.10 F1-measure) on the restricted and unrestricted datasets, respectively. Conclusion Using a statistical feature selection approach that allows the feature extractor to consider only the most informative features from the feature space significantly improves the performance over a baseline that uses all the features from the same feature space. Extracting the assertion value for pneumonia expressions further improves the system performance.


title: The influence of reading speed and line length on the effectiveness of reading from screen

abstract: With such a large volume of material accessible from the World Wide Web, there is an urgent need to increase our knowledge of factors influencing reading from screen. We investigate the effects of two reading speeds (normal and fast) and different line lengths on comprehension, reading rate and scrolling patterns. Scrolling patterns are defined as the way in which readers proceed through the text, pausing and scrolling. Comprehension and reading rate are also examined in relation to scrolling patterns to attempt to identify some characteristics of effective readers. We found a reduction in overall comprehension when reading fast, but the type of information recalled was not dependent on speed. A medium line length (55 characters per line) appears to support effective reading at normal and fast speeds. This produced the highest level of comprehension and was also read faster than short lines. Scrolling patterns associated with better comprehension (more time in pauses and more individual scrolling movements) contrast with scrolling patterns used by faster readers (less time in pauses between scrolling). Consequently, effective readers can only be defined in relation to the aims of the reading task, which may favour either speed or accuracy.


title: Generalization and equilibrium in generative adversarial nets (GANs) (invited talk)

abstract: We show that training of generative adversarial network (GAN) may not have good generalization properties; e.g., training may appear successful but the trained distribution may be far from target distribution in standard metrics. However, generalization does occur for a weaker metric called neural net distance. It is also shown that an approximate pure equilibrium exists in the discriminator/generator game for a special class of generators with natural training objectives when generator capacity and training set sizes are moderate. This existence of equilibrium inspires MIX+GAN protocol, which can be combined with any existing GAN training, and empirically shown to improve some of them.


title: Complete sequence of a cryptic virus from hemp (Cannabis sativa)

abstract: Hemp (Cannabis sativa) was found to be a useful propagation host for hop latent virus, a carlavirus. However, when virus preparations were analysed by electron microscopy, along with the expected filamentous particles, spherical particles with a diameter of around 34 nm were found. RNA from virus preparations was purified, and cDNA was prepared and cloned. Sequence information was used to search databases, and the greatest similarity was found with Primula malacoides virus 1, a putative new member of the genus Partitivirus. The full sequences of RNA 1 and RNA 2 of this new hemp cryptic virus were obtained.


title: HelpingHand: example-based stroke stylization

abstract: Digital painters commonly use a tablet and stylus to drive software like Adobe Photoshop. A high quality stylus with 6 degrees of freedom (DOFs: 2D position, pressure, 2D tilt, and 1D rotation) coupled to a virtual brush simulation engine allows skilled users to produce expressive strokes in their own style. However, such devices are difficult for novices to control, and many people draw with less expensive (lower DOF) input devices. This paper presents a data-driven approach for synthesizing the 6D hand gesture data for users of low-quality input devices. Offline, we collect a library of strokes with 6D data created by trained artists. Online, given a query stroke as a series of 2D positions, we synthesize the 4D hand pose data at each sample based on samples from the library that locally match the query. This framework optionally can also modify the stroke trajectory to match characteristic shapes in the style of the library. Our algorithm outputs a 6D trajectory that can be fed into any virtual brush stroke engine to make expressive strokes for novices or users of limited hardware.


A Comparative Analysis of Machine Learning Classifiers for Twitter Sentiment Analysis


title: Automatic registration of LIDAR and optical images of urban scenes

abstract: Fusion of 3D laser radar (LIDAR) imagery and aerial optical imagery is an efficient method for constructing 3D virtual reality models. One difficult aspect of creating such models is registering the optical image with the LIDAR point cloud, which is characterized as a camera pose estimation problem. We propose a novel application of mutual information registration methods, which exploits the statistical dependency in urban scenes of optical appearance with measured LIDAR elevation. We utilize the well known downhill simplex optimization to infer camera pose parameters. We discuss three methods for measuring mutual information between LIDAR imagery and optical imagery. Utilization of OpenGL and graphics hardware in the optimization process yields registration times dramatically lower than previous methods. Using an initial registration comparable to GPS/INS accuracy, we demonstrate the utility of our algorithm with a collection of urban images and present 3D models created with the fused imagery.


title: Automatic Parameter Selection for Denoising Algorithms Using a No-Reference Measure of Image Content

abstract: Across the field of inverse problems in image and video processing, nearly all algorithms have various parameters which need to be set in order to yield good results. In practice, usually the choice of such parameters is made empirically with trial and error if no "ground-truth" reference is available. Some analytical methods such as cross-validation and Stein's unbiased risk estimate (SURE) have been successfully used to set such parameters. However, these methods tend to be strongly reliant on restrictive assumptions on the noise, and also computationally heavy. In this paper, we propose a no-reference metric Q which is based upon singular value decomposition of local image gradient matrix, and provides a quantitative measure of true image content (i.e., sharpness and contrast as manifested in visually salient geometric features such as edges,) in the presence of noise and other disturbances. This measure 1) is easy to compute, 2) reacts reasonably to both blur and random noise, and 3) works well even when the noise is not Gaussian. The proposed measure is used to automatically and effectively set the parameters of two leading image denoising algorithms. Ample simulated and real data experiments support our claims. Furthermore, tests using the TID2008 database show that this measure correlates well with subjective quality evaluations for both blur and noise distortions.


title: Encoding Provenance Metadata for Social Science Datasets

abstract: Recording provenance is a key requirement for data-centric scholarship, allowing researchers to evaluate the integrity of source data sets and reproduce, and thereby, validate results. Provenance has become even more critical in the web environment in which data from distributed sources and of varying integrity can be combined and derived. Recent work by the W3C on the PROV model provides the foundation for semantically-rich, interoperable, and web-compatible provenance metadata. We apply that model to complex, but characteristic, provenance examples of social science data, describe scenarios that make scholarly use of those provenance descriptions, and propose a manner for encoding this provenance metadata within the widely-used DDI metadata standard.


title: The Impact of Social Connectedness and Internalized Transphobic Stigma on Self-Esteem Among Transgender and Gender Non-Conforming Adults

abstract: ABSTRACTThe transgender and gender non-conforming (TGNC) community continues to represent a notably marginalized population exposed to pervasive discrimination, microaggressions, and victimization. Congruent with the minority stress model, TGNC individuals persistently experience barriers to wellbeing in contemporary society; however, research uncovering resilience-based pathways to health among this population is sparse. This study aimed to explore the impact and interaction between internalized transphobic stigma and a potential buffer against minority stress—social connectedness—on the self-esteem of TGNC identified adults. Data were collected from 65 TGNC identified adults during a national transgender conference. Multiple regression analysis reveals that self-esteem is negatively impacted by internalized transphobia and positively impacted by social connectedness. Social connectedness did not significantly moderate the relationship between internalized transphobia and self-esteem. Micro and macro int...


title: Design of low cost Class-E power amplifier for HF band applications

abstract: HF band (3~30 MHz) has been widely used in many communication and other systems, in military and civil applications. Generally the circuit design in this band is not a challenge; however this is not the case in some circuits, especially in the case of power amplifier design. One of the most important parameters in power amplifier design for civil applications is the product cost. On the other hand power amplifier is the most expensive part of a transceiver or other high power RF system. Consequently low cost high performance power amplifier is very attractive in HF band. In most of designs, high power RF transistor is the most expensive circuit component. This paper presents a new design approach for Class-E power amplifier in HF band, by which the capability of RF switch is considerably improved. Thanks to the proposed approach, we have successfully designed a 30 MHz Class-E power amplifier, using the extra low cost, commercially off-the shelf (COTS) VMOS components. The reported works with COTS VMOS devices are in the range of few hundreds of kilohertz. The cost for the device used in our work is about 100 times lower than the conventional Gemini MOSFET devices. The designed PA exploits 98% power efficiency and 81% power added efficiency with 70 watts output power.


title: Autonomous semantic mapping for robots performing everyday manipulation tasks in kitchen environments

abstract: In this work we report about our efforts to equip service robots with the capability to acquire 3D semantic maps. The robot autonomously explores indoor environments through the calculation of next best view poses, from which it assembles point clouds containing spatial and registered visual information. We apply various segmentation methods in order to generate initial hypotheses for furniture drawers and doors. The acquisition of the final semantic map makes use of the robot's proprioceptive capabilities and is carried out through the robot's interaction with the environment. We evaluated the proposed integrated approach in the real kitchen in our laboratory by measuring the quality of the generated map in terms of the map's applicability for the task at hand (e.g. resolving counter candidates by our knowledge processing system).


title: Trust in Electronic Commerce: A New Model for Building Online Trust in B2C

abstract: The rapid growth in the electronic commerce over the internet has fuelled predictions and speculations about what makes a business to consumer (B2C) web site effective. Increasing use of the World Wide Web as a B2C commercial tool raises interest in understanding the key issues in building relationships with customers on the internet. Trust is believed to be the key to these relationships. In this paper, an overall model has presented for building online trust in this context. This model outlines some of the key factors that are related in this area and suggests a framework based on these factors. With respect to the position and importance of the trust in online commerce, this model helps businesses in order to capture, sustain and construct long-term relationships with their consumers. Keywords: E-Trust, Reverse engineering, Business-consumer web sites, online shopping, Web design


title: Online Impression Management: Personality Traits and Concerns for Secondary Goals as Predictors of Self-Presentation Tactics on Facebook

abstract: This study investigates the utility of personality traits and secondary goals as predictors of self-presentation tactics employed by Facebook users. A structural equation model of self-presentation tactics on Facebook was proposed and tested. Although fit of the initial model was good, the final model, eliminating three paths and adding two others, yielded a significantly better fitting model. Findings show that personality traits predicted concern for secondary goals (N = 477) and that secondary goals predicted the use of various self-presentation tactics used on Facebook. Results indicated that these personality traits and secondary goals are both theoretically and empirically sound components for the conceptualization of online impression management.


title: Boredom and Social Identity 1 On Boredom and Social Identity : A Pragmatic Meaning-Regulation Approach

abstract: People who feel bored experience that their current situation is meaningless and are motivated to reestablish a sense of meaningfulness. Building on the literature that conceptualizes social identification as source of meaningfulness, the authors tested the hypothesis that boredom increases the valuation of ingroups and devaluation of outgroups. Indeed, state boredom increased the liking of an ingroup name (Study 1), it increased hypothetical jail sentences given to an outgroup offender (Study 2 and Study 3), especially in comparison to an ingroup offender (Study 3), it increased positive evaluations of participants' ingroups, especially when ingroups were not the most favored ones to begin with (Study 4), and it increased the appreciation of an ingroup symbol, mediated by people's need to engage in meaningful behavior (Study 5). Several measures ruled out that these results could be explained by other affective states. These novel findings are discussed with respect to boredom, social identity, and existential psychology research.


title: Deep Learning for Imaging Flow Cytometry: Cell Cycle Analysis of Jurkat Cells

abstract: Imaging flow cytometry combines the fluorescence sensitivity and high-throughput capabilities of flow cytometry with single-cell imaging, and hence provides high-volume data well-matched to the strengths of deep learning. We present DeepFlow, a data analysis workflow for imaging flow cytometry that combines deep convolutional neural networks with non-linear dimension reduction. DeepFlow uses learned features of the neural network to visualize, organize and biologically interpret single-cell data. Dissecting the cell cycle as a source of cell-to-cell variability is crucial for quantitative single-cell biology. We demonstrate DeepFlow for a large dataset of cell-cycling Jurkat cells. First, we reconstruct the cells’ continuous progression through cell cycle from raw image data. This shows that DeepFlow can learn a continuous distance measure between categorical phenotypes. Second, we are able to detect and separate a subpopulation of dead cells, although the data set had been cleaned using established approaches. DeepFlow detects this morphologically abnormal subpopulation in an unsupervised manner. Third, in label-free classification of cell cycle phases, we reach a 6-fold reduction in error rate as compared to a recent approach based on boosting on a series of image features. In contrast to previous methods, DeepFlow’s predictions are fast enough to consider integration with the imaging flow cytometry measurement process. Author Summary We present DeepFlow, a deep learning based data analysis workflow optimized for the requirements of imaging flow cytometry. We use it to analyze a large data set of a certain type of human T cells (Jurkat cells), which undergo cell cycle. DeepFlow enables reconstructing the continuous cell cycle progression of these cells, and separates dead from living cells. We show how learned features of the neural network can be visualized and biologically interpreted. When used to classify the cell cycle stage, DeepFlow performs significantly better than previous approaches.


title: Full and Fractional Counting in Bibliometric Networks

abstract: In their study entitled"Constructing bibliometric networks: A comparison between full and fractional counting,"Perianes-Rodriguez, Waltman,&van Eck (2016; henceforth abbreviated as PWvE) provide arguments for the use of fractional counting at the network level as different from the level of publications. Whereas fractional counting in the latter case divides the credit among co-authors (countries, institutions, etc.), fractional counting at the network level can normalize the relative weights of links and thereby clarify the structures in the network. PWvE, however, propose a counting scheme for fractional counting that is one among other possible ones. Alternative schemes proposed by Batagelj and Cerin\v{s}ek (2013) and Park, Yoon,&Leydesdorff (2016; henceforth abbreviated as PYL) are discussed in an appendix. However, our approach is not correctly identified as identical to their Equation A3. Here below, we distinguish three approaches analytically; routines for applying these approaches to bibliometric data are also provided.


title: Energy-credit scheduler: An energy-aware virtual machine scheduler for cloud systems

abstract: Virtualization facilitates the provision of flexible resources and improves energy efficiency through the consolidation of virtualized servers into a smaller number of physical servers. As an increasingly essential component of the emerging cloud computing model, virtualized environments bill their users based on processor time or the number of virtual machine instances. However, accounting based only on the depreciation of server hardware is not sufficient because the cooling and energy costs for data centers will exceed the purchase costs for hardware. This paper suggests a model for estimating the energy consumption of each virtual machine without dedicated measurement hardware. Our model estimates the energy consumption of a virtual machine based on in-processor events generated by the virtual machine. Based on this estimation model, we also propose a virtual machine scheduling algorithm that can provide computing resources according to the energy budget of each virtual machine. The suggested schemes are implemented in the Xen virtualization system, and an evaluation shows that the suggested schemes estimate and provide energy consumption with errors of less than 5% of the total energy consumption. ? We conceptualize an energy-cost-aware billing policy for cloud systems. ? We propose a per-VM energy consumption estimation model. ? We identify the relationships between in-processor activities and processor energy consumption. ? We design and evaluate an energy-budget-aware virtual machine scheduler.


title: EEG signal classification using PCA, ICA, LDA and support vector machines

abstract: In this work, we proposed a versatile signal processing and analysis framework for Electroencephalogram (EEG). Within this framework the signals were decomposed into the frequency sub-bands using DWT and a set of statistical features was extracted from the sub-bands to represent the distribution of wavelet coefficients. Principal components analysis (PCA), independent components analysis (ICA) and linear discriminant analysis (LDA) is used to reduce the dimension of data. Then these features were used as an input to a support vector machine (SVM) with two discrete outputs: epileptic seizure or not. The performance of classification process due to different methods is presented and compared to show the excellent of classification process. These findings are presented as an example of a method for training, and testing a seizure prediction method on data from individual petit mal epileptic patients. Given the heterogeneity of epilepsy, it is likely that methods of this type will be required to configure intelligent devices for treating epilepsy to each individual's neurophysiology prior to clinical operation.


title: Emulsifying properties of an ultrafiltered protein from minced fish wash water

abstract: Centrifugation and ultrafiltration were used to concentrate soluble proteins in the water used to wash minced muscle of sardine caught at two different times of year. The proteins thus extracted were largely of less than 67 kDa molecular weight. Part of the resulting concentrate was frozen-stored and part freeze-dried. Irrespective of the season of capture, samples exhibited high emulsifying capacity and emulsion stability, values being higher in freeze-dried samples. Both properties were virtually unaffected by solutions, with NaCl concentrations ranging from 0 to 3%.


title: A Novel Programmable Parallel CRC Circuit

abstract: A new hardware scheme for computing the transition and control matrix of a parallel cyclic redundancy checksum is proposed. This opens possibilities for parallel high-speed cyclic redundancy checksum circuits that reconfigure very rapidly to new polynomials. The area requirements are lower than those for a realization storing a precomputed matrix. An additional simplification arises as only the polynomial needs to be supplied. The derived equations allow the width of the data to be processed in parallel to be selected independently of the degree of the polynomial. The new design has been simulated and outperforms a recently proposed architecture significantly in speed, area, and energy efficiency.


title: Improved wound healing in transtibial amputees receiving supplementary nutrition

abstract: The objective of this prospective study of matched controls was to find out whether supplementary nutrition would improve wound healing and decrease mortality in patients undergoing transtibial amputation for occlusive arterial disease. The nutritional status of 32 consecutive transtibial amputees was assessed and 28 were classified as malnourished. Supplementary nutrition was given reaching an average intake of 2098 kcal/day for a total of 11 days. In 24 patients, at least 5 days of preoperative supplementary nutrition were given, followed by postoperative treatment for a total of 11 days. Four patients who had an immediate operation were given only postoperative treatment, and 4 were excluded. The controls were 32 amputees in another hospital and matching procedures were carried out with corrections for diabetes, sex, age, smoking habits, previous vascular surgery and living conditions before amputation. Healing, including those healed before death in both groups, occurred in 26 of the nutrition group compared to 13 in the control group, which was statistically significant. Nine patients died within 6 months in the nutrition group compared to 14 of the controls (not significant). Malnutrition was present in nearly 90% of transtibial amputees and supplementary nutrition improved healing, but not mortality.


title: The spread of fake news by social bots

abstract: The massive spread of fake news has been identified as a major global risk and has been alleged to influence elections and threaten democracies. Communication, cognitive, social, and computer scientists are engaged in efforts to study the complex causes for the viral diffusion of digital misinformation and to develop solutions, while search and social media platforms are beginning to deploy countermeasures. However, to date, these efforts have been mainly informed by anecdotal evidence rather than systematic data. Here we analyze 14 million messages spreading 400 thousand claims on Twitter during and following the 2016 U.S. presidential campaign and election. We find evidence that social bots play a key role in the spread of fake news. Accounts that actively spread misinformation are significantly more likely to be bots. Automated accounts are particularly active in the early spreading phases of viral claims, and tend to target influential users. Humans are vulnerable to this manipulation, retweeting bots who post false news. Successful sources of false and biased claims are heavily supported by social bots. These results suggests that curbing social bots may be an effective strategy for mitigating the spread of online misinformation.


title: Integrating Automated Product Derivation and Individual User Interface Design

abstract: Software Product Lines, in conjunction with model- driven product derivation, are successful examples for extensive automation and reuse in software development. However, often each single product requires an individual, tailored user interface of its own to achieve the desired usability. Moreover, in some cases (e.g., online shops, games) it is even mandatory that each product has an individual, unique user interface of its own. Usually, this results in manual user interface design independent from the model-driven product derivation. Consequently, each product configuration has to be mapped manually to a corresponding user interface which can become a tedious and error-prone task for large and complex product lines. This paper addresses this problem by integrating concepts from SPL product derivation and Model-based User Interface Development. This facilitates both (1) a systematic and semi-automated creation of user interfaces during product derivation while (2) still supporting for individual, creative design.


title: Assessing programming language impact on development and maintenance: a study on c and c++

abstract: Billions of dollars are spent every year for building and maintaining software. To reduce these costs we must identify the key factors that lead to better software and more productive development. One such key factor, and the focus of our paper, is the choice of programming language. Existing studies that analyze the impact of choice of programming language suffer from several deficiencies with respect to methodology and the applications they consider. For example, they consider applications built by different teams in different languages, hence fail to control for developer competence, or they consider small-sized, infrequently-used, short-lived projects. We propose a novel methodology which controls for development process and developer competence, and quantifies how the choice of programming language impacts software quality and developer productivity. We conduct a study and statistical analysis on a set of long-lived, widely-used, open source projects - Firefox, Blender, VLC, and MySQL. The key novelties of our study are: (1) we only consider projects which have considerable portions of development in two languages, C and C++, and (2) a majority of developers in these projects contribute to both C and C++ code bases. We found that using C++ instead of C results in improved software quality and reduced maintenance effort, and that code bases are shifting from C to C++. Our methodology lays a solid foundation for future studies on comparative advantages of particular programming languages.


Augmenting Information Retrieval by Knowledge Infusion


title: A Multi-Gene Genetic Programming Application for Predicting Students Failure at School

abstract: Several efforts to predict student failure rate (SFR) at school accurately still remains a core problem area faced by many in the educational sector. The procedure for forecasting SFR are rigid and most often times require data scaling or conversion into binary form such as is the case of the logistic model which may lead to lose of information and effect size attenuation. Also, the high number of factors, incomplete and unbalanced dataset, and black boxing issues as in Artificial Neural Networks and Fuzzy logic systems exposes the need for more efficient tools. Currently the application of Genetic Programming (GP) holds great promises and has produced tremendous positive results in different sectors. In this regard, this study developed GPSFARPS, a software application to provide a robust solution to the prediction of SFR using an evolutionary algorithm known as multi-gene genetic programming. The approach is validated by feeding a testing data set to the evolved GP models. Result obtained from GPSFARPS simulations show its unique ability to evolve a suitable failure rate expression with a fast convergence at 30 generations from a maximum specified generation of 500. The multi-gene system was also able to minimize the evolved model expression and accurately predict student failure rate using a subset of the original expression


Reading Comprehension with Deep Learning


Using the Software CMM in Small Organizations


title: Inferring user interests in microblogging social networks: a survey

abstract: With the growing popularity of microblogging services such as Twitter in recent years, an increasing number of users are using these services in their daily lives. The huge volume of information generated by users raises new opportunities in various applications and areas. Inferring user interests plays a significant role in providing personalized recommendations on microblogging services, and also on third-party applications providing social logins via these services, especially in cold-start situations. In this survey, we review user modeling strategies with respect to inferring user interests from previous studies. To this end, we focus on four dimensions of inferring user interest profiles: (1) data collection, (2) representation of user interest profiles, (3) construction and enhancement of user interest profiles, and (4) the evaluation of the constructed profiles. Through this survey, we aim to provide an overview of state-of-the-art user modeling strategies for inferring user interest profiles on microblogging social networks with respect to the four dimensions. For each dimension, we review and summarize previous studies based on specified criteria. Finally, we discuss some challenges and opportunities for future work in this research domain.


title: Mental Toughness and Success in Sport: A Review and Prospect

abstract: Background : Mental toughness (MT) is often referred to as one of the most important psychological attributes underpinning the success of athletes. Although selected studies have examined this common supposition, research in this area has yet to be synthesized. Objective : The purpose of this study was to review the quantitative literature on MT, competitive standard, achievement level, and performance (competitive and non-competitive) in sport. Method : Searches using a specified key term were performed in 10 databases during August 2016. Following an initial screening of the unique records that were identified (N = 1025), 19 peer-reviewed articles met the eligibility criteria for inclusion in the review. Results : The majority of MT-competitive standard studies (N = 10) found total (66.7%) or subcomponent (71.4%) MT differences, with mentally tougher athletes participating at higher levels of competition. Of the remaining studies (N = 9), most indicated (77.8%) mentally tougher athletes tend to achieve more or perform better. Conclusion : Collectively, the results point to mentally tougher athletes’ superior levels of success. The findings are discussed alongside the limitations associated with this part of the current MT in sport literature, with several important areas outlined for scholars to consider and pursue when conducting future research.


Machine Learning Approaches for Failure Type Detection and Predictive Maintenance


title: Probabilistic Horn abduction and Bayesian networks

abstract: This paper presents a simple framework for Horn-clause abduction, with probabilities associated with hypotheses. The framework incorporates assumptions about the rule base and independence assumptions amongst hypotheses. It is shown how any probabilistic knowledge representable in a discrete Bayesian belief network can be represented in this framework. The main contribution is in finding a relationship between logical and probabilistic notions of evidential reasoning. This provides a useful representation language in its own right, providing a compromise between heuristic and epistemic adequacy. It also shows how Bayesian networks can be extended beyond a propositional language. This paper also shows how a language with only (unconditionally) independent hypotheses can represent any probabilistic knowledge, and argues that it is better to invent new hypotheses to explain dependence rather than having to worry about dependence in the language.


title: Design and modelling of a variable stiffness manipulator for surgical robots

abstract: Abstract In Natural Orifice Transluminal Endoscopic Surgery (NOTES), a surgical robot that can access the human colon or stomach via natural orifices should have sufficient flexibility to pass through tortuous paths and to be operated in a confined space. In addition, the robot should possess an acceptable stiffness level to hold payloads during the surgery. This paper presents a new design concept for variable stiffness manipulators using thermoplastic material Polyethylene Terephthalate (PET) and a flexible stainless steel sheath as a heating media. The stiffness phases of PET can be actively adjusted through temperature. Experiments at different conditions showed that the proposed design was at least as flexible as a typical commercial endoscope in compliant mode and at least 9 times stiffer than the endoscope in stiff mode. In addition, flexural modulus of the proposed manipulator with respect to temperature, current, and time was modeled and validated through both simulation and experiments. A tendon-driven flexible robotic arm integrated with a variable stiffness spine was also developed, and ex vivo tests on fresh porcine tissue were conducted. The manipulator in compliant mode can be easily controlled through the tendons, and it is able to hold its shape against considerably large loads in stiff mode. The results demonstrate not only the high potential of the design concept for the future medical application but also the first steps toward building a complete surgical robotic system with fully controlled variable stiffness.


title: BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model

abstract: We propose a hybrid model of differential privacy that considers a combination of regular and opt-in users who desire the differential privacy guarantees of the local privacy model and the trusted curator model, respectively. We demonstrate that within this model, it is possible to design a new type of blended algorithm that improves the utility of obtained data, while providing users with their desired privacy guarantees. ::: We apply this algorithm to the task of privately computing the head of the search log and show that the blended approach provides significant improvements in the utility of the data compared to related work. ::: Specifically, on two large search click data sets, comprising 1.75 and 16 GB, respectively, our approach attains NDCG values exceeding 95% across a range of privacy budget values.


title: Capability Model for Open Data: An Empirical Analysis

abstract: Creating superior competitiveness is central to open data organization's survivability in the fast changing and competitive open data market. In their quest to develop and increase competitiveness and survivability, many of these organizations are moving towards developing open data capabilities. Research-based knowledge on open data capabilities and how they relate to each other remains sparse, however, with most of the open data literature focusing on social and economic value of open data, not capabilities required. By exploring the related literature on business and organizational capabilities and linking the findings to the empirical evidence collected through the survey of 49 open data organizations around the world, this study develops an open data capability model. The model emerged from our deductive research process improves both theoretical and practical understanding of open data capabilities and their relationships required to help increase competitiveness and survivability of these types of organizations.


title: Node Similarity with q -Grams for Real-World Labeled Networks

abstract: We study node similarity in labeled networks, using the label sequences found in paths of bounded length q leading to the nodes. (This recalls the q-grams employed in document resemblance, based on the Jaccard distance.) When applied to networks, the challenge is two-fold: the number of q-grams generated from labeled paths grows exponentially with q, and their frequency should be taken into account: this leads to a variation of the Jaccard index known as Bray-Curtis index for multisets. We describe nSimGram, a suite of fast algorithms for node similarity with q-grams, based on a novel blend of color coding, probabilistic counting, sketches, and string algorithms, where the universe of elements to sample is exponential. We provide experimental evidence that our measure is effective and our running times scale to deal with large real-world networks.


Developing and Educational Design Pattern Language for MOOCs


title: Modeling and simulation of a single phase grid connected photovoltaic system

abstract: This paper gives a complete computer simulation program of a single phase grid connected PV system using Matlab/Simulink and SimPowerSystem tool in order to monitor the performance of each unit of the system during a selected day in the year representing a sunny day and another cloudy day using the hourly data of load demand, solar radiation and temperature at The Kharga Oasis site, as a case study. The system consists of a PV array subsystem as the primary source of energy, the electric grid as an auxiliary source of energy, the battery bank as a stand by source that feeds the electrical load in case of grid failure. This paper also focuses on the operation control of the system. This control is on/off switch control according to modes of operation of the system and there is a control of inverter using PI controller to achieve the maximum power point of the PV array. Finally this paper gives the simulation results of the required system output parameters; PV output power, grid power, load power, battery power, and battery voltage and state of charge.


title: The Use of Mobile Devices in Aiding Dietary Assessment and Evaluation

abstract: There is a growing concern about chronic diseases and other health problems related to diet including obesity and cancer. The need to accurately measure diet (what foods a person consumes) becomes imperative. Dietary intake provides valuable insights for mounting intervention programs for prevention of chronic diseases. Measuring accurate dietary intake is considered to be an open research problem in the nutrition and health fields. In this paper, we describe a novel mobile telephone food record that will provide an accurate account of daily food and nutrient intake. Our approach includes the use of image analysis tools for identification and quantification of food that is consumed at a meal. Images obtained before and after foods are eaten are used to estimate the amount and type of food consumed. The mobile device provides a unique vehicle for collecting dietary information that reduces the burden on respondents that are obtained using more classical approaches for dietary assessment. We describe our approach to image analysis that includes the segmentation of food items, features used to identify foods, a method for automatic portion estimation, and our overall system architecture for collecting the food intake information.


title: A primer on deep learning in genomics

abstract: Deep learning methods are a class of machine learning techniques capable of identifying highly complex patterns in large datasets. Here, we provide a perspective and primer on deep learning applications for genome analysis. We discuss successful applications in the fields of regulatory genomics, variant calling and pathogenicity scores. We include general guidance for how to effectively use deep learning methods as well as a practical guide to tools and resources. This primer is accompanied by an interactive online tutorial.


title: Behavioral control of unmanned aerial vehicle manipulator systems

abstract: In this paper a behavioral control framework is developed to control an unmanned aerial vehicle-manipulator (UAVM) system, composed by a multirotor aerial vehicle equipped with a robotic arm. The goal is to ensure vehicle-arm coordination and manage complex multi-task missions, where different behaviors must be encompassed in a clear and meaningful way. In detail, a control scheme, based on the null space-based behavioral paradigm, is proposed to handle the coordination between the arm and vehicle motion. To this aim, a set of basic functionalities (elementary behaviors) are designed and combined in a given priority order, in order to attain more complex tasks (compound behaviors). A supervisor is in charge of switching between the compound behaviors according to the mission needs and the sensory feedback. The method is validated on a real testbed, consisting of a multirotor aircraft with an attached 6 Degree of Freedoms manipulator, developed within the EU-funded project ARCAS (Aerial Robotics Cooperative Assembly System). At the the best of authors' knowledge, this is the first time that an UAVM system is experimentally tested in the execution of complex multi-task missions. The results show that, by properly designing a set of compound behaviors and a supervisor, vehicle-arm coordination in complex missions can be effectively managed.


Using dermoscopy to detect tinea of vellus hair


title: 3D Reconstruction of “In-the-Wild” Faces in Images and Videos

abstract: 3D Morphable Models (3DMMs) are powerful statistical models of 3D facial shape and texture, and are among the state-of-the-art methods for reconstructing facial shape from single images. With the advent of new 3D sensors, many 3D facial datasets have been collected containing both neutral as well as expressive faces. However, all datasets are captured under controlled conditions. Thus, even though powerful 3D facial shape models can be learnt from such data, it is difficult to build statistical texture models that are sufficient to reconstruct faces captured in unconstrained conditions (“in-the-wild”). In this paper, we propose the first “in-the-wild” 3DMM by combining a statistical model of facial identity and expression shape with an “in-the-wild” texture model. We show that such an approach allows for the development of a greatly simplified fitting procedure for images and videos, as there is no need to optimise with regards to the illumination parameters. We have collected three new benchmarks that combine “in-the-wild” images and video with ground truth 3D facial geometry, the first of their kind, and report extensive quantitative evaluations using them that demonstrate our method is state-of-the-art.


title: Effects of Speed, Agility, Quickness Training Method on Power Performance in Elite Soccer Players

abstract: The purpose of this study was to evaluate the effects of the SAQ training method on power performance in soccer players. Soccer players were assigned randomly to two groups: experimental group (EG ; n _ 50) and control group (CG ; n_ 50). Power performance was assessed by a test of quickness – the 5m sprint (SP5), a test of acceleration – the 10m sprint (SP10), tests of maximal speed – the 20m (SP20) and the 30m sprint (SP30) along with Bosco jump tests – squat jump (SJ), countermovement jump (CMJ), maximal countermovement jump (MAX) and continuous jumps performed with legs extended (CJS). The initial testing procedure took place at the beginning of the in-season period. The eightweek specific SAQ training program was implemented after which final testing took place. The results of the two-way analysis of variance indicated that the experimental group improved significantly (p<.05) in 5m (1.43sec vs. 1.39sec) and in 10m (2.15sec vs. 2.07sec) sprints, and they also improved their jumping performance in countermovement (44.04cm vs. 4.48cm) and continuous jumps (41.08cm vs. 41.39cm) performed with legs extended (p<.05). The SAQ training program appears to be an effective way of improving some segments of power performance in young soccer players during in-season period. Soccer coaches could use this information in the process of planning in-season training. Without proper planning of the SAQ training soccer players will most likely be confronted with decrease in power performance during in-season period.


title: CENTRIST: A Visual Descriptor for Scene Categorization

abstract: CENsus TRansform hISTogram (CENTRIST), a new visual descriptor for recognizing topological places or scene categories, is introduced in this paper. We show that place and scene recognition, especially for indoor environments, require its visual descriptor to possess properties that are different from other vision domains (e.g., object recognition). CENTRIST satisfies these properties and suits the place and scene recognition task. It is a holistic representation and has strong generalizability for category recognition. CENTRIST mainly encodes the structural properties within an image and suppresses detailed textural information. Our experiments demonstrate that CENTRIST outperforms the current state of the art in several place and scene recognition data sets, compared with other descriptors such as SIFT and Gist. Besides, it is easy to implement and evaluates extremely fast.


title: Commitment of Newcomers and Old-timers to Online Health Support Communities

abstract: For online communities to be successful, they must retain an adequate number of members who contribute to the community. The amount and type of communication members receive can play an important role in generating and sustaining members' commitment to it. However, the communication that members find valuable may change with their tenure in the community. This paper examines how the communication members receive in an health support community influences their commitment and how this influence changes with their tenure in the community. Commitment was operationalized with three measures: self-reported attachment, continued participation in the community, and responding to others. Results show that receiving communication was generally associated with increased commitment across the three measures, with its impact increasing with members' tenure. However, the average amount of informational and emotional support members received per message was associated with decreased commitment. Results have implications for interventions to encourage members' commitment to their communities throughout their history in the community.


title: Asymptotic Loss in Privacy due to Dependency in Gaussian Traces

abstract: The rapid growth of the Internet of Things (IoT) necessitates employing privacy-preserving techniques to protect users' sensitive information. Even when user traces are anonymized, statistical matching can be employed to infer sensitive information. In our previous work, we have established the privacy requirements for the case that the user traces are instantiations of discrete random variables and the adversary knows only the structure of the dependency graph, i.e., whether each pair of users is connected. In this paper, we consider the case where data traces are instantiations of Gaussian random variables and the adversary knows not only the structure of the graph but also the pairwise correlation coefficients. We establish the requirements on anonymization to thwart such statistical matching, which demonstrate the significant degree to which knowledge of the pairwise correlation coefficients further significantly aids the adversary in breaking user anonymity.


title: Optimum Auto Exposure based on High-Dynamic-Range Histogram

abstract: This paper introduces a new approach for optimum auto exposure (AE) based on the high-dynamic-range histogram (HDH) of a scene. The exposure control is optimal in terms of recorded information of the scene. The advantages over AE based on mean values of images will be shown as well as a proof of concept implementation and results. Futher more possible extensions to this approach are discussed. The consideration of the HDH for auto exposure enables new possibilities for auto exposure control for mutliple-slope cameras. An AE method for multiple-slope cameras is proposed which controls exposure as well as the transition curve parameters in order to record a maximum of information from the scene. It considers controllable dynamic range as well as corse quantization in bright image regions.


title: Towards Semi-automatic Bug Triage and Severity Prediction Based on Topic Model and Multi-feature of Bug Reports

abstract: Bug fixing is an essential activity in the software maintenance, because most of the software systems have unavoidable defects. When new bugs are submitted, triagers have to find and assign appropriate developers to fix the bugs. However, if the bugs are at first assigned to inappropriate developers, they may later have to be reassigned to other developers. That increases the time and cost for fixing bugs. Therefore, finding appropriate developers becomes a key to bug resolution. When triagers assign a new bug report, it is necessary to decide how quickly the bug report should be addressed. Thus, the bug severity is an important factor in bug fixing. In this paper, we propose a novel method for the bug triage and bug severity prediction. First, we extract topic(s) from historical bug reports in the bug repository and find bug reports related to each topic. When a new bug report arrives, we decide the topic(s) to which the report belongs. Then we utilize multi-feature to identify corresponding reports that have the same multi-feature (e.g., Component, product, priority and severity) with the new bug report. Thus, given a new bug report, we are able to recommend the most appropriate developer to fix each bug and predict its severity. To evaluate our approach, we not only measured the effectiveness of our study by using about 30,000 golden bug reports extracted from three open source projects (Eclipse, Mozilla, and Net beans), but also compared some related studies. The results show that our approach is likely to effectively recommend the appropriate developer to fix the given bug and predict its severity.


title: The soliton transform and a possible application to nonlinear Alfven waves in space

abstract: We apply the inverse scattering transform (IST) based upon the Derivative Nonlinear Schrodinger (DNLS) equation to a complex time series of nonlinear Alfven wave data generated by numerical simulation. The IST describes the long-time evolution of quasi-parallel Alfven waves more efficiently than the Fourier transform, which is adapted to linear, not nonlinear, problems. When we add dissipation, so the conditions for the validity of the DNLS are not strictly satisfied, the IST continues to provide a compact description of the wave-field in terms of a small number of decaying envelope solitons. Since large amplitude Alfven waves and other nonlinear waves play essential roles in various space environments—the solar wind is one obvious example—we suggest that it may be of interest to investigate how inverse scattering transforms can be developed into practical tools for the analysis of space data.


Preventing "Overfitting" of Cross-Validation Data


title: Secure message transfer using RSA algorithm and improved playfair cipher in cloud computing

abstract: Nowadays people around the world are interested in sharing information among themselves, but they are worried about the security aspect while sharing any information. For this situation cryptography has been a boon to human kind which is intended to secure transmission of data. The objective of this paper is to provide security to the data we send and also securing the key that we are encrypting the data. This paper consists of three stages, the first stage includes encryption of text using playfair cipher of 9×6 matrix. In the second stage, XOR operation is performed between the encrypted text and the key which it has been encrypted. In the final stage encryption of key is done using RSA algorithm and continued with a XOR operation between the encrypted text and the encrypted key. Then the final encrypted text and the new encrypted key is sent to the receiver to decrypt the original message. Thus, this proposed algorithm provides an extra layer of security to the existed algorithms.


title: The packer filter: an efficient mechanism for user-level network code

abstract: Code to implement network protocols can be either inside the kernel of an operating system or in user-level processes. Kernel-resident code is hard to develop, debug, and maintain, but user-level implementations typically incur significant overhead and perform poorly. The performance of user-level network code depends on the mechanism used to demultiplex received packets. Demultiplexing in a user-level process increases the rate of context switches and system calls, resulting in poor performance. Demultiplexing in the kernel eliminates unnecessary overhead. This paper describes the packet filter , a kernel-resident, protocol-independent packet demultiplexer. Individual user processes have great flexibility in selecting which packets they will receive. Protocol implementations using the packet filter perform quite well, and have been in production use for several years.


title: CRISPR/Cas9-mediated genome editing and gene replacement in plants: Transitioning from lab to field.

abstract: The CRISPR/Cas9 genome engineering system has ignited and swept through the scientific community like wildfire. Owing largely to its efficiency, specificity, and flexibility, the CRISPR/Cas9 system has quickly become the preferred genome-editing tool of plant scientists. In plants, much of the early CRISPR/Cas9 work has been limited to proof of concept and functional studies in model systems. These studies, along with those in other fields of biology, have led to the development of several utilities of CRISPR/Cas9 beyond single gene editing. Such utilities include multiplexing for inducing multiple cleavage events, controlling gene expression, and site specific transgene insertion. With much of the conceptual CRISPR/Cas9 work nearly complete, plant researchers are beginning to apply this gene editing technology for crop trait improvement. Before rational strategies can be designed to implement this technology to engineer a wide array of crops there is a need to expand the availability of crop-specific vectors, genome resources, and transformation protocols. We anticipate that these challenges will be met along with the continued evolution of the CRISPR/Cas9 system particularly in the areas of manipulation of large genomic regions, transgene-free genetic modification, development of breeding resources, discovery of gene function, and improvements upon CRISPR/Cas9 components. The CRISPR/Cas9 editing system appears poised to transform crop trait improvement.


title: 1st Workshop on Recommender Systems for Technology Enhanced Learning Recommender System for Predicting Student Performance

abstract: Abstract Recommender systems are widely used in many areas, especially in e-commerce. Recently, they are also applied in e-learning tasks such as recommending resources (e.g. papers, books,..) to the learners (students). In this work, we propose a novel approach which uses recommender system techniques for educational data mining, especially for predicting student performance. To validate this approach, we compare recommender system techniques with traditional regression methods such as logistic/linear regression by using educational data for intelligent tutoring systems. Experimental results show that the proposed approach can improve prediction results.


title: Flux-weakening control of PMSM based on single current regulator and variable q-axis voltage

abstract: Flux-weakening control of PMSM based on single current regulator has high dynamic performance. But the fixed given q-axis voltage decreases the motor's efficiency and load capability. This paper gives a new method of variable q-axis voltage. It can adjust q-axis voltage fully using the motor DC voltage to make motor working at the optimal operating points. It makes the motor efficiency and improves load capability. This method is easy to achieve and does not depend on motor parameters. The results of simulation and experiment verify the method in this paper.


title: Strain gauge sensors based on thermoplastic nanocomposite for monitoring inflatable structures

abstract: In this work we present an investigation on a conductive thermoplastic nanocomposite used as flexible and stretchable strain gauge to be applied on inflatable structures for both terrestrial and extra-terrestrial applications. In particular we analysed the sensor behaviour by using an one-dimensional expansion system for increasing strain up to 5% and we studied the device changing in temperature. The strain gauge was also tested after several expansion cycles thus analysing the sensor response in time and ageing effects. Finally we tested the sensor on an inflatable naval fender in order to reproduce the sensor behaviour in real conditions.


Understanding the Effect of Interdependency and Vulnerability on the Performance of Civil Infrastructure


title: Real-time, realistic full-body 3D reconstruction and texture mapping from multiple Kinects

abstract: Multi-party 3D Tele-Immersive (TI) environments, supporting realistic interaction among distant users, is the future of tele-conferencing. Real-time, full-body 3D reconstruction, an important task for TI applications, is addressed in this paper. A volumetric method for the reconstruction of watertight models of moving humans is presented, along with details for appropriate texture-mapping to enhance the visual quality. The reconstruction uses the input from multiple consumer depth cameras and specifically Kinect sensors. The presented results verify the effectiveness of the proposed methodologies, with respect to the visual quality and frame rates.


title: End-to-End IoT Security Middleware for Cloud-Fog Communication

abstract: IoT (Internet of Things) devices such as sensors have been actively used in 'fogs' to provide critical data during e.g., disaster response scenarios or in-home healthcare. Since IoT devices typically operate in resource-constrained computing environments at the network-edge, data transfer performance to the cloud as well as end-to-end security have to be robust and customizable. In this paper, we present the design and implementation of a middleware featuring "intermittent" and "flexible" end-to-end security for cloud-fog communications. Intermittent security copes with unreliable network connections, and flexibility is achieved through security configurations that are tailored to application needs. Our experiment results show how our middleware that leverages static pre-shared keys forms a promising solution for delivering light-weight, fast and resource-aware security for a variety of IoT-based applications.


Big Data in Cloud Computing: Features and Issues


title: Run-time detection of heap-based overflows

abstract: Buffer overflows belong to the most common class of attacks on today's Internet. Although stack-based variants are still by far more frequent and well-understood, heap-based overflows have recently gained more attention. Several real-world exploits have been published that corrupt heap management information and allow arbitrary code execution with the privileges of the victim process.This paper presents a technique that protects the heap management information and allows for run-time detection of heap-based overflows. We discuss the structure of these attacks and our proposed detection scheme that has been implemented as a patch to the GNU Lib C. We report the results of our experiments, which demonstrate the detection effectiveness and performance impact of our approach. In addition, we discuss different mechanisms to deploy the memory protection.


title: Automated generation of diverse and challenging scenarios for test and evaluation of autonomous vehicles

abstract: We propose a novel method for generating test scenarios for a black box autonomous system that demonstrate critical transitions in its performance modes. In complex environments it is possible for an autonomous system to fail at its assigned mission even if it complies with requirements for all subsystems and throws no faults. This is particularly true when the autonomous system may have to choose between multiple exclusive objectives. The standard approach of testing robustness through fault detection is directly stimulating the system and detecting violations of the system requirements. Our approach differs by instead running the autonomous system through full missions in a simulated environment and measuring performance based on high-level mission criteria. The result is a method of searching for challenging scenarios for an autonomous system under test that exercise a variety of performance modes. We utilize adaptive sampling to intelligently search the state space for test scenarios which exist on the boundary between distinct performance modes. Additionally, using unsupervised clustering techniques we can group scenarios by their performance modes and sort them by those which are most effective at diagnosing changes in the autonomous system's behavior.


title: iTest: testing software with mobile crowdsourcing

abstract: In recent years, a lot of crowdsourcing systems have emerged and lead to many successful crowdsourcing systems like Wiki-pedia, Amazon Mechanical Turk and Waze. In the field of software engineering, crowdtesting has acquired increased interest and adoption, especially among personal developers and smaller companies. In this paper, we present iTest which combines mobile crowdsourcing and software testing together to support the testing of mobile application and web services. iTest is a framework for software developers to submit their software and conveniently get the test results from the crowd testers. Firstly, we analyze the key problems need to be solved in a mobile crowdtesting platform; Secondly, we present the architecture of iTest framework; Thirdly, we introduce the workflow of testing web service in iTest and propose an algorithm for solving the tester selection problem mentioned in Section 2; Then the development kit to support testing mobile application is explained; Finally, we perform two experiments to illustrate that both the way to access network and tester's location influence the performance of web service.


title: The treatment gap in mental health care.

abstract: Mental disorders are highly prevalent and cause considerable suffering and disease burden. To compound this public health problem, many individuals with psychiatric disorders remain untreated although effective treatments exist. We examine the extent of this treatment gap. We reviewed community-based psychiatric epidemiology studies that used standardized diagnostic instruments and included data on the percentage of individuals receiving care for schizophrenia and other non-affective psychotic disorders, major depression, dysthymia, bipolar disorder, generalized anxiety disorder (GAD), panic disorder, obsessive-compulsive disorder (OCD), and alcohol abuse or dependence. The median rates of untreated cases of these disorders were calculated across the studies. Examples of the estimation of the treatment gap for WHO regions are also presented. Thirty-seven studies had information on service utilization. The median treatment gap for schizophrenia, including other non-affective psychosis, was 32.2%. For other disorders the gap was: depression, 56.3%; dysthymia, 56.0%; bipolar disorder, 50.2%; panic disorder, 55.9%; GAD, 57.5%; and OCD, 57.3%. Alcohol abuse and dependence had the widest treatment gap at 78.1%. The treatment gap for mental disorders is universally large, though it varies across regions. It is likely that the gap reported here is an underestimate due to the unavailability of community-based data from developing countries where services are scarcer. To address this major public health challenge, WHO has adopted in 2002 a global action programme that has been endorsed by the Member States.


Deliberate practice and the modifiability of body and mind: Toward a science of the structure and acquisition of expert and elite performance.


title: Exploratory data analysis using clusters and stories

abstract: Exploratory data analysis aims to study datasets through the use of iterative, investigative, and visual analytic algorithms. Due to the difficulty in managing and accessing the growing volume of unstructured data, exploratory analysis of datasets has become harder than ever and an interest to data mining researchers. In this dissertation, we study new algorithms for exploratory analysis of data collections using clusters and stories. Clustering brings together similar entities whereas stories connect dissimilar objects. The former helps organize datasets into regions of interest, and the latter explores latent information by connecting the dots between disjoint instances. This dissertation specifically focuses on five different research aspects to demonstrate the applicability and usefulness of clusters and stories as exploratory data analysis tools. In the area of clustering, we investigate whether clustering algorithms can be automatically “alternatized” and how they can be guided to obtain alternative results using flexible constraints as “scatter-gather” operations. We demonstrate the application of these ideas in many application domains, including studying the bat biosonar system and designing sustainable products. In the area of storytelling, we develop algorithms that can generate stories using distance, clique, and syntactic constraints. We explore the use of storytelling for studying document collections in the biomedical literature and intelligence analysis domain.


title: A reasonable solution to the broadband congestion control problem

abstract: The control of congestion in asynchronous transfer mode (ATM)-based networks is an important and complex subject that must be properly addressed in order to permit efficient use of network capacity while providing good quality of service to all network users. This congestion control problem is characterized here, and the performance-related aspects of some key congestion control features are modelled. Congestion controls for a broadband network will include features for admission control, buffer and queue management, traffic enforcement and reactive control. This paper identifies the leading alternatives for each of these congestion control features, presents an approach for comparing these alternatives based on the relative efficiencies achievable with each, and suggests one reasonable set of congestion control features. Our approch for comparing congestion control alternatives is based on a suitable model for the congestion-related functions of an ATM-based network, togther with a characterization of the traffic loads imposed by various broadband services.


title: Enabling secure and effective near-duplicate detection over encrypted in-network storage

abstract: Near-duplicate detection (NDD) plays an essential role for effective resource utilization and possible traffic alleviation in many emerging network architectures, leveraging in-network storage for various content-centric services. As innetwork storage grows, data security has become one major concern. Though encryption is viable for in-network data protection, current techniques are still lacking for effectively locating encrypted near-duplicate data, making the benefits of NDD practically invalidated. Besides, adopting encrypted innetwork storage further complicates the user authorization when locating near-duplicate data from multiple content providers under different keys. In this paper, we propose a secure and effective NDD system over encrypted in-network storage supporting multiple content providers. Our design bridges locality-sensitive hashing (LSH) with a newly developed cryptographic primitive, multi-key searchable encryption, which allows the user to send only one encrypted query to access near-duplicate data encrypted under different keys. It relieves the users from multiple rounds of interactions or sending multiple different queries respectively. As simply applying LSH does not ensure the detection quality, we then leverage Yao's garbled circuits to build a secure protocol to obtain highly accurate results, without user-side post-processing. We formally analyze the security strength. Experiments demonstrate our system achieves practical performance with comparable accuracy to plaintext.


title: Exploring Attitudes towards English as a Lingua Franca in the East Asian Context

abstract: It is a well-established fact that during the past four centuries, the English language has spread around the world, and that as a result, it is used for a wide range of purposes by many millions of people for whom it is not a mother tongue in the traditional sense of the term. This means that there are more English users nowadays in the Outer Circle (i.e. in the countries colonized by the British in the ‘second diaspora’, see B. Kachru 1992) than there are English users in the Inner Circle (i.e. in Britain and the mother tongue English countries colonized by the British in the ‘first diaspora’). English in the individual countries of the Outer Circle, meanwhile, has become Englishes: nativized varieties of English each with its own flavour and characteristics appropriate to its speakers’ local social and professional uses and to local institutionalized functions. Thus, we can talk of Indian English, Malaysian English, Singapore English, Nigerian English, and so on.


title: Accuracy analysis of range detection for SFCW portable through-wall radar

abstract: This paper discusses the accuracy of range detection for stepped-frequency continuous-wave (SFCW) portable through-wall radar (TWR) and its analysis through simulation software. The radar which is portably constructed of some modular devices has operating frequency range between 1600MHz and 2500MHz. Here, the use of simulation software aims to do modelling to investigate information contained in transmitted and received signals. The simulation of SFCW radar for range detection in the designated frequency was able to range some targets located at the distance of 2.6m and 5.2m. From the result, it shows that the SFCW method was suitable for designing the portable TWR which could range some targets at the distance more than 5m from the radar.


title: The population genetics of the Jewish people

abstract: Adherents to the Jewish faith have resided in numerous geographic locations over the course of three millennia. Progressively more detailed population genetic analysis carried out independently by multiple research groups over the past two decades has revealed a pattern for the population genetic architecture of contemporary Jews descendant from globally dispersed Diaspora communities. This pattern is consistent with a major, but variable component of shared Near East ancestry, together with variable degrees of admixture and introgression from the corresponding host Diaspora populations. By combining analysis of monoallelic markers with recent genome-wide variation analysis of simple tandem repeats, copy number variations, and single-nucleotide polymorphisms at high density, it has been possible to determine the relative contribution of sex-specific migration and introgression to map founder events and to suggest demographic histories corresponding to western and eastern Diaspora migrations, as well as subsequent microevolutionary events. These patterns have been congruous with the inferences of many, but not of all historians using more traditional tools such as archeology, archival records, linguistics, comparative analysis of religious narrative, liturgy and practices. Importantly, the population genetic architecture of Jews helps to explain the observed patterns of health and disease-relevant mutations and phenotypes which continue to be carefully studied and catalogued, and represent an important resource for human medical genetics research. The current review attempts to provide a succinct update of the more recent developments in a historical and human health context.


title: Demonstration of the Myria big data management service

abstract: In this demonstration, we will showcase Myria, our novel cloud service for big data management and analytics designed to improve productivity. Myria's goal is for users to simply upload their data and for the system to help them be self-sufficient data science experts on their data -- self-serve analytics. Using a web browser, Myria users can upload data, author efficient queries to process and explore the data, and debug correctness and performance issues. Myria queries are executed on a scalable, parallel cluster that uses both state-of-the-art and novel methods for distributed query processing. Our interactive demonstration will guide visitors through an exploration of several key Myria features by interfacing with the live system to analyze big datasets over the web.


title: Rural–Urban Linkages and Sustainable Regional Development: The Role of Entrepreneurs in Linking Peripheries and Centers

abstract: Urban and rural areas differ in economic, social and environmental terms. Due to the diverging dynamics in urban and rural areas, the social and economic distance between them might increase in the future even more. Rural entrepreneurs with linkages to urban areas are able to bridge the rural–urban divide by accessing some of the urban features, such as knowledge and markets, while at the same time profiting from the advantages of their peripheral location. This paper highlights exploratory results from qualitative interviews with rural entrepreneurs, and we illustrate entrepreneurial linkages to urban centers. The interview data show that rural entrepreneurs with rural–urban linkages develop sensibility for core market demands and trends, they valuate rural assets, and they combine rural and urban sources of knowledge for innovation. Through their entrepreneurial activity, rural entrepreneurs with linkages to urban areas might constitute an opposite force to polarizing concentration of economic activities in cities. Hence, rural entrepreneurs with urban linkages might contribute to sustainable economic relationships between urban and rural areas.


title: Formalising Traffic Rules for Accountability of Autonomous Vehicles

abstract: One significant barrier in introducing autonomous driving is the liability issue of a collision; e.g. when two autonomous vehicles collide, it is unclear which vehicle should be held accountable. To solve this issue, we view traffic rules from legal texts as requirements for autonomous vehicles. If we can prove that an autonomous vehicle always satisfies these requirements during its operation, then it cannot be held responsible in a collision. We present our approach by formalising a subset of traffic rules from the Vienna Convention on Road Traffic for highway scenarios in Isabelle/HOL.


title: Musical genre classification using support vector machines

abstract: Automatic musical genre classification is very useful for music indexing and retrieval. In this paper, an efficient and effective automatic musical genre classification approach is presented. A set of features is extracted and used to characterize music content. A multi-layer classifier based on support vector machines is applied to musical genre classification. Support vector machines are used to obtain the optimal class boundaries between different genres of music by learning from training data. Experimental results of multi-layer support vector machines illustrate good performance in musical genre classification and are more advantageous than traditional Euclidean distance based method and other statistic learning methods.


title: Analysis of the age of Panax ginseng based on telomere length and telomerase activity

abstract: Ginseng, which is the root of Panax ginseng (Araliaceae), has been used in Oriental medicine as a stimulant and dietary supplement for more than 7,000 years. Older ginseng plants are substantially more medically potent, but ginseng age can be simulated using unscrupulous cultivation practices. Telomeres progressively shorten with each cell division until they reach a critical length, at which point cells enter replicative senescence. However, in some cells, telomerase maintains telomere length. In this study, to determine whether telomere length reflects ginseng age and which tissue is best for such an analysis, we examined telomerase activity in the main roots, leaves, stems, secondary roots and seeds of ginseng plants of known age. Telomere length in the main root (approximately 1 cm below the rhizome) was found to be the best indicator of age. Telomeric terminal restriction fragment (TRF) lengths, which are indicators of telomere length, were determined for the main roots of plants of different ages through Southern hybridization analysis. Telomere length was shown to be positively correlated with plant age, and a simple mathematical model was formulated to describe the relationship between telomere length and age for P. ginseng.


title: James Watson's most inconvenient truth: race realism and the moralistic fallacy.

abstract: Summary Recent editorials in this journal have defended the right of eminent biologist James Watson to raise the unpopular hypothesis that people of sub-Saharan African descent score lower, on average, than people of European or East Asian descent on tests of general intelligence. As those editorials imply, the scientific evidence is substantial in showing a genetic contribution to these differences. The unjustified ill treatment meted out to Watson therefore requires setting the record straight about the current state of the evidence on intelligence, race, and genetics. In this paper, we summarize our own previous reviews based on 10 categories of evidence: The worldwide distribution of test scores; the g factor of mental ability; heritability differences; brain size differences; trans-racial adoption studies; racial admixture studies; regression-to-the-mean effects; related life-history traits; human origins research; and the poverty of predictions from culture-only explanations. The preponderance of evidence demonstrates that in intelligence, brain size, and other life-history variables, East Asians average a higher IQ and larger brain than Europeans who average a higher IQ and larger brain than Africans. Further, these group differences are 50–80% heritable. These are facts, not opinions and science must be governed by data. There is no place for the “moralistic fallacy” that reality must conform to our social, political, or ethical desires.


title: Branch-and-cut for linear programs with overlapping SOS1 constraints

abstract: SOS1 constraints require that at most one of a given set of variables is nonzero. In this article, we investigate a branch-and-cut algorithm to solve linear programs with SOS1 constraints. We focus on the case in which the SOS1 constraints overlap. The corresponding conflict graph can algorithmically be exploited, for instance, for improved branching rules, preprocessing, primal heuristics, and cutting planes. In an extensive computational study, we evaluate the components of our implementation on instances for three different applications. We also demonstrate the effectiveness of this approach by comparing it to the solution of a mixed-integer programming formulation, if the variables appearing in SOS1 constraints ar bounded.


title: The Impact of Group Music Therapy on Depression and Cognition in Elderly Persons With Dementia

abstract: OBJECTIVE ::: The aims of this study were to determine the effectiveness of group music therapy for improving depression and delaying the deterioration of cognitive functions in elderly persons with dementia. ::: ::: ::: METHOD ::: The study had a prospective, parallel-group design with permuted-block randomization. Older persons with dementia (N = 104) were randomly assigned to the experimental or control group. The experimental group received 12 sessions of group music therapy (two 30-min sessions per week for 6 weeks), and the control group received usual care. Data were collected 4 times: (1) 1 week before the intervention, (2) the 6th session of the intervention, (3) the 12th session of the intervention, and (4) 1 month after the final session. ::: ::: ::: RESULTS ::: Group music therapy reduced depression in persons with dementia. Improvements in depression occurred immediately after music therapy and were apparent throughout the course of therapy. The cortisol level did not significantly decrease after the group music therapy. Cognitive function significantly improved slightly at the 6th session, the 12th session, and 1 month after the sessions ended; in particular, short-term recall function improved. The group music therapy intervention had the greatest impact in subjects with mild and moderate dementia. ::: ::: ::: CONCLUSION ::: The group music intervention is a noninvasive and inexpensive therapy that appeared to reduce elders' depression. It also delayed the deterioration of cognitive functions, particularly short-term recall function. Group music therapy may be an appropriate intervention among elderly persons with mild and moderate dementia.


Automatic Question Generation using Relative Pronouns and Adverbs


A Highly Accurate Feature Fusion Network For Vehicle Detection In Surveillance Scenarios


STDnet: A ConvNet for Small Target Detection


title: Camera Localization in Outdoor Garden Environments Using Artificial Landmarks

abstract: In this paper, we present an outdoor monocular camera localization system based on artificial markers and test its performance in one of the test gardens of the TrimBot2020 project, in Wageningen. We use ArUco markers to construct a map of the environment and to subsequently localize the camera position within it. We combine the localization algorithm based on ArUco with a Kalman filter to smooth the trajectory and improve the localization stability with respect to fast movements of the camera, and blurred or noisy images. We recorded two sequences, with resolution 480p and l080p respectively, in the TrimBot2020 garden. We compare the localization performance of ArUco with a keypoint-based approach, namely ORB-SLAM2. We analyze and discuss the strengths and problems of both marker- and keypoint-based approaches on the considered sequences. The performed comparison suggests that the two approaches might be fused to jointly improve re-localization and reduce the drift in pose estimation.


title: Two-Wheeler Safety System for Accident Prevention, Detection and Reporting

abstract: The objective of this paper is to build a safety system which is integrated with the smart helmet and intelligent bike to reduce the probability of two-wheeler accidents and drunk and drive cases. The pressure sensor and the accelerometer sensor checks if the person is wearing the helmet or not. Alcohol sensors detects the alcoholic content in the rider’s breath. If the rider is not wearing the helmet or there is alcohol content found in rider’s breath, the bike remains off. The bike will not start until the rider wears the helmet and there is no alcoholic content present stating that rider did not consume alcohol. When the rider crashes, helmet hits the ground and sensors detect the motion and tilts of helmet and reports the occurrence of an accident and sends information of the location of accident to the family members of the rider and the emergency contact numbers.


title: The Number of Moves of the Largest Disc in Shortest Paths on Hanoi Graphs

abstract: In contrast to the widespread interest in the Frame-Stewart conjecture (FSC) about the optimal number of moves in the classical Tower of Hanoi task with more than three pegs, this is the first study of the question of investigating shortest paths in Hanoi graphs $H_p^n$ in a more general setting. Here $p$ stands for the number of pegs and $n$ for the number of discs in the Tower of Hanoi interpretation of these graphs. The analysis depends crucially on the number of largest disc moves (LDMs). The patterns of these LDMs will be coded as binary strings of length $p-1$ assigned to each pair of starting and goal states individually. This will be approached both analytically and numerically. The main theoretical achievement is the existence, at least for all $n\geqslant p(p-2)$, of optimal paths where $p-1$ LDMs are necessary. Numerical results, obtained by an algorithm based on a modified breadth-first search making use of symmetries of the graphs, lead to a couple of conjectures about some cases not covered by our ascertained results. These, in turn, may shed some light on the notoriously open FSC.


title: NetInfoMiner: High-level information extraction from network traffic

abstract: Due to the rapid increase of the Internet traffic encryption HTTPS, and the newly adopted protocols HTTP2 and SPDY, the need for a comprehensive high-level information extraction tool that supports the new protocols becomes essential for critical applications such as digital and network forensic and web penetration testing. In spite of the availability of big data from the Internet traffic, current network data mining tools do not support encrypted network traffic and the new protocols. This paper proposes a new tool for extracting high-level information such as visited links, user credentials and session cookies from HTTP and HTTPS protocols. It also allows extraction of user credentials and session cookies from HTTP2 and SPDY.


title: Reinforcement Learning of Hierarchical Skills on the Sony Aibo robot

abstract: Humans frequently engage in activities for their own sake rather than as a step towards solving a specific task. During such behavior, which psychologists refer to as being intrinsically motivated, we often develop skills that allow us to exercise mastery over our environment. Reference [7] have recently proposed an algorithm for intrinsically motivated reinforcement learning (IMRL) aimed at constructing hierarchies of skills through self-motivated interaction of an agent with its environment. While they were able to successfully demonstrate the utility of IMRL in simulation, we present the first realization of this approach on a real robot. To this end, we implemented a control architecture for the Sony-AIBO robot that extends the IMRL algorithm to this platform. Through experiments, we examine whether the Aibo is indeed able to learn useful skill hierarchies.


title: Face2Face: real-time face capture and reenactment of RGB videos

abstract: Face2Face is an approach for real-time facial reenactment of a monocular target video sequence (e.g., Youtube video). The source sequence is also a monocular video stream, captured live with a commodity webcam. Our goal is to animate the facial expressions of the target video by a source actor and re-render the manipulated output video in a photo-realistic fashion. To this end, we first address the under-constrained problem of facial identity recovery from monocular video by non-rigid model-based bundling. At run time, we track facial expressions of both source and target video using a dense photometric consistency measure. Reenactment is then achieved by fast and efficient deformation transfer between source and target. The mouth interior that best matches the re-targeted expression is retrieved from the target sequence and warped to produce an accurate fit. Finally, we convincingly re-render the synthesized target face on top of the corresponding video stream such that it seamlessly blends with the real-world illumination. We demonstrate our method in a live setup, where Youtube videos are reenacted in real time. This live setup has also been shown at SIGGRAPH Emerging Technologies 2016, by Thies et al. where it won the Best in Show Award.


title: A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks

abstract: The past decade has witnessed the rapid evolution in blockchain technologies, which has attracted tremendous interests from both the research communities and industries. The blockchain network was originated from the Internet financial sector as a decentralized, immutable ledger system for transactional data ordering. Nowadays, it is envisioned as a powerful backbone/framework for decentralized data processing and data-driven self-organization in flat, open-access networks. In particular, the plausible characteristics of decentralization, immutability, and self-organization are primarily owing to the unique decentralized consensus mechanisms introduced by blockchain networks. This survey is motivated by the lack of a comprehensive literature review on the development of decentralized consensus mechanisms in blockchain networks. In this paper, we provide a systematic vision of the organization of blockchain networks. By emphasizing the unique characteristics of decentralized consensus in blockchain networks, our in-depth review of the state-of-the-art consensus protocols is focused on both the perspective of distributed consensus system design and the perspective of incentive mechanism design. From a game-theoretic point of view, we also provide a thorough review of the strategy adopted for self-organization by the individual nodes in the blockchain backbone networks. Consequently, we provide a comprehensive survey of the emerging applications of blockchain networks in a broad area of telecommunication. We highlight our special interest in how the consensus mechanisms impact these applications. Finally, we discuss several open issues in the protocol design for blockchain consensus and the related potential research directions.


Introduction: Continuity, Change and the Family Farm


title: POSTER: A Logic Based Network Forensics Model for Evidence Analysis

abstract: Modern-day attackers tend to use sophisticated multi-stage/multi-host attack techniques and anti-forensics tools to cover their attack traces. Due to the current limitations of intrusion detection and forensic analysis tools, reconstructing attack scenarios from evidence left behind by the attackers of an enterprise system is challenging. In particular, reconstructing attack scenarios by using the information from IDS alerts and system logs that have a large number of false positives is a big challenge. In this paper, we present a model and an accompanying software tool that systematically addresses how to resolve the above problems to reconstruct the attack scenario. These problems include a large amount of data including non-relevant data and evidence destroyed by anti-forensic techniques. Our system is based on a Prolog system using known vulnerability databases and an anti-forensics database that we plan to extend to a standardized database like the NIST National Vulnerability Database (NVD). In this model, we use different methods, including mapping the evidence to system vulnerabilities, inductive reasoning and abductive reasoning to reconstruct attack scenarios. The goal of this work is to reduce the investigators' time and effort in reaching definite conclusion about how an attack occurred. Our results indicate that such a reasoning system can be useful for network forensics analysis.


title: Analyzing and predicting viral tweets

abstract: Twitter and other microblogging services have become indispensable sources of information in today's web. Understanding the main factors that make certain pieces of information spread quickly in these platforms can be decisive for the analysis of opinion formation and many other opinion mining tasks. This paper addresses important questions concerning the spread of information on Twitter. What makes Twitter users retweet a tweet? Is it possible to predict whether a tweet will become "viral", i.e., will be frequently retweeted? To answer these questions we provide an extensive analysis of a wide range of tweet and user features regarding their influence on the spread of tweets. The most impactful features are chosen to build a learning model that predicts viral tweets with high accuracy. All experiments are performed on a real-world dataset, extracted through a public Twitter API based on user IDs from the TREC 2011 microblog corpus.


title: Arduino based automatic wireless weather station with remote graphical application and alerts

abstract: In this paper we have proposed, developed and tested a hardware module based on Arduino Uno Board and Zigbee wireless technology, which measures the meteorological data, including air temperature, dew point temperature, barometric pressure, relative humidity, wind speed and wind direction. This information is received by a specially designed application interface running on a PC connected through Zigbee wireless link. The proposed system is also a mathematical model capable of generating short time local alerts based on the current weather parameters. It gives an on line and real time effect. We have also compared the data results of the proposed system with the data values of Meteorological Station Chandigarh and Snow & Avalanche Study Establishment Chandigarh Laboratory. The results have come out to be very precise. The idea behind to this work is to monitor the weather parameters, weather forecasting, condition mapping and warn the people from its disastrous effects.


title: Ransomware: A Rising Threat of new age Digital Extortion

abstract: This article attempts to discover the surreptitious features of ransomware and to address it in information systems security research. It intends to elicit attention with regard to ransomware, a newly emerged cyber threat using such encryption technology as RSA, and to help both academic researchers and IT practitioners understand the technological characteristics of ransomware, along with its severity analysis. As ransomware infections continue to rise and attacks employing refined algorithm become increasingly sophisticated, data protection faces serious challenges. The article discusses future trends and research directions related to ransomware, and provides prevention strategies for SMEs.


title: Factors in automatic musical genre classification of audio signals

abstract: Automatic musical genre classification is an important tool for organizing the large collections of music that are becoming available to the average user. In addition, it provides a structured way of evaluating musical content features that does not require extensive user studies. The paper provides a detailed comparative analysis of various factors affecting automatic classification performance, such as choice of features and classifiers. Using recent machine learning techniques, such as support vector machines, we improve on previously published results using identical data collections and features.


title: KissKissBan: a competitive human computation game for image annotation

abstract: In this paper, we propose a competitive human computation game, KissKissBan (KKB), for image annotation. KKB is different from other human computation games since it integrates both collaborative and competitive elements in the game design. In a KKB game, one player, the blocker, competeswith the other two collaborative players, the couples; while the couples try to find consensual descriptions about an image, the blocker's mission is to prevent the couples from reaching consensus. Because of its design, KKB possesses two nice properties over the traditional human computation game. First, since the blocker is encouraged to stop the couples from reaching consensual descriptions, he will try to detect and prevent coalition between the couples; therefore, these efforts naturally form a player-levelcheating-proof mechanism. Second, to evade the restrictions set by the blocker, the couples would endeavor to bring up a more diverse set of image annotations. Experiments hosted on Amazon Mechanical Turk and a gameplay survey involving 17 participants have shown that KKB is a fun and efficient game for collecting diverse image annotations.