title: A dual-systems perspective on addiction: contributions from neuroimaging and cognitive training

abstract: Dual-systems theories explain lapses in self-control in terms of a conflict between automatic and deliberative modes of behavioral control. Numerous studies have now tested whether the brain areas that control behavior are organized in a manner consistent with dual-systems models. Brain regions directly associated with the mesolimbic dopamine system, the nucleus accumbens (NAcc) and ventromedial prefrontal cortex (vmPFC) in particular, capture some of the features assumed by automatic processing. Regions in the lateral prefrontal cortex (lPFC) are more closely linked to deliberative processing and the exertion of self-control in the suppression of impulses. While identifying these regions crudely supports dual-system theories, important modifications to what constitutes automatic and deliberative behavioral control are also suggested. Experiments have identified various means by which automatic processes may be sculpted. Additional work decomposes deliberative processes into component functions such as generalized working memory, reappraisal of emotional stimuli, and prospection. The importance of deconstructing dual-systems models into specific cognitive processes is clear for understanding and treating addiction. We discuss intervention possibilities suggested by recent research, and focus in particular on cognitive training approaches to bolster deliberative control processes that may aid quit attempts.


title: An Empirical Analysis of Anonymity in Zcash

abstract: Among the now numerous alternative cryptocurrencies ::: derived from Bitcoin, Zcash is often touted as the ::: one with the strongest anonymity guarantees, due to its ::: basis in well-regarded cryptographic research. In this ::: paper, we examine the extent to which anonymity is ::: achieved in the deployed version of Zcash. We investigate ::: all facets of anonymity in Zcash’s transactions, ranging ::: from its transparent transactions to the interactions ::: with and within its main privacy feature, a shielded pool ::: that acts as the anonymity set for users wishing to spend ::: coins privately. We conclude that while it is possible to ::: use Zcash in a private way, it is also possible to shrink its ::: anonymity set considerably by developing simple heuristics ::: based on identifiable patterns of usage.


title: Using Simpson's Paradox to Discover Interesting Patterns in Behavioral Data

abstract: We describe a data-driven discovery method that leverages Simpson's paradox to uncover interesting patterns in behavioral data. Our method systematically disaggregates data to identify subgroups within a population whose behavior deviates significantly from the rest of the population. Given an outcome of interest and a set of covariates, the method follows three steps. First, it disaggregates data into subgroups, by conditioning on a particular covariate, so as minimize the variation of the outcome within the subgroups. Next, it models the outcome as a linear function of another covariate, both in the subgroups and in the aggregate data. Finally, it compares trends to identify disaggregations that produce subgroups with different behaviors from the aggregate. We illustrate the method by applying it to three real-world behavioral datasets, including Q\&A site Stack Exchange and online learning platforms Khan Academy and Duolingo.


title: SRA: Fast Removal of General Multipath for ToF Sensors

abstract: A major issue with Time of Flight sensors is the presence of multipath interference. We present Sparse Reflections Analysis (SRA), an algorithm for removing this interference which has two main advantages. First, it allows for very general forms of multipath, including interference with three or more paths, diffuse multipath resulting from Lambertian surfaces, and combinations thereof. SRA removes this general multipath with robust techniques based on L 1 optimization. Second, due to a novel dimension reduction, we are able to produce a very fast version of SRA, which is able to run at frame rate. Experimental results on both synthetic data with ground truth, as well as real images of challenging scenes, validate the approach.


title: Inverse perspective mapping based Urban road markings detection

abstract: A robust road markings detection algorithm is a fundamental component of intelligent vehicles' autonomous navigation in urban environment. This paper presents an algorithm for detecting road markings including zebra crossings, stop lines and lane markings to provide road information for intelligent vehicles. First, to eliminate the impact of the perspective effect, an Inverse Perspective Mapping (IPM) transformation is applied to the images grabbed by the camera; the region of interest (ROI) was extracted from IPM image by a low level processing. Then, different algorithms are adopted to extract zebra crossings, stop lines and lane markings. The experiments on a large number of street scenes in different conditions demonstrate the effectiveness of the proposed algorithm.


title: Practical memory checking with Dr. Memory

abstract: Memory corruption, reading uninitialized memory, using freed memory, and other memory-related errors are among the most difficult programming bugs to identify and fix due to the delay and non-determinism linking the error to an observable symptom. Dedicated memory checking tools are invaluable for finding these errors. However, such tools are difficult to build, and because they must monitor all memory accesses by the application, they incur significant overhead. Accuracy is another challenge: memory errors are not always straightforward to identify, and numerous false positive error reports can make a tool unusable. A third obstacle to creating such a tool is that it depends on low-level operating system and architectural details, making it difficult to port to other platforms and difficult to target proprietary systems like Windows. This paper presents Dr. Memory, a memory checking tool that operates on both Windows and Linux applications. Dr. Memory handles the complex and not fully documented Windows environment, and avoids reporting false positive memory leaks that plague traditional leak locating algorithms. Dr. Memory employs efficient instrumentation techniques; a direct comparison with the state-of-the-art Valgrind Memcheck tool reveals that Dr. Memory is twice as fast as Memcheck on average and up to four times faster on individual benchmarks.


title: DOI:10.1068/b31097 An algorithmic definition of the axial map

abstract: The fewest-line axial map, often simply referred to as the 'axial map', is one of the primary tools of space syntax. Its natural language definition has allowed researchers to draw consistent maps that present a concise description of architectural space; it has been established that graph measures obtained from the map are useful for the analysis of pedestrian movement patterns and activities related to such movement: for example, the location of services or of crime. However, the definition has proved difficult to translate into formal language by mathematicians and algorithmic implementers alike. This has meant that space syntax has been criticised for a lack of rigour in the definition of one of its fundamental representations. Here we clarify the original definition of the fewest-line axial map and show that it can be implemented algorithmically. We show that the original definition leads to maps similar to those currently drawn by hand, and we demonstrate that the differences between the two may be accounted for in terms of the detail of the algorithm used. We propose that the analytical power of the axial map in empirical studies derives from the efficient representation of key properties of the spatial configuration that it captures.


title: High Quality Uniform Random Number Generation Using LUT Optimised State-transition Matrices

abstract: This paper presents a family of uniform random number generators designed for efficient implementation in Lookup table (LUT) based FPGA architectures. A generator with a period of 2 k ???1 can be implemented using k flip-flops and k LUTs, and provides k random output bits each cycle. Each generator is based on a binary linear recurrence, with a state-transition matrix designed to make best use of all available LUT inputs in a given FPGA architecture, and to ensure that the critical path between all registers is a single LUT. This class of generator provides a higher sample rate per area than LFSR and Combined Tausworthe generators, and operates at similar or higher clock-rates. The statistical quality of the generators increases with k, and can be used to pass all common empirical tests such as Diehard, Crush and the NIST cryptographic test suite. Theoretical properties such as global equidistribution can also be calculated, and best and average case statistics shown. Due to the large number of random bits generated per cycle these generators can be used as a basis for generators with even higher statistical quality, and an example involving combination through addition is demonstrated.


title: Beyond Normalization: Pragmatics of Word Form in Text Messages

abstract: Non-standard spellings in text messages often convey extra pragmatic information not found in the standard word form. However, text message normalization systems that transform non-standard text message spellings to standard form tend to ignore this information. To address this problem, this paper examines the types of extra pragmatic information that are conveyed by non-standard word forms. Empirical analysis of our data shows that 40% of non-standard word forms contain emotional information not found in the standard form, and 38% contain additional emphasis. This extra information can be important to downstream applications such as text-to-speech synthesis. We further investigated the automatic detection of non-standard forms that display additional information. Our empirical results show that character level features can provide important cues for such detection.


title: 3D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detection

abstract: Cameras are a crucial exteroceptive sensor for self-driving cars as they are low-cost and small, provide appearance information about the environment, and work in various weather conditions. They can be used for multiple purposes such as visual navigation and obstacle detection. We can use a surround multi-camera system to cover the full 360-degree field-of-view around the car. In this way, we avoid blind spots which can otherwise lead to accidents. To minimize the number of cameras needed for surround perception, we utilize fisheye cameras. Consequently, standard vision pipelines for 3D mapping, visual localization, obstacle detection, etc. need to be adapted to take full advantage of the availability of multiple cameras rather than treat each camera individually. In addition, processing of fisheye images has to be supported. In this paper, we describe the camera calibration and subsequent processing pipeline for multi-fisheye-camera systems developed as part of the V-Charge project. This project seeks to enable automated valet parking for self-driving cars. Our pipeline is able to precisely calibrate multi-camera systems, build sparse 3D maps for visual navigation, visually localize the car with respect to these maps, generate accurate dense maps, as well as detect obstacles based on real-time depth map extraction.


Human Body Skeleton Detection and Tracking


title: Addressing the cold start problem in tag-based recommender systems

abstract: Folksonomies have become a powerful tool to describe, discover, search, and navigate ::: online resources (e.g., pictures, videos, blogs) on the Social Web. Unlike taxonomies and ::: ontologies, which impose a hierarchical categorisation on content, folksonomies directly ::: allow end users to freely create and choose the categories (in this case, tags) that best ::: describe a piece of information. However, the freedom aafforded to users comes at a cost: ::: as tags are defined informally, the retrieval of information becomes more challenging. ::: Different solutions have been proposed to help users discover content in this highly dynamic ::: setting. However, they have proved to be effective only for users who have already heavily ::: used the system (active users) and who are interested in popular items (i.e., items tagged ::: by many other users). ::: In this thesis we explore principles to help both active users and more importantly new or ::: inactive users (cold starters) to find content they are interested in even when this content ::: falls into the long tail of medium-to-low popularity items (cold start items). We investigate ::: the tagging behaviour of users on content and show how the similarities between users and ::: tags can be used to produce better recommendations. We then analyse how users create ::: new content on social tagging websites and show how preferences of only a small portion ::: of active users (leaders), responsible for the vast majority of the tagged content, can be ::: used to improve the recommender system's scalability. We also investigate the growth of ::: the number of users, items and tags in the system over time. We then show how this ::: information can be used to decide whether the benefits of an update of the data structures ::: modelling the system outweigh the corresponding cost. ::: In this work we formalize the ideas introduced above and we describe their implementation. ::: To demonstrate the improvements of our proposal in recommendation efficacy and ::: efficiency, we report the results of an extensive evaluation conducted on three different ::: social tagging websites: CiteULike, Bibsonomy and MovieLens. Our results demonstrate ::: that our approach achieves higher accuracy than state-of-the-art systems for cold start ::: users and for users searching for cold start items. Moreover, while accuracy of our technique ::: is comparable to other techniques for active users, the computational cost that it ::: requires is much smaller. In other words our approach is more scalable and thus more ::: suitable for large and quickly growing settings.


title: Multi-faceted assessment of trademark similarity

abstract: A novel method for the assessment of trademark similarity is proposed.The method blends together visual, semantic and phonetic similarity.It produces an aggregated score based on the individual assessments.Evaluation using information retrieval measures and human judgment. Trademarks are intellectual property assets with potentially high reputational value. Their infringement may lead to lost revenue, lower profits and damages to brand reputation. A test normally conducted to check whether a trademark is highly likely to infringe other existing, already registered, trademarks is called a likelihood of confusion test. One of the most influential factors in this test is establishing similarity in appearance, meaning or sound. However, even though the trademark registration process suggests a multi-faceted similarity assessment, relevant research in expert systems mainly focuses on computing individual aspects of similarity between trademarks. Therefore, this paper contributes to the knowledge in this field by proposing a method, which, similar to the way people perceive trademarks, blends together the three fundamental aspects of trademark similarity and produces an aggregated score based on the individual visual, semantic and phonetic assessments. In particular, semantic similarity is a new aspect, which has not been considered by other researchers in approaches aimed at providing decision support in trademark similarity assessment. Another specific scientific contribution of this paper is the innovative integration, using a fuzzy engine, of three independent assessments, which collectively provide a more balanced and human-centered view on potential infringement problems. In addition, the paper introduces the concept of degree of similarity since the line between similar and dissimilar trademarks is not always easy to define especially when dealing with blending three very different assessments. The work described in the paper is evaluated using a database comprising 1400 trademarks compiled from a collection of real legal cases of trademark disputes. The evaluation involved two experiments. The first experiment employed information retrieval measures to test the classification accuracy of the proposed method while the second used human collective opinion to examine correlations between the trademark scoring/rating and the ranking of the proposed method, and human judgment. In the first experiment, the proposed method improved the F-score, precision and accuracy of classification by 12.5%, 35% and 8.3%, respectively, against the best score computed using individual similarity. In the second experiment, the proposed method produced a perfect positive Spearman rank correlation score of 1.00 in the ranking task and a pairwise Pearson correlation score of 0.92 in the rating task. The test of significance conducted on both scores rejected the null hypotheses of the experiment and showed that both scores correlated well with collective human judgment. The combined overall assessment could add value to existing support systems and be beneficial for both trademark examiners and trademark applicants. The method could be further used in addressing recent cyberspace phenomena related to trademark infringement such as customer hijacking and cybersquatting.


title: Compact Dual-Band Slot Antenna at the Corner of the Ground Plane

abstract: A compact design of a dual-band (2.4-2.485 GHz and 5.15-5.825 GHz) slot antenna is proposed for WLAN applications. To achieve compact and practical design, the proposed antenna is composed of narrow slots, and placed in the 10 times 10 mm2 corner region on a typical FR4 printed circuit board for WLAN personal-digital-assistance (PDA) phones. The detailed design considerations are described in this communication. Also, simulation and measurement regarding the reflection coefficient and radiation pattern are conducted. Good agreement between both results is obtained.


title: A high PSRR Class-D audio amplifier IC based on a self-adjusting voltage reference

abstract: In a wide range of applications, audio amplifiers require a large Power Supply Rejection Ratio (PSRR) that the current Class-D architecture cannot reach. This paper proposes a self-adjusting internal voltage reference scheme that sets the bias voltages of the amplifier without losing on output dynamics. This solution relaxes the constraints on gain and feedback resistors matching that were previously the limiting factor for the PSRR. Theory of operation, design and IC evaluation in a Class-D amplifier in CMOS 0.25µm will be shown in this paper. The use of this voltage reference increased the amplifier's PSRR by 15dB, with only a 140µA increase in current consumption.


title: Enhancement ofIn VitroTranslation by Gold Nanoparticle−DNA Conjugates

abstract: Gold nanoparticle (AuNP)-DNA conjugates can enhance in vitro translation of a protein. Enhancement occurs via a combination of nonspecific adsorption of translation-related molecules and the ribosome to the AuNP-DNA and specific binding to the mRNA of interest. AuNP-DNA conjugates enhanced protein production of fluorescent proteins (mCherry, eGFP) in retic lysate mixes by 65-100%. Gel electrophoresis was used to probe nonspecific adsorption of the AuNP-DNA conjugates to the translation machinery. It was determined that nonspecific adsorption is critical for enhancement, and if it was eliminated, expression enhancement did not occur. The interaction of the mRNA with the DNA on the AuNP surface influenced the amount of enhancement and was probed by expression in the presence of RNase H. These results suggest that higher translation enhancement occurs when the DNA on the AuNP forms an incomplete duplex with the mRNA. Tuning the balance between nonspecific adsorption and specific binding of the AuNP-DNA conjugates could result in the translation enhancement of a specific gene in a mixture.


title: Accurate Weakly-Supervised Deep Lesion Segmentation using Large-Scale Clinical Annotations: Slice-Propagated 3D Mask Generation from 2D RECIST

abstract: Volumetric lesion segmentation from computed tomography (CT) images is a powerful means to precisely assess multiple time-point lesion/tumor changes. However, because manual 3D segmentation is prohibitively time consuming, current practices rely on an imprecise surrogate called response evaluation criteria in solid tumors (RECIST). Despite their coarseness, RECIST markers are commonly found in current hospital picture and archiving systems (PACS), meaning they can provide a potentially powerful, yet extraordinarily challenging, source of weak supervision for full 3D segmentation. Toward this end, we introduce a convolutional neural network (CNN) based weakly supervised slice-propagated segmentation (WSSS) method to 1) generate the initial lesion segmentation on the axial RECIST-slice; 2) learn the data distribution on RECIST-slices; 3) extrapolate to segment the whole lesion slice by slice to finally obtain a volumetric segmentation. To validate the proposed method, we first test its performance on a fully annotated lymph node dataset, where WSSS performs comparably to its fully supervised counterparts. We then test on a comprehensive lesion dataset with 32,735 RECIST marks, where we report a mean Dice score of 92% on RECIST-marked slices and 76% on the entire 3D volumes.


title: Floral dip : a simplified method for Agrobacterium-mediated transformation of Arabidopsis thaliana

abstract: Summary The Agrobacterium vacuum infiltration method has made it possible to transform Arabidopsis thaliana without plant tissue culture or regeneration. In the present study, this method was evaluated and a substantially modified transformation method was developed. The labor-intensive vacuum infiltration process was eliminated in favor of simple dipping of developing floral tissues into a solution containing Agrobacterium tumefaciens, 5% sucrose and 500 microliters per litre of surfactant Silwet L-77. Sucrose and surfactant were critical to the success of the floral dip method. Plants inoculated when numerous immature floral buds and few siliques were present produced transformed progeny at the highest rate. Plant tissue culture media, the hormone benzylamino purine and pH adjustment were unnecessary, and Agrobacterium could be applied to plants at a range of cell densities. Repeated application of Agrobacterium improved transformation rates and overall yield of transformants approximately twofold. Covering plants for 1 day to retain humidity after inoculation also raised transformation rates twofold. Multiple ecotypes were transformable by this method. The modified method should facilitate high-throughput transformation of Arabidopsis for efforts such as T-DNA


title: Design of a UWB unidirectional radiation compound spiral antenna

abstract: The design of a modified unidirectional radiation miniaturized compound spiral antenna fed by an exponentially tapered balun is shown in this paper. The compound spiral antenna is designed by combining a typical equiangular spiral antenna with Archimedean spiral line. By taking advantage of a modified reflector to make the antenna into a microstrip form, the structure realizes a typical unidirectional radiation pattern for planar spiral antennas, which naturally increases radiation gain. In the range from 5 to 20 GHz, simulated radiation patterns, axial ratio plots and VSWR plots show good performance. The comparison with the compound spiral antenna and traditional UWB antennas shows the improvement of the axial ratio.


title: Technical Section: An evaluation of descriptors for large-scale image retrieval from sketched feature lines

abstract: We address the problem of fast, large scale sketch-based image retrieval, searching in a database of over one million images. We show that current retrieval methods do not scale well towards large databases in the context of interactively supervised search and propose two different approaches for which we objectively evaluate that they significantly outperform existing approaches. The proposed descriptors are constructed such that both the full color image and the sketch undergo exactly the same preprocessing steps. We first search for an image with similar structure, analyzing gradient orientations. Then, best matching images are clustered based on dominant color distributions, to offset the lack of color-based decision during the initial search. Overall, the query results demonstrate that the system offers intuitive access to large image databases using a user-friendly sketch-and-browse interface.


© 2004 INFORMS Organizational Citizenship Behavior in the People’s Republic of China


title: Consumer-friendly food allergen detection: moving towards smartphone-based immunoassays

abstract: In this critical review, we provide a comprehensive overview of immunochemical food allergen assays and detectors in the context of their user-friendliness, through their connection to smartphones. Smartphone-based analysis is centered around citizen science, putting analysis into the hands of the consumer. Food allergies represent a significant worldwide health concern and consumers should be able to analyze their foods, whenever and wherever they are, for allergen presence. Owing to the need for a scientific background, traditional laboratory-based detection methods are generally unsuitable for the consumer. Therefore, it is important to develop simple, safe, and rapid assays that can be linked with smartphones as detectors to improve user accessibility. Smartphones make excellent detection systems because of their cameras, embedded flash functions, portability, connectivity, and affordability. Therefore, this review has summarized traditional laboratory-based methods for food allergen detection such as enzyme-linked-immunosorbent assay, flow cytometry, and surface plasmon resonance, and the potential to modernize these methods by interfacing them with a smartphone readout system, based on the aforementioned smartphone characteristics. This is the first review focusing on smartphone-based food-allergen detection methods designed with the intention of being consumer-friendly. Graphical abstract A smartphone-based food allergen detection system in three easy steps (1) sample preparation, (2) allergen detection on a smartphone using antibodies, which then transmits the data wirelessly, (3) analytical results sent straight to smartphone A smartphone-based food allergen detection system in three easy steps (1) sample preparation, (2) allergen detection on a smartphone using antibodies, which then transmits the data wirelessly, (3) analytical results sent straight to smartphone


title: Cloud Computing Data Protection -- A Literature Review and Analysis

abstract: Cloud Computing technologies are gaining increased attention both in academia and practice. Despite of its relevance and potential for more IT flexibility and its beneficial effects on costs, legal uncertainties regarding the data processing especially between large economies still exist on the customer and provider side. Against this background, this contribution aims at providing an overview of privacy issues and legal frameworks for data protection in Cloud environments discussed in recent scientific literature. Due to the overall complexity concerning international law, we decided to primarily focus on data traffic between the United States of America and the European Union. The result of our research revealed significant differences in the jurisdiction and consciousness for data protection in these two economies. As a consequence for further Cloud Computing research we identify a large number of problems that need to be addressed.


title: A parameter-free hedging algorithm

abstract: We study the problem of decision-theoretic online learning (DTOL). Motivated by practical applications, we focus on DTOL when the number of actions is very large. Previous algorithms for learning in this framework have a tunable learning rate parameter, and a barrier to using online-learning in practical applications is that it is not understood how to set this parameter optimally, particularly when the number of actions is large. ::: ::: In this paper, we offer a clean solution by proposing a novel and completely parameter-free algorithm for DTOL. We introduce a new notion of regret, which is more natural for applications with a large number of actions. We show that our algorithm achieves good performance with respect to this new notion of regret; in addition, it also achieves performance close to that of the best bounds achieved by previous algorithms with optimally-tuned parameters, according to previous notions of regret.


title: Rotation Invariant Multi-View Color Face Detection Based on Skin Color and Adaboost Algorithm

abstract: As the training of Adaboost is complicated or does not work well in the multi-view face detection with large plane-rotated angle, this paper proposes a rotation invariant multi-view color face detection method combining skin color segmentation and multi-view Adaboost algorithm. First the possible face region is fast detected by skin color table, and the skin-color background adhesion of face is separated by color clustering. After region merging, the candidate face region is received. Then the face direction in plane is calculated by K-L transform, and finally the candidate face region corrected by rotating is scanned by multi-view Adaboost classifier to locate the face accurately. The experiments show that the method can effectively detect the plane large-angle multi-view face image which the conventional Adaboost can not do. It can be effectively applied to the cases of multi-view and multi-face image with complex background.


title: Modeling Data Mining Applications for Prediction of Prepaid Churn in Telecommunication Services

abstract: This paper defines an advanced methodology for modeling applications based on Data Mining methods that represents a logical framework for development of Data Mining applications. Methodology suggested here for Data Mining modeling process has been applied and tested through Data Mining applications for predicting Prepaid users churn in the telecom industry. The main emphasis of this paper is defining of a successful model for prediction of potential Prepaid churners, in which the most important part is to identify the very set of input variables that are high enough to make the prediction model precise and reliable. The data available on Prepaid users are much more inadequate as compared to Postpaid users, especially when it comes to customer data domain. This fact adds to complexity of modeling process of predictive Data Mining applications related to Prepaid churn. Several models have been created and compared on the basis of different Data Mining methods and algorithms (neural networks, decision trees, logistic regression). For the modeling examples we used WEKA analysis tool – an open source Data Mining tool written in Java (developed at the University of Waikato, New Zealand).


title: Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer’s Disease using structural MR and FDG-PET images

abstract: Alzheimer's Disease (AD) is a progressive neurodegenerative disease. Amnestic mild cognitive impairment (MCI) is a common first symptom before the conversion to clinical impairment where the individual becomes unable to perform activities of daily living independently. Although there is currently no treatment available, the earlier a conclusive diagnosis is made, the earlier the potential for interventions to delay or perhaps even prevent progression to full-blown AD. Neuroimaging scans acquired from MRI and metabolism images obtained by FDG-PET provide in-vivo view into the structure and function (glucose metabolism) of the living brain. It is hypothesized that combining different image modalities could better characterize the change of human brain and result in a more accuracy early diagnosis of AD. In this paper, we proposed a novel framework to discriminate normal control(NC) subjects from subjects with AD pathology (AD and NC, MCI subjects convert to AD in future). Our novel approach utilizing a multimodal and multiscale deep neural network was found to deliver a 85.68\% accuracy in the prediction of subjects within 3 years to conversion. Cross validation experiments proved that it has better discrimination ability compared with results in existing published literature.


title: Pre-processing and Feature Extraction Techniques for EEGBCI Applications- A Review of Recent Research

abstract: The electrical waveforms generated by brain named electroencephalogram (EEG) signals, require certain special processing for using them as part of applications. EEG signals need special pre-processing to enable brain computer interfaces (BCI) capture essential details of the signal and use them for specific applications, including deriving decisions. In this paper, we focus on some of the recent works reported in the area of EEG pre-processing. Further, we discuss some of the reported works related to feature extraction of EEG signals for application in drowsiness detection and development of assistive technologies for persons with special need. Keywords: EEG signals, signal pre-processing, feature extraction, electroencephalography.


title: A review and critique of emotional intelligence measures

abstract: Summary Emotional intelligence measures vary widely in both their content and in their method of assessment. In particular, emotional intelligence measures tend to use either a self-report personality-based approach, an informant approach, or an ability-based assessment procedure. In this paper, the measurement and psychometric properties of four of the major emotional intelligence measures (Emotional Competence Inventory, Emotional Quotient Inventory, Multifactor Emotional Intelligence Scale, Mayer–Salovey–Caruso Emotional Intelligence Test) are reviewed, the comparability of these measures is examined, and some conclusions and suggestions for future research on emotional intelligence measures are provided. Copyright # 2005 John Wiley & Sons, Ltd.


title: Single-Layer Circularly Polarized Antenna With Fan-Beam Endfire Radiation

abstract: A single-layer single-fed endfire antenna with circularly polarized radiation has been designed and evaluated. The concept is the superposition of two closely spaced complementary dipoles. In terms of implementations, a shorted parallel-plate cavity serves as the magnetic dipole, while double-side-printed conductor arms act as the electric dipole. To satisfy the required magnitude/phase relationship between two dipoles, a T-shaped double side slot-coupled line and a meandered double-side parallel strip line are introduced. With analyses of equivalent models, the operation mechanism and design guidelines can be obtained. Taking 5.8-GHz ISM band (5.725–5.875 GHz) for example, the measured results show that the designed antenna has an impedance bandwidth $({| {S_{11}} | from 5.7 to 5.9 GHz (3.5%) and a 3-dB axial-ratio (AR) bandwidth from 5.65 to 5.9 GHz (4.3%). The effective beamwidth in the azimuth/elevation plane is 85°/168° at 5.8 GHz.


title: E-swish: Adjusting Activations to Different Network Depths

abstract: Activation functions have a notorious impact on neural networks on both training and testing the models against the desired problem. Currently, the most used activation function is the Rectified Linear Unit (ReLU). This paper introduces a new and novel activation function, closely related with the new activation $Swish = x * sigmoid(x)$ (Ramachandran et al., 2017) which generalizes it. We call the new activation $E-swish = \beta x * sigmoid(x)$. We show that E-swish outperforms many other well-known activations including both ReLU and Swish. For example, using E-swish provided 1.5% and 4.6% accuracy improvements on Cifar10 and Cifar100 respectively for the WRN 10-2 when compared to ReLU and 0.35% and 0.6% respectively when compared to Swish. The code to reproduce all our experiments can be found at https://github.com/EricAlcaide/E-swish


title: Self-disclosure and student satisfaction with Facebook

abstract: The purpose of the current study was to extend existing research examining the specific motives for creating and maintaining Facebook, and to connect these motives to users' levels of self-disclosure and satisfaction with Facebook. Undergraduate students identified their reasons for using Facebook, privacy and self-disclosure settings, and satisfaction with Facebook's ability to meet these motivational needs. Results indicated that the most common motives for using Facebook were relationship maintenance, passing time, and entertainment, although gender differences in motives for creating a Facebook page were observed. Males and females also differed in the overall level of self-disclosure as well as the type of information presented on their Facebook pages. Levels of self-disclosure, but not privacy levels, were associated with greater levels of satisfaction with Facebook to meet certain motivational goals.


PHTHEIROSPERMUM GLANDULOSUM——A NEW RECORDED SPECIES OF SCROPHULARIACEAE FROM YUNNAN, CHINA


Systematicity in the Lexicon: On Having Your Cake and Eating It Too


title: Hardiness commitment, gender, and age differentiate university academic performance

abstract: Background The increasing diversity of students, particularly in age, attending university has seen a concomitant interest in factors predicting academic success. ::: ::: Aims This 2-year correlational study examined whether age, gender (demographic variables), and hardiness (cognitive/emotional variable) differentiate and predict university final degree grade point average (GPA) and final-year dissertation mark. ::: ::: Sample Data are reported from a total of 134 university undergraduate students. ::: ::: Method Participants provided baseline data in questionnaires administered during the first week of their second year of undergraduate study and gave consent for their academic progress to be tracked. Final degree GPA and dissertation mark were the academic performance criteria. ::: ::: Results Mature-age students achieved higher final degree GPA compared to young undergraduates. Female students significantly outperformed their male counterparts in each measured academic assessment criteria. Female students also reported a significantly higher mean score on hardiness commitment compared to male students. Commitment was the most significant positive correlate of academic achievement. Final degree GPA and dissertation mark were significantly predicted by commitment, and commitment and gender, respectively. ::: ::: Conclusions The findings have implications for universities targeting academic support services to maximize student scholastic potential. Future research should incorporate hardiness, gender, and age with other variables known to predict academic success.


title: Influencing users towards better passwords: persuasive cued click-points

abstract: Usable security has unique usability challenges because the need for security often means that standard human-computer-interaction approaches cannot be directly applied. An important usability goal for authentication systems is to support users in selecting better passwords, thus increasing security by expanding the effective password space. In click-based graphical passwords, poorly chosen passwords lead to the emergence of hotspots -- portions of the image where users are more likely to select click-points, allowing attackers to mount more successful dictionary attacks. We use persuasion to influence user choice in click-based graphical passwords, encouraging users to select more random, and hence more secure, click-points. Our approach is to introduce persuasion to the Cued Click-Points graphical password scheme (Chiasson, van Oorschot, Biddle, 2007). Our resulting scheme significantly reduces hotspots while still maintaining its usability.


title: Monte Carlo Q-learning for General Game Playing

abstract: Recently, the interest in reinforcement learning in game playing has been renewed. This is evidenced by the groundbreaking results achieved by AlphaGo. General Game Playing (GGP) provides a good testbed for reinforcement learning, currently one of the hottest fields of AI. In GGP, a specification of games rules is given. The description specifies a reinforcement learning problem, leaving programs to find strategies for playing well. Q-learning is one of the canonical reinforcement learning methods, which is used as baseline on some previous work (Banerjee & Stone, IJCAI 2007). We implement Q-learning in GGP for three small board games (Tic-Tac-Toe, Connect-Four, Hex). We find that Q-learning converges, and thus that this general reinforcement learning method is indeed applicable to General Game Playing. However, convergence is slow, in comparison to MCTS (a reinforcement learning method reported to achieve good results). We enhance Q-learning with Monte Carlo Search. This enhancement improves performance of pure Q-learning, although it does not yet out-perform MCTS. Future work is needed into the relation between MCTS and Q-learning, and on larger problem instances.


title: A Fabry–Pérot Antenna With Two-Dimensional Electronic Beam Scanning

abstract: A novel fixed-frequency electronically steerable Fabry–Perot Antenna (FPA) with electronic two-dimensional (2-D) (azimuth and elevation) steering capability is presented. The configuration is based on a centrally fed Fabry–Perot cavity (FPC) loaded with a tunable high impedance surface (HIS). Varactor diodes are used to electronically tune the HIS reflection coefficient, forming four azimuthal sectors that are independently controlled by four control signals, respectively. It is demonstrated that this simple configuration generates a pencil beam that can be pointed to eight discrete azimuthal directions, whereas continuous elevation scanning is also attained simultaneously for each azimuthal direction by controlling the leaky-wave propagation constant. The theory, simulation analysis, and experimental results obtained from a prototype operating at 5.5 GHz are presented to validate the antenna design.


Naive Bayes Classification Technique for Opinion Mining in Data Mining S


title: Extreme Binning: Scalable, parallel deduplication for chunk-based file backup

abstract: Data deduplication is an essential and critical component of backup systems. Essential, because it reduces storage space requirements, and critical, because the performance of the entire backup operation depends on its throughput. Traditional backup workloads consist of large data streams with high locality, which existing deduplication techniques require to provide reasonable throughput. We present Extreme Binning, a scalable deduplication technique for non-traditional backup workloads that are made up of individual files with no locality among consecutive files in a given window of time. Due to lack of locality, existing techniques perform poorly on these workloads. Extreme Binning exploits file similarity instead of locality, and makes only one disk access for chunk lookup per file, which gives reasonable throughput. Multi-node backup systems built with Extreme Binning scale gracefully with the amount of input data; more backup nodes can be added to boost throughput. Each file is allocated using a stateless routing algorithm to only one node, allowing for maximum parallelization, and each backup node is autonomous with no dependency across nodes, making data management tasks robust with low overhead.


title: Patterns of Multidimensionality of Embedded Network Ties in Emerging Entrepreneurial Firms

abstract: The social components of embedded network ties areexamined as well as how these components are related to trust, and whether themultidimensionality of these ties creates different types of embeddedness. Thisinvestigation contributes to the conceptual understanding of embeddedness andproposes a theoretical base for the multidimensionality of embeddedness. Eight case studies of emerging entrepreneurial firms from the computerindustry in a western U.S. state are presented, and 66 embedded network tiesacross the eight cases are examined. Significant differences exist among theembedded network ties, especially in dimensions of trust and socialrelationships. Three types of trust relationships (personal, goodwill, andeconomic) are found, as well as three types of social relationships (personal,dyadic interaction, and social capital). Embedded ties are classified intoseven types or patterns. Differences between these types of ties may be crucialfor effective governance of the external relationships of emergingentrepreneurial firms. The results suggest that full embeddedness would contribute to successfulperformance of the firm. If emerging entrepreneurial firms assume all embeddednetwork ties are similar and are governed by relational governance alone, thefirm may encounter governance difficulties. (TNM)


Health App Use Among US Mobile Phone Owners: A National Survey


title: A new approach to simultaneous localization and map building with learning: NeoSLAM (Neuro-Evolutionary Optimizing)

abstract: This paper addresses a novel approach to the solution of the Simultaneous Localization and Mapping (SLAM) problem bared on a Neuro Evolutionary Optimization (NeoSLAM) method. The proposed algorithm first casts SLAM as a global optimization problem using the cost function which represents the quality of robot pose trajectory and the feature positions in world coordinate frame. In our algorithm, the neural network trained to estimate the pose difference of the two consecutive positions accurately from the corresponding sensor data and the previous pose difference. The cost function is formulated as the importance of the full SLAM assumptions of EKF. Evolutionary Programming (EP) is used to evolve the neural model that is most consistent with the actual data measurement. Prediction and correction is simultaneously performed in our neural model that combines both the motion model and sensor model. By way of learning and evolution, our algorithm does not need prior assumption on the motion and sensor models, and therefore shows a robust performance regardless of the actual noise type. Further, our method can generate an accurate map even without the data association step, paving the way to deal with practical applications. Both the simulation and real experimental results conducted made various environments and noise/sensor types demonstrate that NeoSLAM ensures a consistently robust and accurate performance.


title: CompRec-Trip: A composite recommendation system for travel planning

abstract: Classical recommender systems provide users with a list of recommendations where each recommendation consists of a single item, e.g., a book or a DVD. However, applications such as travel planning can benefit from a system capable of recommending packages of items, under a user-specified budget and in the form of sets or sequences. In this context, there is a need for a system that can recommend top-k packages for the user to choose from. In this paper, we propose a novel system, CompRec-Trip, which can automatically generate composite recommendations for travel planning. The system leverages rating information from underlying recommender systems, allows flexible package configuration and incorporates users' cost budgets on both time and money. Furthermore, the proposed CompRec-Trip system has a rich graphical user interface which allows users to customize the returned composite recommendations and take into account external local information.


title: Richer and cleaner? A study on carbon dioxide emissions in developing countries

abstract: The Climate Change debate has drawn attention to the problem of greenhouse gases emissions into the atmosphere. One of the most important issues in the policy debate is the role that should be played by developing countries in joining the commitment of developed countries to reduce GHG emissions, and particularly CO2 emissions. This debate calls into play the relationship between energy consumption, CO2 emissions and economic development. In this paper we use a panel data model for 110 world countries to estimate the relationship between CO2 emissions and GDP and to produce emission forecast. The paper contains three major results: (i) the empirical relationship between carbon dioxide and income is well described by non linear Gamma and Weibull specifications as opposed to more usual linear and log-linear functional forms; (ii) our single equation reduced form model is comparable in terms of forecasted emissions with other more complex, less data driven models; (iii) despite the decreasing marginal propensity to pollute, our forecasts show that future global emissions will rise. The average world growth of CO2 emissions between 2000 and 2020 is about 2.2% per year, while that of Non Annex 1 countries is posted at 3.3% per year.


title: Combining Machine Learning Techniques and Natural Language Processing to Infer Emotions Using Spanish Twitter Corpus

abstract: In the recent years, microblogging services, as Twitter, have become a popular tool for expressing feelings, opinions, broadcasting news, and communicating with friends. Twitter users produced more than 340 million tweets per day which may be consider a rich source of user information. We take a supervised approach to the problem, but leverage existing hashtags in Twitter for building our training data. Finally, we tested the Spanish emotional corpus applying two different machine learning algorithms for emotion identification reaching about 65% accuracy.


title: Differential remoteness and emotional tone modulate the neural correlates of autobiographical memory

abstract: Summary Autobiographical memory relies on complex interactions between episodic memory contents, associated emotions and a sense of self-continuity along the time axis of one’s life history. The neural correlates underlying autobiographical memory are known to primarily comprise areas of prefrontal cortex, medial and lateral temporal cortex, as well as posterior cingulate and retrosplenial cortex. By contrast, the effect of encoding and/or storage parameters such as the emotional tone of the memories retrieved or the length of the timeinterval between the initial encoding of information and retrieval remains to be clarified. Using blocked design functional MRI and statistical parametric mapping, we investigated the impact of remoteness (factor 1: recent, remote) and emotional valence (factor 2: positive, negative) on the neural correlates of autobiographical memory retrieval. Changes in neural activity (P < 0.05, corrected) related to autobiographical memory retrieval (irrespective of remoteness and emotional tone) relative to baseline were observed bilaterally in medial and lateral temporal, temporal‐occipital, posterior cingulate and frontal cortices. Recent (relative to remote) memories were associated with differentially increased neural activity bilaterally in the retrosplenial cortex and the hippocampal region, whereas remote (relative to recent) memories did not show any statistically significant differential neural activations. Positive (relative to negative) memories bilaterally activated the orbitofrontal cortex, the temporal pole, as well as medial temporal areas, with the activation peak being in the entorhinal region. By contrast, negative (relative to positive) memories differentially increased neural activity in the right middle temporal gyrus only. The data suggest differential functional roles for temporal, prefrontal and retrosplenial regions during autobiographical memory retrieval depending on the remoteness and the emotional valence of the memories retrieved. In particular, our findings support the ‘classic’ model of long-term memory processing, which suggests a time-limited differential involvement of the hippocampus in memory consolidation. Interestingly, the observation of such a time-dependent involvement of the hippocampal region in memory consolidation corresponds to the course of retrograde amnesia observed in demented patients, with the loss of recent memories appearing during early stages of the disease when conspicuous neurofibrillary changes are restricted mainly to the hippocampal and parahippocampal regions. Only during later stages, as the neurofibrillary changes spread out to neocortical association areas, do remote memories also become impaired. We conclude that the brain regions involved in autobiographical memory retrieval are influenced by the triggered memories’ emotional significance and their relationship to the individual time axis.


title: NEK1 mutations cause short-rib polydactyly syndrome type majewski.

abstract: Defects of ciliogenesis have been implicated in a wide range of human phenotypes and play a crucial role in signal transduction and cell-cycle coordination. We used homozygosity mapping in two families with autosomal-recessive short-rib polydactyly syndrome Majewski type to identify mutations in NEK1 as an underlying cause of this lethal osteochondrodysplasia. NEK1 encodes a serine/threonine kinase with proposed function in DNA double-strand repair, neuronal development, and coordination of cell-cycle-associated ciliogenesis. We found that absence of functional full-length NEK1 severely reduces cilia number and alters ciliar morphology in vivo. We further substantiate a proposed digenic diallelic inheritance of ciliopathies by the identification of heterozygous mutations in NEK1 and DYNC2H1 in an additional family. Notably, these findings not only increase the broad spectrum of ciliar disorders, but suggest a correlation between the degree of defective microtubule or centriole elongation and organization and the severity of the resulting phenotype.


title: A Survey on Offline Handwriting Recognition Systems

abstract: Handwriting recognition is one of the most challenging and fascinating areas in the field of image processing and machine learning. Its various practical applications include digitizing documents, reading handwritten notes and addresses on postcards, reading the amounts on checks and so on. This paper provides an overview of the different techniques used in the different phases of offline handwriting recognition.


title: IoT-based dynamic street light control for smart cities use cases

abstract: This paper presents a real-world proven solution for dynamic street light control and management which relies on an open and flexible Internet of Things architecture. Substantial contribution is brought at the interoperability level using novel device connection concept based on model-driven communication agents to speed up the integration of sensors and actuators to Internet of Things platforms. The paper shows also results from real-world tests with deployed dynamic street lights in urban spaces. The proposed dynamic light control solution permits an energy saving of about 56% compared to classical static, time-based street light control.


title: The Driving Conditions for Obtaining Subnanosecond High-Voltage Pulses From a Silicon-Avalanche-Shaper Diode

abstract: Silicon-avalanche-shaper (SAS) diodes are fast-closing switches capable of producing high-voltage pulses with a rise time of ~100 ps. The SAS can be driven by a positive, nanosecond scale, high-voltage pulse applied to its cathode where the magnitude of the driving pulse is correlated to the magnitude of the pulse at the SAS anode (output). Drift-step-recovery diodes (DSRDs) are fast-opening switches capable of producing high-voltage pulses with a rise time of the order of 1 ns. Thus, DSRDs are good candidates for driving SAS diodes. In this paper, the SAS output is studied with respect to its driving conditions. First, the SAS output is examined with respect to the magnitude and rise time of the driving pulse, utilizing three DSRDs to produce pulses with various rise times from 0.5 to 5 ns. In addition, the effect of the driving pulse repetition frequency (PRF) on the SAS output is studied. An experimental demonstration using a 1.5-kV SAS fabricated at the Ioffe Physical Technical Institute shows the advantage of driving the SAS with the short, 0.5 ns, pulses, and the degradation of performance due to high PRF, up to 10 MHz.


title: Enhancing an artefact management system with traceability recovery features

abstract: We present a traceability recovery method and tool based on latent semantic indexing (LSI) in the context of an artefact management system. The tool highlights the candidate links not identified yet by the software engineer and the links identified but missed by the tool, probably due to inconsistencies in the usage of domain terms in the traced software artefacts. We also present a case study of using the traceability recovery tool on software artefacts belonging to different categories of documents, including requirement, design, and testing documents, as well as code components.


title: Strategic Alignment of Cloud-Based Architectures for Big Data

abstract: Big Data is an increasingly significant topic for management and IT departments. In the beginning, Big Data applications were large on premise installations. Today, cloud services are used increasingly to implement Big Data applications. This can be done on different ways supporting different strategic enterprise goals. Therefore, we develop a framework that enumerates the alternatives for implementing Big Data applications using cloud-services and identify the strategic goals supported by these Alternatives. The created framework clarifies the options for Big Data initiatives using cloud-computing and thus improves the strategic alignment of Big Data applications.


title: Speed Estimation From a Tri-axial Accelerometer Using Neural Networks

abstract: We propose a speed estimation method with human body accelerations measured on the chest by a tri-axial accelerometer. To estimate the speed we segmented the acceleration signal into strides measuring stride time, and applied two neural networks into the patterns parameterized from each stride calculating stride length. The first neural network determines whether the subject walks or runs, and the second neural network with different node interactions according to the subject's status estimates stride length. Walking or running speed is calculated with the estimated stride length divided by the measured stride time. The neural networks were trained by patterns obtained from 15 subjects and then validated by 2 untrained subjects' patterns. The result shows good agreement between actual and estimated speeds presenting the linear correlation coefficient r = 0.9874. We also applied the method to the real field and track data.


title: Interrater reliability: the kappa statistic

abstract: The kappa statistic is frequently used to test interrater reliability. The importance of rater reliability lies in the fact that it represents the extent to which the data collected in the study are correct representations of the variables measured. Measurement of the extent to which data collectors (raters) assign the same score to the same variable is called interrater reliability. While there have been a variety of methods to measure interrater reliability, traditionally it was measured as percent agreement, calculated as the number of agreement scores divided by the total number of scores. In 1960, Jacob Cohen critiqued use of percent agreement due to its inability to account for chance agreement. He introduced the Cohen’s kappa, developed to account for the possibility that raters actually guess on at least some variables due to uncertainty. Like most correlation statistics, the kappa can range from −1 to +1. While the kappa is one of the most commonly used statistics to test interrater reliability, it has limitations. Judgments about what level of kappa should be acceptable for health research are questioned. Cohen’s suggested interpretation may be too lenient for health related studies because it implies that a score as low as 0.41 might be acceptable. Kappa and percent agreement are compared, and levels for both kappa and percent agreement that should be demanded in healthcare studies are suggested.


title: Achieving Flexible and Self-Contained Data Protection in Cloud Computing

abstract: For enterprise systems running on public clouds in which the servers are outside the control domain of the enterprise, access control that was traditionally executed by reference monitors deployed on the system servers can no longer be trusted. Hence, a self-contained security scheme is regarded as an effective way for protecting outsourced data. However, building such a scheme that can implement the access control policy of the enterprise has become an important challenge. In this paper, we propose a self-contained data protection mechanism called RBAC-CPABE by integrating role-based access control (RBAC), which is widely employed in enterprise systems, with the ciphertext-policy attribute-based encryption (CP-ABE). First, we present a data-centric RBAC (DC-RBAC) model that supports the specification of fine-grained access policy for each data object to enhance RBAC’s access control capabilities. Then, we fuse DC-RBAC and CP-ABE by expressing DC-RBAC policies with the CP-ABE access tree and encrypt data using CP-ABE. Because CP-ABE enforces both access control and decryption, access authorization can be achieved by the data itself. A security analysis and experimental results indicate that RBAC-CPABE maintains the security and efficiency properties of the CP-ABE scheme on which it is based, but substantially improves the access control capability. Finally, we present an implemented framework for RBAC-CPABE to protect privacy and enforce access control for data stored in the cloud.


title: Changes of Phospholipid-Metabolizing and Lysosomal Enzymes in Hypoglossal Nucleus and Ventral Horn Motoneurons During Regeneration of Craniospinal Nerves

abstract: In order to study the biochemical changes associated with the cell body response to axonal crush injury, two systems, hypoglossal nucleus and spinal cord ventral horn, were used. The time intervals chosen were7, 14, and 28 days after unilateral crushing of the right hypoglossal nerve and cervicothoracic nerves of the rabbit. Non-crushed, contralateral nerves were used as controls. Three groups of enzyme activities were tested: (a) phospholipase A2, acyl CoA:2-acyl-sn-glycero-3-phosphocholine acyl-transferase, and choline phosphotransferase, as indicators of phospholipid degradation and biosynthesis; (b) seven hydrolases, namely, β-D-glucuronidase, β-iV-acetyl-d-hex-osaminidase, arylsulfatase A, galactosylceramidase, GM1-ganglioside β-galactosidase, and acid RNase, as indicators of lysosomal activity; and (c) free and inhibitor-bound alkaline RNase, as an index of RNA metabolism. Changes could be grouped into three distinct patterns. Compared to contralateral control, choline phosphotransferase showed a slight increase, whereas phospholipase A2 and most lyso somal hydrolases showed a significant increase of activity, especially evident in the ventral spinal cord neurons 14–28 days after crushing. These changes correlate with known increases of membrane and organelle numbers, including lysosomes, in motor and sensory neurons during peripheral regeneration. In contrast, free and acid alkaline RNase activity significantly decreased in the injured sides compared to the controls. This change can probably be correlated with a stabilization of RNAs needed for increased protein synthesis. No changes in total alkaline RNase and acyltrans-ferase activities in either regeneration model were observed.


title: Evaluating the effectiveness of explanations for recommender systems

abstract: When recommender systems present items, these can be accompanied by explanatory information. Such explanations can serve seven aims: effectiveness, satisfaction, transparency, scrutability, trust, persuasiveness, and efficiency. These aims can be incompatible, so any evaluation needs to state which aim is being investigated and use appropriate metrics. This paper focuses particularly on effectiveness (helping users to make good decisions) and its trade-off with satisfaction. It provides an overview of existing work on evaluating effectiveness and the metrics used. It also highlights the limitations of the existing effectiveness metrics, in particular the effects of under- and overestimation and recommendation domain. In addition to this methodological contribution, the paper presents four empirical studies in two domains: movies and cameras. These studies investigate the impact of personalizing simple feature-based explanations on effectiveness and satisfaction. Both approximated and real effectiveness is investigated. Contrary to expectation, personalization was detrimental to effectiveness, though it may improve user satisfaction. The studies also highlighted the importance of considering opt-out rates and the underlying rating distribution when evaluating effectiveness.


title: Optimizing protocol for efficient microspore embryogenesis and doubled haploid development in different maturity groups of cauliflower (B. oleracea var. botrytis L.) in India

abstract: Microspore culture technique is widely used in Brassicas for development of haploids and doubled haploids (DH) in breeding programmes and genetic studies. Indian cauliflower, a group of vegetable Brassica; with tolerance to higher temperature and moisture, has been selected in India for last 200 years. In India, cauliflowers are grown throughout the year in different climatic zones based on their temperature adaptation. For the first time, we report microspore embryogenesis in different maturity groups of Indian cauliflowers. The main objective of our study was to optimize protocol for efficient microspore embryogenesis in different types of cauliflower cultivated in India. We studied different factors like bud size, developmental stage of the microspores, culture density and heat shock treatments for efficient microspore embryogenesis in four different genotypes representing four different maturity groups. Among different factors, the bud size and microspore developmental stage was found to be more critical for successful microspore embryogenesis. The optimum bud size for high frequency microspore embryogenesis for the two early maturity groups (September–October maturity) was 4.0–4.5 mm, whereas, it was 4.5–5.0 mm for the cultivars grown for November onwards harvesting. These optimized bud sizes contained the highest percent of viable microspores and maximum number of microspores at late uninucleate to early binucleate stage. The microspore density of 6–8 × 104 cells per mL was found most suitable for efficient microspore embryogenesis. A post-culture incubation of microspores at 30.0 °C/32.5 °C for 24 h followed by maintenance at 25 °C was found optimum for induction of microspore embryos in different maturity group of Indian cauliflower. The flow cytometry analysis of 646 microspore derived plants revealed that more than 60 % plants were spontaneous diploids and less than 15 % were haploids. However, the genotypes from different groups showed varied response in different culture density and temperature treatments. Thus, it is necessary to optimize genotype specific protocol for efficient microspore embryogenesis before its routine application. The protocol developed in this study will facilitate doubled haploids based breeding and genetic studies of all types of cauliflower grown in India.


title: Group sparse representation based classification for multi-feature multimodal biometrics

abstract: Group sparse representation based classification algorithm is proposed for feature-level multimodal biometrics.The algorithm is able to handle missing features in multimodal scenario.Experimental results on WVU and real world LEA databases show efficacy of the proposed algorithm. Multimodal biometrics technology consolidates information obtained from multiple sources at sensor level, feature level, match score level, and decision level. It is used to increase robustness and provide broader population coverage for inclusion. Due to the inherent challenges involved with feature-level fusion, combining multiple evidences is attempted at score, rank, or decision level where only a minimal amount of information is preserved. In this paper, we propose the Group Sparse Representation based Classifier (GSRC) which removes the requirement for a separate feature-level fusion mechanism and integrates multi-feature representation seamlessly into classification. The performance of the proposed algorithm is evaluated on two multimodal biometric datasets. Experimental results indicate that the proposed classifier succeeds in efficiently utilizing a multi-feature representation of input data to perform accurate biometric recognition.


title: Density-aware compressive crowdsensing

abstract: Crowdsensing systems collect large-scale sensor data from mobile devices to provide a wide-area view of phenomena including traffic, noise and air pollution. Because such data often exhibits sparse structure, it is natural to apply compressive sensing (CS) for data sampling and recovery. However in practice, crowd participants are often distributed highly unevenly across the sensing area, and thus the numbers of observations collected over different areas may vary wildly -- an issue we call density disparity. Density disparity leads to inaccuracy in low density areas, and potentially undermines the recovery performance if conventional compressive sensing is applied directly, which equally treats data from areas of different density.To address this challenge, we propose a probabilistic accuracy estimator, based on which we devise two recovery algorithms: Threshold Recovery (TR) and Weighted Recovery (WR). As general-purpose recovery algorithms, TR and WR improve the performance of CS in the scenarios with density disparity, and also provide better guarantees in terms of $\ell_2$-norm accuracy compared with conventional CS recovery algorithms. We also conduct extensive experiments based on synthetic and real-life datasets. Our results show that TR/WR typically reduce $\ell_2$-norm error by more than 60\% compared to state-of-the-art baselines.


title: Web server workload characterization: the search for invariants

abstract: The phenomenal growth in popularity of the World Wide Web (WWW, or the Web) has made WWW traffic the largest contributor to packet and byte traffic on the NSFNET backbone. This growth has triggered recent research aimed at reducing the volume of network traffic produced by Web clients and servers, by using caching, and reducing the latency for WWW users, by using improved protocols for Web interaction.Fundamental to the goal of improving WWW performance is an understanding of WWW workloads. This paper presents a workload characterization study for Internet Web servers. Six different data sets are used in this study: three from academic (i.e., university) environments, two from scientific research organizations, and one from a commercial Internet provider. These data sets represent three different orders of magnitude in server activity, and two different orders of magnitude in time duration, ranging from one week of activity to one year of activity.Throughout the study, emphasis is placed on finding workload invariants: observations that apply across all the data sets studied. Ten invariants are identified. These invariants are deemed important since they (potentially) represent universal truths for all Internet Web servers. The paper concludes with a discussion of caching and performance issues, using the invariants to suggest performance enhancements that seem most promising for Internet Web servers.


title: Scaling MapReduce Vertically and Horizontally

abstract: Glass wing is a MapReduce framework that uses OpenCL to exploit multi-core CPUs and accelerators. However, compute device capabilities may vary significantly and require targeted optimization. Similarly, the availability of resources such as memory, storage and interconnects can severely impact overall job performance. In this paper, we present and analyze how MapReduce applications can improve their horizontal and vertical scalability by using a well controlled mixture of coarse- and fine-grained parallelism. Specifically, we discuss the Glass wing pipeline and its ability to overlap computation, communication, memory transfers and disk access. Additionally, we show how Glass wing can adapt to the distinct capabilities of a variety of compute devices by employing fine-grained parallelism. We experimentally evaluated the performance of five MapReduce applications and show that Glass wing outperforms Hadoop on a 64-node multi-core CPU cluster by factors between 1.2 and 4, and factors from 20 to 30 on a 23-node GPU cluster. Similarly, we show that Glass wing is at least 1.5 times faster than GPMR on the GPU cluster.


title: Greedy Algorithms for Packing Unequal Circles into a Rectangular Container ∗†

abstract: In this paper, we study the problem of packing unequal circles into a two-dimensional rectangular container. We solve this problem by proposing two greedy algorithms. The first algorithm, denoted by B1.0, selects the next circle to place according to the maximum-hole degree rule, that is inspired from human activity in packing. The second algorithm, denoted by B1.5, improves B1.0 with a self-look-ahead search strategy. The comparisons with the published methods on several instances taken from the literature show the good performance of our approach.


title: Health information systems: Failure, success and improvisation

abstract: Summary Background and purpose The generalised assumption of health information systems (HIS) success is questioned by a few commentators in the medical informatics field. They point to widespread HIS failure. The purpose of this paper was therefore to develop a better conceptual foundation for, and practical guidance on, health information systems failure (and success). Methods Literature and case analysis plus pilot testing of developed model. Results Defining HIS failure and success is complex, and the current evidence base on HIS success and failure rates was found to be weak. Nonetheless, the best current estimate is that HIS failure is an important problem. The paper therefore derives and explains the “design–reality gap” conceptual model. This is shown to be robust in explaining multiple cases of HIS success and failure, yet provides a contingency that encompasses the differences which exist in different HIS contexts. The design–reality gap model is piloted to demonstrate its value as a tool for risk assessment and mitigation on HIS projects. It also throws into question traditional, structured development methodologies, highlighting the importance of emergent change and improvisation in HIS. Conclusions The design–reality gap model can be used to address the problem of HIS failure, both as a post hoc evaluative tool and as a pre hoc risk assessment and mitigation tool. It also validates a set of methods, techniques, roles and competencies needed to support the dynamic improvisations that are found to underpin cases of HIS success.


title: Overview of Annotation Creation: Processes&Tools

abstract: Creating linguistic annotations requires more than just a reliable annotation scheme. Annotation can be a complex endeavour potentially involving many people, stages, and tools. This chapter outlines the process of creating end-to-end linguistic annotations, identifying specific tasks that researchers often perform. Because tool support is so central to achieving high quality, reusable annotations with low cost, the focus is on identifying capabilities that are necessary or useful for annotation tools, as well as common problems these tools present that reduce their utility. Although examples of specific tools are provided in many cases, this chapter concentrates more on abstract capabilities and problems because new tools appear continuously, while old tools disappear into disuse or disrepair. The two core capabilities tools must have are support for the chosen annotation scheme and the ability to work on the language under study. Additional capabilities are organized into three categories: those that are widely provided; those that often useful but found in only a few tools; and those that have as yet little or no available tool support.


title: A High-Throughput Distributed Shared-Buffer NoC Router

abstract: Router microarchitecture plays a central role in the performance of an on-chip network (NoC). Buffers are needed in routers to house incoming flits which cannot be immediately forwarded due to contention. This buffering can be done at the inputs or the outputs of a router, corresponding to an input-buffered router (IBR) or an output-buffered router (OBR). OBRs are attractive because they can sustain higher throughputs and have lower queuing delays under high loads than IBRs. However, a direct implementation of an OBR requires a router speedup equal to the number of ports, making such a design prohibitive under aggressive clocking needs and limited power budgets of most NoC applications. In this paper, we propose a new router design that aims to emulate an OBR practically, based on a distributed shared-buffer (DSB) router architecture. We introduce innovations to address the unique constraints of NoCs, including efficient pipelining and novel flow-control. We also present practical DSB configurations that can reduce the power overhead with negligible degradation in performance. The proposed DSB router achieves up to 19% higher throughput on synthetic traffic and reduces packet latency by 60% on average for SPLASH-2 benchmarks with high contention, compared to a state-of-art pipelined IBR. On average, the saturation throughput of DSB routers is within 10% of the theoretically ideal saturation throughput under the synthetic workloads evaluated.


title: Language to Logical Form with Neural Attention

abstract: Semantic parsing aims at mapping natural language to machine interpretable meaning representations. Traditional approaches rely on high-quality lexicons, manually-built templates, and linguistic features which are either domainor representation-specific. In this paper we present a general method based on an attention-enhanced encoder-decoder model. We encode input utterances into vector representations, and generate their logical forms by conditioning the output sequences or trees on the encoding vectors. Experimental results on four datasets show that our approach performs competitively without using hand-engineered features and is easy to adapt across domains and meaning representations.


title: Motion signal processing

abstract: Techniques from the image and signal processing domain can be successfully applied to designing, modifying, and adapting animated motion. For this purpose, we introduce multiresolution motion filtering, multitarget motion interpolation with dynamic timewarping, waveshaping and motion displacement mapping. The techniques are well-suited for reuse and adaptation of existing motion data such as joint angles, joint coordinates or higher level motion parameters of articulated figures with many degrees of freedom. Existing motions can be modified and combined interactively and at a higher level of abstraction than conventional systems support. This general approach is thus complementary to keyframing, motion capture, and procedural animation.


title: Magic Moments in situated mediascapes

abstract: In this paper, we describe the situation and factors that lead to "Magic Moments" in mediascape experiences and discuss the implications for how to design these magic moments without them appearing contrived. We introduce a framework for Experience Design and describe a set of design heuristics which should extend the field of HCI to encompass aspects of user experience, mobility, the outside environment and facets of the new medium. The distinctive feature of mediascapes is their link to the physical environment. The findings are primarily based on analysis of public reaction to Riot! 1831, a mediascape in the form of an interactive drama which is based on the actual riots that took place in a public square in Bristol, England in 1831.


title: Detecting Deceptive Opinions with Profile Compatibility

abstract: We propose using profile compatibility to differentiate genuine and fake product reviews. For each product, a collective profile is derived from a separate collection of reviews. Such a profile contains a number of aspects of the product, together with their descriptions. For a given unseen review about the same product, we build a test profile using the same approach. We then perform a bidirectional alignment between the test and the collective profile, to compute a list of aspect-wise compatible features. We adopt Ott et al. (2011)’s op spam v1.3 dataset for identifying truthful vs. deceptive reviews. We extend the recently proposed N-GRAM+SYN model of Feng et al. (2012a) by incorporating profile compatibility features, showing such an addition significantly improves upon their state-ofart classification performance.


title: HEVC still image coding and high efficiency image file format

abstract: The High Efficiency Video Coding (HEVC) standard includes support for a large range of image representation formats and provides an excellent image compression capability. The High Efficiency Image File Format (HEIF) offers a convenient way to encapsulate HEVC coded images, image sequences and animations together with associated metadata into a single file. This paper discusses various features and functionalities of the HEIF file format and compares the compression efficiency of HEVC still image coding to that of JPEG 2000. According to the experimental results HEVC provides about 25% bitrate reduction compared to JPEG 2000, while keeping the same objective picture quality.


title: Constrained model predictive control implementation for a heavy-duty gas turbine power plant

abstract: In this paper, model predictive control (MPC) strategy is implemented to a GE9001E gas turbine power plant. A linear model is developed for the gas turbine using conventional mathematical models and ARX identification procedure. Also a process control model is identified for system outputs prediction. The controller is designed in order to adjust the exhaust gas temperature and the rotor speed by compressor inlet guide vane (IGV) position and fuel signals. The proposed system is simulated under load demand disturbances. It is shown that MPC controller can maintain the rotor speed and exhaust gas temperature more accurately in comprehension with both SpeedTronic™ control system and conventional PID control.


title: Baby steps: evaluation of a system to support record-keeping for parents of young children

abstract: Parents of young children often want to keep a variety of records on their children's early years, for the purposes of preservation of memories or at the request of their pediatrician. However, time constraints, motivation, and forgetfulness may hinder their ability to keep consistent records. We developed a system, Baby Steps, which is designed to improve the record-keeping process. In this paper, we present the results of a 3-month deployment study of this technology with 8 families and their pediatricians. The study showed that when compared to a control condition, experimental design features of Baby Steps encouraged parents to more frequently collect and review records, provided higher confidence in reporting, and improved parent-pediatrician communication.


title: User-aware conversational agents

abstract: Conversational agents are becoming increasingly popular. These systems present an extremely rich and challenging research space for addressing many aspects of user awareness and adaptation, such as user profiles, contexts, personalities, emotions, social dynamics, conversational styles, etc. Adaptive interfaces are of long-standing interest for the HCI community. Meanwhile, new machine learning approaches are introduced in the current generation of conversational agents, such as deep learning, reinforcement learning, and active learning. It is imperative to consider how various aspects of user-awareness should be handled by these new techniques. The goal of this workshop is to bring together researchers in HCI, user modeling, and the AI and NLP communities from both industry and academia, who are interested in advancing the state-of-the-art on the topic of user-aware conversational agents. Through a focused and open exchange of ideas and discussions, we will work to identify central research topics in user-aware conversational agents and develop a strong interdisciplinary foundation to address them.


title: Essential features of serious games design in higher education: Linking learning attributes to game mechanics

abstract: This paper consolidates evidence and material from a range of specialist and disciplinary fields to provide an evidence-based review and synthesis on the design and use of serious games in higher education. Search terms identified 165 papers reporting conceptual and empirical evidence on how learning attributes and game mechanics may be planned, designed and implemented by university teachers interested in using games, which are integrated into lesson plans and orchestrated as part of a learning sequence at any scale. The findings outline the potential of classifying the links between learning attributes and game mechanics as a means to scaffold teachers’ understanding of how to perpetuate learning in optimal ways while enhancing the in-game learning experience. The findings of this paper provide a foundation for describing methods, frames and discourse around experiences of design and use of serious games, linked to methodological limitations and recommendations for further research in this area.


title: Color histogram features based image classification in content-based image retrieval systems

abstract: In content-based image retrieval systems (CBIR) the most efficient and simple searches are the color based searches. Although this methods can be improved if some prepocessing steps are used. In this paper one of the prepocessing algorithms, the image classification is analyzed. In CBIR image classification has to be computationally fast and efficient. In this paper a new approach is introduced, which based on low level image histogram features. The main advantage of this method is the very quick generation and comparison of the applied feature vectors.


title: MOCA: a lightweight mobile cloud offloading architecture

abstract: We present our work on MOCA, a lightweight Mobile Cloud Offloading Architecture, which uses an in-network cloud platform to provide offloading resources. MOCA integrates with existing mobile network architectures without requiring significant changes, and utilizes software defined networking techniques in the data plane to redirect appropriate traffic to and from the cloud platform. We show the feasibility of MOCA by a prototype implementation using a LTE/EPC mobile testbed.


title: Optimal Defense Policies for Partially Observable Spreading Processes on Bayesian Attack Graphs

abstract: The defense of computer networks from intruders is becoming a problem of great importance as networks and devices become increasingly connected. We develop an automated approach to defending a network against continuous attacks from intruders, using the notion of Bayesian attack graphs to describe how attackers combine and exploit system vulnerabilities in order to gain access and progress through a network. We assume that the attacker follows a probabilistic spreading process on the attack graph and that the defender can only partially observe the attacker's capabilities at any given time. This leads to the formulation of the defender's problem as a partially observable Markov decision process (POMDP). We define and compute optimal defender countermeasure policies, which describe the optimal countermeasure action to deploy given the current information.


Adaptive Acceleration of Sparse Coding via Matrix Factorization


title: Design of Thyristor Digital Trigger Circuit Based on Microcontroller Unit

abstract: In the current paper, a kind of thyristor digital trigger circuit, based on 8-bit microcontroller unit, is designed. The circuit can be used to trigger the three-phase full-controlled-bridge rectifier circuits. It adopts single phase voltage as the synchronization signal; 6-channel trigger pulses from it are very symmetrical and the pulses can be shifted within a cycle of a sine wave from 0 degree to 180 degree. The circuit consists of a microcontroller unit, a keyboard, a LED display, synchronization shift phase module and trigger pulses output module and so on. Its structure is simple; its performance is high stable and reliable; its anti-interference ability is strong and its operation is easy. The hardware structure of the trigger circuit and its principle are the emphasis of this paper and especially the process of generating of the trigger pulses has a detailed description.


D-VITA: AVisual Interactive Text Analysis System Using Dynamic Topic Mining


title: The Application of RFID to Healthcare Management of Nursing House

abstract: The economic development and the changes of social demographic structure have resulted in the problems of population aging sharply and elders' chronic diseases getting serious. Population aging has become a critical problem in modern societies, and senior houses have become a primary option for the life care of elders. However, the regulations related to long-term care are not keeping up with the speed of population aging; also, the lack of caregivers cause the elders not receiving enough care on either outdoor or indoor activities from senior houses. Besides the lack of manpower, an effective healthcare management system is needed to solve the major problem. RFID used to apply on the identification in logistics industry and this technology has become more mature. RFID is now gradually applied to medical business and healthcare for elders. This study intends to replace traditional manual operation with the establishment of RFID positioning monitor and management system in order to reduce manpower as well as continuously transmitting data to the back-end server for real-time monitoring and post enquiry; the data in the database could also become the reference for risk evaluation and healthcare security. Moreover, the system provides not only personal tracker but alerts to secure healthcare so as to reduce chance of accidents happening in nursing homes. The integration of RFID with healthcare operation and information flow is proposed in this study, and the efficient management is also proposed to enhance the management benefits of nursing homes; therefore, this study is focusing on installing the equipment regarding to environment of senior house. The RFID auto positioning monitor system could real-time and continuously monitor and transmit seniors' physiological signals wirelessly through active RFID Reader and active RFID Tag to promote the quality of healthcare. Besides, the active RFID Tag could be embedded in a wristband with RFID electronic label, tied with elders to receive real-time elders' body temperature, location, and conditions for the information system. Caregivers in nursing homes therefore could understand the current situation of elders and monitor the seniors' situations to prevent accidents.


title: Right colon cancer: long-term results after curative surgery and prognostic significance of duration of symptoms.

abstract: Between 1976 and 1986, a consecutive series of 195 patients with right colon cancer were admitted at the First Department of Surgery of the University of Rome. Patients were divided into five groups according to the absence of intestinal symptoms (group 1) and the duration of the symptomatic phase (groups 2-5). Analysis of the preoperative investigation showed that colonoscopy was able to identify 33 (26.2%) of tumors misdiagnosed by double contrast X-ray barium enema. We did not notice any correlation among symptom duration and tumor stage, tumor differentiation, operability rate, and survival. Patients admitted in the asymptomatic phase presented tumors of less advanced stage and, thus, had the best survival rate (71.4%). In this group, patients promptly treated had a better survival rate (87.5%) than those treated after 2 months (50%). Our study suggests that only neoplasms diagnosed in asymptomatic patients and treated promptly are related to a good survival rate. We, therefore, emphasize the importance of education of patients and family physicians about right colon cancer and the necessity to investigate all patients with a suspected right colon cancer by means of colonoscopy.


title: The Role of Empirical Rheology in Flour Quality Control

abstract: Rheology, as a branch of physics, studies the deformation and flow of matter in response to an applied stress or strain. According to the materials’ behaviour, they can be classified as Newtonian or non-Newtonian (Steffe, 1996; Schramm, 2004). The most of the foodstuffs exhibit properties of non-Newtonian viscoelastic systems (Abang Zaidel et al., 2010). Among them, the dough can be considered as the most unique system from the point of material science. It is viscoelastic system which exhibits shear-thinning and thixotropic behaviour (Weipert, 1990). This behaviour is the consequence of dough complex structure in which starch granules (75-80%) are surrounded by three-dimensional protein (20-25%) network (Bloksma, 1990, as cited in Weipert, 2006). Wheat proteins are consisted of gluten proteins (80-85% of total wheat protein) which comprise of prolamins (in wheat gliadins) and glutelins (in wheat glutenins) and non gluten proteins (15-20% of the total wheat proteins) such as albumins and globulins (Veraverbeke & Delcour, 2002). Gluten complex is a viscoelastic protein responsible for dough structure formation. Among the cereal technologists, rheology is widely recognized as a valuable tool in quality assessment of flour. Hence, in the cereal scientific community, rheological measurements are generally employed throughout the whole processing chain in order to monitor the mechanical properties, molecular structure and composition of the material, to imitate materials’ behaviour during processing and to anticipate the quality of the final product (Dobraszczyk & Morgenstern, 2003). Rheology is particularly important technique in revealing the influence of flour constituents and additives on dough behaviour during breadmaking. There are many test methods available to measure rheological properties, which are commonly divided into empirical (descriptive, imitative) and fundamental (basic) (Scott Blair, 1958 as cited in Weipert, 1990). Although being criticized due to their shortcomings concerning inflexibility in defining the level of deforming force, usage of strong deformation forces, interpretation of results in relative non-SI units, large sample requirements and its impossibility to define rheological parameters such as stress, strain, modulus or viscosity (Weipert, 1990; Dobraszczyk & Morgenstern, 2003), empirical rheological measurements are still indispensable in the cereal quality laboratories. According to the empirical rheological parameters it is possible to determine the optimal flour quality for a particular purpose. The empirical techniques used for dough quality


title: Linear cross-modal hashing for efficient multimedia search

abstract: Most existing cross-modal hashing methods suffer from the scalability issue in the training phase. In this paper, we propose a novel cross-modal hashing approach with a linear time complexity to the training data size, to enable scalable indexing for multimedia search across multiple modals. Taking both the intra-similarity in each modal and the inter-similarity across different modals into consideration, the proposed approach aims at effectively learning hash functions from large-scale training datasets. More specifically, for each modal, we first partition the training data into $k$ clusters and then represent each training data point with its distances to $k$ centroids of the clusters. Interestingly, such a k-dimensional data representation can reduce the time complexity of the training phase from traditional O(n2) or higher to O(n), where $n$ is the training data size, leading to practical learning on large-scale datasets. We further prove that this new representation preserves the intra-similarity in each modal. To preserve the inter-similarity among data points across different modals, we transform the derived data representations into a common binary subspace in which binary codes from all the modals are "consistent" and comparable. nThe transformation simultaneously outputs the hash functions for all modals, which are used to convert unseen data into binary codes. Given a query of one modal, it is first mapped into the binary codes using the modal's hash functions, followed by matching the database binary codes of any other modals. Experimental results on two benchmark datasets confirm the scalability and the effectiveness of the proposed approach in comparison with the state of the art.


title: Cataloging GitHub Repositories

abstract: GitHub is one of the largest and most popular repository hosting service today, having about 14 million users and more than 54 million repositories as of March 2017. This makes it an excellent platform to find projects that developers are interested in exploring. GitHub showcases its most popular projects by cataloging them manually into categories such as DevOps tools, web application frameworks, and game engines. We propose that such cataloging should not be limited only to popular projects. We explore the possibility of developing such cataloging system by automatically extracting functionality descriptive text segments from readme files of GitHub repositories. These descriptions are then input to LDA-GA, a state-of-the-art topic modeling algorithm, to identify categories. Our preliminary experiments demonstrate that additional meaningful categories which complement existing GitHub categories can be inferred. Moreover, for inferred categories that match GitHub categories, our approach can identify additional projects belonging to them. Our experimental results establish a promising direction in realizing automatic cataloging system for GitHub.