title: Factors affecting online repurchase intention

abstract: Purpose – The purpose of this paper is to investigate the effects of online shopping experience and habit in relation to adjusted expectations for enhancing online repurchase intention. Design/methodology/approach – The authors employed partial least square (PLS) as a technique used to analyze the measurement and structural models. Data for this research were collected from 240 Taiwanese online shoppers who had experienced online shopping at least four times. Findings – The result of this study indicates that online shopping habit acts as a moderator of both customer satisfaction and adjusted expectations, whereas online shopping experience can be considered a key driver for customer satisfaction. Furthermore, the research findings confirm that customer satisfaction is a vital driver of adjusted expectations and online repurchase intention. Adjusted expectations do mediate the impact of online repurchase intention. Research limitations/implications – This paper highlights the effect of online shopping exp...


title: Applying Naive Bayes Data Mining Technique for Classification of Agricultural Land Soils

abstract: Summary The advances in computing and information storage have provided vast amounts of data. The challenge has been to extract knowledge from this raw data that has lead to new methods and techniques such as data mining that can bridge the knowledge gap. This research aimed to assess these new data mining techniques and apply them to a soil science database to establish if meaningful relationships can be found. A large data set of Soil database is extracted from the Department of Soil Sciences and Agricultural Chemistry, S V Agricultural College, Tirupati, The database contains measurements of soil profile data from various locations of Chandragiri Mandal, Chittoor District. The research establishes whether Soils are Classified Using various data mining techniques. In addition, comparison was made between Naive bayes classification and analyse the most effective technique. The outcome of the research may have many benefits, t o agriculture, soil management and environmental.


title: Spatially Coupled Codes Optimized for Magnetic Recording Applications

abstract: Spatially coupled (SC) codes are a class of sparse graph-based codes known to have capacity-approaching performance. SC codes are constructed based on an underlying low-density parity-check (LDPC) code, by first partitioning the underlying block code and then putting replicas of the components together. Significant recent research efforts have been devoted to the asymptotic, ensemble-averaged study of SC codes, as these coupled variants of the existing LDPC codes offer excellent properties. While the asymptotic analysis is important, due to simplifying assumptions and averaging effects, results from the asymptotic domain are not readily translatable to the practical, finite-length setting. Despite this chasm, the finite-length analysis of SC codes is still largely unexplored. In this paper, we tackle the problem of optimized design of SC codes in the context of magnetic-recording (MR) applications. In particular, we identify combinatorial structures in the graphical representation of the code that are detrimental in the MR setting. An intriguing observation is that for the same SC code, the problematic objects for the MR channels are combinatorially different from the additive white Gaussian noise (AWGN) setting, thus necessitating a careful code design approach for the MR applications. We first demonstrate that the choice of the so-called cutting vector, which guides the code partitioning in the SC code design, directly affects the cardinality of these problematic objects. In particular, we show that the number of problematic objects is the highest - and consequently that the performance is the worst - in the case of the degenerate cutting vector, which precisely corresponds to uncoupled LDPC block codes. We, therefore, show that coupling always improves the performance and that the degree of improvement is dependent on the choice of the cutting vector. We then extend our analysis to different column weights and present SC codes that outperform block codes with unoptimized edge weights by more than 3.5 orders of magnitude and that also outperform both optimized block codes and unoptimized SC codes by more than two orders of magnitude. Through presented examples, we demonstrate high performance of the proposed code design methodology.


X Silver nanoparticles : sensing and imaging applications


title: Interactive animation of ocean waves

abstract: We present an adaptive scheme for the interactive animation and display of ocean waves far from the coast. Relying on a procedural wave model, the method restricts computations to the visible part of the ocean surface, adapts the geometric resolution to the viewing distance and only considers the visible waves wavelengths. This yields real-time performances, even when the camera moves. The method allows the user to interactively fly over an unbounded animated ocean, which was not possible using previous approaches.


title: Learning what matters - Sampling interesting patterns

abstract: In the field of exploratory data mining, local structure in data can be described by patterns and discovered by mining algorithms. Although many solutions have been proposed to address the redundancy problems in pattern mining, most of them either provide succinct pattern sets or take the interests of the user into account—but not both. Consequently, the analyst has to invest substantial effort in identifying those patterns that are relevant to her specific interests and goals.


title: Performance of 5.8-GHz multi-polarization rectenna for linearly/circularly polarized wave reception

abstract: This paper demonstrates the performance of a 5.8GHz multi-polarization rectenna integrating four dual-polarized antennas. The proposed multi-polarization rectenna divides an RF wave of any polarization into two orthogonal components and converts each component to DC. Good RF-to-DC conversion performance was obtained regardless of the polarization angle for linearly polarized wave reception. Conversion efficiency for circularly polarized wave was twice as high as that of parallel connection of two-patch differential rectenna.


title: The random subspace method for constructing decision forests

abstract: Much of previous attention on decision trees focuses on the splitting criteria and optimization of tree sizes. The dilemma between overfitting and achieving maximum accuracy is seldom resolved. A method to construct a decision tree based classifier is proposed that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity. The classifier consists of multiple trees constructed systematically by pseudorandomly selecting subsets of components of the feature vector, that is, trees constructed in randomly chosen subspaces. The subspace method is compared to single-tree classifiers and other forest construction methods by experiments on publicly available datasets, where the method's superiority is demonstrated. We also discuss independence between trees in a forest and relate that to the combined classification accuracy.


title: Chemical structure recognition: a rule-based approach

abstract: In chemical literature much information is given in the form of diagrams depicting molecules. In order to access this ::: information diagrams have to be recognised and translated into a processable format. We present an approach that models ::: the principal recognition steps for molecule diagrams in a strictly rule based system, providing rules to identify the main ::: components - atoms and bonds - as well as to resolve possible ambiguities. The result of the process is a translation into ::: a graph representation that can be used for further processing. We show the effectiveness of our approach by describing ::: its embedding into a full recognition system and present an experimental evaluation that demonstrates how our current ::: implementation outperforms the leading open source system currently available.


title: Learning Sparse Task Relations in Multi-Task Learning

abstract: In multi-task learning, when the number of tasks is large, pairwise task relations exhibit sparse patterns since usually a task cannot be helpful to all of the other tasks and moreover, sparse task relations can reduce the risk of overfitting compared with the dense ones. In this paper, we focus on learning sparse task relations. Based on a regularization framework which can learn task relations among multiple tasks, we propose a SParse covAriance based mulTi-taSk (SPATS) model to learn a sparse covariance by using the ??? l regularization. The resulting objective function of the SPATS method is convex, which allows us to devise an alternating method to solve it. Moreover, some theoretical properties of the proposed model are studied. Experiments on synthetic and real-world datasets demonstrate the effectiveness of the proposed method.


title: Geometry Guided Convolutional Neural Networks for Self-Supervised Video Representation Learning

abstract: It is often laborious and costly to manually annotate videos for training high-quality video recognition models, so there has been some work and interest in exploring alternative, cheap, and yet often noisy and indirect training signals for learning the video representations. However, these signals are still coarse, supplying supervision at the whole video frame level, and subtle, sometimes enforcing the learning agent to solve problems that are even hard for humans. In this paper, we instead explore geometry, a grand new type of auxiliary supervision for the self-supervised learning of video representations. In particular, we extract pixel-wise geometry information as flow fields and disparity maps from synthetic imagery and real 3D movies, respectively. Although the geometry and high-level semantics are seemingly distant topics, surprisingly, we find that the convolutional neural networks pre-trained by the geometry cues can be effectively adapted to semantic video understanding tasks. In addition, we also find that a progressive training strategy can foster a better neural network for the video recognition task than blindly pooling the distinct sources of geometry cues together. Extensive results on video dynamic scene recognition and action recognition tasks show that our geometry guided networks significantly outperform the competing methods that are trained with other types of labeling-free supervision signals.


title: Baseband attacks: remote exploitation of memory corruptions in cellular protocol stacks

abstract: Published attacks against smartphones have concentrated on software running on the application processor. With numerous countermeasures like ASLR, DEP and code signing being deployed by operating system vendors, practical exploitation of memory corruptions on this processor has become a time-consuming endeavor. At the same time, the cellular baseband stack of most smart-phones runs on a separate processor and is significantly less hardened, if at all. ::: ::: In this paper we demonstrate the risk of remotely exploitable memory corruptions in cellular baseband stacks. We analyze two widely deployed baseband stacks and give exemplary cases of memory corruptions that can be leveraged to inject and execute arbitrary code on the baseband processor. The vulnerabilities can be triggered over the air interface using a rogue GSM base station, for instance using OpenBTS together with a USRP software defined radio.


title: Dynamics of Business Models – Strategizing, Critical Capabilities and Activities for Sustained Value Creation

abstract: Much progress has been made recently in developing the business model concept. However, one issue remains poorly understood, despite its importance for managers, policy makers, and academics alike, namely, how companies change and develop their business models to achieve sustained value creation. Companies which manage to create value over extended periods of time successfully shape, adapt and renew their business models to fuel such value creation. Drawing on findings from a research program on continuously growing firms, this paper identifies three critical capabilities, namely an orientation towards experimenting with and exploiting new business opportunities; a balanced use of resources; as well as achieving coherence between leadership, culture, and employee commitment, together shaping key strategizing actions. Moreover, we illustrate how each of these capabilities is supported by different sets of specific activities. Jointly, these three capabilities, their activities and the strategizing actions act as complementarities for value creation. We conclude the paper by suggesting implications for research and practitioners, providing a tool for managers which allows them to reflect on and identify critical issues relevant for changing and developing their business model to sustain value creation.


title: IEPAD: information extraction based on pattern discovery

abstract: research in information extraction (IE) regards the generation of wrappers that can extract particular information from semi- structured Web documents. Similar to compiler generation, the extractor is actually a driver program, which is accompanied with the generated extraction rule. Previous work in this field aims to learn extraction rules from users' training example. In this paper, we propose IEPAD, a system that automatically discovers extraction rules from Web pages. The system can automatically identify record boundary by repeated pattern mining and multiple sequence alignment. The discovery of repeated patterns are realized through a data structure call PAT trees. Additionally, repeated patterns are further extended by pattern alignment to comprehend all record instances. This new track to IE involves no human effort and content-dependent heuristics. Experimental results show that the constructed extraction rules can achieve 97 percent extraction over fourteen popular search engines.


title: Persuasive Technologies for Sustainable Urban Mobility

abstract: In recent years, the persuasive interventions for inducing sustainable urban mobility behaviours has become a very active research field. This review paper systematically analyses existing approaches and prototype systems and describes and classifies the persuasive strategies used for changing behaviour in the domain of transport. It also studies the results and recommendations derived from pilot studies, and as a result of this analysis highlights the need for personalizing and tailoring persuasive technology to various user characteristics. We also discuss the possible role of context-aware persuasive systems for increasing the number of sustainable choices. Finally, recommendations for future investigations on scholarly persuasive systems are proposed.


title: Reconstruction of Nasal Skin Cancer Defects with Local Flaps

abstract: Reconstruction of nasal defects must preserve the integrity of complex facial functions and expressions, as well as facial symmetry and a pleasing aesthetic outcome. The reconstructive modality of choice will depend largely on the location, size, and depth of the surgical defect. Individualized therapy is the best course, and numerous flaps have been designed to provide coverage of a variety of nasal-specific defects. We describe our experience in the aesthetic reconstruction of nasal skin defects following oncological surgery. The use of different local flaps for nasal skin cancer defects is reported in 286 patients. Complications in this series were one partial flap dehiscence that healed by secondary intention, two forehead flaps, and one bilobed flap with minimal rim necrosis that resulted in an irregular scar requiring revision. Aesthetic results were deemed satisfactory by all patients and the operating surgeons. The color and texture matches were aesthetically good, and the nasal contour was distinct in all patients. All scars were inconspicuous and symmetrical. No patient had tenting or a flat nose.


title: Evolving Generalised Maze Solvers

abstract: This paper presents a study of the efficacy of comparative controller design methods that aim to produce generalised problem solving behaviours. In this case study, the goal was to use neuro-evolution to evolve generalised maze solving behaviours. That is, evolved robot controllers that solve a broad range of mazes. To address this goal, this study compares objective, non-objective and hybrid approaches to direct the search of a neuro-evolution controller design method. The objective based approach was a fitness function, the non-objective based approach was novelty search, and the hybrid approach was a combination of both. Results indicate that, compared to the fitness function, the hybrid and novelty search evolve significantly more maze solving behaviours that generalise to larger and more difficult maze sets. Thus this research provides empirical evidence supporting novelty and hybrid novelty-objective search as approaches for potentially evolving generalised problem solvers.


title: GiLA: GitHub label analyzer

abstract: Reporting bugs, asking for new features and in general giving any kind of feedback is a common way to contribute to an Open-Source Software (OSS) project. In GitHub, the largest code hosting service for OSS, this feedback is typically expressed as new issues for the project managed by an issue-tracking system available in each new project repository. Among other features, the issue tracker allows creating and assigning labels to issues with the goal of helping the project community to better classify and manage those issues (e.g., facilitating the identification of issues for top priority components or candidate developers that could solve them). Nevertheless, as the project grows a manual browsing of the project issues is no longer feasible. In this paper we present GiLA, a tool which generates a set of visualizations to facilitate the analysis of issues in a project depending on their label-based categorization. We believe our visualizations are useful to see the most popular labels (and their relationships) in a project, identify the most active community members for those labels and compare the typical issue evolution for each label category.


title: Regional brain responses to pleasant and unpleasant IAPS pictures: Different networks

abstract: Abstract Purpose : The purpose of this study was to investigate the brain circuitry involved in the processing of both positive and negative emotions in normal healthy subjects. Method : we have recruited 15 healthy volunteers (9 males and 6 females, age range 30–60). In this block-design fMRI study, we compared the blood oxygen level dependant (BOLD) signal change as response to pleasant and unpleasant IAPS pictures, each compared to a neutral condition. Results : Pleasant pictures versus neutral condition contrast demonstrated significant activation ( p FDRcorrected p FDRcorrected Conclusion : Amygdala is mainly involved in the processing of negative emotions. Although an overlap in regions involved in the processing of pleasant and unpleasant IAPS pictures exists, the neural network for each is unique.


title: Numerical modelling of coastal seiches in Malta

abstract: Abstract Long period waves with periodicities ranging from a few minutes to several tens of minutes are observed throughout the year on the northern coastal area of the Maltese Islands. These coastal seiches, locally known as ‘ milghuba ’, closely resemble tsunamis in their frequencies and behaviour, but result predominantly from meteorological forcing rather than seismic events. These waves are present uninterrupted on the sea level trace, and during ‘quiet’ periods they characterise the ‘background’ sea level oscillations. Occasionally the ‘ milghuba ’ intensifies to large amplitude fluctuations reaching a range of up to 1 m in extent. The generation and propagation of seiche oscillations that occur in two adjacent wide-mouthed embayments on the northern coast of Malta is investigated by numerical simulations using a 2D version of the free surface non-linear Princeton Ocean Model. The study is targeted to investigate the mechanism by which the coastal sea level responds to signals generated in the open sea. In particular, the numerical experiments permit a full characterization of the dynamics of the seiches in the two coupled embayments and their forcing by the open sea long wave field. The simulations confirm how the topographic features specific to these basins have a predominant role in the transformation and amplification of long waves near the coast. In the case of the wide-mouthed embayments under study, the two-way coupling with the open sea area appears to be most important, and triggers a greater variability in the basin response. The intensity and spectral character of the sea level oscillations inside the embayments are found to be greatly dependent on the nature and incidence of the long period waves in the open sea. Yet the seiche oscillations are still dominated by the fundamental and the first bay modes. Standing oscillations generated along the cross-shore direction on the shelf (leaky waves) are more effective in forcing the extreme oscillations in the embayments rather than edge waves propagating alongshore. The results show the effectiveness of the Princeton Ocean Model in describing the response functions of water level in the embayments to an offshore disturbance, and can describe the associated water currents.


title: Overview on Additive Manufacturing Technologies

abstract: This paper provides an overview on the main additive manufacturing/3D printing technologies suitable for many satellite applications and, in particular, radio-frequency components. In fact, nowadays they have become capable of producing complex net-shaped or nearly net-shaped parts in materials that can be directly used as functional parts, including polymers, metals, ceramics, and composites. These technologies represent the solution for low-volume, high-value, and highly complex parts and products.


title: Improving medical diagnosis performance using hybrid feature selection via relieff and entropy based genetic search (RF-EGA) approach: application to breast cancer prediction

abstract: In this research a new hybrid prediction algorithm for breast cancer has been made from a breast cancer data set. Many approaches are available in diagnosing the medical diseases like genetic algorithm, ant colony optimization, particle swarm optimization, cuckoo search algorithm, etc., The proposed algorithm uses a ReliefF attribute reduction with entropy based genetic algorithm for breast cancer detection. The hybrid combination of these techniques is used to handle the dataset with high dimension and uncertainties. The data are obtained from the Wisconsin breast cancer dataset; these data have been categorized based on different properties. The performance of the proposed method is evaluated and the results are compared with other well known feature selection methods. The obtained result shows that the proposed method has a remarkable ability to generate reduced-size subset of salient features while yielding significant classification accuracy for large datasets.


title: Wearable Learning: Multiplayer Embodied Games for Math

abstract: We present a new technology-based paradigm to support embodied mathematics educational games, using wearable devices in the form of SmartPhones and SmartWatches for math learning, for full classes of students in formal in-school education settings. The Wearable Learning Games Engine is web based infrastructure that enables students to carry one mobile device per child, as they embark on math team-based activities that require physical engagement with the environment. These Wearable Tutors serve as guides and assistants while students manipulate, measure, estimate, discern, discard and find mathematical objects that satisfy specified constraints. Multi-player math games that use this infrastructure have yielded both cognitive and affective benefits. Beyond math game play, the Wearable Games Engine Authoring Tool enables students to create games themselves for other students to play; in this process, students engage in computational thinking and learn about finite-state machines. We present the infrastructure, games, and results for a series of experiments on both game play and game creation.


title: "Out of the Fr-Eye-ing Pan": Towards Gaze-Based Models of Attention during Learning with Technology in the Classroom

abstract: Attention is critical to learning. Hence, advanced learning technologies should benefit from mechanisms to monitor and respond to learners' attentional states. We study the feasibility of integrating commercial off-the-shelf (COTS) eye trackers to monitor attention during interactions with a learning technology called GuruTutor. We tested our implementation on 135 students in a noisy computer-enabled high school classroom and were able to collect a median 95% valid eye gaze data in 85% of the sessions where gaze data was successfully recorded. Machine learning methods were employed to develop automated detectors of mind wandering (MW) -- a phenomenon involving a shift in attention from task-related to task-unrelated thoughts that is negatively correlated with performance. Our student-independent, gaze-based models could detect MW with an accuracy (F1 of MW = 0.59) significantly greater than chance (F1 of MW = 0.24). Predicted rates of mind wandering were negatively related to posttest performance, providing evidence for the predictive validity of the detector. We discuss next steps towards developing gaze-based, attention-aware, learning technologies that can be deployed in noisy, real-world environments.


title: Generalization of Back propagation to Recurrent and Higher Order Neural Networks

abstract: A general method for deriving backpropagation algorithms for networks with recurrent and higher order networks is introduced. The propagation of activation in these networks is determined by dissipative differential equations. The error signal is backpropagated by integrating an associated differential equation. The method is introduced by applying it to the recurrent generalization of the feedforward backpropagation network. The method is extended to the case of higher order networks and to a constrained dynamical system for training a content addressable memory. The essential feature of the adaptive algorithms is that adaptive equation has a simple outer product form. ::: ::: Preliminary experiments suggest that learning can occur very rapidly in networks with recurrent connections. The continuous formalism makes the new approach more suitable for implementation in VLSI.


title: The impact of the tradeoff between incentives and spillovers on intellectual property rights system

abstract: This paper builds a model for evaluating the influence of innovation spillovers on innovation activity and benefits, based on the tradeoff between innovation incentives and spillovers. Some major findings are: 1) The decline in innovation activity caused by spillovers is partially offset by the beneficially distributive externalities from spillovers; 2) The relationship between total innovation benefit and spillover ratio takes on approximately inverted U-shaped, so do workers' benefit and innovators' benefit. Spillover ratios corresponding to workers' and innovators' benefit maximization constitute a Pareto optimal ratio interval of spillover. Moreover, the ratio corresponding to total innovation benefit maximization is within the interval; 3) The shape of total innovation benefit curve depends on the nature of innovation and intellectual property rights system. In addition, the smoother the total innovation benefit curve is, the wider the Pareto optimal ratio interval of spillover becomes. This study sharpens our understanding on innovation spillover effects. In practice, it sheds light on design and implementation of intellectual property rights system.


title: The Effects of Preexercise Caffeinated-Coffee Ingestion on Endurance Performance: An Evidence-Based Review

abstract: Endurance athletes commonly ingest caffeine as a means to enhance training intensity and competitive performance. A widely-used source of caffeine is coffee, however conflicting evidence exists regarding the efficacy of coffee in improving endurance performance. In this context, the aims of this evidence-based review were threefold: 1) to evaluate the effects of preexercise coffee on endurance performance, 2) to evaluate the effects of coffee on perceived exertion during endurance performance, and 3) to translate the research into usable information for athletes to make an informed decision regarding the intake of caffeine via coffee as a potential ergogenic aid. Searches of three major databases were performed using terms caffeine and coffee, or coffee-caffeine, and endurance, or aerobic. Included studies (n = 9) evaluated the effects of caffeinated coffee on human subjects, provided the caffeine dose administered, administered caffeine ≥ 45 min before testing, and included a measure of endurance perform...


title: Search-based software engineering: Trends, techniques and applications

abstract: In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This article1 provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.


title: Human mobility characterization from cellular network data

abstract: Anonymous location data from cellular phone networks sheds light on how people move around on a large scale.


title: How Feasible Is Baby-Led Weaning as an Approach to Infant Feeding? A Review of the Evidence

abstract: Baby-Led Weaning (BLW) is an alternative method for introducing complementary foods to infants in which the infant feeds themselves hand-held foods instead of being spoon-fed by an adult. The BLW infant also shares family food and mealtimes and is offered milk (ideally breast milk) on demand until they self-wean. Anecdotal evidence suggests that many parents are choosing this method instead of conventional spoon-feeding of purées. Observational studies suggest that BLW may encourage improved eating patterns and lead to a healthier body weight, although it is not yet clear whether these associations are causal. This review evaluates the literature with respect to the prerequisites for BLW, which we have defined as beginning complementary foods at six months (for safety reasons), and exclusive breastfeeding to six months (to align with WHO infant feeding guidelines); the gross and oral motor skills required for successful and safe self-feeding of whole foods from six months; and the practicalities of family meals and continued breastfeeding on demand. Baby-Led Weaning will not suit all infants and families, but it is probably achievable for most. However, ultimately, the feasibility of BLW as an approach to infant feeding can only be determined in a randomized controlled trial. Given the popularity of BLW amongst parents, such a study is urgently needed.


Foundations of Intelligent Systems


title: Markov Random Field Model-Based Edge-Directed Image Interpolation

abstract: This paper presents an edge-directed image interpolation algorithm. In the proposed algorithm, the edge directions are implicitly estimated with a statistical-based approach. In opposite to explicit edge directions, the local edge directions are indicated by length-16 weighting vectors. Implicitly, the weighting vectors are used to formulate geometric regularity (GR) constraint (smoothness along edges and sharpness across edges) and the GR constraint is imposed on the interpolated image through the Markov random field (MRF) model. Furthermore, under the maximum a posteriori-MRF framework, the desired interpolated image corresponds to the minimal energy state of a 2-D random field given the low-resolution image. Simulated annealing methods are used to search for the minimal energy state from the state space. To lower the computational complexity of MRF, a single-pass implementation is designed, which performs nearly as well as the iterative optimization. Simulation results show that the proposed MRF model-based edge-directed interpolation method produces edges with strong geometric regularity. Compared to traditional methods and other edge-directed interpolation methods, the proposed method improves the subjective quality of the interpolated edges while maintaining a high PSNR level.


title: Continuous Authentication on Mobile Devices by Analysis of Typing Motion Behavior.

abstract: Smartphones have become the standard personal device to store private or sensitive information. Widely used as every day gadget, however, they are susceptible to get lost or stolen. To protect information on a smartphone from being physically accessed by attackers, a lot of authentication methods have been proposed in recent years. Each one of them suffers from certain drawbacks, either they are easy to circumvent, vulnerable against shoulder surfing attacks, or cumbersome to use. In this paper, we present an alternative approach for user authentication that is based on the smartphone’s sensors. By making use of the user’s biometrical behavior while entering text into the smartphone, we transparently authenticate the user in an ongoing-fashion. In a field study, we asked more than 300 participants to enter some short sentences into a smartphone while all available sensor events were recorded to determine a typing motion fingerprint of the user. After the proper feature extraction, a machine learning classifier based on Support Vector Machines (SVM) is used to identify the authorized user. The results of our study are twofold: While our approach is able to continuously authenticate some users with high precision, there also exist participants for which no accurate motion fingerprint can be learned. We analyze these difference in detail and provide guidelines for similar problems.


title: Flexible End-to-End Dialogue System for Knowledge Grounded Conversation

abstract: In knowledge grounded conversation, domain knowledge plays an important role in a special domain such as Music. The response of knowledge grounded conversation might contain multiple answer entities or no entity at all. Although existing generative question answering (QA) systems can be applied to knowledge grounded conversation, they either have at most one entity in a response or cannot deal with out-of-vocabulary entities. We propose a fully data-driven generative dialogue system GenDS that is capable of generating responses based on input message and related knowledge base (KB). To generate arbitrary number of answer entities even when these entities never appear in the training set, we design a dynamic knowledge enquirer which selects different answer entities at different positions in a single response, according to different local context. It does not rely on the representations of entities, enabling our model deal with out-of-vocabulary entities. We collect a human-human conversation data (ConversMusic) with knowledge annotations. The proposed method is evaluated on CoversMusic and a public question answering dataset. Our proposed GenDS system outperforms baseline methods significantly in terms of the BLEU, entity accuracy, entity recall and human evaluation. Moreover,the experiments also demonstrate that GenDS works better even on small datasets.


title: Machine-Learning Research Four Current Directions

abstract: Machine-learning research has been making great progress in many directions. This article summarizes four of these directions and discusses some current open problems. The four directions are (1) the improvement of classification accuracy by learning ensembles of classifiers, (2) methods for scaling up supervised learning algorithms, (3) reinforcement learning, and (4) the learning of complex stochastic models.


title: Familiarity effects in EEG-based emotion recognition

abstract: Although emotion detection using electroencephalogram (EEG) data has become a highly active area of research over the last decades, little attention has been paid to stimulus familiarity, a crucial subjectivity issue. Using both our experimental data and a sophisticated database (DEAP dataset), we investigated the effects of familiarity on brain activity based on EEG signals. Focusing on familiarity studies, we allowed subjects to select the same number of familiar and unfamiliar songs; both resulting datasets demonstrated the importance of reporting self-emotion based on the assumption that the emotional state when experiencing music is subjective. We found evidence that music familiarity influences both the power spectra of brainwaves and the brain functional connectivity to a certain level. We conducted an additional experiment using music familiarity in an attempt to recognize emotional states; our empirical results suggested that the use of only songs with low familiarity levels can enhance the performance of EEG-based emotion classification systems that adopt fractal dimension or power spectral density features and support vector machine, multilayer perceptron or C4.5 classifier. This suggests that unfamiliar songs are most appropriate for the construction of an emotion recognition system.


title: Multi-scale Convolutional Neural Networks for Lung Nodule Classification

abstract: We investigate the problem of diagnostic lung nodule classification using thoracic Computed Tomography (CT) screening. Unlike traditional studies primarily relying on nodule segmentation for regional analysis, we tackle a more challenging problem on directly modelling raw nodule patches without any prior definition of nodule morphology. We propose a hierarchical learning framework—Multi-scale Convolutional Neural Networks (MCNN)—to capture nodule heterogeneity by extracting discriminative features from alternatingly stacked layers. In particular, to sufficiently quantify nodule characteristics, our framework utilizes multi-scale nodule patches to learn a set of class-specific features simultaneously by concatenating response neuron activations obtained at the last layer from each input scale. We evaluate the proposed method on CT images from Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), where both lung nodule screening and nodule annotations are provided. Experimental results demonstrate the effectiveness of our method on classifying malignant and benign nodules without nodule segmentation.


title: AutoDict: Automated Dictionary Discovery

abstract: An attribute dictionary is a set of attributes together with a set of common values of each attribute. Such dictionaries are valuable in understanding unstructured or loosely structured textual descriptions of entity collections, such as product catalogs. Dictionaries provide the supervised data for learning product or entity descriptions. In this demonstration, we will present Auto Dict, a system that analyzes input data records, and discovers high quality dictionaries using information theoretic techniques. To the best of our knowledge, Auto Dict is the first end-to-end system for building attribute dictionaries. Our demonstration will showcase the different information analysis and extraction features within Auto Dict, and highlight the process of generating high quality attribute dictionaries.


title: Plasma pyrolysis and gasification of plastics waste - a review

abstract: Plasma pyrolysis integrates thermo-chemical properties of plasma using pyrolysis process for safe disposal of solid wastes. It is an environment friendly technology to generate valuable byproducts by converting municipal solid waste, biomedical waste and hazardous wastes at 800-1000°C. Plasma Pyrolysis of plastic wastes generates pyrolysis gas, which can be utilized for energy recovery via different applications.


title: An analog front-end enables electrical impedance spectroscopy system on-chip for biomedical applications

abstract: The increasing number of applications of electrical bioimpedance measurements in biomedical practice, together with continuous advances in textile technology, has encouraged several researchers to make the first attempts to develop portable, even wearable, electrical bioimpedance measurement systems. The main target of these systems is personal and home monitoring. Analog Devices has made available AD5933, a new system-on-chip fully integrated electrical impedance spectrometer, which might allow the implementation of minimum-size instrumentation for electrical bioimpedance measurements. However, AD5933 as such is not suitable for most applications of electrical bioimpedance. In this work, we present a relatively simple analog front-end that adapts AD5933 to a four-electrode strategy, allowing its use in biomedical applications for the first time. The resulting impedance measurements exhibit a very good performance in aspects like load dynamic range and accuracy. This type of minimum-size, system-on-chip-based bioimpedance measurement system would lead researchers to develop and implement light and wearable electrical bioimpedance systems for home and personal health monitoring applications, a new and huge niche for medical technology development.


title: An Improved DPA Countermeasure Based on Uniform Distribution Random Power Generator for IoT Applications

abstract: In order to secure the communication in Internet of Things era, a low overhead power analysis countermeasure called uniform distribution random power generator is proposed for stream ciphers. To alter the power signature of protected circuit, ring oscillators, which act as unit power generators, are grouped in a systematic approach to generate additional power with uniform probability distribution. Experiments from the proposed evaluation framework demonstrate that the secret key of unprotected device can be disclosed within 103 power traces with standard differential power analysis attack, whereas the same attack mounted on the proposed scheme cannot be disclosed even after 106 measurements. Moreover, the area and the power overhead are reduced by at least 29% and 22% compared with the state-of-the-art works, respectively.


title: Web Application Models are more than Conceptual Models

abstract: In this paper we argue that Web applications are a particular kind of hypermedia application and show how to model their navigational structure. We argue that if we need to design applications combining hypermedia navigation with complex transactional behaviors (as in E-commerce systems), we need a systematic development approach. We present the main ideas underlying the Object-Oriented Hypermedia Design Method (OOHDM) and show that Web applications are built as views of conceptual models. We present the abstraction primitives used to design conceptual and navigational structure of Web applications and describe the view definition language. We introduce navigational contexts as the structuring mechanism for the navigational space. Further work on designing Web applications with OOHDM is also presented.


Clinical Relation Extraction with Deep Learning


In silico Drug Design : Prospective for Drug Lead Discovery


title: Fix your classifier: the marginal value of training the last weight layer

abstract: Neural networks are commonly used as models for classification for a wide variety of tasks. Typically, a learned affine transformation is placed at the end of such models, yielding a per-class value used for classification. This classifier can have a vast number of parameters, which grows linearly with the number of possible classes, thus requiring increasingly more resources. In this work we argue that this classifier can be fixed, up to a global scale constant, with little or no loss of accuracy for most tasks, allowing memory and computational benefits. Moreover, we show that by initializing the classifier with a Hadamard matrix we can speed up inference as well. We discuss the implications for current understanding of neural network models.


HE LOSS SURFACE AND EXPRESSIVITY OF DEEP CONVOLUTIONAL NEURAL NETWORKS


title: A Novel Two Switch Non-inverting Buck-Boost Converter based Maximum Power Point Tracking System

abstract: A solar module can’t transfer maximum power to the load itself due to impedance mismatch. A maximum power point tracking (MPPT) system could be employed to have the maximum power. A new MPPT system has been developed using Buck-Boost type DC-DC converter. The system is highly efficient and robust. PIC16F73 microcontroller has been used to control the DC-DC converter output. PV module output power is measured using microcontroller. The output power is compared with the previous module output power and the duty cycle of the converter is adjusted continuously to track MPP. This process repeats until the output power reaches near to the maximum power point.In this paper, a maximum power point tracking (MPPT) system is developed using two-switch non-inverting buck-boost converter. Perturb and observe (P & O) MPPT algorithm is used to transfer maximum power from the PV panel which is executed using a microcontroller. DOI: http://dx.doi.org/10.11591/ijece.v3i4.2772


Correctness by construction: Better can also be cheaper


title: Diversity driven Attention Model for Query-based Abstractive Summarization

abstract: Abstractive summarization aims to generate a shorter version of the document covering all the salient points in a compact and coherent fashion. On the other hand, query-based summarization highlights those points that are relevant in the context of a given query. The encode-attend-decode paradigm has achieved notable success in machine translation, extractive summarization, dialog systems, etc. But it suffers from the drawback of generation of repeated phrases. In this work we propose a model for the query-based summarization task based on the encode-attend-decode paradigm with two key additions (i) a query attention model (in addition to document attention model) which learns to focus on different portions of the query at different time steps (instead of using a static representation for the query) and (ii) a new diversity based attention model which aims to alleviate the problem of repeating phrases in the summary. In order to enable the testing of this model we introduce a new query-based summarization dataset building on debatepedia. Our experiments show that with these two additions the proposed model clearly outperforms vanilla encode-attend-decode models with a gain of 28\% (absolute) in ROUGE-L scores.


title: Aggregating inconsistent information: Ranking and clustering

abstract: We address optimization problems in which we are given contradictory pieces of input information and the goal is to find a globally consistent solution that minimizes the extent of disagreement with the respective inputs. Specifically, the problems we address are rank aggregation, the feedback arc set problem on tournaments, and correlation and consensus clustering. We show that for all these problems (and various weighted versions of them), we can obtain improved approximation factors using essentially the same remarkably simple algorithm. Additionally, we almost settle a long-standing conjecture of Bang-Jensen and Thomassen and show that unless NP⊆BPP, there is no polynomial time algorithm for the problem of minimum feedback arc set in tournaments.


title: OpenKN: An open knowledge computational engine for network big data

abstract: With the coming of the era of big data, it is most urgent to establish the knowledge computational engine for the purpose of discovering implicit and valuable knowledge from the huge, rapidly dynamic, and complex network data. In this paper, we first survey the mainstream knowledge computational engines from four aspects and point out their deficiency. To cover these shortages, we propose the open knowledge network (OpenKN), which is a self-adaptive and evolutionable knowledge computational engine for network big data. To the best of our knowledge, this is the first work of designing the end-to-end and holistic knowledge processing pipeline in regard with the network big data. Moreover, to capture the evolutionable computing capability of OpenKN, we present the evolutionable knowledge network for knowledge representation. A case study demonstrates the effectiveness of the evolutionable computing of OpenKN.


title: Inductively coupled wireless power transfer with class-E2 DC-DC converter

abstract: This paper proposes an inductive coupled wireless power transfer (WPT) system with class-E2 dc-dc converter along with its design procedure. The proposed WPT system can achieve high power-conversion efficiency at high frequencies because it satisfies the class-E zero-voltage switching and zero-derivative-voltage switching conditions on both the inverter and the rectifier. By using the class-E inverter as a transmitter and the class-E rectifier as a receiver, high power-delivery efficiency can be achieved in the designed WPT system. By using a numerical design procedure proposed in the previous work, it is possible to design the WPT system without considering the impedance matching for satisfying the class-E ZVS/ZDS conditions. The experimental results of the design example showed the overall efficiency of 85.1 % at 100 W output power and 200 kHz operating frequency.


title: SPICE simulation and evaluation of a GaN-based synchronous full-bridge resonant converter

abstract: This paper presents a SPICE-based simulation for a full-bridge converter applying Gallium Nitride (GaN)-based switches. The evaluated converter is concerned to a resonant topology which achieves extra-high efficiency when using an LLC network. The studied GaN-based switch model considers resistances and capacitances which are equivalent to each terminal of the transistor. Otherwise, the current through the device is modeled by a voltage controlled current source. Furthermore, high-frequency parasitic inductances are included in the circuitry in order to achieve a suitable and relevant simulation, since these parameters are important to a proper modeling of the switch dynamic losses. A comparative analysis regarding silicon-based devices is carried out highlighting the GaN improved efficiency for this full-bridge topology.


title: Investigation of lattice-free maximum mutual information-based acoustic models with sequence-level Kullback-Leibler divergence

abstract: Lattice-free maximum mutual information (LFMMI) was recently proposed as a mixture of the ideas of hidden-Markov-model-based acoustic models (AMs) and connectionist-temporal-classification-based AMs. In this paper, we investigate LFMMI from various perspectives of model combination, teacher-student training, and unsupervised speaker adaptation. Especially, we thoroughly investigate the use of the “sequence-level” Kullback-Leibler divergence with its novel and simple error derivation to enhance LFMMI-based AMs. In our experiment, we used the corpus of spontaneous Japanese (CSJ). Our best AM was an ensemble of three types of time delay neural networks and one long short-term memory-based network, and it finally achieved a WER of 6.94%, which is, to the best of our knowledge, the best published result for the CSJ.


An Empirical Investigation of the Antecedents and Consequences of Privacy Uncertainty in the Context of Mobile Apps


title: Toward integrated motion planning and control using potential fields and torque-based steering actuation for autonomous driving

abstract: This paper proposes an integrated motion planning and control approach for autonomous car navigation. Existing approaches to autonomous vehicle navigation typically plan a trajectory and pass it on to a steering controller that commands steering wheel angle (SWA) or curvature at every timestep to minimize tracking error. However, this approach exhibits large amounts of control effort, and ignores other criteria such as smoothness or the importance of staying on plan at different times. Conversely, our proposed approach leverages the concept of potential fields to represent a driving corridor with a desired tracking error tolerance and direct torque-based steering control to smoothly steer the vehicle with a much smaller control effort. Further, using potential fields allows us to naturally incorporate obstacles in the driving corridor to circumvent them, with typically no need for explicit trajectory planning. We compare our approach to a standard steering controller in experiments with a real-world autonomous vehicle platform. Results show that our proposed approach achieves similar path tracking performance as a high-gain SWA controller, but with much less actuator effort.


title: Building measure-based prediction models for UML class diagram maintainability

abstract: The usefulness of measures for the analysis and design of object oriented (OO) software is increasingly being recognized in the field of software engineering research. In particular, recognition of the need for early indicators of external quality attributes is increasing. We investigate through experimentation whether a collection of UML class diagram measures could be good predictors of two main subcharacteristics of the maintainability of class diagrams: understandability and modifiability. Results obtained from a controlled experiment and a replica support the idea that useful prediction models for class diagrams understandability and modifiability can be built on the basis of early measures, in particular, measures that capture structural complexity through associations and generalizations. Moreover, these measures seem to be correlated with the subjective perception of the subjects about the complexity of the diagrams. This fact shows, to some extent, that the objective measures capture the same aspects as the subjective ones. However, despite our encouraging findings, further empirical studies, especially using data taken from real projects performed in industrial settings, are needed. Such further study will yield a comprehensive body of knowledge and experience about building prediction models for understandability and modifiability.


title: Ranking explanatory sentences for opinion summarization

abstract: We introduce a novel sentence ranking problem called explanatory sentence extraction (ESE) which aims to rank sentences in opinionated text based on their usefulness for helping users understand the detailed reasons of sentiments (i.e., "explanatoriness"). We propose and study several general methods for scoring the explanatoriness of a sentence. We create new data sets and propose a new measure for evaluation. Experiment results show that the proposed methods are effective, outperforming a state of the art sentence ranking method for standard text summarization.


title: Auditing Black-Box Models Using Transparent Model Distillation With Side Information

abstract: Black-box risk scoring models permeate our lives, yet are typically proprietary or opaque. We propose a transparent model distillation approach to audit such models. Model distillation was first introduced to transfer knowledge from a large, complex teacher model to a faster, simpler student model without significant loss in prediction accuracy. To this we add a third criterion - transparency. To gain insight into black-box models, we treat them as teachers, training transparent student models to mimic the risk scores assigned by the teacher. Moreover, we use side information in the form of the actual outcomes the teacher scoring model was intended to predict in the first place. By training a second transparent model on the outcomes, we can compare the two models to each other. When comparing models trained on risk scores to models trained on outcomes, we show that it is necessary to calibrate the risk-scoring model's predictions to remove distortion that may have been added to the black-box risk-scoring model during or after its training process. We also show how to compute confidence intervals for the particular class of transparent student models we use - tree-based additive models with pairwise interactions (GA2Ms) - to support comparison of the two transparent models. We demonstrate the methods on four public datasets: COMPAS, Lending Club, Stop-and-Frisk, and Chicago Police.


Single Image Reflection Separation with Perceptual Losses Supplementary Material


title: Learning Color Names for Real-World Applications

abstract: Color names are required in real-world applications such as image retrieval and image annotation. Traditionally, they are learned from a collection of labeled color chips. These color chips are labeled with color names within a well-defined experimental setup by human test subjects. However, naming colors in real-world images differs significantly from this experimental setting. In this paper, we investigate how color names learned from color chips compare to color names learned from real-world images. To avoid hand labeling real-world images with color names, we use Google image to collect a data set. Due to the limitations of Google image, this data set contains a substantial quantity of wrongly labeled data. We propose several variants of the PLSA model to learn color names from this noisy data. Experimental results show that color names learned from real-world images significantly outperform color names learned from labeled color chips for both image retrieval and image annotation.


title: Impact of the Semantic Web on Modeling and Simulation

abstract: More specifically, this chapter considers the issue of using semantics in Modeling and Simulation (M&S). The impetus for this is the large initiative to develop the next generation Web, the Semantic Web being developed by the Artificial Intelligence, Database and Information Retrieval communities. A complimentary parallel track is represented by the Model Driven Architecture (MDA) approach being developed by OMG and the Software Engineering Community. The goal of this initiate is for all software development to be model driven. Semantics (and the Semantic Web) will likely impact the M&S Community in two ways. First, the community should develop ontology to delineate, define and relate the concepts in the field. Ontology for modeling and simulation should be logically connected to more general (or higherlevel) ontology e.g., one for mathematics such as Monet (Caprotti, Dewar, and Turi 2004) or one for general knowledge upper ontology such as the Suggested Upper Merged Ontology (SUMO) (Niles and Pease 2001). Second, simulation models, model components and other artifacts should be provided with richer semantic descriptions. The least disruptive way to do this is through annotation in which the artifacts refer to semantic models (e.g., a concept in an ontology). The fact that the Semantic Web is being developed and simulation artifacts can be semantically annotated, begs the question of why do it. This question relates to the basic motivation for having the Semantic Web and Semantic Web Services. For the M&S community, semantics represented in ontology provides standard terminology to the community and beyond, so that common understanding of concepts and relationships can be achieved, which, in turn, increases the potential for application interoperability and reuse of simulation artifacts. Semantic Web technology


title: Silencing Hardware Backdoors

abstract: Hardware components can contain hidden backdoors, which can be enabled with catastrophic effects or for ill-gotten profit. These backdoors can be inserted by a malicious insider on the design team or a third-party IP provider. In this paper, we propose techniques that allow us to build trustworthy hardware systems from components designed by untrusted designers or procured from untrusted third-party IP providers. We present the first solution for disabling digital, design-level hardware backdoors. The principle is that rather than try to discover the malicious logic in the design -- an extremely hard problem -- we make the backdoor design problem itself intractable to the attacker. The key idea is to scramble inputs that are supplied to the hardware units at runtime, making it infeasible for malicious components to acquire the information they need to perform malicious actions. We show that the proposed techniques cover the attack space of deterministic, digital HDL backdoors, provide probabilistic security guarantees, and can be applied to a wide variety of hardware components. Our evaluation with the SPEC 2006 benchmarks shows negligible performance loss (less than 1% on average) and that our techniques can be integrated into contemporary microprocessor designs.


title: The Oracle Problem in Software Testing: A Survey

abstract: Testing involves examining the behaviour of a system in order to discover potential faults. Given an input for a system, the challenge of distinguishing the corresponding desired, correct behaviour from potentially incorrect behavior is called the “test oracle problem”. Test oracle automation is important to remove a current bottleneck that inhibits greater overall test automation. Without test oracle automation, the human has to determine whether observed behaviour is correct. The literature on test oracles has introduced techniques for oracle automation, including modelling, specifications, contract-driven development and metamorphic testing. When none of these is completely adequate, the final source of test oracle information remains the human, who may be aware of informal specifications, expectations, norms and domain specific information that provide informal oracle guidance. All forms of test oracles, even the humble human, involve challenges of reducing cost and increasing benefit. This paper provides a comprehensive survey of current approaches to the test oracle problem and an analysis of trends in this important area of software testing research and practice.


title: Generating natural motion in an android by mapping human motion

abstract: One of the main aims of humanoid robotics is to develop robots that are capable of interacting naturally with people. However, to understand the essence of human interaction, it is crucial to investigate the contribution of behavior and appearance. Our group's research explores these relationships by developing androids that closely resemble human beings in both aspects. If humanlike appearance causes us to evaluate an android's behavior from a human standard, we are more likely to be cognizant of deviations from human norms. Therefore, the android's motions must closely match human performance to avoid looking strange, including such autonomic responses as the shoulder movements involved in breathing. This paper proposes a method to implement motions that look human by mapping their three-dimensional appearance from a human performer to the android and then evaluating the verisimilitude of the visible motions using a motion capture system. This approach has several advantages over current research, which has focused on copying a person's moving joint angles to a robot: (1) in an android robot with many degrees of freedom and kinematics that differs from that of a human being, it is difficult to calculate which joint angles would make the robot's posture appear similar to the human performer; and (2) the motion that we perceive is at the robot's surface, not necessarily at its joints, which are often hidden from view.


title: Robust Safety for Autonomous Vehicles through Reconfigurable Networking

abstract: Autonomous vehicles bring the promise of enhancing the consumer experience in terms of comfort and convenience and, in particular, the safety of the autonomous vehicle. Safety functions in autonomous vehicles such as Automatic Emergency Braking and Lane Centering Assist rely on computation, information sharing, and the timely actuation of the safety functions. One opportunity to achieve robust autonomous vehicle safety is by enhancing the robustness of in-vehicle networking architectures that support built-in resiliency mechanisms. Software Defined Networking (SDN) is an advanced networking paradigm that allows fine-grained manipulation of routing tables and routing engines and the implementation of complex features such as failover, which is a mechanism of protecting in-vehicle networks from failure, and in which a standby link automatically takes over once the main link fails. In this paper, we leverage SDN network programmability features to enable resiliency in the autonomous vehicle realm. We demonstrate that a Software Defined In-Vehicle Networking (SDIVN) does not add overhead compared to Legacy In-Vehicle Networks (LIVNs) under non-failure conditions and we highlight its superiority in the case of a link failure and its timely delivery of messages. We verify the proposed architectures benefits using a simulation environment that we have developed and we validate our design choices through testing and simulations


title: Support Vector Machines for Paraphrase Identification and Corpus Construction

abstract: The lack of readily-available large corpora of aligned monolingual sentence pairs is a major obstacle to the development of Statistical Machine Translation-based paraphrase models. In this paper, we describe the use of annotated datasets and Support Vector Machines to induce larger monolingual paraphrase corpora from a comparable corpus of news clusters found on the World Wide Web. Features include: morphological variants; WordNet synonyms and hypernyms; loglikelihood-based word pairings dynamically obtained from baseline sentence alignments; and formal string features such as word-based edit distance. Use of this technique dramatically reduces the Alignment Error Rate of the extracted corpora over heuristic methods based on position of the sentences in the text.


title: Targeted recombination between homologous chromosomes for precise breeding in tomato

abstract: Homologous recombination (HR) between parental chromosomes occurs stochastically. Here, we report on targeted recombination between homologous chromosomes upon somatic induction of DNA double-strand breaks (DSBs) via CRISPR-Cas9. We demonstrate this via a visual and molecular assay whereby DSB induction between two alleles carrying different mutations in the PHYTOENE SYNTHASE (PSY1) gene results in yellow fruits with wild type red sectors forming via HR-mediated DSB repair. We also show that in heterozygote plants containing one psy1 allele immune and one sensitive to CRISPR, repair of the broken allele using the unbroken allele sequence template is a common outcome. In another assay, we show evidence of a somatically induced DSB in a cross between a psy1 edible tomato mutant and wild type Solanum pimpinellifolium, targeting only the S. pimpinellifolium allele. This enables characterization of germinally transmitted targeted somatic HR events, demonstrating that somatically induced DSBs can be exploited for precise breeding of crops.


title: Systems Competition and Network Effects

abstract: This paper discusses firm behavior, market performance, and the public and private institutions that arise in systems markets, i.e., markets where consumers use compatible components together to generate benefits. In such markets, which include communications networks and 'hardware/software' networks, popular products are inherently more valuable. These 'network effects' can drive corporate strategies and are critical in understanding innovation in many high-technology markets. The discussion here emphasizes the dynamics of consumer adoption decisions in the presence of network effects, competition between incompatible systems, and how suppliers choose which components are compatible and which are not.


Solar Energy and Photovoltaic Systems


title: Learning Procedures from Text : Codifying How-to Procedures in Deep Neural Networks

abstract: A lot of knowledge about procedures and how-tos are described in text. Recently, extracting semantic relations from the procedural text has been actively explored. Prior work mostly has focused on finding relationships among verb-noun pairs or clustering of extracted pairs. In this paper, we investigate the problem of learning individual procedure-specific relationships (e.g. is method of, is alternative of, or is subtask of) among sentences. To identify the relationships, we propose an end-to-end neural network architecture, which can selectively learn important procedure-specific relationships. Using this approach, we could construct a how-to knowledge base from the largest procedure sharing-community, wiki-how.com. The evaluation of our approach shows that it outperforms the existing entity relationship extraction algorithms.


title: Natural Language Inference over Interaction Space

abstract: Natural Language Inference (NLI) task requires an agent to determine the logical relationship between a natural language premise and a natural language hypothesis. We introduce Interactive Inference Network (IIN), a novel class of neural network architectures that is able to achieve high-level understanding of the sentence pair by hierarchically extracting semantic features from interaction space. We show that an interaction tensor (attention weight) contains semantic information to solve natural language inference, and a denser interaction tensor contains richer semantic information. One instance of such architecture, Densely Interactive Inference Network (DIIN), demonstrates the state-of-the-art performance on large scale NLI copora and large-scale NLI alike corpus. It's noteworthy that DIIN achieve a greater than 20% error reduction on the challenging Multi-Genre NLI (MultiNLI) dataset with respect to the strongest published system.


title: Some notes on discount factor restrictions for dynamic optimization problems

abstract: We consider dynamic optimization problems on one-dimensional state spaces. Un- der standard smoothness and convexity assumptions, the optimal solutions are characterized by an optimal policy function h mapping the state space into itself. There exists an extensive literature on the relation between the size of the discount factor of the dynamic optimization problem on the one hand and the properties of the dynamical system xt+1 = h(xt) on the other hand. The purpose of this paper is to survey some of the most important contributions of this literature and to modify or improve them in various directions. We deal in particular with the topological entropy of the dynamical system, with its Lyapunov exponents, and with its periodic orbits.


title: Integration of Hyperspectral Imagery and Sparse Sonar Data for Shallow Water Bathymetry Mapping

abstract: Accurate and rapid mapping of shallow water bathymetry is essential for the safe operation of many industries. Here, we propose a new approach to shallow water bathymetry mapping that integrates hyperspectral image and sparse sonar data. Our approach includes two main steps: dimensional reduction of Hyperion images and interpolation of sparse sonar data. First, we propose a new algorithm, i.e., a sonar-based semisupervised Laplacian eigenmap (LE) using both spatial and spectral distance, for dimensional reduction of Hyperion imagery. Second, we develop a new algorithm to interpolate sparse sonar points using a 3-D information diffusion method with homogeneous regions. These homogeneous regions are derived from the segmentation of the dimensional reduction results based on depth. We conduct the experimental comparison to confirm the applicability of the dimensional reduction and interpolation methods and their advantages over previously described methods. The proposed dimensional reduction method achieves better dimensional results than unsupervised method and semisupervised LE method (using only spectral distance). Furthermore, the bathymetry retrieved using the proposed method is more precise than that retrieved using common interpolation methods.


title: Blockchain - From the Anarchy of Cryptocurrencies to the Enterprise (Keynote Abstract)

abstract: A blockchain is a public ledger for recording transactions, maintained by many nodes without central authority through a distributed cryptographic protocol. All nodes validate the information to be appended to the blockchain, and a consensus protocol ensures that the nodes agree on a unique order in which entries are appended. Distributed protocols tolerating faults and adversarial attacks, coupled with cryptographic tools are needed for this. The recent interest in blockchains has revived research on consensus protocols, ranging from the proof-of-work method in Bitcoin's "mining" protocol to classical Byzantine agreement. Going far beyond its use in cryptocurrencies, blockchain is today viewed as a promising technology to simplify trusted exchanges of data and goods among companies. In this context, the Hyperledger Project has been established in early 2016 as an industry-wide collaborative effort to develop an open-source blockchain. This talk will present an overview of blockchain concepts, cryptographic building blocks and consensus mechanisms. It will also introduce Hyperledger Fabric, an implementation of blockchain technology intended for enterprise applications. Being one of the key partners in the Hyperledger Project, IBM is actively involved in the development of this blockchain platform.


title: A Neural Implementation of Wald’s Sequential Probability Ratio Test

abstract: Difficult decisions often require evaluation of samples of evidence acquired sequentially. A sensible strategy is to accumulate evidence, weighted by its reliability, until sufficient support is attained. An optimal statistical approach would accumulate evidence in units of logarithms of likelihood ratios (logLR) to a desired level. Studies of perceptual decisions suggest that the brain approximates an analogous procedure, but a direct test of accumulation, in units of logLR, to a threshold in units of cumulative logLR is lacking. We trained rhesus monkeys to make decisions based on a sequence of evanescent, visual cues assigned different logLR, hence different reliability. Firing rates of neurons in the lateral intraparietal area (LIP) reflected the accumulation of logLR and reached a stereotyped level before the monkeys committed to a decision. The monkeys' choices and reaction times, including their variability, were explained by LIP activity in the context of accumulation of logLR to a threshold.


title: Comparative study of full adder circuit with 32nm MOSFET, DG-FinFET and CNTFET

abstract: In this paper, we study the performance of Full Adder circuit with three different FET devices, MOSFET, Double-Gate (DG) FinFET and CNTFET, in 32nm technology. The full adder circuit is implemented using two different logic styles, the conventional 28-transistor (28T) CMOS logic and Gate Diffusion Input (GDI) logic. Power consumption, delay and Power Delay Product (PDP) are investigated using LTspice and HSPICE simulations. CNTFET shows the best performance among the three with the least power consumption and delay.


title: Modeling Semantic Containment and Exclusion in Natural Language Inference

abstract: We propose an approach to natural language inference based on a model of natural logic, which identifies valid inferences by their lexical and syntactic features, without full semantic interpretation. We greatly extend past work in natural logic, which has focused solely on semantic containment and monotonicity, to incorporate both semantic exclusion and implicativity. Our system decomposes an inference problem into a sequence of atomic edits linking premise to hypothesis; predicts a lexical entailment relation for each edit using a statistical classifier; propagates these relations upward through a syntax tree according to semantic properties of intermediate nodes; and composes the resulting entailment relations across the edit sequence. We evaluate our system on the FraCaS test suite, and achieve a 27% reduction in error from previous work. We also show that hybridizing an existing RTE system with our natural logic system yields significant gains on the RTE3 test suite.


title: Digital Democracy : Reimagining Pathways to Political Participation

abstract: Recently, research revolving around blogs has flourished. Usually, academics illus- trate what blogs are, motivations to blog, and, only to some extent, their role in politics. Along these lines, we examine the impact of digital politics by looking specifically at blog readers. Although blog readers might be considered at the forefront of a new technological revolution, and people have specu- lated about their participatory habits both online and off, little research has specifically looked at this growing proportion of the population. This article models factors that predict traditional and online forms of participation, presenting a portrait of a new type of political advocate.


title: Identity Authentication and Capability Based Access Control (IACAC) for the Internet of Things

abstract: In the last few years the Internet of Things (IoT) has seen widespread application and can be found in each field. Authentication and access control are important and critical functionalities in the context of IoT to enable secure communication between devices. Mobility, dynamic network topology and weak physical security of low power devices in IoT networks are possible sources for security vulnerabilities. It is promising to make authentication and access control attack resistant and lightweight in a resource constrained and distributed IoT environment. This paper presents the Identity Authentication and Capability based Access Control (IACAC) model with protocol evaluation and performance analysis. To protect IoT from manin-themiddle, replay and denial of service (Dos) attacks, the concept of capability for access control is introduced. The novelty of this model is that it presents an integrated approach of authentication and access control for IoT devices. The results of other related study have also been analyzed to validate and support our findings. Finally, the proposed protocol is evaluated by using security protocol verification tool and verification results shows that IACAC is secure against aforementioned attacks. This paper discusses performance analysis of the protocol in terms of computational time compared to other existing solutions. This paper also addresses challenges in IoT and security attacks are modelled with the use cases to give an actual view of IoT networks.


title: Capturing user reading behaviors for personalized document summarization

abstract: We propose a new personalized document summarization method that observes a user's personal reading preferences. These preferences are inferred from the user's reading behaviors, including facial expressions, gaze positions, and reading durations that were captured during the user's past reading activities. We compare the performance of our algorithm with that of a few peer algorithms and software packages. The results of our comparative study show that our algorithm can produce more superior personalized document summaries than all the other methods in that the summaries generated by our algorithm can better satisfy a user's personal preferences.


title: Global Learning of Typed Entailment Rules

abstract: Extensive knowledge bases of entailment rules between predicates are crucial for applied semantic inference. In this paper we propose an algorithm that utilizes transitivity constraints to learn a globally-optimal set of entailment rules for typed predicates. We model the task as a graph learning problem and suggest methods that scale the algorithm to larger graphs. We apply the algorithm over a large data set of extracted predicate instances, from which a resource of typed entailment rules has been recently released (Schoenmackers et al., 2010). Our results show that using global transitivity information substantially improves performance over this resource and several baselines, and that our scaling methods allow us to increase the scope of global learning of entailment-rule graphs.


title: A field camera for MR sequence monitoring and system analysis

abstract: PURPOSE: MR image formation and interpretation relies on highly accurate dynamic magnetic fields of high fidelity. A range of mechanisms still limit magnetic field fidelity, including magnet drifts, eddy currents, and finite linearity and stability of power amplifiers used to drive gradient and shim coils. Addressing remaining errors by means of hardware, sequence, or signal processing optimizations, calls for immediate observation by magnetic field monitoring. The present work presents a stand-alone monitoring system delivering insight into such field imperfections for MR sequence and system analysis. ::: METHODS: A flexible NMR field probe-based stand-alone monitoring system, built on a software-defined-radio approach, is introduced and used to sense field dynamics up to third-order in space in a selection of situations with different time scales. ::: RESULTS: Highly sensitive trajectories are measured and successfully used for image reconstruction. Further field perturbations due to mechanical oscillations and thermal field drifts following demanding gradient use and external interferences are studied. ::: CONCLUSION: A flexible and versatile monitoring system is presented, delivering camera-like access to otherwise hardly accessible field dynamics with nanotesla resolution. Its stand-alone nature enables field analysis even during unknown MR system states. Magn Reson Med, 2015. © 2015 Wiley Periodicals, Inc.


title: Rehabilitation posture correction using deep neural network

abstract: The rehabilitation treatment is important because it helps a patient restore physical sensory and mental capabilities. The patient whose symptoms are moderately relieved, or outpatient, usually rehabilitate the individual alone. Improper exercise or posture can slow the recovery of the patient or even worsen the patient's health status when doing rehabilitation exercise alone. The best way is to receive home visiting treatment from professional therapist until cured. However, such way is a burden on the patient in terms of cost. This paper proposes the novel model that corrects the improper postures of the patient when having rehabilitating exercise alone. We use Microsoft Kinect to recognize the posture of the patient by extracting the human skeleton. We will adopt deep neural network to analyze the extracted human skeleton, in order to determine whether the posture is correct or not. The data for training our model will be correct postures and incorrect postures and detailed data collection plan is provided in this paper. The implementation and experiment will be performed in the future work.


title: Vector analysis of femtosecond laser-assisted astigmatic keratotomy after deep anterior lamellar keratoplasty and penetrating keratoplasty

abstract: Purpose ::: To compare the refractive and keratometric changes induced by femtosecond laser astigmatic keratotomy (AK) for suture-out post-keratoplasty astigmatism in deep anterior lamellar keratoplasty (group I) and penetrating keratoplasty (group II).


title: An intelligent market making strategy in algorithmic trading

abstract: Market making (MM) strategies have played an important role in the electronic stock market. However, the MM strategies without any forecasting power are not safe while trading. In this paper, we design and implement a twotier framework, which includes a trading signal generator based on a supervised learning approach and an event-driven MM strategy. The proposed generator incorporates the information within order book microstructure and market news to provide directional predictions. The MM strategy in the second tier trades on the signals and prevents itself from profit loss led by market trending. Using half a year price tick data from Tokyo Stock Exchange (TSE) and Shanghai Stock Exchange (SSE), and corresponding Thomson Reuters news of the same time period, we conduct the back-testing and simulation on an industrial near-to-reality simulator. From the empirical results, we find that 1) strategies with signals perform better than strategies without any signal in terms of average daily profit and loss (PnL) and sharpe ratio (SR), and 2) correct predictions do help MM strategies readjust their quoting along with market trending, which avoids the strategies triggering stop loss procedure that further realizes the paper loss.


title: On the Logic and Purpose of Significance Testing

abstract: There has been much recent attention given to the problems involved with the traditional approach to null hypothesis significance testing (NHST). Many have suggested that, perhaps, NHST should be abandoned altogether in favor of other bases for conclusions such as confidence intervals and effect size estimates (e.g., Schmidt, 1996). The purposes of this article are to (a) review the function that data analysis is supposed to serve in the social sciences, (b) examine the ways in which these functions are performed by NHST, (c) examine the case against NHST, and (d) evaluate interval-based estimation as an alternative to NHST.


Treatment of Acrophobia in Virtual Reality: a Pilot Study


title: Understanding cognitive presence in an online and blended community of inquiry: Assessing outcomes and processes for deep approaches to learning

abstract: This paper focuses on deep and meaningful learning approaches and outcomes associated with online and blended communities of inquiry. Applying mixed methodology for the research design, the study used transcript analysis, learning outcomes, perceived learning, satisfaction, and interviews to assess learning processes and outcomes. The findings for learning processes and outcomes indicated that students in both online and blended courses were able to reach high levels of cognitive presence and learning outcomes. The results suggest that cognitive presence in a community of inquiry is associated with perceived and actual learning outcomes. It is recommended that future research efforts focus on quantitative measures to establish links between cognitive presence and the quality of learning outcomes. [ABSTRACT FROM AUTHOR]


title: Evidence-Based Psychological Assessment

abstract: ABSTRACTIn recent years there has been increasing emphasis on evidence-based practice in psychology (EBPP), and as is true in most health care professions, the primary focus of EBPP has been on treatment. Comparatively little attention has been devoted to applying the principles of EBPP to psychological assessment, despite the fact that assessment plays a central role in myriad domains of empirical and applied psychology (e.g., research, forensics, behavioral health, risk management, diagnosis and classification in mental health settings, documentation of neuropsychological impairment and recovery, personnel selection and placement in organizational contexts). This article outlines the central elements of evidence-based psychological assessment (EBPA), using the American Psychological Association's tripartite definition of EBPP as integration of the best available research with clinical expertise in the context of patient characteristics, culture, and preferences. After discussing strategies for conceptua...


title: Design and simulation evaluation of cooperative adaptive cruise control for a platoon of vehicles

abstract: The semnificative growth of the number of vehicles around the world can conduct to a condensation of the highway traffic. Therefore, the research to find optimal solutions for a higher vehicle flow on highways in a shorter time and with less accidents is ongoing. The organization of vehicles in platoons represents an appropriate method for increasing the roads' capacities. This type of organization permits the decreasing of the distances between vehicles using an electronic coupling made by suitable devices. This functionality allows the simultaneous acceleration or deceleration of all vehicles in the platoon. Moreover, the intelligent vehicles are capable to automatically join or leave the platoon. Another advantage of this system is the decreasing of the fuel consumption and implicitly of the pollution. In this paper, a design approach for cooperative adaptive cruise control (CACC) of vehicle platooning is presented together with a performance evaluation through simulations. The proposed CACC system is based only on the communication with the directly preceding vehicle and consists of a cruise control system for the leader vehicle and a combination of feedback and feed-forward control systems for the followers. An ACC feedback controller is used to keep the desired inter-vehicle distance and a feed-forward one to reduce the effect of measurable disturbances.


title: Malware classification using gray-scale images and ensemble learning

abstract: Malware authors can easily generate obfuscated and metamorphic malware to evade the detection of anti-virus software using automated toolkits. To identify these variations, we design an efficient system to detect and classify the malicious software. We construct a controlled disassembly files from malware. And we convert disassembly files into gray-scale images. In order to improve the efficiency, we use the local mean method to compress gray-scale images, which are mapped into feature vectors. To classify malware, we propose a novel ensemble learning which is based on K-means and the diversity selection. Finally, our experiments show that our method is able to effectively classify the malware.


title: A General Model for Testing Mediation and Moderation Effects

abstract: This paper describes methods for testing mediation and moderation effects in a dataset, both together and separately. Investigations of this kind are especially valuable in prevention research to obtain information on the process by which a program achieves its effects and whether the program is effective for subgroups of individuals. A general model that simultaneously estimates mediation and moderation effects is presented, and the utility of combining the effects into a single model is described. Possible effects of interest in the model are explained, as are statistical methods to assess these effects. The methods are further illustrated in a hypothetical prevention program example.


title: Acquiring OWL ontologies from XML documents

abstract: Converting non-Semantic Web encoded information to ontology-based languages such as the Web Ontology Lan-guage (OWL) is an important knowledge acquisition chal-lenge. In many domains, a large amount of information is represented in eXtensible Markup Language (XML), which has driven the development of general-purpose tools for converting XML to OWL. These tools suffer from a variety of shortcomings, however, including a requirement for multi-stage mapping processes and limited mapping tech-niques. A general shortcoming is that the mapping methods are not OWL-centric and thus limit the complexity of the generated OWL ontologies. To address these shortcomings, we developed an OWL-based language that can transform XML documents to arbitrary OWL ontologies. The lan-guage is based on the Manchester OWL Syntax and ex-tends it with XPath queries to support references to XML documents. Our language provides a compact, user-friendly approach for converting XML to OWL.


title: Scoring reputation in online social networks

abstract: Reputation management plays a key role in improving brand awareness and mastering the impact of harmful events. Aware of its importance, companies invest a lot in acquiring powerful social media monitoring tools (SMM). Several SMM tools are available today in the market, they help marketers and firms' managers follow relevant insights that inform them about what people think about their brand, who speaks about it, when and how it occurs. For this, SMM tools use several metrics concerning messages sent in social media about a brand, but few give a scoring to that brand's reputation. Our contribution through this work is to introduce the reputation score measured by our Intelligent Reputation Measuring System (IRMS) and compare it with existing SMM tools metrics.


title: An improved LLC resonant inverter for induction heating with asymmetrical control

abstract: This paper proposes a modified LLC resonant load configuration of a full-bridge inverter for induction heating applications by using asymmetrical voltage cancellation control. The proposed control method is implemented in a full-bridge inverter topology. With the use of a phase-locked loop control, the operating frequency is automatically adjusted to maintain a small constant lagging phase angle under load parameter variation. The output power is controlled using the asymmetrical voltage cancellation technique. The LLC resonant tank is designed with a matching transformer in between the series inductor and paralleled LC resonant tank for short circuit protection capability of the matching transformer and the induction coil. The validity of the proposed method is verified through computer simulation and hardware experiment at the operating frequency of 108.7 to 110.6 kHz.


title: Glucose Sensing for Diabetes Monitoring: Recent Developments

abstract: This review highlights recent advances towards non-invasive and continuous glucose monitoring devices, with a particular focus placed on monitoring glucose concentrations in alternative physiological fluids to blood.


title: Effects of Positive Suggestions on the Need for Red Blood Cell Transfusion in Orthopedic Surgery

abstract: Abstract This study examined whether positive suggestions applied without a hypnotic induction in the perioperative period reduces the need for red blood cell transfusions in patients who underwent total hip or knee arthroplasties with spinal anesthesia. No hypnotic assessment was performed. Ninety-five patients were randomly assigned to the suggestion group (n = 45) and to the control group (n = 50). Patients in the suggestion group received verbal suggestions before and audiotaped suggestions during the surgery for reducing blood loss, anxiety, postoperative pain, and fast recovery. Our study showed that using positive suggestions in the perioperative period significantly decreases the necessity for transfusion.


title: Document concept lattice for text understanding and summarization

abstract: We argue that the quality of a summary can be evaluated based on how many concepts in the original document(s) that can be preserved after summarization. Here, a concept refers to an abstract or concrete entity or its action often expressed by diverse terms in text. Summary generation can thus be considered as an optimization problem of selecting a set of sentences with minimal answer loss. In this paper, we propose a document concept lattice that indexes the hierarchy of local topics tied to a set of frequent concepts and the corresponding sentences containing these topics. The local topics will specify the promising sub-spaces related to the selected concepts and sentences. Based on this lattice, the summary is an optimized selection of a set of distinct and salient local topics that lead to maximal coverage of concepts with the given number of sentences. Our summarizer based on the concept lattice has demonstrated competitive performance in Document Understanding Conference 2005 and 2006 evaluations as well as follow-on tests.


title: A Dual-Band Multiport MIMO Slot Antenna for WLAN Applications

abstract: A novel compact dual-band multiple-input–multiple-output (MIMO) antenna design for IEEE 802.11 applications is proposed. The antenna operates at 4.9–5.725 GHz with four ports and at 2.4–2.5 GHz with two ports and utilizes a dual-band dual-port MIMO antenna to cover the 2.4/5-GHz bands alongside two single-band antennas to cover the 5-GHz band. This allows simultaneous operation at both WLAN frequencies while having only four ports in total. Isolation between ports is better than 12 dB and is achieved with no reconfigurable elements. The overall antenna size is compact, occupying $46\,\times 20\,\times 1.6\:\mathrm{\,\text{mm}^{3}}$ , and is printed on an FR-4 printed circuit board. The proposed antenna is investigated by simulation and measurement, and results include radiation patterns, efficiency, ${S}$ -parameters, signal correlations, and branch power ratios between ports. These show that in typical wireless environments, envelope cross correlations of less than 0.3 between the ports are obtained.


title: To belong or not to belong, that is the question: Terror management and identification with gender and ethnicity.

abstract: The terror management prediction that reminders of death motivate in-group identification assumes people view their identifications positively. However, when the in-group is framed negatively, mortality salience should lead to disidentification. Study 1 found that mortality salience increased women's perceived similarity to other women except under gender-based stereotype threat. In Study 2, mortality salience and a negative ethnic prime led Hispanic as well as Anglo participants to derogate paintings attributed to Hispanic (but not Anglo-American) artists. Study 3 added a neutral prime condition and used a more direct measure of psychological distancing. Mortality salience and the negative prime led Hispanic participants to view themselves as especially different from a fellow Hispanic. Implications for understanding in-group derogation and disidentification are briefly discussed.