title: The Effect of E-WOM and Perceived Value on the Purchase Decision of Foods by Using the Go-Food Application as Mediated by Trust

abstract: Purpose: This study examined the effect of electronic word of mouth and perceived value on purchase decisions of foods when using the Go-Food Application, which is a purchase decision mediated by trust. The increasing the number of Go-Food Application users makes this study a timely survey. Methodology/Approach: This research explored how the Go-Food Application is able to obtain more consumers compared to similar mobile apps. This research is quantitative in nature by testing the hypotheses. The sample size used was 175 respondents from a non-probability sampling with a convenience sampling technique. The analysis uses a SEM model analyzed using the AMOS program 24. Findings: The results based on Confirmatory Factor Analysis (CFA) shows that each variable is valid and reliable, and the result of Goodness of Fit shows all variables fit the data. The result of hypotheses testing show: (1) electronic word of mouth affects significantly and positively on trust; (2) perceived value affects significantly and positively on trust; (3) electronic word of mouth affects significantly and positively on purchase decision; (4) perceived value affects significantly and positively on purchase decision; (5) trust affects significantly and positively on purchase decision. Research Limitation/implication: The respondents were only those who live in Jakarta and those who use Go-Food application owned by Go-Jek Indonesia Ltd., exclude its other application such as Go-Car, Go-Mart, Go-Send, Go-Box, Go-Pulsa, Go-Massage, Go-Clean. Originality/Value of paper: Trust proved as mediation variable between of electronic word of mouth and perceived value and purchase decision by using Go-Food application. So this research has distribution to Go-Jek Indonesia Ltd. to maintain the costumer trust to get sustainable competitive advantage.


title: Design and start-up of spherical robot with internal pendulum

abstract: Mobile robots are the subject of many studies in robotics. Nevertheless, there is still a lot to be done in this field of knowledge, especially if it comes to ball-shaped constructions. This document focuses on a design and manual remote control of a mobile spherical robot with an internal pendulum like mechanism.


title: Evaluating the Search Phase of Neural Architecture Search

abstract: Neural Architecture Search (NAS) aims to facilitate the design of deep networks for new tasks. Existing techniques rely on two stages: searching over the architecture space and validating the best architecture. NAS algorithms are currently compared solely based one their results on the downstream task. While intuitive, this fails to explicitly evaluate the effectiveness of their search strategies. In this paper, we propose to evaluate the NAS search phase. To this end, we compare the quality of the solutions obtained by NAS search policies with that of random architecture selection. We find that: (i) On average, the state-of-the-art NAS algorithms perform similarly to the random policy; (ii) the widely-used weight sharing strategy degrades the ranking of the NAS candidates to the point of not reflecting their true performance, thus reducing the effectiveness of the search process. We believe that our evaluation framework will be key to designing NAS strategies that consistently discover architectures superior to random ones.


title: End-to-End Learning of Deep Visual Representations for Image Retrieval

abstract: While deep learning has become a key ingredient in the top performing methods for many computer vision tasks, it has failed so far to bring similar improvements to instance-level image retrieval. In this article, we argue that reasons for the underwhelming results of deep methods on image retrieval are threefold: (1) noisy training data, (2) inappropriate deep architecture, and (3) suboptimal training procedure. We address all three issues. First, we leverage a large-scale but noisy landmark dataset and develop an automatic cleaning method that produces a suitable training set for deep retrieval. Second, we build on the recent R-MAC descriptor, show that it can be interpreted as a deep and differentiable architecture, and present improvements to enhance it. Last, we train this network with a siamese architecture that combines three streams with a triplet loss. At the end of the training process, the proposed architecture produces a global image representation in a single forward pass that is well suited for image retrieval. Extensive experiments show that our approach significantly outperforms previous retrieval approaches, including state-of-the-art methods based on costly local descriptor indexing and spatial verification. On Oxford 5k, Paris 6k and Holidays, we respectively report 94.7, 96.6, and 94.8 mean average precision. Our representations can also be heavily compressed using product quantization with little loss in accuracy.


title: Experimental Verification of the Oscillating Paddling Gait for an ePaddle-EGM Amphibious Locomotion Mechanism

abstract: An eccentric paddle locomotion mechanism based on the epicyclic gear mechanism (ePaddle-EGM), which was proposed to enhance the mobility of amphibious robots in multiterrain tasks, can perform various terrestrial and aquatic gaits. Two of the feasible aquatic gaits are the rotational paddling gait and the oscillating paddling gait. The former one has been studied in our previous work, and a capacity of generating vectored thrust has been found. In this letter, we focus on the oscillating paddling gait by measuring the generated thrusts of the gait on an ePaddle-EGM prototype module. Experimental results verify that the oscillating paddling gait can generate vectored thrust by changing the location of the paddle shaft as well. Furthermore, we compare the oscillating paddling gait with the rotational paddling gait at the vectored thrusting property, magnitude of the thrust, and the gait efficiency.


title: Prediction of excess weight loss after laparoscopic Roux-en-Y gastric bypass: data from an artificial neural network

abstract: Introduction ::: Laparoscopic Roux-en-Y gastric bypass (LRYGB) has become the gold standard for surgical weight loss. The success of LRYGB may be measured by excess body mass index loss (%EBMIL) over 25 kg/m2, which is partially determined by multiple patient factors. In this study, artificial neural network (ANN) modeling was used to derive a reasonable estimate of expected postoperative weight loss using only known preoperative patient variables. Additionally, ANN modeling allowed for the discriminant prediction of achievement of benchmark 50 % EBMIL at 1 year postoperatively.


Hydrogen peroxide priming modulates abiotic oxidative stress tolerance: insights from ROS detoxification and scavenging


title: LOGAN: Membership Inference Attacks Against Generative Models

abstract: Generative models estimate the underlying distribution of a dataset to generate realistic samples according to that distribution. In this paper, we present the first membership inference attacks against generative models: given a data point, the adversary determines whether or not it was used to train the model. Our attacks leverage Generative Adversarial Networks (GANs), which combine a discriminative and a generative model, to detect overfitting and recognize inputs that were part of training datasets, using the discriminator's capacity to learn statistical differences in distributions. ::: We present attacks based on both white-box and black-box access to the target model, against several state-of-the-art generative models, over datasets of complex representations of faces (LFW), objects (CIFAR-10), and medical images (Diabetic Retinopathy). We also discuss the sensitivity of the attacks to different training parameters, and their robustness against mitigation strategies, finding that defenses are either ineffective or lead to significantly worse performances of the generative models in terms of training stability and/or sample quality.


title: A high-accuracy method for fine registration of overlapping point clouds

abstract: This paper presents a high-accuracy method for fine registration of two partially overlapping point clouds that have been coarsely registered. The proposed algorithm, which is named dual interpolating point-to-surface method, is principally a modified variant of point-to-surface Iterative Closest Point (ICP) algorithm. The original correspondences are established by adopting a dual surface fitting approach using B-spline interpolation. A novel auxiliary pair constraint based on the surface fitting approach, together with surface curvature information, is employed to remove unreliable point matches. The combined constraint directly utilizes global rigid motion consistency in conjunction with local geometric invariant to reject false correspondences precisely and efficiently. The experimental results involving a number of realistic point clouds demonstrate that the new method can obtain accurate and robust fine registration for pairwise 3D point clouds. This method addresses highest accuracy alignment with less focus on recovery from poor coarse registrations.


Home Automation and Security System Using Android ADK


title: Disintegration and Bayesian inversion via string diagrams

abstract: The notions of disintegration and Bayesian inversion are fundamental in conditional probability theory. They produce channels, as conditional probabilities, from a joint state, or from an already given channel (in opposite direction). These notions exist in the literature, in concrete situations, but are presented here in abstract graphical formulations. The resulting abstract descriptions are used for proving basic results in conditional probability theory. The existence of disintegration and Bayesian inversion is discussed for discrete probability, and also for measure-theoretic probability --- via standard Borel spaces and via likelihoods. Finally, the usefulness of disintegration and Bayesian inversion is illustrated in several examples.


title: ZigBee wireless sensor network for radiation monitoring at nuclear facilities

abstract: Wireless Sensor Networks (WSN) is increasing its market share in many industrial, health, surveillance and nuclear applications. This paper describes the design of a WSN that allows smart real time monitoring of radiation levels at nuclear facilities. We develop a local low cost prototype of wireless sensor node equipped with a radiation sensor. The wireless node based on ZigBee technology and Arduino platform, it is implemented using TI CC2530 chips. The radiation sensor is based on Geiger Muller tube (GMT) as a reliable detector for the radioactive particulates in the gaseous effluent vented from nuclear facilities. The WSN allows the operators to record and control the radiation levels emitted to the environment and it is supported with warning system for the early detection of radiation release. We evaluated the performance of the radiation sensor by the accuracy of the readings while the wireless link performance is represented in terms of the Received Signal Strength Indicator (RSSI), the Link Quality Indicator (LQI) and the Packet Reception Ratio (PRR).


title: Schema extraction from XML collections

abstract: XML Schema language has been proposed to replace Document Type Definitions (DTDs) as schema mechanism for XML data. This language consistently extends grammar-based constructions with constraint- and pattern-based ones and have a higher expressive power than DTDs. As schemas remain optional for XML, we address the problem of XML Schema extraction. We model the XML schema as extended context-free grammars and develop a novel extraction algorithm inspired by methods of grammatical inference. The algorithm copes also with the schema determinism requirement imposed by XML DTDs and XML Schema languages.


title: Business Process Models. Change Management

abstract: In recent years, the role of process models in the development of enterprise software systems has increased continuously. Today, process models are used at different levels in the development process. For instance, in Service-Oriented Architectures (SOA), high-level business process models become input for the development of IT systems, and in running IT systems executable process models describe choreographies of Web Services. A key driver behind this development is the necessity for a closer alignment of business and IT requirements, to reduce the reaction times in software development to frequent changes in competitive markets.Typically in these scenarios, process models are developed, maintained, and transformed in a team environment by several stakeholders that are often from different business units, resulting in different versions. To obtain integrated process models comprising the changes applied to different versions, the versions need to be consolidated by means of model change management. Change management for process models can be compared to widely used concurrent versioning systems (CVS) and consists of the following major activities: matching of process models, detection of differences, computation of dependencies and conflicts between differences, and merging of process models.Although in general model-driven development (MDD) is accepted as a well-established development approach, there are still some shortcomings that let developers decide against MDD and for more traditional development paradigms. These shortcomings comprise a lack of fully integrated and fully featured development environments for MDD, such as a comprehensive support for model change management.In this thesis, we present a framework for process model change management. The framework is based on an intermediate representation for process models that serves as an abstraction of specific process modeling languages and focuses on common syntactic and semantic core concepts for the modeling of workflow in process models. Based on the intermediate representation, we match process models in versioning scenarios and compute differences between process models generically. Further, we consider the analysis of dependencies between differences and show how conflicts between differences can be computed by taking into account the semantics of the modeling language.As proof-of concept, we have implemented major parts of this framework in terms of a prototype. The detection of differences and dependencies contributed also to the Compare & Merge framework for the IBM WebSphere Business Modeler V 7.0 [1] (WBM), which was released as a product in fall 2009.


title: Fundamental Principles in Bacterial Physiology - History, Recent progress, and the Future with Focus on Cell Size Control: A Review

abstract: Bacterial physiology is a branch of biology that aims to understand overarching principles of cellular reproduction. Many important issues in bacterial physiology are inherently quantitative, and major contributors to the field have often brought together tools and ways of thinking from multiple disciplines. This article presents a comprehensive overview of major ideas and approaches developed since the early 20th century for anyone who is interested in the fundamental problems in bacterial physiology. This article is divided into two parts. In the first part (sections 1-3), we review the first 'golden era' of bacterial physiology from the 1940s to early 1970s and provide a complete list of major references from that period. In the second part (sections 4-7), we explain how the pioneering work from the first golden era has influenced various rediscoveries of general quantitative principles and significant further development in modern bacterial physiology. Specifically, section 4 presents the history and current progress of the 'adder' principle of cell size homeostasis. Section 5 discusses the implications of coarse-graining the cellular protein composition, and how the coarse-grained proteome 'sectors' re-balance under different growth conditions. Section 6 focuses on physiological invariants, and explains how they are the key to understanding the coordination between growth and the cell cycle underlying cell size control in steady-state growth. Section 7 overviews how the temporal organization of all the internal processes enables balanced growth. In the final section 8, we conclude by discussing the remaining challenges for the future in the field.


title: Recursive Context Propagation Network for Semantic Scene Labeling

abstract: We propose a deep feed-forward neural network architecture for pixel-wise semantic scene labeling. It uses a novel recursive neural network architecture for context propagation, referred to as rCPN. It first maps the local visual features into a semantic space followed by a bottom-up aggregation of local information into a global representation of the entire image. Then a top-down propagation of the aggregated information takes place that enhances the contextual information of each local feature. Therefore, the information from every location in the image is propagated to every other location. Experimental results on Stanford background and SIFT Flow datasets show that the proposed method outperforms previous approaches. It is also orders of magnitude faster than previous methods and takes only 0.07 seconds on a GPU for pixel-wise labeling of a 256 x 256 image starting from raw RGB pixel values, given the super-pixel mask that takes an additional 0.3 seconds using an off-the-shelf implementation.


Unpacking the effectivity paradox of strategy workshops: do strategy workshops produce strategic change?


title: Peptidergic innervation of the temporomandibular disk in the rat

abstract: The peptidergic innervation of the temporomandibular disk was investigated in the postnatal young rat by using an indirect immunofluorescence method. Calcitonin gene-related peptide-containing nerve fibers were located around the blood vessels and terminated as free nerve endings in the disk. These nerve fibers may be of a sensory nature.


title: CPB: a classification-based approach for burst time prediction in cascades

abstract: Studying the bursty nature of cascades in social media is practically important in many real applications such as product sales prediction, disaster relief, and stock market prediction. Although both the cascade size prediction and the burst patterns of the cascades have been extensively studied, how to predict when a burst will come remains an open problem. It is challenging for traditional time-series-based models such as regression models to address this task directly. Firstly, times-series-based prediction models focus on predicting the future values based on previously observed ones. It is hard to apply them to predict the time of a bursts with the "quick rise-and-fall" pattern. Secondly, besides the cascade popularity, a lot of other side information like user profile and social relation are available in social media. Although the potential utility of such information can be high, it is also hard for time-series-based models to capture and integrate these rich information with diverse formats seamlessly. This paper proposes a classification-based approach for burst time prediction by exploiting rich knowledge in information diffusion. Particularly, we first propose a time-window-based transformation to predict in which time window the burst will appear. By dividing the time spans of all the cascades into the same number of time windows K, the cascades with diverse time spans can thus be handled uniformly. To exploit the rich and heterogenous information in social media, we next propose a scale-independent feature extraction framework to model the heterogenous knowledge in a scale-independent manner. Systematical evaluations are conducted on the Sina Weibo reposting dataset and MemeTracker dataset. Besides the superior performance of the proposed approach, we also observe that: (1) surprisingly, social/structure knowledge is more indicative of the bursts than the cascade popularity information, especially for the bursts occurring in a farther future. (2) Larger cascades are harder to predict as the spreading process of the cascades with higher popularity is usually more diverse and fluctuant. (3) The proposed approach is robust in the sense that the result is not much sensitive to the popularity of the training cascades.


title: Ensembles for unsupervised outlier detection: challenges and research questions a position paper

abstract: Ensembles for unsupervised outlier detection is an emerging topic that has been neglected for a surprisingly long time (although there are reasons why this is more difficult than supervised ensembles or even clustering ensembles). Aggarwal recently discussed algorithmic patterns of outlier detection ensembles, identified traces of the idea in the literature, and remarked on potential as well as unlikely avenues for future transfer of concepts from supervised ensembles. Complementary to his points, here we focus on the core ingredients for building an outlier ensemble, discuss the first steps taken in the literature, and identify challenges for future research.


title: Modeling of lithium-ion battery using MATLAB/simulink

abstract: Lithium-ion battery is potentially to be adopted as energy storage system for green technology applications due to its high power density and high energy density. An accurate battery model in simulation platform is very important to design an efficient battery-powered system. In this paper, an electrical battery model is developed in MATLAB/Simulink. The structure of model is explained in detail, and a battery model for a lithium ferro phosphate battery is presented. The developed battery model is validated from the experiment results. From the comparison, it reveals that the developed model is capable of predicting current-voltage performance accurately. Although the model has been developed for lithium ferro phosphate battery, it is expected that it can be applied for other types of battery.


title: About relationship between business text patterns and financial performance in corporate data

abstract: This study uses text and data mining to investigate the relationship between the text patterns of annual reports published by US listed companies and sales performance. Taking previous research a step further, although annual reports show only past and present financial information, analyzing text content can identify sentences or patterns that indicate the future business performance of a company. First, we examine the relation pattern between business risk factors and current business performance. For this purpose, we select companies belonging to two categories of US SIC (Standard Industry Classification) in the IT sector, 7370 and 7373, which include Twitter, Facebook, Google, Yahoo, etc. We manually collect sales and business risk information for a total of 54 companies that submitted an annual report (Form 10-K) for the last three years in these two categories. To establish a correlation between patterns of text and sales performance, four hypotheses were set and tested. To verify the hypotheses, statistical analysis of sales, statistical analysis of text sentences, sentiment analysis of sentences, clustering, dendrogram visualization, keyword extraction, and word-cloud visualization techniques are used. The results show that text length has some correlation with sales performance, and that patterns of frequently appearing words are correlated with the sales performance. However, a sentiment analysis indicates that the positive or negative tone of a report is not related to sales performance.


Treatment of genital lesions with diode laser vaporization


mTORC1-Independent Reduction of Retinal Protein Synthesis in Type 1 Diabetes


title: Investigating the Importance of Shape Features, Color Constancy, Color Spaces and Similarity Measures in Open-Ended 3D Object Recognition

abstract: Despite the recent success of state-of-the-art 3D object recognition approaches, service robots are frequently failed to recognize many objects in real human-centric environments. For these robots, object recognition is a challenging task due to the high demand for accurate and real-time response under changing and unpredictable environmental conditions. Most of the recent approaches use either the shape information only and ignore the role of color information or vice versa. Furthermore, they mainly utilize the $L_n$ Minkowski family functions to measure the similarity of two object views, while there are various distance measures that are applicable to compare two object views. In this paper, we explore the importance of shape information, color constancy, color spaces, and various similarity measures in open-ended 3D object recognition. Towards this goal, we extensively evaluate the performance of object recognition approaches in three different configurations, including \textit{color-only}, \textit{shape-only}, and \textit{ combinations of color and shape}, in both offline and online settings. Experimental results concerning scalability, memory usage, and object recognition performance show that all of the \textit{combinations of color and shape} yields significant improvements over the \textit{shape-only} and \textit{color-only} approaches. The underlying reason is that color information is an important feature to distinguish objects that have very similar geometric properties with different colors and vice versa. Moreover, by combining color and shape information, we demonstrate that the robot can learn new object categories from very few training examples in a real-world setting.


title: The Pervasive Fridge A Smart Computer System Against Uneaten Food Loss

abstract: Food waste or food loss is food that is discarded or lost uneaten. The work presented in this paper is related to our researches in the field of pervasive and ubiquitous computing. Our "Pervasive Fridge" prototype allows users to be notified proactively, when a food arrives to its expiration date. Speech and image recognition are also integrated in our prototype. This system combines various resources in order to scan barcode, identify and store data related to products, with a smartphone. Later, notifications are sent freely to consumers by mail, SMS (with no charge) and pop-up, to avoid uneaten food loss.


title: Symbol recognition in documents: a collection of techniques?

abstract: Abstract. Various techniques have been used or simply proposed, during the last 10 years, for recognizing symbols in documents. Differences depend on both application field and researcher's cultural background. We bring the methods most commonly used in the representation, description, and classification phases to the attention of the reader, and we discuss the main recognition strategies. We briefly review a number of papers, that are representative of the various approaches.


title: Social Media Analytics and Business Value: A Theoretical Framework and Case Study

abstract: Social media analytics (SMA) uses advanced techniques to analyze patterns in social media data to enable informed and insightful decision-making. It provides organizations with new ways to create value and gain competitive advantage. In this paper, we present a theoretical framework that explains how organizations create value with SMA. We use the framework as a lens for a case study involving a large financial institution (Bankco) that used SMA as a critical component of a major and highly successful marketing campaign. Bankco successfully distinguished itself from its competitors with a marketing campaign based on the creative and innovative use of a number of social media channels. SMA provided Bankco with important insights into customer sentiments, engagement and brand awareness. A number of important lessons learned about effective use of SMA are discussed.


title: Time series representation and similarity based on local autopatterns

abstract: Time series data mining has received much greater interest along with the increase in temporal data sets from different domains such as medicine, finance, multimedia, etc. Representations are important to reduce dimensionality and generate useful similarity measures. High-level representations such as Fourier transforms, wavelets, piecewise polynomial models, etc., were considered previously. Recently, autoregressive kernels were introduced to reflect the similarity of the time series. We introduce a novel approach to model the dependency structure in time series that generalizes the concept of autoregression to local autopatterns. Our approach generates a pattern-based representation along with a similarity measure called learned pattern similarity (LPS). A tree-based ensemble-learning strategy that is fast and insensitive to parameter settings is the basis for the approach. Then, a robust similarity measure based on the learned patterns is presented. This unsupervised approach to represent and measure the similarity between time series generally applies to a number of data mining tasks (e.g., clustering, anomaly detection, classification). Furthermore, an embedded learning of the representation avoids pre-defined features and an extraction step which is common in some feature-based approaches. The method generalizes in a straightforward manner to multivariate time series. The effectiveness of LPS is evaluated on time series classification problems from various domains. We compare LPS to eleven well-known similarity measures. Our experimental results show that LPS provides fast and competitive results on benchmark datasets from several domains. Furthermore, LPS provides a research direction and template approach that breaks from the linear dependency models to potentially foster other promising nonlinear approaches.


title: Low-shot visual object recognition

abstract: Low-shot visual learning - the ability to recognize novel object categories from very few examples - is a hallmark of human visual intelligence. Existing machine learning approaches fail to generalize in the same way. To make progress on this foundational problem, we present a novel protocol to evaluate low-shot learning on complex images where the learner is permitted to first build a feature representation. Then, we propose and evaluate representation regularization techniques that improve the effectiveness of convolutional networks at the task of low-shot learning, leading to a 2x reduction in the amount of training data required at equal accuracy rates on the challenging ImageNet dataset.


title: Qualitative content analysis in nursing research: concepts, procedures and measures to achieve trustworthiness

abstract: Qualitative content analysis in nursing research : Concepts, procedures and measures to achieve trustworthiness.


title: Bandit Structured Prediction for Neural Sequence-to-Sequence Learning

abstract: Bandit structured prediction describes a stochastic optimization framework where learning is performed from partial feedback. This feedback is received in the form of a task loss evaluation to a predicted output structure, without having access to gold standard structures. We advance this framework by lifting linear bandit learning to neural sequence-to-sequence learning problems using attention-based recurrent neural networks. Furthermore, we show how to incorporate control variates into our learning algorithms for variance reduction and improved generalization. We present an evaluation on a neural machine translation task that shows improvements of up to 5.89 BLEU points for domain adaptation from simulated bandit feedback.


Algebraic Reasoning for Probabilistic Concurrent Systems


title: Pharmacological and psychosocial treatments for adolescents with ADHD: An updated systematic review of the literature

abstract: Abstract Smith, Waschbusch, Willoughby, and Evans (2000) reviewed a small treatment literature on ADHD in adolescents and concluded that methylphenidate stimulant medication was a well-established treatment and behavior therapy (BT) demonstrated preliminary efficacy. This review extends and updates the findings of the prior one based on the previous 15 years of research. Studies published since 1999 were identified and coded using standard criteria and effect sizes were calculated where appropriate. Highlights of the last 15 years of research include an expansion of pharmacological treatment options and developmentally appropriate psychosocial treatment packages for adolescents with ADHD. Additionally, nonstimulant medications (e.g., atomoxetine) are now approved for the treatment of ADHD in adolescence. The review concludes that medication and BT produce a similar range of therapeutic effects on the symptoms of adolescents with ADHD. However, results suggest that BT may produce greater overall benefits on measures of impairment. There was no evidence that cognitive enhancement trainings, such as working memory training or neurofeedback improved the functioning of adolescents with ADHD. Whether to use medication, BT, or their combination to treat an adolescent with ADHD is complicated and we provide evidence-informed guidelines for treatment selection. The reviewed evidence does not support current American Academy of Pediatrics and American Academy of Child and Adolescent Psychiatry professional guidelines, which state that stimulant medication is the preferred treatment for adolescents with ADHD. Recommendations for assessment, practice guidelines, and future research are discussed.


title: Dynamic Frequency Control Support by Energy Storage to Reduce the Impact of Wind and Solar Generation on Isolated Power System's Inertia

abstract: In electrical islands, frequency excursions are sizeable and automatic load shedding is often required in response to disturbances. Moreover, the displacement of conventional generation with wind and solar plants, which usually do not provide inertial response, further weakens these power systems. Fast-acting storage, by injecting power within instants after the loss of a generating unit, can back up conventional generation assets during the activation of their primary reserve. This paper relies on dynamic simulations to study the provision of such a dynamic frequency control support by energy storage systems in the French island of Guadeloupe with large shares of wind or solar generation. The results show that fast-acting storage, by acting as a synthetic inertia, can mitigate the impact of these sources on the dynamic performance of the studied island grid in the case of a major generation outage. The other concerns raised by renewables (e.g., variability, forecast accuracy, low voltage ride-through, etc.) have not been addressed within this project.


title: Human-computer interaction for complex pattern recognition problems, to appear

abstract: We review some applications of human-computer interaction that alleviate the complexity of visual recognition by partitioning it into human and machine tasks to exploit the differences between human and machine capabilities. Human involvement offers advantages, both in the design of automated pattern classification systems, and at the operational level of some image retrieval and classification tasks. Recent development of interactive systems has benefited from the convergence of computer vision and psychophysics in formulating visual tasks as computational processes. Computer-aided classifier design and exploratory data analysis are already well established in pattern recognition and machine learning, but interfaces and functionality are improving. On the operational side, earlier recognition systems made use of human talent only in preprocessing and in coping with rejects. Most current content-based image retrieval systems make use of relevance feedback without direct image interaction. In contrast, some visual object classification systems can exploit such interaction. They require, however, a domain-specific visible model that makes sense to both human and computer.


title: Failure to detect changes to people during a real-world interaction.

abstract: Recent research on change detection has documented surprising failures to detect visual changes occurring between views of a scene, suggesting the possibility that visual representations contain few details. Although these studies convincingly demonstrate change blindness for objects in still images and motion pictures, they may not adequately assess the capacity to represent objects in the real world. Here we examine and reject the possibility that change blindness in previous studies resulted from passive viewing of 2-D displays. In one experiment, an experimenter initiated a conversation with a pedestrian, and during the interaction, he was surreptitiously replaced by a different experimenter. Only half of the pedestrians detected the change. Furthermore, successful detection depended on social group membership; pedestrians from the same social group as the experimenters detected the change but those from a different social group did not. A second experiment further examined the importance of this effect of social group. Provided that the meaning of the scene is unchanged, changes to attended objects can escape detection even when they occur during a natural, real-world interaction. The discussion provides a set of guidelines and suggestions for future research on change blindness.


title: Modular meta-learning

abstract: Many prediction problems, such as those that arise in the context of robotics, have a simplifying underlying structure that could accelerate learning. In this paper, we present a strategy for learning a set of neural network modules that can be combined in different ways. We train different modular structures on a set of related tasks and generalize to new tasks by composing the learned modules in new ways. We show this improves performance in two robotics-related problems.


title: Islamic Banking and Finance: Recent Empirical Literature and Directions for Future Research

abstract: This paper examines the recent empirical literature in Islamic banking and finance, highlights the main findings and provides a guide for future research. Early studies focus on the efficiency, production technology and general performance features of Islamic versus conventional banks, whereas more recent work looks at profit and loss-sharing (PLS) behaviour, competition, risks as well as other dimensions such as small business lending and financial inclusion. Apart from key exceptions, the empirical literature suggests no major differences between Islamic and conventional banks in terms of their efficiency, competition and risk features (although small Islamic banks are found to be less risky than their conventional counterparts). There is some evidence that Islamic finance aids inclusion and financial sector development. Results from the empirical finance literature, dominated by studies that focus on the risk/return features of mutual funds, finds that Islamic funds perform as well, if not better, than conventional funds - there is little evidence that they perform worse than standard industry benchmarks. Some recent evidence, however, suggests that Islamic bond (Sukuk) issuance destroys value for shareholders.


title: Sentiment Analysis Based on Expanded Aspect and Polarity-Ambiguous Word Lexicon

abstract: This paper focuses on the task of disambiguating polarity-ambiguous words and the task is reduced to sentiment classification of aspects, which we refer to sentiment expectation instead of semantic orientation widely used in previous researches. Polarity-ambiguous words refer to words like" large, small, high, low ", which pose a challenging task on sentiment analysis. In order to disambiguate polarity-ambiguous words, this paper constructs the aspect and polarity-ambiguous lexicon using a mutual bootstrapping algorithm. So the sentiment of polarity-ambiguous words in context can be decided collaboratively by the sentiment expectation of the aspects and polarity-ambiguous words' prior polarity.At sentence level, experiments show that our method is effective in sentiment analysis. Keywords—polarity-ambiguous word; aspect; sentiment analysis


title: Structural Topic Model for Latent Topical Structure Analysis

abstract: Topic models have been successfully applied to many document analysis tasks to discover topics embedded in text. However, existing topic models generally cannot capture the latent topical structures in documents. Since languages are intrinsically cohesive and coherent, modeling and discovering latent topical transition structures within documents would be beneficial for many text analysis tasks. ::: ::: In this work, we propose a new topic model, Structural Topic Model, which simultaneously discovers topics and reveals the latent topical structures in text through explicitly modeling topical transitions with a latent first-order Markov chain. Experiment results show that the proposed Structural Topic Model can effectively discover topical structures in text, and the identified structures significantly improve the performance of tasks such as sentence annotation and sentence ordering.


title: A design of HTM spatial pooler for face recognition using memristor-CMOS hybrid circuits

abstract: Hierarchical Temporal Memory (HTM) is a machine learning algorithm that is inspired from the working principles of the neocortex, capable of learning, inference, and prediction for bit-encoded inputs. Spatial pooler is an integral part of HTM that is capable of learning and classifying visual data such as objects in images. In this paper, we propose a memristor-CMOS circuit design of spatial pooler and exploit memristors capabilities for emulating the synapses, where the strength of the weights is represented by the state of the memristor. The proposed design is validated on a challenging application of single image per person face recognition problem using AR database resulting in a recognition accuracy of 80%.


From Correlation to Hierarchy : Practical Topic Modeling via Spectral Inference


title: Monte-Carlo Tree Search in Dragline Operation Planning

abstract: Draglines are one of the largest earthmoving machines in surface mining. They are employed to remove the overburden near the surface, giving access to a seam of the target mineral or coal underneath. Their excavation productivity is significantly affected by the high-level operation strategy of where to position the dragline, what to dig at each position and where to dump the removed material. This letter explores the potential of using the Monte-Carlo tree search (MCTS) algorithm for planning this operation strategy. To this end, we adapt the MCTS algorithm to compute the dragline positioning sequences when a fixed digging and dumping strategy is applied at each position. The performance of the adapted MCTS algorithm is compared to a previously developed A* search algorithm and a greedy algorithm. Simulation results show that the MCTS algorithm is able to find near-optimal positioning sequences using significantly less time and memory than the A* algorithm. The solutions from the MCTS algorithm also largely outperform the solutions from the greedy algorithm in all the test scenarios. These results suggest that the MCTS algorithm can be used to plan the operation strategy when decisions about the digging and dumping operations are also considered.


title: Sockeye: A Toolkit for Neural Machine Translation

abstract: We describe Sockeye (version 1.12), an open-source sequence-to-sequence toolkit for Neural Machine Translation (NMT). Sockeye is a production-ready framework for training and applying models as well as an experimental platform for researchers. Written in Python and built on MXNet, the toolkit offers scalable training and inference for the three most prominent encoder-decoder architectures: attentional recurrent neural networks, self-attentional transformers, and fully convolutional networks. Sockeye also supports a wide range of optimizers, normalization and regularization techniques, and inference improvements from current NMT literature. Users can easily run standard training recipes, explore different model settings, and incorporate new ideas. In this paper, we highlight Sockeye's features and benchmark it against other NMT toolkits on two language arcs from the 2017 Conference on Machine Translation (WMT): English-German and Latvian-English. We report competitive BLEU scores across all three architectures, including an overall best score for Sockeye's transformer implementation. To facilitate further comparison, we release all system outputs and training scripts used in our experiments. The Sockeye toolkit is free software released under the Apache 2.0 license.


title: Distributional Similarity of Words with Different Frequencies

abstract: Distributional semantics tries to characterize the meaning of words by the contexts in which they occur. Similarity of words hence can be derived from the similarity of contexts. Contexts of a word are usually vectors of words appearing near to that word in a corpus. It was observed in previous research that similarity measures for the context vectors of two words depend on the frequency of these words. In the present paper we investigate this dependency in more detail for one similarity measure, the Jensen-Shannon divergence. We give an empirical model of this dependency and propose the deviation of the observed Jensen-Shannon divergence from the divergence expected on the basis of the frequencies of the words as an alternative similarity measure. We show that this new similarity measure is superior to both the Jensen-Shannon divergence and the cosine similarity in a task, in which pairs of words, taken from Wordnet, have to be classied as being synonyms or not.


title: Bayesian Calibration of a Natural History Model with Application to a Population Model for Colorectal Cancer

abstract: BACKGROUND ::: Cancer natural history models are essential when evaluating screening/preventative interventions or changes to diagnostic pathways. Natural history models commonly use a state transition structure, but it is often not possible to observe the state transition probabilities required for parameterization. ::: ::: ::: AIM ::: . The work aimed to accurately represent the uncertainty in the parameters of a state transition model for the natural history of colorectal cancer by embedding the problem in the framework of Bayesian inference. ::: ::: ::: METHODS ::: The Metropolis-Hastings algorithm was used to estimate natural history parameters and screening test characteristics by generating multiple sets of parameters from the posterior distribution, which is the probability distribution that is compatible with the observed data. Observed data included colorectal cancer incidence categorized by age and stage, autopsy data on polyp prevalence, and cancer and polyp detection rates from the first round of screening with the fecal occult blood test in England. The approach was implemented using Visual Basic. ::: ::: ::: RESULTS ::: The results were subsequently examined for convergence using the package CODA in R 2.8.0. Outputs from fitting were samples from the joint posterior distribution of the natural history parameters given the epidemiological data. The parameter sets obtained are shown to have a good fit to all the observed data sets. These parameter sets are used when running probabilistic sensitivity analysis. ::: ::: ::: CONCLUSION ::: The advantages of this strategy are that it draws efficiently from a high-dimensional correlated parameter space. The algorithm is simple to code and runs overnight on a standard desktop PC. Using this method, the parameter sets are drawn according to their posterior probability given calibration data, and thus they correctly summarize the residual uncertainty in the parameter space.


Author Identification on Twitter


title: Targeted advertising and advertising avoidance

abstract: I examine how the increasing ability of firms to target their advertisements to particular individuals influences market outcomes when consumers have access to advertising avoidance tools. While firms generally benefit from increased targeting, consumers need not. I also show that there may be too little blocking of advertisements in equilibrium, and consider the role of targeted advertising when niche firms compete for ad inventory against mass-market firms.


title: Characteristics of skin-electrode impedance for a novel screw electrode

abstract: Our group has developed a novel dry electrode, the skin screw electrode, for EEG measurement. This electrode can be conveniently and rapidly installed on the human scalp requiring no electrolyte application and skin preparation. In this paper, we further evaluate the performance of this electrode by investigating its frequency-dependent electrical impedance at the skin-electrode interface. Comparing with the traditional disc electrode, we found that the two types of electrodes showed very different spectral properties. We also found that the impedance of the screw electrode decreases after coating with gold.


title: Introducing Undergraduate Students to Electrochemistry: A Two-Week Discovery Chemistry Experiment

abstract: Within the framework of a laboratory-focused, guided-inquiry pedagogy, students discover the Nernst equation, the spontaneity of galvanic cells, concentration cells, and the use of electrochemical data to calculate equilibrium constants. The laboratory experiment we describe here is a continuation of curriculum reform and pedagogical innovation at College of the Holy Cross that we call Discovery Chemistry. During this two-week Discovery Chemistry experiment students also compare multiple reduction half-reactions versus a common oxidation, which facilitates student data pooling and the incorporation of the pooled data into lecture discussions, hallmarks of Discovery Chemistry.


title: FogGIS: Fog Computing for geospatial big data analytics

abstract: Cloud Geographic Information Systems (GIS) has emerged as a tool for analysis, processing and transmission of geospatial data. The Fog computing is a paradigm where Fog devices help to increase throughput and reduce latency at the edge of the client. This paper developed a Fog-based framework named Fog GIS for mining analytics from geospatial data. We built a prototype using Intel Edison, an embedded microprocessor. We validated the FogGIS by doing preliminary analysis. including compression, and overlay analysis. Results showed that Fog computing hold a great promise for analysis of geospatial data. We used several open source compression techniques for reducing the transmission to the cloud.


title: Correlation Between the MRI Changes in the Lumbar Multifidus Muscles and Leg Pain

abstract: AIM ::: In the assessment of the lumbar spine by magnetic resonance imaging (MRI), changes in the paraspinal muscles are frequently overlooked. In this study, our objective was to investigate the relationships between lumbar multifidus (MF) muscle atrophy and low back pain (LBP), leg pain and intevertebral disc degeneration. ::: ::: ::: METHODS ::: A retrospective study of 78 patients (aged 17-72) with LBP presenting with back pain with or without associated leg pain was undertaken. Their MR images were visually analysed for signs of lumbar MF muscle atrophy, disc degeneration and nerve root compression. The clinical history in each case was obtained from their case notes and pain drawing charts. ::: ::: ::: RESULTS ::: MF muscle atrophy was present in 80% of the patients with LBP. The correlation between MF muscle atrophy and leg pain was found to be significant (P < 0.01). However, the relationships between muscle atrophy and radiculopathy symptoms, nerve root compression, herniated nucleus pulposus and number of degenerated discs were statistically not significant. ::: ::: ::: CONCLUSION ::: Examination of the paraspinal muscles looking for atrophy of MF muscle should be considered when assessing MR images of lumbar spine. This may explain the referred leg pain in the absence of other MR abnormalities.


title: The structure of ill structured problems

abstract: Reviews of the state of the professional practice in Requirements Engineering (RE) stress that the RE process is both complex and hard to describe, and suggest there is a significant difference between competent and "approved" practice. "Approved" practice is reflected by (in all likelihood, in fact, has its genesis in) RE education, so that the knowledge and skills taught to students do not match the knowledge and skills required and applied by competent practitioners.


title: Movie review analysis: Emotion analysis of IMDb movie reviews

abstract: Movie ratings and reviews at sites such as IMDb or Amazon are commonly used by moviegoers to decide which movie to watch or buy next. Currently, moviegoers base their decisions as to which movie to watch by looking at the ratings of movies as well as reading some of the reviews at IMDb or Amazon. This paper argues that there is a better way: reviewers movie scores and reviews can be analyzed with respect to their emotion content, aggregated and projected onto a movie, resulting in an emotion map for a movie. One can then make a decision on which movie to watch next by selecting those movies having emotion maps with certain emotion map patterns desirable for him/her. This paper is a first step towards the above-listed scenario.


title: Comparative Study of PEGASIS and PDCH Protocols in Wireless Sensor Network

abstract: In wireless sensor network (WSN) [1], energy efficiency is one of the very important issues. Protocols in WSNs are broadly classified as Hierarchical, Flat and Location Based routing protocols. Hierarchical routing is used to perform efficient routing in WSN. Here we concentrate on Hierarchical Routing protocols, different types of Hierarchical routing protocols, and PEGASIS (Power-Efficient Gathering in Sensor Information Systems) [2, 3] based routing protocols like PDCH (PEGASIS with Double Cluster Head) [4] protocol which makes every nodes load balance and extend the network lifetime. EEPB (Energy-Efficient PEGASISBased protocol) [5] which is a chain-based protocol to adopt a threshold when constructing chain to decrease the formation of long link to select the leader. This paper presents the hierarchical routing protocol PEGASIS and a comparative study on various versions of PEGASIS protocols. General Terms Routing protocols, Hierarchical routing, Wireless sensor networks


title: Design and Implementation of a Learning Analytics Toolkit for Teachers

abstract: Introduction Learning Management Systems (LMS) or Virtual Learning Environments (VLE) are widely used and have become part of the common toolkits of educators (Schroeder, 2009). One of the main goals of the integration of traditional teaching methods with technology enhancements is the improvement of teaching and learning quality in large university courses with many students. But does utilizing a VLE automatically improve teaching and learning? In our experience, many teachers just upload existing files, like lecture slides, handouts and exercises, when starting to use a VLE. Thereby availability of learning resources is improved. For improving teaching and learning it could be helpful to create more motivating, challenging, and engaging learning materials and e.g., collaborative scenarios to improve learning among large groups of students. Teachers could e.g., use audio and video recordings of their lectures or provide interactive, demonstrative multimedia examples and quizzes. If they put effort in the design of such online learning activities, they need tools that help them observe the consequences of their actions and evaluate their teaching interventions. They need to have appropriate access to data to assess changing behaviors and performances of their students to estimate the level of improvement that has been achieved in the learning environment. With the establishment of TEL, a new research field, called Learning Analytics, is emerging (Elias, 2011). This research field borrows and synthesizes techniques from different related fields, such as Educational Data Mining (EDM), Academic Analytics, Social Network Analysis or Business Intelligence (BI), to harness them for converting educational data into useful information and thereon to motivate actions, like self-reflecting ones previous teaching or learning activities, to foster improved teaching and learning. The main goal of BI is to turn enterprise data into useful information for management decision support. However, Learning Analytics, Academic Analytics, as well as EDM more specifically focus on tools and methods for exploring data coming from educational contexts. While Academic Analytics take a university-wide perspective, including also e.g., organizational and financial issues (Campbell & Oblinger, 2007), Learning Analytics as well as EDM focus specifically on data about teaching and learning. Siemens (2010) defines Learning Analytics as "the use of intelligent data, learner-produced data, and analysis models to discover information and social connections, and to predict and advise on learning." It can support teachers and students to take action based on the evaluation of educational data. However, the technology to deliver this potential is still very young and research on understanding the pedagogical usefulness of Learning Analytics is still in its infancy (Johnson et al., 2011b; Johnson et al., 2012). It is a current goal at RWTH Aachen University to enhance its VLE--the learning and teaching portal L2P (Gebhardt et al., 2007)--with user-friendly tools for Learning Analytics, in order to equip their teachers and tutors with means to evaluate the effectiveness of TEL within their instructional design and courses offered. These teachers still face difficulties, deterring them from integrating cyclical reflective research activities, comparable to Action Research, into everyday practice. Action Research is characterized by a continuing effort to closely interlink, relate and confront action and reflection, to reflect upon one's conscious and unconscious doings in order to develop one's actions, and to act reflectively in order to develop one's knowledge." (Altrichter et al., 2005, p. 6). A pre-eminent barrier is the additional workload, originating from tasks of collecting, integrating, and analyzing raw data from log files of their VLE (Altenbernd-Giani et al., 2009). To tackle these issues, we have developed the "exploratory Learning Analytics Toolkit" (eLAT). …


title: The clinico-radiological paradox in multiple sclerosis revisited.

abstract: The use of magnetic resonance imaging as a surrogate outcome measure in clinical trials, or even as a prognosticator in the assessment of the natural evolution, assumes a close relationship between extent and rate of development of magnetic resonance imaging abnormalities with the clinical status and rate of development of disability. While it may seem obvious that patients who develop new lesions are worse off than those without new lesions, the association between clinical findings and radiological extent of involvement is generally poor. In this review, various confounders are discussed, including inappropriate clinical rating, lack of histopathological specificity (especially for axonal loss), neglect of spinal cord involvement, underestimation of damage to the normal appearing brain tissue (both white and gray matter), and masking effects of cortical adaptation. It is concluded that much progression has been made in magnetic resonance techniques so that the clinico-radiological dissociation has indeed proved to be a paradox. Thus, the relevance of normal appearing brain tissue damage, residual brain volume, spinal cord damage and cerebral plasticity had to be reiterated. The increased awareness of the subtle interplay between these dimensions should be kept in mind when magnetic resonance is used as a surrogate outcome measure. This corroborates with conventional wisdom that one should not rely on a single magnetic resonance measure, but take full advantage of the fact that magnetic resonance is able to provide multidimensional information.


title: A Survey on Off-Line Cursive Script Recognition

abstract: This paper presents a survey on off-line Cursive Word Recognition. The approaches to the problem are described in detail. Each step of the process leading from raw data to the final result is analyzed. This survey is divided into two parts, the first one dealing with the general aspects of Cursive Word Recognition, the second one focusing on the applications presented in the literature.


title: EXPLORING THE IMPACT OF RESTRICTIVE DEFAULT PRIVACY SETTINGS ON THE PRIVACY CALCULUS ON SOCIAL NETWORK SITES

abstract: Research about self-disclosure on social network sites (SNS) is based on the assumption that all entered personal information is publicly available by default. Literature lacks empirical insights on how restrictive default privacy settings influence the privacy calculus and the self-disclosure on SNS. To gain empirical insight we analyzed how the perception of users regarding the benefits and privacy costs of self-disclosure on SNS changes by making users aware of restrictive default privacy settings. The results of our exploratory study using Facebook show that making users aware of restrictive default privacy settings significantly changes the perceived benefit of relationship maintenance and the perceived privacy costs of privacy concerns and the likelihood of a privacy violation.


title: The Mechanism of Synchronous Precise Regulation of Two Shrimp White Spot Syndrome Virus Targets by a Viral MicroRNA

abstract: MicroRNAs (miRNAs), important factors in animal innate immunity, suppress the expressions of their target genes by binding to target mRNA's 3' untranslated regions (3'UTRs). However, the mechanism of synchronous regulation of multiple targets by a single miRNA remains unclear. In this study, the interaction between a white spot syndrome virus (WSSV) miRNA (WSSV-miR-N32) and its two viral targets (wsv459 and wsv322) was characterized in WSSV-infected shrimp. The outcomes indicated that WSSV-encoded miRNA (WSSV-miR-N32) significantly inhibited virus infection by simultaneously targeting wsv459 and wsv322. The silencing of wsv459 or wsv322 by siRNA led to significant decrease of WSSV copies in shrimp, showing that the two viral genes were required for WSSV infection. WSSV-miR-N32 could mediate 5'-3' exonucleolytic digestion of its target mRNAs, which stopped at the sites of target mRNA 3'UTRs close to the sequence complementary to the miRNA seed sequence. The complementary bases (to the target mRNA sequence) of a miRNA 9th-18th non-seed sequence were essential for the miRNA targeting. Therefore, our findings presented novel insights into the mechanism of miRNA-mediated suppression of target gene expressions, which would be helpful for understanding the roles of miRNAs in innate immunity of invertebrate.


title: Optic Disk Detection in Fundus Image Based on Structured Learning

abstract: Automated optic disk (OD) detection plays an important role in developing a computer aided system for eye diseases. In this paper, we propose an algorithm for the OD detection based on structured learning. A classifier model is trained based on structured learning. Then, we use the model to achieve the edge map of OD. Thresholding is performed on the edge map, thus a binary image of the OD is obtained. Finally, circle Hough transform is carried out to approximate the boundary of OD by a circle. The proposed algorithm has been evaluated on three public datasets and obtained promising results. The results (an area overlap and Dices coefficients of 0.8605 and 0.9181, respectively, an accuracy of 0.9777, and a true positive and false positive fraction of 0.9183 and 0.0102) show that the proposed method is very competitive with the state-of-the-art methods and is a reliable tool for the segmentation of OD.


title: IM2GPS: estimating geographic information from a single image

abstract: Estimating geographic information from an image is an excellent, difficult high-level computer vision problem whose time has come. The emergence of vast amounts of geographically-calibrated image data is a great reason for computer vision to start looking globally - on the scale of the entire planet! In this paper, we propose a simple algorithm for estimating a distribution over geographic locations from a single image using a purely data-driven scene matching approach. For this task, we leverage a dataset of over 6 million GPS-tagged images from the Internet. We represent the estimated image location as a probability distribution over the Earthpsilas surface. We quantitatively evaluate our approach in several geolocation tasks and demonstrate encouraging performance (up to 30 times better than chance). We show that geolocation estimates can provide the basis for numerous other image understanding tasks such as population density estimation, land cover estimation or urban/rural classification.


title: Segmenting salient objects from images and videos

abstract: In this paper we introduce a new salient object segmentation method, which is based on combining a saliency measure with a conditional random field (CRF) model. The proposed saliency measure is formulated using a statistical framework and local feature contrast in illumination, color, and motion information. The resulting saliency map is then used in a CRF model to define an energy minimization based segmentation approach, which aims to recover well-defined salient objects. The method is efficiently implemented by using the integral histogram approach and graph cut solvers. Compared to previous approaches the introduced method is among the few which are applicable to both still images and videos including motion cues. The experiments show that our approach outperforms the current state-of-the-art methods in both qualitative and quantitative terms.


title: Corralling a Band of Bandit Algorithms

abstract: We study the problem of combining multiple bandit algorithms (that is, online learning algorithms with partial feedback) with the goal of creating a master algorithm that performs almost as well as the best base algorithm if it were to be run on its own. The main challenge is that when run with a master, base algorithms unavoidably receive much less feedback and it is thus critical that the master not starve a base algorithm that might perform uncompetitively initially but would eventually outperform others if given enough feedback. We address this difficulty by devising a version of Online Mirror Descent with a special mirror map together with a sophisticated learning rate scheme. We show that this approach manages to achieve a more delicate balance between exploiting and exploring base algorithms than previous works yielding superior regret bounds. ::: Our results are applicable to many settings, such as multi-armed bandits, contextual bandits, and convex bandits. As examples, we present two main applications. The first is to create an algorithm that enjoys worst-case robustness while at the same time performing much better when the environment is relatively easy. The second is to create an algorithm that works simultaneously under different assumptions of the environment, such as different priors or different loss structures.


title: Set-top box virtualization as a personal cloud server for 5G users

abstract: In the immediate future, the new networking paradigms SDN, NFV and MEC, together with the incoming 5G mobile network generation, will allow users to receive very- interactive high-quality services from the Telco Operators, thanks to the characteristic of virtualizing network and application functions, and running them very close to the users. In this context, this paper proposes a prototype of a virtual Set-Top Box (STB), the virtualization of a smart device (SD) that is running on the edge of the network, and is able to follow the user when he is in mobility.


title: SphereReID: Deep Hypersphere Manifold Embedding for Person Re-Identification

abstract: Abstract Many current successful Person Re-Identification (ReID) methods train a model with the softmax loss function to classify images of different persons and obtain the feature vectors at the same time. However, the underlying feature embedding space is ignored. In this paper, we use a modified softmax function, termed Sphere Softmax, to solve the classification problem and learn a hypersphere manifold embedding simultaneously. A balanced sampling strategy is also introduced. Finally, we propose a convolutional neural network called SphereReID adopting Sphere Softmax and training a single model end-to-end with a new warming-up learning rate schedule on four challenging datasets including Market-1501, DukeMTMC-reID, CHHK-03, and CUHK-SYSU. Experimental results demonstrate that this single model outperforms the state-of-the-art methods on all four datasets without fine-tuning or re-ranking. For example, it achieves 94.4% rank-1 accuracy on Market-1501 and 83.9% rank-1 accuracy on DukeMTMC-reID. The code and trained weights of our model will be released.


title: Adoption , Adoption Seeking , and Relinquishment for Adoption in the United States

abstract: Objectives—This report presents national data on adoption and adoption-related behaviors among ever-married women 18‐44 years of age in the United States, according to selected characteristics of the women. Trends are shown in the prevalence of adoption and relinquishment of children for adoption. For 1995, the report shows demand for adoption and women’s preferences for characteristics of the child. Methods—Data are based on nationally representative samples of women 15‐44 years of age from the 1973, 1982, 1988, and 1995 National Surveys of Family Growth (NSFG). Results—The percent of ever-married women 18‐44 years of age who have ever adopted a child declined from 2.1 percent in 1973 to 1.3 percent in 1995. Of the 9.9 million women who had ever considered adoption, 16 percent had taken steps toward adoption, and 31 percent of these had actually adopted a child. Older women, nulliparous women, women with fecundity impairment, and women who have used infertility services were more likely to have considered adoption, to have taken concrete steps toward adoption, and to have actually adopted a child. In response to the questions about preferred characteristics of an adopted child, women expressed strong preferences with respect to age, sex, race, and disability level of the child, but were willing to accept children with the less-desired traits. Between 1989 and 1995, about 1 percent of babies born to never-married women were relinquished for adoption, down from 9 percent among such babies born before 1979. Conclusions—Federally supported adoption data collection sources corroborate the decline in adoption shown by the NSFG over the past 25 years. Demand for adoption in the United States varies, depending on whether demand is conceptualized liberally as ‘‘ever having considered adoption’’ or more narrowly, as ‘‘currently taking concrete steps toward adoption.’’ The narrow definition was fulfilled by 232,000 ever-married women in 1995.


title: LibSEAL: revealing service integrity violations using trusted execution

abstract: Users of online services such as messaging, code hosting and collaborative document editing expect the services to uphold the integrity of their data. Despite providers' best efforts, data corruption still occurs, but at present service integrity violations are excluded from SLAs. For providers to include such violations as part of SLAs, the competing requirements of clients and providers must be satisfied. Clients need the ability to independently identify and prove service integrity violations to claim compensation. At the same time, providers must be able to refute spurious claims. We describe LibSEAL, a SEcure Audit Library for Internet services that creates a non-repudiable audit log of service operations and checks invariants to discover violations of service integrity. LibSEAL is a drop-in replacement for TLS libraries used by services, and thus observes and logs all service requests and responses. It runs inside a trusted execution environment, such as Intel SGX, to protect the integrity of the audit log. Logs are stored using an embedded relational database, permitting service invariant violations to be discovered using simple SQL queries. We evaluate LibSEAL with three popular online services (Git, ownCloud and Dropbox) and demonstrate that it is effective in discovering integrity violations, while reducing throughput by at most 14%.


title: Rethinking segregation and integration: contributions of whole-brain modelling

abstract: The brain balances the segregation and integration of incoming information to facilitate flexible cognition and behaviour. In this Opinion article, Deco and colleagues argue that whole-brain computational modelling based on neuroimaging data can provide insights into these segregation and integration processes.


title: An Efficient DSP-FPGA-Based Real-Time Implementation Method of SVM Algorithms for an Indirect Matrix Converter

abstract: This paper proposes a real-time DSP- and FPGA-based implementation method of a space vector modulation (SVM) algorithm for an indirect matrix converter (IMC). Therefore, low-cost and compact control platform is built using a 32-bit fixed-point DSP (TMS320F2812) operating at 150 MHz and a SPARTAN 3E FPGA operating at 50 MHz. The method consists in using the event-manager modules of the DSP to build specified pulses at its PWM output peripherals, which are fed to the digital input ports of a FPGA. Moreover, a simple logical processing and delay times are thereafter implemented in the FPGA so as to synthesize the suitable gate pulse patterns for the semiconductor-controlled devices. It is shown that the proposed implementation method enables high switching frequency operation with high pulse resolution as well as a negligible propagation time for the generation of the gating pulses. Experimental results from an IMC prototype confirm the practical feasibility of the proposed technique.


title: Implementing a no-loss state in the game of Tic-Tac-Toe using a customized Decision Tree Algorithm

abstract: The game of Tic-Tac-Toe is one of the most commonly known games. This game doesn't allow one to win all the time and a significant proportion of games played results in a draw. This study is aimed at evolving of no-loss strategies in the game using Decision Tree Algorithm and comparing them with existing methodologies, mainly focused on the implementation of the game using the Minimax algorithm. The Minimax algorithm does provide an optimal No-Loss Strategy by assuming that both players play optimally. So the question that comes out is what happens when the opponent plays un-optimally, in these cases the minimax proved to play non optimal moves, even though it wins at the next state rather than the expected state. Thus this paper provides a clear study of those trivial states and provides an optimal game play using an decision tree algorithm independent of the opponent's game strategy.


title: Feature-based volumetric terrain generation

abstract: Two-dimensional heightfields are the most common data structure used for storing and rendering of terrain in offline rendering and especially real-time computer graphics. By its very nature, a heightfield cannot store terrain structures with multiple vertical layers such as overhanging cliffs, caves, or arches. This restriction does not apply to volumetric data structures. However, the workflow of manual modelling and editing of volumetric terrain usually is tedious and very time-consuming. Therefore, we propose to use three-dimensional curve-based primitives to efficiently model prominent, large-scale terrain features. We present a technique for volumetric generation of a complete terrain surface from the sparse input data by means of diffusion-based algorithms. By combining an efficient, feature-based toolset with a volumetric terrain representation, the modelling workflow is accelerated and simplified while retaining the full artistic freedom of volumetric terrains. All stages of our method are GPU-accelerated using compute shaders to ensure interactive editing of terrain.


title: Effects of Early-Life Abuse Differ across Development: Infant Social Behavior Deficits Are Followed by Adolescent Depressive-Like Behaviors Mediated by the Amygdala

abstract: Abuse during early life, especially from the caregiver, increases vulnerability to develop later-life psychopathologies such as depression. Although signs of depression are typically not expressed until later life, signs of dysfunctional social behavior have been found earlier. How infant abuse alters the trajectory of brain development to produce pathways to pathology is not completely understood. Here we address this question using two different but complementary rat models of early-life abuse from postnatal day 8 (P8) to P12: a naturalistic paradigm, where the mother is provided with insufficient bedding for nest building; and a more controlled paradigm, where infants undergo olfactory classical conditioning. Amygdala neural assessment (c-Fos), as well as social behavior and forced swim tests were performed at preweaning (P20) and adolescence (P45). Our results show that both models of early-life abuse induce deficits in social behavior, even during the preweaning period; however, depressive-like behaviors were observed only during adolescence. Adolescent depressive-like behavior corresponds with an increase in amygdala neural activity in response to forced swim test. A causal relationship between the amygdala and depressive-like behavior was suggested through amygdala temporary deactivation (muscimol infusions), which rescued the depressive-like behavior in the forced swim test. Our results indicate that social behavior deficits in infancy could serve as an early marker for later psychopathology. Moreover, the implication of the amygdala in the ontogeny of depressive-like behaviors in infant abused animals is an important step toward understanding the underlying mechanisms of later-life mental disease associated with early-life abuse.


title: Embracing Big Data in Complex Educational Systems: The Learning Analytics Imperative and the Policy Challenge

abstract: In the new era of big educational data, learning analytics (LA) offer the possibility of implementing real–time assessment and feedback systems and processes at scale that are focused on improvement of learning, development of self–regulated learning skills, and student success. However, to realize this promise, the necessary shifts in the culture, technological infrastructure, and teaching practices of higher education, from assessment–for–accountability to assessment–for–learning, cannot be achieved through piecemeal implementation of new tools. We propose here that the challenge of successful institutional change for learning analytics implementation is a wicked problem that calls for new adaptive forms of leadership, collaboration, policy development and strategic planning. Higher education institutions are best viewed as complex systems underpinned by policy, and we introduce two policy and planning frameworks developed for complex systems that may offer institutional teams practical guidance in their project of optimizing their educational systems with learning analytics.


Development of a control architecture for the ROBUDEM outdoor mobile robot platform


title: Guest Editorial Understanding Free/Open Source Software Development Processes

abstract: This article introduces a special issue of Software Process—Improvement and Practice focusing on processes found in free or open source software development (F/OSSD) projects. It seeks to provide a background overview of research in this area through a review of selected empirical studies of F/OSSD processes. The results and findings from a survey of empirical studies of F/OSSD give rise to an interesting variety of opportunities and challenges for understanding these processes, which are identified along the way. Overall, what becomes clear is that studies of F/OSSD processes reveal a more diverse set of different types of processes than have typically been examined in conventional software development projects. The articles in this special issue further advance understanding of what processes characterize and shape F/OSSD. Copyright © 2006 John Wiley & Sons, Ltd.


title: Scenario-Based Analysis of Software Architecture

abstract: Despite advances in clarifying high level design needs, analyzing a system's ability to meet desired quality criteria is still difficult. The authors propose using scenarios to make analysis more straightforward. In their case study report, they analyze lessons learned with this approach. They developed the Software Architecture Analysis Method, an approach that uses scenarios to gain information about a system's ability to meet desired quality attributes. Scenarios are brief narratives of expected or anticipated system uses from both user and developer views and they provide a look at how the system satisfies quality attributes in various use contexts.


CLIPS : Customized Levels of IoT Privacy and Security


title: Interpreting Latent Variables in Factor Models via Convex Optimization

abstract: Latent or unobserved phenomena pose a significant difficulty in data analysis as they induce complicated and confounding dependencies among a collection of observed variables. Factor analysis is a prominent multivariate statistical modeling approach that addresses this challenge by identifying the effects of (a small number of) latent variables on a set of observed variables. However, the latent variables in a factor model are purely mathematical objects that are derived from the observed phenomena, and they do not have any interpretation associated to them. A natural approach for attributing semantic information to the latent variables in a factor model is to obtain measurements of some additional plausibly useful covariates that may be related to the original set of observed variables, and to associate these auxiliary covariates to the latent variables. In this paper, we describe a systematic approach for identifying such associations. Our method is based on solving computationally tractable convex optimization problems, and it can be viewed as a generalization of the minimum-trace factor analysis procedure for fitting factor models via convex optimization. We analyze the theoretical consistency of our approach in a high-dimensional setting as well as its utility in practice via experimental demonstrations with real data.


title: A hybrid deep architecture for robotic grasp detection

abstract: The robotic grasp detection is a great challenge in the area of robotics. Previous work mainly employs the visual approaches to solve this problem. In this paper, a hybrid deep architecture combining the visual and tactile sensing for robotic grasp detection is proposed. We have demonstrated that the visual sensing and tactile sensing are complementary to each other and important for the robotic grasping. A new THU grasp dataset has also been collected which contains the visual, tactile and grasp configuration information. The experiments conducted on a public grasp dataset and our collected dataset show that the performance of the proposed model is superior to state of the art methods. The results also indicate that the tactile data could help to enable the network to learn better visual features for the robotic grasp detection task.


title: TOWARDS A SPECTRUM OF GRAPH CONVOLUTIONAL NETWORKS

abstract: We present our ongoing work on understanding the limitations of graph convolutional networks (GCNs) as well as our work on generalizations of graph convolutions for representing more complex node attribute dependencies. Based on an analysis of GCNs with the help of the corresponding computation graphs, we propose a generalization of existing GCNs where the aggregation operations are (a) determined by structural properties of the local neighborhood graphs and (b) not restricted to weighted averages. We show that the proposed approach is strictly more expressive while requiring only a modest increase in the number of parameters and computations. We also show that the proposed generalization is identical to standard convolutional layers when applied to regular grid graphs.


Springer Handbook on Speech Processing and Speech Communication 1 SPEECH RECOGNITION WITH WEIGHTED FINITE-STATE TRANSDUCERS


title: Explaining watson: polymath style

abstract: Our paper is actually two contributions in one. First, we argue that IBM's Jeopardy! playing machine needs a formal semantics. We present several arguments as we discuss the system. We also situate the work in the broader context of contemporary AI. Our second point is that the work in this area might well be done as a broad collaborative project. Hence our "Blue Sky" contribution is a proposal to organize a polymath-style effort aimed at developing formal tools for the study of state of the art question-answer systems, and other large scale NLP efforts whose architectures and algorithms lack a theoretical foundation.


title: Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks

abstract: We introduce a simple semi-supervised learning approach for images based on in-painting using an adversarial loss. Images with random patches removed are presented to a generator whose task is to fill in the hole, based on the surrounding pixels. The in-painted images are then presented to a discriminator network that judges if they are real (unaltered training images) or not. This task acts as a regularizer for standard supervised training of the discriminator. Using our approach we are able to directly train large VGG-style networks in a semi-supervised fashion. We evaluate on STL-10 and PASCAL datasets, where our approach obtains performance comparable or superior to existing methods.


title: To Bathe or Not to Bathe With Chlorhexidine Gluconate: Is It Time to Take a Stand for Preadmission Bathing and Cleansing?

abstract: any health care facilities have incorporated an antiseptic skin cleansing protocol, often referred to as preoperative bathing and cleansing, to reduce the endogenous microbial burden on the skin of patients undergoing elective surgery, with the aim of reducing the risk of surgical site infections (SSIs). According to a recent study by Injean et al, 91% of all facilities that perform coronary artery bypass surgery in California have a standardized preoperative bathing and cleansing protocol for patients. Historically, this practice has been endorsed by national and international organizations, such as the Hospital Infection Control Practice Advisory Committee and the Centers for Disease Control and Prevention, the Association for Professionals in Infection Control and Epidemiology (APIC), AORN, the Institute for Healthcare Improvement (IHI), and the National Institute for Health and Care Excellence (NICE), which recommend bathing and/or cleansing with an antiseptic agent before surgery as a component of a broader strategy to reduce SSIs. The 2008 Society for Healthcare Epidemiology of America (SHEA)/ Infectious Diseases Society of America (IDSA)/Surgical Infection Society (SIS) strategies to prevent SSIs in acute care hospitals declined to recommend a specific application policy regarding selection of an antiseptic agent for preoperative bathing but acknowledged that the (maximal) antiseptic benefits of chlorhexidine gluconate (CHG) are dependent on achieving adequate skin surface concentrations.


title: ModelTracker: Redesigning Performance Analysis Tools for Machine Learning

abstract: Model building in machine learning is an iterative process. The performance analysis and debugging step typically involves a disruptive cognitive switch from model building to error analysis, discouraging an informed approach to model building. We present ModelTracker, an interactive visualization that subsumes information contained in numerous traditional summary statistics and graphs while displaying example-level performance and enabling direct error examination and debugging. Usage analysis from machine learning practitioners building real models with ModelTracker over six months shows ModelTracker is used often and throughout model building. A controlled experiment focusing on ModelTracker's debugging capabilities shows participants prefer ModelTracker over traditional tools without a loss in model performance.


Evolution of Subsumption Using Genetic Programming


title: An algorithmic overview of surface registration techniques for medical imaging

abstract: This paper presents a literature survey of automatic 3D surface registration techniques emphasizing the mathematical and algorithmic underpinnings of the subject. The relevance of surface registration to medical imaging is that there is much useful anatomical information in the form of collected surface points which originate from complimentary modalities and which must be reconciled. Surface registration can be roughly partitioned into three issues: choice of transformation, elaboration of surface representation and similarity criterion, and matching and global optimization. The first issue concerns the assumptions made about the nature of relationships between the two modalities, e.g. whether a rigid-body assumption applies, and if not, what type and how general a relation optimally maps one modality onto the other. The second issue determines what type of information we extract from the 3D surfaces, which typically characterizes their local or global shape, and how we organize this information into a representation of the surface which will lead to improved efficiency and robustness in the last stage. The last issue pertains to how we exploit this information to estimate the transformation which best aligns local primitives in a globally consistent manner or which maximizes a measure of the similarity in global shape of two surfaces. Within this framework, this paper discusses in detail each surface registration issue and reviews the state-of-the-art among existing techniques.


title: Prevention of Falls in the Elderly

abstract: This study seeks to find answers to the main research question; how can falls in the elderly be prevented. However, in order to provide answers to this main research question, it is important to identify the causes of such falls in the elderly and last but not the least, how the consequences of these falls are managed once they occur. Qualitative content analysis was the methodological choice for this study because we had access to sufficient scientific articles on fall prevention in the elderly from which to analyze and provide answers to our research questions. The analysis of these articles was guided by Elo & Kyngas’ (2008) abstraction process model for inductive content analysis. Dorothea Orem’s Self – care Deficit theory provided the theoretical road map for this study. It defines self-care as, “the performance or practice of activities that individuals initiate and perform on their own behalf to maintain life, health, and well-being, while self-care deficit delineates when nursing is needed. The theory’s three steps of nursing process namely – assessment, diagnosis and planning, implement, evaluation further enriched the findings. At the end, we arrived at some interesting conclusions. For instance, falls in the elderly is a vicious circle. As such, any prevention and or management strategies must be holistic. They should include physical, psychological and environmental the habitat of the elderly. Nevertheless, they are some limitations that could be improved upon. For instance, the thesis could have benefitted from more articles than the fifteen articles analysed. Secondly, a mixed method content analysis comprising inductive and deductive strategies would have brought out other perspectives than relying on the inductive approach alone. These limitations notwithstanding, the study sheds some light on fall prevention measures in the elderly and how these can be improved.


A Conceptual Framework for Data Quality in Knowledge Discovery Tasks (FDQ-KDT): A Proposal.


title: IncSQL: Training Incremental Text-to-SQL Parsers with Non-Deterministic Oracles

abstract: We present a sequence-to-action parsing approach for the natural language to SQL task that incrementally fills the slots of a SQL query with feasible actions from a pre-defined inventory. To account for the fact that typically there are multiple correct SQL queries with the same or very similar semantics, we draw inspiration from syntactic parsing techniques and propose to train our sequence-to-action models with non-deterministic oracles. We evaluate our models on the WikiSQL dataset and achieve an execution accuracy of 83.7% on the test set, a 2.1% absolute improvement over the models trained with traditional static oracles assuming a single correct target SQL query. When further combined with the execution-guided decoding strategy, our model sets a new state-of-the-art performance at an execution accuracy of 87.1%.


title: A wearable device for fall detection elderly people using tri dimensional accelerometer

abstract: A fall detection device is needed to provide information to paramedics or family members when an elderly is falling. Helping for elderly falling can avoid fatal injuries or loss of life. In order for the falling device comfortably taken by the elderly, we proposed a wearable device that lightweight, using battery for power supply, and a low-energy consumption. Our proposed device consists of: 3-dimensional accelerometer as a sensor, a microcontroller and a communication device. The sensor provides accelerations of elderly body movements. Then, the microcontroller identifies position body and a falling from three-axis accelerations. We use parameter threshold in our proposed fall detection as a method that has success detect 75% in fall forward and 95% in fall backward. The proposed device also has a 100% success in providing information on normal activities, such as: standing or sitting, supine, face down, left and right, while the success rate for the e-health device by cooking hack is 92%.


title: Histology and ultrastructure of the thymus during development in tilapia, Oreochromis niloticus

abstract: The thymus in teleost fishes plays an important role in producing functionally competent T-lymphocytes. However, the thymus in tilapia is not well known, which greatly hampers investigations into the immune responses of tilapia infected by aquatic pathogens. The histological structure and ultrastructure of the thymus in Oreochromis niloticus, including embryos and larvae at different developmental stages, juveniles, and adult fish, were systematically investigated using whole mount in situ hybridization (WISH), and light and transmission electron microscopy (TEM). The position of the thymus primordium was first labeled in the embryo at 2 days post-fertilization (dpf) with the thymus marker gene recombination activating gene 1 (Rag1), when the water temperature was 27 °C. Obvious structures of the thymus were easily observed in 4-dpf embryos. At this stage, the thymus was filled with stem cells. At 6 dpf, the thymus differentiated into the cortex and medulla. The shape of the thymus was ‘broad bean’-like during the early stages from 4 to 10 dpf, and became wedge-shaped in fish larvae at 20 dpf. At 6 months post-fertilization (mpf), the thymus differentiated into the peripheral zone, central zone, and inner zone. During this stage, myoid cells and adipocytes appeared in the inner zone following thymus degeneration. Then, the thymus displayed more advanced degeneration by 1 year post-fertilization (ypf), and the separation of cortex and medulla was not observed at this stage. The thymic trabecula and lobule were absent during the entire course of development. However, the typical Hassall's corpuscle was present and underwent degeneration. Additionally, TEM showed that the thymic tissues contained a wide variety of cell types, namely lymphocytes, macrophages, epithelial cells, fibroblasts, and mastocytes.


Effects of Fitness Applications with SNS: How Do They Influence Physical Activity


title: A Systematic Literature Review on Combining Ontology with Bayesian Network to Support Logical and Probabilistic Reasoning

abstract: Reasoning in ontology is currently limited to logical reasoning. It is because ontology does not have a standard for probabilistic reasoning. Various approaches have been made by researchers to add the ability for ontological reasoner to do probabilistic reasoning. The approach is done by combining ontology with Bayesian network that does have probabilistic reasoning abilities. This study mapped out various approaches performed in combining ontologies with Bayesian networks to realize logical and probabilistic reasoning simultaneously. We use a systematic literature review method to identify the primary studies on combining ontology with Bayesian network following a predefined review protocol. We searched from four indexing services (SCOPUS, IEEE Xplore, ACM Digital Library, and SpringerLink) and got the result of 74 papers accepted for the review. We extracted properties from these studies and found 8 motivations of the studies, 5 contexts, 4 factors involved, and 5 techniques used in combining ontology with Bayesian network. The aim and the context are clearly stated in most of the studies, while most of the authors did not completely discuss the threats in their papers. As for the method and findings, most of the studies describe and discuss it moderately.


title: A survey of network flow applications

abstract: It has been over 16 years since Cisco's NetFlow was patented in 1996. Extensive research has been conducted since then and many applications have been developed. In this survey, we have reviewed an extensive number of studies with emphasis on network flow applications. First, we provide a brief introduction to sFlow, NetFlow and network traffic analysis. Then, we review the state of the art in the field by presenting the main perspectives and methodologies. Our analysis has revealed that network security has been an important research topic since the beginning. Advanced methodologies, such as machine learning, have been very promising. We provide a critique of the studies surveyed about datasets, perspectives, methodologies, challenges, future directions and ideas for potential integration with other Information Technology infrastructure and methods. Finally, we concluded this survey.