title: Tourist behaviour towards self-service hotel technology adoption: trust and subjective norm as key antecedents.

abstract: Abstract The hospitality industry is expanding with an ever evolving technology adoption process and novel possibilities of adoption of new technologies are constantly being explored. Over the years, several research perspectives have offered different adoption models for the implementation of such technologies. The current study extends the utility of technology acceptance model (TAM) through analysis of additional antecedent beliefs in order to predict tourists' attitude towards self-service technologies (SSTs) in the offline hospitality context. The paper further examines the impact of trust and subjective norm on consumers' (1) attitude and (2) behavioural intention towards adopting self-service hotel technologies (SSHTs). Results indicate that trust has a more significant impact on tourists' attitude, though both trust and subjective norm considerably affect tourists' behavioural intention towards adopting new technologies. Conclusion includes managerial and theoretical contributions of the study, its limitations and future research directions.


title: Mobile Storage Augmentation in Mobile Cloud Computing: Taxonomy, Approaches, and Open Issues

abstract: Worldwide employment of mobile devices in various critical domains, particularly healthcare, disaster recovery, and education has revolutionized data generation rate. However, rapidly rising data volume intensifies data storage and battery limitations of mobile devices. Mobile Cloud Computing (MCC) as the state-of-the-art mobile computing aims to augment mobile storage by leveraging infinite cloud resources to provide unlimited storage capabilities with energy-dissipation prevention. Researchers have already surveyed varied MCC aspects and its challenges, but successful futuristic Mobile Storage Augmentation (MSA) approaches demand deep insight into the current storage augmentation solutions that highlights critical challenges, which are lacking. This paper thoroughly investigates the main MSA issues in three domains of mobile computing, cloud computing, and MCC to present a taxonomy. Also, it examines several credible MSA approaches and mechanisms in MCC, classifies characteristics of cloud-based storage resources, and presents open issues that direct future research.


title: A Broadband $\pm 45^{\circ}$ Dual-Polarized Antenna With Y-Shaped Feeding Lines

abstract: A broadband $\pm 45^{\circ}$ dual-polarized base station antenna is proposed for 2G/3G/LTE bands. The proposed antenna has Y-shaped feeding lines to impart a wide impedance bandwidth to it, and a rectangular box-shaped reflector to enhance its stability in radiation patterns over the operating frequencies. A prototype of the proposed antenna is designed, fabricated, and tested. The measured results show achievement of impedance bandwidth of 45% for $\hbox {SWR} at both ports, port-to-port isolation $>$ 25 dB , a stable radiation pattern with 3-dB beamwidth $68^{\circ}\pm 2^{\circ}$ at H-plane and V-plane, and a stable antenna gain of 8.2 $\pm$ 0.6 dBi from 1.7 to 2.7 GHz. A broadband dual-polarized antenna array with five elements for the band is developed for base station application. Measurements show achievement of array gain of 14.8 $\pm$ 1.4 dBi, 3-dB beamwidth $66.56^{\circ}\pm 2.22^{\circ}$ at H-plane at port 1 and $64.85^{\circ}\pm 4.96^{\circ}$ at port 2 with $\hbox {SWR} over the operating frequency band. Simulation results have a good agreement with measurement ones.


title: of Printed Yagi Antenna Based on MIC

abstract: To achieve better antenna performance, the optimization algorithms, changing the parameters of antenna configuration, have been widely applied in antenna design field. However, different physical parameters have significantly different impact on the optimization results and the awareness of their influence will improve the optimization efficiency. In this paper, Maximal Information Coefficient (MIC) is utilized to detect pairwise correlations for configuration and performance parameters of printed Yagi antenna. Finally, the relationships between these parameters have been derived by analyzing the large data sets.


title: Gaithashing: A two-factor authentication scheme based on gait features

abstract: Abstract Recently, gait recognition has attracted much attention as a biometric feature for real-time person authentication. The main advantage of gait is that it can be observed at a distance in an unobtrusive manner. However, the security of an authentication system, based only on gait features, can be easily broken. A malicious actor can observe the gait of an unsuspicious person and extract the related biometric template in a trivial manner and without being noticed. Another major issue of gait as an identifier has to do with their high intra-variance, since human silhouettes can be significantly modified, when for example the user holds a bag or wears a coat. This paper proposes gaithashing, a two-factor authentication that interpolates between the security features of biohash and the recognition capabilities of gait features to provide a high accuracy and secure authentication system. A novel characteristic of gaithashing is that it enrolls three different human silhouettes types. During authentication, the new extracted gait features and the enrollment ones are fused using weighted sums. By selecting appropriate weight values, the proposed scheme eliminates the noise and distortions caused by different silhouette types and achieves to authenticate a user independently of his/her silhouette. Apart from high accuracy, the proposed scheme provides revocability in case of a biometric template compromise. The performance of the proposed scheme is evaluated by carrying out a comprehensive set of experiments. Numerical results show that gaithashing outperforms existing solutions in terms of authentication performance, while at the same time achieves to secure the gait features.


title: Cost-Aware Collaborative Filtering for Travel Tour Recommendations

abstract: Advances in tourism economics have enabled us to collect massive amounts of travel tour data. If properly analyzed, this data could be a source of rich intelligence for providing real-time decision making and for the provision of travel tour recommendations. However, tour recommendation is quite different from traditional recommendations, because the tourist’s choice is affected directly by the travel costs, which includes both financial and time costs. To that end, in this article, we provide a focused study of cost-aware tour recommendation. Along this line, we first propose two ways to represent user cost preference. One way is to represent user cost preference by a two-dimensional vector. Another way is to consider the uncertainty about the cost that a user can afford and introduce a Gaussian prior to model user cost preference. With these two ways of representing user cost preference, we develop different cost-aware latent factor models by incorporating the cost information into the probabilistic matrix factorization (PMF) model, the logistic probabilistic matrix factorization (LPMF) model, and the maximum margin matrix factorization (MMMF) model, respectively. When applied to real-world travel tour data, all the cost-aware recommendation models consistently outperform existing latent factor models with a significant margin.


title: Using Six Sigma and Lean Methodologies to Improve OR Throughput

abstract: ABSTRACT •IMPROVING PATIENT FLOW in the perioperative environment is challenging, but it has positive implications for both staff members and for the facility. •ONE FACILITY IN VERMONT improved patient throughput by incorporating Six Sigma and Lean methodologies for patients undergoing elective procedures. •THE RESULTS OF THE PROJECT were significantly improved patient flow and increased teamwork and pride among perioperative staff members. AORN J 86 (July 2007) 73-82. © AORN, Inc, 2007.


Translation-based Recommendation: A Scalable Method for Modeling Sequential Behavior


Reverse Auctions : Benefits , Challenges , and Best Practices


title: Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking

abstract: To help accelerate progress in multi-target, multi-camera tracking systems, we present (i) a new pair of precision-recall measures of performance that treats errors of all types uniformly and emphasizes correct identification over sources of error; (ii) the largest fully-annotated and calibrated data set to date with more than 2 million frames of 1080 p, 60 fps video taken by 8 cameras observing more than 2,700 identities over 85 min; and (iii) a reference software system as a comparison baseline. We show that (i) our measures properly account for bottom-line identity match performance in the multi-camera setting; (ii) our data set poses realistic challenges to current trackers; and (iii) the performance of our system is comparable to the state of the art.


title: Genetic variation, morphology and pathogenicity of Ceratocystis fimbriata on Hevea brasiliensis in Brazil

abstract: Ceratocystis fimbriata causes diseases on a wide variety of plants in Brazil, including rubber tree (Hevea brasiliensis), on which it causes gray mold or moldy rot on tapping panels affecting latex yield. However, C. fimbriata isolated from rubber tree have not been critically studied. Phylogenetic analyses of sequences of ITS rDNA and a mating type gene placed rubber tree isolates from Acre and Bahia among Brazilian isolates of C. fimbriata from other hosts. In the analyses of 14 microsatellite loci, the rubber tree isolates from Bahia were identical to each other and had alleles similar to those of Brazilian isolates from mango and eucalyptus. The microsatellite alleles of the Acre rubber tree isolates were identical to each other but distinct from other Brazilian isolates. The rubber tree isolates were morphologically indistinguishable from each other and very similar to the isolates of C. fimbriata on Ipomoea batatas, on which the species was originally described. Based on inoculation experiments results, the Bahia and Acre rubber tree isolates do not appear to be host specialized, which is typical for Brazilian isolates of C. fimbriata sensu stricto.


title: Reinforcement Learning for Visual Object Detection

abstract: One of the most widely used strategies for visual object detection is based on exhaustive spatial hypothesis search. While methods like sliding windows have been successful and effective for many years, they are still brute-force, independent of the image content and the visual category being searched. In this paper we present principled sequential models that accumulate evidence collected at a small set of image locations in order to detect visual objects effectively. By formulating sequential search as reinforcement learning of the search policy (including the stopping condition), our fully trainable model can explicitly balance for each class, specifically, the conflicting goals of exploration – sampling more image regions for better accuracy –, and exploitation – stopping the search efficiently when sufficiently confident about the target's location. The methodology is general and applicable to any detector response function. We report encouraging results in the PASCAL VOC 2012 object detection test set showing that the proposed methodology achieves almost two orders of magnitude speed-up over sliding window methods.


title: Should I Stay or Should I Go? The Effects of Control Mechanisms on App Developers’ Intention to Stick with a Platform

abstract: Although control mechanisms have been widely studied in IS research within and between organizations, there is still a lack of research on control mechanisms applied in software-based platforms, on which the complex interactions between a platform owner and a myriad of third-party developers have to be coordinated. Drawing on IS control literature and self-determination theory, our study presents the findings of a laboratory experiment with 138 participants in which we examined how formal (i.e., output and process) and informal (i.e., self) control mechanisms affect third-party developers’ intentions to stick with a mobile app development platform. We demonstrate that selfcontrol plays a significantly more important role than formal control modes in increasing platform stickiness. We also find that the relationship between control modes and platform stickiness is fully mediated by developers’ perceived autonomy. Taken together, our study highlights the theoretically important finding that self-determination and self-regulation among third-party developers are stronger driving forces for staying with a platform than typical hierarchical control mechanisms. Implications for research and practice are discussed.


title: Solving a stochastic demand multi-product supplier selection model with service level and budget constraints using Genetic Algorithm

abstract: This study presents a stochastic demand multi-product supplier selection model with service level and budget constraints using Genetic Algorithm. Recently, much attention has been given to stochastic demand due to uncertainty in the real world. Conflicting objectives also exist between profit, service level and resource utilization. In this study, the relationship between the expected profit and the number of trials as well as between the expected profit and the combination of mutation and crossover rates are investigated to identify better parameter values to efficiently run the Genetic Algorithm. Pareto optimal solutions and return on investment are analyzed to provide decision makers with the alternative options of achieving the proper budget and service level. The results show that the optimal value for the return on investment and the expected profit are obtained with a certain budget and service level constraint.


title: A free simulation experiment to examine the effects of social commerce website quality and customer psychological empowerment on customers' satisfaction

abstract: This research was designed to theoretically address and empirically examine research issues related to customer’s satisfaction with social commerce. To investigate these research issues, data were collected using a written survey as part of a free simulation experiment. In this experiment, 136 participants were asked to evaluate two social commerce websites using an instrument designed to measure relationships between s-commerce website quality, customer psychological empowerment and customer satisfaction. A total of 278 usable s-commerce site evaluations were collected and analyzed. The results showed that customer satisfaction with social commerce is correlated with social commerce sites quality and customer psychological empowerment.


title: Providing culturally appropriate care: A literature review

abstract: Abstract Objectives As part of a study that explored how midwives incorporate cultural sensitivity, into their practice, the literature was reviewed to ascertain how the concept of culture has been, defined and what recommendations have been made as to how to provide culturally appropriate care, to individuals from Indigenous and/or ethnic minority backgrounds. Design A systematic review of the literature was undertaken. Data sources Electronic databases including Medline, Cinahl, Socio-file and Expanded Academic Index, were accessed. Review methods Several key search terms were used for example, midwife, midwives, midwifery, nurse, nurses, nursing, culture or cultural, diversity, sensitivity, competency and empowerment. The, results relating to midwifery were few; therefore ‘nursing' was included which increased the amount, of material. References that were deemed useful from bibliographies of relevant texts and journal, articles were included. The inclusion criteria were articles that provided information about culture, and/or the culturally appropriate care of individuals from Indigenous and/or ethnically, culturally and, linguistically diverse backgrounds. Materials reviewed for this paper satisfied the inclusion criteria. Results There are two main approaches to culture; the first focuses on the cognitive aspects of culture, the ‘values, beliefs and traditions' of a particular group, identified by language or location such as, ‘Chinese women' or ‘Arabic speaking women'. This approach views culture as static and unchanging, and fails to account for diversity within groups. The second approach incorporates culture within a wider, structural framework, focusing on social position to explain health status rather than on individual behaviours and beliefs. It includes perspectives on the impact of the colonial process on the ongoing relationships of Indigenous and non-Indigenous people and how this affects health and health care. Conclusion Most of the literature focuses on the cognitive aspects of culture and recommends learning about the culture of specific groups which is presumed to apply to everyone. This generic approach can, lead to stereotyping and a failure to identify the needs of the individual receiving care. The concept of, cultural safety derived from the second approach to culture and practice has potential but evidence to show how it is being incorporated into practice is lacking and health professionals appear to be unclear about its meaning.


title: Forecast the price of chemical products with multivariate data

abstract: Sales price of staple commodities plays an important role in human life and reflects production and sales of enterprises, so predicting the price accurately is of great significance. The price of chemical products has the characteristics of time series, nonlinear, unstable, etc, and has relationship with multiple variables which are affected by seasons, national policy and macro-economy. Therefore, predicting their price has become a challenging task. In this paper we propose a new prediction algorithm that exploits multivariate data with analysis including crawled web data related to chemical products and expert experience data. History data is first disposed and analyzed to build statistic and machine learning forecasting models. Then sentiment analysis is performed based on related data crawled from the internet measured by text analyzing techniques. Finally expert experience on forecasting the price is used to optimize the prediction results. We use methanol as an example to evaluate the accuracy of prediction results tracked for eight months, the MAPE (average absolute percent error) of our method is 2.91% better than other models. Compared with traditional prediction models, our model based on multivariate data has higher accuracy.


title: Use of Electronic Health Records in U.S. Hospitals

abstract: BACKGROUND ::: Despite a consensus that the use of health information technology should lead to more efficient, safer, and higher-quality care, there are no reliable estimates of the prevalence of adoption of electronic health records in U.S. hospitals. ::: ::: ::: METHODS ::: We surveyed all acute care hospitals that are members of the American Hospital Association for the presence of specific electronic-record functionalities. Using a definition of electronic health records based on expert consensus, we determined the proportion of hospitals that had such systems in their clinical areas. We also examined the relationship of adoption of electronic health records to specific hospital characteristics and factors that were reported to be barriers to or facilitators of adoption. ::: ::: ::: RESULTS ::: On the basis of responses from 63.1% of hospitals surveyed, only 1.5% of U.S. hospitals have a comprehensive electronic-records system (i.e., present in all clinical units), and an additional 7.6% have a basic system (i.e., present in at least one clinical unit). Computerized provider-order entry for medications has been implemented in only 17% of hospitals. Larger hospitals, those located in urban areas, and teaching hospitals were more likely to have electronic-records systems. Respondents cited capital requirements and high maintenance costs as the primary barriers to implementation, although hospitals with electronic-records systems were less likely to cite these barriers than hospitals without such systems. ::: ::: ::: CONCLUSIONS ::: The very low levels of adoption of electronic health records in U.S. hospitals suggest that policymakers face substantial obstacles to the achievement of health care performance goals that depend on health information technology. A policy strategy focused on financial support, interoperability, and training of technical support staff may be necessary to spur adoption of electronic-records systems in U.S. hospitals.


title: A survey of dimensionality reduction techniques based on random projection

abstract: Dimensionality reduction techniques play important roles in the analysis of big data. Traditional dimensionality reduction approaches, such as principal component analysis (PCA) and linear discriminant analysis (LDA), have been studied extensively in the past few decades. However, as the dimensionality of data increases, the computational cost of traditional dimensionality reduction methods grows exponentially, and the computation becomes prohibitively intractable. These drawbacks have triggered the development of random projection (RP) techniques, which map high-dimensional data onto a low-dimensional subspace with extremely reduced time cost. However, the RP transformation matrix is generated without considering the intrinsic structure of the original data and usually leads to relatively high distortion. Therefore, in recent years, methods based on RP have been proposed to address this problem. In this paper, we summarize the methods used in different situations to help practitioners to employ the proper techniques for their specific applications. Meanwhile, we enumerate the benefits and limitations of the various methods and provide further references for researchers to develop novel RP-based approaches.


title: The MAC unreliability problem in IEEE 802.15.4 wireless sensor networks

abstract: In recent years, the number of sensor network deployments for real-life applications has rapidly increased and it is expected to expand even more in the near future. Actually, for a credible deployment in a real environment three properties need to be fulfilled, i.e., energy efficiency, scalability and reliability. In this paper we focus on IEEE 802.15.4 sensor networks and show that they can suffer from a serious MAC unreliability problem, also in an ideal environment where transmission errors never occur. This problem arises whenever power management is enabled - for improving the energy efficiency - and results in a very low delivery ratio, even when the number of nodes in the network is very low (e.g., 5). We carried out an extensive analysis, based on simulations and real measurements, to investigate the ultimate reasons of this problem. We found that it is caused by the default MAC parameter setting suggested by the 802.15.4 standard. We also found that, with a more appropriate parameter setting, it is possible to achieve the desired level of reliability (as well as a better energy efficiency). However, in some scenarios this is possible only by choosing parameter values formally not allowed by the standard.


title: Generalized random shapelet forests

abstract: Shapelets are discriminative subsequences of time series, usually embedded in shapelet-based decision trees. The enumeration of time series shapelets is, however, computationally costly, which in addition to the inherent difficulty of the decision tree learning algorithm to effectively handle high-dimensional data, severely limits the applicability of shapelet-based decision tree learning from large (multivariate) time series databases. This paper introduces a novel tree-based ensemble method for univariate and multivariate time series classification using shapelets, called the generalized random shapelet forest algorithm. The algorithm generates a set of shapelet-based decision trees, where both the choice of instances used for building a tree and the choice of shapelets are randomized. For univariate time series, it is demonstrated through an extensive empirical investigation that the proposed algorithm yields predictive performance comparable to the current state-of-the-art and significantly outperforms several alternative algorithms, while being at least an order of magnitude faster. Similarly for multivariate time series, it is shown that the algorithm is significantly less computationally costly and more accurate than the current state-of-the-art.


title: Personalised gaming: a motivation and overview of literature

abstract: This article focuses on personalised games, which we define as games that utilise player models for the purpose of tailoring the game experience to the individual player. The main contribution of the article is a motivation for personalised gaming, supported by an extensive overview of scientific literature. The motivatin concerns (a) the psychological foundation, (b) the effect on player satisfaction, (c) the contribution to game development, and (d) the requirement for achieving ambitions. The provided overview of scientific literature goes into the subject of player modelling, as well as eight adaptive components: (1) space adaptation, (2) mission/task adaptation, (3) character adaptation, (4) game mechanics adaptation, (5) narrative adaptation, (6) music/sound adaptation, (7) player matching (multiplayer), and (8) difficulty scaling. In the concluding sections, the relationship to procedural content generation is discussed, as well as the generalisation to other domains.


title: Using ArchiMate to Represent ITIL Metamodel

abstract: Enterprise Architecture (EA) and ITIL, two distinct governance approaches with different perspectives, have become recently dominant between practitioners. However, parallel EA and ITIL projects can lead to wasted resources and a duplication of costs and efforts. This paper proposes an integration by approaching ITIL from an EA perspective and proposes a mapping of ITIL concepts to EA, and a set of models representing the ITIL metamodel using the ArchiMate modeling language. Our goal is twofold: on one hand to give the architect the elements, relationships and models that represent best practices in IT service management, and, on the other, to formally model ITIL for knowledge sharing, stakeholder communication and to contribute to ITIL discussion and validation. For evaluation we shall use interviews and the Wand and Weber ontological method.


title: An open-source OCR evaluation tool

abstract: This paper describes an open-source tool which computes statistics of the differences between a reference text an the output of an OCR engine. It also facilitates the spotting of mismatches by generating an aligned bitext where the differences are highlighted and cross linked. The tool accepts a variety of input formats (both for the reference and the OCR) and can also be also used to compare the output of two different OCR engines. Some considerations on the criteria to compare the textual content of two files, at character and word level, are also discussed here.


title: Compression Artifacts Removal Using Convolutional Neural Networks

abstract: This paper shows that it is possible to train large and deep convolutional neural networks (CNN) for JPEG compression ::: artifacts reduction, and that such networks can provide significantly better reconstruction quality compared to ::: previously used smaller networks as well as to any other state-of-the-art methods. We were able to train networks ::: with 8 layers in a single step and in relatively short time by combining residual learning, skip architecture, and ::: symmetric weight initialization. We provide further insights into convolution networks for JPEG artifact reduction ::: by evaluating three different objectives, generalization with respect to training dataset size, and generalization with ::: respect to JPEG quality level.


title: Collaborative Deep Ranking: A Hybrid Pair-Wise Recommendation Algorithm with Implicit Feedback

abstract: Collaborative Filtering with Implicit Feedbacks e.g., browsing or clicking records, named as CF-IF, is demonstrated to be an effective way in recommender systems. Existing works of CF-IF can be mainly classified into two categories, i.e., point-wise regression based and pair-wise ranking based, where the latter one relaxes assumption and usually obtains better performance in empirical studies. In real applications, implicit feedback is often very sparse, causing CF-IF based methods to degrade significantly in recommendation performance. In this case, side information e.g., item content is usually introduced and utilized to address the data sparsity problem. Nevertheless, the latent feature representation learned from side information by topic model may not be very effective when the data is too sparse. To address this problem, we propose collaborative deep ranking CDR, a hybrid pair-wise approach with implicit feedback, which leverages deep feature representation of item content into Bayesian framework of pair-wise ranking model in this paper. The experimental analysis on a real-world dataset shows CDR outperforms three state-of-art methods in terms of recall metric under different sparsity level.


title: Why Do Customer Relationship Management Applications Affect Customer Satisfaction

abstract: This research evaluates the effect of customer relationship management (CRM) on customer knowledge and customer satisfaction. An analysis of archival data for a cross-section of U.S. firms shows that the use of CRM applications is positively associated with improved customer knowledge and improved customer satisfaction. This article also shows that gains in customer knowledge are enhanced when firms share their customer-related information with their supply chain partners.


title: Backlash Agent: A trading strategy based on Directional Change

abstract: Directional Change (DC) is a technique to summarize price movements in a financial market. According to the DC concept, data is sampled only when the magnitude of price change is significant according to the investor. Unlike with time series, DC samples data at irregular time intervals. In this paper, we propose a contrarian trading strategy that is based on the DC concept. We examine the profitability of our trading strategy using three currency pairs: EUR/CHF, GBP/CHF and EUR/USD. The results show that our proposed trading strategy is profitable with Alpha over than 10 in some cases. However, counting the bid and ask prices can decrease considerably the profits under particular settings.


title: What Will 5G Be?

abstract: What will 5G be? What it will not be is an incremental advance on 4G. The previous four generations of cellular technology have each been a major paradigm shift that has broken backward compatibility. Indeed, 5G will need to be a paradigm shift that includes very high carrier frequencies with massive bandwidths, extreme base station and device densities, and unprecedented numbers of antennas. However, unlike the previous four generations, it will also be highly integrative: tying any new 5G air interface and spectrum together with LTE and WiFi to provide universal high-rate coverage and a seamless user experience. To support this, the core network will also have to reach unprecedented levels of flexibility and intelligence, spectrum regulation will need to be rethought and improved, and energy and cost efficiencies will become even more critical considerations. This paper discusses all of these topics, identifying key challenges for future research and preliminary 5G standardization activities, while providing a comprehensive overview of the current literature, and in particular of the papers appearing in this special issue.


title: A Communication-Assisted Protection Strategy for Inverter-Based Medium-Voltage Microgrids

abstract: This paper proposes a communication-assisted protection strategy implementable by commercially available microprocessor-based relays for the protection of medium-voltage microgrids. Even though the developed protection strategy benefits from communications, it offers a backup protection strategy to manage communication network failures. The paper also introduces the structure of a relay that enables the proposed protection strategy. Comprehensive simulation studies are carried out to verify the effectiveness of the proposed protection strategy under different fault scenarios, in the PSCAD/EMTDC software environment.


title: Happy People Live Longer: Subjective Well-Being Contributes to Health and Longevity

abstract: Seven types of evidence are reviewed that indicate that high subjective well-being (such as life satisfaction, absence of negative emotions, optimism, and positive emotions) causes better health and longevity. For example, prospective longitudinal studies of normal populations provide evidence that various types of subjective well-being such as positive affect predict health and longevity, controlling for health and socioeconomic status at baseline. Combined with experimental human and animal research, as well as naturalistic studies of changes of subjective well-being and physiological processes over time, the case that subjective well-being influences health and longevity in healthy populations is compelling. However, the claim that subjective well-being lengthens the lives of those with certain diseases such as cancer remains controversial. Positive feelings predict longevity and health beyond negative feelings. However, intensely aroused or manic positive affect may be detrimental to health. Issues such as causality, effect size, types of subjective well-being, and statistical controls are discussed.


title: Group Sparse Coding

abstract: Bag-of-words document representations are often used in text, image and video processing. While it is relatively easy to determine a suitable word dictionary for text documents, there is no simple mapping from raw images or videos to dictionary terms. The classical approach builds a dictionary using vector quantization over a large set of useful visual descriptors extracted from a training set, and uses a nearest-neighbor algorithm to count the number of occurrences of each dictionary word in documents to be encoded. More robust approaches have been proposed recently that represent each visual descriptor as a sparse weighted combination of dictionary words. While favoring a sparse representation at the level of visual descriptors, those methods however do not ensure that images have sparse representation. In this work, we use mixed-norm regularization to achieve sparsity at the image level as well as a small overall dictionary. This approach can also be used to encourage using the same dictionary words for all the images in a class, providing a discriminative signal in the construction of image representations. Experimental results on a benchmark image classification dataset show that when compact image or dictionary representations are needed for computational efficiency, the proposed approach yields better mean average precision in classification.


title: Multi-dimensional sequential pattern mining

abstract: Sequential pattern mining, which finds the set of frequent subsequences in sequence databases, is an important data-mining task and has broad applications. Usually, sequence patterns are associated with different circumstances, and such circumstances form a multiple dimensional space. For example, customer purchase sequences are associated with region, time, customer group, and others. It is interesting and useful to mine sequential patterns associated with multi-dimensional information.In this paper, we propose the theme of multi-dimensional sequential pattern mining, which integrates the multidimensional analysis and sequential data mining. We also thoroughly explore efficient methods for multi-dimensional sequential pattern mining. We examine feasible combinations of efficient sequential pattern mining and multi-dimensional analysis methods, as well as develop uniform methods for high-performance mining. Extensive experiments show the advantages as well as limitations of these methods. Some recommendations on selecting proper method with respect to data set properties are drawn.


title: CT of Gastric Emergencies.

abstract: This pictorial essay reviews pertinent gastric anatomy and suggests specific imaging protocols for evaluating the stomach. Common and uncommon gastric conditions that may prompt evaluation in the emergency department are addressed, with a focus on clinical symptoms, imaging findings, differentiating features, and potential pitfalls.


title: A Hybrid Intelligence/Multi-agent System Approach for Mining Information Assurance Data

abstract: Organizations of all sizes wrestle with the problem of "coping with information overload." They ingest more and more data, in new and varied formats every day, and struggle more and more vigorously to find the nuggets of knowledge hidden away within the vast amounts of information. Furthermore, due to the various and pervasive types of noise in the haystack of data, it is increasingly and exceedingly difficult to discern between the shining false shards and the true needles of knowledge. In the grander scheme of our work we intend to demonstrate that a hybrid intelligence/multi-agent systems-based overarching layer, which collates, compares and contrasts input from several traditional data mining applications below it, will yield far more accurate results than any one application acting on its own.


title: Visual Gesture Recognition

abstract: Presents a method for recognising human-hand gestures using a model based approach. A finite state machine is used to model four qualitatively distinct phases of a generic gesture. Fingertips are tracked in multiple frames to compute motion trajectories. The trajectories are then used for finding the start and stop position of the gesture. Gestures are represented as a list of vectors and are then matched to stored gesture vector models using table lookup based on vector displacements. Results are presented showing recognition of seven gestures using images sampled at 4 Hz on a SPARC-1 without any special hardware. The seven gestures are representatives for actions of left, right, up, down, grab, rotate, and stop.


title: A fully homomorphic encryption scheme with symmetric keys with application to private data processing in clouds

abstract: With single computer's computation power not sufficing, need for sharing resources to manipulate and manage data through clouds is increasing rapidly. Hence, it is favorable to delegate computations or store data with a third party, the cloud provider. However, delegating data to third party poses the risks of data disclosure during computation. The problem can be addressed by carrying out computation without decrypting the encrypted data. The results are also obtained encrypted and can be decrypted at the user side. This requires modifying functions in such a way that they are still executable while privacy is ensured or to search an encrypted database. Homomorphic encryption provides security to cloud consumer data while preserving system usability. We propose a symmetric key homomorphic encryption scheme based on matrix operations with primitives that make it easily adaptable for different needs in various cloud computing scenarios.


title: A Logic for Default Reasoning

abstract: The need to make default assumptions is frequently encountered in reasoning about incompletely specified worlds. Inferences sanctioned by default are best viewed as beliefs which may well be modified or rejected by subsequent observations. It is this property which leads to the non-monotonicity of any logic of defaults. ::: ::: In this paper we propose a logic for default reasoning. We then specialize our treatment to a very large class of commonly occuring defaults. For this class we develop a complete proof theory and show how to interface it with a top down resolution theorem prover. Finally, we provide criteria under which the revision of derived beliefs must be effected.


title: Probabilistic interval method for phased array sensitivity analysis

abstract: This paper presents a novel probabilistic method based on Interval Analysis for predicting the power pattern of a linear array whose element excitation amplitudes are subject to errors. Probabilistic upper and lower bounds of the power pattern have been defined by means of the Interval Arithmetic according to the probability density functions used to model the statistical behavior of the excitation amplitude uncertainties.


title: Mechanisms Employed by Trichoderma Species in the Biological Control of Plant Diseases: The History and Evolution of Current Concepts

abstract: Fungal species belonging to the genus Trichoderma are worldwide in occurrence and easily isolated from soil, decaying wood, and other forms of plant organic matter. They are, for the most part, classified as imperfect fungi, in that they have no known sexual stage. Rapid growth rate in culture and the production of numerous spores (conidia) that are varying shades of green characterize fungi in this genus. The reverse side of colonies is often uncolored, buff, yellow, amber, or yellow-green, and many species produce prodigious quantities of thick-walled spores (chlamydospores) in submerged mycelium (8). The potential of Trichoderma species as biocontrol agents of plant diseases was first recognized in the early 1930s (31), and in subsequent years, control of many diseases has been added to the list (1,3,5,7,9,11,19, 23,29,34,37,40). This has culminated in the commercial production of several Trichoderma species for the protection and growth enhancement of a number of crops in the United States (24), and in the production of Trichoderma species and mixtures of species in India, Israel, New Zealand, and Sweden (D. R. Fravel, personal communication). One of the most interesting aspects of the science of biological control is the study of the mechanisms employed by biocontrol agents to effect disease control. Past research indicates that the mechanisms are many and varied, even within the genus Trichoderma. In order to make the most effective use of biocontrol agents for the control of plant diseases, we must understand how the agents work and what their limitations are. We can then develop effective means of culturing, storing, applying, and utilizing biocontrol agents so that we harness their best effort for disease control. The selected research papers cited in this article were chosen because they illustrate what has been learned about mechanisms involved in biocontrol with Trichoderma species.


title: Massive scale cyber traffic analysis: a driver for graph database research

abstract: We consider cyber traffic analysis (TA) as a challenge problem for research in graph database systems. TA involves observing and analyzing connections between clients, servers, hosts, and actors within IP networks, over time, to detect suspicious patterns. Towards that end, NetFlow (or more generically, IPFLOW) data are available from routers and servers which summarize coherent groups of IP packets flowing through the network. The ability to cast IPFLOW data as a massive graph and query it interactively is potentially transformative for cybersecurity, but issues of scale and data complexity pose challenges for current technology. In this paper, we outline requirements and opportunities for graph-structured IPFLOW analytics based on our experience with real IPFLOW databases. We describe real use cases from the security domain, cast them as graph patterns, show how to express them in two graph-oriented query languages (SPARQL and Datalog), and use these examples to motivate a new class of "hybrid" graph-relational systems.


title: Defining ETL worfklows using BPMN and BPEL

abstract: Decisional systems are crucial for enterprise improvement. They allow the consolidation of heterogeneous data from distributed enterprise data stores into strategic indicators. An essential component of this data consolidation is the Extract, Transform, and Load (ETL) process. In the research literature there has been very few work defining conceptual models for ETL processes. At the same time, there are currently many tools that manage such processes. However, each tool uses its own model, which is not necessarily able to communicate with the models of other tools. In this paper, we propose a platform-independent conceptual model of ETL processes based on the Business Process Model Notation (BPMN) standard. We also show how such a conceptual model can be implemented using Business Process Execution Language (BPEL), a standard executable language for specifying interactions with web services.


title: Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos

abstract: Every moment counts in action recognition. A comprehensive understanding of human activity in video requires labeling every frame according to the actions occurring, placing multiple labels densely over a video sequence. To study this problem we extend the existing THUMOS dataset and introduce MultiTHUMOS, a new dataset of dense labels over unconstrained internet videos. Modeling multiple, dense labels benefits from temporal relations within and across classes. We define a novel variant of long short-term memory deep networks for modeling these temporal relations via multiple input and output connections. We show that this model improves action labeling accuracy and further enables deeper understanding tasks ranging from structured retrieval to action prediction.


title: A high efficiency bridgeless flyback PFC converter for adapter application

abstract: This paper proposes a high efficiency low cost AC/DC converter for adapter application. In order to achieve high efficiency and low cost for adapter with universal AC input, a single stage bridgeless flyback PFC converter with peak current clamping technique was proposed. Compared with conventional flyback PFC converter, the conduction loss is reduced due to bridgeless structure. And the size of transformer can also be significantly reduced due to lower peak current, which results in lower cost and higher power density. Detailed operation principles and design considerations are illustrated. Experimental results from a 90W prototype with universal input and 20V/4.5A output are presented to verify the operation and performance of the proposed converter. The minimum efficiency at full load is above 91% over the entire input range.


title: Effects of cetirizine in dogs with chronic atopic dermatitis: a randomized, double blind, placebo-controlled trial

abstract: It was the aim of this study to evaluate the effect of cetirizine in dogs with atopic dermatitis (AD), fulfilling Favrot's diagnostic clinical criteria. In a randomized, double blind, placebo-controlled study, the dogs received either 3 mg/kg cetirizine orally once daily (n=27), or a placebo (n=23) for 14 days, without concomitant medication. The effect was evaluated by means of a pruritus visual analog scale at the start (D0) and at D14. After 14 days, cetirizine had clearly no effect on the pruritus in dogs with chronic AD and there was no significant difference between the two groups. It is concluded that cetirizine (and likely H1 histamine receptor antagonists in general) should not be recommended for the control of pruritus in dogs with a long term allergy.


title: Relational contracts and the theory of the firm

abstract: Relational contracts—informal agreements sustained by the value of future relationships—are prevalent within and between firms. We develop repeated-game models showing why and how relational contracts within firms (vertical integration) differ from those between (nonintegration). We show that integration affects the parties' temptations to renege on a given relational contract, and hence affects the best relational contract the parties can sustain. In this sense, the integration decision can be an instrument in the service of the parties' relationship. Our approach also has implications for joint ventures, alliances, and networks, and for the role of management within and between firms.


title: Toward dynamic programming-based management in reconfigurable battery packs

abstract: Well-designed battery energy management algorithms are integral and important parts of battery management and maintenance in various applications ranging from smart grid backup systems to Electric and Hybrid Electric Vehicles (EV/HEV). Management in smart reconfigurable battery systems tend to be more complicated, flexible, and sophisticated since the systematic ability allows access to individual cells for monitoring and control purposes in a real-time fashion. Therefore, in this study, based on our previous work on adaptive reconfigurable battery network, a dynamic programming based management strategy is proposed and validated to fully utilize systematic ability and to optimize energy efficiency while keeping cell to cell states balanced and ensuring safety. To validate the proposed algorithm, battery model is first set up using experimental data on 26650 lithium ion batteries with the help of Arbin test-bed. Simulations are then conducted using the established battery models to figure out the gain in terms of energy efficiency of the proposed algorithm compared with traditional fixed battery system design. The comparison indicates the effeteness of the algorithm. Furthermore, the proposed method is applicable to battery packs with different types of battery cells in terms of capacity and electrochemistry, which is the key idea of software defined battery.


title: Enhanced Automatic X-Ray Bone Image Segmentation using Wavelets and Morphological Operators

abstract: X-ray bone segmentation is a vital step in X-Ray image analysis techniques. the main aim of segmentation is to subdivide the various portions, so that it can help medical practitioners (i) During the study of bone structure, (ii) Identification of bone fracture, (iii) Measurement of fracture treatment, (iv) Treatment planning prior to surgery. It is considered as a challenging task because the bone x-ray images are complex in nature and the output of segmentation algorithm is affected due to various factors like partial volume effect, intensity inhomogeneity, presence of noise and artifacts and Closeness in gray level of different soft tissues


title: A Theoretical and Computational Framework for Isometry Invariant Recognition of Point Cloud Data

abstract: Point clouds are one of the most primitive and fundamental manifold representations. Popular sources of point clouds are three-dimensional shape acquisition devices such as laser range scanners. Another important field where point clouds are found is in the representation of high-dimensional manifolds by samples. With the increasing popularity and very broad applications of this source of data, it is natural and important to work directly with this representation, without having to go through the intermediate and sometimes impossible and distorting steps of surface reconstruction. A geometric framework for comparing manifolds given by point clouds is presented in this paper. The underlying theory is based on Gromov-Hausdorff distances, leading to isometry invariant and completely geometric comparisons. This theory is embedded in a probabilistic setting as derived from random sampling of manifolds, and then combined with results on matrices of pairwise geodesic distances to lead to a computational implementation of the framework. The theoretical and computational results presented here are complemented with experiments for real three-dimensional shapes.


Primary cutaneous marginal zone B-cell lymphoma


title: Multimode and Wideband Printed Loop Antenna Based on Degraded Split-Ring Resonators

abstract: In this paper, a compound multimode printed loop antenna based on degraded spilt-ring resonators (SRRs) for broadband wireless applications is proposed. The proposed antenna consists of an outer split ring and one inner closed ring with a big difference in size, which can be considered as degraded SRRs. By virtue of the orthogonal radiating mode of two rings, the compound multimode loop antenna can achieve multi-band resonances. Collaborating with the two coupled rings, a parasitic strip is placed close to the feed gap of the inner ring, which not only induces a new resonant frequency at upper band but also greatly improves the impedance matching in all high-frequency bands. The proposed antenna not only covers the first split ring mode at 816 MHz but also exhibits a broadband property covering from 2.3 to 4.6 GHz (reflection coefficient $|S_{11}| fractional bandwidth). The antenna operational principle and physical mechanism are analyzed by carrying out the studies of mode analysis and surface current distribution. To demonstrate the effectiveness of the proposed antenna, an antenna prototype is fabricated and tested. It is numerically and experimentally proved that broadband and multimode characteristics, stable radiation patterns with a peak gain of 1.2 and 5.1 dBi in the low- and high-frequency bands respectively, and more than 80% efficiency can be achieved. With merits of a compact size of $0.12\lambda _{0}\times 0.11\lambda _{0}$ ( $\lambda _{0}$ is the wavelength at the lowest operating frequency), uniplanar and simple printed structure and wide bandwidth characteristics make it suitable for a wide range of wireless applications.


title: Reliability of Ashworth and Modified Ashworth Scales in Children with Spastic Cerebral Palsy

abstract: Measurement of spasticity is a difficult and unresolved problem, partly due to its complexity and the fact that there are many factors involved. In the assessment of spasticity in the pediatric disabled population, methods that are easily used in practice are ordinal scales that still lack reliability. A prospective cross-sectional observational study was planned to determine the reliability of the Ashworth Scale (AS) and the Modified Ashworth Scale (MAS) in children with spastic cerebral palsy (CP).


title: SECURITY SUBCULTURES IN AN ORGANIZATION- EXPLORING

abstract: Security culture is considered as an important factor in overcoming the problem with employees’ lack of compliance with Information Security (IS) policies. Within one organization different subcultures might transcribe to different and sometimes even conflicting, values. In this paper we study such value conflicts and their implications on IS management and practice. Shein’s (1999) model of organizational culture is used as a tool supporting analysis of our empirical data. We found that value conflicts exists between different security cultures within the same organization and that users anchor their values related to IS in their professional values. Thus our empirical results highlight value conflicts as an important factor to take into account when security culture is developed in an organization. Moreover, we found Shein’s model as a useful tool for analysis of value conflicts between different subcultures in an organization.


Information Needs and Information Usage of the Older Person Club’s Members in Bangkok


title: Deep-Net: Deep Neural Network for Cyber Security Use Cases

abstract: Deep neural networks (DNNs) have witnessed as a powerful approach in this year by solving long-standing Artificial intelligence (AI) supervised and unsupervised tasks exists in natural language processing, speech processing, computer vision and others. In this paper, we attempt to apply DNNs on three different cyber security use cases: Android malware classification, incident detection and fraud detection. The data set of each use case contains real known benign and malicious activities samples. The efficient network architecture for DNN is chosen by conducting various trails of experiments for network parameters and network structures. The experiments of such chosen efficient configurations of DNNs are run up to 1000 epochs with learning rate set in the range [0.01-0.5]. Experiments of DNN performed well in comparison to the classical machine learning algorithms in all cases of experiments of cyber security use cases. This is due to the fact that DNNs implicitly extract and build better features, identifies the characteristics of the data that lead to better accuracy. The best accuracy obtained by DNN and XGBoost on Android malware classification 0.940 and 0.741, incident detection 1.00 and 0.997 fraud detection 0.972 and 0.916 respectively.


Draft version for NIPS 2004 Euclidean Embedding of Co-occurrence Data


title: Recovering 6D Object Pose: Multi-modal Analyses on Challenges

abstract: A large number of studies analyse object detection and pose estimation at visual level in 2D, discussing the effects of challenges such as occlusion, clutter, texture, etc., on the performances of the methods, which work in the context of RGB modality. Interpreting the depth data, the study in this paper presents thorough multi-modal analyses. It discusses the above-mentioned challenges for full 6D object pose estimation in RGB-D images comparing the performances of several 6D detectors in order to answer the following questions: What is the current position of the computer vision community for maintaining"automation"in robotic manipulation? What next steps should the community take for improving"autonomy"in robotics while handling objects? Direct comparison of the detectors is difficult, since they are tested on multiple datasets with different characteristics and are evaluated using widely varying evaluation protocols. To deal with these issues, we follow a threefold strategy: five representative object datasets, mainly differing from the point of challenges that they involve, are collected. Then, two classes of detectors are tested on the collected datasets. Lastly, the baselines' performances are evaluated using two different evaluation metrics under uniform scoring criteria. Regarding the experiments conducted, we analyse our observations on the baselines along with the challenges involved in the interested datasets, and we suggest a number of insights for the next steps to be taken, for improving the autonomy in robotics.


title: Design and implementation of water level control using gain scheduling PID back calculation integrator Anti Windup

abstract: Conventional PID controller is a simplest known controller and has been used in almost all process industries for controlling process parameter at desired set point. However, the weakness of the controller is largely depend on the parameter of the controlled object. As the dynamics process of a water tank is nonlinear, it also has nonlinear behaviour between the input and output. This paper demonstrates that Gain Scheduling PID with Back Calculation Integrator Antiwindup enables to enhance the response of automatic water level control system performance. A mathematical model of a first order tank system is considered and simulated to determine fine tunes and schedules for the controller parameter based on trial and error experiment so as to adapt with all operating points. The system performance comparison of various setpoint given to the system is performed to prove that the Gain Scheduling PID with Back Calculation Integrator Antiwindup outpaces conventional PID controller.


title: An intelligent content-based image retrieval system for clinical decision support in brain tumor diagnosis

abstract: Accurate diagnosis is crucial for successful treatment of the brain tumor. Accordingly in this paper, we propose an intelligent content-based image retrieval (CBIR) system which retrieves similar pathology bearing magnetic resonance (MR) images of the brain from a medical database to assist the radiologist in the diagnosis of the brain tumor. A single feature vector will not perform well for finding similar images in the medical domain as images within the same disease class differ by severity, density and other such factors. To handle this problem, the proposed CBIR system uses a two-step approach to retrieve similar MR images. The first step classifies the query image as benign or malignant using the features that discriminate the classes. The second step then retrieves the most similar images within the predicted class using the features that distinguish the subclasses. In order to provide faster image retrieval, we propose an indexing method called clustering with principal component analysis (PCA) and KD-tree which groups subclass features into clusters using modified K-means clustering and separately reduces the dimensionality of each cluster using PCA. The reduced feature set is then indexed using a KD-tree. The proposed CBIR system is also made robust against misalignment that occurs during MR image acquisition. Experiments were carried out on a database consisting of 820 MR images of the brain tumor. The experimental results demonstrate the effectiveness of the proposed system and show the viability of clinical application.


title: The harsh rule of the goals: Data-driven performance indicators for football teams

abstract: Sports analytics in general, and football (soccer in USA) analytics in particular, have evolved in recent years in an amazing way, thanks to automated or semi-automated sensing technologies that provide high-fidelity data streams extracted from every game. In this paper we propose a data-driven approach and show that there is a large potential to boost the understanding of football team performance. From observational data of football games we extract a set of pass-based performance indicators and summarize them in the H indicator. We observe a strong correlation among the proposed indicator and the success of a team, and therefore perform a simulation on the four major European championships (78 teams, almost 1500 games). The outcome of each game in the championship was replaced by a synthetic outcome (win, loss or draw) based on the performance indicators computed for each team. We found that the final rankings in the simulated championships are very close to the actual rankings in the real championships, and show that teams with high ranking error show extreme values of a defense/attack efficiency measure, the Pezzali score. Our results are surprising given the simplicity of the proposed indicators, suggesting that a complex systems' view on football data has the potential of revealing hidden patterns and behavior of superior quality.


title: Performance appraisal as an antecedent to innovation: An analysis of its importance relative to other human resource practices

abstract: Orientation: Innovation is essential to organisational survival, and several studies have shown that performance appraisals (PAs) contribute to innovation. Research purpose: The purpose of this study was to examine the importance of PA as an antecedent to innovation relative to other human resource practices (HRPs). Motivation for the study: The specific HRP drivers of innovation among employees and across organisations are not well specified, hindering appropriate resource allocation. Research approach/design and method: The PA–innovation link was investigated among 3180 employees across 53 South African organisations, utilising a cross-sectional survey design involving quantitative data, and focusing on correlation and regression analyses. Main findings: Human resource practices accounted for approximately 10% of the variance in innovation when considering individual employees. Performance appraisal was neither a common nor a unique predictor of innovation. When focusing on the link across organisations, a significant HRPs–innovation link was established in approximately 60% of organisations, with PA playing a significant role as the predictor of innovation in 10 (out of 53) organisations. Practical/managerial implications: Other HRPs, specifically supervisor support and staffing, played a much bigger role than PA in driving innovation of individuals, also across organisations. This specifies the relative importance of PA amongst other HRPs. Contribution/value-add: General managers, human resource practitioners and researchers can now use data-driven evidence to select specific HRPs which significantly enhance innovation among employees and across organisations.


title: MalDozer: Automatic framework for android malware detection using deep learning

abstract: Abstract Android OS experiences a blazing popularity since the last few years. This predominant platform has established itself not only in the mobile world but also in the Internet of Things (IoT) devices. This popularity, however, comes at the expense of security, as it has become a tempting target of malicious apps. Hence, there is an increasing need for sophisticated, automatic, and portable malware detection solutions. In this paper, we propose MalDozer, an automatic Android malware detection and family attribution framework that relies on sequences classification using deep learning techniques. Starting from the raw sequence of the app's API method calls, MalDozer automatically extracts and learns the malicious and the benign patterns from the actual samples to detect Android malware. MalDozer can serve as a ubiquitous malware detection system that is not only deployed on servers, but also on mobile and even IoT devices. We evaluate MalDozer on multiple Android malware datasets ranging from 1 K to 33 K malware apps, and 38 K benign apps. The results show that MalDozer can correctly detect malware and attribute them to their actual families with an F1-Score of 96%–99% and a false positive rate of 0.06%–2%, under all tested datasets and settings.


title: BIER — Boosting Independent Embeddings Robustly

abstract: Learning similarity functions between image pairs with deep neural networks yields highly correlated activations of large embeddings. In this work, we show how to improve the robustness of embeddings by exploiting independence in ensembles. We divide the last embedding layer of a deep network into an embedding ensemble and formulate training this ensemble as an online gradient boosting problem. Each learner receives a reweighted training sample from the previous learners. This leverages large embedding sizes more effectively by significantly reducing correlation of the embedding and consequently increases retrieval accuracy of the embedding. Our method does not introduce any additional parameters and works with any differentiable loss function. We evaluate our metric learning method on image retrieval tasks and show that it improves over state-ofthe- art methods on the CUB-200-2011, Cars-196, Stanford Online Products, In-Shop Clothes Retrieval and VehicleID datasets by a significant margin.


title: A hybrid framework combining solar energy harvesting and wireless charging for wireless sensor networks

abstract: Recently, there have been a growing number of applications that power wireless sensor networks (WSNs) by wireless charging technology. Although previous studies indicate that wireless charging can deliver energy reliably, it still faces regulatory challenges to provide high power density without incurring health risks. In particular, in clustered WSNs there exists a mismatch between the high energy demands from cluster heads and the relatively low energy supplies that wireless charging can provide. Fortunately, solar energy harvesting can provide high power density which is also risk-free. However, it is subject to weather dynamics. Therefore, in this paper, we propose a hybrid framework that combines the two technologies — cluster heads are equipped with solar panels to scavenge solar energy and the rest of nodes are powered by wireless charging. First, we study a placement problem on how to deploy solar-powered cluster heads that can minimize overall cost and propose a distributed 1.61(1 + ∊)2-approximation algorithm for the placement. Second, we establish an energy balance in the network and explore how to maintain such balance when sunlight is unavailable. Third, we consider combining wireless charging and mobile data gathering in a joint tour in such networks, and propose a polynomial-time scheduling algorithm. Our extensive simulation demonstrates that the hybrid framework can reduce battery depletion by 20% and save system cost by 25% compared to previous results.


title: Identifying Personal Genomes by Surname Inference

abstract: Sharing sequencing data sets without identifiers has become a common practice in genomics. Here, we report that surnames can be recovered from personal genomes by profiling short tandem repeats on the Y chromosome (Y-STRs) and querying recreational genetic genealogy databases. We show that a combination of a surname with other types of metadata, such as age and state, can be used to triangulate the identity of the target. A key feature of this technique is that it entirely relies on free, publicly accessible Internet resources. We quantitatively analyze the probability of identification for U.S. males. We further demonstrate the feasibility of this technique by tracing back with high probability the identities of multiple participants in public sequencing projects.


title: The endocannabinoid anandamide inhibits neuronal progenitor cell differentiation through attenuation of the Rap1/B-Raf/ERK pathway.

abstract: Endocannabinoids are neuromodulators that act as retrograde synaptic messengers inhibiting the release of different neurotransmitters in cerebral areas such as hippocampus, cortex, and striatum. However, little is known about other roles of the endocannabinoid system in brain. In the present work we provide substantial evidence that the endocannabinoid anandamide (AEA) regulates neuronal differentiation both in culture and in vivo. Thus AEA, through the CB(1) receptor, inhibited cortical neuron progenitor differentiation to mature neuronal phenotype. In addition, human neural stem cell differentiation and nerve growth factor-induced PC12 cell differentiation were also inhibited by cannabinoid challenge. AEA decreased PC12 neuronal-like generation via CB(1)-mediated inhibition of sustained extracellular signal-regulated kinase (ERK) activation, which is responsible for nerve growth factor action. AEA thus inhibited TrkA-induced Rap1/B-Raf/ERK activation. Finally, immunohistochemical analyses by confocal microscopy revealed that adult neurogenesis in dentate gyrus was significantly decreased by the AEA analogue methanandamide and increased by the CB(1) antagonist SR141716. These data indicate that endocannabinoids inhibit neuronal progenitor cell differentiation through attenuation of the ERK pathway and suggest that they constitute a new physiological system involved in the regulation of neurogenesis.


title: Value, rareness, competitive advantage, and performance: a conceptual-level empirical investigation of the resource-based view of the firm

abstract: The resource-based view of the firm (RBV) hypothesizes that the exploitation of valuable, rare resources and capabilities contributes to a firm's competitive advantage, which in turn contributes to its performance. Despite this notion, few empirical studies test these hypotheses at the conceptual level. In response to this gap, this study empirically examines the relationships between value, rareness, competitive advantage, and performance. The results suggest that value and rareness are related to competitive advantage, that competitive advantage is related to performance, and that competitive advantage mediates the rareness-performance relationship. These findings have important academic and practitioner implications which are then discussed. Copyright © 2008 John Wiley & Sons, Ltd.


title: Decoupling control of bridle rolls for steel mill drive system

abstract: A decoupling controller between the tension and speed of a steel processing line is proposed for bridle rolls of the steel mill drive system. With the controller, the tension of the steel strip is kept as a reference value, regardless of the line speed variations, and the speed of the strip is also kept, regardless of the tension variations. The controller generates a torque reference of each roll driving motor according to the average roll speed and required tension, which is in contrast to the conventional controller, where one regulates line speed and the other tension. The effectiveness and the performance of the controller is verified by computer simulations and prototype experiments.


title: Investigating neural network based query-by-example keyword spotting approach for personalized wake-up word detection in Mandarin Chinese

abstract: We use query-by-example keyword spotting (QbyE-KWS) approach to solve the personalized wake-up word detection problem for small-footprint, low-computational cost on-device applications. QbyE-KWS takes keywords as templates, and matches the templates across an audio stream via DTW to see if the keyword is included. In this paper, we use neural networks as acoustic models to extract DNN/LSTM phoneme posterior features and LSTM embedding features. Specifically, we investigate the LSTM embedding feature extractor for different modeling units in Mandarin, spanning from phonemes to words. We also study the performances of two popular DTW approaches: S-DTW and SLN-DTW. SLN-DTW manages to accurately and effectively search the keyword in a long audio stream without the segmentation procedure that is used in S-DTW approaches. Our study shows that DNN phoneme posterior plus SLN-DTW approach achieves the highest computation efficiency and the state-of-the-art performance with 78% relative miss rate reduction as compared with the S-DTW approach. Word level LSTM embedding feature shows superior performance as compared with other embedding units.


title: Breast cancer surgery survivability prediction using bayesian network and support vector machines

abstract: Predicting the survival status of patients who will undergo breast cancer surgery is highly important, where it indicates whether conducting a surgery is the best solution for the presented medical case or not. Since this is a case of life or death, the need to explore better prediction techniques to ensure accurate survival status prediction cannot be overemphasized. In this paper we evaluate the performance of support vector machine (SVM) and Bayesian network (BN) in predicting the survival state of breast cancer patients after having a surgery. The experiments on both techniques have been carried out using Weka software package. Empirical results from simulations showed that support vector machine outperformed Bayesian network in this task, where support vector machine achieved better accuracy of 74.44% while Bayesian network had its best accuracy of 67.56%.


title: Using Augmented Reality to Elicit Pretend Play for Children with Autism

abstract: Children with autism spectrum condition (ASC) suffer from deficits or developmental delays in symbolic thinking. In particular, they are often found lacking in pretend play during early childhood. Researchers believe that they encounter difficulty in generating and maintaining mental representation of pretense coupled with the immediate reality. We have developed an interactive system that explores the potential of Augmented Reality (AR) technology to visually conceptualize the representation of pretense within an open-ended play environment. Results from an empirical study involving children with ASC aged 4 to 7 demonstrated a significant improvement of pretend play in terms of frequency, duration and relevance using the AR system in comparison to a non computer-assisted situation. We investigated individual differences, skill transfer, system usability and limitations of the proposed AR system. We discuss design guidelines for future AR systems for children with ASC and other pervasive developmental disorders.


A Two-Process View of Trust and Distrust Building in Recommendation Agents: A Process-Tracing Study


title: SPoT: A Trainable Sentence Planner

abstract: Sentence planning is a set of inter-related but distinct tasks, one of which is sentence scoping, i.e. the choice of syntactic structure for elementary speech acts and the decision of how to combine them into one or more sentences. In this paper, we present SPoT, a sentence planner, and a new methodology for automatically training SPoT on the basis of feedback provided by human judges. We reconceptualize the task into two distinct phases. First, a very simple, randomized sentence-plan-generator (SPG) generates a potentially large list of possible sentence plans for a given text-plan input. Second, the sentence-plan-ranker (SPR) ranks the list of output sentence plans, and then selects the top-ranked plan. The SPR uses ranking rules automatically learned from training data. We show that the trained SPR learns to select a sentence plan whose rating on average is only 5% worse than the top human-ranked sentence plan.


title: On-Line Sequential Extreme Learning Machine

abstract: The primitive Extreme Learning Machine (ELM) [1, 2, 3] with additive neurons and RBF kernels was implemented in batch mode. In this paper, its sequential modification based on recursive least-squares (RLS) algorithm, which referred as Online Sequential Extreme Learning Machine (OS-ELM), is introduced. Based on OS-ELM, Online Sequential Fuzzy Extreme Learning Machine (Fuzzy-ELM) is also introduced to implement zero order TSK model and first order TSK model. The performance of OS-ELM and Fuzzy-ELM are evaluated and compared with other popular sequential learning algorithms, and experimental results on some real benchmark regression problems show that the proposedOnlineSequentialExtreme Learning Machine (OS-ELM) produces better generalization performance at very fast learning speed.


title: From RankNet to LambdaRank to LambdaMART: An Overview

abstract: LambdaMART is the boosted tree version of LambdaRank, which is based on RankNet. RankNet, LambdaRank, and LambdaMART have proven to be very successful algorithms for solving real world ranking problems: for example an ensemble of LambdaMART rankers won Track 1 of the 2010 Yahoo! Learning To Rank Challenge. The details of these algorithms are spread across several papers and reports, and so here we give a self-contained, detailed and complete description of them.


title: Protection Devices for Aircraft Electrical Power Distribution Systems: State of the Art

abstract: The development of all electric aircraft (AEC) has provided new opportunities in the field of electronic devices and power electronics. One of the most interesting areas is focused on the protection devices field and the management of the loads by means of the solid state power controllers (SSPC). This is mainly due to the great increase of these devices in the electrical power distribution system architectures used in the new airplanes such as A380 and B787. This paper presents a survey of conventional and future trends of protection devices in onboard platforms. Moreover a virtual test bench is developed in order to analyze the potential problems that could appear by using SSPC in new onboard platforms. In addition an experimental validation with commercial SSPC and its model are presented.


title: Tackling the x-ray cargo inspection challenge using machine learning

abstract: The current infrastructure for non-intrusive inspection of cargo containers cannot accommodate exploding com-merce volumes and increasingly stringent regulations. There is a pressing need to develop methods to automate parts of the inspection workﬂow, enabling expert operators to focus on a manageable number of high-risk images. To tackle this challenge, we developed a modular framework for automated X-ray cargo image inspection. Employing state-of-the-art machine learning approaches, including deep learning, we demonstrate high performance for empty container veriﬁcation and speciﬁc threat detection. This work constitutes a signiﬁcant step towards the partial automation of X-ray cargo image inspection.


title: Two-Wheel Self-Balancing of a Four-Wheeled Vehicle [Applications of Control]

abstract: Cars and trucks are susceptible to accidents due to rollover. In the United States in 2005, 21.1% of a total of 54,718 deaths in vehicle crashes were caused by rollover [1]. Significant research has therefore been de voted to detecting and preventing rollover through active control. Numerous approaches attempt to detect or pre dict wheel liftoff using onboard sensing and a combina tion of automatic steering and braking to keep the wheels on the ground [2]-[4]. Rather than focusing on how to keep the wheels on the ground, it is also useful to under stand how to control a vehicle while two wheels are in the air. This understanding may enable the design of control laws that can safely return the vehicle to the ground after inadvertent tip-up.


title: Small-signal Discrete-time Modeling of Digitally Controlled DC-DC Converters

abstract: The paper presents an exact small-signal discrete-time model for digitally controlled DC-DC converters. The model, which is based on well-known approaches to discrete-time modeling and the standard Z-transform, takes into account modulator effects and delays in the control loop. The model is well suited for direct digital design of digital compensators


title: Neural Headline Generation with Minimum Risk Training

abstract: Automatic headline generation is an important research area within text summarization and sentence compression. Recently, neural headline generation models have been proposed to take advantage of well-trained neural networks in learning sentence representations and mapping sequence to sequence. Nevertheless, traditional neural network encoder utilizes maximum likelihood estimation for parameter optimization, which essentially constraints the expected training objective within word level instead of sentence level. Moreover, the performance of model prediction significantly relies on training data distribution. To overcome these drawbacks, we employ minimum risk training strategy in this paper, which directly optimizes model parameters with respect to evaluation metrics and statistically leads to significant improvements for headline generation. Experiment results show that our approach outperforms state-of-the-art systems on both English and Chinese headline generation tasks.


title: Modeling and Verification of Online Shopping Business Processes by Considering Malicious Behavior Patterns

abstract: Recently, online shopping integrating third-party payment platforms (TPPs) introduces new security challenges due to complex interactions between Application Programming Interfaces (APIs) of Merchants and TPPs. Malicious clients may exploit security vulnerabilities by calling APIs in an arbitrary order or playing various roles. To deal with the security issue in the early stages of system development, this paper presents a formal method for modeling and verification of online shopping business processes with malicious behavior patterns considered based on Petri nets. We propose a formal model called E-commerce Business Process Net to model a normal online shopping business process that represent intended functions, and malicious behavior patterns representing a potential attack that violates the security goals at the requirement analysis phase. Then, we synthesize the normal business process and malicious behavior patterns by an incremental modeling method. According to the synthetic model, we analyze whether an online shopping business process is resistant to the known malicious behavior patterns. As a result, our approach can make the software design provably secured from the malicious attacks at process design time and, thus, reduces the difficulty and cost of modification for imperfect systems at the release phase. We demonstrate our approach through a case study.


title: Internet Addiction Prevalence and Quality of (Real) Life: A Meta-Analysis of 31 Nations Across Seven World Regions

abstract: Abstract Internet addiction (IA) has emerged as a universal issue, but its international estimates vary vastly. This multinational meta-analysis fills this gap by providing estimates of its global prevalence. Two hypotheses were formulated to explain the cross-national variations. The accessibility hypothesis predicts that IA prevalence is positively related to Internet penetration rate and GDP per capita, whereas the quality of (real) life hypothesis predicts that IA prevalence is inversely related to a global national index of life satisfaction and specific national indices of environmental quality. Multiple search strategies were used in an attempt to retrieve all empirical reports from 1996 to 2012 that adopted the Young Diagnostic Questionnaire or Internet Addiction Test for assessing generalized IA. The data set comprised 164 prevalence figures derived from 80 reports, including 89,281 participants from 31 nations across seven world regions. A random effects meta-analysis showed a global prevalence ...


Chapter 52 Prototyping Tools and Techniques


title: Multi-view reconstruction using photo-consistency and exact silhouette constraints: a maximum-flow formulation

abstract: This paper describes a novel approach for reconstructing a closed continuous surface of an object from multiple calibrated color images and silhouettes. Any accurate reconstruction must satisfy (1) photo-consistency and (2) silhouette consistency constraints. Most existing techniques treat these cues identically in optimization frameworks where silhouette constraints are traded off against photo-consistency and smoothness priors. Our approach strictly enforces silhouette constraints, while optimizing photo-consistency and smoothness in a global graph-cut framework. We transform the reconstruction problem into computing max-flow/min-cut in a geometric graph, where any cut corresponds to a surface satisfying exact silhouette constraints (its silhouettes should exactly coincide with those of the visual hull); a minimum cut is the most photo-consistent surface amongst them. Our graph-cut formulation is based on the rim mesh, (the combinatorial arrangement of rims or contour generators from many views) which can be computed directly from the silhouettes. Unlike other methods, our approach enforces silhouette constraints without introducing a bias near the visual hull boundary and also recovers the rim curves. Results are presented for synthetic and real datasets.


title: A fast-lock DLL with power-on reset circuit

abstract: This paper describes a fast-lock delay-lock loop (DLL) with power-on reset (POR) circuit. The POR circuit and coarse tune (CT) circuit are proposed to overcome the problems of the false locking associated with conventional DLL's and offer the faster locking time. Moreover, the proposed VCDL can reduce dynamic switching power dissipation and noise. The chip is fabricated in a 0.35 /spl mu/m CMOS process. From the measurement results, the DLL can operate correctly from 100 to 190 MHz and generate equally spaced eight-phase clocks. When the input clock frequency is 100 MHz, the measured output clock peak-to-peak jitter and rms jitter are 56 ps and 12.44 ps, respectively. And when the input clock frequency is 190 MHz, the measured output clock peak-to-peak jitter and rms jitter are 46 ps and 8.463 ps, respectively. Besides, the maximum lock time is 43 clock cycles at 150 MHz.


title: A Turkish Version of the Critical‐Care Pain Observation Tool: Reliability and Validity Assessment

abstract: PURPOSE ::: The study aim was to evaluate the validity and reliability of the Critical-Care Pain Observation Tool in critically ill patients. ::: ::: ::: DESIGN ::: A repeated measures design was used for the study. ::: ::: ::: METHODS ::: A convenience sample of 66 patients who had undergone open-heart surgery in the cardiovascular surgery intensive care unit in Ordu, Turkey, was recruited for the study. The patients were evaluated by using the Critical-Care Pain Observation Tool at rest, during a nociceptive procedure (suctioning), and 20 minutes after the procedure while they were conscious and intubated after surgery. ::: ::: ::: FINDING ::: The Turkish version of the Critical-Care Pain Observation Tool has shown statistically acceptable levels of validity and reliability. Inter-rater reliability was supported by moderate-to-high-weighted κ coefficients (weighted κ coefficient = 0.55 to 1.00). For concurrent validity, significant associations were found between the scores on the Critical-Care Pain Observation Tool and the Behavioral Pain Scale scores. Discriminant validity was also supported by higher scores during suctioning (a nociceptive procedure) versus non-nociceptive procedures. The internal consistency of the Critical-Care Pain Observation Tool was 0.72 during a nociceptive procedure and 0.71 during a non-nociceptive procedure. ::: ::: ::: CONCLUSIONS ::: The validity and reliability of the Turkish version of the Critical-Care Pain Observation Tool was determined to be acceptable for pain assessment in critical care, especially for patients who cannot communicate verbally.


title: Power Quality Enhancement of Smart Households Using a Multilevel-THSeAF With a PR Controller

abstract: In this paper a multilevel transformerless hybrid series active filter is proposed to enhance the power quality of a single-phase residential household. The proposed topology reflects new trends of consumers toward electronic polluting loads and integration of renewable sources which in fact may lead to the scope of a reliable and sustainable supply. This paper contributes to improvement of power quality for a modern single-phase system and emphasis integration of a compensator with energy storage capacity to ensure a sustainable supply. A proportional resonant (P+R) regulator is implemented in the controller to prevent current harmonic distortions of various non-linear loads to flow into the utility. The main significant features of the proposed topology include the great capability to correct the power factor as well as cleaning the grid simultaneously, while protecting consumers from voltage disturbances, sags, and swells during a grid perturbation. It investigates aspects of harmonic compensation and assesses the influence of the controller’s choice and time delay during a real-time implementation. Combinations of analysis and experimental results performed on a laboratory setup are presented for validation.


One of Us, One of Them, or One of Those? The Construction of Identity and Sexuality in Relation to HIV/AIDS


title: 3-D gesture-based scene navigation in medical imaging applications using Time-of-Flight cameras

abstract: For a lot of applications, and particularly for medical intra-operative applications, the exploration of and navigation through 3-D image data provided by sensors like ToF (time-of-flight) cameras, MUSTOF (multisensor-time-of-flight) endoscopes or CT (computed tomography) [8], requires a user-interface which avoids physical interaction with an input device. Thus, we process a touchless user-interface based on gestures classified by the data provided by a ToF camera. Reasonable and necessary user interactions are described. For those interactions a suitable set of gestures is introduced. A user-interface is then proposed, which interprets the current gesture and performs the assigned functionality. For evaluating the quality of the developed user-interface we considered the aspects of classification rate, real-time applicability, usability, intuitiveness and training time. The results of our evaluation show that our system, which provides a classification rate of 94.3% at a framerate of 11 frames per second, satisfactorily addresses all these quality requirements.


title: Study on Virtual Control of a Robotic Arm via a Myo Armband for the Self- Manipulation of a Hand Amputee

abstract: This paper proposes a Myo device that has electromyography (EMG) sensors for detecting electrical activities from different parts of the forearm muscles; it also has a gyroscope and an accelerometer. EMG sensors detect and provide very clear and important data from muscles compared with other types of sensors. The Myo armband sends data from EMG, gyroscope, and accelerometer sensors to a computer via Bluetooth and uses these data to control a virtual robotic arm which was built in Unity 3D. Virtual robotic arms based on EMG, gyroscope, and accelerometer sensors have different features. A robotic arm based on EMG is controlled by using the tension and relaxation of muscles. Consequently, a virtual robotic arm based on EMG is preferred for a hand amputee to a virtual robotic arm based on a gyroscope and an accelerometer


title: Forecasting Economics and Financial Time Series: ARIMA vs. LSTM

abstract: Forecasting time series data is an important subject in economics, business, and finance. Traditionally, there are several techniques to effectively forecast the next lag of time series data such as univariate Autoregressive (AR), univariate Moving Average (MA), Simple Exponential Smoothing (SES), and more notably Autoregressive Integrated Moving Average (ARIMA) with its many variations. In particular, ARIMA model has demonstrated its outperformance in precision and accuracy of predicting the next lags of time series. With the recent advancement in computational power of computers and more importantly developing more advanced machine learning algorithms and approaches such as deep learning, new algorithms are developed to forecast time series data. The research question investigated in this article is that whether and how the newly developed deep learning-based algorithms for forecasting time series data, such as"Long Short-Term Memory (LSTM)", are superior to the traditional algorithms. The empirical studies conducted and reported in this article show that deep learning-based algorithms such as LSTM outperform traditional-based algorithms such as ARIMA model. More specifically, the average reduction in error rates obtained by LSTM is between 84 - 87 percent when compared to ARIMA indicating the superiority of LSTM to ARIMA. Furthermore, it was noticed that the number of training times, known as"epoch"in deep learning, has no effect on the performance of the trained forecast model and it exhibits a truly random behavior.


title: Non-negative mixtures

abstract: This is the author's accepted pre-print of the article, first published as M. D. Plumbley, A. Cichocki and R. Bro. Non-negative mixtures. In P. Comon and C. Jutten (Ed), Handbook of Blind Source Separation: Independent Component Analysis and Applications. Chapter 13, pp. 515-547. Academic Press, Feb 2010. ISBN 978-0-12-374726-6 DOI: 10.1016/B978-0-12-374726-6.00018-7


An Efficient Off-line Electronic Cash System Based On The Representation Problem


title: Optimal Hand-Eye Calibration

abstract: This paper presents a calibration method for eye-in-hand systems in order to estimate the hand-eye and the robot-world transformations. The estimation takes place in terms of a parametrization of a stochastic model. In order to perform optimally, a metric on the group of the rigid transformations SE(3) and the corresponding error model are proposed for nonlinear optimization. This novel metric works well with both common formulations AX=XB and AX=ZB, and makes use of them in accordance with the nature of the problem. The metric also adapts itself to the system precision characteristics. The method is compared in performance to earlier approaches.


title: On the evolution of fingertip grasping manifolds

abstract: Efficient and accurate planning of fingertip grasps is essential for dexterous in-hand manipulation. In this work, we present a system for fingertip grasp planning that incrementally learns a heuristic for hand reachability and multi-fingered inverse kinematics. The system consists of an online execution module and an offline optimization module. During execution the system plans and executes fingertip grasps using Canny's grasp quality metric and a learned random forest based hand reachability heuristic. In the offline module, this heuristic is improved based on a grasping manifold that is incrementally learned from the experiences collected during execution. The system is evaluated both in simulation and on a Schunk-SDH dexterous hand mounted on a KUKA-KR5 arm. We show that, as the grasping manifold is adapted to the system's experiences, the heuristic becomes more accurate, which results in an improved performance of the execution module. The improvement is not only observed for experienced objects, but also for previously unknown objects of similar sizes.


Multiple Growing Cell Structures


title: Dimensionality Reduction for Binary Data through the Projection of Natural Parameters

abstract: Principal component analysis (PCA) for binary data, known as logistic PCA, has become a popular alternative to dimensionality reduction of binary data. It is motivated as an extension of ordinary PCA by means of a matrix factorization, akin to the singular value decomposition, that maximizes the Bernoulli log-likelihood. We propose a new formulation of logistic PCA which extends Pearson's formulation of a low dimensional data representation with minimum error to binary data. Our formulation does not require a matrix factorization, as previous methods do, but instead looks for projections of the natural parameters from the saturated model. Due to this difference, the number of parameters does not grow with the number of observations and the principal component scores on new data can be computed with simple matrix multiplication. We derive explicit solutions for data matrices of special structure and provide computationally efficient algorithms for solving for the principal component loadings. Through simulation experiments and an analysis of medical diagnoses data, we compare our formulation of logistic PCA to the previous formulation as well as ordinary PCA to demonstrate its benefits.