title: Nonlinear virtual colon unfolding

abstract: The majority of virtual endoscopy techniques tries to simulate a real endoscopy. A real endoscopy does not always give the optimal information due to the physical limitations it is subject to. In this paper, we deal with the unfolding of the surface of the colon as a possible visualization technique for diagnosis and polyp detection. A new two-step technique is presented which deals with the problems of double appearance of polyps and nonuniform sampling that other colon unfolding techniques suffer from. In the first step, a distance map from a central path induces nonlinear rays for unambiguous parameterization of the surface. The second step compensates for locally varying distortions of the unfolded surface. A technique similar to magnification fields in information visualization is hereby applied. The technique produces a single view of a complete, virtually dissected colon.


title: eHint: An Efficient Protocol for Uploading Small-Size IoT Data

abstract: IoT (Internet of Things) has attracted a lot of attention recently. IoT devices need to report their data or status to base stations at various frequencies. The IoT communications observed by a base station normally exhibit the following characteristics: (1) massively connected, (2) lightly loaded per packet, and (3) periodical or at least mostly predictable. The current design principals of communication networks, when applied to IoT scenarios, however, do not fit well to these requirements. When a large number of devices contend to send small packets, the signaling overhead is not cost-effective. To address this problem, our previous work [1] proposes the Hint protocol, which is slot-based and schedule- oriented for uploading IoT devices' data. In this work, we extend [1] to support data transmissions for multiple resource blocks. We assume that the uplink payloads from IoT devices are small, each taking very few slots (or resource blocks), but devices are massive. The main idea is to "encode" information in a tiny broadcast that allows each device to "decode" its transmission slots, thus significantly reducing transmission overheads and contention overheads. Our simulation results verify that the protocol can significantly increase channel utilization compared with traditional schemes.


title: Aspirations and realizations: The smart city of Seattle

abstract: Smart city initiatives have been launched on every continent. That notwithstanding the concept of “smart city” has remained ambiguous. We systematically interviewed officials of an acclaimed Smart City (Seattle) and explicitly asked the officials for their own definitions of “smart city,” which we then compared to the respective projects run by that City. While the definitions given by the practitioners were found different from those in the literature, the smart city projects lived up and matched the practitioner definitions to a high degree. We document the projects and their expected and realized benefits, which illustrate where a leading City government is headed in terms of smart government. However, “Smart City” initiatives in local government might be only a steppingstone in making the greater urban space a “smart city,” which appears to be a more challenging undertaking.


title: Inferring social ties across heterogenous networks

abstract: It is well known that different types of social ties have essentially different influence on people. However, users in online social networks rarely categorize their contacts into "family", "colleagues", or "classmates". While a bulk of research has focused on inferring particular types of relationships in a specific social network, few publications systematically study the generalization of the problem of inferring social ties over multiple heterogeneous networks. In this work, we develop a framework for classifying the type of social relationships by learning across heterogeneous networks. The framework incorporates social theories into a factor graph model, which effectively improves the accuracy of inferring the type of social relationships in a target network by borrowing knowledge from a different source network. Our empirical study on five different genres of networks validates the effectiveness of the proposed framework. For example, by leveraging information from a coauthor network with labeled advisor-advisee relationships, the proposed framework is able to obtain an F1-score of 90% (8-28% improvements over alternative methods) for inferring manager-subordinate relationships in an enterprise email network.


title: Eugenic World Building and Disability: The Strange World of Kazuo Ishiguro's Never Let Me Go

abstract: A crucial challenge for critical disability studies is developing an argument for why disabled people should inhabit our democratic, shared public sphere. The ideological and material separation of citizens into worthy and unworthy based on physiological variations imagined as immutable differences is what I call eugenic world building. It is justified by the idea that social improvement and freedom of choice require eliminating devalued human traits in the interest of reducing human suffering, increasing life quality, and building a more desirable citizenry. In this essay, I outline the logic of inclusive and eugenic world building, define and explain the role of the "normate" in eugenic logic, and provide a critical disability studies reading of the 2005 novel Never Let Me Go by Kazuo Ishiguro and its 2010 film adaptation. I argue that the ways of being in the world we think of as disabilities must be understood as the natural variations, abilities, and limitations inherent in human embodiment. When this happens, disability will be understood not as a problem to be eliminated but, rather, as a valid way of being in the world that must be accommodated through a sustaining and sustainable environment designed to afford access for a wide range of human variations.


title: New Iterative Geometric Methods for Robust Perceptual Image Hashing

abstract: We propose a novel and robust hashing paradigm that uses iterative geometric techniques and relies on observations that main geometric features within an image would approximately stay invariant under small perturbations. A key goal of this algorithm is to produce sufficiently randomized outputs which are unpredictable, thereby yielding properties akin to cryptographic MACs. This is a key component for robust multimedia identification and watermarking (for synchronization as well as content dependent key generation). Our algorithm withstands standard benchmark (e.g Stirmark) attacks provided they do not cause severe perceptually significant distortions. As verified by our detailed experiments, the approach is relatively media independent and works for audio as well.


title: Reverse engineering of the interaction diagrams from C++ code

abstract: In object oriented programming, the functionalities of a system result from the interactions (message exchanges) among the objects allocated by the system. While designing object interactions is far more complex than designing the object structure in forward engineering, the problem of understanding object interactions during code evolution is even harder, because the related information is spread across the code. In this paper, a technique for the automatic extraction of UML interaction diagrams from C++ code is proposed. The algorithm is based on a static, conservative flow analysis that approximates the behavior of the system in any execution and for any possible input. Applicability of the approach to large software is achieved by means of two mechanisms: partial analysis and focusing. Usage of our method on a real world, large C++ system confirmed its viability.


title: Use of Speaker’s Gaze and Syntax in Verb Learning

abstract: Speaker eye gaze and gesture are known to help child and adult listeners establish communicative alignment and learn object labels. Here we consider how learners use these cues, along with linguistic information, to acquire abstract relational verbs. Test items were perspective verb pairs (e.g., chase/flee, win/lose), which pose a special problem for observational accounts of word learning because their situational contexts overlap very closely; the learner must infer the speaker’s chosen perspective on the event. Two cues to the speaker’s perspective on a depicted event were compared and combined: (a) the speaker’s eye gaze to an event participant (e.g., looking at the Chaser vs. looking at the Flee-er) and (b) the speaker’s linguistic choice of which event participant occupies Subject position in his utterance. Participants (3-, 4-, and 5-year-olds) were eye-tracked as they watched a series of videos of a man describing drawings of perspective events (e.g., a rabbit chasing an elephant). The speaker looked at one of the two characters and then uttered either an utterance that was referentially uninformative (He’s mooping him) or informative (The rabbit’s mooping the elephant/The elephant’s mooping the rabbit) because of the syntactic positioning of the nouns. Eye-tracking results showed that all participants regardless of age followed the speaker’s gaze in both uninformative and informative contexts. However, verb-meaning choices were responsive to speaker’s gaze direction only in the linguistically uninformative condition. In the presence of a linguistically informative context, effects of speaker gaze on meaning were minimal for the youngest children to nonexistent for the older populations. Thus children, like adults, can use multiple cues to inform verb-meaning choice but rapidly learn that the syntactic positioning of referring expressions is an especially informative source of evidence for these decisions.


Implementation of Pattern-Mixture Models Using Standard SAS / STAT Procedures


title: 3D anomaly inspection system for sewer pipes using stereo vision and novel image processing

abstract: This paper describes and evaluates a novel 3D inspection system to detect anomalies in sewer pipes using stereo vision coupled with novel image processing algorithms. Currently, most commercial pipe inspection systems are designed with one or more Closed-circuit Television (CCTV) cameras. These systems are slow, costly and have limited accuracy (caused by human and environmental factors). More sophisticated systems (Laser-based, Infrared Thermography, Ultrasonic-based and Ground Penetrating Radar) suffer from: low resolution, high noise, high operational costs and an inability to detect water infiltration. The main objective of this research is to apply stereo vision and robust image processing to generate 3D images of anomalies in sewer pipes in order to achieve high efficiency and accuracy for pipe inspection. The results show that various types of defects are successfully detectable. In addition, the correspondence time can be reduced by up to 45% and the accuracy of disparity maps is maintained compared to traditional local correspondence algorithms. Each component of the proposed system was tested individually with real and simulated data sets.


title: Mixing Coins of Different Quality: A Game-Theoretic Approach

abstract: Cryptocoins based on public distributed ledgers can differ in their quality due to different subjective values users assign to coins depending on the unique transaction history of each coin. We apply game theory to study how qualitative differentiation between coins will affect the behavior of users interested in improving their anonymity through mixing services. We present two stylized models of mixing with perfect and imperfect information and analyze them for three distinct quality propagation policies: poison, haircut, and seniority. In the game of perfect information, mixing coins of high quality remains feasible under certain conditions, while imperfect information eventually leads to a mixing market where only coins of the lowest quality are mixed.


title: The short winding path coupled inductor voltage regulators

abstract: In today's CPU Voltage Regulator(VR) industry, coupled inductor VRs have begun to replace the non-coupled VRs for their better performances. However, today's coupled inductor implementations have long winding paths, which is not preferred in the high current CPU VR application. In this paper, several short winding path coupled inductor structures for 2 and more phase coupled inductors are proposed. Their precise reluctance models are illustrated and their design guidelines based on their precise reluctance models are also shown. The experimental results verify the proposed structures, their models and their design procedures.


title: Detecting Falls with Wearable Sensors Using Machine Learning Techniques

abstract: Falls are a serious public health problem and possibly life threatening for people in fall risk groups. We develop an automated fall detection system with wearable motion sensor units fitted to the subjects' body at six different positions. Each unit comprises three tri-axial devices (accelerometer, gyroscope, and magnetometer/compass). Fourteen volunteers perform a standardized set of movements including 20 voluntary falls and 16 activities of daily living (ADLs), resulting in a large dataset with 2520 trials. To reduce the computational complexity of training and testing the classifiers, we focus on the raw data for each sensor in a 4 s time window around the point of peak total acceleration of the waist sensor, and then perform feature extraction and reduction. Most earlier studies on fall detection employ rule-based approaches that rely on simple thresholding of the sensor outputs. We successfully distinguish falls from ADLs using six machine learning techniques (classifiers): the k-nearest neighbor (k-NN) classifier, least squares method (LSM), support vector machines (SVM), Bayesian decision making (BDM), dynamic time warping (DTW), and artificial neural networks (ANNs). We compare the performance and the computational complexity of the classifiers and achieve the best results with the k-NN classifier and LSM, with sensitivity, specificity, and accuracy all above 99%. These classifiers also have acceptable computational requirements for training and testing. Our approach would be applicable in real-world scenarios where data records of indeterminate length, containing multiple activities in sequence, are recorded.


title: An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring

abstract: Accurate equipment remaining useful life prediction is critical to effective condition based maintenance for improving reliability and reducing overall maintenance cost. In this paper, an artificial neural network (ANN) based method is developed for achieving more accurate remaining useful life prediction of equipment subject to condition monitoring. The ANN model takes the age and multiple condition monitoring measurement values at the present and previous inspection points as the inputs, and the life percentage as the output. A function generalized from the Weibull failure rate function is used to fit each condition monitoring measurement series for a failure history, and the fitted measurement values are used to form the ANN training set so as to reduce the effects of the noise factors that are irrelevant to the equipment degradation. A validation mechanism is introduced in the ANN training process to improve the prediction performance of the ANN model. The proposed ANN method is validated using real-world vibration monitoring data collected from pump bearings in the field. A comparative study is performed between the proposed ANN method and an adapted version of a reported method, and the results demonstrate the advantage of the proposed method in achieving more accurate remaining useful life prediction.


title: Customer Relationship Management (CRM) systems, intermediation and disintermediation: The case of INSG

abstract: Whilst our knowledge of Customer Relationship Management (CRM) systems continues to evolve, there is still much to learn. This paper offers some relatively rare insights on the use of CRM systems and the strategic impact on the processes of intermediation and disintermediation in order to improve customer service. This research was conducted from April 2007 to 2008 using an interpretative case study approach. The case involved working with a leading international insurance company (given the pseudonym of INSG) and some of their intermediary customer service agents. The research highlights some design characteristics and philosophical insights regarding CRM system approaches and also offers some useful practical insights on the impact of CRM in changes to the deployment of some intermediaries, leading to a process of disintermediation, in order to improve customer service. In summary, some theoretical and practical implications are highlighted and discussed.


title: Switching hybrid for cold-starting context-aware recommender systems

abstract: Finding effective solutions for cold-starting Context-Aware Recommender Systems (CARSs) is important because usually low quality recommendations are produced for users, items or contextual situations that are new to the system. In this paper, we tackle this problem with a switching hybrid solution that exploits a custom selection of two CARS algorithms, each one suited for a particular cold-start situation, and switches between these algorithms depending on the detected recommendation situation (new user, new item or new context). We evaluate the proposed algorithms in an off-line experiment by using various contextually-tagged rating datasets. We illustrate some significant performance differences between the considered algorithms and show that they can be effectively combined into the proposed switching hybrid to cope with different types of cold-start problems.


Kernel PCA for Feature Extraction with Information Complexity


title: Violence Against Women in Jordan

abstract: This study aimed to describe the social and cultural etiology of violence against women in Jordan. A sample of houses was randomly selected from all 12 Governorates in Jordan, resulting in a final sample of 1,854 randomly selected women. ANOVA analysis showed significant differences in violence against women as a result of women’s education, F = 4.045, α = 0.003, women who work, F = 3.821, α = 0.001, espouser to violence F = 17.896, α = 0.000, experiencing violence during childhood F = 12.124, α = 0.000, and wife’s propensity to leave the marital relationship F = 12.124, α = 0.000. However, no differences were found in violence against women because of the husband’s education, husband’s work, or having friends who belief in physical punishment of kids. Findings showed women experienced 45 % or witnessed 55 % violence during their childhood. Almost all 98 % of the sample was subjected to at least one type of violence. Twenty-eight percent of the sample believed a husband has the right to control a woman’s behavior and 93 % believed a wife is obliged to obey a husband. After each abusive incidence, women felt insecure, ashamed, frightened, captive and stigmatized.


title: Why would corporations behave in socially responsible ways? An institutional theory of corporate social responsibility

abstract: I offer an institutional theory of corporate social responsibility consisting of a series of propositions specifying the conditions under which corporations are likely to behave in socially responsible ways. I argue that the relationship between basic economic conditions and corporate behavior is mediated by several institutional conditions: public and private regulation, the presence of nongovernmental and other independent organizations that monitor corporate behavior, institutionalized norms regarding appropriate corporate behavior, associative behavior among corporations themselves, and organized dialogues among corporations and their stakeholders. Concerns about corporate social responsibility have grown significantly during the last two decades. Not only has the issue become commonplace in the business press and among business and political leaders (Buhr & Graf


title: Acne Conglobata Associated with Hidradenitis Suppurativa, Disorders of Follicular Occlusion (Case Report)

abstract: Abstract The author is presenting the case of a 23-year-old female patient with a severe form of acne conglobata, with the first symptoms of the disease occurring as far back as the prepubertal age. In the past year the disease has combined with hidradenitis suppurativa (to be referred to henceforth as “HS”), Hurley stage I, in the axillae and both sides of the inguinal region, with a family history of acne conglobata (both her mother and brother were affected). Further examinations ruled out inflammatory bowel disease because of a lack of further associated symptoms, except for sideropenic anaemia (lesser form) and lower serum values of vitamin D. Up until now the disease has been resistant to treatment, including the long-term treatment of methylprednisolone in combination with isotretinoid as well as dapsone and antibiotics.


title: Increasing reading comprehension and engagement through concept-oriented reading instruction

abstract: Based on an engagement perspective of reading development, we investigated the extent to which an instructional framework of combining motivation support and strategy instruction (Concept-Oriented Reading Instruction—CORI) influenced reading outcomes for third-grade children. In CORI, five motivational practices were integrated with six cognitive strategies for reading comprehension. In the first study, we compared this framework to an instructional framework emphasizing Strategy Instruction (SI), but not including motivation support. In the second study, we compared CORI to SI and to a traditional instruction group (TI), and used additional measures of major constructs. In both studies, class-level analyses showed that students in CORI classrooms were higher than SI and/or TI students on measures of reading comprehension, reading motivation, and reading strategies. A widespread goal of education in the elementary grades is reading comprehension for all students. Reading comprehension becomes especially important in the later elementary grades (Sweet & Snow, 2003) and provides the basis for a substantial amount of learning in secondary school (Kirsch et al., 2002). Without the skills of reading comprehension and the motivation for reading to learn, students’ academic progress is limited (Alvermann & Earle, 2003). In view of the prominence of reading comprehension, a vital issue for educational psychology is investigating the characteristics of effective instruction for reading comprehension (Hiebert & Raphael, 1996). The growing knowledge base about instruction for reading comprehension is rightly directed toward identifying classroom practices with known effects on specific aspects of reading, and a major focus of this research has been on identifying effective reading strategies that increase children’s comprehension (Block & Pressley, 2002; Duke & Pearson, 2002; National Reading Panel, 2000). However, the evidence rests primarily on instructional research in which single cognitive strategies, such as questioning, are taught in controlled experiments. Relatively little investigation has been conducted on how multiple strategies can be combined in long-term comprehension instruction within the classroom, and


title: Attention-Aware Deep Reinforcement Learning for Video Face Recognition

abstract: In this paper, we propose an attention-aware deep reinforcement learning (ADRL) method for video face recognition, which aims to discard the misleading and confounding frames and find the focuses of attentions in face videos for person recognition. We formulate the process of finding the attentions of videos as a Markov decision process and train the attention model through a deep reinforcement learning framework without using extra labels. Unlike existing attention models, our method takes information from both the image space and the feature space as the input to make better use of face information that is discarded in the feature learning process. Besides, our approach is attention-aware, which seeks different attentions of videos for the recognition of different pairs of videos. Our approach achieves very competitive video face recognition performance on three widely used video face datasets.


title: Design of Android type Humanoid Robot Albert HUBO

abstract: To celebrate the 100th anniversary of the announcement of the special relativity theory of Albert Einstein, KAIST HUBO team and Hanson robotics team developed android type humanoid robot Albert HUBO which may be the world's first expressive human face on a walking biped robot. The Albert HUBO adopts the techniques of the HUBO design for Albert HUBO body and the techniques of Hanson robotics for Albert HUBO's head. Its height and weight are 137 cm and 57 Kg. Albert HUBO has 66 DOFs (31 for head motions and 35 for body motions) And head part uses `fubber' materials for smooth artificial skin and 28 servo motors for face movements and 3 servo motors for neck movements are used for generating a full range of facial expressions such as laugh, sadness, angry, surprised, etc. and body is modified with HUBO(KHR-3) introduced in 2004 to join with Albert HUBO's head and 35 DC motors are embedded for imitating various human-like body motions


title: Extracting semantic representations from word co-occurrence statistics: A computational study

abstract: In a previous article, we presented a systematic computational study of the extraction of semantic representations from the word–word co-occurrence statistics of large text corpora. The conclusion was that semantic vectors of pointwise mutual information values from very small co-occurrence windows, together with a cosine distance measure, consistently resulted in the best representations across a range of psychologically relevant semantic tasks. This article extends that study by investigating the use of three further factors—namely, the application of stop-lists, word stemming, and dimensionality reduction using singular value decomposition (SVD)—that have been used to provide improved performance elsewhere. It also introduces an additional semantic task and explores the advantages of using a much larger corpus. This leads to the discovery and analysis of improved SVD-based methods for generating semantic representations (that provide new state-of-the-art performance on a standard TOEFL task) and the identification and discussion of problems and misleading results that can arise without a full systematic study.


title: Micromolding of photocrosslinkable chitosan hydrogel for spheroid microarray and co-cultures

abstract: Bioengineering approaches, such as co-cultures of multiple cell types, that aim to mimic the physiological microenvironment may be beneficial for optimizing cell function and for engineering tissues in vitro. This study describes a novel method for preparing a spheroid microarray on microfabricated hydrogels, alone or in co-cultures. Photocrosslinkable chitosan was synthesized and utilized for fabricating hydrogel microstructures through a micromolding process. The chitosan surface was initially cell repellent but became increasingly cell adhesive over time. By using this unique property of chitosan hydrogels, it was possible to generate patterned co-cultures of spheroids and support cells. In this scheme, cells were initially microarrayed within low shear stress regions of microwells. Human hepatoblastoma cells, Hep G2, seeded in these wells formed spheroids with controlled sizes and shapes and stably secreted albumin during the culture period. The change of cell adhesive properties in the chitosan surface facilitated the adhesion and growth of a second cell type, NIH-3T3 fibroblast, and therefore enabled co-cultures of hepatocyte spheroids and fibroblast monolayers. This co-culture system could be a useful platform for studying heterotypic cell–cell interactions, for drug screening, and for developing implantable bioartificial organs.


title: Sustainable Transport in Freiburg: Lessons from Germany’s Environmental Capital

abstract: This article examines changes in transport and land-use policies in Germany over the last 40 years that have encouraged more walking, bicycling and public transport use. It focuses on a case study of policy changes in the city of Freiburg, where over the last three decades, the number of bicycle trips tripled, public transport ridership doubled, and the share of trips by automobile declined from 38% to 32%. Since 1990, motorization rates have leveled-off and per-capita CO2 emissions from transport have fallen—despite strong economic growth. The analysis identifies policies that are transferable to car-oriented countries around the world.


title: Political Neuroscience: The Beginning of a Beautiful Friendship

abstract: The emergence of political neuroscience—an interdisciplinary venture involving political science, psychology, and cognitive neuroscience—has piqued the interests of scholars as well as the mass public. In this chapter, we review evidence pertaining to four areas of inquiry that have generated most of the research in political neuroscience to date: (1) racial prejudice and intergroup relations; (2) the existence of partisan bias and motivated political cognition; (3) the nature of left-right differences in political orientation; and (4) the dimensional structure of political attitudes. Although these topics are well-known to political psychologists, the application of models and methods from neuroscience has renewed interest in each of them and yielded novel insights. There is reason to believe that many other areas of political psychology await similarly promising renewals and that innovative methods will continue to advance our understanding of the physiological processes involved in political cognition, evaluation, judgment, and behavior. We address limitations, criticisms, and potential pitfalls of existing work—including the “chicken-and-egg problem”—and propose an ambitious agenda for the next generation of research in political neuroscience.


title: Interference reduction in ECG signal acquisition: Ground electrode removal (case study)

abstract: An ECG signal is susceptible to a significant interference due to the surrounding sources of noise. The identification of these sources is necessary to acquire an ECG signal since its level is low (few millivolts) and is also immersed in a noisy environment. We propose the elimination of the ground electrode to reduce common-mode interference. We provide a qualitative model for these sources of interference and prove the effectiveness of ground electrode removal in interference reduction through an experimental design.


title: A novel 4H-SiC pinched barrier rectifier

abstract: In this paper, design, fabrication and experimental analysis of a novel 4H-SiC pinched barrier rectifier (PBR) are presented. The operation mechanism of the pinched barrier is analyzed by numerical simulation and energy band diagrams. PN junction depth (X j ) and spacing (S) parameters in the diode structure are carefully designed to achieve a prototype optimal structure, and both forward and reverse characteristics are compared with those of JBS diodes by experimental results. It is demonstrated that, only by simply adjusting the PN junction depth and spacing parameters, the pinched barrier diode can obtain relatively optimal performance close to that of JBS diodes. The addition of channel doping concentration (Nch) consideration for the structure design can achieve further improvements and superior performances than JBS diodes. In comparison with the optimum JBS diode design (S=1.2pm), for an on-set voltage control of 0.8V, PBR diode with optimum design sets of S and Nch can achieve a wider trade-off window (S≤0.8pm) between the device forward and reverse performances. Furthermore, PBR diode can achieve superior performance with lower forward voltage drop (1.3V) than JBS diode (1.6V) for the same reverse blocking capability (∼1600V). The performance limit of PBR diode is also explored in this paper. For a minimum limit on-set voltage control of 0.6 V, a lowest voltage drop of 1.1V as well as a wide design window can be achieved.


title: Comparative Study on Generative Adversarial Networks

abstract: In recent years, there have been tremendous advancements in the field of machine learning. These advancements have been made through both academic as well as industrial research. Lately, a fair amount of research has been dedicated to the usage of generative models in the field of computer vision and image classification. These generative models have been popularized through a new framework called Generative Adversarial Networks. Moreover, many modified versions of this framework have been proposed in the last two years. We study the original model proposed by Goodfellow et al. as well as modifications over the original model and provide a comparative analysis of these models.


title: Spotlight on Anti‐CGRP Monoclonal Antibodies in Migraine: The Clinical Evidence to Date

abstract: Migraine, a common neurovascular brain disorder, represents a severe and widespread health problem; along with medication-induced (medication-overuse) headache, it is the third-leading cause of disability worldwide. Currently, its therapeutic management remains unsatisfactory for several reasons; up to 40% of migraineurs are eligible for prophylactic treatment, but there are issues of efficacy, safety, and adherence. In recent years the evidence on the role of calcitonin gene-related peptide (CGRP) in migraine pathophysiology has been consolidated, so new and promising treatments for migraine pain and its possible prevention have been developed. The following review reports the results of the clinical trials conducted so far with each of the new monoclonal antibodies targeting CGRP or its receptor, with particular reference to safety, tolerance, and efficacy in migraine prevention. Moreover, the pharmacological characterization and further developments of each monoclonal antibody are reported, based on current knowledge.


title: A study of enterprise resource planning (ERP) system performance measurement using the quantitative balanced scorecard approach

abstract: This study proposed an ERP performance measurement framework based on BSC.The non-additive fuzzy integral is applied to measure the ERP performance.The proposed approach reflects the interdependence among performance criteria.The proposed approach reflects the fuzziness existing in experts' judgments. Enterprise resource planning (ERP) systems have been used in integrating information and accelerating its distribution across functions and departments with the aim to increase organizations' operational performance. Thus, it is worth measuring ERP system performance based on its impact to critical performance of an organization: this requires a systematic method that bridges ERP performance measurement and key organizational performance. The hierarchical balanced scorecard (HBSC) model with respect to multiple criteria decision-making is such a systematic approach to ERP performance measurement. An ERP evaluation framework that integrates the balanced scorecard dimensions, linguistic variables, and non-additive fuzzy integral provides an objective approach to measuring both the performance level of the ERP system and its contribution to the strategic objectives of high-tech firms. Taking Taiwan's high-tech firms as an example, this study demonstrates the effectiveness of this integrated approach to measure the performance of ERP systems at the post-implementation stage under evaluators' subjective, uncertainty, and vagueness judgments.


title: Value Sensitive Design and Information Systems

abstract: Value Sensitive Design is a theoretically grounded approach to the design of technology that accounts for human values in a principled and comprehensive manner throughout the design process. It employs an integrative and iterative tripartite methodology, consisting of conceptual, empirical, and technical investigations. We explicate Value Sensitive Design by drawing on three case studies. The first study concerns information and control of web browser cookies, implicating the value of informed consent. The second study concerns using high-definition plasma displays in an office environment to provide a “window” to the outside world, implicating the values of physical and psychological well-being and privacy in public spaces. The third study concerns an integrated land use, transportation, and environmental simulation system to support public deliberation and debate on major land use and transportation decisions, implicating the values of fairness, accountability, and support for the democratic process, as well as a highly diverse range of values that might be held by different stakeholders, such as environmental sustainability, opportunities for business expansion, or walkable neighborhoods. We conclude with direct and practical suggestions for how to engage in Value Sensitive Design.


title: REVEL: tactile feedback technology for augmented reality

abstract: REVEL is an augmented reality (AR) tactile technology that allows for change to the tactile feeling of real objects by augmenting them with virtual tactile textures using a device worn by the user. Unlike previous attempts to enhance AR environments with haptics, we neither physically actuate objects or use any force- or tactile-feedback devices, nor require users to wear tactile gloves or other apparatus on their hands. Instead, we employ the principle of reverse electrovibration where we inject a weak electrical signal anywhere on the user body creating an oscillating electrical field around the user's fingers. When sliding his or her fingers on a surface of the object, the user perceives highly distinctive tactile textures augmenting the physical object. By tracking the objects and location of the touch, we associate dynamic tactile sensations to the interaction context. REVEL is built upon our previous work on designing electrovibration-based tactile feedback for touch surfaces [Bau, et al. 2010]. In this paper we expand tactile interfaces based on electrovibration beyond touch surfaces and bring them into the real world. We demonstrate a broad range of application scenarios where our technology can be used to enhance AR interaction with dynamic and unobtrusive tactile feedback.


title: Modeling Dialogue Acts with Content Word Filtering and Speaker Preferences

abstract: We present an unsupervised model of dialogue act sequences in conversation. By modeling topical themes as transitioning more slowly than dialogue acts in conversation, our model de-emphasizes content-related words in order to focus on conversational function words that signal dialogue acts. We also incorporate speaker tendencies to use some acts more than others as an additional predictor of dialogue act prevalence beyond temporal dependencies. According to the evaluation presented on two dissimilar corpora, the CNET forum and NPS Chat corpus, the effectiveness of each modeling assumption is found to vary depending on characteristics of the data. De-emphasizing content-related words yields improvement on the CNET corpus, while utilizing speaker tendencies is advantageous on the NPS corpus. The components of our model complement one another to achieve robust performance on both corpora and outperform state-of-the-art baseline models.


title: Finding Coherent Motions and Semantic Regions in Crowd Scenes: A Diffusion and Clustering Approach

abstract: This paper addresses the problem of detecting coherent motions in crowd scenes and subsequently constructing semantic regions for activity recognition. We first introduce a coarse-to-fine thermal-diffusion-based approach. It processes input motion fields (e.g., optical flow fields) and produces a coherent motion filed, named as thermal energy field. The thermal energy field is able to capture both motion correlation among particles and the motion trends of individual particles which are helpful to discover coherency among them. We further introduce a two-step clustering process to construct stable semantic regions from the extracted time-varying coherent motions. Finally, these semantic regions are used to recognize activities in crowded scenes. Experiments on various videos demonstrate the effectiveness of our approach.


title: Building a Conversational Agent Overnight with Dialogue Self-Play

abstract: We propose Machines Talking To Machines (M2M), a framework combining automation and crowdsourcing to rapidly bootstrap end-to-end dialogue agents for goal-oriented dialogues in arbitrary domains. M2M scales to new tasks with just a task schema and an API client from the dialogue system developer, but it is also customizable to cater to task-specific interactions. Compared to the Wizard-of-Oz approach for data collection, M2M achieves greater diversity and coverage of salient dialogue flows while maintaining the naturalness of individual utterances. In the first phase, a simulated user bot and a domain-agnostic system bot converse to exhaustively generate dialogue "outlines", i.e. sequences of template utterances and their semantic parses. In the second phase, crowd workers provide contextual rewrites of the dialogues to make the utterances more natural while preserving their meaning. The entire process can finish within a few hours. We propose a new corpus of 3,000 dialogues spanning 2 domains collected with M2M, and present comparisons with popular dialogue datasets on the quality and diversity of the surface forms and dialogue flows.


title: 3D Object Representations for Fine-Grained Categorization

abstract: While 3D object representations are being revived in the context of multi-view object class detection and scene understanding, they have not yet attained wide-spread use in fine-grained categorization. State-of-the-art approaches achieve remarkable performance when training data is plentiful, but they are typically tied to flat, 2D representations that model objects as a collection of unconnected views, limiting their ability to generalize across viewpoints. In this paper, we therefore lift two state-of-the-art 2D object representations to 3D, on the level of both local feature appearance and location. In extensive experiments on existing and newly proposed datasets, we show our 3D object representations outperform their state-of-the-art 2D counterparts for fine-grained categorization and demonstrate their efficacy for estimating 3D geometry from images via ultra-wide baseline matching and 3D reconstruction.


title: Dynamic time warping constraint learning for large margin nearest neighbor classification

abstract: Nearest neighbor (NN) classifier with dynamic time warping (DTW) is considered to be an effective method for time series classification. The performance of NN-DTW is dependent on the DTW constraints because the NN classifier is sensitive to the used distance function. For time series classification, the global path constraint of DTW is learned for optimization of the alignment of time series by maximizing the nearest neighbor hypothesis margin. In addition, a reduction technique is combined with a search process to condense the prototypes. The approach is implemented and tested on UCR datasets. Experimental results show the effectiveness of the proposed method.


title: Detecting Table Region in PDF Documents Using Distant Supervision

abstract: Superior to state-of-the-art approaches which compete in table recognition with 67 annotated government reports in PDF format released by {\it ICDAR 2013 Table Competition}, this paper contributes a novel paradigm leveraging large-scale unlabeled PDF documents to open-domain table detection. We integrate the paradigm into our latest developed system ({\it PdfExtra}) to detect the region of tables by means of 9,466 academic articles from the entire repository of {\it ACL Anthology}, where almost all papers are archived by PDF format without annotation for tables. The paradigm first designs heuristics to automatically construct weakly labeled data. It then feeds diverse evidences, such as layouts of documents and linguistic features, which are extracted by {\it Apache PDFBox} and processed by {\it Stanford NLP} toolkit, into different canonical classifiers. We finally use these classifiers, i.e. {\it Naive Bayes}, {\it Logistic Regression} and {\it Support Vector Machine}, to collaboratively vote on the region of tables. Experimental results show that {\it PdfExtra} achieves a great leap forward, compared with the state-of-the-art approach. Moreover, we discuss the factors of different features, learning models and even domains of documents that may impact the performance. Extensive evaluations demonstrate that our paradigm is compatible enough to leverage various features and learning models for open-domain table region detection within PDF files.


Tutorial 10 Kalman and Particle filters


title: Self-organizing neural integration of pose-motion features for human action recognition

abstract: The visual recognition of complex, articulated human movements is fundamental for a wide range of artificial systems oriented toward human-robot communication, action classification, and action-driven perception. These challenging tasks may generally involve the processing of a huge amount of visual information and learning-based mechanisms for generalizing a set of training actions and classifying new samples. To operate in natural environments, a crucial property is the efficient and robust recognition of actions, also under noisy conditions caused by, for instance, systematic sensor errors and temporarily occluded persons. Studies of the mammalian visual system and its outperforming ability to process biological motion information suggest separate neural pathways for the distinct processing of pose and motion features at multiple levels and the subsequent integration of these visual cues for action perception. We present a neurobiologically-motivated approach to achieve noise-tolerant action recognition in real time. Our model consists of self-organizing Growing When Required (GWR) networks that obtain progressively generalized representations of sensory inputs and learn inherent spatio-temporal dependencies. During the training, the GWR networks dynamically change their topological structure to better match the input space. We first extract pose and motion features from video sequences and then cluster actions in terms of prototypical pose-motion trajectories. Multi-cue trajectories from matching action frames are subsequently combined to provide action dynamics in the joint feature space. Reported experiments show that our approach outperforms previous results on a dataset of full-body actions captured with a depth sensor, and ranks among the best results for a public benchmark of domestic daily actions.


title: DroidBot: A Lightweight UI-Guided Test Input Generator for Android

abstract: As many automated test input generation tools for Android need to instrument the system or the app, they cannot be used in some scenarios such as compatibility testing and malware analysis. We introduce DroidBot, a lightweight UI-guided test input generator, which is able to interact with an Android app on almost any device without instrumentation. The key technique behind DroidBot is that it can generate UI-guided test inputs based on a state transition model generated on-the-fly, and allow users to integrate their own strategies or algorithms. DroidBot is lightweight as it does not require app instrumentation, thus users do not need to worry about the inconsistency between the tested version and the original version. It is compatible with most Android apps, and able to run on almost all Android-based systems, including customized sandboxes and commodity devices. Droidbot is released as an open-source tool on GitHub, and the demo video can be found at https://youtu.be/3-aHG_SazMY.


Making sense of interoperability : Protocols and Standardization initiatives in IOT


title: Web 3.0 Emerging

abstract: While Web 3.0 technologies are difficult to define precisely, the outline of emerging applications has become clear over the past year. We can thus essentially view Web 3.0 as semantic Web technologies integrated into, or powering, large-scale Web applications. The base of Web 3.0 applications resides in the resource description framework (RDF) for providing a means to link data from multiple Web sites or databases. With the SPARQL query language, a SQL-like standard for querying RDF data, applications can use native graph-based RDF stores and extract RDF data from traditional databases.


title: An Investigation on Mobile Banking Adoption and Usage: A Case Study of Mauritius

abstract: The purpose of this paper is to investigate m-banking adoption and usage in Mauritius; a service relatively new in the island. The aim is to gauge awareness level and to identify those factors that inhibit or motivate m-banking usage in Mauritius. An online survey was carried out using a standard questionnaire. Convenience sampling method was used. Out of the 211 people who responded to the survey, only 169 responses were deemed to be usable. It was found that awareness of local m-banking services is quite high and usage level is reasonable. Convenience, time and effort savings, privacy, ubiquitous access to banking services, compatibility with lifestyle and banking needs were identified as the main factors motivating mbanking adoption. Perceived security risk and reliability were found to be the main obstacles to m-banking usage. It was also found that m-banking usage is not associated with age, gender and salary. There is, however, an association between education and m-banking usage. As to the limitation of the study, the use of convenience sampling limits the generalisation of the findings of the study. The study has practical implications for local banks either offering or planning to launch m-banking services in Mauritius as factors preventing and encouraging usage of m-banking are discussed. Relatively few or no investigations on m-banking adoption and usage in Mauritius have been made. The constructs of Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT) are integrated and extended with perceived risk and cost to ascertain mbanking usage and adoption. The study, therefore, offers valuable insights on m-banking in Mauritius.


title: Development and validation of the Self-Regulation of Eating Behaviour Questionnaire for adults

abstract: Background ::: Eating self-regulatory capacity can help individuals to cope with the obesogenic environment and achieve, as well as maintain, a healthy weight and diet. At present, there is no comprehensive, reliable and valid questionnaire for assessing this capacity and measuring change in response to self-regulation interventions in adults. This paper reports the development of the Self-regulation of Eating Behaviour Questionnaire (SREBQ) for use in UK adults, and presents evidence for its reliability and construct validity. ::: ::: The development of the SREBQ involved generation of an item pool, followed by two pilot studies (Samples 1 and 2) and a test of the questionnaire’s underlying factor structure (Sample 3). The final version of the SREBQ was then assessed for reliability and construct validity (Sample 4).


title: Categorical Colormap Optimization with Visualization Case Studies

abstract: Mapping a set of categorical values to different colors is an elementary technique in data visualization. Users of visualization software routinely rely on the default colormaps provided by a system, or colormaps suggested by software such as ColorBrewer . In practice, users often have to select a set of colors in a semantically meaningful way (e.g., based on conventions, color metaphors, and logological associations), and consequently would like to ensure their perceptual differentiation is optimized. In this paper, we present an algorithmic approach for maximizing the perceptual distances among a set of given colors. We address two technical problems in optimization, i.e., (i) the phenomena of local maxima that halt the optimization too soon, and (ii) the arbitrary reassignment of colors that leads to the loss of the original semantic association. We paid particular attention to different types of constraints that users may wish to impose during the optimization process. To demonstrate the effectiveness of this work, we tested this technique in two case studies. To reach out to a wider range of users, we also developed a web application called Colourmap Hospital.


title: Separation and Analysis of Fetal-ECG Signals From Compressed Sensed Abdominal ECG Recordings

abstract: Objective: Analysis of fetal electrocardiogram (f-ECG) waveforms as well as fetal heart-rate (fHR) evaluation provide important information about the condition of the fetus during pregnancy. A continuous monitoring of f-ECG, for example using the technologies already applied for adults ECG tele-monitor-ing (e.g., Wireless Body Sensor Networks (WBSNs)), may increase early detection of fetal arrhythmias. In this study, we propose a novel framework, based on compressive sensing (CS) theory, for the compression and joint detection/classification of mother and fetal heart beats. Methods : Our scheme is based on the sparse representation of the components derived from independent component analysis (ICA), which we propose to apply directly in the compressed domain. Detection and classification is based on the activated atoms in a specifically designed reconstruction dictionary. Results : Validation of the proposed compression and detection framework has been done on two publicly available datasets, showing promising results (sensitivity S = 92.5 $\%$ , P += 92 $\%$ , F 1 = 92.2 $\%$ for the Silesia dataset and S = 78 $\%$ , P += 77 $\%$ , F 1 = 77.5 $\%$ for the Challenge dataset A, with average reconstruction quality PRD = 8.5 $\%$ and PRD = 7.5 $\%$ , respectively). Conclusion : The experiments confirm that the proposed framework may be used for compression of abdominal f-ECG and to obtain real-time information of the fHR, providing a suitable solution for real time, very low-power f-ECG monitoring. Significance : To the authors’ knowledge, this is the first time that a framework for the low-power CS compression of fetal abdominal ECG is proposed combined with a beat detector for an fHR estimation.


title: IPFS - Content Addressed, Versioned, P2P File System

abstract: The InterPlanetary File System (IPFS) is a peer-to-peer distributed file system that seeks to connect all computing devices with the same system of files. In some ways, IPFS is similar to the Web, but IPFS could be seen as a single BitTorrent swarm, exchanging objects within one Git repository. In other words, IPFS provides a high throughput content-addressed block storage model, with content-addressed hyper links. This forms a generalized Merkle DAG, a data structure upon which one can build versioned file systems, blockchains, and even a Permanent Web. IPFS combines a distributed hashtable, an incentivized block exchange, and a self-certifying namespace. IPFS has no single point of failure, and nodes do not need to trust each other.


title: Interactome Networks and Human Disease

abstract: Complex biological systems and cellular networks may underlie most genotype to phenotype relationships. Here, we review basic concepts in network biology, discussing different types of interactome networks and the insights that can come from analyzing them. We elaborate on why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease.


title: Beyond strategic information systems: towards an IS capability

abstract: Abstract The evolution of information technology (IT) in organizations is often portrayed as following three eras—Data Processing, Management Information Systems, and Strategic Information Systems (SIS)—each displaying distinct characteristics regarding the application of IT and having different objectives. While investments in IT continue to made for both efficiency and effectiveness purposes, the SIS era is premised on management proactively seeking out opportunities for competitive advantage through IT, with approaches to information systems (IS) strategy formulation accommodating the requirement for both alignment of IS/IT investments with corporate strategy and assessing the disruptive impact of technology and the options for its use in shaping business strategy. Frameworks, methodologies and tools have been developed to support the objectives of the SIS era, yet the mechanisms through which organizations achieve repeated and sustained value from IT has received scant attention. Drawing on resource-based theory, this paper proposes a perspective on the management of IT in organizations that specifically considers how organizations can continuously derive and leverage value through IT. The analysis moves beyond a focus on identifying ‘strategic systems’ and develops the concept of an IS capability, suggesting that it heralds the arrival of a new era. The paper presents a model of an IS capability, outlines its core components and illustrates its application.


title: High performance concrete with steel slag aggregate

abstract: The effect of the steel slag aggregate aging on mechanical properties of the high performance concrete is analysed in the paper. The effect of different aging periods of steel slag aggregate on mechanical properties of high performance concrete is studied. It was observed that properties of this concrete are affected by the steel slag aggregate aging process. The compressive strength increases with an increase in the aging period of steel slag aggregate. The flexural strength, Young’s modulus, and impact strength of concrete, increase at the rate similar to that of the compressive strength. The workability and the abrasion loss of concrete decrease with an increase of the steel slag aggregate aging period.


title: Shared and nonshared neural networks of cognitive and affective theory-of-mind: A neuroimaging study using cartoon picture stories

abstract: Theory of mind (ToM) refers to the ability to represent one's own and others' cognitive and affective mental states. Recent imaging studies have aimed to disentangle the neural networks involved in cognitive as opposed to affective ToM, based on clinical observations that the two can functionally dissociate. Due to large differences in stimulus material and task complexity findings are, however, inconclusive. Here, we investigated the neural correlates of cognitive and affective ToM in psychologically healthy male participants (n = 39) using functional brain imaging, whereby the same set of stimuli was presented for all conditions (affective, cognitive and control), but associated with different questions prompting either a cognitive or affective ToM inference. Direct contrasts of cognitive versus affective ToM showed that cognitive ToM recruited the precuneus and cuneus, as well as regions in the temporal lobes bilaterally. Affective ToM, in contrast, involved a neural network comprising prefrontal cortical structures, as well as smaller regions in the posterior cingulate cortex and the basal ganglia. Notably, these results were complemented by a multivariate pattern analysis (leave one study subject out), yielding a classifier with an accuracy rate of more than 85% in distinguishing between the two ToM-conditions. The regions contributing most to successful classification corresponded to those found in the univariate analyses. The study contributes to the differentiation of neural patterns involved in the representation of cognitive and affective mental states of others. Hum Brain Mapp, 36:29–39, 2015. © 2014 Wiley Periodicals, Inc.


title: The entrepreneur's business model: toward a unified perspective

abstract: Highly emphasized in entrepreneurial practice, business models have received limited attention from researchers. No consensus exists regarding the definition, nature, structure, and evolution of business models. Still, the business model holds promise as a unifying unit of analysis that can facilitate theory development in entrepreneurship. This article synthesizes the literature and draws conclusions regarding a number of these core issues. Theoretical underpinnings of a firm’s business model are explored. A six-component framework is proposed for characterizing a business model, regardless of venture type. These components are applied at three different levels. The framework is illustrated using a successful mainstream company. Suggestions are made regarding the manner in which business models might be expected to emerge and evolve over time.


title: Three-Dimensional Object Detection and Layout Prediction Using Clouds of Oriented Gradients

abstract: We develop new representations and algorithms for three-dimensional (3D) object detection and spatial layout prediction in cluttered indoor scenes. RGB-D images are traditionally described by local geometric features of the 3D point cloud. We propose a cloud of oriented gradient (COG) descriptor that links the 2D appearance and 3D pose of object categories, and thus accurately models how perspective projection affects perceived image boundaries. We also propose a "Manhattan voxel" representation which better captures the 3D room layout geometry of common indoor environments. Effective classification rules are learned via a structured prediction framework that accounts for the intersection-over-union overlap of hypothesized 3D cuboids with human annotations, as well as orientation estimation errors. Contextual relationships among categories and layout are captured via a cascade of classifiers, leading to holistic scene hypotheses with improved accuracy. Our model is learned solely from annotated RGB-D images, without the benefit of CAD models, but nevertheless its performance substantially exceeds the state-of-the-art on the SUN RGB-D database. Avoiding CAD models allows easier learning of detectors for many object categories.


title: Web-based models for natural language processing

abstract: Previous work demonstrated that Web counts can be used to approximate bigram counts, suggesting that Web-based frequencies should be useful for a wide variety of Natural Language Processing (NLP) tasks. However, only a limited number of tasks have so far been tested using Web-scale data sets. The present article overcomes this limitation by systematically investigating the performance of Web-based models for several NLP tasks, covering both syntax and semantics, both generation and analysis, and a wider range of n-grams and parts of speech than have been previously explored. For the majority of our tasks, we find that simple, unsupervised models perform better when n-gram counts are obtained from the Web rather than from a large corpus. In some cases, performance can be improved further by using backoff or interpolation techniques that combine Web counts and corpus counts. However, unsupervised Web-based models generally fail to outperform supervised state-of-the-art models trained on smaller corpora. We argue that Web-based models should therefore be used as a baseline for, rather than an alternative to, standard supervised models.


title: Gecko Inspired Surface Climbing Robots

abstract: Many applications call for robots to perform tasks in workspaces where traditional vehicles cannot reach. Using robots to perform these tasks can afford better human safety as well as lower cost operations. This paper focuses on the development of gecko inspired synthetic dry adhesives for wall climbing robots which can scale vertical walls. Many applications are of great interest for this kind of robot such as inspection, repair, cleaning, and exploration. The fabrication of synthetic dry adhesives inspired by nature is discussed as well as the design of prototype wall climbing robots. Results are presented and discussed to show the feasibility of novel gecko inspired robots


title: The Blame Game: The Effect of Responsibility and Social Stigma on Empathy for Pain

abstract: This investigation combined behavioral and functional neuroimaging measures to explore whether perception of pain is modulated by the target's stigmatized status and whether the target bore responsibility for that stigma. During fMRI scanning, participants were exposed to a series of short video clips featuring age-matched individuals experiencing pain who were (a) similar to the participant (healthy), (b) stigmatized but not responsible for their stigmatized condition (infected with AIDS as a result of an infected blood transfusion), or (c) stigmatized and responsible for their stigmatized condition (infected with AIDS as a result of intravenous drug use). Explicit pain and empathy ratings for the targets were obtained outside of the MRI environment, along with a variety of implicit and explicit measures of AIDS bias. Results showed that participants were significantly more sensitive to the pain of AIDS transfusion targets as compared with healthy and AIDS drug targets, as evidenced by significantly higher pain and empathy ratings during video evaluation and significantly greater hemodynamic activity in areas associated with pain processing (i.e., right anterior insula, anterior midcingulate cortex, periaqueductal gray). In contrast, significantly less activity was observed in the anterior midcingulate cortex for AIDS drug targets as compared with healthy controls. Further, behavioral differences between healthy and AIDS drug targets were moderated by the extent to which participants blamed AIDS drug individuals for their condition. Controlling for both explicit and implicit AIDS bias, the more participants blamed these targets, the less pain they attributed to them as compared with healthy controls. The present study reveals that empathic resonance is moderated early in information processing by a priori attitudes toward the target group.


title: New E-Commerce User Interest Patterns

abstract: The number of online purchases is increasing constantly. Companies have recognized the related opportunities and they are using online channels progressively. In order to acquire potential customers, companies often try to gain a better understanding through the use of web analytics. One of the most useful sources are web log files. Basically, these provide an abundance of important information about the user behavior on a website, such as the path or access time. Mining this so-called clickstream data in the most comprehensive way has become an important task in order to predict the behavior of online customers, optimize webpages, and give personalized recommendations. As the number of customers constantly rises, the volume of the generated data log files also increases, both in terms of size and quantity. Thus, for certain companies, the currently used technologies are no longer sufficient. In this work, a comprehensive workflow will be proposed using a clustering algorithm in a Hadoop ecosystem to investigate user interest patterns. The complete workflow will be demonstrated on an application scenario of one of the largest business-to-business (B2B) electronic commerce websites in Germany. Furthermore, an experimental evaluation method will be applied to verify the applicability and efficiency of the used algorithm, along with the associated framework.


title: A Survey on Fault Management in Software-Defined Networks

abstract: Software-defined networking (SDN) is an emerging paradigm that has become increasingly popular in recent years. The core idea is to separate the control and data planes, allowing the construction of network applications using high-level abstractions that are translated to network devices through a southbound interface. SDN architecture is composed of three layers: 1) infrastructure layer, responsible exclusively for data forwarding; 2) control layer, which maintains the network view and provides core network abstractions; and 3) application layer, which uses abstractions provided by the control layer to implement network applications. SDN provides features, such as flexibility and programmability, that are key enablers to meet current network requirements (e.g., multi-tenant cloud networks and elastic optical networks). However, along with its benefits, SDN also brings new issues. In this survey we focus on issues related to fault management. Different fault management threat vectors are introduced by each layer, as well as by the interface between layers. Nevertheless, besides addressing fault management issues of its architecture, SDN also must handle the same problems faced by legacy networks. However, programmability and centralized management might be used to provide flexibility to deal with those issues. This paper presents an overview of fault management in SDN. The major contributions of this paper are as follows: 1) identification of the main fault management issues in SDN and classification according to the affected layers; 2) survey of efforts that address those issues and classification according to the affected planes, issues concerned, general approaches, and features; and 3) discussion about trade-offs of different approaches and their suitability for different scenarios.


title: Enabling Private Continuous Queries For Revealed User Locations

abstract: A rotary screen mounted in the median area of a preferably spherical enclosure, the rear face of which being made opaque except in its central portion, while its front face is perfectly transparent, a projector being placed rearwardly of the enclosure in central axis of the portion which is not opaque in order to enable light rays from the prospector to pass through the enclosure to reach the rotary screen.


title: Evaluating a Semantic Network Automatically Constructed from Lexical Co-occurrence on a Word Sense Disambiguation Task

abstract: We describe the extension and objective evaluation of a network1 of semantically related noun senses (or concepts) that has been automatically acquired by analyzing lexical cooccurrence in Wikipedia. The acquisition process makes no use of the metadata or links that have been manually built into the encyclopedia, and nouns in the network are automatically disambiguated to their corresponding noun senses without supervision. For this task, we use the noun sense inventory of WordNet 3.0. Thus, this work can be conceived of as augmenting the WordNet noun ontology with unweighted, undirected related-to edges between synsets. Our network contains 208,832 such edges. ::: ::: We evaluate our network's performance on a word sense disambiguation (WSD) task and show: a) the network is competitive with WordNet when used as a stand-alone knowledge source for two WSD algorithms; b) combining our network with WordNet achieves disambiguation results that exceed the performance of either resource individually; and c) our network outperforms a similar resource that has been automatically derived from semantic annotations in the Wikipedia corpus.


title: Teacher Attitudes to Multicultural Curriculum Development

abstract: This study reports teacher attitudes towards multicultural curricula developments in a sample of New South Wales state primary schools and the assimilation orientation of the respondents prior to t...


title: Story Generation with Crowdsourced Plot Graphs

abstract: Story generation is the problem of automatically selecting a sequence of events that meet a set of criteria and can be told as a story. Story generation is knowledge-intensive; traditional story generators rely on a priori defined domain models about fictional worlds, including characters, places, and actions that can be performed. Manually authoring the domain models is costly and thus not scalable. We present a novel class of story generation system that can generate stories in an unknown domain. Our system (a) automatically learns a domain model by crowdsourcing a corpus of narrative examples and (b) generates stories by sampling from the space defined by the domain model. A large-scale evaluation shows that stories generated by our system for a previously unknown topic are comparable in quality to simple stories authored by untrained humans.


title: Paenibacillus favisporus sp . nov . , a xylanolytic bacterium isolated from cow faeces

abstract: During a search for xylan-degrading micro-organisms, a sporulated bacterium was recovered from recent and old cow dung and rectal samples. The isolates were identified as members of a novel species of the genus Paenibacillus, based on 16S rRNA gene sequences. According to the results of phylogenetic analysis, the most closely related species was Paenibacillus azoreducens. Phenotypic and chemotaxonomic analyses and DNA–DNA hybridization experiments also showed that the isolates belonged to a novel species of the genus Paenibacillus. The novel species is a facultatively anaerobic, motile, Gram-variable, sporulated rod. The spores of this rod-shaped micro-organism occur in slightly swollen sporangia and are honeycomb-shaped. The main fatty acid is anteiso-branched C15 : 0. Growth was observed with many carbohydrates, including xylan, as the only carbon source and gas production was not observed from glucose. The novel species produces a wide variety of hydrolytic enzymes, such as xylanases, cellulases, amylases, gelatinase, urease and β-galactosidase. On the contrary, it does not produce caseinase, phenylalanine deaminase or lysine decarboxylase. According to the data obtained in this work, the strains belong to a novel species, for which the name Paenibacillus favisporus sp. nov. is proposed (type strain, GMP01T=LMG 20987T=CECT 5760T).


title: 28 Sept layout B.C ok fr

abstract: The plasmodium of the slime mould Physarum polycephalum is a large amoeba-like cell consisting of a dendritic network of tube-like structures (pseudopodia). It changes its shape as it crawls over a plain agar gel and, if food is placed at two different points, it will put out pseudopodia that connect the two food sources. Here we show that this simple organism has the ability to find the minimum-length solution between two points in a labyrinth.


title: JBits : Java based interface for reconfigurable computing

abstract: The JBitsTM software is a set of JavaTM classes which provide an Application Programming Interface (API) to access the Xilinx FPGA bitstream. The interface operates on either bitstreams generated by Xilinx design tools, or on bitstreams read back from actual hardware. This permits all configurable resources like Look-up tables, routing and the flip-flops in the FPGA to be individually configured under software control.


title: Discovering users' topics of interest on twitter: a first look

abstract: Twitter, a micro-blogging service, provides users with a framework for writing brief, often-noisy postings about their lives. These posts are called "Tweets." In this paper we present early results on discovering Twitter users' topics of interest by examining the entities they mention in their Tweets. Our approach leverages a knowledge base to disambiguate and categorize the entities in the Tweets. We then develop a "topic profile," which characterizes users' topics of interest, by discerning which categories appear frequently and cover the entities. We demonstrate that even in this early work we are able to successfully discover the main topics of interest for the users in our study.


Tuning Of Fuzzy PID Controllers


PECAN WEEVIL, CURCULIO CARYAE (COLEOPTERA: CURCULIONIDAE): 2. ESTIMATION OF ADULT POPULATIONS


title: Electric Coupling Structure of Substrate Integrated Waveguide (SIW) for the Application of 140-GHz Bandpass Filter on LTCC

abstract: Millimeter-wave (mmW) bandpass filters (BPFs) using substrate integrated waveguide (SIW) are proposed in this paper. The propagation constants of two different types of electromagnetic bandgap (EBG) units are discussed and compared for their passbands and stopbands performance. In among, the slotted-SIW unit shows a very good lower and upper-stopband performance. The mmW BPF with three cascaded uniform slotted-SIW-based EBG units is constructed and designed at 40-GHz. This EBG filter exhibits good out-of-band performance. To further improve the in-band performance, a third-order mmW BPF with nonuniformly cascaded slotted-SIW unit is designed at 140 GHz. The filter is investigated with the theory of electric coupling mechanism. The extracted coupling coefficient (K) and quality factor (Q) are used to determine the filter circuit dimensions. To prove the validity, the two proposed structures are fabricated in a single-circuit layer using low temperature co-fired ceramic technology and measured at 40 and 140 GHz, respectively. The measured results are in good agreement with the simulated results in such high frequency. The measured insertion losses at 40 GHz and 140 GHz are 0.72 and 1.913 dB, respectively.


title: Autoencoding the retrieval relevance of medical images

abstract: Content-based image retrieval (CBIR) of medical images is a crucial task that can contribute to a more reliable diagnosis if applied to big data. Recent advances in feature extraction and classification have enormously improved CBIR results for digital images. However, considering the increasing accessibility of big data in medical imaging, we are still in need of reducing both memory requirements and computational expenses of image retrieval systems. This work proposes to exclude the features of image blocks that exhibit a low encoding error when learned by a $n/p/n$ autoencoder ($p\!<\!n$). We examine the histogram of autoendcoding errors of image blocks for each image class to facilitate the decision which image regions, or roughly what percentage of an image perhaps, shall be declared relevant for the retrieval task. This leads to reduction of feature dimensionality and speeds up the retrieval process. To validate the proposed scheme, we employ local binary patterns (LBP) and support vector machines (SVM) which are both well-established approaches in CBIR research community. As well, we use IRMA dataset with 14,410 x-ray images as test data. The results show that the dimensionality of annotated feature vectors can be reduced by up to 50% resulting in speedups greater than 27% at expense of less than 1% decrease in the accuracy of retrieval when validating the precision and recall of the top 20 hits.


title: Memory-enhanced Decoder for Neural Machine Translation

abstract: We propose to enhance the RNN decoder in a neural machine translator (NMT) with external memory, as a natural but powerful extension to the state in the decoding RNN. This memory-enhanced RNN decoder is called \textsc{MemDec}. At each time during decoding, \textsc{MemDec} will read from this memory and write to this memory once, both with content-based addressing. Unlike the unbounded memory in previous work\cite{RNNsearch} to store the representation of source sentence, the memory in \textsc{MemDec} is a matrix with pre-determined size designed to better capture the information important for the decoding process at each time step. Our empirical study on Chinese-English translation shows that it can improve by $4.8$ BLEU upon Groundhog and $5.3$ BLEU upon on Moses, yielding the best performance achieved with the same training set.


title: Autonomous pallet localization and picking for industrial forklifts: a robust range and look method

abstract: A combined double-sensor architecture, laser and camera, and a new algorithm named RLPF are presented as a solution to the problem of identifying and localizing a pallet, the position and angle of which are a priori known with large uncertainty. Solving this task for autonomous robot forklifts is of great value for logistics industry. The state-of-the-art is described to show how our approach overcomes the limitations of using either laser ranging or vision. An extensive experimental campaign and uncertainty analysis are presented. For the docking task, new dynamic nonlinear path planning which takes into account vehicle dynamics is proposed.


title: Age-related cognitive decline during normal aging: the complex effect of education.

abstract: Abstract The purpose of this study was to further analyze the effects of education on cognitive decline during normal aging. An 806-subject sample was taken from five different Mexican regions. Participants ranged in age from 16 to 85 years. Subjects were grouped into four educational levels: illiterate, 1–4, 5–9, and 10 or more years of education, and four age ranges: 16–30, 31–50, 51–65, and 66–85 years. A brief neuropsychological test battery (NEUROPSI), standardized and normalized in Spanish, was administered. The NEUROPSI test battery includes assessment of orientation, attention, memory, language, visuoperceptual abilities, motor skills, and executive functions. In general, test scores were strongly associated with level of educational, and differences among age groups were smaller than differences among education groups. However, there was an interaction between age and education such as that among illiterate individuals scores of participants 31–50 years old were higher than scores of participants 16–30 years old for over 50% of the tests. Different patterns of interaction among educational groups were distinguished. It was concluded that: (a) The course of life-span changes in cognition are affected by education. Among individuals with a low level of education, best neuropsychological test performance is observed at an older age than among higher-educated subjects; and (b) there is not a single relationship between age-related cognitive decline and education, but different patterns may be found, depending upon the specific cognitive domain.


title: Sequential method for the determination of uranium, thorium and 226Ra by liquid scintillation alpha spectrometry.

abstract: Abstract A new procedure for the determination of uranium, thorium and 226 Ra from the same aliquot of an aqueous sample using extractant scintillators and liquid scintillation alpha spectrometry is proposed. The procedure is designed such that the same aqueous phase can be used in all the stages, with slight modifications. The procedure is thus very simple, requiring little manipulation of the sample. Testing of the procedure was performed obtaining satisfactory results and high reproducibility.


Learning What's Easy: Fully Differentiable Neural Easy-First Taggers


title: A Study of Evoked Potentials From Ear-EEG

abstract: A method for brain monitoring based on measuring the electroencephalogram (EEG) from electrodes placed in-the-ear (ear-EEG) was recently proposed. The objective of this study is to further characterize the ear-EEG and perform a rigorous comparison against conventional on-scalp EEG. This is achieved for both auditory and visual evoked responses, over steady-state and transient paradigms, and across a population of subjects. The respective steady-state responses are evaluated in terms of signal-to-noise ratio and statistical significance, while the qualitative analysis of the transient responses is performed by considering grand averaged event-related potential (ERP) waveforms. The outcomes of this study demonstrate conclusively that the ear-EEG signals, in terms of the signal-to-noise ratio, are on par with conventional EEG recorded from electrodes placed over the temporal region.


The Development of Multimodal Lexical Resources


title: A Distributed Demand Response Algorithm and Its Application to PHEV Charging in Smart Grids

abstract: This paper proposes a distributed framework for demand response and user adaptation in smart grid networks. In particular, we borrow the concept of congestion pricing in Internet traffic control and show that pricing information is very useful to regulate user demand and hence balance network load. User preference is modeled as a willingness to pay parameter which can be seen as an indicator of differential quality of service. Both analysis and simulation results are presented to demonstrate the dynamics and convergence behavior of the algorithm. Based on this algorithm, we then propose a novel charging method for plug-in hybrid electric vehicles (PHEVs) in a smart grid, where users or PHEVs can adapt their charging rates according to their preferences. Simulation results are presented to demonstrate the dynamic behavior of the charging algorithm and impact of different parameters on system performance.


title: Language Detection For Short Text Messages In Social Media

abstract: With the constant growth of the World Wide Web and the number of documents in different languages accordingly, the need for reliable language detection tools has increased as well. Platforms such as Twitter with predominantly short texts are becoming important information resources, which additionally imposes the need for short texts language detection algorithms. In this paper, we show how incorporating personalized user-specific information into the language detection algorithm leads to an important improvement of detection results. To choose the best algorithm for language detection for short text messages, we investigate several machine learning approaches. These approaches include the use of the well-known classifiers such as SVM and logistic regression, a dictionary based approach, and a probabilistic model based on modified Kneser-Ney smoothing. Furthermore, the extension of the probabilistic model to include additional user-specific information such as evidence accumulation per user and user interface language is explored, with the goal of improving the classification performance. The proposed approaches are evaluated on randomly collected Twitter data containing Latin as well as non-Latin alphabet languages and the quality of the obtained results is compared, followed by the selection of the best performing algorithm. This algorithm is then evaluated against two already existing general language detection tools: Chromium Compact Language Detector 2 (CLD2) and langid, where our method significantly outperforms the results achieved by both of the mentioned methods. Additionally, a preview of benefits and possible applications of having a reliable language detection algorithm is given.


title: A reference model for reputation systems

abstract: Recent advances in ICT have led to a vast and expeditious development of e-services and technology. Trust is a fundamental aspect for the acceptance and adoption of these new services. Reputation is commonly employed as the measure of the trustworthiness of users in on-line communities. However, to facilitate their acceptance, reputation systems should be able to deal with the trust challenges and needs of those services. The aim of this survey is to propose a framework for the analysis of reputation systems. We elicit the requirements for reputations metrics along with the features necessary to achieve such requirements. The identied requirements and features form a reference framework which allows an objective evaluation and comparison of reputation systems. We demonstrate its applicability by analyzing and classifying a number of existing reputation systems. Our framework can serve as a reference model for the analysis of reputation systems. It is also helpful for the design of new reputation systems as it provides an analysis of the implications of design choices.


title: Radial-Flux Permanent-Magnet Limited-Angle Torque Motors

abstract: Slotless radial-flux permanent-magnet limited-angle torque motor with disturbed armature winding is analyzed in this paper. For the first time, the outer rotor structure is investigated for this type of the rotary actuator. The exact 2-D analytical modeling is applied to obtain the magnetic field parameters of the windings and excitation for both the outer rotor and the conventional inner rotor topologies. Also, the governing and geometrical equations are derived based on the analysis outcomes. Then, they are validated by applying the finite-elements tool. Both topologies are designed and compared with each other considering a variety of utilization parameters of these actuators. Avoiding saturation is considered as a designing criterion, which corresponds to the maximum torque production without distortion of linear characteristics in short-time operations. Experimental results show good accuracy of the theoretical design and numerical simulations. Comparison of the results shows significantly better performance of the new proposed actuator with the outer rotor structure against the conventional inner rotor type.


title: Impact of hardy ornamental nursery stock (HONS) systems on the environment: losses of nutrients and agrochemicals

abstract: Abstract Hardy ornamental nursery stock (HONS) is container grown in systems employing frequent irrigation and considerable applications of nutrients and pesticides. These container systems are frequently isolated from the underlying soil by an impermeable membrane resulting in near-surface drainage from the beds. An experiment was established to examine the losses of nitrate, phosphorus and selected agrochemicals from a peat-based growing medium for HONS. Concentrations of nitrate-N in water draining from the beds exceeded 200 mg l−1 and phosphorus (P) exceeded 20 mg l−1, which are considerably in excess of the EC limit for drinking water and also represent a considerable eutrophication potential. Concentrations of pesticides in water draining from the beds were considerably in excess of the limit imposed by the EC Drinking Water Directive, of 0.1 μg l−1 for any one pesticide and 0.5 μg l−1 total, and could pose a threat to some aquatic environments. Peak concentrations observed were: oxadiazon 1500 μg l−1, fonofos 72 μg l−1, furalaxlyl 1500 μg l−1 and simazine 317 μg l−1. The use of sand beds irrigated by a sub-surface system did not noticeably reduce losses of either pesticides or nutrients, unless used in conjunction with a re-circulation system


title: Design of a Switch-Level Analog Model for Verilog

abstract: This paper describes a modeling extension to Verilog called "Switch-Level Analog." It is inspired by the switch-level transistor modeling facility of Verilog, but extends the value domain from Logic to Reals and is based on linear relationships between the currents of branches and the voltages of nodes, rather than the charge relationships of nodes of earlier switch-level models. This capability allows the modeling of many types of modern circuit blocks that exploit the current-source (saturation mode) and resistive (linear mode) properties of transistors. The model is implemented as a PLI library. Modeling examples and performance data are presented.


title: Study on network traffic prediction techniques

abstract: We briefly describe a number of traffic predictors (such as ARIMA, FARIMA, ANN and wavelet-based predictors) and analyze their computational complexity. We compare their performance with MSE, NMSE and computational complexity by simulating the predictors on four wireless network traffic traces and decide the most suitable network traffic predictor based on acceptable performance and accuracy.


title: Towards Deep Semantic Analysis Of Hashtags

abstract: Hashtags are semantico-syntactic constructs used across various social networking and microblogging platforms to enable users to start a topic specific discussion or classify a post into a desired category. Segmenting and linking the entities present within the hashtags could therefore help in better understanding and extraction of information shared across the social media. However, due to lack of space delimiters in the hashtags (e.g #nsavssnowden), the segmentation of hashtags into constituent entities (“NSA” and “Edward Snowden” in this case) is not a trivial task. Most of the current state-of-the-art social media analytics systems like Sentiment Analysis and Entity Linking tend to either ignore hashtags, or treat them as a single word. In this paper, we present a context aware approach to segment and link entities in the hashtags to a knowledge base (KB) entry, based on the context within the tweet. Our approach segments and links the entities in hashtags such that the coherence between hashtag semantics and the tweet is maximized. To the best of our knowledge, no existing study addresses the issue of linking entities in hashtags for extracting semantic information. We evaluate our method on two different datasets, and demonstrate the effectiveness of our technique in improving the overall entity linking in tweets via additional semantic information provided by segmenting and linking entities in a hashtag.


title: Emotionally-Relevant Features for Classification and Regression of Music Lyrics

abstract: This research addresses the role of lyrics in the music emotion recognition process. Our approach is based on several state of the art features complemented by novel stylistic, structural and semantic features. To evaluate our approach, we created a ground truth dataset containing 180 song lyrics, according to Russell's emotion model. We conduct four types of experiments: regression and classification by quadrant, arousal and valence categories. Comparing to the state of the art features (ngrams - baseline), adding other features, including novel features, improved the F-measure from 69.9, 82.7 and 85.6 percent to 80.1, 88.3 and 90 percent, respectively for the three classification experiments. To study the relation between features and emotions (quadrants) we performed experiments to identify the best features that allow to describe and discriminate each quadrant. To further validate these experiments, we built a validation set comprising 771 lyrics extracted from the AllMusic platform, having achieved 73.6 percent F-measure in the classification by quadrants. We also conducted experiments to identify interpretable rules that show the relation between features and emotions and the relation among features. Regarding regression, results show that, comparing to similar studies for audio, we achieve a similar performance for arousal and a much better performance for valence.


title: Double-Rotor Flux-Switching Permanent Magnet Machine With Yokeless Stator

abstract: In this paper, novel design of a flux-switching permanent magnet machine (FSPMM) with a yokeless stator and two rotors has been proposed. The proposed machine has been found to perform on par with a 6-slot/13-pole C-core conventional FSPMM in terms of torque output. Additionally, the proposed machine has no significant unbalanced magnetic forces, which is a major challenge in conventional FSPMM with odd number of rotor poles. Step-by-step parametric optimization has been performed using finite-element models of the machine in order to achieve the highest torque with the given magnetic and electrical constraints.


title: A design theory for systems that support emergent knowledge processes

abstract: This paper addresses the design problem of providing IT support for emerging knowledge processes (EKPs). EKPs are organizational activity patterns that exhibit three characteristics in combination: an emergent process of deliberations with no best structure or sequence; requirements for knowledge that are complex (both general and situational), distributed across people, and evolving dynamically; and an actor set that is unpredictable in terms of job roles or prior knowledge. Examples of EKPs include basic research, new product development, strategic business planning, and organization design. EKPs differ qualitatively from semi-structured decision making processes; therefore, they have unique requirements that are not all thoroughly supported by familiar classes of systems, such as executive information systems, expert systems, electronic communication systems, organizational memory systems, or repositories. Further, the development literature on familiar classes of systems does not provide adequate guidance on how to build systems that support EKPs. Consequently, EKPs require a new IS design theory, as explicated by Walls et al. (1992). ::: ::: We created such a theory while designing and deploying a system for the EKP of organization design. The system was demonstrated through subsequent empirical analysis to be successful in supporting the process. Abstracting from the experience of building this system, we developed an IS design theory for EKP support systems. This new IS design theory is an important theoretical contribution, because it both provides guidance to developers and sets an agenda for academic research. EKP design theory makes the development process more tractable for developers by restricting the range of effective features (or rules for selecting features) and the range of effective development practices to a more manageable set. EKP design theory also sets an agenda for academic research by articulating theory-based principles that are subject to empirical, as well as practical, validation.


title: Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments

abstract: The research purpose is to investigate learner self-regulation in e-learning environments. In order to better understand learner attitudes toward e-learning, 196 university students answer a questionnaire survey after use an e-learning system few months. The statistical results showed that perceived satisfaction, perceived usefulness, and interactive learning environments were all found to predict perceived self-regulation in e-learning environments. Further, perceived usefulness can be influenced by interactive learning environments, perceived self-efficacy, and perceived satisfaction. In addition, perceived satisfaction can be affected by interactive learning environments, perceived self-efficacy, and perceived anxiety. Finally, the study proposes a conceptual model to investigate learner self-regulation in e-learning environments.