title: 2003) "Electronic survey methodology: A case study in reaching hard-to-involve internet users

abstract: Using the Internet to conduct quantitative research presents challenges not found in conventional research. Paper-based survey quality criteria cannot be completely adapted to electronic formats. Electronic surveys have distinctive technological, demographic, and response characteristics that affect their design, use, and implementation. Survey design, participant privacy and confidentiality, sampling and subject solicitation, distribution methods and response rates, and survey piloting are critical methodological components that must be addressed. In this article, quality criteria for electronic survey design and use based on an investigation of recent electronic survey literature are presented. The application of these criteria to reach a hard-to-involve online population-nonpublic participants of online communities (also known as "lurkers")-and survey them on their community participation, a topic not salient to the purpose of their online communities is demonstrated in a case study. The results show t...


title: Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction

abstract: This paper proposes a novel approach for relation extraction from free text which is trained to jointly use information from the text and from existing knowledge. Our model is based on scoring functions that operate by learning low-dimensional embeddings of words, entities and relationships from a knowledge base. We empirically show on New York Times articles aligned with Freebase relations that our approach is able to efficiently use the extra information provided by a large subset of Freebase data (4M entities, 23k relationships) to improve over methods that rely on text features alone.


title: Applying neural network analysis on heart rate variability data to assess driver fatigue

abstract: Research highlights? In this study we show heart rate variability which can be used as a passive means to quantify drowsiness. Frequency domain components of HRV is used for early detection of fatigue. ? We also designed a neural network based artificial intelligent algorithm which detects whether the given HRV signal is in alert of fatigue state. ? The frequency domain components of HRV were also used to distinguish between parasympathetic (HF) and sympathetic (LF) activities using LF/HF ratio. Statistical analysis was also performed to identify the difference between LF/HF ratio during alert and fatigue states. Long duration driving is a significant cause of fatigue related accidents on motorways. Fatigue caused by driving for extended hours can acutely impair driver's alertness and performance. This papers presents an artificial intelligence based system which could detect early onset of fatigue in drivers using heart rate variability (HRV) as the human physiological measure. The detection performance of neural network was tested using a set of electrocardiogram (ECG) data recorded under laboratory conditions. The neural network gave an accuracy of 90%. This HRV based fatigue detection technique can be used as a fatigue countermeasure.


title: Stress detection from speech and Galvanic Skin Response signals

abstract: The problem of stress-management has been receiving an increasing attention in related research communities due to a wider recognition of potential problems caused by chronic stress and due to the recent developments of technologies providing non-intrusive ways of collecting continuously objective measurements to monitor person's stress level. Experimental studies have shown already that stress level can be judged based on the analysis of Galvanic Skin Response (GSR) and speech signals. In this paper we investigate how classification techniques can be used to automatically determine periods of acute stress relying on information contained in GSR and/or speech of a person.


title: Color Transform Based Approach for Disease Spot Detection on Plant Leaf

abstract: In this research, an algorithm for disease spot segmentation using image processing techniques in plant leaf is implemented. This is the first and important phase for automatic detection and classification of plant diseases. Disease spots are different in color but not in intensity, in comparison with plant leaf color. So we color transform of RGB image can be used for better segmentation of disease spots. In this paper a comparison of the effect of CIELAB, HSI and YCbCr color space in the process of disease spot detection is done. Median filter is used for image smoothing. Finally threshold can be calculated by applying Otsu method on color component to detect the disease spot. An algorithm which is independent of background noise, plant type and disease spot color was developed and experiments were carried out on different "Monocot" and "Dicot" family plant leaves with both, noise free (white) and noisy background.


title: Cat affected with sarcoptic mange in Y. S. R. District of Andhra Pradesh, India

abstract: Sarcoptes scabiei infestation in cats was rare. A 1-year-old tom cat was presented to the clinic with recurrent dermatitis, emaciation, severe itching and presence of skin lesions over the face, forehead and ears. Microscopic examination of skin scrapings from the skin lesions revealed the presence of mites. Based on the morphology of the mites, sarcoptic mange was confirmed. Cat was treated with parenteral ivermectin and supportive therapy. Complete recovery was observed by the fifteenth day of treatment.


title: Survey on NoSQL database

abstract: With the development of the Internet and cloud computing, there need databases to be able to store and process big data effectively, demand for high-performance when reading and writing, so the traditional relational database is facing many new challenges. Especially in large scale and high-concurrency applications, such as search engines and SNS, using the relational database to store and query dynamic user data has appeared to be inadequate. In this case, NoSQL database created. This paper describes the background, basic characteristics, data model of NoSQL. In addition, this paper classifies NoSQL databases according to the CAP theorem. Finally, the mainstream NoSQL databases are separately described in detail, and extract some properties to help enterprises to choose NoSQL.


title: PROVID: Progressive and Multimodal Vehicle Reidentification for Large-Scale Urban Surveillance

abstract: Compared with person reidentification, which has attracted concentrated attention, vehicle reidentification is an important yet frontier problem in video surveillance and has been neglected by the multimedia and vision communities. Since most existing approaches mainly consider the general vehicle appearance for reidentification while overlooking the distinct vehicle identifier, such as the license plate number, they attain suboptimal performance. In this paper, we propose PROVID, a PROgressive Vehicle re-IDentification framework based on deep neural networks. In particular, our framework not only utilizes the multimodality data in large-scale video surveillance, such as visual features, license plates, camera locations, and contextual information, but also considers vehicle reidentification in two progressive procedures: coarse-to-fine search in the feature domain, and near-to-distant search in the physical space. Furthermore, to evaluate our progressive search framework and facilitate related research, we construct the VeRi dataset, which is the most comprehensive dataset from real-world surveillance videos. It not only provides large numbers of vehicles with varied labels and sufficient cross-camera recurrences but also contains license plate numbers and contextual information. Extensive experiments on the VeRi dataset demonstrate both the accuracy and efficiency of our progressive vehicle reidentification framework.


title: Cotton leaf disease identification using pattern recognition techniques

abstract: Leaf diseases on cotton plant must be identified early and accurately as it can prove detrimental to the yield. The proposed work presents a pattern recognition system for identification and classification of three cotton leaf diseases i.e. Bacterial Blight, Myrothecium and Alternaria. The images required for this work are captured from the fields at Central Institute of Cotton Research Nagpur, and the cotton fields in Buldana and Wardha district. Active contour model is used for image segmentation and Hu's moments are extracted as features for the training of adaptive neuro-fuzzy inference system. The classification accuracy is found to be 85 percent.


title: 1 Smart Distribution: Coupled Microgrids

abstract: The distribution system provides major opportunities for smart grid concepts. One way to approach distribution system problems is to rethinking our distribution system to include the integration of high levels of distributed energy resources, using microgrid concepts. Basic objectives are improved reliability, promote high penetration of renewable sources, dynamic islanding, and improved generation efficiencies through the use of waste heat. Managing significant levels of distributed energy resources (DERs) with a wide and dynamic set of resources and control points can become overwhelming. The best way to manage such a system is to break the distribution system down into small clusters or microgrids, with distributed optimizing controls coordinating multimicrogrids. The Consortium for Electric Reliability Technology Solutions (CERTSs) concept views clustered generation and associated loads as a grid resource or a “microgrid.” The clustered sources and loads can operate in parallel to the grid or as an island. This grid resource can disconnect from the utility during events (i.e., faults, voltage collapses), but may also intentionally disconnect when the quality of power from the grid falls below certain standards. This paper focuses on DER-based distribution, the basics of microgrids, possibility of smart distribution systems using coupled microgrid and the current state of autonomous microgrid technology.


title: A Functional and Structural Mongolian Scots Pine (Pinus sylvestris var. mongolica) Model Integrating Architecture, Biomass and Effects of Precipitation

abstract: Mongolian Scots pine (Pinus sylvestris var. mongolica) is one of the principal tree species in the network of Three-North Shelterbelt for windbreak and sand stabilisation in China. The functions of shelterbelts are highly correlated with the architecture and eco-physiological processes of individual tree. Thus, model-assisted analysis of canopy architecture and function dynamic in Mongolian Scots pine is of value for better understanding its role and behaviour within shelterbelt ecosystems in these arid and semiarid regions. We present here a single-tree functional and structural model, derived from the GreenLab model, which is adapted for young Mongolian Scots pines by incorporation of plant biomass production, allocation, allometric rules and soil water dynamics. The model is calibrated and validated based on experimental measurements taken on Mongolian Scots pines in 2007 and 2006 under local meteorological conditions. Measurements include plant biomass, topology and geometry, as well as soil attributes and standard meteorological data. After calibration, the model allows reconstruction of three-dimensional (3D) canopy architecture and biomass dynamics for trees from one- to six-year-old at the same site using meteorological data for the six years from 2001 to 2006. Sensitivity analysis indicates that rainfall variation has more influence on biomass increment than on architecture, and the internode and needle compartments and the aboveground biomass respond linearly to increases in precipitation. Sensitivity analysis also shows that the balance between internode and needle growth varies only slightly within the range of precipitations considered here. The model is expected to be used to investigate the growth of Mongolian Scots pines in other regions with different soils and climates.


title: Docking and scoring in virtual screening for drug discovery: methods and applications

abstract: Computational approaches that 'dock' small molecules into the structures of macromolecular targets and 'score' their potential complementarity to binding sites are widely used in hit identification and lead optimization. Indeed, there are now a number of drugs whose development was heavily influenced by or based on structure-based design and screening strategies, such as HIV protease inhibitors. Nevertheless, there remain significant challenges in the application of these approaches, in particular in relation to current scoring schemes. Here, we review key concepts and specific features of small-molecule-protein docking methods, highlight selected applications and discuss recent advances that aim to address the acknowledged limitations of established approaches.


title: Better Control for a Solar Energy System: Using Improved Enhanced Phase-Locked Loop-Based Control Under Variable Solar Intensity

abstract: An improved enhanced phase-locked loop (EPL)-based control algorithm for a double-stage solar photovoltaic (PV) grid-interfaced power generating system is presented in this article. It also mitigates power quality (PQ) problems in a three-phase, four-wire (3P4W) distribution system under variable solar intensity. The proposed solar PV grid-interfaced system consists of a solar PV array, a boost converter, a four-leg voltage source converter (VSC), and connected linear/nonlinear loads. The suggested improved EPL based synchronization method is implemented to meet grid requirements such as PQ improvement, distortion-free signals under variable solar intensity, and load unbalancing.


title: Prospect theory on the brain? Toward a cognitive neuroscience of decision

abstract: Most decisions must be made without advance knowledge of their consequences. Economists and psychologists have devoted much attention to modeling decisions made under conditions of risk in which options can be characterized by a known probability distribution over possible outcomes. The descriptive shortcomings of classical economic models motivated the development of prospect theory (D. Kahneman, A. Tversky, Prospect theory: An analysis of decision under risk. Econometrica, 4 (1979) 263‐291; A. Tversky, D. Kahneman, Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5 (4) (1992) 297‐323) the most successful behavioral model of decision under risk. In the prospect theory, subjective value is modeled by a value function that is concave for gains, convex for losses, and steeper for losses than for gains; the impact of probabilities are characterized by a weighting function that overweights low probabilities and underweights moderate to high probabilities. We outline the possible neural bases of the components of prospect theory, surveying evidence from human imaging, lesion, and neuropharmacology studies as well as animal neurophysiology studies. These results provide preliminary suggestions concerning the neural bases of prospect theory that include a broad set of brain regions and neuromodulatory systems. These data suggest that focused studies of decision making in the context of quantitative models may provide substantial leverage towards a fuller understanding of the cognitive neuroscience of decision making. D 2005 Elsevier B.V. All rights reserved. Theme: Neural basis of behavior Topic: Cognition


title: Monitoring of Large-Area IoT Sensors Using a LoRa Wireless Mesh Network System: Design and Evaluation

abstract: Although many techniques exist to transfer data from the widely distributed sensors that make up the Internet of Things (IoT) (e.g., using 3G/4G networks or cables), these methods are associated with prohibitively high costs, making them impractical for real-life applications. Recently, several emerging wireless technologies have been proposed to provide long-range communication for IoT sensors. Among these, LoRa has been examined for long-range performance. Although LoRa shows good performance for long-range transmission in the countryside, its radio signals can be attenuated over distance, and buildings, trees, and other radio signal sources may interfere with the signals. Our observations show that in urban areas, LoRa requires dense deployment of LoRa gateways (GWs) to ensure that indoor LoRa devices can successfully transfer data back to remote GWs. Wireless mesh networking is a solution for increasing communication range and packet delivery ratio (PDR) without the need to install additional GWs. This paper presents a LoRa mesh networking system for large-area monitoring of IoT applications. We deployed 19 LoRa mesh networking devices over an $800\,\,\text {m} \times 600$ m area on our university campus and installed a GW that collected data at 1-min intervals. The proposed LoRa mesh networking system achieved an average 88.49% PDR, whereas the star-network topology used by LoRa achieved only 58.7% under the same settings. To the best of our knowledge, this is the first academic study discussing LoRa mesh networking in detail and evaluating its performance via real experiments.


title: Cultural institutions in the digital age: British Museum’s use of Facebook Insights

abstract: Over the last decade, museums have experienced a strong cultural shift from objectoriented ::: towards audience-oriented strategic approaches. This paper reviews the relevant ::: literature on this transition and provides supportive evidence of the impact that social ::: media have upon the relationship between cultural organisations and their audiences. Data ::: deriving from the use of the British Museum’s Facebook page provides a comprehensive ::: evaluation of the museum’s general social media strategy. Given the dominance and ::: widespread use of Facebook and other social media, the degree of engagement of cultural ::: organisations with them, seem to have become an inseparable factor on the scale of ::: effectiveness of the relation with their audiences and the levels of visitor attendance. This ::: research aims, by using one of the most prestigious museums in Britain as a case study, to ::: provide an initial comprehensive set of insights into the use of social media by cultural ::: organisations. The findings of this study are based on research conducted for the first ::: author’s postgraduate studies at the Centre for Digital Humanities, Department of ::: Information studies, University College London.


title: Multi-view action recognition by cross-domain learning

abstract: This paper proposes a novel multi-view human action recognition method by discovering and sharing common knowledge among different video sets captured in multiple viewpoints. To our knowledge, we are the first to treat a specific view as target domain and the others as source domains and consequently formulate the multi-view action recognition into the cross-domain learning framework. First, the classic bag-of-visual word framework is implemented for visual feature extraction in individual viewpoints. Then, we propose a cross-domain learning method with block-wise weighted kernel function matrix to highlight the saliency components and consequently augment the discriminative ability of the model. Extensive experiments are implemented on IXMAS, the popular multi-view action dataset. The experimental results demonstrate that the proposed method can consistently outperform the state of the arts.


title: SANE: System for Fine Grained Named Entity Typing on Textual Data

abstract: Assignment of fine-grained types to named entities is gaining popularity as one of the major Information Extraction tasks due to its applications in several areas of Natural Language Processing. Existing systems use huge knowledge bases to improve the accuracy of the fine-grained types. We designed and developed SANE, a system that uses Wikipedia categories to fine grain the type of the named entities recognized in the textual data. ::: ::: The main contribution of this work is building a named entity typing system without the use of knowledge bases. Through our experiments, 1) we establish the usefulness of Wikipedia categories to Named Entity Typing and 2) we show that SANE's performance is on par with the state-of-the-art.


title: Deep learning detecting fraud in credit card transactions

abstract: Credit card fraud resulted in the loss of $3 billion to North American financial institutions in 2017. The rise of digital payments systems such as Apple Pay, Android Pay, and Venmo has meant that loss due to fraudulent activity is expected to increase. Deep Learning presents a promising solution to the problem of credit card fraud detection by enabling institutions to make optimal use of their historic customer data as well as real-time transaction details that are recorded at the time of the transaction. In 2017, a study found that a Deep Learning approach provided comparable results to prevailing fraud detection methods such as Gradient Boosted Trees and Logistic Regression. However, Deep Learning encompasses a number of topologies. Additionally, the various parameters used to construct the model (e.g. the number of neurons in the hidden layer of a neural network) also influence its results. In this paper, we evaluate a subsection of Deep Learning topologies — from the general artificial neural network to topologies with built-in time and memory components such as Long Short-term memory — and different parameters with regard to their efficacy in fraud detection on a dataset of nearly 80 million credit card transactions that have been pre-labeled as fraudulent and legitimate. We utilize a high performance, distributed cloud computing environment to navigate past common fraud detection problems such as class imbalance and scalability. Our analysis provides a comprehensive guide to sensitivity analysis of model parameters with regard to performance in fraud detection. We also present a framework for parameter tuning of Deep Learning topologies for credit card fraud detection to enable financial institutions to reduce losses by preventing fraudulent activity.


title: Rehabilitation in amyotrophic lateral sclerosis: Why it matters

abstract: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease that results in a constellation of problematic symptoms and a high patient and caregiver burden. Multidisciplinary care includes rehabilitation interventions that have the goal of assisting people to teach their fullest potential despite the presence of a disabling disease. Given the progressive nature of ALS, the clinician must be aware of the expected disease trajectory and apply appropriate interventions at each stage. This review will present rehabilitation strategies that can be utilized to maximize patient independence, function, safety, and quality of life, and to minimize disease-related symptoms. The role of bracing, exercise, assistive devices, and adaptive equipment will be discussed. At each disease stage, an experienced rehabilitation team is well positioned to make a significant impact on the life of ALS patients.


title: Double-sided printed bow-tie antenna for UWB communications

abstract: This letter proposes a double-sided printed bow-tie antenna for ultra wide band (UWB) applications. The frequency band considered is 3.1-10.6 GHz, which has been approved by the Federal Communications Commission as a commercial UWB band. The proposed antenna has a return loss less than 10 dB, phase linearity, and gain flatness over the above frequency band.


title: The Underground Economy, the Demand for Currency Approach and the Analysis of Discrepancies: Some Recent European Experience

abstract: Recent estimates of the size of the "underground economy" have used the so-called "demand for currency approach." One of the assumptions made by these studies is that official statistics do not take into account the underground economy when estimating GDP. After setting some definitions, the paper presents a brief critical review of the method and results obtained for the European Union using this approach. It points out that the different concepts of unreported and unrecorded activities are incorrectly considered to be equivalent. The third section, after a review of the method of estimating the underground economy using the discrepancy approach, presents the new results of the authors which give an indication of the amount of the unreported activities already included in official national accounts statistics in the EU. The results of the discrepancy approach disprove the widespread belief that official statistics only include officially recorded transactions and reinforce the critical view on the results obtained with the currency-demand approach. Copyright 2001 by The International Association for Research in Income and Wealth.


title: Inhibition of HDAC6 Protein Enhances Bortezomib-induced Apoptosis in Head and Neck Squamous Cell Carcinoma (HNSCC) by Reducing Autophagy*

abstract: Chemoresistance is a major barrier to effective chemotherapy of solid tumors, including head and neck squamous cell carcinoma (HNSCC). Recently, autophagy, a highly conservative intracellular recycling system, has shown to be associated with chemoresistance in cancer cells. However, little is known about how autophagy plays a role in the development of chemoresistance in HNSCC and how autophagy is initiated when HNSCC cells undergo cytotoxic stress. Here, we report that autophagy was activated when HNSCC cells are treated with the proteasome inhibitor bortezomib, proposed as an alternative chemotherapeutic agent for both primary and cisplatin-resistant HNSCC cells. Ablation of histone deacetylase 6 (HDAC6) expression and its activity in HNSCC cells significantly inhibited autophagy induction by altering the phosphorylation status of mammalian target of rapamycin and enhanced the bortezomib cytotoxicity. Similarly, a combination regimen of bortezomib and the histone deacetylase inhibitor trichostatin A abolished HDAC6 activity and decreased autophagy induction while significantly enhancing bortezomib-induced apoptosis in HNSCC cells. These data uncover a novel molecular mechanism indicating that HDAC6 may serve as a critical causal link between autophagy, apoptosis, and the cell survival response in HNSCC. A combination regimen resulting in regression of autophagy improves chemotherapeutic efficacy, thereby providing a new strategy to overcome chemoresistance and to improve the treatment and survival of HNSCC patients.


title: ENTREPRENEURSHIP: THE DIFFERENTIAL EFFECTS ON VENTURING, INNOVATION AND RENEWAL PROCESSES

abstract: Most corporate entrepreneurship studies have focused on either innovation, venturing or strategic renewal making comparison between the antecedents of all three aspects of corporate entrepreneurship difficult. Moreover, studies on corporate entrepreneurship hardly address organizational antecedents, while simultaneously managing and organizing CE and mainstream activities has been seen as a major challenge for incumbent firms. Our findings show that organizational ambidexterity has strong and differential effects on venturing, innovation and renewal. We find, for example, that innovation is affected by horizontal integration, while strategic renewal is significantly influenced by integration on top management team level.


title: Design Optimization of High-Speed and Low-Power Operational Transconductance Amplifier Using gm/ID Lookup Table Methodology

abstract: We propose a design optimization flow for a high-speed and low-power operational transconductance amplifier (OTA) using a gm/ID lookup table design methodology in scaled CMOS. This methodology advantages from using gm/ID as a primary design parameter to consider all operation regions including strong, moderate, and weak inversion regions, and enables the lowest power design. SPICE-based lookup table approach is employed to optimize the operation region specified by the gm/ID with sufficient accuracy for short-channel transistors. The optimized design flow features 1) a proposal of the worst-case design scenario for specification and gm/ID lookup table generations from worst-case SPICE simulations, 2) an optimization procedure accomplished by the combination of analytical and simulation-based approaches in order to eliminate tweaking of circuit parameters, and 3) an additional use of gm/ID subplots to take second-order effects into account. A gain-boosted folded-cascode OTA for a switched capacitor circuit is adopted as a target topology to explore the effectiveness of the proposed design methodology for a circuit with complex topology. Analytical expressions of the gain-boosted folded-cascode OTA in terms of DC gain, frequency response and output noise are presented, and detailed optimization of gm/IDs as well as circuit parameters are illustrated. The optimization flow is verified for the application to a residue amplifier in a 10-bit 125MS/s pipeline A/D converter implemented in a 0.18µm CMOS technology. The optimized circuit satisfies the required specification for all corner simulations without additional tweaking of circuit parameters. We finally explore the possibility of applying this design methodology as a technology migration tool, and illustrate the failure analysis by comparing the differences in the gm/ID characteristics.


title: Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning

abstract: While great strides have been made in using deep learning algorithms to solve supervised learning tasks, the problem of unsupervised learning - leveraging unlabeled examples to learn about the structure of a domain - remains a difficult unsolved challenge. Here, we explore prediction of future frames in a video sequence as an unsupervised learning rule for learning about the structure of the visual world. We describe a predictive neural network ("PredNet") architecture that is inspired by the concept of "predictive coding" from the neuroscience literature. These networks learn to predict future frames in a video sequence, with each layer in the network making local predictions and only forwarding deviations from those predictions to subsequent network layers. We show that these networks are able to robustly learn to predict the movement of synthetic (rendered) objects, and that in doing so, the networks learn internal representations that are useful for decoding latent object parameters (e.g. pose) that support object recognition with fewer training views. We also show that these networks can scale to complex natural image streams (car-mounted camera videos), capturing key aspects of both egocentric movement and the movement of objects in the visual scene, and the representation learned in this setting is useful for estimating the steering angle. Altogether, these results suggest that prediction represents a powerful framework for unsupervised learning, allowing for implicit learning of object and scene structure.


title: Data Quality Measures and Data Cleansing for Research Information Systems

abstract: 10 pages.-- Published in the Journal of Digital Information Management, Vol 16 Iss 1 (Feb 2018)


title: ZM-Net: Real-time Zero-shot Image Manipulation Network

abstract: Many problems in image processing and computer vision (e.g. colorization, style transfer) can be posed as 'manipulating' an input image into a corresponding output image given a user-specified guiding signal. A holy-grail solution towards generic image manipulation should be able to efficiently alter an input image with any personalized signals (even signals unseen during training), such as diverse paintings and arbitrary descriptive attributes. However, existing methods are either inefficient to simultaneously process multiple signals (let alone generalize to unseen signals), or unable to handle signals from other modalities. In this paper, we make the first attempt to address the zero-shot image manipulation task. We cast this problem as manipulating an input image according to a parametric model whose key parameters can be conditionally generated from any guiding signal (even unseen ones). To this end, we propose the Zero-shot Manipulation Net (ZM-Net), a fully-differentiable architecture that jointly optimizes an image-transformation network (TNet) and a parameter network (PNet). The PNet learns to generate key transformation parameters for the TNet given any guiding signal while the TNet performs fast zero-shot image manipulation according to both signal-dependent parameters from the PNet and signal-invariant parameters from the TNet itself. Extensive experiments show that our ZM-Net can perform high-quality image manipulation conditioned on different forms of guiding signals (e.g. style images and attributes) in real-time (tens of milliseconds per image) even for unseen signals. Moreover, a large-scale style dataset with over 20,000 style images is also constructed to promote further research.


title: IT Infrastructure-Monitoring Tools

abstract: Monitoring is critical to IT system health and thus to businesses' bottom line. This article discusses current tools that monitor networks to detect issues, ensure the components' availability, and measure the resources those components use.


title: RIOT: One OS to Rule Them All in the IoT

abstract: The Internet of Things (IoT) embodies a wide spectrum of machines ranging from sensors powered by 8-bits microcontrollers, to devices powered by processors roughly equivalent to those found in entry-level smartphones. Neither traditional operating systems (OS) currently running on internet hosts, nor typical OS for sensor networks are capable to fulfill all at once the diverse requirements of such a wide range of devices. Hence, in order to avoid redundant developments and maintenance costs of IoT products, a novel, unifying type of OS is needed. The following analyzes requirements such an OS should fulfill, and introduces RIOT, a new OS satisfying these demands.


title: Protection: principles and practice

abstract: The protection mechanisms of computer systems control the access to objects, especially information objects. The range of responsibilities of these mechanisms includes at one extreme completely isolating executing programs from each other, and at the other extreme permitting complete cooperation and shared access among executing programs. Within this range one can identify at least seven levels at which protection mechanisms can be conceived as being required, each level being more difficult than its predecessor to implement: 1. No sharing at all (complete isolation). 2. Sharing copies of programs or data files. 3. Sharing originals of programs or data files. 4. Sharing programming systems or subsystems. 5. Permitting the cooperation of mutually suspicious subsystems---e.g., as with debugging or proprietary subsystems. 6. Providing "memoryless" subsystems---i.e., systems which, having performed their tasks, are guaranteed to have kept no secret record of the task performed (an income-tax computing service, for example, must be allowed to keep billing information on its use by customers but not to store information secretly on customers' incomes). 7. Providing "certified" subsystems---i.e., those whose correctness has been completely validated and is guaranteed a priori.


title: Strategic Value of Cloud Computing in Healthcare Organisations Using the Balanced Scorecard Approach

abstract: The evolution of Cloud Computing over the past few years has the potential to provide many benefits for healthcare organisations. However, healthcare organisations still need to discover the strategic values of adopting such a technology model. The paper discusses the strategic value of implementing Cloud Computing solutions in a Saudi hospital based on the Balanced Scorecard Approach. The paper also presents the strategy map and the KPIs that were used by the hospital. The results of this paper (KPIs, strategy map.) could act as guidelines for similar projects and similar organisations, while taking into consideration the uniqueness of each organisation.


title: Secure coding practices in Java: challenges and vulnerabilities

abstract: The Java platform and its third-party libraries provide useful features to facilitate secure coding. However, misusing them can cost developers time and effort, as well as introduce security vulnerabilities in software. We conducted an empirical study on StackOverflow posts, aiming to understand developers' concerns on Java secure coding, their programming obstacles, and insecure coding practices. We observed a wide adoption of the authentication and authorization features provided by Spring Security---a third-party framework designed to secure enterprise applications. We found that programming challenges are usually related to APIs or libraries, including the complicated cross-language data handling of cryptography APIs, and the complex Java-based or XML-based approaches to configure Spring Security. In addition, we reported multiple security vulnerabilities in the suggested code of accepted answers on the StackOverfow forum. The vulnerabilities included disabling the default protection against Cross-Site Request Forgery (CSRF) attacks, breaking SSL/TLS security through bypassing certificate validation, and using insecure cryptographic hash functions. Our findings reveal the insufficiency of secure coding assistance and documentation, as well as the huge gap between security theory and coding practices.


title: Health Information Systems: a Survey of Frame-works for Developing Countries

abstract: Objectives: The objective of this paper is to perform a survey of excellent research on health information systems (HIS) analysis and design, and their underlying theoretical frameworks. It classifies these frameworks along major themes, and analyzes the different approaches to HIS development that are practical in resource-constrained environments. Method: Literature review based on PubMed citations and conference proceedings, as well as Internet searches on information systems in general, and health information systems in particular. Results: The field of health information systems development has been studied extensively. Despite this, failed implementations are still common. Theoretical frameworks for HIS development are available that can guide implementers. Conclusion: As awareness, acceptance, and demand for health information systems increase globally, the variety of approaches and strategies will also follow. For developing countries with scarce resources, a trial-and-error approach can be very costly. Lessons from the successes and failures of initial HIS implementations have been abstracted into theoretical frameworks. These frameworks organize complex HIS concepts into methodologies that standardize techniques in implementation. As globalization continues to impact healthcare in the developing world, demand for more responsive health systems will become urgent. More comprehensive frameworks and practical tools to guide HIS implementers will be imperative.


title: A Damping Scheme for Switching Ringing of Full SiC MOSFET by Air Core PCB Circuit

abstract: In this paper, a resonant damping circuit to suppress the switching ringing of full SiC mosfet is proposed. It reveals that the ringing phenomenon caused by parasitic impedances of switching circuit can be damped out using air core PCB transformer which has a properly designed secondary side circuit. The design method for PCB transformer and the secondary circuit are developed considering the physical dimension applied to the PCB transformer inserted between full SiC mosfet module and snubber capacitor. Experimental results using 1200 V–180 A full SiC mosfet module validate the design method and the performance of the proposed air core PCB circuit. Thanks to the damping circuit, the resonant component due to the switching ringing has been reduced to a half compared to that of system without damping circuit.


title: Pushing the Envelope of Sound

abstract: Normal hearing depends on the amplification of sound in the cochlea. In this issue of Neuron, Fettiplace and colleagues show that prestin-based amplification is a viable mechanism as it is not limited by the outer hair cell's membrane time constant as previously surmised.


title: Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures

abstract: With the emergence of ever-growing advanced vehicular applications, the challenges to meet the demands from both communication and computation are increasingly prominent. Without powerful communication and computational support, various vehicular applications and services will still stay in the concept phase and cannot be put into practice in the daily life. Thus, solving this problem is of great importance. The existing solutions, such as cellular networks, roadside units (RSUs), and mobile cloud computing, are far from perfect because they highly depend on and bear the cost of additional infrastructure deployment. Given tremendous number of vehicles in urban areas, putting these underutilized vehicular resources into use offers great opportunity and value. Therefore, we conceive the idea of utilizing vehicles as the infrastructures for communication and computation, named vehicular fog computing (VFC), which is an architecture that utilizes a collaborative multitude of end-user clients or near-user edge devices to carry out communication and computation, based on better utilization of individual communication and computational resources of each vehicle. By aggregating abundant resources of individual vehicles, the quality of services and applications can be enhanced greatly. In particular, by discussing four types of scenarios of moving and parked vehicles as the communication and computational infrastructures, we carry on a quantitative analysis of the capacities of VFC. We unveil an interesting relationship among the communication capability, connectivity, and mobility of vehicles, and we also find out the characteristics about the pattern of parking behavior, which benefits from the understanding of utilizing the vehicular resources. Finally, we discuss the challenges and open problems in implementing the proposed VFC system as the infrastructures. Our study provides insights for this novel promising paradigm, as well as research topics about vehicular information infrastructures.


title: Adult attachment, working models, and relationship quality in dating couples.

abstract: Three studies were conducted to examine the correlates of adult attachment. In Study 1, an 18-item scale to measure adult attachment style dimensions was developed based on Kazan and Shaver's (1987) categorical measure. Factor analyses revealed three dimensions underlying this measure: the extent to which an individual is comfortable with closeness, feels he or she can depend on others, and is anxious or fearful about such things as being abandoned or unloved. Study 2 explored the relation between these attachment dimensions and working models of self and others. Attachment dimensions were found to be related to self-esteem, expressiveness, instrumentality, trust in others, beliefs about human nature, and styles of loving. Study 3 explored the role of attachment style dimensions in three aspects of ongoing dating relationships: partner matching on attachment dimensions; similarity between the attachment of one's partner and caregiving style of one's parents; and relationship quality, including communication, trust, and satisfaction. Evidence was obtained for partner matching and for similarity between one's partner and one's parents, particularly for one's opposite-sex parent. Dimensions of attachment style were strongly related to how each partner perceived the relationship, although the dimension of attachment that best predicted quality differed for men and women. For women, the extent to which their partner was comfortable with closeness was the best predictor of relationship quality, whereas the best predictor for men was the extent to which their partner was anxious about being abandoned or unloved. It is generally believed that the nature and quality of one's close relationships in adulthood are strongly influenced by affective events that took place during childhood, particularly within the child-caretaker relationship. Yet, only recently have social psychologists begun to integrate work on adult love relationships with developmental theory and research on the nature and functioning of parent-child relations (Hartup & Rubin, 1986; Kazan & Shaver, 1987;Hinde, 1979;HindeS Shaver & Hazan, 1988; Shaver, Hazan, & Bradshaw, 1988; Shaver R Weiss, 1982,1986). Of particular interest has been the extent to which a child's early attachment relationships with caretakers shape important beliefs about the self and social world, which then guide relationships in adulthood. Recently, Hazan and Shaver (1987) have used infant attachment theory (Ainsworth, Blehar, Waters, & Wall, 1978; Bowlby, 1982,1973,1980) as a framework for examining how adult love relationships are related to early parent-child interactions. The


title: Speech production: Wernicke, Broca and beyond

abstract: We investigated the brain systems engaged during propositional speech (PrSp) and two forms of non- propositional speech (NPrSp): counting and reciting overlearned nursery rhymes. Bilateral cerebral and cerebellar regions were involved in the motor act of articulation, irrespective of the type of speech. Three additional, left-lateralized regions, adjacent to the Sylvian sulcus, were activated in common: the most posterior part of the supratemporal plane, the lateral part of the pars opercularis in the posterior inferior frontal gyrus and the anterior insula. Therefore, both NPrSp and PrSp were dependent on the same discrete subregions of the anatomically ill-defined areas of Wernicke and Broca. PrSp was also dependent on a predominantly left-lateralized neural system distributed between multi-modal and amodal regions in posterior inferior parietal, anterolateral and medial temporal and medial prefrontal cortex. The lateral prefrontal and paracingulate cortical activity observed in previous studies of cued word retrieval was not seen with either NPrSp or PrSp, demonstrating that normal brain- language representations cannot be inferred from explicit metalinguistic tasks. The evidence from this study indicates that normal communicative speech is dependent on a number of left hemisphere regions remote from the classic language areas of Wernicke and Broca. Destruction or disconnection of discrete left extrasylvian and perisylvian cortical regions, rather than the total extent of damage to perisylvian cortex, will account for the qualitative and quantitative differences in the impaired speech production observed in aphasic stroke patients.


title: Optimizing MLC-based STT-RAM caches by dynamic block size reconfiguration

abstract: The use of STT-RAM as on-chip caches has been widely studied. However, existing works focused mainly on single-level cell (SLC) design while the potential of multi-level cell (MLC) STT-RAM has not yet been fully explored. It is expected that MLC STT-RAM can achieve 2× the storage density of SLC and thus improves system performance. Unfortunately, at the device level, the two-step read/write scheme introduces performance and energy overhead. In this paper, we propose an architectural design to dynamically reconfigure the cache block size for a MLC STT-RAM last-level cache. Our approach place certain hot data chunks in smaller blocks so as to benefit from the lower latency and energy, while keeping the rest in larger blocks to maintain an overall hit rate. Experiment shows that our strategy reduces the performance and energy penalty of MLC STT-RAM caches with a slightly higher miss rate. On average, IPC is increased by 4.6% while energy consumption is reduced by 23.5% compared to the conventional MLC STT-RAM cache.


title: Motion control of a novel spherical robot equipped with a flywheel

abstract: An omni-directional rolling spherical robot equipped with a high-rate flywheel (BYQ-V) is presented, the gyroscopic effects of high-rate flywheel can further enhance the dynamic stability of the spherical robot. This robot is designed for territory or lunar exploration in the future. The mechanical structure and control system of the robot are given particularly. Using the constrained Lagrangian method, the simplified dynamic model of the robot is derived under some assumptions, Moreover, a Linear Quadratic Regulator (LQR) controller and Percentage Derivative (PD) controller are designed to implement the pose and velocity control of the robot respectively, Finally, the dynamic model and the controllers are validated through simulation study and prototype experiment.


title: LCCT-Z-Source inverters

abstract: Many renewable generation units (e.g. PVs, fuel cells, small wind turbines) require integrated DC-AC converters providing efficient high step-up power conversion. On the basis of recent developments in impedance source (Z-source) inverters the present paper proposes an integration of quasi-Z-source inverter (qZSI) with a built-in high frequency transformer. Two new type LCCT-Z-Source inverters are presented by author. Proposed inverters characterize continuous input current, improved relationship between boost coefficient and modulation index and improved EMI performance. Application of four element (Inductor — Capacitor — Capacitor — Transformer) LCCT impedance network provides higher voltage gain than obtained in quasi-Z-source inverter. The advantage of proposed topology over other recently developed trans Z-source inverters (TZSI) is that two built-in capacitors block DC currents in transformer windings and prevents core saturation. Simulation and experimental results are shown to verify the proposed topologies.


title: Information Security Practices in Latin America: The case of Bolivia

abstract: REQUIRED) In this paper, we present a social/behavioral study of individual information security practices of internet users in Latin America, specifically presenting the case of Bolivia. The research model uses social cognitive theory in order to explain the individual cognitive factors that influence information security behavior. The model includes individuals' beliefs about their abilities to competently use computer information security tools and information security awareness in the determination of effective information security practices. The operationalization of constructs that are part of our research model, such as information security practice as the dependent variable, self-efficacy and information security awareness as independent variables , are presented both in Spanish and English. In this study, we offer the analysis of a survey of 255 Internet users from Bolivia who replied to our survey and provided responses about their information security behavior. A discussion about information security awareness and practices is presented.


title: A survey of power estimation techniques in VLSI circuits

abstract: With the advent of portable and high-density microelectronic devices, the power dissipation of very large scale integrated (VLSI) circuits is becoming a critical concern. Accurate and efficient power estimation during the design phase is required in order to meet the power specifications without a costly redesign process. In this paper, we present a review of the power estimation techniques that have recently been proposed. >


title: Millimeter-wave radar for civil applications

abstract: This contribution gives an overview of civil state-of-the-art and also some novel applications of radar sensors in the millimeter-wave frequency range, together with some general principles and a number of examples of such radar systems.


title: Small-Size Coupled-Fed Antenna With Two Printed Distributed Inductors for Seven-Band WWAN/LTE Mobile Handset

abstract: A compact small-size coupled-fed antenna with two symmetrical printed meandered inductive strips as two distributed inductors for seven-band operation covering the GSM850/900/1800//1900/UMTS2100/LTE2300/2500 bands in the internal mobile handset application is proposed. Wideband operation is achieved by incorporating a feeding strip and two radiating strips of different lengths, which are both disposed on the no-ground board space of the proposed antenna. The desired lower bands of GSM850/900 operation can be achieved by the two radiating strips of different lengths, which provide a quarter-wavelength dual-resonance mode at 810 and 962 MHz. While the feeding strip can provide a quarter-wavelength resonant mode at around 1.78 GHz, and other resonant modes at 2.45 GHz are contributed by the higher-order resonant modes of the two radiating strips, covering the desired upper bands of DCS1800/PCS1900/UMTS2100/LTE2300/2500 operation. In addition, the proposed antenna only occupies a planar size of 15×30 mm2 and is easily printed on one surface of a FR4 substrate, which makes it suitable for practical mobile applications. Detailed considerations of the design and main parameters are studied in this communication and the proposed antenna is successfully simulated, fabricated, and measured.


title: Colorization using optimization

abstract: Colorization is a computer-assisted process of adding color to a monochrome image or movie. The process typically involves segmenting images into regions and tracking these regions across image sequences. Neither of these tasks can be performed reliably in practice; consequently, colorization requires considerable user intervention and remains a tedious, time-consuming, and expensive task.In this paper we present a simple colorization method that requires neither precise image segmentation, nor accurate region tracking. Our method is based on a simple premise; neighboring pixels in space-time that have similar intensities should have similar colors. We formalize this premise using a quadratic cost function and obtain an optimization problem that can be solved efficiently using standard techniques. In our approach an artist only needs to annotate the image with a few color scribbles, and the indicated colors are automatically propagated in both space and time to produce a fully colorized image or sequence. We demonstrate that high quality colorizations of stills and movie clips may be obtained from a relatively modest amount of user input.


title: 2-Point-based outlier rejection for camera-IMU systems with applications to micro aerial vehicles

abstract: This paper presents a novel method to perform the outlier rejection task between two different views of a camera rigidly attached to an Inertial Measurement Unit (IMU). Only two feature correspondences and gyroscopic data from IMU measurerments are used to compute the motion hypothesis. By exploiting this 2-point motion parametrization, we propose two algorithms to remove wrong data associations in the feature-matching process for case of a 6DoF motion. We show that in the case of a monocular camera mounted on a quadrotor vehicle, motion priors from IMU can be used to discard wrong estimations in the framework of a 2-point-RANSAC based approach. The proposed methods are evaluated on both synthetic and real data.


title: Practical Use of Microservices in Moving Workloads to the Cloud

abstract: Most enterprises believe the cloud will become the new home for applications; however, not all applications are ready for the cloud. Containers and microservices make it easier to move applications to the cloud. The application developer charged with refactoring the application must think about how to best redesign the applications to become containerized and service oriented. In essence, you're turning a monolithic application into something that's more complex and distributed. However, the real objective is for it to become more productive, agile, and cost effective.


title: Using Summarization to Discover Argument Facets in Online Ideological Dialog

abstract: More and more of the information available on the web is dialogic, and a significant portion of it takes place in online forum conversations about current social and political topics. We aim to develop tools to summarize what these conversations are about. What are the CENTRAL PROPOSITIONS associated with different stances on an issue; what are the abstract objects under discussion that are central to a speaker’s argument? How can we recognize that two CENTRAL PROPOSITIONS realize the same FACET of the argument? We hypothesize that the CENTRAL PROPOSITIONS are exactly those arguments that people find most salient, and use human summarization as a probe for discovering them. We describe our corpus of human summaries of opinionated dialogs, then show how we can identify similar repeated arguments, and group them into FACETS across many discussions of a topic. We define a new task, ARGUMENT FACET SIMILARITY (AFS), and show that we can predict AFS with a .54 correlation score, versus an ngram system baseline of .39 and a semantic textual similarity system baseline of .45.


title: Personalized Recommendation with Confidence

abstract: This paper presents a personalized recommendation system mining online product reviews, fusing opinions together and providing a ranked order of a set similar products. We define three attributes of opinion summary: opinion coverage, opinion consistency and opinion consensus. Confidence factor is computed based on these attributes. A user specifies the relative importance of each product feature. The quantitive summary reflects the user's preference, the opinion synopsis and the confidence measurement.


title: Unsupervised Learning of the Morphology of a Natural Language

abstract: This study reports the results of using minimum description length (MDL) analysis to model unsupervised learning of the morphological segmentation of European languages, using corpora ranging in size from 5,000 words to 500,000 words. We develop a set of heuristics that rapidly develop a probabilistic morphological grammar, and use MDL as our primary tool to determine whether the modifications proposed by the heuristics will be adopted or not. The resulting grammar matches well the analysis that would be developed by a human morphologist.In the final section, we discuss the relationship of this style of MDL grammatical analysis to the notion of evaluation metric in early generative grammar.


title: Chimps in the wild show stirrings of culture.

abstract: TNFCIWA Our closest primate relatives exhibit traditions of grooming, calling, and tool use that a growing number of researchers say add up to a primitive form of culture. Their argument rests on examples in which one chimp learns from another, and on the seemingly arbitrary differences in habits between chimpanzee groups at different sites. But some researchers argue that our primate cousins do not learn as we do, by imitation and instruction, and most agree that primates don9t seem to be able to build on previous inventions as humans do.


title: Uncertainty Theory: A Branch of Mathematics for Modeling Human Uncertainty

abstract: Uncertainty theory is a branch of mathematics based on normality, monotonicity, self-duality, countable subadditivity, and product measure axioms. Uncertainty is any concept that satisfies the axioms of uncertainty theory. Thus uncertainty is neither randomness nor fuzziness. It is also known from some surveys that a lot of phenomena do behave like uncertainty. How do we model uncertainty? How do we use uncertainty theory? In order to answer these questions, this book provides a self-contained, comprehensive and up-to-date presentation of uncertainty theory, including uncertain programming, uncertain risk analysis, uncertain reliability analysis, uncertain process, uncertain calculus, uncertain differential equation, uncertain logic, uncertain entailment, and uncertain inference. Mathematicians, researchers, engineers, designers, and students in the field of mathematics, information science, operations research, system science, industrial engineering, computer science, artificial intelligence, finance, control, and management science will find this work a stimulating and useful reference.


title: Discriminative Covariance Oriented Representation Learning for Face Recognition with Image Sets

abstract: For face recognition with image sets, while most existing works mainly focus on building robust set models with hand-crafted feature, it remains a research gap to learn better image representations which can closely match the subsequent image set modeling and classification. Taking sample covariance matrix as set model in the light of its recent promising success, we present a Discriminative Covariance oriented Representation Learning (DCRL) framework to bridge the above gap. The framework constructs a feature learning network (e.g. a CNN) to project the face images into a target representation space, and the network is trained towards the goal that the set covariance matrix calculated in the target space has maximum discriminative ability. To encode the discriminative ability of set covariance matrices, we elaborately design two different loss functions, which respectively lead to two different representation learning schemes, i.e., the Graph Embedding scheme and the Softmax Regression scheme. Both schemes optimize the whole network containing both image representation mapping and set model classification in a joint learning manner. The proposed method is extensively validated on three challenging and large scale databases for the task of face recognition with image sets, i.e., YouTube Celebrities, YouTube Face DB and Point-and-Shoot Challenge.


title: Deep Learning Based NLOS Identification with Commodity WLAN Devices

abstract: Identifying line-of-sight (LOS) and non-LOS channel conditions can improve the performance of many wireless applications, such as signal strength-based localization algorithms. For this purpose, channel state information (CSI) obtained by commodity IEEE 802.11n devices can be used, because it contains information about channel impulse response (CIR). However, because of the limited sampling rate of the devices, a high-resolution CIR is not available, and it is difficult to detect the existence of an LOS path from a single CSI measurement, but it can be inferred from the variation pattern of CSI over time. To this end, we propose a recurrent neural network (RNN) model, which takes a series of CSI to identify the corresponding channel condition. We collect numerous measurement data under an indoor office environment, train the proposed RNN model, and compare the performance with those of existing schemes that use handcrafted features. The proposed method efficiently learns a nonlinear relationship between input and output, and thus, yields high accuracy even for data obtained in a very short period.


title: The Nucleus Accumbens: An Interface Between Cognition, Emotion, and Action

abstract: Nearly 40 years of research on the function of the nucleus accumbens (NAc) has provided a wealth of information on its contributions to behavior but has also yielded controversies and misconceptions regarding these functions. A primary tenet of this review is that, rather than serving as a “reward” center, the NAc plays a key role in action selection, integrating cognitive and affective information processed by frontal and temporal lobe regions to augment the efficiency and vigor of appetitively or aversively motivated behaviors. Its involvement in these functions is most prominent when the appropriate course of action is ambiguous, uncertain, laden with distractors, or in a state of flux. To this end, different subregions of the NAc play dissociable roles in refining action selection, promoting approach toward motivationally relevant stimuli, suppressing inappropriate actions so that goals may be obtained more efficiently, and encoding action outcomes that guide the direction of subsequent ones.


title: Hug the Elephant: Migrating a Legacy Data Analytics Application to Hadoop Ecosystem

abstract: Big data applications that rely on relational databases gradually expose limitations on scalability and performance. In recent years, Hadoop ecosystem has been widely adopted as an evolving solution. This paper presents the migration of a legacy data analytics application in a provincial data center. The target platform follows "no one size fits all" method. Considering different workloads, data storage is hybrid with distributed file system (HDFS) and distributed NoSQL database. Beyond the architecture re-design, we focus on the problem of data model transformation from relational database to NoSQL database. We propose a query-aware approach to free developers from tedious manual work. The approach generates query-specific views (NoView) for NoSQL and re-structures the views to align with NoSQL's data model. Our results show that the migrated application achieves high scalability and high performance. We believe that our practice provides valuable insights (such as NoSQL data modeling methodology), and the techniques can be easily applied to other similar migrations.


title: Search Engine Optimization of E-Commerce Websites

abstract: Search engine optimization, called SEO for short, is important to websites, which will improve the rank for search engines and get more page views. This paper puts forward its principle, basic strategy and emphasizes the special aspects of e-commerce website optimization, such as dynamic web pages, redirect web pages and similar or repeated pages, then gives some tactics, such as making dynamic pages static, reducing redirect pages and revising similar pages.


Distributed Representations for Building Profiles of Users and Items from Text Reviews


title: Yet another survey on image segmentation: Region and boundary information integration

abstract: Image segmentation has been, and still is, a relevant research area in Computer Vision, and hundreds of segmentation algorithms have been proposed in the last 30 years. However, it is well known that elemental segmentation techniques based on boundary or region information often fail to produce accurate segmentation results. Hence, in the last few years, there has been a tendency towards algorithms which take advantage of the complementary nature of such information. This paper reviews different segmentation proposals which integrate edge and region information and highlights 7 different strategies and methods to fuse such information. In contrast with other surveys which only describe and compare qualitatively different approaches, this survey deals with a real quantitative comparison. In this sense, key methods have been programmed and their accuracy analyzed and compared using synthetic and real images. A discussion justified with experimental results is given and the code is available on Internet.


Photoacoustic Imaging in Gastroenterology: Advances and Needs


title: Automatic Generation of Typographic Font from a Small Font Subset

abstract: The automated generation of fonts containing a large number of characters is in high demand. For example, a typical Japanese font requires over 1000 characters. Unfortunately, professional typographers create the majority of fonts, resulting in significant financial and time investments for font generation. The main contribution of this article is the development of a method that automatically generates a target typographic font containing thousands of characters, from a small subset of character images in the target font. We generate characters other than the subset so that a complete font is obtained. We propose a novel font generation method with the capability to deal with various fonts, including a font composed of distinctive strokes, which are difficult for existing methods to handle. We demonstrated the proposed method by generating 2965 characters in 47 fonts. Moreover, objective and subjective evaluations verified that the generated characters are similar to the original characters.


title: A Low-Computational Approach on Gaze Estimation With Eye Touch System

abstract: Among various approaches to eye tracking systems, light-reflection based systems with non-imaging sensors, e.g., photodiodes or phototransistors, are known to have relatively low complexity; yet, they provide moderately accurate estimation of the point of gaze. In this paper, a low-computational approach on gaze estimation is proposed using the Eye Touch system, which is a light-reflection based eye tracking system, previously introduced by the authors. Based on the physical implementation of Eye Touch, the sensor measurements are now utilized in low-computational least-squares algorithms to estimate arbitrary gaze directions, unlike the existing light reflection-based systems, including the initial Eye Touch implementation, where only limited predefined regions were distinguished. The system also utilizes an effective pattern classification algorithm to be able to perform left, right, and double clicks based on respective eye winks with significantly high accuracy. In order to avoid accuracy problems for sensitive sensor biasing hardware, a robust custom microcontroller-based data acquisition system is developed. Consequently, the physical size and cost of the overall Eye Touch system are considerably reduced while the power efficiency is improved. The results of the experimental analysis over numerous subjects clearly indicate that the proposed eye tracking system can classify eye winks with 98% accuracy, and attain an accurate gaze direction with an average angular error of about 0.93 °. Due to its lightweight structure, competitive accuracy and low-computational requirements relative to video-based eye tracking systems, the proposed system is a promising human-computer interface for both stationary and mobile eye tracking applications.


title: Morphological Analysis and Disambiguation for Dialectal Arabic

abstract: The many differences between Dialectal Arabic and Modern Standard Arabic (MSA) pose a challenge to the majority of Arabic natural language processing tools, which are designed for MSA. In this paper, we retarget an existing state-of-the-art MSA morphological tagger to Egyptian Arabic (ARZ). Our evaluation demonstrates that our ARZ morphology tagger outperforms its MSA variant on ARZ input in terms of accuracy in part-of-speech tagging, diacritization, lemmatization and tokenization; and in terms of utility for ARZ-toEnglish statistical machine translation.


title: Development in Children and Adolescents Whose Mothers Have Borderline Personality Disorder

abstract: A mother's mental illness may have a profound effect on her child's development, including an increased risk of the child developing the same disorder. From a developmental psychopathology perspective, offspring provide an opportunity to examine pathways to disorder versus resilience. Borderline personality disorder (BPD) is a severe disorder diagnosed in early adulthood involving stormy relationships, an unstable sense of identity, and self-destructive behavior. Interestingly, the domains of dysfunction are conceptually similar to developmental tasks in early childhood reworked in adolescence: attachment, self development, and self-regulation. Early deviation may increase the risk for later disorder. There are five empirical studies of children whose mothers have BPD, two conducted from a developmental perspective. This article proposes a theoretical framework and an innovative methodology with which to extend this research, and suggests an intervention to bring development back on track if necessary.


title: Cooperation, Fast and Slow: Meta-Analytic Evidence for a Theory of Social Heuristics and Self-Interested Deliberation

abstract: Does cooperating require the inhibition of selfish urges? Or does “rational” self-interest constrain cooperative impulses? I investigated the role of intuition and deliberation in cooperation by meta-analyzing 67 studies in which cognitive-processing manipulations were applied to economic cooperation games (total N = 17,647; no indication of publication bias using Egger’s test, Begg’s test, or p-curve). My meta-analysis was guided by the Social Heuristics Hypothesis, which proposes that intuition favors behavior that typically maximizes payoffs, whereas deliberation favors behavior that maximizes one’s payoff in the current situation. Therefore, this theory predicts that deliberation will undermine pure cooperation (i.e., cooperation in settings where there are few future consequences for one’s actions, such that cooperating is not in one’s self-interest) but not strategic cooperation (i.e., cooperation in settings where cooperating can maximize one’s payoff). As predicted, the meta-analysis revealed 17.3% more pure cooperation when intuition was promoted relative to deliberation, but no significant difference in strategic cooperation between more intuitive and more deliberative conditions.


title: Teaching based on augmented reality for a technical creative design course

abstract: Student creativity is currently attracting considerable attention. An increasing number of high schools in China are trying to improve the learning motivation and creativity of students, as well as the teaching efficiency of creative design, by introducing augmented reality (AR) technology into creative design courses. However, many teachers have only limited knowledge of AR, and software developers are not familiar with general creative design education, which makes it difficult to incorporate AR in such courses. In many high schools in China, the lack of relevant teaching facilities and creative design equipment means that the environment in which the technology curriculum is applied still has a long way to go to meet the real requirement of curriculum. To address these problems, we present a general technical creative design teaching scheme that includes AR. Our approach is based on the ARCS model of motivational design, social psychology, and a computational model of creativity. Two teaching aids are introduced to support this teaching scheme: "AR Creative-Classroom," which explains the domain-relevant knowledge of creative design, and "AR Creative-Builder," which helps students to build actual AR scenes. The results of a pilot study show that the proposed teaching scheme significantly improves learning motivation, student creativity, and the teaching of creative design. Design a creative design teaching scheme with the introduction of AR.Develop two AR-based and teaching scheme related teaching aids.Demonstrate the AR-based teaching scheme improves the learning motivation.Demonstrate the scheme helps students to experience the complete creative design process.Demonstrate the AR-based teaching scheme improve the creativity of the outputs.


title: Demonstration Abstract: IoTSuite - A Framework to Design, Implement, and Deploy IoT Applications

abstract: Internet of Things (IoT) application development is technically challenging and complex due to several factors such as the presence of different IoT protocols, interoperability issues, lack of industry standards and dealing with the heterogeneity that exists both in Physical and Internet worlds. Furthermore, developers involved in the IoT application development have to address issues pertaining to different life-cycles ranging from design, implementation to deployment (deployment complexities due to the possibilities of wide range of devices). Manual effort involved in all above three phases for heterogeneous devices is a time-consuming and error-prone process. We demonstrate IoTSuite which is an application development platform consisting of a suite of tools motivated by the need to simplify IoT application development. The IoTSuite supports several features such as automatic code generation and integration with a set of modeling languages. It takes high-level specification as input that abstract heterogeneity related complexity. It integrates compiler and deployment module to provide automation at different phases of application development process.


title: Massively Parallel Parsing: A Strongly Interactive Model of Natural Language Interpretation*

abstract: This is a description of research in developing a natural language processing system with modular knowledge sources but strongly interactive processing. The system offers insights into a variety of linguistic phenomena and allows easy testing of a variety of hypotheses. Language interpretation takes place on a activation network which is dynamically created from input, recent context, and long-term knowledge. Initially ambiguous and unstable, the network settles on a single interpretation, using a parallel, analog relaxation process. We also describe a parallel model for the representation of context and of the priming of concepts. Examples illustrating contextual influence on meaning interpretation and “semantic garden path” sentence processing, among other issues, are included.


title: Peak-End Effects on Player Experience in Casual Games

abstract: The peak-end rule is a psychological heuristic observing that people's retrospective assessment of an experience is strongly influenced by the intensity of the peak and final moments of that experience. We examine how aspects of game player experience are influenced by peak-end manipulations to the sequence of events in games that are otherwise objectively identical. A first experiment examines players' retrospective assessments of two games (a pattern matching game based on Bejeweled and a point-and-click reaction game) when the sequence of difficulty is manipulated to induce positive, negative and neutral peak-end effects. A second experiment examines assessments of a shootout game in which the balance between challenge and skill is similarly manipulated. Results across the games show that recollection of challenge was strongly influenced by peak-end effects; however, results for fun, enjoyment, and preference to repeat were varied -- sometimes significantly in favour of the hypothesized effects, sometimes insignificant, but never against the hypothesis.


title: Ultraminiaturized High-Speed Permanent-Magnet Generators for Milliwatt-Level Power Generation

abstract: This paper presents the design, fabrication, and characterization of millimeter-scale rotary electromagnetic generators. The axial-flux synchronous machines consist of a three-phase microfabricated surface-wound copper coil and a multipole permanent-magnet (PM) rotor measuring 2 mm in diameter. Several machines with various geometries and numbers of magnetic poles and turns per pole are designed and compared. Moreover, the use of different PM materials is investigated. Multipole magnetic rotors are modeled using finite element analysis to analyze magnetic field distributions. In operation, the rotor is spun above the microfabricated stator coils using an off-the-shelf air-driven turbine. As a result of design choices, the generators present different levels of operating frequency and electrical output power. The four-pole six-turn/pole NdFeB generator exhibits up to 6.6 mWrms of ac electrical power across a resistive load at a rotational speed of 392 000 r/min. This milliwatt-scale power generation indicates the feasibility of such ultrasmall machines for low-power applications. [2008-0078].


title: Fuel Variability Effects on Turbulent, Lean Premixed Flames at High Pressures

abstract: Fuel flexibility will be a key issue for the operation of future stationary gas turbines because of the increasing amount of off-spec natural gas qualities from new resources and upcoming new fuels derived from biomass which will be more important in the near future. The performance of gas turbines in terms of flame stability and low emission combustion must be at least maintained also with these new fuels. Therefore, the impact of fuel variation on combustion characteristics must be known for the combustor design. This paper addresses the effect of hydrogen and propane addition on flame characteristics like lean blowout (LBO), emissions (NOx , CO), flame positions and turbulent flame speeds for flames at gas turbine relevant conditions. Hydrogen enriched fuels are typical constituents of gasification fuels such as those obtained from biomass, while propane is considered a typical higher hydrocarbon present in off-spec natural gas. Turbulent, lean premixed flames of different fuels (methane, methane/hydrogen and methane/propane) have been studied in a generic, axis-symmetric, high-pressure gas turbine combustor. Flame stabilization is achieved aerodynamically via a recirculation zone induced by the combustor geometry with sudden expansion. Turbulence at the combustor inlet is generated using a turbulence grid (perforated plate). LBO limits are detected using the global OH chemiluminescence flame signal collected with a photo-multiplier and a data acquisition system together with the exhaust gas temperature measured with a thermocouple. The species concentrations (CO2 , O2 , CO, NOx ) are measured by exhaust gas analyzers. Flame front positions and turbulent flame speeds are determined with Laser Induced Fluorescence measurements of the OH radical (OH-PLIF). Flame characteristics will be presented for the following fuel/air mixtures at a mixture preheating temperature of 673 K: pure methane, H2 -enriched flames containing up to 50% hydrogen by volume, methane/propane mixtures containing up to 50% propane by volume. LBO limits, NOx emissions will be presented for different pressures. Most probable flame front positions and turbulent flame speeds are presented at a pressure of 5 bars for fuel mixtures between pure methane and 50% of each additive (propane and hydrogen). Experiments have revealed that a premixed mixture of 50% hydrogen and 50% methane, by volume, can significantly extend the lean blowout limit by up to 22% compared to pure methane. Because of a 120 K lower flame temperature a drastic reduction of the NOx emission (about 57%) is observed. Addition of hydrogen also significantly decreases the flame position (50%), changes the shape of the flame front and because of a higher global reaction rate increases the turbulent flame speed. Experiments with different methane/propane mixtures showed an increase (approximately 25–30%) of the NOx concentration at a propane content of 50%. Additionally, the flame stabilizes closer to the combustor inlet for higher propane contents.Copyright © 2007 by ASME


Morpho-syntactically Annotated Amharic Treebank


title: Manual Training of Mental Rotation in Children.

abstract: Abstract When deciding whether two stimuli rotated in space are identical or mirror reversed, subjects employ mental rotation to solve the task. In children mental rotation can be trained by extensive repetition of the task, but the improvement seems to rely on the retrieval of previously learned stimuli. We assumed that due to the close relation between mental and manual rotation in children a manual training should improve the mental rotation process itself. The manual training we developed indeed ameliorated mental rotation and the training effect was not limited to learned stimuli. While boys outperformed girls in the mental rotation test before the manual rotation training, we found no gender differences in the results of the manual rotation task.


title: An Overview of Sentiment Analysis in Social Media and Its Applications in Disaster Relief

abstract: Sentiment analysis refers to the class of computational and natural language processing based techniques used to identify, extract or characterize subjective information, such as opinions, expressed in a given piece of text. The main purpose of sentiment analysis is to classify a writer’s attitude towards various topics into positive, negative or neutral categories. Sentiment analysis has many applications in different domains including, but not limited to, business intelligence, politics, sociology, etc. Recent years, on the other hand, have witnessed the advent of social networking websites, microblogs, wikis and Web applications and consequently, an unprecedented growth in user-generated data is poised for sentiment mining. Data such as web-postings, Tweets, videos, etc., all express opinions on various topics and events, offer immense opportunities to study and analyze human opinions and sentiment. In this chapter, we study the information published by individuals in social media in cases of natural disasters and emergencies and investigate if such information could be used by first responders to improve situational awareness and crisis management. In particular, we explore applications of sentiment analysis and demonstrate how sentiment mining in social media can be exploited to determine how local crowds react during a disaster, and how such information can be used to improve disaster management. Such information can also be used to help assess the extent of the devastation and find people who are in specific need during an emergency situation. We first provide the formal definition of sentiment analysis in social media and cover traditional and the state-of-the-art approaches while highlighting contributions, shortcomings, and pitfalls due to the composition of online media streams. Next we discuss the relationship among social media, disaster relief and situational awareness and explain how social media is used in these contexts with the focus on sentiment analysis. In order to enable quick analysis of real-time geo-distributed data, we will detail applications of visual analytics with an emphasis on sentiment visualization . Finally, we conclude the chapter with a discussion of research challenges in sentiment analysis and its application in disaster relief.


title: Birth preparedness and complication readiness among the women beneficiaries of selected rural primary health centers of Dakshina Kannada district, Karnataka, India

abstract: Introduction ::: Birth preparedness and complication readiness (BPCR) is a strategy to promote timely use of skilled maternal and neonatal care during childbirth. According to World Health Organization, BPCR should be a key component of focused antenatal care. Dakshina Kannada, a coastal district of Karnataka state, is categorized as a high-performing district (institutional delivery rate >25%) under the National Rural Health Mission. However, a substantial proportion of women in the district experience complications during pregnancy (58.3%), childbirth (45.7%), and postnatal (17.4%) period. There is a paucity of data on BPCR practice and the factors associated with it in the district. Exploring this would be of great use in the evidence-based fine-tuning of ongoing maternal and child health interventions. ::: ::: ::: Objective ::: To assess BPCR practice and the factors associated with it among the beneficiaries of two rural Primary Health Centers (PHCs) of Dakshina Kannada district, Karnataka, India. ::: ::: ::: Methods ::: A facility-based cross-sectional study was conducted among 217 pregnant (>28 weeks of gestation) and recently delivered (in the last 6 months) women in two randomly selected PHCs from June -September 2013. Exit interviews were conducted using a pre-designed semi-structured interview schedule. Information regarding socio-demographic profile, obstetric variables, and knowledge of key danger signs was collected. BPCR included information on five key components: identified the place of delivery, saved money to pay for expenses, mode of transport identified, identified a birth companion, and arranged a blood donor if the need arises. In this study, a woman who recalled at least two key danger signs in each of the three phases, i.e., pregnancy, childbirth, and postpartum (total six) was considered as knowledgeable on key danger signs. Optimal BPCR practice was defined as following at least three out of five key components of BPCR. ::: ::: ::: Outcome measures ::: Proportion, Odds ratio, and adjusted Odds ratio (adj OR) for optimal BPCR practice. ::: ::: ::: Results ::: A total of 184 women completed the exit interview (mean age: 26.9±3.9 years). Optimal BPCR practice was observed in 79.3% (95% CI: 73.5–85.2%) of the women. Multivariate logistic regression revealed that age >26 years (adj OR = 2.97; 95%CI: 1.15–7.7), economic status of above poverty line (adj OR = 4.3; 95%CI: 1.12–16.5), awareness of minimum two key danger signs in each of the three phases, i.e., pregnancy, childbirth, and postpartum (adj OR = 3.98; 95%CI: 1.4–11.1), preference to private health sector for antenatal care/delivery (adj OR = 2.9; 95%CI: 1.1–8.01), and woman’s discussion about the BPCR with her family members (adj OR = 3.4; 95%CI: 1.1–10.4) as the significant factors associated with optimal BPCR practice. ::: ::: ::: Conclusion ::: In this study population, BPCR practice was better than other studies reported from India. Healthcare workers at the grassroots should be encouraged to involve women’s family members while explaining BPCR and key danger signs with a special emphasis on young (<26 years) and economically poor women. Ensuring a reinforcing discussion between woman and her family members may further enhance the BPCR practice.


title: 77 GHz lens-based multistatic MIMO radar with colocated antennas for automotive applications

abstract: With the fast development of highly integrated 77 GHz SiGe-MMICs, cost effective and reliable radar systems are feasible to significantly increase driving safety in all vehicle classes. New features have to be implemented in these sensors to fulfill the ascending requirements for future systems. Important demands will be the detection and separation of multiple targets in a range cell and the exact measurement of angles in azimuth and elevation. In this paper we present a new lens based 77 GHz MIMO radar system with SiGe MMICs. The center of attention in our investigations is the comparison between conventional lens based radar configurations and lens based MIMO radar configurations.


title: Cardea: Context-Aware Visual Privacy Protection from Pervasive Cameras

abstract: The growing popularity of mobile and wearable devices with built-in cameras, the bright prospect of camera related applications such as augmented reality and life-logging system, the increased ease of taking and sharing photos, and advances in computer vision techniques have greatly facilitated people's lives in many aspects, but have also inevitably raised people's concerns about visual privacy at the same time. Motivated by recent user studies that people's privacy concerns are dependent on the context, in this paper, we propose Cardea, a context-aware and interactive visual privacy protection framework that enforces privacy protection according to people's privacy preferences. The framework provides people with fine-grained visual privacy protection using: i) personal privacy profiles, with which people can define their context-dependent privacy preferences; and ii) visual indicators: face features, for devices to automatically locate individuals who request privacy protection; and iii) hand gestures, for people to flexibly interact with cameras to temporarily change their privacy preferences. We design and implement the framework consisting of the client app on Android devices and the cloud server. Our evaluation results confirm this framework is practical and effective with 86% overall accuracy, showing promising future for context-aware visual privacy protection from pervasive cameras.


title: Construct Specification and Misspecification within the Application of Push-Pull-Mooring Theory of Switching Behaviour

abstract: This paper principally addresses the issue of construct specification and misspecification within the context of the application of Push-Pull-Mooring (PPM) theory of consumer switching behaviour (CSB), and what implications it has for practicing marketing researchers. Despite the prospects of the PPM framework as a dominant paradigm for current and future research, there is evidence of divergent specification of constructs for the PPM framework in empirical studies. This study addresses this problem by applying empirical guidelines to determine the correct specification for the PPM framework, and uses an empirical illustration to demonstrate the potential consequence of misspecifying the PPM constructs in business. The findings indicate that about 67% level of construct misspecification exist in available research applying the PPM model in CSB literature. Pull, Push, and Mooring effects are identified as multi-dimensional construct that should follow a first-order reflective, second-order formative specification in the application of the full model. The findings also indicate that misspecification has potentially negative consequence on the validity, content and parsimony of measurement models in the PPM framework. Recommendations for practicing researchers are discussed. The paper furthers our understanding of construct specification and misspecification in theory development in business research.


title: Preoperative hair removal and surgical site infections: network meta-analysis of randomized controlled trials.

abstract: Preoperative hair removal has been used to prevent surgical site infections (SSIs) or to prevent hair from interfering with the incision site. We aimed to update the meta-analysis of published randomized controlled trials about hair removal for the prevention of SSIs, and conduct network meta-analyses to combine direct and indirect evidence and to compare chemical depilation with clipping. The PubMed, ScienceDirect and Cochrane databases were searched for randomized controlled trials analysing different hair removal techniques and no hair removal in similar groups. Paired and network meta-analyses were conducted. Two readers independently assessed the study limitations for each selected article according to the Grading of Recommendations Assessment, Development and Evaluation (GRADE) method. Nineteen studies met the inclusion criteria. No study compared clipping with chemical depilation. Network meta-analyses with shaving as the reference showed significantly fewer SSIs with clipping, chemical depilation, or no depilation [relative risk 0.55, 95% confidence interval 0.38-0.79; 0.60, 0.36-0.97; and 0.56, 0.34-0.96, respectively]. No significant difference was observed between the absence of depilation and chemical depilation or clipping (1.05, 0.55-2.00; 0.97, 0.51-1.82, respectively] or between chemical depilation and clipping (1.09, 0.59-2.01). This meta-analysis of 19 randomized controlled trials confirmed the absence of any benefit of depilation to prevent surgical site infection, and the higher risk of surgical site infection when shaving is used for depilation. Chemical depilation and clipping were compared for the first time. The risk of SSI seems to be similar with both methods.


title: Image classification using neural networks and ontologies

abstract: The advent of extremely powerful home PC and the growth of the Internet have made the appearance of multimedia documents a common sight in the computer world. In the world of unstructured data composed of images and other media types, classification often comes at the price of countless hours of manual labor. This research aims to present a scalable system capable of examining images and accurately classifying the image based on its visual content. When retrieving images based on a user's query, the system yields a minimal amount of irrelevant information (high precision) and ensures a maximum amount of relevant information (high recall).


title: Diverse activation functions in deep learning

abstract: We introduce the concept of diverse activation functions, and apply them into Convolutional Auto-Encoder (CAE) to develop diverse activation CAE (DaCAE), which considerably reduces the reconstruction loss. In contrast to vanilla CAE only with activation functions of the same types, DaCAE incorporates diverse activations by considering their cooperation and location. In terms of the reconstruction capability, DaCAE significantly outperforms vanilla CAE and full connected Auto-Encoder, and we conclude rules of thumb on designing diverse activations networks. Based on the high quality of the latent bottleneck features extracted from DaCAE, we demonstrate a satisfying advantage that fuzzy rules classifier performs better than softmax layer in supervised learning. These results could be seen as new research points in the attempts at using diverse activations to train deep neural networks and combining fuzzy inference systems with deep learning.


Towards a quantitative model of Mandarin Chinese perception of English


title: An angle-based interest model for text recommendation

abstract: Building an interest model is the key to realize personalized text recommendation. Previous interest models neglect the fact that a user may have multiple angles of interest. Different angles of interest provide different requests and criteria for text recommendation. This paper proposes an interest model that consists of two kinds of angles: persistence and pattern, which can be combined to form complex angles. The model uses a new method to represent the long-term interest and the short-term interest, and distinguishes the interest in object and the interest in the link structure of objects. Experiments with news-scale text data show that the interest in object and the interest in link structure have real requirements, and it is effective to recommend texts according to the angles. The model represents the multiple basic angles of reading interest.The basic angles reflect the pattern and persistence of reading interest.The model represents a complex angle by combining basic angles.Different angles correspond to different sets of required texts.