title: On 3-D motion estimation from 2-D sonar image flow

abstract: Visual odometry for autonomous terrestrial, aerial and marine robots involves 3-D motion/trajectory computation by tracking features in video sequences. Where optical systems are ineffective due to restricted visibility and high turbidity levels, high-frequency 2-D forward-scan sonar can provide images with target details, and attractive trade-off in range, resolution, and data rate. Automated processing of sonar video images enables numerous key capabilities, including target tracking, 3-D estimation of camera and object motions, and scene analysis and classification in terms of target types and their structures, etc. We investigate the computation of 3-D sonar motion by tracking the 2-D images of scene features. The cases of pure rotation, pure translation, and general motion are analyzed separately to gain deeper insight into the inherent characteristics of the underlying ambiguities and complexities. We analyze performance based on results of experiments with both synthetic and real data.


title: A new 4/6 pole-changing double layer winding for three phase electrical machines

abstract: The paper presents a new double layer three phase 4 to 6 pole-changing winding, with 48 slots and 6 terminals. In order to obtain a higher fundamental winding factor when winding is 6-pole connected, a degree of asymmetry is accepted for the winding when 4-pole connected. The new winding is compared with a well known two-layer winding presented in literature. An experimental motor equipped with the new winding was built on a 4-pole base and frame 100 (2.2/1.5 kW) and tested. At the same time, 2D FEM field-circuit models were developed to simulate the operation of the experimental model equipped with the newly proposed winding, and model equipped with the well known two-layer winding.


“The Early Specialised Bird Catches the Worm!” – A Specialised Sampling Model in the Development of Football Talents


title: A Digital Twin-Based Approach for Designing and Multi-Objective Optimization of Hollow Glass Production Line

abstract: Various new national advanced manufacturing strategies, such as Industry 4.0, Industrial Internet, and Made in China 2025, are issued to achieve smart manufacturing, resulting in the increasing number of newly designed production lines in both developed and developing countries. Under the individualized designing demands, more realistic virtual models mirroring the real worlds of production lines are essential to bridge the gap between design and operation. This paper presents a digital twin-based approach for rapid individualized designing of the hollow glass production line. The digital twin merges physics-based system modeling and distributed real-time process data to generate an authoritative digital design of the system at pre-production phase. A digital twin-based analytical decoupling framework is also developed to provide engineering analysis capabilities and support the decision-making over the system designing and solution evaluation. Three key enabling techniques as well as a case study in hollow glass production line are addressed to validate the proposed approach.


title: News personalization using the CF-IDF semantic recommender

abstract: When recommending news items, most of the traditional algorithms are based on TF-IDF, i.e., a term-based weighting method which is mostly used in information retrieval and text mining. However, many new technologies have been made available since the introduction of TF-IDF. This paper proposes a new method for recommending news items based on TF-IDF and a domain ontology. It is demonstrated that adapting TF-IDF with the semantics of a domain ontology, resulting in Concept Frequency - Inverse Document Frequency (CF-IDF), yields better results than using the original TF-IDF method. CF-IDF is built and tested in Athena, a recommender extension to the Hermes news personalization framework. Athena employs a user profile to store concepts or terms found in news items browsed by the user. The framework recommends new articles to the user using a traditional TF-IDF recommender and the CF-IDF recommender. A statistical evaluation of both methods shows that the use of an ontology significantly improves the performance of a traditional recommender.


title: Real-time Analysis and Visualization of the YFCC100m Dataset

abstract: With the Yahoo Flickr Creative Commons 100 Million (YFCC100m) dataset, a novel dataset was introduced to the computer vision and multimedia research community. To maximize the benefit for the research community and utilize its potential, this dataset has to be made accessible by tools allowing to search for target concepts within the dataset and mechanism to browse images and videos of the dataset. Following best practice from data collections, such as ImageNet and MS COCO, this paper presents means of accessibility for the YFCC100m dataset. This includes a global analysis of the dataset and an online browser to explore and investigate subsets of the dataset in real-time. Providing statistics of the queried images and videos will enable researchers to refine their query successively, such that the users desired subset of interest can be narrowed down quickly. The final set of image and video can be downloaded as URLs from the browser for further processing.


title: Big data analysis for heart disease detection system using map reduce technique

abstract: In today's world, the enormous information in health care is to be processed in order to identify, diagnose, detect and prevent the various diseases. Big data analysis is the challenging one because big data contain large amount of records. It is proposed to develop a centralized patient monitoring system using big data. In the proposed system, large set of medical records are taken as input. From this medical dataset, it is aimed to extract the needed information from the record of heart patients using map reduce technique. Heart disease is a major health problem and it is the leading causes of death throughout the world. Early detection of heart disease has become an important issue in the medical research fields. For heart disease detection, some features are analyzed such as RR interval, QRS interval and QT interval. The classification process states whether the patient is normal or abnormal and in the detection step using map reduce technique to detect the disease and reduce the dataset. Thus, the proposed system helps to classify a large and complex medical dataset and detect the heart disease.


title: Action-Driven Object Detection with Top-Down Visual Attentions

abstract: A dominant paradigm for deep learning based object detection relies on a "bottom-up" approach using "passive" scoring of class agnostic proposals. These approaches are efficient but lack of holistic analysis of scene-level context. In this paper, we present an "action-driven" detection mechanism using our "top-down" visual attention model. We localize an object by taking sequential actions that the attention model provides. The attention model conditioned with an image region provides required actions to get closer toward a target object. An action at each time step is weak itself but an ensemble of the sequential actions makes a bounding-box accurately converge to a target object boundary. This attention model we call AttentionNet is composed of a convolutional neural network. During our whole detection procedure, we only utilize the actions from a single AttentionNet without any modules for object proposals nor post bounding-box regression. We evaluate our top-down detection mechanism over the PASCAL VOC series and ILSVRC CLS-LOC dataset, and achieve state-of-the-art performances compared to the major bottom-up detection methods. In particular, our detection mechanism shows a strong advantage in elaborate localization by outperforming Faster R-CNN with a margin of +7.1% over PASCAL VOC 2007 when we increase the IoU threshold for positive detection to 0.7.


title: LabelMe: Online Image Annotation and Applications

abstract: Central to the development of computer vision systems is the collection and use of annotated images spanning our visual world. Annotations may include information about the identity, spatial extent, and viewpoint of the objects present in a depicted scene. Such a database is useful for the training and evaluation of computer vision systems. Motivated by the availability of images on the Internet, we introduced a web-based annotation tool that allows online users to label objects and their spatial extent in images. To date, we have collected over 400 000 annotations that span a variety of different scene and object classes. In this paper, we show the contents of the database, its growth over time, and statistics of its usage. In addition, we explore and survey applications of the database in the areas of computer vision and computer graphics. Particularly, we show how to extract the real-world 3-D coordinates of images in a variety of scenes using only the user-provided object annotations. The output 3-D information is comparable to the quality produced by a laser range scanner. We also characterize the space of the images in the database by analyzing 1) statistics of the co-occurrence of large objects in the images and 2) the spatial layout of the labeled images.


title: Regret minimization in games with incomplete information

abstract: Extensive games are a powerful model of multiagent decision-making scenarios with incomplete information. Finding a Nash equilibrium for very large instances of these games has received a great deal of recent attention. In this paper, we describe a new technique for solving large games based on regret minimization. In particular, we introduce the notion of counterfactual regret, which exploits the degree of incomplete information in an extensive game. We show how minimizing counterfactual regret minimizes overall regret, and therefore in self-play can be used to compute a Nash equilibrium. We demonstrate this technique in the domain of poker, showing we can solve abstractions of limit Texas Hold'em with as many as 1012 states, two orders of magnitude larger than previous methods.


title: Big Data, Health Informatics, and the Future of Cardiovascular Medicine.

abstract: “Medicine is going to become an information science. In 10 years or so, we may have billions of data points on each individual, and the real challenge will be to develop information technology that can reduce that to real hypotheses about that individual.”—Leroy Hood [(1)][1] ::: ::: A physician


title: Self-generated Zigzag Turning of Bombyx mori Males during Pheromone-mediated Upwind Walking(Physology)

abstract: This invention is directed to hollow filament membranes derived from esters of cellulose and characterized by a density gradient in the filament wall structure. When employed in reverse osmosis operations the filaments exhibit extraordinary flux and rejection properties. A continuous fabricating method is also provided which comprises continuously extruding filaments into a controlled evaporation zone thence directing the filaments into a cold water coagulation bath, thereafter passing the same to a wash bath and finally to an annealing zone.


TAXONOMY, DISTRIBUTION AND SEASONAL OCCURRENCE OF THE GENUS GALAXAURA IN THE VICINITY OF THE SETO MARINE BIOLOGICAL LABORATORY


title: Modelling Intelligent Phishing Detection System for E-banking Using Fuzzy Data Mining

abstract: Detecting and identifying any phishing websites in real-time, particularly for e-banking is really a complex and dynamic problem involving many factors and criteria. Because of the subjective considerations and the ambiguities involved in the detection, Fuzzy Data Mining (DM) Techniques can be an effective tool in assessing and identifying phishing websites for e-banking since it offers a more natural way of dealing with quality factors rather than exact values. In this paper, we present novel approach to overcome the ‘fuzziness’ in the e-banking phishing website assessment and propose an intelligent resilient and effective model for detecting e-banking phishing websites. The proposed model is based on Fuzzy logic (FL) combined with Data Mining algorithms to characterize the e-banking phishing website factors and to investigate its techniques by classifying there phishing types and defining six e-banking phishing website attack criteria’s with a layer structure. The proposed e-banking phishing website model showed the significance importance of the phishing website two criteria’s (URL & Domain Identity) and (Security & Encryption) in the final phishing detection rate result, taking into consideration its characteristic association and relationship with each others as showed from the fuzzy data mining classification and association rule algorithms. Our phishing model also showed the insignificant trivial influence of the (Page Style & Content) criteria along with (Social Human Factor) criteria in the phishing detection final rate result.


title: Automatic optic disc segmentation with peripapillary atrophy elimination

abstract: Optic disc segmentation from retinal fundus image is a fundamental but important step for automatic glaucoma diagnosis. In this paper, an optic disc segmentation method is proposed based on peripapillary atrophy elimination. The elimination is done through edge filtering, constraint elliptical Hough transform and peripapillary atrophy detection. With the elimination, edges that are likely from non-disc structures especially peripapillary atrophy are excluded to make the segmentation more accurate. The proposed method has been tested in a database of 650 images with disc boundaries marked by trained professionals manually. The experimental results by the proposed method show average m 1 , m 2 and m VD of 10.0%, 7.4% and 4.9% respectively. It can be used to compute cup to disc ratio as well as other features for application in automatic glaucoma diagnosis systems.


title: Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists

abstract: Background ::: Chest radiograph interpretation is critical for the detection of thoracic diseases, including tuberculosis and lung cancer, which affect millions of people worldwide each year. This time-consuming task typically requires expert radiologists to read the images, leading to fatigue-based diagnostic error and lack of diagnostic expertise in areas of the world where radiologists are not available. Recently, deep learning approaches have been able to achieve expert-level performance in medical image interpretation tasks, powered by large network architectures and fueled by the emergence of large labeled datasets. The purpose of this study is to investigate the performance of a deep learning algorithm on the detection of pathologies in chest radiographs compared with practicing radiologists. ::: ::: ::: Methods and findings ::: We developed CheXNeXt, a convolutional neural network to concurrently detect the presence of 14 different pathologies, including pneumonia, pleural effusion, pulmonary masses, and nodules in frontal-view chest radiographs. CheXNeXt was trained and internally validated on the ChestX-ray8 dataset, with a held-out validation set consisting of 420 images, sampled to contain at least 50 cases of each of the original pathology labels. On this validation set, the majority vote of a panel of 3 board-certified cardiothoracic specialist radiologists served as reference standard. We compared CheXNeXt’s discriminative performance on the validation set to the performance of 9 radiologists using the area under the receiver operating characteristic curve (AUC). The radiologists included 6 board-certified radiologists (average experience 12 years, range 4–28 years) and 3 senior radiology residents, from 3 academic institutions. We found that CheXNeXt achieved radiologist-level performance on 11 pathologies and did not achieve radiologist-level performance on 3 pathologies. The radiologists achieved statistically significantly higher AUC performance on cardiomegaly, emphysema, and hiatal hernia, with AUCs of 0.888 (95% confidence interval [CI] 0.863–0.910), 0.911 (95% CI 0.866–0.947), and 0.985 (95% CI 0.974–0.991), respectively, whereas CheXNeXt’s AUCs were 0.831 (95% CI 0.790–0.870), 0.704 (95% CI 0.567–0.833), and 0.851 (95% CI 0.785–0.909), respectively. CheXNeXt performed better than radiologists in detecting atelectasis, with an AUC of 0.862 (95% CI 0.825–0.895), statistically significantly higher than radiologists' AUC of 0.808 (95% CI 0.777–0.838); there were no statistically significant differences in AUCs for the other 10 pathologies. The average time to interpret the 420 images in the validation set was substantially longer for the radiologists (240 minutes) than for CheXNeXt (1.5 minutes). The main limitations of our study are that neither CheXNeXt nor the radiologists were permitted to use patient history or review prior examinations and that evaluation was limited to a dataset from a single institution. ::: ::: ::: Conclusions ::: In this study, we developed and validated a deep learning algorithm that classified clinically important abnormalities in chest radiographs at a performance level comparable to practicing radiologists. Once tested prospectively in clinical settings, the algorithm could have the potential to expand patient access to chest radiograph diagnostics.


title: Learning Robust Bed Making using Deep Imitation Learning with DART

abstract: Bed-making is a universal home task that can be challenging for senior citizens due to reaching motions. Automating bed-making has multiple technical challenges such as perception in an unstructured environments, deformable object manipulation, obstacle avoidance and sequential decision making. We explore how DART, an LfD algorithm for learning robust policies, can be applied to automating bed making without fiducial markers with a Toyota Human Support Robot (HSR). By gathering human demonstrations for grasping the sheet and failure detection, we can learn deep neural network policies that leverage pre-trained YOLO features to automate the task. Experiments with a 1/2 scale twin bed and distractors placed on the bed, suggest policies learned on 50 demonstrations with DART achieve 96% sheet coverage, which is over 200% better than a corner detector baseline using contour detection.


title: Improving Schools, Improving School Health Education, Improving Public Health: The Role of SOPHE Members

abstract: The reciprocal relationship between health and education has garnered increased attention among public health professionals. The evidence is clear that the level of an individual’s education is rel...


title: Action Anticipation By Predicting Future Dynamic Images

abstract: Human action-anticipation methods predict what is the future action by observing only a few portion of an action in progress. This is critical for applications where computers have to react to human actions as early as possible such as autonomous driving, human-robotic interaction, assistive robotics among others. In this paper, we present a method for human action anticipation by predicting the most plausible future human motion. We represent human motion using Dynamic Images and make use of tailored loss functions to encourage a generative model to produce accurate future motion prediction. Our method outperforms the currently best performing action-anticipation methods by 4% on JHMDB-21, 5.2% on UT-Interaction and 5.1% on UCF 101-24 benchmarks.


title: Steps Toward Participatory Design of Social Robots: Mutual Learning with Older Adults with Depression

abstract: This paper presents the results of research aimed at developing a methodology for the participatory design of social robots, which are meant to be incorporated into various social contexts (e.g. home, work) and establish social relations with people. In contrast to the dominant technologically driven robot development process, we aim to develop a socially robust and responsible approach to robot design using Participatory Design (PD) methods. The PD process builds on participants' self-identified issues and concerns, and develops robot concepts according to participants' interpretations of the capabilities and potential applications of robotic technologies. We present methodological insights from an ongoing PD project aimed at designing socially assistive robots with older adults diagnosed with depression and their therapists, and identify remaining challenges in this project. We particularly focus on supporting mutual learning between researchers and participants and on promoting active participation of older adults as "designers" (rather than consumers) as foundational aspects of PD. We conclude with reflections regarding how this work can contribute to the further development of social robots and relevant PD methodologies.


title: Deep Learning and Its Applications in Biomedicine

abstract: Abstract Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images , electroencephalography, genomic and protein sequences. Learning from these data facilitates the understanding of human health and disease. Developed from artificial neural networks, deep learning -based algorithms show great promise in extracting features and learning patterns from complex data. The aim of this paper is to provide an overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field. We first introduce the development of artificial neural network and deep learning. We then describe two main components of deep learning, i.e. , deep learning architectures and model optimization. Subsequently, some examples are demonstrated for deep learning applications, including medical image classification, genomic sequence analysis, as well as protein structure classification and prediction. Finally, we offer our perspectives for the future directions in the field of deep learning.


title: Enabling phenotypic big data with PheNorm

abstract: Objective ::: Electronic health record (EHR)-based phenotyping infers whether a patient has a disease based on the information in his or her EHR. A human-annotated training set with gold-standard disease status labels is usually required to build an algorithm for phenotyping based on a set of predictive features. The time intensiveness of annotation and feature curation severely limits the ability to achieve high-throughput phenotyping. While previous studies have successfully automated feature curation, annotation remains a major bottleneck. In this paper, we present PheNorm, a phenotyping algorithm that does not require expert-labeled samples for training. ::: ::: ::: Methods ::: The most predictive features, such as the number of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes or mentions of the target phenotype, are normalized to resemble a normal mixture distribution with high area under the receiver operating curve (AUC) for prediction. The transformed features are then denoised and combined into a score for accurate disease classification. ::: ::: ::: Results ::: We validated the accuracy of PheNorm with 4 phenotypes: coronary artery disease, rheumatoid arthritis, Crohn's disease, and ulcerative colitis. The AUCs of the PheNorm score reached 0.90, 0.94, 0.95, and 0.94 for the 4 phenotypes, respectively, which were comparable to the accuracy of supervised algorithms trained with sample sizes of 100-300, with no statistically significant difference. ::: ::: ::: Conclusion ::: The accuracy of the PheNorm algorithms is on par with algorithms trained with annotated samples. PheNorm fully automates the generation of accurate phenotyping algorithms and demonstrates the capacity for EHR-driven annotations to scale to the next level - phenotypic big data.


Cultural Framing of Computer/Video Games


title: Simultaneous video defogging and stereo reconstruction

abstract: We present a method to jointly estimate scene depth and recover the clear latent image from a foggy video sequence. In our formulation, the depth cues from stereo matching and fog information reinforce each other, and produce superior results than conventional stereo or defogging algorithms. We first improve the photo-consistency term to explicitly model the appearance change due to the scattering effects. The prior matting Laplacian constraint on fog transmission imposes a detail-preserving smoothness constraint on the scene depth. We further enforce the ordering consistency between scene depth and fog transmission at neighboring points. These novel constraints are formulated together in an MRF framework, which is optimized iteratively by introducing auxiliary variables. The experiment results on real videos demonstrate the strength of our method.


title: Evaluating the Intangible Benefits of Business Intelligence: Review & Research Agenda

abstract: A Business Intelligence (BI) system is a technology that provides significant business value by improving the effectiveness of managerial decision-making. In an uncertain and highly competitive business environment, the value of strategic information systems such as these is easily recognised. High adoption rates and investment in BI software and services suggest that these systems are a principal provider of decision support in the current marketplace. Most business investments are screened using some form of evaluation process or technique. The benefits of BI are such that traditional evaluation techniques have difficulty in identifying the soft, intangible benefits often provided by BI. This paper, forming the first part of a larger research project, aims to review current evaluation techniques that address intangible benefits, presents issues relating to the evaluation of BI in industry, and suggests a research agenda to advance what is presently a limited body of knowledge relating to the evaluation of BI intangible benefits.


title: Regulation of Photovoltaic Voltage

abstract: In photovoltaic power systems, both photovoltaic modules and switching-mode converters present nonlinear and time-variant characteristics, which result in a difficult control problem. This paper presents an in-depth analysis and modeling to discover the inherent features of a photovoltaic power system. The method of successive linearization simplifies the nonlinear problem back to the linear case. This paper also presents the use of Youla parameterization to design a stable control system for regulating the photovoltaic voltage. The experimental and simulation results demonstrate the effectiveness of the presented analysis, design, and implementation.


title: MobileDeepPill: A Small-Footprint Mobile Deep Learning System for Recognizing Unconstrained Pill Images

abstract: Correct identification of prescription pills based on their visual appearance is a key step required to assure patient safety and facilitate more effective patient care. With the availability of high-quality cameras and computational power on smartphones, it is possible and helpful to identify unknown prescription pills using smartphones. Towards this goal, in 2016, the U.S. National Library of Medicine (NLM) of the National Institutes of Health (NIH) announced a nationwide competition, calling for the creation of a mobile vision system that can recognize pills automatically from a mobile phone picture under unconstrained real-world settings. In this paper, we present the design and evaluation of such mobile pill image recognition system called MobileDeepPill. The development of MobileDeepPill involves three key innovations: a triplet loss function which attains invariances to real-world noisiness that deteriorates the quality of pill images taken by mobile phones; a multi-CNNs model that collectively captures the shape, color and imprints characteristics of the pills; and a Knowledge Distillation-based deep model compression framework that significantly reduces the size of the multi-CNNs model without deteriorating its recognition performance. Our deep learning-based pill image recognition algorithm wins the First Prize (champion) of the NIH NLM Pill Image Recognition Challenge. Given its promising performance, we believe MobileDeepPill helps NIH tackle a critical problem with significant societal impact and will benefit millions of healthcare personnel and the general public.


title: A Rule‐Based Strategy for the Semantic Annotation of Geodata

abstract: The ability to represent geospatial semantics is of great importance when building geospatial applications for the Web. This ability will enhance discovery, retrieval and translation of geographic information as well as the reuse of geographic information in different contexts. The problem of generating semantic annotations has been recognized as one of the most serious obstacles for realizing the Geospatial Semantic Web vision. We present a rule-based strategy for the semantic annotation of geodata that combines Semantic Web and Geospatial Web Services technology. In our approach, rules are employed to partially automate the annotation process. Rules define conditions for identifying geospatial concepts. Based on these rules, spatial analysis procedures are implemented that allow for inferring whether or not a feature in a dataset represents an instance of a geospatial concept. This automated evaluation of features in the dataset generates valuable information for the creation and refinement of semantic annotations on the concept level. The approach is illustrated by a case study on annotating data sources containing representations of lowlands. The presented strategy lays the foundations for the specification of a semantic annotation tool for geospatial web services that supports data providers in annotating their sources according to multiple domain views.


title: Modeling of a photovoltaic system with different MPPT techniques using MATLAB/Simulink

abstract: Solar photovoltaic energy is one of the most growing technologies in the world with a growth rate of 35–40% per year. In this paper, a step-by-step modeling of a PV system is presented. Additionally, a Maximum Power Point Tracking (MPPT) algorithm finds the maximum power for the operation of the PV system during variations of solar irradiance and ambient temperature. Different MPPT techniques are simulated, namely the Perturbation and Observation (P&O) method, the incremental conductance and a fuzzy logic control, which can improve the efficiency of the PV system generation dramatically. The proposed model is implemented in MATLAB/ Simulink. The simulation results have shown that P&O and incremental conductance methods may fail under rapidly changing climatic conditions and present oscillations around the MPP during the steady state period. On the other hand, fuzzy logic control can track the system to the MPP very fast and accurately, even during rapid variations of atmospheric conditions.


title: A Behavior-Authoring Framework for Multiactor Simulations

abstract: Interest has been growing in the behavioral animation of autonomous actors in virtual worlds. However, authoring complicated interactions between multiple actors in a way that balances control flexibility and automation remains a considerable challenge. A proposed behavior-authoring framework gives users complete control over the domain of the system: the state space, action space, and cost of executing actions. To specialize actors, the framework uses effect and cost modifiers, which modify existing action definitions, and constraints, which prune action choices in a state-dependent manner. The framework groups actors with common or conflicting goals to form a composite domain, and a multiagent planner generates complicated interactions between multiple actors. The Web extra is a video that shows how multiactor simulations should aim to strike a happy medium between the automation of generation and the flexibility of specification.


title: Unscented Filtering for Spacecraft Attitude Estimation

abstract: A new spacecraft attitude estimation approach based on the unscented filter is derived. For nonlinear systems the unscented filter uses a carefully selected set of sample points to map the probability distribution more accurately than the linearization of the standard extended Kalman filter, leading to faster convergence from inaccurate initial conditions in attitude estimation problems. The filter formulation is based on standard attitude-vector measurements using a gyro-based model for attitude propagation. The global attitude parameterization is given by a quaternion, whereas a generalized three-dimensional attitude representation is used to define the local attitude error. A multiplicative quaternion-error approach is derived from the local attitude error, which guarantees that quaternion normalization is maintained in the filter. Simulation results indicate that the unscented filter is more robust than the extended Kalman filter under realistic initial attitude-error conditions.


title: The Literature Review of Technology Acceptance Model: A Study of the Bibliometric Distributions

abstract: Technology acceptance model plays a signified issue in the information systems field since this theory was introduced by Davis in 1989. This paper investigates the features of technology acceptance model literature based on bibliometric method. By searching the ISI Web of knowledge database under the keyword of “technology acceptance model,” 689 articles were retrieved and analyzed though growth of the literatures and citation, document type, publication countries, subject area, keyword analysis are addressed. The distribution of journal paper was also examined using Bradford’s law and Lotka’s law. As the result, this research found that technology acceptance model literature has a steady growth as well as the citations. Relevant articles were concentrating on computer science, information systems, management, information science, and library science. The author productivity distribution data in technology acceptance literature was consistent with Lotka’s law. Furthermore, eight core journals were identified utilizing the Bradford’s law.


title: A low-power structured light sensor for outdoor scene reconstruction and dominant material identification

abstract: We introduce a compact structured light device that utilizes a commercially available MEMS mirror-enabled hand-held laser projector. Without complex re-engineering, we show how to exploit the projector's high-speed MEMS mirror motion and laser light-sources to suppress ambient illumination, enabling low-cost and low-power reconstruction of outdoor scenes in sunlight. We discuss how the line-striping acts as a kind of “light-probe”, creating distinctive patterns of light scattered by different types of materials. We investigate visual features that can be computed from these patterns and can reliably identify the dominant material characteristic of a scene, i.e. where most of the objects consist of either diffuse (wood), translucent (wax), reflective (metal) or transparent (glass) materials.


title: @spam: the underground on 140 characters or less

abstract: In this work we present a characterization of spam on Twitter. We find that 8% of 25 million URLs posted to the site point to phishing, malware, and scams listed on popular blacklists. We analyze the accounts that send spam and find evidence that it originates from previously legitimate accounts that have been compromised and are now being puppeteered by spammers. Using clickthrough data, we analyze spammers' use of features unique to Twitter and the degree that they affect the success of spam. We find that Twitter is a highly successful platform for coercing users to visit spam pages, with a clickthrough rate of 0.13%, compared to much lower rates previously reported for email spam. We group spam URLs into campaigns and identify trends that uniquely distinguish phishing, malware, and spam, to gain an insight into the underlying techniques used to attract users. Given the absence of spam filtering on Twitter, we examine whether the use of URL blacklists would help to significantly stem the spread of Twitter spam. Our results indicate that blacklists are too slow at identifying new threats, allowing more than 90% of visitors to view a page before it becomes blacklisted. We also find that even if blacklist delays were reduced, the use by spammers of URL shortening services for obfuscation negates the potential gains unless tools that use blacklists develop more sophisticated spam filtering.


title: The self-normalized estimator for counterfactual learning

abstract: This paper identifies a severe problem of the counterfactual risk estimator typically used in batch learning from logged bandit feedback (BLBF), and proposes the use of an alternative estimator that avoids this problem. In the BLBF setting, the learner does not receive full-information feedback like in supervised learning, but observes feedback only for the actions taken by a historical policy. This makes BLBF algorithms particularly attractive for training online systems (e.g., ad placement, web search, recommendation) using their historical logs. The Counterfactual Risk Minimization (CRM) principle [1] offers a general recipe for designing BLBF algorithms. It requires a counterfactual risk estimator, and virtually all existing works on BLBF have focused on a particular unbiased estimator. We show that this conventional estimator suffers from a propensity overfitting problem when used for learning over complex hypothesis spaces. We propose to replace the risk estimator with a self-normalized estimator, showing that it neatly avoids this problem. This naturally gives rise to a new learning algorithm - Normalized Policy Optimizer for Exponential Models (Norm-POEM) - for structured output prediction using linear rules. We evaluate the empirical effectiveness of Norm-POEM on several multi-label classification problems, finding that it consistently outperforms the conventional estimator.


title: Time to exercise!: an aide-memoire stroke app for post-stroke arm rehabilitation

abstract: A majority of Stroke survivors have an arm impairment (up to 80%), which persists over the long term (>12 months). Physiotherapy experts believe that a rehabilitation Aide-Memoire could help these patients [25]. Hence, we designed, with the input of physiotherapists, Stroke experts and former Stroke patients, the Aide-Memoire Stroke (AIMS) App to help them remember to exercise more frequently. We evaluated its use in a controlled field evaluation on a smartphone, tablet and smartwatch. Since one of the main features of the app is to remind Stroke survivors to exercise we also investigated reminder modalities (i.e., visual, vibrate, audio, speech). One key finding is that Stroke survivors opted for a combination of modalities to remind them to conduct their exercises. Also, Stroke survivors seem to prefer smartphones compared to other mobile devices due to their ease of use, usability, familiarity and being easier to handle with one arm.


title: Semi-Automatic Segmentation and Classification of Pap Smear Cells

abstract: Cytologic screening has been widely used for detecting the cervical cancers. In this study, a semiautomatic PC-based cellular image analysis system was developed for segmenting nuclear and cytoplasmic contours and for computing morphometric and textual features to train support vector machine (SVM) classifiers to classify four different types of cells and to discriminate dysplastic from normal cells. A software program incorporating function, including image reviewing and standardized denomination of file names, was also designed to facilitate and standardize the workflow of cell analyses. Two experiments were conducted to verify the classification performance. The cross-validation results of the first experiment showed that average accuracies of 97.16% and 98.83%, respectively, for differentiating four different types of cells and in discriminating dysplastic from normal cells have been achieved using salient features (8 for four-cluster and 7 for two-cluster classifiers) selected with SVM recursive feature addition. In the second experiment, 70% (837) of the cell images were used for training and 30% (361) for testing, achieving an accuracy of 96.12% and 98.61% for four-cluster and two-cluster classifiers, respectively. The proposed system provides a feasible and effective tool in evaluating cytologic specimens.


title: Directions for new developments on statistical design and analysis of small population group trials

abstract: Most statistical design and analysis methods for clinical trials have been developed and evaluated where at least several hundreds of patients could be recruited. These methods may not be suitable to evaluate therapies if the sample size is unavoidably small, which is usually termed by small populations. The specific sample size cut off, where the standard methods fail, needs to be investigated. In this paper, the authors present their view on new developments for design and analysis of clinical trials in small population groups, where conventional statistical methods may be inappropriate, e.g., because of lack of power or poor adherence to asymptotic approximations due to sample size restrictions. Following the EMA/CHMP guideline on clinical trials in small populations, we consider directions for new developments in the area of statistical methodology for design and analysis of small population clinical trials. We relate the findings to the research activities of three projects, Asterix, IDeAl, and InSPiRe, which have received funding since 2013 within the FP7-HEALTH-2013-INNOVATION-1 framework of the EU. As not all aspects of the wide research area of small population clinical trials can be addressed, we focus on areas where we feel advances are needed and feasible. The general framework of the EMA/CHMP guideline on small population clinical trials stimulates a number of research areas. These serve as the basis for the three projects, Asterix, IDeAl, and InSPiRe, which use various approaches to develop new statistical methodology for design and analysis of small population clinical trials. Small population clinical trials refer to trials with a limited number of patients. Small populations may result form rare diseases or specific subtypes of more common diseases. New statistical methodology needs to be tailored to these specific situations. The main results from the three projects will constitute a useful toolbox for improved design and analysis of small population clinical trials. They address various challenges presented by the EMA/CHMP guideline as well as recent discussions about extrapolation. There is a need for involvement of the patients’ perspective in the planning and conduct of small population clinical trials for a successful therapy evaluation.


title: Demonstration of a Photonic Integrated Mode Coupler With MDM and WDM Transmission

abstract: In this letter, 3.072-Tb/s (six spatial and polarization modes × 4 wavelength-division multiplexing (WDM) ×128-Gb/s 16QAM) transmission over 30 km of few-mode fiber (FMF) is demonstrated employing a photonic integrated mode coupler based on silicon-on-insulator (SoI) technology. A 2-D top coupling solution with five small vertical grating couplers is proposed for coupling between an SoI chip and an FMF, guiding LP01 and LP11 modes. Push-pull and center launch configurations for exciting LP11 and LP01 modes, respectively, through mode-profile matching are analyzed and implemented on the SoI chip.


title: Advantages and Limitations of using Successor Features for Transfer in Reinforcement Learning

abstract: One question central to Reinforcement Learning is how to learn a feature representation that supports algorithm scaling and re-use of learned information from different tasks. Successor Features approach this problem by learning a feature representation that satisfies a temporal constraint. We present an implementation of an approach that decouples the feature representation from the reward function, making it suitable for transferring knowledge between domains. We then assess the advantages and limitations of using Successor Features for transfer.


title: Love is more than just a kiss: a neurobiological perspective on love and affection

abstract: Abstract Love, attachment, and truth of human monogamy have become important research themes in neuroscience. After the introduction of functional Magnetic Resonance Imaging (fMRI) and Positron Emission Tomography (PET), neuroscientists have demonstrated increased interest in the neurobiology and neurochemistry of emotions, including love and affection. Neurobiologists have studied pair-bonding mechanisms in animal models of mate choice to elucidate neurochemical mechanisms underlying attachment and showed possible roles for oxytocin, vasopressin, and dopamine and their receptors in pair-bonding and monogamy. Unresolved is whether these substances are also critically involved in human attachment. The limited number of available imaging studies on love and affection is hampered by selection bias on gender, duration of a love affair, and cultural differences. Brain activity patterns associated with romantic love, shown with fMRI, overlapped with regions expressing oxytocin receptors in the animal models, but definite proof for a role of oxytocin in human attachment is still lacking. There is also evidence for a role of serotonin, cortisol, nerve growth factor, and testosterone in love and attachment. Changes in brain activity related to the various stages of a love affair, gender, and cultural differences are unresolved and will probably become important research themes in this field in the near future. In this review we give a resume of the current knowledge of the neurobiology of love and attachment and we discuss in brief the truth of human monogamy.


title: Event analysis on TRECVid 2008 LondonGatwick dataset

abstract: In this paper we perform event analysis on a challenging surveillance dataset without any artificial events. We analyze low-level and high-level features such as motion vectors, change detection and pedestrian detections for recognition of events. We performed detection of three events namely person runs, elevator no entry and opposing flow. The event detection is performed on 60 hours of TRECVid 2008 event detection dataset.


title: Outdoor face recognition using enhanced near infrared imaging

abstract: In this paper, we present a robust and accurate system for outdoor (as well as indoor) face recognition, based on a recently developed enhanced near-infrared (ENIR) imaging device. Using a narrow band NIR laser generator instead of LED lights for active frontal illumination, the ENIR device can provide face images of good quality even under sunlight. Experiments show that the ENIR system performs similarly to the existing NIR system when used indoors, but outperforms it significantly outdoors especially under sunlight.


Understanding Emotions: A Dataset of Tweets to Study Interactions between Affect Categories.


A Comparative Study of Extremely Low-Resource Transliteration of the World's Languages.


The Impact of Social Media as a Marketing Tool on Purchasing Decisions (Case Study on SAMSUNG for Electrical Home Appliances)


title: Compound micromachines powered by acoustic streaming

abstract: This paper presents the design, fabrication, and operation of compound micromachines powered by acoustic streaming. The machine components were directly incorporated around pillars serving as shafts without further assembly steps using a single-step in situ polymerization process controlled by a programmable projector. Two strategies were presented for harvesting acoustic energy using sharp-edged structures. The first method is based on on-board pumping of fluids and the second method involves engineering of rotors. The implementation of these strategies resulted in the construction of microscale turbines and engines that can be coupled to gear trains for adaptable transmission of mechanical power. We provide a number of further improvements that may together lead to development of compact yet powerful robotic manipulation systems inside microfluidic devices.


title: A Flexible Approach to Automated RNN Architecture Generation

abstract: The process of designing neural architectures requires expert knowledge and extensive trial and error. While automated architecture search may simplify these requirements, the recurrent neural network (RNN) architectures generated by existing methods are limited in both flexibility and components. We propose a domain-specific language (DSL) for use in automated architecture search which can produce novel RNNs of arbitrary depth and width. The DSL is flexible enough to define standard architectures such as the Gated Recurrent Unit and Long Short Term Memory and allows the introduction of non-standard RNN components such as trigonometric curves and layer normalization. Using two different candidate generation techniques, random search with a ranking function and reinforcement learning, we explore the novel architectures produced by the RNN DSL for language modeling and machine translation domains. The resulting architectures do not follow human intuition yet perform well on their targeted tasks, suggesting the space of usable RNN architectures is far larger than previously assumed.


title: Nonce-based authenticated key establishment over OAuth 2.0 IoT proof-of-possession architecture

abstract: The Internet of Things will scale to billions of devices in the next coming years. A secure communication framework is needed to interconnect all these objects, by taking into account their intrinsic constrained in terms of energy, cpu and memory; Several proposals relying on adapting existing well-known and standardized security solutions exist, but we believe there is still a gap for most-constrained nodes to provide fine-grained authorization and secure establishment of fresh cryptographic keys. We propose a mechanism that runs on top of the OAuth Authorization architecture and provides the bootstrapping of fresh authenticated symmetric cryptographic material between previously unknown parties using a nonce-based protocol. We set up an energy measurement platform to evaluate our proposal and compare it with existing work.


title: Real Time Robust Human Detection and Tracking System

abstract: In this paper, we present a real time robust human detection and tracking system for video surveillance which can be used in varying environments. This system consists of human detection, human tracking and false object detection. The human detection utilizes the background subtraction to segment the blob and use codebook to classify human being from other objects. The optimal design algorithm of the codebook is proposed. The tracking is performed at two levels: human classification and individual tracking .The color histogram of human body is used as the appearance model to track individuals. In order to reduce the false alarm, the algorithms of the false object detection are also provided.


title: Information systems strategy for small and medium sized enterprises: an organisational perspective

abstract: Information systems strategy (ISS) use is under-developed for, and under-researched in, small and medium sized enterprises (SMEs). This paper reviews existing approaches to ISS for SMEs, finding them now out-dated and technology-focussed. The paper develops an approach that reflects on the role of information as a strategic resource. It argues that ISS recommendations in small firms need to take account of organisational change issues as much as information system implementation. The revised approach to ISS for SMEs is demonstrated through its application to over 40 cases in the UK West Midlands. The influences from the processes on outcomes are discussed and the effectiveness of tools and techniques recommended is assessed.


title: Traceability information model for very small entities with ISO/IEC 29110

abstract: Requirements traceability is an important quality factor in the life cycle of the software, there is a lot of software standards that demands his implementation, for example, ISO/IEC 29110, a standard for very small entities; however, projects of small organizations do not perform an adequate application of traceability. In the literature there are proposals with a high level of abstraction; the realization of a traceability information model for small software development organizations provides a narrow alternative, reducing the gap between the theory and the reality in software development. We present a traceability information model for small organizations within the framework of ISO/IEC 29110, in this way organizations access an illustrative and reusable traceability solution that allows them to improve the quality of software development. The article contemplates the definition of the models.


title: The Web as a graph: Measurements, models and methods

abstract: The pages and hyperlinks of the World-Wide Web may be viewed as nodes and edges in a directed graph. This graph is a fascinating object of study: it has several hundred million nodes today, over a billion links, and appears to grow exponentially with time. There are many reasons -- mathematical, sociological, and commercial -- for studying the evolution of this graph. In this paper we begin by describing two algorithms that operate on the Web graph, addressing problems from Web search and automatic community discovery. We then report a number of measurements and properties of this graph that manifested themselves as we ran these algorithms on the Web. Finally, we observe that traditional random graph models do not explain these observations, and we propose a new family of random graph models. These models point to a rich new sub-field of the study of random graphs, and raise questions about the analysis of graph algorithms on the Web.


title: Microservices Science and Engineering

abstract: In this paper we offer an overview on the topic of Microservices Science and Engineering (MSE) and we provide a collection of bibliographic references and links relevant to understand an emerging field. We try to clarify some misunderstandings related to microservices and Service-Oriented Architectures, and we also describe projects and applications our team have been working on in the recent past, both regarding programming languages construction and intelligent buildings.


title: Exploring subscription renewal intention of operational cloud enterprise systems-a socio-technical approach

abstract: Despite the fact that customer retention is crucial for providers of cloud enterprise systems, only little attention has been directed towards investigating the antecedents of subscription renewal in an organizational context. This is even more surprising, as cloud services are usually offered as subscription-based pricing models with the (theoretical) possibility of immediate service cancellation, strongly opposing classical long-term IT-Outsourcing contracts or license-based payment plans of on premise enterprise systems. To close this research gap an empirical study was undertaken. Firstly, a conceptual model was drawn from theories of social psychology, organizational system continuance and IS success. The model was subsequently tested using survey responses of senior management within companies which adopted cloud enterprise systems. Gathered data was then analysed using PLS. The results indicate that subscription renewal intention is influenced by both – social-related and technology-specific factors – which are able to explain 50.4% of the variance in the dependent variable. Beneath the cloud enterprise systems specific contributions, the work advances knowledge in the area of organizational system continuance, as well as IS success.


title: Validity in Qualitative Research

abstract: Much contemporary dialogue has centered on the difficulty of establishing validity criteria in qualitative research. Developing validity standards in qualitative research is challenging because of the necessity to incorporate rigor and subjectivity as well as creativity into the scientific process. This article explores the extant issues related to the science and art of qualitative research and proposes a synthesis of contemporary viewpoints. A distinction between primary and secondary validity criteria in qualitative research is made with credibility, authenticity, criticality, and integrity identified as primary validity criteria and explicitness, vividness, creativity, thoroughness, congruence, and sensitivity identified as secondary validity criteria.


title: Boosting the Quality of Approximate String Matching by Synonyms

abstract: A string-similarity measure quantifies the similarity between two text strings for approximate string matching or comparison. For example, the strings “Sam” and “Samuel” can be considered to be similar. Most existing work that computes the similarity of two strings only considers syntactic similarities, for example, number of common words or q-grams. While this is indeed an indicator of similarity, there are many important cases where syntactically-different strings can represent the same real-world object. For example, “Bill” is a short form of “William,” and “Database Management Systems” can be abbreviated as “DBMS.” Given a collection of predefined synonyms, the purpose of this article is to explore such existing knowledge to effectively evaluate the similarity between two strings and efficiently perform similarity searches and joins, thereby boosting the quality of approximate string matching. In particular, we first present an expansion-based framework to measure string similarities efficiently while considering synonyms. We then study efficient algorithms for similarity searches and joins by proposing two novel indexes, called SI-trees and QP-trees, which combine signature-filtering and length-filtering strategies. In order to improve the efficiency of our algorithms, we develop an estimator to estimate the size of candidates to enable an online selection of signature filters. This estimator provides strong low-error, high-confidence guarantees while requiring only logarithmic space and time costs, thus making our method attractive both in theory and in practice. Finally, the experimental results from a comprehensive study of the algorithms with three real datasets verify the effectiveness and efficiency of our approaches.


title: Privacy, confidentiality, and electronic medical records.

abstract: The enhanced availability of health information in an electronic format is strategic for industry-wide efforts to improve the quality and reduce the cost of health care, yet it brings a concomitant concern of greater risk for loss of privacy among health care participants. The authors review the conflicting goals of accessibility and security for electronic medical records and discuss nontechnical and technical aspects that constitute a reasonable security solution. It is argued that with guiding policy and current technology, an electronic medical record may offer better security than a traditional paper record.


title: A nonlinear model of information-seeking behavior

abstract: This paper offers a new, nonlinear model of information-seeking behavior, which contrasts with earlier stage models of information behavior and represents a potential cornerstone for a shift toward a new perspective for understanding user information behavior. The model is based on the findings of a study on interdisciplinary information-seeking behavior. The study followed a naturalistic inquiry approach using interviews of 45 academics. The interview results were inductively analyzed and an alternative framework for understanding information-seeking behavior was developed. This model illustrates three core processes and three levels of contextual interaction, each composed of several individual activities and attributes. These interact dynamically through time in a nonlinear manner. The behavioral patterns are analogous to an artist's palette, in which activities remain available throughout the course of information-seeking. In viewing the processes in this way, neither start nor finish points are fixed, and each process may be repeated or lead to any other until either the query or context determine that information-seeking can end. The interactivity and shifts described by the model show information-seeking to be nonlinear, dynamic, holistic, and flowing. The paper offers four main implications of the model as it applies to existing theory and models, requirements for future research, and the development of information literacy curricula. Central to these implications is the creation of a new nonlinear perspective from which user information-seeking can be interpreted.


title: Energy Delay Trade-Off in Cloud Offloading for Mutli-Core Mobile Devices

abstract: Cloud offloading is considered a promising approach to energy conservation and storage/computation enhancement for resource limited mobile devices. In this paper, we present a Lyapunov optimization based scheme for cloud offloading scheduling, as well as download scheduling for cloud execution output, for multiple applications running in a mobile device with a multi-core CPU. We derive an online algorithm and prove performance bounds for the proposed algorithm with respect to average power consumption and average queue length, which is indicative of delay, and reveal the fundamental trade-off between the two optimization goals.


title: Enabling IoT interoperability through opportunistic smartphone-based mobile gateways

abstract: In the near future, all our everyday things and objects will be both connected to the Internet and equipped with enough sensing, acting and processing capabilities to exploit the full potential benefits of the so called Internet of Things (IoT) paradigm. Even the simplest objects will become smart because they will be interconnected to other objects to share and collect data from the environments in which they are placed thus paving the way to novel application services, computing and communication scenarios.In this context, interoperability among different standards and communication technologies is still a significant challenge that we have started to address by proposing a smartphone-based mobile gateway acting as a flexible and transparent interface between different IoT devices and the Internet. The presented unified, high-level and extendible software architecture supports opportunistic IoT devices discovery, control and management coupled with data processing, collection and diffusion functionalities.A specific testbed on common smartphones with different hardware and software capabilities was deployed to evaluate the real feasibility of the designed solution measuring the system performance in terms of energy consumption, memory and CPU usage in high and low load scenarios. According to the obtained results, the implemented software architecture for multi-standard and multi-technology interoperation presents a reduced use of hardware resources in front of a relatively high energy consumption value, mostly due to the simultaneously active radio interfaces combined with a small battery capacity, that limits the smartphone lifetime. Nevertheless, the presented general approach is still remarkable because this latter aspect will most likely be exceeded, in a short time, thanks to daily technological advancements in both batteries and radio interfaces.


title: Modified bowtie antenna for GPR applications

abstract: This paper presents a novel antenna for Ground Penetrating Radar (GPR) applications. It consists of two crossed bowtie antennas loaded with an annular ring. Numerical and experimental results are reported demonstrating that the proposed antenna exhibits both a broadband impedance bandwidth and very good radiation properties.


title: Cost of photovoltaic energy systems as determined by balance-of-system costs

abstract: The effect of the balance-of-system (BOS), i.e., the total system less the modules, on photovoltaic energy system costs is discussed for multikilowatt, flat-plate systems. Present BOS costs are in the range of 10 to 16 dollars per peak watt (1978 dollars). BOS costs represent approximately 50% of total system cost. The possibility of future BOS cost reduction is examined. It is concluded that, given the nature of BOS casts and the lack of a comprehensive national effort focussed on cost reduction, it is unlikely that BOS costs will decline greatly in the next several years. This prognosis is contrasted with the expectations of the Department of Energy National Photovoltaic Program goals and pending legislation in the Congress which require a BOS cost reduction of an order of magnitude or more by the mid-1980s. INTRODUCTION In the commercialization of any of the solar energy technologies, cost is an important consideration. In the early days of technology development before a large body 2 of experience has been accumulated cost numbers are difficult to ascertain. Often the numbers are alloyed with a measure of unsubstantiated speculation and partisan desire. Yet realistic cost estimates are vital to the deliberation of public policy, the formulation of implementation strategy, and t;:e assessment of cost-benefit. In this light, this repl.x t attempts to clarify a major issue concerning the cost of solar photovoltaic energy systems. Photovoltaic system costs are commonly divided into two parts: the photovoltaic module and the balance-of-system (BOS). Historically, the preponderance of effort in analyzing and defining photovoltaic costs has centered on the production of solar cells and modules; little effort has been expended in understanding BOS costs. This emphasis most likely is the result of early perceptions of priorities by the Energy Research and Develcpment Administration (now Department of Energy DOE) at a time when module costs were relatively high. Preoccupation with module cost continues to the present. This can readily be observed in publications ranging from government documents to technical journals to news articles; module cost (or more commonly what is termed array cost) is the sine qua non in discussing the economics of photovoltaic energy. By way of example, see references 1 and 2. 1Module is the smallest, electrically interconnected, environmentally protected assembly of solar cells. The DOE National Photovoltaic Program goals are expressed in terms of module price, dollars per watt.


title: LCL filter design for three-phase two-level power factor correction using line impedance stabilization network

abstract: This paper presents LCL filter design method for three-phase two-level power factor correction (PFC) using line impedance stabilization network (LISN). A straightforward LCL filter design along with variation in grid impedance is not simply achievable and inevitably lead to an iterative solution for filter. By introducing of fast power switches for PFC applications such as silicon-carbide, major current harmonics around the switching frequency drops in the region that LISN can actively provide well-defined impedance for measuring the harmonics (i.e. 9 kHz- 30MHz). Therefore, LISN can be replaced with unknown grid impedance at high frequency, simplify the model of the filter, and provide repetitive measurements. In this paper, all the filter parameters are derived with analyzing the behavior of the converter at high frequency with presence of LISN impedance. The minimum required filter capacitor is derived using the current ripple behavior of converter-side inductor. The grid-side inductor is achieved as a function of LISN impedance to fulfill the grid regulation. To verify the analyses, an LCL filter is designed for a 5 kW SiC-based PFC. The simulation and experimental results support the validity of the method.


title: Major life changes and behavioral markers in social media: case of childbirth

abstract: We explore the harnessing of social media as a window on changes around major life events in individuals and larger populations. We specifically examine patterns of activity, emotional, and linguistic correlates for childbirth and postnatal course. After identifying childbirth events on Twitter, we analyze daily posting patterns and language usage before and after birth by new mothers, and make inferences about the status and dynamics of changes in emotions expressed following childbirth. We find that childbirth is associated with some changes for most new mothers, but approximately 15% of new mothers show significant changes in their online activity and emotional expression postpartum. We observe that these mothers can be distinguished by linguistic changes captured by shifts in a relatively small number of words in their social media posts. We introduce a greedy differencing procedure to identify the type of language that characterizes significant changes in these mothers during postpartum. We conclude with a discussion about how such characterizations might be applied to recognizing and understanding health and well-being in women following childbirth.


Partially-Supervised Image Captioning


title: High-Frequency Transformer Isolated Bidirectional DC–DC Converter Modules With High Efficiency Over Wide Load Range for 20 kVA Solid-State Transformer

abstract: This paper presents the design of new high-frequency transformer isolated bidirectional dc-dc converter modules connected in input-series-output-parallel (ISOP) for 20-kVA-solid-state transformer. The ISOP modular structure enables the use of low-voltage MOSFETs, featuring low on-state resistance and resulted conduction losses, to address medium-voltage input. A phase-shift dual-half-bridge (DHB) converter is employed to achieve high-frequency galvanic isolation, bidirectional power flow, and zero voltage switching (ZVS) of all switching devices, which leads to low switching losses even with high-frequency operation. Furthermore, an adaptive inductor is proposed as the main energy transfer element of a phase-shift DHB converter so that the circulating energy can be optimized to maintain ZVS at light load and minimize the conduction losses at heavy load as well. As a result, high efficiency over wide load range and high power density can be achieved. In addition, current stress of switching devices can be reduced. A planar transformer adopting printed-circuit-board windings arranged in an interleaved structure is designed to obtain low core and winding loss, solid isolation, and identical parameters in multiple modules. Moreover, the modular structure along with a distributed control provides plug-and-play capability and possible high-level fault tolerance. The experimental results on 1 kW DHB converter modules switching at 50 kHz are presented to validate the theoretical analysis.


title: Discriminatively Embedded K-Means for Multi-view Clustering

abstract: In real world applications, more and more data, for example, image/video data, are high dimensional and repre-sented by multiple views which describe different perspectives of the data. Efficiently clustering such data is a challenge. To address this problem, this paper proposes a novel multi-view clustering method called Discriminatively Embedded K-Means (DEKM), which embeds the synchronous learning of multiple discriminative subspaces into multi-view K-Means clustering to construct a unified framework, and adaptively control the intercoordinations between these subspaces simultaneously. In this framework, we firstly design a weighted multi-view Linear Discriminant Analysis (LDA), and then develop an unsupervised optimization scheme to alternatively learn the common clustering indicator, multiple discriminative subspaces and weights for heterogeneous features with convergence. Comprehensive evaluations on three benchmark datasets and comparisons with several state-of-the-art multi-view clustering algorithms demonstrate the superiority of the proposed work.


title: Optimized Continuous Collision Detection for Deformable Triangle Meshes

abstract: We present different approaches for accelerating the process of continuous collision detection for deformable triangle meshes. The main focus is upon the collision detection for simulated virtual clothing, especially for situations involving a high number of contact points between the triangle meshes, such as multi-layered garments. We show how the culling efficiency of bounding volume hierarchies may be increased by introducing additional bounding volumes for edges and vertices of the triangle mesh. We present optimized formulas for computing the time of collision for these primitives analytically, and describe an efficient iterative scheme that ensures that all collisions are treated in the correct chronological order.


title: Textons, contours and regions: cue integration in image segmentation

abstract: The paper makes two contributions: it provides (1) an operational definition of textons, the putative elementary units of texture perception, and (2) an algorithm for partitioning the image into disjoint regions of coherent brightness and texture, where boundaries of regions are defined by peaks in contour orientation energy and differences in texton densities across the contour. B. Julesz (1981) introduced the term texton, analogous to a phoneme in speech recognition, but did not provide an operational definition for gray-level images. We re-invent textons as frequently co-occurring combinations of oriented linear filter outputs. These can be learned using a K-means approach. By mapping each pixel to its nearest texton, the image can be analyzed into texton channels, each of which is a point set where discrete techniques such as Voronoi diagrams become applicable. Local histograms of texton frequencies can be used with a /spl chi//sup 2/ test for significant differences to find texture boundaries. Natural images contain both textured and untextured regions, so we combine this cue with that of the presence of peaks of contour energy derived from outputs of odd- and even-symmetric oriented Gaussian derivative filters. Each of these cues has a domain of applicability, so to facilitate cue combination we introduce a gating operator based on a statistical test for isotropy of Delaunay neighbors. Having obtained a local measure of how likely two nearby pixels are to belong to the same region, we use the spectral graph theoretic framework of normalized cuts to find partitions of the image into regions of coherent texture and brightness. Experimental results on a wide range of images are shown.


title: AN IMPROVED ROUND ROBIN CPU SCHEDULING ALGORITHM

abstract: One of the most important components of the computer resource is the CPU. CPU scheduling involves a careful examination of pending processes to determine the most efficient way to service the requests. CPU scheduling is the basis of multiprogrammed operating systems. Most CPU scheduling algorithms concentrate on maximizing CPU utilization and throughput and minimizing turnaround time, waiting time, response time and number of context switching for a set of requests. Some of the popular CPU scheduling algorithms are First-Come-First-Served (FCFS), Shortest Job First (SJF), Priority Scheduling and Round Robin (RR). FCFS is the simplest form of CPU scheduling algorithm. This algorithm is simple to implement, but it generally does not provide the fastest service. Round Robin being the most popular choice in time shared system, but it may not be suitable for real time systems because of larger waiting time, turnaround time and more number of context switches. This paper describes an improvement in RR. A simulator program has been designed and tested the Improved Round Robin (IRR). After improvement in RR it has been found that the waiting time and turnaround time have been reduced drastically.


title: Analysis Matrix for Smart Cities

abstract: The current digital revolution has ignited the evolution of communications grids and the development of new schemes for productive systems. Traditional technologic scenarios have been challenged, and Smart Cities have become the basis for urban competitiveness. The citizen is the one who has the power to set new scenarios, and that is why a definition of the way people interact with their cities is needed, as is commented in the first part of the article. At the same time, a lack of clarity has been detected in the way of describing what Smart Cities are, and the second part will try to set the basis for that. For all before, the information and communication technologies that manage and transform 21st century cities must be reviewed, analyzing their impact on new social behaviors that shape the spaces and means of communication, as is posed in the experimental section, setting the basis for an analysis matrix to score the different elements that affect a Smart City environment. So, as the better way to evaluate what a Smart City is, there is a need for a tool to score the different technologies on the basis of their usefulness and consequences, considering the impact of each application. For all of that, the final section describes the main objective of this article in practical scenarios, considering how the technologies are used by citizens, who must be the main concern of all urban development.


title: A 40 Gb/s Serial Link Transceiver in 28 nm CMOS Technology

abstract: A SerDes operating at 40 Gb/s optimized for chip-to-chip communication is presented. Equalization consists of 2-tap feed-forward equalizers (FFE) in both transmitter and receiver, a 3-stage continuous-time linear equalizer (CTLE) and discrete-time equalizers including a 17-tap decision feedback equalizer (DFE) and a 3-tap sampled-FFE in the receiver. The SerDes is realized in 28-nm CMOS technology with 23.2 mW/Gb/s power efficiency at 40 Gb/s.


title: An FPGA-Based Multiple-Axis Motion Control Chip

abstract: This paper presents the design and implementation of a multiple-axis motion control chip using a field-programmable gate array (FPGA). This multiple-axis motion control chip is designed to control a multiple-axis motion system such as a robotic arm manipulator or a computer numerical control machine. The proposed motion control chip has many functions. These include velocity profile generation, interpolation calculation, inverse kinematics calculation, proportional-integral-derivative control, feedback count, pulse integration, data conversion, clock generation, and external interfacing. These functions are designed using the VHSIC hardware description language and implemented on an FPGA according to the electronic design automation design methodology. This allows for a highly sampled, accurate, flexible, compact, low-power, and low-cost motion control system. The detailed design of the proposed motion control chip is presented. A multiple-axis motion control system using this chip is implemented, and its performance is measured. The multiple-axis motion control system is implemented on a platform consisting of a chip-based multiple-axis motion controller, analog ac servo drivers, a selective compliant assembly robot arm robot, and a host personal computer.


title: Recommending citations for academic papers

abstract: We approach the problem of academic literature search by considering an unpublished manuscript as a query to a search system. We use the text of previous literature as well as the citation graph that connects it to find relevant related material. We evaluate our technique with manual and automatic evaluation methods, and find an order of magnitude improvement in mean average precision as compared to a text similarity baseline.


title: Beyond objects: a software design paradigm based on process control

abstract: A standard demonstration problem in object-oriented programming is the design of an automobile cruise control. This design exercise demonstrates object-oriented techniques well, but it does not ask whether the object-oriented paradigm is the best one for the task. Here we examine the alternative view that cruise control is essentially a control problem. We present a new software organization paradigm motivated by process control loops. The control view leads us to an architecture that is dominated by analysis of a classical feedback loop rather than by the identification of discrete stateful components to treat as objects. The change in architectural model calls attention to important questions about the cruise control task that aren't addressed in an object-oriented design.


title: WEB DESIGN IN E-COMMERCE: A THEORY AND EMPIRICAL ANALYSIS

abstract: In the Internet market, websites are the main interface between online merchants and their customers. Effective website design plays a critical role in attracting and maintaining customers’ interest and in influencing their purchase behavior. Despite the singular significance of website design, little theoretical knowledge is available regarding how web-design elements impact the purchase behavior of online shoppers. By drawing on the theory of planned behavior and interpersonal influence, we develop and empirically test a conceptual model for the process by which web design elements could influence the purchase intention of online customers. This research provides a theoretical framework for the design decisions regarding websites in order to accommodate the salient features of online shopping.


title: The emerging world of motor neuroprosthetics: a neurosurgical perspective.

abstract: A MOTOR NEUROPROSTHETIC device, or brain computer interface, is a machine that can take some type of signal from the brain and convert that information into overt device control such that it reflects the intentions of the user's brain. In essence, these constructs can decode the electrophysiological signals representing motor intent. With the parallel evolution of neuroscience, engineering, and rapid computing, the era of clinical neuroprosthetics is approaching as a practical reality for people with severe motor impairment. Patients with such diseases as spinal cord injury, stroke, limb loss, and neuromuscular disorders may benefit through the implantation of these brain computer interfaces that serve to augment their ability to communicate and interact with their environment. In the upcoming years, it will be important for the neurosurgeon to understand what a brain computer interface is, its fundamental principle of operation, and what the salient surgical issues are when considering implantation. We review the current state of the field of motor neuroprosthetics research, the early clinical applications, and the essential considerations from a neurosurgical perspective for the future.


PoeTryMe : a versatile platform for poetry generation


title: A Multi-task Learning Approach for Improving Product Title Compression with User Search Log Data

abstract: It is a challenging and practical research problem to obtain effective compression of lengthy product titles for E-commerce. This is particularly important as more and more users browse mobile E-commerce apps and more merchants make the original product titles redundant and lengthy for Search Engine Optimization. Traditional text summarization approaches often require a large amount of preprocessing costs and do not capture the important issue of conversion rate in E-commerce. This paper proposes a novel multi-task learning approach for improving product title compression with user search log data. In particular, a pointer network-based sequence-to-sequence approach is utilized for title compression with an attentive mechanism as an extractive method and an attentive encoder-decoder approach is utilized for generating user search queries. The encoding parameters (i.e., semantic embedding of original titles) are shared among the two tasks and the attention distributions are jointly optimized. An extensive set of experiments with both human annotated data and online deployment demonstrate the advantage of the proposed research for both compression qualities and online business values.


title: Blending of Two and Three-way Interactions for Modeling Multi-relational Data

abstract: Much work has been recently proposed to model relational data, especially in the multi-relational case, where di↵erent kinds of relationships are used to connect the various data entities. Previous attempts either consist of powerful systems with high capacity to model complex connectivity patterns, which unfortunately usually end up overfitting on rare relationships, or in approaches that trade capacity for simplicity in order to fairly model all relationships, frequent or not. In this paper, we propose a happy medium obtained by complementing a high-capacity model with a simpler one, both pre-trained separately and jointly fine-tuned. We show that our approach outperforms existing models on different types of relationships, and achieves state-of-the-art results on two benchmarks of the literature.


title: Kernel-Based Learning of Hierarchical Multilabel Classification Models

abstract: We present a kernel-based algorithm for hierarchical text classification where the documents are allowed to belong to more than one category at a time. The classification model is a variant of the Maximum Margin Markov Network framework, where the classification hierarchy is represented as a Markov tree equipped with an exponential family defined on the edges. We present an efficient optimization algorithm based on incremental conditional gradient ascent in single-example subspaces spanned by the marginal dual variables. The optimization is facilitated with a dynamic programming based algorithm that computes best update directions in the feasible set. ::: ::: Experiments show that the algorithm can feasibly optimize training sets of thousands of examples and classification hierarchies consisting of hundreds of nodes. Training of the full hierarchical model is as efficient as training independent SVM-light classifiers for each node. The algorithm's predictive accuracy was found to be competitive with other recently introduced hierarchical multi-category or multilabel classification learning algorithms.


title: Social Networking Sites Security: Quo Vadis

abstract: Social networking sites have been studied extensively within the past five years, especially in the area of information security. Within this paper we discuss these emerging web services both regarding possible attack vectors as well as defense strategies. Our results suggest that a gap between attack and defense strategies exists. Furthermore we found that research focuses mainly on Facebook, while scant attention is paid to other social networking sites.


title: Switched Reluctance Motor Drive With External Rotor for Fan in Air Conditioner

abstract: This paper describes the developed three-phase 6/8 poles switched reluctance external rotor motor drive for a fan in air conditioner. The external rotor core structure and the internal stator core structure, the three-phase windings arrangement, the slotted claw, and the setting structure of the photoelectric transducers on the rotor position detector are illustrated. The electromagnetic field calculation results are given. The three-phase asymmetric bridge power converter was used in the drive system. The block diagram of the switched reluctance external rotor motor drive with closed-loop rotor speed control is given. The closed-loop rotor speed control is implemented using a fuzzy logic algorithm. The experimental tests of the developed prototype are made for driving the fan from 200 to 950 r/min. The comparison results of the two systems show that the input power and input phase current RMS value are lower in the developed three-phase 6/8 poles external rotor switched reluctance motor drive prototype with the fan than those in the induction motor variable-frequency variable-speed drive with the fan.


title: The value of inferring the internal state of traffic participants for autonomous freeway driving

abstract: Safe interaction with human drivers is one of the primary challenges for autonomous vehicles. In order to plan driving maneuvers effectively, the vehicle's control system must infer and predict how humans will behave based on their latent internal state (e.g., intentions and aggressiveness). This research uses a simple model for human behavior with unknown parameters that make up the internal states of the traffic participants and presents a method for quantifying the value of estimating these states and planning with their uncertainty explicitly modeled. An upper performance bound is established by an omniscient Monte Carlo Tree Search (MCTS) planner that has perfect knowledge of the internal states. A baseline lower bound is established by planning with MCTS assuming that all drivers have the same internal state. MCTS variants are then used to solve a partially observable Markov decision process (POMDP) that models the internal state uncertainty to determine whether inferring the internal state offers an advantage over the baseline. Applying this method to a freeway lane changing scenario reveals that there is a significant performance gap between the upper bound and baseline. POMDP planning techniques come close to closing this gap, especially when important hidden model parameters are correlated with measurable parameters.


title: Images from self-occlusion

abstract: We propose a complete system for designing, simulating, and fabricating surfaces with shading due to self-occlusion that induce desired input images. Our work is based on a simple observation. Consider a cylindrical hole (a pit) in a planar surface. As the depth of the hole increases, the radiance emitted from the surface patch that contains the hole decreases. This is because more light is trapped and absorbed in the hole. First, we propose a measurement-based approach that derives a mapping between average albedo of the surface patch containing the hole and the hole depth. Given this mapping and an input image, we show how to produce a distribution of holes with varied depth that approximates the image well. We demonstrate that by aligning holes with image features we can obtain reproductions that look better than those resulting from regular hole patterns -- despite using slightly less holes. We validate this method on a variety of images and corresponding surfaces fabricated with a computer-controlled milling machine and a 3D printer.


Lightweight Cryptography for the Internet of Things


title: Explicative human activity recognition using adaptive association rule-based classification

abstract: Computational social sensing is enabled by the Internet of Things at large scale. Using sensors, e. g., implemented in mobile and wearable devices, human behavior and activities can then be investigated, e.g., using according models and patterns. However, the obtained models are often not explicative, i. e., interpretable, transparent, and explanation-aware, which makes assessment and validation difficult for humans. This paper proposes a novel explicative classification approach featuring interpretable and explainable models. For this purpose, we embed a framework for building rule-based classifiers using class association rules. For evaluation, we apply two real-world datasets: One collected in the domain of personalized health using wearable sensors (accelerometers), the second one utilizing smartphone sensors for activity recognition. Our results indicate, that the proposed approach outperforms the baselines clearly, concerning both accuracy and complexity of the resulting predictive models.


title: CodeWorkout: Short Programming Exercises with Built-in Data Collection

abstract: Learning programming techniques can be challenging and frustrating for many students. Many instructors use drill-and-practice strategies to help students develop basic programming techniques and improve their confidence. Online systems that provide short programming exercises with immediate, automated feedback are often seen as a valuable approach to drill-and-practice. However, the relationship between practicing with short programming exercises and performance on larger programming assignments or exams is unclear. This paper describes CodeWorkout, an open-source drill-and-practice system that supports short programming exercises and multiple choice questions. CodeWorkout combines an open, gradual engagement model that allows any student to practice exercises, whether or not they have an account or are enrolled in a course, together with powerful course management features that support graded assignments, quizzes, and even practicum style exams. It also provides a rich data collection and evaluation infrastructure for educational research purposes. We report on initial experiences using CodeWorkout in a CS1 course, including student perceptions of the tool and its benefits.


Novel reduction-sensitive micelles for triggered intracellular drug release


title: Understanding Malicious Behavior in Crowdsourcing Platforms: The Case of Online Surveys

abstract: Crowdsourcing is increasingly being used as a means to tackle problems requiring human intelligence. With the ever-growing worker base that aims to complete microtasks on crowdsourcing platforms in exchange for financial gains, there is a need for stringent mechanisms to prevent exploitation of deployed tasks. Quality control mechanisms need to accommodate a diverse pool of workers, exhibiting a wide range of behavior. A pivotal step towards fraud-proof task design is understanding the behavioral patterns of microtask workers. In this paper, we analyze the prevalent malicious activity on crowdsourcing platforms and study the behavior exhibited by trustworthy and untrustworthy workers, particularly on crowdsourced surveys. Based on our analysis of the typical malicious activity, we define and identify different types of workers in the crowd, propose a method to measure malicious activity, and finally present guidelines for the efficient design of crowdsourced surveys.


title: Graphene Field-Effect Transistors for Radio-Frequency Flexible Electronics

abstract: Flexible radio-frequency (RF) electronics require materials which possess both exceptional electronic properties and high-strain limits. While flexible graphene field-effect transistors (GFETs) have demonstrated significantly higher strain limits than FETs fabricated from thin films of Si and III-V semiconductors, to date RF performance has been comparatively worse, limited to the low GHz frequency range. However, flexible GFETs have only been fabricated with modestly scaled channel lengths. In this paper, we fabricate GFETs on flexible substrates with short channel lengths of 260 nm. These devices demonstrate extrinsic unity-power-gain frequencies, f ::: max ::: , up to 7.6 GHz and strain limits of 2%, representing strain limits an order of magnitude higher than the flexible technology with next highest reported f ::: max ::: .


title: Target dependent score normalization techniques and their application to signature verification

abstract: Score normalization methods in biometric verification, which encompass the more traditional user-dependent decision thresholding techniques, are reviewed from a test hypotheses point of view. These are classified into test dependent and target dependent methods. The focus of the paper is on target dependent methods, which are further classified into impostor-centric, target-centric and target-impostor. These are applied to an on-line signature verification system on signature data from SVC 2004. In particular, a target-centric technique based on a variant of the cross-validation procedure provides the best relative performance improvement both for skilled (19%) and random forgeries (53%) as compared to the raw verification performance without score normalization (7.14% EER and 1.06% EER for skilled and random forgeries respectively).


title: Next Basket Recommendation with Neural Networks

abstract: One crucial task in recommendation is to predict what a user will buy next given her shopping history. In this paper, we propose a novel neural network to complete this task. The model consists of an embedding layer, a hidden layer and an output layer. Firstly, the distributed representations of the user and the items bought before are obtained and used to form a feature vector by the embedding layer. Then the hidden layer transforms the feature vector to another space by a non-linear operator. Finally, the softmax operator is adopted to output the probabilities of next items. We can see that the model elegantly involves both the user's general interest and the sequential dependencies between items for prediction. Experimental results on two real datasets prove the effectiveness of our model.


title: IoT-Based Sensor Data Fusion for Occupancy Sensing Using Dempster–Shafer Evidence Theory for Smart Buildings

abstract: With the advent of Internet of Things technologies and smart buildings, the need for building automation systems that automatically performs computations without the intervention of humans have also increased. This paper deals with detection of occupancy in a room from various ambient sources like temperature, humidity, light, and CO2. With the help of this system, remote monitoring of the building as well as leveraging control on the indoor parameters through HVAC control systems is possible at real-time. This paper adopts Dempster–Shafer evidence theory for fusing sensory information collected from heterogeneous sensors, assigns probability mass assignments (PMAs) to the raw sensor readings, and finally performs mass combination to derive a conclusion about the occupancy status in a room. A PMA function has been proposed for this purpose. The results reveal a substantially high percentage of accuracy (up to 99.09%) which was observed to increase with the increase in number of fusion parameters.