title: Adaptive gain sliding mode observer for state of charge estimation based on combined battery equivalent circuit model in electric vehicles

abstract: Abstract An adaptive gain sliding mode observer (AGSMO) for battery state of charge (SOC) estimation based on a combined battery equivalent circuit model (CBECM) is presented. The error convergence of the AGSMO for the SOC estimation is proved by Lyapunov stability theory. Comparing with conventional sliding mode observers for the SOC estimation, the AGSMO can minimise chattering levels and improve the accuracy by adaptively adjusting switching gains to compensate modelling errors. To design the AGSMO for the SOC estimation, the state equations of the CBECM are derived to capture dynamics of a battery. A lithium-polymer battery (LiPB) is used to conduct experiments for extracting parameters of the CBECM and verifying the effectiveness of the proposed AGSMO for the SOC estimation.


title: Detection and Segmentation of Cell Nuclei in Virtual Microscopy Images: A Minimum-Model Approach

abstract: Automated image analysis of cells and tissues has been an active research field in medical informatics for decades but has recently attracted increased attention due to developments in computer and microscopy hardware and the awareness that scientific and diagnostic pathology require novel approaches to perform objective quantitative analyses of cellular and tissue specimens. Model-based approaches use a priori information on cell shape features to obtain the segmentation, which may introduce a bias favouring the detection of cell nuclei only with certain properties. In this study we present a novel contour-based “minimum-model” cell detection and segmentation approach that uses minimal a priori information and detects contours independent of their shape. This approach avoids a segmentation bias with respect to shape features and allows for an accurate segmentation (precision = 0.908; recall = 0.859; validation based on ∼8000 manually-labeled cells) of a broad spectrum of normal and disease-related morphological features without the requirement of prior training.


title: Structural neuroplasticity in the sensorimotor network of professional female ballet dancers

abstract: Evidence suggests that motor, sensory, and cognitive training modulates brain structures involved in a specific practice. Functional neuroimaging revealed key brain structures involved in dancing such as the putamen and the premotor cortex. Intensive ballet dance training was expected to modulate the structures of the sensorimotor network, for example, the putamen, premotor cortex, supplementary motor area (SMA), and the corticospinal tracts. We investigated gray (GM) and white matter (WM) volumes, fractional anisotropy (FA), and mean diffusivity (MD) using magnetic resonance-based morphometry and diffusion tensor imaging in 10 professional female ballet dancers compared with 10 nondancers. In dancers compared with nondancers, decreased GM volumes were observed in the left premotor cortex, SMA, putamen, and superior frontal gyrus, and decreased WM volumes in both corticospinal tracts, both internal capsules, corpus callosum, and left anterior cingulum. FA was lower in the WM underlying the dancers' left and right premotor cortex. There were no significant differences in MD between the groups. Age of dance commencement was negatively correlated with GM and WM volume in the right premotor cortex and internal capsule, respectively, and positively correlated with WM volume in the left precentral gyrus and corpus callosum. Results were not influenced by the significantly lower body mass index of the dancers. The present findings complement the results of functional imaging studies in experts that revealed reduced neural activity in skilled compared with nonskilled subjects. Reductions in brain activity are accompanied by local decreases in GM and WM volumes and decreased FA.


title: Unifying collaborative and content-based filtering

abstract: Collaborative and content-based filtering are two paradigms that have been applied in the context of recommender systems and user preference prediction. This paper proposes a novel, unified approach that systematically integrates all available training information such as past user-item ratings as well as attributes of items or users to learn a prediction function. The key ingredient of our method is the design of a suitable kernel or similarity function between user-item pairs that allows simultaneous generalization across the user and item dimensions. We propose an on-line algorithm (JRank) that generalizes perceptron learning. Experimental results on the EachMovie data set show significant improvements over standard approaches.


title: Chapter 9 A SURVEY OF CLUSTERING ALGORITHMS FOR GRAPH DATA

abstract: In this chapter, we will provide a survey of clustering algorithms for graph data. We will discuss the different categories of clustering algorithms and recent efforts to design clustering methods for various kinds of graphical data. Clustering algorithms are typically of two types. The first type consists of node clustering algorithms in which we attempt to determine dense regions of the graph based on edge behavior. The second type consists of structural clustering algorithms, in which we attempt to cluster the different graphs based on overall structural behavior. We will also discuss the applicability of the approach to other kinds of data such as semi-structured data, and the utility of graph mining algorithms to such representations.


title: Collaborative Filtering Based Recommendation System: A survey

abstract: the most common technique used for recommendations is collaborative filtering. Recommender systems based on collaborative filtering predict user preferences for products or services by learning past user-item relationships from a group of user who share the same preferences and taste. In this paper we have explored various aspects of collaborative filtering recommendation system. We have categorized collaborative filtering recommendation system and shown how the similarity is computed. The desired criteria for selection of data set are also listed. The measures used for evaluating the performance of collaborative filtering recommendation system are discussed along with the challenges faced by the recommendation system. Types of rating that can be collected from the user to rate items are also discussed along with the uses of collaborative filtering recommendation system.


title: Towards a decision-making structure for selecting a research design in empirical software engineering

abstract: Several factors make empirical research in software engineering particularly challenging as it requires studying not only technology but its stakeholders' activities while drawing concepts and theories from social science. Researchers, in general, agree that selecting a research design in empirical software engineering research is challenging, because the implications of using individual research methods are not well recorded. The main objective of this article is to make researchers aware and support them in their research design, by providing a foundation of knowledge about empirical software engineering research decisions, in order to ensure that researchers make well-founded and informed decisions about their research designs. This article provides a decision-making structure containing a number of decision points, each one of them representing a specific aspect on empirical software engineering research. The article provides an introduction to each decision point and its constituents, as well as to the relationships between the different parts in the decision-making structure. The intention is the structure should act as a starting point for the research design before going into the details of the research design chosen. The article provides an in-depth discussion of decision points in relation to the research design when conducting empirical research.


title: 3D Surface Detail Enhancement from a Single Normal Map

abstract: In 3D reconstruction, the obtained surface details are mainly limited to the visual sensor due to sampling and quantization in the digitalization process. How to get a fine-grained 3D surface with low-cost is still a challenging obstacle in terms of experience, equipment and easyto-obtain. This work introduces a novel framework for enhancing surfaces reconstructed from normal map, where the assumptions on hardware (e.g., photometric stereo setup) and reflection model (e.g., Lambertion reflection) are not necessarily needed. We propose to use a new measure, angle profile, to infer the hidden micro-structure from existing surfaces. In addition, the inferred results are further improved in the domain of discrete geometry processing (DGP) which is able to achieve a stable surface structure under a selectable enhancement setting. Extensive simulation results show that the proposed method obtains significantly improvements over uniform sharpening method in terms of both subjective visual assessment and objective quality metric.


title: Budget-Aware Deep Semantic Video Segmentation

abstract: In this work, we study a poorly understood trade-off between accuracy and runtime costs for deep semantic video segmentation. While recent work has demonstrated advantages of learning to speed-up deep activity detection, it is not clear if similar advantages will hold for our very different segmentation loss function, which is defined over individual pixels across the frames. In deep video segmentation, the most time consuming step represents the application of a CNN to every frame for assigning class labels to every pixel, typically taking 6-9 times of the video footage. This motivates our new budget-aware framework that learns to optimally select a small subset of frames for pixelwise labeling by a CNN, and then efficiently interpolates the obtained segmentations to yet unprocessed frames. This interpolation may use either a simple optical-flow guided mapping of pixel labels, or another significantly less complex and thus faster CNN. We formalize the frame selection as a Markov Decision Process, and specify a Long Short-Term Memory (LSTM) network to model a policy for selecting the frames. For training the LSTM, we develop a policy-gradient reinforcement-learning approach for approximating the gradient of our non-decomposable and non-differentiable objective. Evaluation on two benchmark video datasets show that our new framework is able to significantly reduce computation time, and maintain competitive video segmentation accuracy under varying budgets.


title: User experience in do-it-yourself-style smart homes

abstract: Do-it-yourself (DIY)-style smart home products enable users to create their own smart homes by installing sensors and actuators. DIY smart home products are a potential solution to current problems related to home automation products, such as inflexible user controls and high costs of installation. Although the expected user experience of DIY smart home products is different from that of previous home automation products, research on DIY smart home products is still in its early stages. In this paper, we report a 3-week in situ observational study involving eight households. The results suggest six stages of the DIY smart home usage cycle and design implications for improving the user experience of DIY smart home products.


title: Providing Effective Real-time Feedback in Simulation-based Surgical Training

abstract: Virtual reality simulation is becoming popular as a training platform in surgical education. However, one important aspect of simulation-based surgical training that has not received much attention is the provision of automated real-time performance feedback to support the learning process. Performance feedback is actionable advice that improves novice behaviour. In simulation, automated feedback is typically extracted from prediction models trained using data mining techniques. Existing techniques suffer from either low effectiveness or low efficiency resulting in their inability to be used in real-time. In this paper, we propose a random forest based method that finds a balance between effectiveness and efficiency. Experimental results in a temporal bone surgery simulation show that the proposed method is able to extract highly effective feedback at a high level of efficiency.


title: Computational and Statistical Tradeoffs in Learning to Rank

abstract: For massive and heterogeneous modern datasets, it is of fundamental interest to provide guarantees on the accuracy of estimation when computational resources are limited. In the application of learning to rank, we provide a hierarchy of rank-breaking mechanisms ordered by the complexity in thus generated sketch of the data. This allows the number of data points collected to be gracefully traded off against computational resources available, while guaranteeing the desired level of accuracy. Theoretical guarantees on the proposed generalized rank-breaking implicitly provide such trade-offs, which can be explicitly characterized under certain canonical scenarios on the structure of the data.


title: Radiofrequency Ablation vs Endoscopic Surveillance for Patients With Barrett Esophagus and Low-Grade Dysplasia

abstract: IMPORTANCE ::: Barrett esophagus containing low-grade dysplasia is associated with an increased risk of developing esophageal adenocarcinoma, a cancer with a rapidly increasing incidence in the western world. ::: ::: ::: OBJECTIVE ::: To investigate whether endoscopic radiofrequency ablation could decrease the rate of neoplastic progression. ::: ::: ::: DESIGN, SETTING, AND PARTICIPANTS ::: Multicenter randomized clinical trial that enrolled 136 patients with a confirmed diagnosis of Barrett esophagus containing low-grade dysplasia at 9 European sites between June 2007 and June 2011. Patient follow-up ended May 2013. ::: ::: ::: INTERVENTIONS ::: Eligible patients were randomly assigned in a 1:1 ratio to either endoscopic treatment with radiofrequency ablation (ablation) or endoscopic surveillance (control). Ablation was performed with the balloon device for circumferential ablation of the esophagus or the focal device for targeted ablation, with a maximum of 5 sessions allowed. ::: ::: ::: MAIN OUTCOMES AND MEASURES ::: The primary outcome was neoplastic progression to high-grade dysplasia or adenocarcinoma during a 3-year follow-up since randomization. Secondary outcomes were complete eradication of dysplasia and intestinal metaplasia and adverse events. ::: ::: ::: RESULTS ::: Sixty-eight patients were randomized to receive ablation and 68 to receive control. Ablation reduced the risk of progression to high-grade dysplasia or adenocarcinoma by 25.0% (1.5% for ablation vs 26.5% for control; 95% CI, 14.1%-35.9%; P < .001) and the risk of progression to adenocarcinoma by 7.4% (1.5% for ablation vs 8.8% for control; 95% CI, 0%-14.7%; P = .03). Among patients in the ablation group, complete eradication occurred in 92.6% for dysplasia and 88.2% for intestinal metaplasia compared with 27.9% for dysplasia and 0.0% for intestinal metaplasia among patients in the control group (P < .001). Treatment-related adverse events occurred in 19.1% of patients receiving ablation (P < .001). The most common adverse event was stricture, occurring in 8 patients receiving ablation (11.8%), all resolved by endoscopic dilation (median, 1 session). The data and safety monitoring board recommended early termination of the trial due to superiority of ablation for the primary outcome and the potential for patient safety issues if the trial continued. ::: ::: ::: CONCLUSIONS AND RELEVANCE ::: In this randomized trial of patients with Barrett esophagus and a confirmed diagnosis of low-grade dysplasia, radiofrequency ablation resulted in a reduced risk of neoplastic progression over 3 years of follow-up. ::: ::: ::: TRIAL REGISTRATION ::: trialregister.nl Identifier: NTR1198.


title: Geometric shaped facial feature extraction for face recognition

abstract: Facial feature extraction is the process of extracting face component features like eyes, nose, mouth, etc from human face image. Facial feature extraction is very much important for the initialization of processing techniques like face tracking, facial expression recognition or face recognition. Among all facial features, eye localization and detection is essential, from which locations of all other facial features are identified. However, the existing face recognition techniques failed to identify the exact person. In order to overcome this issue, Geometric Shaped Facial Feature Extraction for Face Recognition (GSF2EFR) is designed for identifying the exact person by finding the center and corners of the eye using eye detection and eye localization modules. An input facial image is given where the face map gets initialized and processed in the form of coarse-to-fine manner with two modules. In the first module, eye detector is used to detect the eye pattern using Gabor filter. In the second module, the location of eye center is found using SVM classifier to reduce the eye localization time. Finally, after locating the center and corners of eye fiducial points are found from which the face of an individual gets recognized. The better performance of proposed GSF2EFR system is measured in terms of eye detection accuracy, eye localization time and true positive rate.


title: A concept for global access to information on geographical databases

abstract: Monitoring of the ecological as well as economical aspects of the planet Earth will in future require more sophisticated data obtaining systems (e. g. satellites) and data processing devices (e. g. GIS). In order to process the great amount of data, detailed information on geographical databases has to be provided in an easy way. In this paper a concept of an information system on geographical databases is presented which encompasses a centrally managed international directory with summary information as well as local information interfaces geographical databases. An information interface provides detailed information on the extent, structure and geographical meaning of the data stored in the geographical database belonging to it. To describe this information a conceptual scheme, based on an object oriented approach, is developed.


title: Fault Classification using Pseudomodal Energies and Neuro-fuzzy modelling

abstract: This paper presents a fault classification method which makes use of a Takagi-Sugeno neuro-fuzzy model and Pseudomodal energies calculated from the vibration signals of cylindrical shells. The calculation of Pseudomodal Energies, for the purposes of condition monitoring, has previously been found to be an accurate method of extracting features from vibration signals. This calculation is therefore used to extract features from vibration signals obtained from a diverse population of cylindrical shells. Some of the cylinders in the population have faults in different substructures. The pseudomodal energies calculated from the vibration signals are then used as inputs to a neuro-fuzzy model. A leave-one-out cross-validation process is used to test the performance of the model. It is found that the neuro-fuzzy model is able to classify faults with an accuracy of 91.62%, which is higher than the previously used multilayer perceptron.


Linking product design to consumer behavior: the moderating role of consumption experience


title: Crisis Construction and Organizational Learning: Capability Building in Catching-up at Hyundai Motor

abstract: Effective organizational learning requires high absorptive capacity, which has two major elements: prior knowledge base and intensity of effort. Hyundai Motor Company, the most dynamic automobile producer in developing countries, pursued a strategy of independence in developing absorptive capacity. In its process of advancing from one phase to the next through the preparation for and acquisition, assimilation, and improvement of foreign technologies, Hyundai acquired migratory knowledge to expand its prior knowledge base and proactively constructed crises as a strategic means of intensifying its learning effort. Unlike externally evoked crises, proactively constructed internal crises present a clear performance gap, shift learning orientation from imitation to innovation, and increase the intensity of effort in organizational learning. Such crisis cons truction is an evocative and galvanizing device in the personal repertoires of proactive top managers. A similar process of opportunistic learning is also evident in other industries in Korea.


title: Attentive Recurrent Neural Network for Weak-supervised Multi-label Image Classification

abstract: Multi-label image classification is a fundamental and challenging task in computer vision, and recently achieved significant progress by exploiting semantic relations among labels. However, the spatial positions of labels for multi-labels images are usually not provided in real scenarios, which brings insuperable barrier to conventional models. In this paper, we propose an end-to-end attentive recurrent neural network for multi-label image classification under only image-level supervision, which learns the discriminative feature representations and models the label relations simultaneously. First, inspired by attention mechanism, we propose a recurrent highlight network (RHN) which focuses on the most related regions in the image to learn the discriminative feature representations for different objects in an iterative manner. Second, we develop a gated recurrent relation extractor (GRRE) to model the label relations using multiplicative gates in a recurrent fashion, which learns to decide how multiple labels of the image influence the relation extraction. Extensive experiments on three benchmark datasets show that our model outperforms the state-of-the-arts, and performs better on small-object categories and under the scenario with large number of labels.


Pan Olympus : Sensor Privacy through Utility Aware


An Accurate Bioimpedance Measurement System for Blood Pressure Monitoring


title: 1 A Network Architecture for Heterogeneous Mobile Computing

abstract: This article summarizes the results of the BARWAN project, which focused on enabling truly useful mobile networking across an extremely wide variety of real-world networks and mobile devices. We present the overall architecture, summarize key results, and discuss four broad lessons learned along the way. The architecture enables seamless roaming in a single logical overlay network composed of many heterogeneous (mostly wireless) physical networks, and provides significantly better TCP performance for these networks. It also provides complex scalable and highly available services to enable powerful capabilities across a very wide range of mobile devices, and mechanisms for automated discovery and configuration of localized services. Four broad themes arose from the project: (1) the power of dynamic adaptation as a generic solution to heterogeneity, (2) the importance of cross-layer information, such as the exploitation of TCP semantics in the link layer, (3) the use of agents in the infrastructure to enable new abilities and to hide new problems from legacy servers and protocol stacks, and (4) the importance of soft state for such agents for simplicity, ease of fault recovery, and scalability.


title: Integrated magnetic design of small planar transformers for LLC resonant converters

abstract: Integrating magnetic parasitics in the design of LCC resonant converters provides a solution to reduce parts count and increase power density. This paper provides an efficient design procedure for small planar transformers which integrate transformer leakage inductance and magnetizing inductance into the resonant tank by employing an accurate parasitic prediction model. Finite element simulations were used to create the models using Design of Experiment (DoE) methodology. A planar transformer prototype was designed and tested within a 2.5W LLC resonant converter and results under different operating modes are included to illustrate the resonant behaviour and to validate the presented design procedure.


title: Definition and Multidimensionality of Security Awareness: Close Encounters of the Second Order

abstract: This study proposes and examines a multidimensional definition of information security awareness. We also investigate its antecedents and analyze its effects on compliance with organizational information security policies. The above research goals are tested through the theoretical lens of technology threat avoidance theory and protection motivation theory. Information security awareness is defined as a second-order construct composed of the elements of threat and coping appraisals supplemented by the responsibilities construct to account for organizational environment. The study was executed in two stages. First, the participants (employees of a municipality) were exposed to a series of phishing messages. Second, the same individuals were asked to participate in a survey designed to examine their security awareness. The research model was tested using PLS-SEM approach. The results indicate that security awareness is in fact a second-order formative construct composed of six components. There are significant differences in security awareness levels between the victims of the phishing experiment and the employees who maintain compliance with security policies. Our study extends the theory by proposing and validating a general, yet practical definition of security awareness. It also bridges the gap between theory and practice - our contextualization of security awareness draws heavily on both fields.


title: The effect of product attractiveness, word of mouth and product quality in elevating the impulsive buying behavior of Garuda Peanut in Surabaya

abstract: Consumer behavior is essentially a decision-making processes by consumers either individuals, groups, and organizations that includes the process of choosing, buying, obtaining, use of goods or services. The main question in consumer behavior research is how consumers make a purchase decision. This study indentifies factors that are statistically significant to impulsive buying to Kacang Garuda (Peanut) product of each gender in Surabaya. By using primary data with the population of people ages 18–40, collected with purposive sampling, by spreading questionnaire. Selected object is Garuda Peanut because peanut products are low involvement products that trigger the occurrence of impulsive buying behavior, as well as Garuda Peanut is the market leader for peanut products in Indonesia. This research limits the factors into three, product attractiveness attributed by unique and interesting package, attractive package color, and package size availability, word of mouth attributed by convincing salesman, info from relatives and info from friends, and quality attributed by reliability, conformance quality and durability. The data shows that product attractiveness and quality are significant in increasing the degree of impulsive buying to both gender, but word of mouth applies only to female gender.


title: Measurement of retinal vessel widths from fundus images based on 2-D modeling

abstract: Changes in retinal vessel diameter are an important sign of diseases such as hypertension, arteriosclerosis and diabetes mellitus. Obtaining precise measurements of vascular widths is a critical and demanding process in automated retinal image analysis as the typical vessel is only a few pixels wide. This paper presents an algorithm to measure the vessel diameter to subpixel accuracy. The diameter measurement is based on a two-dimensional difference of Gaussian model, which is optimized to fit a two-dimensional intensity vessel segment. The performance of the method is evaluated against Brinchmann-Hansen's half height, Gregson's rectangular profile and Zhou's Gaussian model. Results from 100 sample profiles show that the presented algorithm is over 30% more precise than the compared techniques and is accurate to a third of a pixel.


title: Shared autonomy via hindsight optimization for teleoperation and teaming

abstract: In shared autonomy, a user and autonomous system work together to achieve shared goals. To collaborate effectively, the autonomous system must know the user's goal. As such, most prior works follow a predict-then-act model, first predicting the user's goal with high confidence, then assisting given that goal. Unfortunately, confidently predicting the user's goal may not be possible until they have nearly achieved it, causing predict-then-act methods to provide little assistance. However, the system can often provide useful assistance even when confidence for any single goal is low (e.g. move towards multiple goals). In this work, we formalize this insight by modelling shared autonomy as a Partially Observable Markov Decision Process (POMDP), providing assistance that minimizes the expected cost-to-go with an unknown goal. As solving this POMDP optimally is intractable, we use hindsight optimization to approximate. We apply our framework to both shared-control teleoperation and human-robot teaming. Compared to predict-then-act methods, our method achieves goals faster, requires less user input, decreases user idling time, and results in fewer user-robot collisions.


title: Sketching informal presentations

abstract: Informal presentations are a lightweight means for fast and convenient communication of ideas. People communicate their ideas to others on paper and whiteboards, which afford fluid sketching of graphs, words and other expressive symbols. Unlike existing authoring tools that are designed for formal presentations, we created SketchPoint to help presenters design informal presentations via freeform sketching. In SketchPoint, presenters can quickly author presentations by sketching slide content, overall hierarchical structures and hyperlinks. To facilitate the transition from idea capture to communication, a note-taking workspace was built for accumulating ideas and sketching presentation outlines. Informal feedback showed that SketchPoint is a promising tool for idea communication.


title: Federated Internet of Things and Cloud Computing Pervasive Patient Health Monitoring System

abstract: The exponentially growing healthcare costs coupled with the increasing interest of patients in receiving care in the comfort of their own homes have prompted a serious need to revolutionize healthcare systems. This has prompted active research in the development of solutions that enable healthcare providers to remotely monitor and evaluate the health of patients in the comfort of their residences. However, existing works lack flexibility, scalability, and energy efficiency. This article presents a pervasive patient health monitoring (PPHM) system infrastructure. PPHM is based on integrated cloud computing and Internet of Things technologies. In order to demonstrate the suitability of the proposed PPHM infrastructure, a case study for real-time monitoring of a patient suffering from congestive heart failure using ECG is presented. Experimental evaluation of the proposed PPHM infrastructure shows that PPHM is a flexible, scalable, and energy-efficient remote patient health monitoring system.


title: Distributed power system for driving SD 42" PDP television

abstract: The 440 W distributed power system(DPS) is developed for driving PDP(SD42") TV. The half bridge ZVS PWM converter is used in sustain, set and reset for reducing the losses. All of 15 output powers are designed. Each electrode is supplied by these powers. In this system, mainly outputs are Va for address, Vs for sustain, Vset for set up, Ve for reset and Vscan for scan voltage. The experimental results including steady state response and transient response are obtained for these outputs. The measured maximum efficiency through the DPS developed is about 91% in case of 220 V input and full load. And the efficiency of the sustain voltage(Vs) is up to 95.27%.


title: Human action recognition using transfer learning with deep representations

abstract: Human action recognition is an imperative research area in the field of computer vision due to its numerous applications. Recently, with the emergence and successful deployment of deep learning techniques for image classification, object recognition, and speech recognition, more research is directed from traditional handcrafted to deep learning techniques. This paper presents a novel method for human action recognition based on a pre-trained deep CNN model for feature extraction & representation followed by a hybrid Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifier for action recognition. It has been observed that already learnt CNN based representations on large-scale annotated dataset could be transferred to action recognition task with limited training dataset. The proposed method is evaluated on two well-known action datasets, i.e., UCF sports and KTH. The comparative analysis confirms that the proposed method achieves superior performance over state-of-the-art methods in terms of accuracy.


title: Distributed Hierarchical Control Plane of Software Defined Networking

abstract: Single controller implementation of SDN has many problems related to single points of failure, computational complexity growth, reliability and scalability. To solve these problems, multi-controller implementation of Software Defined Network has been introduced. Multiple controller implementation of the SDN control plane for large networks environment solves the scalability and reliability issues introduced by the centralized controller of the SDN. However, there are still open research topics related to controllers placement. Work has been done in placement of controllers and making solution to earlier problem like single point of failure and computational complexity growth. Distributed hierarchical control plane architecture of SDN using multiple controllers of large-scale networks is presented. Simulation result shows that hierarchical architecture solved many problems those were arising earlier in single controller architecture. Distributed hierarchical architecture does not have single point failure problems. Proposed architecture effectively reduces the computational complexity growth of SDN control plane from super-linear to linear.


title: Modeling Modbus TCP for intrusion detection

abstract: DFAs (Deterministic Finite Automata) and DTMCs (Discrete Time Markov Chain) have been proposed for modeling Modbus/TCP for intrusion detection in SCADA (Supervisory Control and Data Acquisition) systems. While these models can be used to learn the behavior of the system, they require the designer to know the appropriate amount of training data for building the model, to retrain models when configuration changes, and to generate understandable alert messages. In this paper, we propose to complement these learned models with the specification approaches. To build a robust model, we need to consider configuration-level specifications in addition to protocol specification. As Modbus/TCP is a simple protocol with handful function code(s) or commands for each communication channel, designing a specification-based approach is suitable for monitoring this communication. We do a comparison of DFA and DTMC approaches in two datasets and illustrate how to use our inferred specification to complement these models.


title: Advanced Cooling for Power Electronics

abstract: Power electronics devices such as MOSFET's, GTO's, IGBT's, IGCT's etc. are now widely used to efficiently deliver electrical power in home electronics, industrial drives, telecommunication, transport, electric grid and numerous other applications. This paper discusses cooling technologies that have evolved in step to remove increasing levels of heat dissipation and manage junction temperatures to achieve goals for efficiency, cost, and reliability. Cooling technologies rely on heat spreading and convection. In applications that use natural or forced air cooling, water heat pipes provide efficient heat spreading for size and weight reduction. Previous concepts are reviewed and an improved heat sink concept with staggered fin density is described for more isothermal cooling. Where gravity can drive liquid flow, thermosiphons provide efficient heat transport to remote fin volumes that can be oriented for natural and/or forced air cooling. Liquid cold plates (LCP's) offer the means to cool high heat loads and heat fluxes including double sided cooling for the highest density packaging. LCP's can be used both in single phase cooling systems with aqueous or oil based coolants and in two-phase cooling systems with dielectric fluids and refrigerants. Previous concepts are reviewed and new concepts including an air cooled heat sink, a thermosiphon heat sink, a vortex flow LCP and a shear flow direct contact cooling concept are described.


title: A 0.18μm CMOS up-conversion mixer for wireless sensor networks application

abstract: This paper presents an up-conversion mixer for 2.4GHz wireless sensor networks (WSN) in 0.18μm RF CMOS technology. It was based on a double-balanced Gilbert cell type. Operational amplifiers (OP) were used in this design to improve the conversion gain. The performance was verified via simulation using Cadence SpectreRF. The post simulation results indicated that under 1.8V power supply the conversion gain was 16.2dB and the input referred 1dB compression point was −20.7dBm and it consumed 5mA current.


title: Polar Transformer Networks

abstract: Convolutional neural networks (CNNs) are inherently equivariant to translation. Efforts to embed other forms of equivariance have concentrated solely on rotation. We expand the notion of equivariance in CNNs through the Polar Transformer Network (PTN). PTN combines ideas from the Spatial Transformer Network (STN) and canonical coordinate representations. The result is a network invariant to translation and equivariant to both rotation and scale. PTN is trained end-to-end and composed of three distinct stages: a polar origin predictor, the newly introduced polar transformer module and a classifier. PTN achieves state-of-the-art on rotated MNIST and the newly introduced SIM2MNIST dataset, an MNIST variation obtained by adding clutter and perturbing digits with translation, rotation and scaling. The ideas of PTN are extensible to 3D which we demonstrate through the Cylindrical Transformer Network


title: Spectral and Temporal Processing in Human Auditory Cortex

abstract: We used positron emission tomography to examine the response of human auditory cortex to spectral and temporal variation. Volunteers listened to sequences derived from a standard stimulus, consisting of two pure tones separated by one octave alternating with a random duty cycle. In one series of five scans, spectral information (tone spacing) remained constant while speed of alternation was doubled at each level. In another five scans, speed was kept constant while the number of tones sampled within the octave was doubled at each level, resulting in increasingly fine frequency differences. Results indicated that (i) the core auditory cortex in both hemispheres responded to temporal variation, while the anterior superior temporal areas bilaterally responded to the spectral variation; and (ii) responses to the temporal features were weighted towards the left, while responses to the spectral features were weighted towards the right. These findings confirm the specialization of the left-hemisphere auditory cortex for rapid temporal processing, and indicate that core areas are especially involved in these processes. The results also indicate a complementary hemispheric specialization in right-hemisphere belt cortical areas for spectral processing. The data provide a unifying framework to explain hemispheric asymmetries in processing speech and tonal patterns. We propose that differences exist in the temporal and spectral resolution of corresponding fields in the two hemispheres, and that they may be related to anatomical hemispheric asymmetries in myelination and spacing of cortical columns.


title: Spotting Fake Reviews via Collective Positive-Unlabeled Learning

abstract: Online reviews have become an increasingly important resource for decision making and product designing. But reviews systems are often targeted by opinion spamming. Although fake review detection has been studied by researchers for years using supervised learning, ground truth of large scale datasets is still unavailable and most of existing approaches of supervised learning are based on pseudo fake reviews rather than real fake reviews. Working with Dianping, the largest Chinese review hosting site, we present the first reported work on fake review detection in Chinese with filtered reviews from Dianping's fake review detection system. Dianping's algorithm has a very high precision, but the recall is hard to know. This means that all fake reviews detected by the system are almost certainly fake but the remaining reviews (unknown set) may not be all genuine. Since the unknown set may contain many fake reviews, it is more appropriate to treat it as an unlabeled set. This calls for the model of learning from positive and unlabeled examples (PU learning). By leveraging the intricate dependencies among reviews, users and IP addresses, we first propose a collective classification algorithm called Multi-typed Heterogeneous Collective Classification (MHCC) and then extend it to Collective Positive and Unlabeled learning (CPU). Our experiments are conducted on real-life reviews of 500 restaurants in Shanghai, China. Results show that our proposed models can markedly improve the F1 scores of strong baselines in both PU and non-PU learning settings. Since our models only use language independent features, they can be easily generalized to other languages.


title: Cityride: A Predictive Bike Sharing Journey Advisor

abstract: In this paper, we present a personal journey advisor application for helping people to navigate the city using the available bike-sharing system. For a given origin and destination, the application suggests the best pair of stations to be used to take and return a city-bike, in order to minimize the overall walking and biking travel time as well as maximizing the probability to find available bikes at the first station and returning slots at the second one. To solve the journey advisor optimization problem, we modeled real mobile bikers' behavior in terms of travel time, and used the predicted availability at every bike station to choose the pair of stations which maximizes a measure of optimality. To develop the application, we built a spatio-temporal prediction system able to estimate the number of available bikes for each station in short and long term, outperforming already developed solutions. The prediction system is based on an underlying spatial interaction network among the bike stations, and takes into account the temporal patterns included in the data. The City ride application was tested with real data from the Dublin bike-sharing system.


title: From Trust in Automation to Decision Neuroscience: Applying Cognitive Neuroscience Methods to Understand and Improve Interaction Decisions Involved in Human Automation Interaction

abstract: Human automation interaction (HAI) systems have thus far failed to live up to expectations mainly because human users do not always interact with the automation appropriately. Trust in automation (TiA) has been considered a central influence on the way a human user interacts with an automation; if TiA is too high there will be overuse, if TiA is too low there will be disuse. However, even though extensive research into TiA has identified specific human automation interaction behaviors, or trust outcomes, a unique mapping between trust states and trust outcomes has yet to be clearly identified. Interaction behaviors have been intensely studied in the domain of HAI and TiA and this has led to a reframing of the issues of problems with HAI in terms of reliance and compliance. We find the behaviorally defined terms reliance and compliance to be useful in their functionality for application in real-world situations. However, we note that once an inappropriate interaction behavior has occurred it is too late to mitigate it. We therefore take a step back and look at the interaction decision that precedes the behavior. We note that the decision neuroscience community has revealed that decisions are fairly stereotyped processes accompanied by measurable psychophysiological correlates. Two literatures were therefore reviewed. TiA literature was extensively reviewed in order to understand the relationship between TiA and trust outcomes, as well as to identify gaps in current knowledge. We note that an interaction decision precedes an interaction behavior and believe that we can leverage knowledge of the psychophysiological correlates of decisions to improve joint system performance. As we believe that understanding the interaction decision will be critical to the eventual mitigation of inappropriate interaction behavior, we reviewed the decision making literature and provide a synopsis of the state of the art understanding of the decision process from a decision neuroscience perspective. We forward hypotheses based on this understanding that could shape a research path toward the ability to mitigate interaction behavior in the real world.


title: Effects of haptic guidance and disturbance on motor learning: Potential advantage of haptic disturbance

abstract: One of the primary goals of haptic guidance is to facilitate the learning of complex human motor skills by providing haptic cues that are helpful to induce desired movements. Nevertheless, a majority of previous studies have found that haptic guidance is ineffective, or sometimes even detrimental, to motor skill learning. In this paper, we propose the opposite concept, haptic disturbance, and evaluate its efficacy. In haptic disturbance, haptic cues that interfere with the movements of a learner are presented during training. We designed two methods of haptic disturbance using repulsive and noise-like forces, respectively. The effects of these methods were experimentally assessed, comparatively with the conventional methods of visual learning only and progressive haptic guidance. The motor task was to track a dot moving on a 2D plane with a haptic interface operated with one arm. We found that during training, the progressive haptic guidance showed the best tracking accuracy, but in immediate and delayed retention tests, the noise-like haptic disturbance led to the best performance. The results suggest high potentials for haptic disturbance to be a general strategy for expediting the motor learning process.


title: Leadership in complex organizations

abstract: Abstract This paper asks how complexity theory informs the role of leadership in organizations. Complexity theory is a science of complexly interacting systems; it explores the nature of interaction and adaptation in such systems and how they influence such things as emergence, innovation, and fitness. We argue that complexity theory focuses leadership efforts on behaviors that enable organizational effectiveness, as opposed to determining or guiding effectiveness. Complexity science broadens conceptualizations of leadership from perspectives that are heavily invested in psychology and social psychology (e.g., human relations models) to include processes for managing dynamic systems and interconnectivity. We develop a definition of organizational complexity and apply it to leadership science, discuss strategies for enabling complexity and effectiveness, and delve into the relationship between complexity theory and other currently important leadership theories. The paper concludes with a discussion of possible implications for research strategies in the social sciences.


title: Improving L-BFGS Initialization for Trust-Region Methods in Deep Learning

abstract: Deep learning algorithms often require solving a highly non-linear and nonconvex unconstrained optimization problem. Generally, methods for solving the optimization problems in machine learning and in deep learning specifically are restricted to the class of first-order algorithms, like stochastic gradient descent (SGD). The major drawback of the SGD methods is that they have the undesirable effect of not escaping saddle-points. Furthermore, these methods require exhaustive trial-and-error to fine-tune many learning parameters. Using the second-order curvature information to find the search direction can help with more robust convergence for the non-convex optimization problem. However, computing the Hessian matrix for the large-scale problems is not computationally practical. Alternatively, quasi-Newton methods construct an approximate of Hessian matrix to build a quadratic model of the objective function. Quasi-Newton methods, like SGD, require only first-order gradient information, but they can result in superlinear convergence, which makes them attractive alternatives. The limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) approach is one of the most popular quasi-Newton methods that construct positive-definite Hessian approximations. Since the true Hessian matrix is not necessarily positive definite, an extra initialization condition is required to be introduced when constructing the L-BFGS matrices to avoid false negative curvature information. In this paper, we propose various choices for initialization methods of the L-BFGS matrices within a trust-region framework. We provide empirical results on the classification task of the MNIST digits dataset to compare the performance of the trust-region algorithm with different L-BFGS initialization methods.


title: Vulnerabilities, Attacks, and Countermeasures in Balise-Based Train Control Systems

abstract: In modern rail transport systems, balises are widely used to exchange track–train information via air-gap interface. In this paper, we first present the vulnerabilities on the standard balise air-gap interface, and then conduct vulnerability simulations using the system parameters that were specified in the European Train Control System. The simulation results show that the vulnerabilities can be exploited to launch effective and practical attacks, which could lead to catastrophic consequences, such as train derailment or collision. To mitigate the vulnerabilities and attacks, we propose to implement a challenge–response authentication process in the air-gap interface in the existing transport infrastructure.


title: Program Synthesis By Sketching

abstract: The goal of software synthesis is to generate programs automatically from high-level specifications. However, efficient implementations for challenging programs require a combination of high-level algorithmic insights and low-level implementation details. Deriving the low-level details is a natural job for a computer, but the synthesizer can not replace the human insight. Therefore, one of the central challenges for software synthesis is to establish a synergy between the programmer and the synthesizer, exploiting the programmer's expertise to reduce the burden on the synthesizer. ::: This thesis introduces sketching, a new style of synthesis that offers a fresh approach to the synergy problem. Previous approaches have relied on meta-programming, or variations of interactive theorem proving to help the synthesizer deduce an efficient implementation. The resulting systems are very powerful, but they require the programmer to master new formalisms far removed from traditional programming models. To make synthesis accessible, programmers must be able to provide their insight effortlessly, using formalisms they already understand. ::: In Sketching, insight is communicated through a partial program, a sketch that expresses the high-level structure of an implementation but leaves holes in place of the low-level details. This form of synthesis is made possible by a new SAT-based inductive synthesis procedure that can efficiently synthesize an implementation from a small number of test cases. This algorithm forms the core of a new counterexample guided inductive synthesis procedure (CEGIS) which combines the inductive synthesizer with a validation procedure to automatically generate test inputs and ensure that the generated program satisfies its specification. With a few extensions, CEGIS can even use its sequential inductive synthesizer to generate concurrent programs; all the concurrency related reasoning is delegated to an off-the-shelf validation procedure. ::: The resulting synthesis system scales to real programming problems from a variety of domains ranging from bit-level ciphers to manipulations of linked datastructures. The system was even used to produce a complete optimized implementation of the AES cipher. The concurrency aware synthesizer was also used to synthesize, in a matter of minutes, the details of a fine-locking scheme for a concurrent set, a sense reversing barrier, and even a solution to the dining philosophers problem. ::: The system was also extended with domain specific knowledge to better handle the problem of implementing stencil computations, an important domain in scientific computing. For this domain, we were able to encode domain specific insight as a problem reduction that converted stencil sketches into simplified sketch problems which CEGIS resolved in a matter of minutes. This specialized synthesizer was used to quickly implement a MultiGrid solver for partial differential equations containing many difficult implementation strategies from the literature. ::: In short, this thesis shows that sketching is a viable approach to making synthesis practical in a general programming context.


title: The PH-tree: a space-efficient storage structure and multi-dimensional index

abstract: We propose the PATRICIA-hypercube-tree, or PH-tree, a multi-dimensional data storage and indexing structure. It is based on binary PATRICIA-tries combined with hypercubes for efficient data access. Space efficiency is achieved by combining prefix sharing with a space optimised implementation. This leads to storage space requirements that are comparable or below storage of the same data in non-index structures such as arrays of objects. The storage structure also serves as a multi-dimensional index on all dimensions of the stored data. This enables efficient access to stored data via point and range queries. We explain the concept of the PH-tree and demonstrate the performance of a sample implementation on various datasets and compare it to other spatial indices such as the kD-tree. The experiments show that for larger datasets beyond 10^7 entries, the PH-tree increasingly and consistently outperforms other structures in terms of space efficiency, query performance and update performance.


title: 7.2 A 28GHz 32-element phased-array transceiver IC with concurrent dual polarized beams and 1.4 degree beam-steering resolution for 5G communication

abstract: Next-generation mobile technology (5G) aims to provide an improved experience through higher data-rates, lower latency, and improved link robustness. Millimeter-wave phased arrays offer a path to support multiple users at high data-rates using high-bandwidth directional links between the base station and mobile devices. To realize this vision, a phased-array-based pico-cell must support a large number of precisely controlled beams, yet be compact and power efficient. These system goals have significant mm-wave radio interface implications, including scalability of the RFIC+antenna-array solution, increase in the number of concurrent beams by supporting dual polarization, precise beam steering, and high output power without sacrificing TX power efficiency. Packaged Si-based phased arrays [1–3] with nonconcurrent dual-polarized TX and RX operation [2,3], concurrent dual-polarized RX operation [3] and multi-IC scaling [3,4] have been demonstrated. However, support for concurrent dual-polarized operation in both RX and TX remains unaddressed, and high output power comes at the cost of power consumption, cooling complexity and increased size. The RFIC reported here addresses these challenges. It supports concurrent and independent dual-polarized operation in TX and RX modes, and is compatible with a volume-efficient, scaled, antenna-in-package array. A new TX/RX switch at the shared antenna interface enables high output power without sacrificing TX efficiency, and a t-line-based phase shifter achieves <1° RMS error and <5° phase steps for precise beam control.


title: A Hybrid Approach Using Topic Modeling and Class-Association Rule Mining for Text Classification: the Case of Malware Detection

abstract: We propose a novel general-purpose hybrid method comprising topic modeling and Class Association Rule Mining (CARM) for text classification in tandem. While topic modeling performs dimension reduction, association rule mining aspect is taken care by Apriori and Frequent Pattern(FP)- growth algorithms, separately. In order to illustrate the effectiveness of the proposed method, malware prediction using two publicly available datasets of API calls has been performed. The proposed model has generated highly accurate class association rules and Area Under the Curve (AUC) compare to the extant models in the literature. With the help of statistical significance test, it is concluded that the performances of both proposed hybrid models, i.e., topic modelina with FP-2rowth and Apriori, are same.


title: Text Simplification as Tree Labeling

abstract: We present a new, structured approach to text simplification using conditional random fields over top-down traversals of dependency graphs that jointly predicts possible compressions and paraphrases. Our model reaches readability scores comparable to word-based compression approaches across a range of metrics and human judgements while maintaining more of the important information.


title: A randomized, efficient, and distributed protocol for the detection of node replication attacks in wireless sensor networks

abstract: Wireless sensor networks are often deployed in hostile environments, where anadversary can physically capture some of the nodes. Once a node is captured, the attackercan re-program it and replicate the node in a large number of clones, thus easily taking over the network. The detection of node replication attacks in a wireless sensor network is therefore a fundamental problem. A few distributed solutions have recently been proposed. However, these solutions are not satisfactory. First, they are energy and memory demanding: A serious drawback for any protocol that is to be used in resource constrained environment such as a sensor network. Further, they are vulnerable to specific adversary models introduced in this paper. The contributions of this work are threefold. First, we analyze the desirable properties of a distributed mechanism for the detection of node replication attacks. Second, we show that the known solutions for this problem do not completely meet our requirements. Third, we propose a new Randomized, Efficient, and Distributed (RED) protocol for the detection of node replication attacks and we show that it is completely satisfactory with respect to the requirements. Extensive simulations also show that our protocol is highly efficient in communication, memory, and computation, that it sets out an improved attack detection probability compared to the best solutions in the literature, and that it is resistant to the new kind of attacks we introduce in this paper, while other solutions are not.


title: Published online in Wiley InterScience THERAPEUTIC POTENTIAL OF ESSENTIAL OILS 1 (www.interscience.wiley.com) DOI: 10.1002/ptr.2072 REVIEW ARTICLE Pharmaceutical and Therapeutic Potentials of Essential Oils and Their Individual Volatile Constituents: A Re

abstract: Essential oils and their volatile constituents are used widely to prevent and treat human disease. The possible role and mode of action of these natural products is discussed with regard to the prevention and treatment of cancer, cardiovascular diseases including atherosclerosis and thrombosis, as well as their bioactivity as antibacterial, antiviral, antioxidants and antidiabetic agents. Their application as natural skin penetration enhancers for transdermal drug delivery and the therapeutic properties of essential oils in aroma and massage therapy will also be outlined. Copyright © 2007 John Wiley & Sons, Ltd.


title: Active Imitation Learning via Reduction to I.I.D. Active Learning

abstract: In standard passive imitation learning, the goal is to learn a target policy by passively observing full execution trajectories of it. Unfortunately, generating such trajectories can require substantial expert effort and be impractical in some cases. In this paper, we consider active imitation learning with the goal of reducing this effort by querying the expert about the desired action at individual states, which are selected based on answers to past queries and the learner's interactions with an environment simulator. We introduce a new approach based on reducing active imitation learning to i.i.d. active learning, which can leverage progress in the i.i.d. setting. Our first contribution, is to analyze reductions for both non-stationary and stationary policies, showing that the label complexity (number of queries) of active imitation learning can be substantially less than passive learning. Our second contribution, is to introduce a practical algorithm inspired by the reductions, which is shown to be highly effective in four test domains compared to a number of alternatives.


title: Semi-supervised Learning with Constraints for Person Identification in Multimedia Data

abstract: We address the problem of person identification in TV series. We propose a unified learning framework for multi-class classification which incorporates labeled and unlabeled data, and constraints between pairs of features in the training. We apply the framework to train multinomial logistic regression classifiers for multi-class face recognition. The method is completely automatic, as the labeled data is obtained by tagging speaking faces using subtitles and fan transcripts of the videos. We demonstrate our approach on six episodes each of two diverse TV series and achieve state-of-the-art performance.


title: Students' working strategies and outcomes in a creativity-supporting learning environment

abstract: This paper describes results from a teaching experiment at the Department of Computer Science at the University of Helsinki, in which we studied students' different working strategies and tried to find patterns between these strategies and the creativity of the students' work. In a typical computer science course in Finland, the teaching is quite strictly structured and the support structures (e.g. lectures, lab sessions) are highly teacher-driven. In contrast, our intention was to create a learning environment where the support structures would focus on supporting creativity to bring forth new ideas and innovation. We were especially interested in the working strategies that students would use outside our learning sessions, the students´ outcomes with regards to creativity, and the interplay between working strategies and the creativity of the outcomes. To put our ideas into practice, we designed a pilot course utilizing practices from research into creativity and intrinsic motivation. To answer our research questions we interviewed all course attendees (n=33) twice, at the beginning and at the end of the course. We chose LEGO® Mindstorms robots as the platform for the project. While further studies are needed, our preliminary results suggest that there is a pattern between working strategy and creativity.


title: Big Data Modeling and Analysis of Microblog Ecosystem

abstract: Recent progress of Web 2.0 applications has witnessed the rapid development of microblog in China, which has already been one of the most important ways for online communications, especially on sharing information. This paper tries to make an in-depth investigation on the big data modeling and analysis of microblog ecosystem in China by using a real dataset containing over 17 million records of SinaWeibo users. First, we present the detailed geography, gender, authentication, education and age analysis of microblog users in this dataset. Then we conduct the numerical features distribution analysis, propose the user influence formula and calculate the influences for different kinds of microblog users. Finally, user content intention analysis is performed to reveal users' most concerns in their daily life.


title: Graph Based Semi-Supervised Learning via Structure Preserving Low-Rank Representation

abstract: Semi-supervised learning works on utilizing both labeled and unlabeled data to improve learning performance, which has been receiving increasing attention in many applications such as clustering and classification. In this paper, we focus on the semi-supervised learning methods developed on data graph whose edge weights are measured by low-rank representation (LRR) coefficients. Specifically, we impose two constraints on LRR when constructing the graph: local affinity and distant repulsion, to preserve the data manifold information. The proposed model, termed structure preserving LRR (SPLRR), can preserve the local geometrical structure and without distorting the distant repulsion property. Using the augmented Lagrange multiplier (ALM) method framework, we derive an efficient approach to optimizing the SPLRR model. Experiments are conducted on four widely used data sets to validate the effectiveness of our proposed SPLRR model and the results demonstrate that SPLRR is an excellent model for graph based semi-supervised learning in comparison with the state-of-the-art methods.


title: Research on direvt torque predictive control system of induction motor based on three-level inverter

abstract: In this paper, aimed at the neutral-point potential unbalance of the three-level neutral point clamped inverter, an improved new control scheme about neutral-point potential balance was proposed and a simplified direct torque prediction scheme was used for induction motor fed by three-level inverter. Firstly, stator voltage vectors to be applied at the next time was preselected by the torque prediction algorithm; Then, based on non-orthogonal 60° coordinate, the corresponding order of SVPWM was employed by the detection of neutral-point voltage ripple's direction and three-phase current. At last, the results verify the feasibility of the proposed scheme. The induction motor responded quickly and torque ripple smaller, with which the neutral-point voltage unbalance is restrained.


title: Two-way powered microcontroller-based plastic bottles ‘drop-and-tap’ reverse vending machine with stored value system using radio frequency identification (RFID) scanner technology

abstract: To help address Metro Manila's garbage problem, the researchers constructed a Reverse Vending Machine to help promote proper waste management system inside Colegio de San Juan de Letran. At the heart of the system is a microcontroller which directs the operation of the various input (sensors and keypad) and output (display and motors) devices attached to it. The machine accepts plastic bottles and credits these as points, which in turn, can be used to buy products. Operation of the machine exhibited accuracy in recognizing RFID accounts, distinguishing between plastic and non-plastic bottles, storage or updating of points of each account, and dispensing products. The machine was able to run on either commercial power or solar power. The addition of the solar panel and battery not only provides a renewable source of energy but also serves as back-up power in the event of commercial power outage. Survey results showed that the Colegio accepts the idea of having a Reverse-Vending Machine and believes that the adoption of the machine will improve the current waste segregation scheme of the Colegio.


title: A rare sugar xylitol. Part II: biotechnological production and future applications of xylitol

abstract: Xylitol is the first rare sugar that has global markets. It has beneficial health properties and represents an alternative to current conventional sweeteners. Industrially, xylitol is produced by chemical hydrogenation of d-xylose into xylitol. The biotechnological method of producing xylitol by metabolically engineered yeasts, Saccharomyces cerevisiae or Candida, has been studied as an alternative to the chemical method. Due to the industrial scale of production, xylitol serves as an inexpensive starting material for the production of other rare sugars. The second part of this mini-review on xylitol will look more closely at the biotechnological production and future applications of the rare sugar, xylitol.


title: Stable multi-target tracking in real-time surveillance video

abstract: The majority of existing pedestrian trackers concentrate on maintaining the identities of targets, however systems for remote biometric analysis or activity recognition in surveillance video often require stable bounding-boxes around pedestrians rather than approximate locations. We present a multi-target tracking system that is designed specifically for the provision of stable and accurate head location estimates. By performing data association over a sliding window of frames, we are able to correct many data association errors and fill in gaps where observations are missed. The approach is multi-threaded and combines asynchronous HOG detections with simultaneous KLT tracking and Markov-Chain Monte-Carlo Data Association (MCM-CDA) to provide guaranteed real-time tracking in high definition video. Where previous approaches have used ad-hoc models for data association, we use a more principled approach based on a Minimal Description Length (MDL) objective which accurately models the affinity between observations. We demonstrate by qualitative and quantitative evaluation that the system is capable of providing precise location estimates for large crowds of pedestrians in real-time. To facilitate future performance comparisons, we make a new dataset with hand annotated ground truth head locations publicly available.


title: Feature selection using principal feature analysis

abstract: Dimensionality reduction of a feature set is a common preprocessing step used for pattern recognition and classification applications. Principal Component Analysis (PCA) is one of the popular methods used, and can be shown to be optimal using different optimality criteria. However, it has the disadvantage that measurements from all the original features are used in the projection to the lower dimensional space. This paper proposes a novel method for dimensionality reduction of a feature set by choosing a subset of the original features that contains most of the essential information, using the same criteria as PCA. We call this method Principal Feature Analysis (PFA). The proposed method is successfully applied for choosing the principal features in face tracking and content-based image retrieval (CBIR) problems. Automated annotation of digital pictures has been a highly challenging problem for computer scientists since the invention of computers. The capability of annotating pictures by computers can lead to breakthroughs in a wide range of applications including Web image search, online picture-sharing communities, and scientific experiments. In our work, by advancing statistical modeling and optimization techniques, we can train computers about hundreds of semantic concepts using example pictures from each concept. The ALIPR (Automatic Linguistic Indexing of Pictures - Real Time) system of fully automatic and high speed annotation for online pictures has been constructed. Thousands of pictures from an Internet photo-sharing site, unrelated to the source of those pictures used in the training process, have been tested. The experimental results show that a single computer processor can suggest annotation terms in real-time and with good accuracy.


title: Stochastic Inventory Systems in a Supply Chain with Asymmetric Information: Cycle Stocks, Safety Stocks, and Consignment Stock

abstract: The two critical factors distinguishing inventory management in a multifirm supply-chain context from the more traditional centrally planned perspective are incentive conflicts and information asymmetries. We study the well-known order quantity/reorder point Q, r model in a two-player context, using a framework inspired by observations during a case study. We show how traditional allocations of decision rights to supplier and buyer lead to inefficient outcomes, and we use principal-agent models to study the effects of information asymmetries about setup cost and backorder cost, respectively. ::: ::: We analyze two “opposite” models of contracting on inventory policies. First, we derive the buyer's optimal menu of contracts when the supplier has private information about setup cost, and we show how consignment stock can help reduce the impact of this information asymmetry. Next, we study consignment and assume the supplier cannot observe the buyer's backorder cost. We derive the supplier's optimal menu of contracts on consigned stock level and show that in this case, the supplier effectively has to overcompensate the buyer for the cost of each stockout. ::: ::: Our theoretical analysis and the case study suggest that consignment stock helps reduce cycle stock by providing the supplier with an additional incentive to decrease batch size, but simultaneously gives the buyer an incentive to increase safety stock by exaggerating backorder costs. This framework immediately points to practical recommendations on how supply-chain incentives should be realigned to overcome existing information asymmetries.


title: An eigensystem realization algorithm for modal parameter identification and model reduction

abstract: A method called the eigensystem realization algorithm is developed for modal parameter identification and model reduction of dynamic systems from test data. A new approach is introduced in conjunction with the singular-value decomposition technique to derive the basic formulation of minimum order realization which is an extended version of the Ho-Kalman algorithm. The basic formulation is then transformed into modal space for modal parameter identification. Two accuracy indicators are developed to quantitatively identify the system and noise modes. For illustration of the algorithm, an example is shown using experimental data from the Galileo spacecraft.


title: White-Box Cryptography and an AES Implementation

abstract: Conventional software implementations of cryptographic algorithms are totally insecure where a hostile user may control the execution environment, or where co-located with malicious software. Yet current trends point to increasing usage in environments so threatened. We discuss encrypted-composed-function methods intended to provide a practical degree of protection against white-box (total access) attacks in untrusted execution environments. As an example, we show how AES can be implemented as a series of lookups in key-dependent tables. The intent is to hide the key by a combination of encoding its tables with random bijections representing compositions rather than individual steps, and extending the cryptographic boundary by pushing it out further into the containing application. We partially justify our AES implementation, and motivate its design, by showing how removal of parts of the recommended implementation allows specified attacks, including one utilizing a pattern in the AES SubBytes table.


title: LZfuzz: a fast compression-based fuzzer for poorly

abstract: Computers make very fast, very accurate mistakes. From a refrigerator magnet. Real-world infrastructure offers many scenarios where protocols (and other details) are not released due to being considered too sensitive or for other reasons. This situation makes it hard to apply fuzzing techniques to test their security and reliability, since their full documentation is only available to their developers, and domain developer expertise does not necessarily intersect with fuzz-testing expertise (nor deployment responsibility). State-of-the-art fuzzing techniques, however, work best when protocol specifications are available. Still, operators whose networks include equipment communicating via proprietary protocols should be able to reap the benefits of fuzz-testing them. In particular, administrators should be able to test proprietary protocols in the absence of end-to-end application-level encryption to understand whether they can withstand injection of bad traffic, and thus be able to plan adequate network protection measures. Such protocols can be observed in action prior to fuzzing, and packet captures can be used to learn enough about the structure of the protocol to make fuzzing more efficient. Various machine learning approaches, e.g. bioinformatics methods, have been proposed for learning models of the targeted protocols. The problem with most of these approaches to date is that, although sometimes quite successful, they are very computationally heavy and thus are hardly practical for application by network administrators and equipment owners who cannot easily dedicate a compute cluster to such tasks. We propose a simple method that, despite its roughness, allowed us to learn facts useful for fuzzing from protocol traces at much smaller CPU and time costs. Our fuzzing approach proved itself empirically in testing actual proprietary SCADA protocols in an isolated control network test environment, and was also successful in triggering flaws in implementations of several popular commodity Internet protocols. Our fuzzer, LZfuzz (pronounced “lazy-fuzz”) relies on a variant of Lempel–Ziv compression algorithm to guess boundaries between the structural units of the protocol, and builds on the well-known free software GPF fuzzer.


title: A technique to improve the drive current of high-voltage I/O transistors in digital CMOS technologies

abstract: By combining a 0.12-/spl mu/m-long 1.2-V thin-oxide transistor with a 0.22-/spl mu/m-long 3.3-V thick-oxide transistor in a 0.13-/spl mu/m CMOS process, a composite MOS transistor structure with a drawn gate length of 0.34 /spl mu/m is realized. Measurements show that at V/sub GS/=1.2 V and V/sub DS/=3.3 V, the composite transistor has more than two times the drain current of the minimum channel length (0.34 /spl mu/m) 3.3-V thick-oxide transistor, while having the same breakdown voltage (V/sub BK/) as the thick-oxide transistor. Exploiting these, it should be possible to implement 3.3-V I/O transistors with better combination of drive current, threshold voltage (V/sub T/) and breakdown voltage in conventional CMOS technologies without adding any process modifications.


title: Data Augmentation using Random Image Cropping and Patching for Deep CNNs

abstract: Deep convolutional neural networks (CNNs) have achieved remarkable results in image processing tasks. However, their high expression ability risks overfitting. Consequently, data augmentation techniques have been proposed to prevent overfitting while enriching datasets. Recent CNN architectures with more parameters are rendering traditional data augmentation techniques insufficient. In this study, we propose a new data augmentation technique called random image cropping and patching (RICAP) which randomly crops four images and patches them to create a new training image. Moreover, RICAP mixes the class labels of the four images, resulting in an advantage of the soft labels. We evaluated RICAP with current state-of-the-art CNNs (e.g., the shake-shake regularization model) by comparison with competitive data augmentation techniques such as cutout and mixup. RICAP achieves a new state-of-the-art test error of 2.19% on CIFAR-10. We also confirmed that deep CNNs with RICAP achieve better results on classification tasks using CIFAR-100 and ImageNet, an image-caption retrieval task using Microsoft COCO, and other computer vision tasks.


title: Neighbor Discovery and Rendezvous Maintenance with Extended Quorum Systems for Mobile Applications

abstract: In many mobile sensing applications, devices need to discover new neighbors and maintain the rendezvous with known neighbors continuously. Due to the limited energy supply, these devices have to duty cycle their radios to conserve the energy and bandwidth, making neighbor discovery and rendezvous maintenance even more challenging. To date, the main mechanism for device discover and rendezvous maintenance in existing solutions is pairwise, direct one-hop communication. We argue that such pairwise direct communication is sufficient but not necessary: there exist unnecessary active slots that can be eliminated, without affecting discovery and rendezvous. In this work, we propose a novel concept of extended quorum system , which leverages indirect discovery to further conserve energy. Specifically, we use quorum graph to capture all possible information flow paths where knowledge about known-neighbors can propagate among devices. By eliminating redundant paths, we can reduce the number of active slots significantly. Since a quorum graph can characterize arbitrary active schedules of mobile devices, our work can be broadly used to improve many existing quorum-based discovery and rendezvous solutions. We comprehensively evaluate $EQS$ in three different scales of networks, and the results show that $EQS$ reduces as much as 55 percent energy consumption with a maximal 5 percent increase in latency for existing solutions. To test the real-world values of $EQS$ , we further propose a taxicab dispatching application called $EQS$ -dispatch to navigate taxicab drivers to the area with less competition based on the discovery results of nearby taxicabs.


title: TSA, an SDN architecture including end terminals

abstract: In this work we integrate user terminals into an SDN architecture that allows global network optimization. We embed an SDN virtual switch into a mobile user terminal in such a way that a centralized controller, using both terminal and network-side information, can decide which access network terminal should connect to. User profiling and terminal interface configuration are integral parts of the architecture.


title: Modelling Relational Data using Bayesian Clustered Tensor Factorization

abstract: We consider the problem of learning probabilistic models for complex relational structures between various types of objects. A model can help us "understand" a dataset of relational facts in at least two ways, by finding interpretable structure in the data, and by supporting predictions, or inferences about whether particular unobserved relations are likely to be true. Often there is a tradeoff between these two aims: cluster-based models yield more easily interpretable representations, while factorization-based approaches have given better predictive performance on large data sets. We introduce the Bayesian Clustered Tensor Factorization (BCTF) model, which embeds a factorized representation of relations in a nonparametric Bayesian clustering framework. Inference is fully Bayesian but scales well to large data sets. The model simultaneously discovers interpretable clusters and yields predictive performance that matches or beats previous probabilistic models for relational data.


title: The Effect of Overlearning on Long-Term Retention

abstract: Once material has been learned to a criterion of one perfect trial, further study within the same session constitutes overlearning. Although overlearning is a popular learning strategy, its effect on long-term retention is unclear. In two experiments presented here, 218 college students learned geography facts (Experiment 1) or word definitions (Experiment 2). The degree of learning was manipulated and measured via multiple test-with-feedback trials, and participants returned for a final cued recall test between one and nine weeks later. The overlearners recalled far more than the low learners at the one-week test, but this difference decreased dramatically thereafter. These data suggest that overlearning (and its concomitant demand for additional study time) is an inefficient strategy for learning material for meaningfully long periods of time.


title: The utility business model and the future of computing services

abstract: The utility business model is shaped by a number of characteristics that are typical in public services: users consider the service a necessity, high reliability of service is critical, the ability to fully utilize capacity is limited, and services are scalable and benefit from economies of scale. This paper examines the utility business model and its future role in the provision of computing services.


GLOBAL BEAUTY INDUSTRY TRENDS IN THE 21 st CENTURY


title: Change Detection Based on Deep Siamese Convolutional Network for Optical Aerial Images

abstract: In this letter, we propose a novel supervised change detection method based on a deep siamese convolutional network for optical aerial images. We train a siamese convolutional network using the weighted contrastive loss. The novelty of the method is that the siamese network is learned to extract features directly from the image pairs. Compared with hand-crafted features used by the conventional change detection method, the extracted features are more abstract and robust. Furthermore, because of the advantage of the weighted contrastive loss function, the features have a unique property: the feature vectors of the changed pixel pair are far away from each other, while the ones of the unchanged pixel pair are close. Therefore, we use the distance of the feature vectors to detect changes between the image pair. Simple threshold segmentation on the distance map can even obtain good performance. For improvement, we use a $k$ -nearest neighbor approach to update the initial result. Experimental results show that the proposed method produces results comparable, even better, with the two state-of-the-art methods in terms of F-measure.


title: An HVS-based location-sensitive definition of mutual information between two images

abstract: Quantitative measure of image information amount is of great importance in many image processing applications, e.g. image compression and image registration. Many commonly used metrics are defined mathematically. However, the ultimate consumers of images are human observers in most situations, thus such measures without consideration of internal mechanism of human visual system (HVS) may not be appropriate. This paper proposes an improved definition of mutual information between two images based on the visual information which is actually perceived by human beings in different subbands of image. This definition is both sensitive to the pixels' spatial location and correlates well with human perceptual feeling than mutual information purely calculated by pixels' grayscale value. Experimental results on images with different noises and JPEG&JPEG2000 compressed images are also given.


title: Ovarian Cancer Survivors: Qualitative Analysis of the Symptom of Sexuality

abstract: PURPOSE/OBJECTIVES ::: To better understand treatment-induced changes in sexuality from the patient perspective, to learn how women manage these changes in sexuality, and to identify what information they want from nurses about this symptom. ::: ::: ::: RESEARCH APPROACH ::: Qualitative descriptive methods. ::: ::: ::: SETTING ::: An outpatient gynecologic clinic in an urban area in the southeastern United States served as the recruitment site for patients. ::: ::: ::: PARTICIPANTS ::: Eight women, ages 33-69, receiving first-line treatment for ovarian cancer participated in individual interviews. Five women, ages 40-75, participated in a focus group and their status ranged from newly diagnosed to terminally ill from ovarian cancer. ::: ::: ::: METHODOLOGIC APPROACH ::: Both individual interviews and a focus group were conducted. Content analysis was used to identify themes that described the experience of women as they became aware of changes in their sexuality. Triangulation of approach, the researchers, and theory allowed for a rich description of the symptom experience. ::: ::: ::: FINDINGS ::: Regardless of age, women reported that ovarian cancer treatment had a detrimental impact on their sexuality and that the changes made them feel "no longer whole." Mechanical changes caused by surgery coupled with hormonal changes added to the intensity and dimension of the symptom experience. Physiologic, psychological, and social factors also impacted how this symptom was experienced. ::: ::: ::: CONCLUSIONS ::: Regardless of age or relationship status, sexuality is altered by the diagnosis and treatment of ovarian cancer. ::: ::: ::: INTERPRETATION ::: Nurses have an obligation to educate women with ovarian cancer about anticipated changes in their sexuality that may come from treatment.


title: Improved PSO neural network system for automatic detecting fertility of hatching eggs

abstract: An improved particle swarm optimization neural network for automatic detection fertility of hatching eggs is presented.RGB images of eggs are obtained using computer vision technique,and are converted into HSI color images.Hue values of the images are extracted as the related feature of egg's surface.The primary components of feature parameters are extracted and selected using primary component analysis(PCA).Structure of multi-layer feedback forward neural network is optimized using an improved particle swarm optimization algorithm.Learning quality and training speed of the neural network are improved.The samples of network training are representative and comprehensive,it improved generalization ability of the neural network.The experimental results show that the neural network model for fertility of hatching eggs detection had a high accuracy and efficiency and the algo-rithm was robust.


Cost Weighting for Neural Machine Translation Domain Adaptation


Biological Width: The Periodontal Restorative Interlink: Short Discussion


title: Impact of the CMS No-Pay Policy on Hospital-Acquired Fall Prevention Related Practice Patterns

abstract: Background and Objectives ::: In October 2008, the Centers for Medicare & Medicaid Services (CMS) stopped reimbursing hospitals for costs related to patient falls. This study aimed to examine whether the CMS no-pay policy influenced four fall prevention practices: bed alarms, sitters, room changes, and physical restraints. ::: ::: ::: Research Design and Methods ::: Using electronic medical record data collected from four hospitals between 2005 and 2010, this secondary observational analysis examined the associations between the CMS no-pay policy and nursing interventions and medical orders related to fall prevention. Multivariable generalized linear mixed models with logit link function and accommodation for matching was used to assess the associations between the CMS no-pay policy and nursing interventions and medical orders. ::: ::: ::: Results ::: After the CMS policy change, nurses were more likely to perform one or more fall-related interventions (adjusted odds ratio (aOR): 1.667; 95% confidence interval (CI): 1.097-2.534). Of the four prevention practices, the use of bed alarms (aOR: 2.343; 95% CI: 1.409-3.897) increased significantly after the CMS policy change. ::: ::: ::: Discussion and Implications ::: The CMS no-pay policy increased utilization of fall prevention strategies despite little evidence that these measures prevent falls.


title: Multilayer liquid crystal polymer based RF frontend module for millimeter wave imaging

abstract: In this paper we proposed an integrated RF front-end module by using multilayer liquid crystal polymer (LCP) substrates in millimeter wave frequency (mmW) range. The module consists of a linearly tapered slot antenna (LTSA), low noise amplifiers (LNA), as well as various passive components, including transmission lines, waveguides, filters, and their transitions. The active LNAs and their related bias circuits are integrated with RF circuits through wire-bonding process to achieve a gain of 12 dB centered at 94GHz. The fabricated devices are characterized and compared with simulated results, showing good agreement. The LTSA has a wide impedance bandwidth from 43 to 90 GHz and high radiation gain. The compact module holds tremendous promise in the high-performance applications, such as mmW imaging, and many other high density RF systems.


title: S parameter-based experimental modeling of high Q MCM inductor with exponential gradient learning algorithm

abstract: Lumped inductors are very desirable passive components in wireless/RF circuits integrated on MCM substrate. This paper models the inductor from on-wafer high frequency measurement by utilizing the S parameter formulation and exponential gradient method. The S parameter formulation enables us to understand the phase shifting effects within the model while the exponential gradient learning algorithm provides us with a more robust and better fitting technique than the gradient descent algorithm. Both the magnitudes and phases of all S parameters fit well for all the inductors we constructed. It is shown that the phase shifting of the distributed effects should not be neglected even in MCM-D technology. The resulting experimental model provides measurement-verified solid ground for circuit design and numerical characterization.


title: Reliability and validity of the test of in-hand manipulation in children ages 5 to 6 years

abstract: The Test of In-Hand Manipulation (TIHM; Case-Smith, 2000) is a five-task test that uses a 9-hole pegboard to examine 2 key components of in-hand manipulation: rotation and translation with stabilization. The authors used Rasch modeling to examine the TIHM’s construct validity, interrater reliability, and test–retest reliability in 45 typically developing children ages 5.5 years to 6.5 years. A version of the test, revised using Rasch modeling, was found to have evidence for adequate construct validity and excellent interrater reliability. However, test–retest reliability over a 2-week retest period was not supported. The TIHM demonstrates potential as a clinically useful assessment of in-hand manipulation. The test does not examine all aspects of in-hand manipulation, however, and it may have limited sensitivity to the performance of finger-to-palm and palm-to-finger translation. Further validation of the test is needed before the TIHM can confidently be used in occupational therapy practice.


title: Design of AVR and ALFC for single area power system including damping control

abstract: This paper presents the combined control for single area power system including automatic voltage control by using automatic voltage regulator (AVR) and automatic frequency control by using automatic load frequency control (ALFC). The paper shows the coupling effects between the two loops, which are studied by extending the linearised ALFC system to incorporate the excitation system. A proportional integral gain controller is included in the AVR loop and conventional integral controller in ALFC loop so that system responses become better in terms of peak deviations and settling time. Damper winding is included in the AVR loop which is present on the rotor of the synchronous generator. Simulation studies reveal that the response of the system becomes better in terms of peak deviations (overshoots and undershoots) and settling time when damper winding is included in the system.


title: The Management of Innovation

abstract: The paper analyzes the organization of the RD (b) to rationalize commonly observed features in research employment contracts, such as shop rights, trailer clauses, and the "hired for" doctrine; (c) to discuss the robustness of the so-called Schumpeterian hypotheses to endogenizing the organization of RD and (d) to provide a rationale for


title: The multiplicity of emotions: A framework of emotional functions in decision making

abstract: A four-fold classification of emotions with respect to their functions in decision making is proposed. It is argued that emotions are not homogenous concerning their role in decision making, but that four distinct functions can be distinguished concerning emotional phenomena. One function is to provide information about pleasure and pain for preference construction, a second function is to enable rapid choices under time pressure, a third function is to focus attention on relevant aspects of a decision problem, and a fourth function is to generate commitment concerning morally and socially significant decisions. The pertinent literature on the relationship between emotion and decision making is reviewed, and it is concluded that most approaches fit into the proposed framework. We argue that a precise conceptualization of emotional phenomena is required to advance our understanding of the complex role of emotions in decision making.


title: A fast randomized algorithm for orthogonal projection

abstract: We describe an algorithm that, given any full-rank matrix A having fewer rows than columns, can rapidly compute the orthogonal projection of any vector onto the null space of A, as well as the orthogonal projection onto the row space of A, provided that both A and its adjoint can be applied rapidly to arbitrary vectors. As an intermediate step, the algorithm solves the overdetermined linear least-squares regression involving the adjoint of A (and so can be used for this, too). The basis of the algorithm is an obvious but numerically unstable scheme; suitable use of a preconditioner yields numerical stability. We generate the preconditioner rapidly via a randomized procedure that succeeds with extremely high probability. In many circumstances, the method can accelerate interior-point methods for convex optimization, such as linear programming (Ming Gu, personal communication).