title: Obesity, the Metabolic Syndrome, and Type 2 Diabetes in Developing Countries: Role of Dietary Fats and Oils

abstract: Developing countries are undergoing rapid nutrition transition concurrent with increases in obesity, the metabolic syndrome, and type 2 diabetes mellitus (T2DM). From a healthy traditional high-fiber, low-fat, low-calorie diet, a shift is occurring toward increasing consumption of calorie-dense foods containing refined carbohydrates, fats, red meats, and low fiber. Data show an increase in the supply of animal fats and increased intake of saturated fatty acid (SFAs) (obtained from coconut oil, palm oil, and ghee [clarified butter]) in many developing countries, particularly in South Asia and South-East Asia. In some South Asian populations, particularly among vegetarians, intake of n-3 polyunsaturated fatty acids (PUFAs) (obtained from flaxseed, mustard, and canola oils) and long-chain (LC) n-3 PUFAs (obtained from fish and fish oils) is low. Further, the effect of supplementation of n-3 PUFAs on metabolic risk factors and insulin resistance, except for demonstrated benefit in terms of decreased triglycerides, needs further investigation among South Asians. Data also show that intake of monounsaturated fatty acids (MUFAs) ranged from 4.7% to 16.4%en in developing countries, and supplementing it from olive, canola, mustard, groundnut, and rice bran oils may reduce metabolic risk. In addition, in some developing countries, intake of n-6 PUFAs (obtained from sunflower, safflower, corn, soybean, and sesame oils) and trans-fatty acids (TFAs) is increasing. These data show imbalanced consumption of fats and oils in developing countries, which may have potentially deleterious metabolic and glycemic consequences, although more research is needed. In view of the rapid rise of T2DM in developing countries, more aggressive public health awareness programs coupled with governmental action and clear country-specific guidelines are required, so as to promote widespread use of healthy oils, thus curbing intake of SFAs and TFAs, and increasing intake of n-3 PUFAs and MUFAs. Such actions would contribute to decelerating further escalation of "epidemics" of obesity, the metabolic syndrome, and T2DM in developing countries.


title: The environment and falls prevention: Do environmental modifications make a difference?

abstract: Background and Aims: Environmental modification is a strategy widely recommended in many falls prevention programs. However the effectiveness of such a strategy has not been well established. This study aimed to evaluate published research related to falls prevention and environmental modification for older people. ::: ::: Method: A review of research conducted between 1993 and 2004 was undertaken. Ageline, Cinahl and OvidMedline databases were searched using falls prevention, home modifications, environmental modifications and occupational therapy as key words. ::: ::: Results: Eighteen articles were found relating to community and institutional dwellings and the relevance of environmental modifications. ::: ::: Conclusion: There is some evidence to support the use of environmental modification as a strategy in falls prevention for older people particularly in association with multidisciplinary/multifactorial programs targeted to those people with a history of falls.


title: The Similarity of Social Behavior between Primary School Children and Their Friends: Differences in the Behavior Type

abstract: The current study investigated the similarity of social behavior between primary school children and their friends. A total of 360 primary school children were administered measurement of social behavior by peer nomination. The results of correlation analysis and regression analysis showed that 1) Primary school children’s pro-social behaviors were significantly positive correlated with their friends’ pro-social behaviors after controlled the variable of gender, but their aggressive behavior was not significantly correlated with their friends. 2) The subsequent results of regression analysis respectively control the variable of gender and age also fully proves this point. The results indicated that the similarity of social behavior between primary school children and their friends were moderated by the type of social behavior.


title: Current Sensing Techniques: A Review

abstract: This paper provides a thorough review of state-of-the-art current sensing techniques. It catalogues the current sensors according to the underlying physical principle in order to point out their strengths and weaknesses.


title: A Hybrid Cloud Approach for Secure Authorized Deduplication

abstract: Data deduplication is one of important data compression techniques for eliminating duplicate copies of repeating data, and has been widely used in cloud storage to reduce the amount of storage space and save bandwidth. To protect the confidentiality of sensitive data while supporting deduplication, the convergent encryption technique has been proposed to encrypt the data before outsourcing. To better protect data security, this paper makes the first attempt to formally address the problem of authorized data deduplication. Different from traditional deduplication systems, the differential privileges of users are further considered in duplicate check besides the data itself. We also present several new deduplication constructions supporting authorized duplicate check in a hybrid cloud architecture. Security analysis demonstrates that our scheme is secure in terms of the definitions specified in the proposed security model. As a proof of concept, we implement a prototype of our proposed authorized duplicate check scheme and conduct testbed experiments using our prototype. We show that our proposed authorized duplicate check scheme incurs minimal overhead compared to normal operations.


title: A weakest-adversary security metric for network configuration security analysis

abstract: A security metric measures or assesses the extent to which a system meets its security objectives. Since meaningful quantitative security metrics are largely unavailable, the security community primarily uses qualitative metrics for security. In this paper, we present a novel quantitative metric for the security of computer networks that is based on an analysis of attack graphs. The metric measures the security strength of a network in terms of the strength of the weakest adversary who can successfully penetrate the network. We present an algorithm that computes the minimal sets of required initial attributes for the weakest adversary to possess in order to successfully compromise a network; given a specific network configuration, set of known exploits, a specific goal state, and an attacker class (represented by a set of all initial attacker attributes). We also demonstrate, by example, that diverse network configurations are not always beneficial for network security in terms of penetrability.


title: The 2016 revision of the World Health Organization classification of lymphoid neoplasms

abstract: A revision of the nearly 8-year-old World Health Organization classification of the lymphoid neoplasms and the accompanying monograph is being published. It reflects a consensus among hematopathologists, geneticists, and clinicians regarding both updates to current entities as well as the addition of a limited number of new provisional entities. The revision clarifies the diagnosis and management of lesions at the very early stages of lymphomagenesis, refines the diagnostic criteria for some entities, details the expanding genetic/molecular landscape of numerous lymphoid neoplasms and their clinical correlates, and refers to investigations leading to more targeted therapeutic strategies. The major changes are reviewed with an emphasis on the most important advances in our understanding that impact our diagnostic approach, clinical expectations, and therapeutic strategies for the lymphoid neoplasms.


title: Architectural Study of HOG Feature Extraction Processor for Real-Time Object Detection

abstract: This paper describes a Histogram of Oriented Gradients (HOG) feature extraction processor for HDTV resolution video (1920 ~ 1080 pixels). It features a simplified HOG algorithm with cell-based scanning and simultaneous Support Vector Machine (SVM) calculation, cell-based pipeline architecture, and parallelized modules. To evaluate the effectiveness of our approach, the proposed architecture is implemented onto a FPGA prototyping board. Results show that the proposed architecture can generate HOG features and detect objects with 40 MHz for SVGA resolution video (800 ~ 600 pixels) at 72 frames per second (fps). The proposed schemes are easily expandable to HDTV resolution video at 30 fps with 76.2 MHz if a high-resolution camera and higher operating frequency are available.


title: Smartphone Apps Using Photoplethysmography for Heart Rate Monitoring: Meta-Analysis

abstract: BACKGROUND ::: Smartphone ownership is rising at a stunning rate. Moreover, smartphones prove to be suitable for use in health care due to their availability, portability, user-friendliness, relatively low price, wireless connectivity, far-reaching computing capabilities, and comprehensive memory. To measure vital signs, smartphones are often connected to a mobile sensor or a medical device. However, by using the white light-emitting diode as light source and the phone camera as photodetector, a smartphone could be used to perform photoplethysmography (PPG), enabling the assessment of vital signs. ::: ::: ::: OBJECTIVE ::: The objective of this meta-analysis was to evaluate the available evidence on the use of smartphone apps to measure heart rate by performing PPG in comparison with a validated method. ::: ::: ::: METHODS ::: PubMed and ISI Web of Knowledge were searched for relevant studies published between January 1, 2009 and December 7, 2016. The reference lists of included studies were hand-searched to find additional eligible studies. Critical Appraisal Skills Programme (CASP) Diagnostic Test Study checklist and some extra items were used for quality assessment. A fixed effects model of the mean difference and a random effects model of Pearson correlation coefficient were applied to pool the outcomes of the studies. ::: ::: ::: RESULTS ::: In total, 14 studies were included. The pooled result showed no significant difference between heart rate measurements with a smartphone and a validated method (mean difference -0.32; 99% CI -1.24 to 0.60; P=.37). In adults, the Pearson correlation coefficient of the relation between heart rate measurement with a smartphone and a validated method was always ≥.90. In children, the results varied depending on measuring point and heart rate. The pooled result showed a strong correlation that was significant (correlation coefficient .951; 95% CI 0.906-0.975; P<.001). The reported limits of agreement showed good agreement between a smartphone and a validated method. There was a moderately strong significant negative correlation between the year of publication of the included studies and the mean difference (r=-.69; P<.001). ::: ::: ::: CONCLUSIONS ::: Smartphone apps measuring heart rate by performing PPG appear to agree with a validated method in an adult population during resting sinus rhythm. In a pediatric population, the use of these apps is currently not validated.


title: Optimization of large join queries: combining heuristics and combinatorial techniques

abstract: We investigate the use of heuristics in optimizing queries with a large number of joins. Examples of such heuristics are the augmentation and local improvement heuristics described in this paper and a heuristic proposed by Krishnamurthy et al. We also study the combination of these heuristics with two general combinatorial optimization techniques, iterative improvement and simulated annealing, that were studied in a previous paper. Several interesting combinations are experimentally compared. For completeness, we also include simple iterative improvement and simulated annealing in our experimental comparisons. We find that two combinations of the augmentation heuristic and iterative improvement perform the best under most conditions. The results are validated using two different cost models and several different synthetic benchmarks.


title: PANAS-t: A Pychometric Scale for Measuring Sentiments on Twitter

abstract: Online social networks have become a major communication platform, where people share their thoughts and opinions about any topic real-time. The short text updates people post in these network contain emotions and moods, which when measured collectively can unveil the public mood at population level and have exciting implications for businesses, governments, and societies. Therefore, there is an urgent need for developing solid methods for accurately measuring moods from large-scale social media data. In this paper, we propose PANAS-t, which measures sentiments from short text updates in Twitter based on a well-established psychometric scale, PANAS (Positive and Negative Affect Schedule). We test the efficacy of PANAS-t over 10 real notable events drawn from 1.8 billion tweets and demonstrate that it can efficiently capture the expected sentiments of a wide variety of issues spanning tragedies, technology releases, political debates, and healthcare.


title: Model-Driven Development of Complex Software: A Research Roadmap

abstract: The term model-driven engineering (MDE) is typically used to describe software development approaches in which abstract models of software systems are created and systematically transformed to concrete implementations. In this paper we give an overview of current research in MDE and discuss some of the major challenges that must be tackled in order to realize the MDE vision of software development. We argue that full realizations of the MDE vision may not be possible in the near to medium-term primarily because of the wicked problems involved. On the other hand, attempting to realize the vision will provide insights that can be used to significantly reduce the gap between evolving software complexity and the technologies used to manage complexity.


title: A New Control Method Utilizing Stiffness Adjustment of Mechanical Elastic Elements for Serial Link Systems

abstract: This paper proposes a tracking control method of sinusoidal motions utilizing stiffness adjustment of mechanical elastic elements for serial link systems. Although dynamics of the controlled objects is nonlinear, the stiffness adjustment realize a condition similar to a resonance of linear systems. We present a controller that adjusts stiffness of the elastic elements to reduce torque requirement of actuators while generating desired motions. The proposed controller works without using dynamics models nor parameters of the controlled objects. Stability of the controller is proved, and tracking errors are guaranteed to converge to a certain region. Simulation results demonstrate the validity of the proposed method. We also present an application of the proposed method to power assist systems.


title: Small Business Credit Scoring and Credit Availability

abstract: U.S. commercial banks are increasingly using credit scoring models to underwrite small business credits. This paper discusses this technology, evaluates the research findings on the effects of this technology on small business credit availability, and links these findings to a number of research and public policy issues.


title: A small-size ultrawideband MIMO antenna with triple band-notched function and high isolation

abstract: In this paper, a small-size ultra-wideband (UWB) multiple-input-multiple-output (MIMO) antenna with triple notched bands and a high isolation, is proposed. Its size is dramatically decreased to 30×26×0.8 mm3 on a cost-effective FR4 substrate. It consists of two rectangular printed monopole (PM) elements and a simple stepped ground stub to enhance wideband isolation. For two different rejected bands of the wireless local area network (WLANs) covering 5.15–5.35 and 5.725–5.825 GHz, four parasitic C-shaped split-ring resonators (PCSRR) are placed on either side of feed line. By etching two inverted U-shaped slots on the center of radiators, the notched frequency at 4 GHz C-band (3.7–4.2 GHz) of satellite communication systems is obtained. Simulated and measured results prove a bandwidth of S 11 12 <−21dB at 3.1–11.2 GHz excluding three independently adjustable rejection bands. Hence, the proposed UWB MIMO antenna has a very small size, a simple structure, a higher isolation and three narrower notched bands which effectively save more useful frequencies. Moreover, it is a good candidate for wireless portable UWB MIMO applications.


title: Comparison of Sparse Representation and Fourier Discriminant Methods: Damage Location Classification in Indirect Lab-scale Bridge Structural Health Monitoring

abstract: This paper presents a novel method for interpreting data to improve the indirect structural health monitoring (SHM) of bridges. The research presented in the study is part of an ongoing study aimed at developing a novel SHM paradigm for the health assessment of bridges. In this paradigm, we envision the use of an instrumented vehicle that assesses a bridge's dynamic characteristics while traveling across the bridge. These characteristics are then correlated to the health of the structure by means of advanced signal processing and pattern recognition approaches. In this paper, we present and compare two classification algorithms that locate the presence of damages at well-defined locations on the structure: sparse representation and the Fourier discriminant methods, and find that the sparse representation method provides superior classification accuracy.


title: Skeletal osteopenia and residual ridge resorption

abstract: Abstract Increased residual ridge resorption resulting from postmenopausal skeletal osteopenia has been a long-standing clinical assumption. This study was conducted to determine the relationship between the degree of residual ridge resorption, sex, and the age of the patient. The technique of measuring mandibular resorption uses panoramic radiographs as described by Wical and Swoope. Six readers received technique instructions and were then tested for uniformity. An analysis of variance demonstrated no significant difference between readers (p > 0.10). A random selection of 459 radiographs of edentulous patients were then measured by using a blind technique to determine the amount of residual ridge resorption. Analysis of this data demonstrated a significantly larger percentage of women with class 3 (severe) residual ridge resorption (p


title: Evaluating I/O scheduling techniques at the forwarding layer and coordinating data server accesses

abstract: In this dissertation, we evaluate I/O scheduling techniques for the I/O forwarding layer of supercomputers. We demonstrate that existing algorithms that work to improve spatiality and request size of the access patterns are only partially effective. We propose TWINS, a new scheduling algorithm to coordinate the access of intermediate I/O nodes to the data servers. Our solution decreases concurrency at the latter, a factor proved to negatively affect performance. We are able to improve read performance from shared files by up to 28% over state-of-the-art scheduling algorithms and by up to 50% over not forwarding I/O. Our collaborations to the HPC field accounted for 16 papers and also motivated interactions with researchers from Argonne, BSC, INRIA, and LNCC.


title: Simulation and Animation of Mechanical Systems

abstract: This paper introduces a process for simulating and animating mechanical components in the context of a larger system. Using the NASA HL-20 flight vehicle’s main landing gear as an example, the paper shows how the CAD assembly can be used as a starting point for creating a dynamic model as well as a realistic animation of the mechanical components. The paper also describes how a dynamic model created from the CAD assembly can be integrated into the larger model of the overall flight vehicle and how the animation created from the CAD assembly can be incorporated into a larger flight vehicle animation. The resulting model and animation are used for detailed analysis and visualization of a main landing gear collapse due to a sudden wind gust shortly before landing. Nomenclature I(n) = Unity matrix of n rows and n columns, with diagonal elements equal to one and off-diagonal elements equal to zero. I. Introduction ODELING of mechanical systems and components often starts with CAD tools. These tools are useful for specifying the detailed three-dimensional (3-D) mechanical design of a component. Once the CAD model of a mechanical component is complete, engineers will often need to design a control system for it. With the control design task accomplished, engineers will want to analyze how the mechanical component affects the performance of the overall vehicle and how the mechanical components work together with other vehicle systems. For example, the landing gear group for an aircraft manufacturer may develop a mechanical design of a landing gear; the controls group may develop a controller for that mechanical design; and the systems engineering group may then model how the landing gear operation affects the dynamics of the overall vehicle and how the hydraulic actuator works with the landing gear mechanical design and control system. Often, different software packages are used for each step of this process, making it difficult to move from mechanical 3-D modeling to control design and then from control design to a system level simulation. This paper introduces a streamlined workflow that enables quick and easy transition from mechanical 3-D modeling to control design and then to system level modeling and simulation. This workflow provides a compelling solution to the control design and system modeling tasks. This solution will be based on using the commercial off-the-shelf software tool, Simulink ® , from The MathWorks, Inc. Using the proposed approach, we will translate a CAD file into an environment where control design can be easily achieved. We will also explain how to easily model and simulate multi-domain systems, including mechanical, hydraulic, and electrical components. We will discuss how to include models of mechanical components into models of larger systems. In addition, we will show how models for environmental conditions, such as wind turbulence and changes in atmospheric pressure and temperature with altitude can be easily integrated into the model of the overall vehicle. As we discuss the overall process, we will specifically focus on two areas of this workflow. First, we will show how engineers can create Simulink models of mechanical components using CAD models as a starting point. Second, we will discuss how to create high quality 3-D animations from these models. We will present a process for exporting CAD models into Virtual Reality Modeling Language (VRML) files. VRML enables high quality online 1


title: Prediction of Stock Price Movements Based on Concept Map Information

abstract: Visualization of textual data may reveal interesting properties regarding the information conveyed in a group of documents. In this paper, we study whether the structure revealed by a visualization method can be used as inputs for improved classifiers. In particular, we study whether the locations of news items on a concept map could be used as inputs for improving the prediction of stock price movements from the news. We propose a method based on information visualization and text classification for achieving this. We apply the proposed approach to the prediction of the stock price movements of companies within the oil and natural gas sector. In a case study, we show that our proposed approach performs better than a naive approach and a bag-of-words approach


title: Resource-Efficient Neural Architect

abstract: Neural Architecture Search (NAS) is a laborious process. Prior work on automated NAS targets mainly on improving accuracy, but lacks consideration of computational resource use. We propose the Resource-Efficient Neural Architect (RENA), an efficient resource-constrained NAS using reinforcement learning with network embedding. RENA uses a policy network to process the network embeddings to generate new configurations. We demonstrate RENA on image recognition and keyword spotting (KWS) problems. RENA can find novel architectures that achieve high performance even with tight resource constraints. For CIFAR10, it achieves 2.95% test error when compute intensity is greater than 100 FLOPs/byte, and 3.87% test error when model size is less than 3M parameters. For Google Speech Commands Dataset, RENA achieves the state-of-the-art accuracy without resource constraints, and it outperforms the optimized architectures with tight resource constraints.


title: Autonomous Shepherding Behaviors of Multiple Target Steering Robots

abstract: This paper presents a distributed coordination methodology for multi-robot systems, based on nearest-neighbor interactions. Among many interesting tasks that may be performed using swarm robots, we propose a biologically-inspired control law for a shepherding task, whereby a group of external agents drives another group of agents to a desired location. First, we generated sheep-like robots that act like a flock. We assume that each agent is capable of measuring the relative location and velocity to each of its neighbors within a limited sensing area. Then, we designed a control strategy for shepherd-like robots that have information regarding where to go and a steering ability to control the flock, according to the robots’ position relative to the flock. We define several independent behavior rules; each agent calculates to what extent it will move by summarizing each rule. The flocking sheep agents detect the steering agents and try to avoid them; this tendency leads to movement of the flock. Each steering agent only needs to focus on guiding the nearest flocking agent to the desired location. Without centralized coordination, multiple steering agents produce an arc formation to control the flock effectively. In addition, we propose a new rule for collecting behavior, whereby a scattered flock or multiple flocks are consolidated. From simulation results with multiple robots, we show that each robot performs actions for the shepherding behavior, and only a few steering agents are needed to control the whole flock. The results are displayed in maps that trace the paths of the flock and steering robots. Performance is evaluated via time cost and path accuracy to demonstrate the effectiveness of this approach.


title: Induction and molecular signature of pathogenic TH17 cells

abstract: Interleukin 17 (IL-17)-producing TH17 cells are often present at the sites of tissue inflammation in autoimmune diseases, which has lead to the conclusion that TH17 are main drivers of autoimmune tissue injury. However, not all TH17 cells are pathogenic, in fact TH17 generated with TGF-β1 and IL-6 produce IL-17 but do not readily induce autoimmune disease without further exposure to IL-23. Here we show that TGF-β3, produced by developing TH17 cells, is dependent on IL-23, which together with IL-6 induces highly pathogenic TH17 cells. Moreover, TGF-β3-induced TH17 cells are functionally and molecularly distinct from TGF-β1-induced TH17 cells and possess a molecular signature that defines pathogenic effector TH17 cells in autoimmune disease.


title: Two-stage query segmentation for information retrieval

abstract: Modeling term dependence has been shown to have a significant positive impact on retrieval. Current models, however, use sequential term dependencies, leading to an increased query latency, especially for long queries. In this paper, we examine two query segmentation models that reduce the number of dependencies. We find that two-stage segmentation based on both query syntactic structure and external information sources such as query logs, attains retrieval performance comparable to the sequential dependence model, while achieving a 50% reduction in query latency.


title: Inverse problems and applications

abstract: M.Yu. Kokurin and A. Bakushinsky, Interatively regularized Gauss-Newton methods under random noise.- L. Beilina, N.T. Thanh, M.V. Klibanov, and J.B. Malmberg, Methods of quantitative reconstruction of shapes and refractive indices from experimental data.- J.B. Malmberg, A posteriori error estimate in the Lagrangian setting for an inverse problem based on a new formulation of Maxwell's system.- E. Karchevskii, A. Spiridonov, and L. Beilina, Determination of permittivity from propagation constant measurements in optical fibers.- L. Angermann, Yu.V. Shestopalov, and V.V. Yatsyk, Eigenmodes of linearised problems of scattering and generation of oscillations on cubically polarisable layers.- S. Soltani, R. Andersson, and B. Andersson, Time resolution in transient kinetics.- L. Beilina and A. Eriksson, Reconstruction of dielectric constants in a cylindrical waveguide.- L. Beilina and I. Gainova, Time-adaptive FEM for distributed parameter identification in mathematical model of HIV infection with drug therapy.- L. Beilina and E. Karchevskii, The layer-stripping algorithm for reconstruction of dielectrics in an optical fiber.- L. Beilina, M. Cristofol, and K. Niinimaki, Simultaneous reconstruction of Maxwell's coefficients from backscattering data.


title: The accuracy of identifying the greater palatine neurovascular bundle: a cadaver study.

abstract: BACKGROUND ::: The palate is a common site for harvesting subepithelial connective tissue grafts (SCTG). The size of SCTG that can be harvested is dictated by the position of the greater palatine neurovascular bundle (GPB). The aims of this cadaver study are to assess the accuracy of predicting the location of the GPB on study models and to evaluate anatomic factors that might influence the predictability. ::: ::: ::: METHODS ::: Eleven fully dentate or partially edentulous maxillary cadavers were used. Study models were fabricated after the greater palatine foramen was identified. The GPB was recognized after dissection, from which the distance to the cemento-enamel junction of the first molar and premolar was measured. Eight periodontists and twelve periodontal residents were asked to estimate the location of the GPB on the study models and the same measurements were taken. Comparisons of the estimated and true GPB position were performed. The correlation between the palatal vault height and the variability of detecting the GPB was investigated. ::: ::: ::: RESULTS ::: The most frequent greater palatine foramen location was between the second and third molars (66.6%). For most cases, there was an underestimation of the location of the GPB up to 4 mm. The interexaminer variability was positively correlated with the vault height. ::: ::: ::: CONCLUSIONS ::: The estimated location of the GPB was commonly closer to the cemento-enamel junction of posterior teeth. Agreement on the location of the GPB was lowered with the presence of high palatal vaults. The results of this study could assist clinicians in planning the location for harvesting SCTG on the hard palate.


title: Cross-eye jamming of monopulse radar

abstract: Radar seeking missiles present a serious threat towards ships and aircraft. In cross-eye jamming a missile seeker is deflected by signals from two onboard antennas transmitting out of phase with signals of nearly equal strength. The limits on cross-eye performance imposed by scattering have been studied when scattering takes place far from the propagation path. It will be shown that a digital cross-eye system with several antenna units can be seriously affected by scattering from sea waves and terrain.


title: Speech Repairs, Intonational Phrases, And Discourse Markers: Modeling Speakers' Utterances In Spoken Dialogue

abstract: Interactive spoken dialogue provides many new challenges for natural language understanding systems. One of the most critical challenges is simply determining the speaker's intended utterances: both segmenting a speaker's turn into utterances and determining the intended words in each utterance. Even assuming perfect word recognition, the latter problem is complicated by the occurrence of speech repairs, which occur where speakers go back and change (or repeat) something they just said. The words that are replaced or repeated are no longer part of the intended utterance, and so need to be identified. Segmenting turns and resolving repairs are strongly interwined with a third task: identifying discourse markers. Because of the interactions, and interactions with POS tagging and speech recognition, we need to address these tasks together and early on in the processing stream. This paper presents a statistical language model in which we redefine the speech recognition problem so that it includes the identification of POS tags, discourse markers, speech repairs, and intonational phrases. By solving these simultaneously, we obtain better results on each task than addressing them separately. Our model is able to identify 72% of turn-internal intonational boundaries with a precision of 71%, 97% of discourse markers with 96% precision, and detect and correct 66% of repairs with 74% precision.


title: Cost and effectiveness analysis of the AH-64 and UH-60 on-board vibrations monitoring system

abstract: The objectives of our program are to evaluate the cost and effectiveness of the AH-64 (Apache) and UH-60 (Blackhawk) helicopters' on-board vibration monitoring (VM) system, to provide an annual cost savings analysis of the vibration management enhancement program (VMEP) for the AH-64 and UH-60 aircraft fleets, and to initially correlate vibration signals with the ULLS-A (logistics) database to create a costs benefits analysis (CBA) model. Logistics (ULLS-A) and vibration (VMU) data were collected for Blackhawks and Apaches from different establishments (South Carolina Army National Guard, Alabama Army National Guard, deployed units in Kosovo, and Korea), warehoused in our database, and analyzed. In addition, all personnel from these bases were surveyed to examine other nontangible benefits of the program. In order to provide a timely and sufficient cost and economic analysis to support the effective allocation and management of resources for Army programs, a CBA model has been developed. Our goal was to develop and maintain cost and economic analyses as effective and efficient tools for decision-making while supporting management decisions by quantifying the resource impact of alternative options. The model utilizes test flight information from the ULLS-A database in order to estimate a cost savings and recovery of the initial cost of the VMU hardware installation and future cost savings for the Apache and Blackhawk helicopters. It includes cost variables such as: test flight hours, hours per flight, cost per flight hour, VMEP investment, number of VMEP helicopters, RT&B flights, and non-RT&B flights. It also includes nontangible variables such as: availability, morale, safety, operational flight hours gained, premature parts failure, mission aborts, and unscheduled maintenance occurrence. As of today, our activities have been highlighted by savings in parts cost, operational support, an increase in mission capability rates, a decrease in maintenance, and an increase in total flight time. Other highlights of nontangible benefits include an increase in confidence for early diagnosis, an increase in attention and performance, an increase in personnel morale, and an increase in safety and sense of safety


title: RHex: A Biologically Inspired Hexapod Runner

abstract: RHex is an untethered, compliant leg hexapod robot that travels at better than one body length per second over terrain few other robots can negotiate at all. Inspired by biomechanics insights into arthropod locomotion, RHex uses a clock excited alternating tripod gait to walk and run in a highly maneuverable and robust manner. We present empirical data establishing that RHex exhibits a dynamical (“bouncing”) gait—its mass center moves in a manner well approximated by trajectories from a Spring Loaded Inverted Pendulum (SLIP)—characteristic of a large and diverse group of running animals, when its central clock, body mass, and leg stiffnesses are appropriately tuned. The SLIP template can function as a useful control guide in developing more complex autonomous locomotion behaviors such as registration via visual servoing, local exploration via visual odometry, obstacle avoidance, and, eventually, global mapping and localization.


title: Corporal punishment by parents and associated child behaviours and experiences: A meta-analytic and theoretical review

abstract: Although the merits of parents using corporal punishment to discipline children have been argued for decades, a thorough understanding of whether and how corporal punishment affects children has not been reached. Toward this end, the author first presents the results of meta-analyses of the association between parental corporal punishment and 11 child behaviors and experiences. Parental corporal punishment was associated with all child constructs, including higher levels of immediate compliance and aggression and lower levels of moral internalization and mental health. The author then presents a process– context model to explain how parental corporal punishment might cause particular child outcomes and considers alternative explanations. The article concludes by identifying 7 major remaining issues for future


title: NORMAL HUMAN VARIATION: REFOCUSSING THE ENHANCEMENT DEBATE

abstract: This article draws attention to several common mistakes in thinking about biomedical enhancement, mistakes that are made even by some supporters of enhancement. We illustrate these mistakes by examining objections that John Harris has recently raised against the use of pharmacological interventions to directly modulate moral decision-making. We then apply these lessons to other influential figures in the debate about enhancement. One upshot of our argument is that many considerations presented as powerful objections to enhancement are really strong considerations in favour of biomedical enhancement, just in a different direction. Another upshot is that it is unfortunate that much of the current debate focuses on interventions that will radically transform normal human capacities. Such interventions are unlikely to be available in the near future, and may not even be feasible. But our argument shows that the enhancement project can still have a radical impact on human life even if biomedical enhancement operated entirely within the normal human range.


title: Learning temporal dynamics of behavior propagation in social networks

abstract: Social influence has been widely accepted to explain people's cascade behaviors and further utilized in many related applications. However, few of existing work studied the direct, microscopic and temporal impact of social influence on people's behaviors in detail. In this paper we concentrate on the behavior modeling and systematically formulate the family of behavior propagation models (BPMs) including the static models (BP and IBP), and their discrete temporal variants (DBP and DIBP). To address the temporal dynamics of behavior propagation over continuous time, we propose a continuous temporal interest-aware behavior propagation model, called CIBP. As a new member of the BPM family, CIBP exploits the continuous-temporal functions (CTFs) to model the fully-continuous dynamic variance of social influence over time. Experiments on real-world datasets evaluated the family of BPMs and demonstrated the effectiveness of our proposed approach.


title: Learning Structured Ordinal Measures for Video based Face Recognition

abstract: This paper presents a structured ordinal measure method for video-based face recognition that simultaneously learns ordinal filters and structured ordinal features. The problem is posed as a non-convex integer program problem that includes two parts. The first part learns stable ordinal filters to project video data into a large-margin ordinal space. The second seeks self-correcting and discrete codes by balancing the projected data and a rank-one ordinal matrix in a structured low-rank way. Unsupervised and supervised structures are considered for the ordinal matrix. In addition, as a complement to hierarchical structures, deep feature representations are integrated into our method to enhance coding stability. An alternating minimization method is employed to handle the discrete and low-rank constraints, yielding high-quality codes that capture prior structures well. Experimental results on three commonly used face video databases show that our method with a simple voting classifier can achieve state-of-the-art recognition rates using fewer features and samples.


Chemical-disease Relations Extraction Based on The Shortest Dependency Path Tree


title: Quantitative Evaluation of Induction Efficiency in Domestic Induction Heating Applications

abstract: Efficiency of electromagnetic energy transference in coupled pot-inductor systems of induction cooktops is analyzed. The influence of different parameters such as pot properties, number of turns of the windings, frequency of the currents, cable types, and ferrite arrangements is studied by means of an analytical model. The proposed model is a combination of two independent models corresponding to the loaded planar coil impedance and the cable losses, respectively. The two models are related using the global magnetic field of the system. The analysis reveals that it is not possible to experimentally determine induction efficiency by measuring unloaded and loaded winding impedances. In this paper, we propose a practical method to obtain the induction efficiency and compare the results with theoretical predictions. Quantitative results corresponding to different pot materials, frequencies, type of cables, and ferrite arrangements are also presented.


Detection and Analysis Cerber Ransomware Using Network Forensics Behavior Based


title: Factors Affecting Consumers’ Green Purchasing Behavior: An Integrated Conceptual Framework

abstract: In this modern era of societal marketing business ethics and social responsibility are becoming the guiding themes for marketing strategies and practices. Within the field of ethics and social responsibility environmental and green marketing topics are the central topics, which are closely related to biodiversity and sustainability. This paper suggests a different approach to assessing the variables of consumers’ green purchasing behavior. Based on thoroughly researched secondary data, this conceptual paper suggests a framework integrating the so far incoherent frameworks as proposed by previous authors. Emanating from this eclectic and chronological literature review, the paper will also propose further missing links that need to be included in the proposed integrated framework. Based on this holistic framework, in a future study, the authors will explain a sustainability index of green consumer behavior, which will be tested empirically in the study. In fact, from the proposed integrated framework, in total eight vital factors/aspects of green/environmental issues are likely to have an impact on consumer green purchasing behavior. Demographic variables will play an intervening or mediating role in the framework.


title: From Dialogue Corpora to Dialogue Systems: Generating a Chatbot with Teenager Personality for Preventing Cyber-Pedophilia

abstract: A conversational agent, also known as chatbot, is a machine conversational system which interacts with human users via natural language. Traditionally, chatbot technology is built under certain set of “manually” elaborated conversational rules. However, given the availability of large and real examples of humans’ interactions in the web, automatically generating these rules is becoming a more feasible option. In this paper we describe an approach for building and training a conversational agent, which holds a teenager personality and it is able to dialogue in Mexican Spanish. By means of this chatter bot we aim at assisting law enforcement officers in the prevention of cyber-pedophilia. Our performed experiments demonstrate that our developed chatbot is able to elaborate comparable lexical and syntactical constructions to those a teenager would produce. As an additional contribution, we compile and release a large dialogue corpus containing real examples of conversations among teenagers.


title: Flight control of tethered kites in autonomous pumping cycles for airborne wind energy

abstract: Energy harvesting based on tethered kites benefits from exploiting higher wind speeds at higher altitudes. The setup considered in this paper is based on a pumping cycle. It generates energy by winching out at high tether forces, driving an electrical generator while flying crosswind. Then it winches in at a stationary neutral position, thus leaving a net amount of generated energy. The focus of this paper is put on the flight control design, which implements an accurate direction control towards target points and allows for a flight with an eight-down pattern. An extended overview on the control system approach, as well as details of each element of the flight controller, are presented. The control architecture is motivated by a simple, yet comprehensive model for the kite dynamics. In addition, winch strategies based on an optimization scheme are presented. In order to demonstrate the real world functionality of the presented algorithms, flight data from a fully automated pumping-cycle operation of a small-scale prototype are given. The setup is based on a 30m$^2$ kite linked to a ground-based 50kW electrical motor/generator by a single line.


Neutropenia and pseudomonas septicemia after sulindac therapy: case report.


title: Exposure Fusion: A Simple and Practical Alternative to High Dynamic Range Photography

abstract: We propose a technique for fusing a bracketed exposure sequence into a high quality image, without converting to High dynamic range (HDR) first. Skipping the physically based HDR assembly step simplifies the acquisition pipeline. This avoids camera response curve calibration and is computationally efficient. It also allows for including flash images in the sequence. Our technique blends multiple exposures, guided by simple quality measures like saturation and contrast. This is done in a multiresolution fashion to account for the brightness variation in the sequence. The resulting image quality is comparable to existing tone mapping operators.


title: SustData: A Public Dataset for ICT4S Electric Energy Research

abstract: Energy and environmental sustainability can benefit a lot from advances in data mining and machine learning techniques. However, those advances rely on the availability of relevant datasets required to develop, improve and validate new techniques. Only recently the first datasets were made publicly available for the energy and sustainability research community. In this paper we present a freely available dataset containing power usage and related information from 50 homes. Here we describe our dataset, the hardware and software setups used when collecting the data and how others can access it. We then discuss potential uses of this data in the future of energy eco- feedback and demand side management research. Index Terms—Public dataset, sustainability, electric energy, feedback.


title: Single incision endoscope-assisted surgery for sagittal craniosynostosis

abstract: OBJECTIVE ::: The objective of this study is to present the novel technique and associated results of a single-incision endoscope-assisted procedure for the treatment of sagittal craniosynostosis. ::: ::: ::: METHODS ::: We retrospectively reviewed the charts of infants who underwent single-incision endoscope-assisted sagittal craniectomy for craniosynostosis at our institution. Demographic data collected included patient age, blood loss, operative time, pre- and post-operative hemoglobin, pre- and post-operative cephalic index (CI), and hospital length of stay. ::: ::: ::: RESULTS ::: Seven consecutive infants underwent surgery for sagittal craniosynostosis using a single-incision endoscopic technique. Average operative time was 87 (±10.5) minutes. Average blood loss was 32 (±13.5) cubic centimeters (cc). Post-operative hemoglobin was an average of 7.1 (±0.2) g/dL. No patients required a blood transfusion intra-operatively or in the post-operative setting. Dural tears were encountered in one patient. The average hospital length of stay was 1.4 (±1.1) days. Difference between pre- and post-operative CI was 8.4 % (±3.5; p < 0.05). ::: ::: ::: CONCLUSIONS ::: We demonstrate the novel use of a single-incision technique for endoscope-assisted sagittal craniosynostosis correction that improves upon the classically described surgical procedure by decreasing invasiveness, while allowing for excellent clinical outcomes.


title: DEVELOPING ACCESSIBILITY MEASUREMENT INDICATORS FOR IRANIAN HEALTH CENTERS

abstract: Background: All over the world there is a pressure to improve the performance and it is not achievable unless we have a performance measurement tool. The purpose of study was to identify a list of indicators for accessibility measurement of Iranian health centers. Methods: The study was conducted in three stages: first, conducting review articles of the literature to identify different indicators for accessibility measurement; second, the Delphi process was used with participating eighteen experts in three Delphi round; third, Analytical Hierarchy Process method was used to weight each indicator. Results: 31 indicators were identified in the literature review. The Delphi method reduced the list to eight indicators. Conclusions: This model can be used as a template for measuring accessibility to the health centers in developing countries.


title: MPDCompress - Matrix Permutation Decomposition Algorithm for Deep Neural Network Compression

abstract: Deep neural networks (DNNs) have become the state-of-the-art technique for machine learning tasks in various applications. However, due to their size and the computational complexity, large DNNs are not readily deployable on edge devices in real-time. To manage complexity and accelerate computation, network compression techniques based on pruning and quantization have been proposed and shown to be effective in reducing network size. However, such network compression can result in irregular matrix structures that are mismatched with modern hardware-accelerated platforms, such as graphics processing units (GPUs) designed to perform the DNN matrix multiplications in a structured (block-based) way. We propose MPDCompress, a DNN compression algorithm based on matrix permutation decomposition via random mask generation. In-training application of the masks molds the synaptic weight connection matrix to a sub-graph separation format. Aided by the random permutations, a hardware-desirable block matrix is generated, allowing for a more efficient implementation and compression of the network. To show versatility, we empirically verify MPDCompress on several network models, compression rates, and image datasets. On the LeNet 300-100 model (MNIST dataset), Deep MNIST, and CIFAR10, we achieve 10 X network compression with less than 1% accuracy loss compared to non-compressed accuracy performance. On AlexNet for the full ImageNet ILSVRC-2012 dataset, we achieve 8 X network compression with less than 1% accuracy loss, with top-5 and top-1 accuracies of 79.6% and 56.4%, respectively. Finally, we observe that the algorithm can offer inference speedups across various hardware platforms, with 4 X faster operation achieved on several mobile GPUs.


title: Byzantine Attack and Defense in Cognitive Radio Networks: A Survey

abstract: The Byzantine attack in cooperative spectrum sensing (CSS), also known as the spectrum sensing data falsification (SSDF) attack in the literature, is one of the key adversaries to the success of cognitive radio networks (CRNs). Over the past couple of years, the research on the Byzantine attack and defense strategies has gained worldwide increasing attention. In this paper, we provide a comprehensive survey and tutorial on the recent advances in the Byzantine attack and defense for CSS in CRNs. Specifically, we first briefly present the preliminaries of CSS for general readers, including signal detection techniques, hypothesis testing, and data fusion. Second, we propose a taxonomy of the existing Byzantine attack behaviors and elaborate on the corresponding attack parameters, which determine where, who, how, and when to launch attacks. Then, from the perspectives of homogeneous or heterogeneous scenarios, we classify the existing defense algorithms, and provide an in-depth tutorial on the state-of-the-art Byzantine defense schemes, commonly known as robust or secure CSS in the literature. Furthermore, we analyze the spear-and-shield relation between Byzantine attack and defense from an interactive game-theoretical perspective. Moreover, we highlight the unsolved research challenges and depict the future research directions.


title: A particle swarm optimization approach for optimum design of PID controller in AVR system

abstract: In this paper, a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters of an AVR system using the particle swarm optimization (PSO) algorithm is presented. This paper demonstrated in detail how to employ the PSO method to search efficiently the optimal PID controller parameters of an AVR system. The proposed approach had superior features, including easy implementation, stable convergence characteristic, and good computational efficiency. Fast tuning of optimum PID controller parameters yields high-quality solution. In order to assist estimating the performance of the proposed PSO-PID controller, a new time-domain performance criterion function was also defined. Compared with the genetic algorithm (GA), the proposed method was indeed more efficient and robust in improving the step response of an AVR system.


title: Machine learning: Trends, perspectives, and prospects

abstract: Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.


title: Advanced Set of Rules to Generate Ontology from Relational Database.

abstract: Nowadays, the majority of data sources in the current web are stored in Relational Data Bases (RDB), the semantic web main idea is to solve the problem of sharing and reusing information between applications and companies in different common areas, based on data stored in RDBs. This article present a complete automatic approach that generate Ontology from a giving relational database based on a set of rules that extract semantics from RDB and transform it to OWL file. Our approach treat most complicated relationship types and constraints like simple and multiple inheritance, transitive chain, disjoint, completeness constraint and N-ary relations. From other side, our solution deals also with mapping data at the same time, think that make this solution more powerful, complete and effective. Our approach composed of four processing stages, analysis, extraction, mapping and finally a verification step before generating the OWL file.


title: Selecting green suppliers based on GSCM practices: Using fuzzy TOPSIS applied to a Brazilian electronics company

abstract: Due to an increased awareness and significant environmental pressures from various stakeholders, companies have begun to realize the significance of incorporating green practices into their daily activities. This paper proposes a framework using Fuzzy TOPSIS to select green suppliers for a Brazilian electronics company; our framework is built on the criteria of green supply chain management (GSCM) practices. An empirical analysis is made, and the data are collected from a set of 12 available suppliers. We use a fuzzy TOPSIS approach to rank the suppliers, and the results of the proposed framework are compared with the ranks obtained by both the geometric mean and the graded mean methods of fuzzy TOPSIS methodology. Then a Spearman rank correlation coefficient is used to find the statistical difference between the ranks obtained by the three methods. Finally, a sensitivity analysis has been performed to examine the influence of the preferences given by the decision makers for the chosen GSCM practices on the selection of green suppliers. Results indicate that the four dominant criteria are Commitment of senior management to GSCM; Product designs that reduce, reuse, recycle, or reclaim materials, components, or energy; Compliance with legal environmental requirements and auditing programs; and Product designs that avoid or reduce toxic or hazardous material use.


title: Directed Functional Brain Connectivity Based on EEG Source Imaging: Methodology and Application to Temporal Lobe Epilepsy

abstract: Objective: The importance of functional brain connectivity to study physiological and pathological brain activity has been widely recognized. Here, we aimed to 1) review a methodological pipeline to investigate directed functional connectivity between brain regions using source signals derived from high-density EEG; 2) elaborate on some methodological challenges; and 3) apply this pipeline to temporal lobe epilepsy (TLE) patients and healthy controls to investigate directed functional connectivity differences in the theta and beta frequency bands during EEG epochs without visible pathological activity. Methods: The methodological pipeline includes: EEG acquisition and preprocessing, electrical-source imaging (ESI) using individual head models and distributed inverse solutions, parcellation of the gray matter in regions of interest, fixation of the dipole orientation for each region, computation of the spectral power in the source space, and directed functional connectivity estimation using Granger-causal modeling. We specifically analyzed how the signal-to-noise ratio (SNR) changes using different approaches for the dipole orientation fixation. We applied this pipeline to 20 left TLE patients, 20 right TLE patients, and 20 healthy controls. Results: Projecting each dipole to the predominant dipole orientation leads to a threefold SNR increase as compared to the norm of the dipoles. By comparing connectivity in TLE versus controls, we found significant frequency-specific outflow differences in physiologically plausible regions. Conclusion: The results suggest that directed functional connectivity derived from ESI can help better understand frequency-specific resting-state network alterations underlying focal epilepsy. Significance: EEG-based directed functional connectivity could contribute to the search of new biomarkers of this disorder.


abstract: Nonnegative Matrix Factorization (NMF) has been one of the most widely used clustering techniques for exploratory data analysis. However, since each data point enters the objective function with squared residue error, a few outliers with large errors easily dominate the objective function. In this article, we propose a Robust Manifold Nonnegative Matrix Factorization (RMNMF) method using e2,1-norm and integrating NMF and spectral clustering under the same clustering framework. We also point out the solution uniqueness issue for the existing NMF methods and propose an additional orthonormal constraint to address this problem. With the new constraint, the conventional auxiliary function approach no longer works. We tackle this difficult optimization problem via a novel Augmented Lagrangian Method (ALM)--based algorithm and convert the original constrained optimization problem on one variable into a multivariate constrained problem. The new objective function then can be decomposed into several subproblems that each has a closed-form solution. More importantly, we reveal the connection of our method with robust K-means and spectral clustering, and we demonstrate its theoretical significance. Extensive experiments have been conducted on nine benchmark datasets, and all empirical results show the effectiveness of our method.


title: Compact Quad-Band Polarization-Insensitive Ultrathin Metamaterial Absorber With Wide Angle Stability

abstract: In this letter, a compact ultrathin quad-band polarization-insensitive metamaterial absorber with a wide angle of absorption is proposed. The unit cell of the proposed structure comprises conductive cross dipoles loaded with split-ring resonators. The proposed absorber exhibits simulated peak absorption of <inline-formula> <tex-math notation="LaTeX">$\text{96.15}\% $</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX"> $\text{99.17}\% $</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$\text{99.75}\%,$</tex-math> </inline-formula> and <inline-formula><tex-math notation="LaTeX">$\text{98.75}\% $</tex-math></inline-formula> at 3.68, 8.58, 10.17, and 14.93 GHz, respectively. The proposed multiband absorber is ultrathin and compact in configuration with a thickness of <inline-formula><tex-math notation="LaTeX">$\text{0.0122}\,\lambda $</tex-math> </inline-formula> and a unit cell size of 0.122 <inline-formula><tex-math notation="LaTeX">$\lambda$</tex-math> </inline-formula> (corresponding to the lowest frequency). Moreover, by understanding the interaction of the unit cell with incident electromagnetic radiation, a conceptual equivalent circuit model is developed, which is used to understand the influence of coupling on the quad band of absorption. The simulated response of the proposed design demonstrates that it has quad-band polarization-insensitive absorption characteristics. In addition, the proposed absorber shows high absorption for an oblique incidence angle up to <inline-formula><tex-math notation="LaTeX">$6{0^ \circ }$</tex-math></inline-formula> for both transverse-electric and transverse-magnetic polarizations.


title: A Sensorimotor Map: Modulating Lateral Interactions for Anticipation and Planning

abstract: Experimental studies of reasoning and planned behavior have provided evidence that nervous systems use internal models to perform predictive motor control, imagery, inference, and planning. Classical (model-free) reinforcement learning approaches omit such a model; standard sensorimotor models account for forward and backward functions of sensorimotor dependencies but do not provide a proper neural representation on which to realize planning. We propose a sensorimotor map to represent such an internal model. The map learns a state representation similar to self-organizing maps but is inherently coupled to sensor and motor signals. Motor activations modulate the lateral connection strengths and thereby induce anticipatory shifts of the activity peak on the sensorimotor map. This mechanism encodes a model of the change of stimuli depending on the current motor activities. The activation dynamics on the map are derived from neural field models. An additional dynamic process on the sensorimotor map (derived from dynamic programming) realizes planning and emits corresponding goal-directed motor sequences, for instance, to navigate through a maze.


title: Single-tube, nested PCR for the diagnosis of Polymyxa betae infection in sugar beet roots and colorimetric analysis of amplified products

abstract: Nested primers for the specific amplification of DNA sequences from the obligate parasitic root-infecting fungus Polymyxa betae in a single-tube reaction are described. The choice of primers, DNA purity, and relative concentration of outer to inner primers were critical to the success of single-tube reactions. The polymerase chain reaction (PCR) test discriminated against background DNA from the host plant and contaminating microorganisms and detected P. betae in as little as 1 pg of total genomic DNA from infected roots. For rapid analysis of amplified products, primers were modified to generate products that could be detected in a colorimetric assay with the commercially available Captagene-GCN4 kit. It was essential to design a PCR protocol that reduced primer dimerization to levels that did not lead to high background absorbance readings. Results from the Captagene-GCN4 test were compared to those obtained by agarose gel analysis of PCR products.


title: Mirrors: design principles for meta-level facilities of object-oriented programming languages

abstract: We identify three design principles for reflection and metaprogramming facilities in object oriented programming languages. Encapsulation: meta-level facilities must encapsulate their implementation. Stratification: meta-level facilities must be separated from base-level functionality. Ontological correspondence: the ontology of meta-level facilities should correspond to the ontology of the language they manipulate. Traditional/mainstream reflective architectures do not follow these precepts. In contrast, reflective APIs built around the concept of mirrors are characterized by adherence to these three principles. Consequently, mirror-based architectures have significant advantages with respect to distribution, deployment and general purpose metaprogramming.


title: Natural speech reveals the semantic maps that tile human cerebral cortex

abstract: It has been proposed that language meaning is represented throughout the cerebral cortex in a distributed ‘semantic system’, but little is known about the details of this network; here, voxel-wise modelling of functional MRI data collected while subjects listened to natural stories is used to create a detailed atlas that maps representations of word meaning in the human brain.


title: A Depression Detection Model Based on Sentiment Analysis in Micro-blog Social Network

abstract: Datasets originating from social networks are valuable to many fields such as sociology and psychology. But the supports from technical perspective are far from enough, and specific approaches are urgently in need. This paper applies data mining to psychology area for detecting depressed users in social network services. Firstly, a sentiment analysis method is proposed utilizing vocabulary and man-made rules to calculate the depression inclination of each micro-blog. Secondly, a depression detection model is constructed based on the proposed method and 10 features of depressed users derived from psychological research. Then 180 users and 3 kinds of classifiers are used to verify the model, whose precisions are all around 80%. Also, the significance of each feature is analyzed. Lastly, an application is developed within the proposed model for mental health monitoring online. This study is supported by some psychologists, and facilitates them in data-centric aspect in turn.


title: A compact formula for the derivative of a 3-D rotation in exponential coordinates

abstract: We present a compact formula for the derivative of a 3-D rotation matrix with respect to its exponential coordinates. A geometric interpretation of the resulting expression is provided, as well as its agreement with other less-compact but better-known formulas. To the best of our knowledge, this simpler formula does not appear anywhere in the literature. We hope by providing this more compact expression to alleviate the common pressure to reluctantly resort to alternative representations in various computational applications simply as a means to avoid the complexity of differential analysis in exponential coordinates.


T-SGX: Eradicating Controlled-Channel Attacks Against Enclave Programs


title: Lost sleep and cyberloafing: Evidence from the laboratory and a daylight saving time quasi-experiment.

abstract: The Internet is a powerful tool that has changed the way people work. However, the ubiquity of the Internet has led to a new workplace threat to productivity—cyberloafing. Building on the ego depletion model of self-regulation, we examine how lost and low-quality sleep influence employee cyberloafing behaviors and how individual differences in conscientiousness moderate these effects. We also demonstrate that the shift to Daylight Saving Time (DST) results in a dramatic increase in cyberloafing behavior at the national level. We first tested the DST–cyberloafing relation through a national quasi-experiment, then directly tested the relation between sleep and cyberloafing in a closely controlled laboratory setting. We discuss the implications of our findings for theory, practice, and future research.


Unsupervised models for morpheme segmentation and morphology learning


title: Cholesterol efflux and the effect of combined treatment with niacin and chromium on aorta of hyperlipidemic rat

abstract: Endothelial cells may play a potential role in cholesterol efflux from peripheral tissues to liver. Cholesterol efflux from cells is essential for activation of the reverse cholesterol transport pathway and cardiovascular health. One of the cholesterol transporters is steroidogenic acute regulatory protein (StAR) which promotes intramitochondrial delivery of cholesterol to the cholesterol side-chain cleavage system. The aim of the present study was to determine the effects of a niacin–chromium complex on aortas of hyperlipidemic rats and on the cholesterol efflux from aorta endothelial cells by examination under light and transmission electron microscopes and evaluating the StAR immunoreactivity, respectively. Aorta lipid peroxidation (LPO) and glutathione (GSH) levels were determined by spectrophotometric methods. After treating hyperlipidemic animals with the complex, the StAR immunoreactivity in endothelial cells increased to achieve cholesterol homeostasis and efflux. Combined treatment with niacin and chromium resulted in an inhibition in the mast cell secretion and a decrease in lipid vacuole size in unilocular adipose tissue surrounding aorta, as well as in a decrease in morphological degenerations observing in aorta of hyperlipidemic rats. Aorta LPO levels increased and GSH levels decreased in the hyperlipidemic group, whereas treatment with niacin and chromium reversed these effects. In conclusion, this study reveals that combined treatment with niacin and chromium prevents the morphological and biochemical changes observed in thoracic aorta of hyperlipidemic rats, and may regulate effectively cardiovascular diseases inducing an increase in StAR levels on endothelial cells.


Superstructured Assembly of Nanocarbons: Fullerenes, Nanotubes, and Graphene.


title: A deep learning based approach for traffic data imputation

abstract: Traffic data is a fundamental component for applications and researches in transportation systems. However, real traffic data collected from loop detectors or other channels often include missing data which affects the relative applications and researches. This paper proposes an approach based on deep learning to impute the missing traffic data. The proposed approach treats the traffic data including observed data and missing data as a whole data item and restores the complete data with the deep structural network. The deep learning approach can discover the correlations contained in the data structure by a layer-wise pre-training and improve the imputation accuracy by conducting a fine-tuning afterwards. The authors analyze the imputation patterns that can be realized with the proposed approach and conduct a series of experiments. The results show that the proposed approach can keep a stable error under different traffic data missing rate. Deep learning is promising in the field of traffic data imputation.


title: Efficient radius neighbor search in three-dimensional point clouds

abstract: Finding all neighbors of a point inside a given radius is an integral part in many approaches using three-dimensional laser range data. We present novel insights to significantly improve the runtime performance of radius neighbor search using octrees. Our contributions are as follows: (1) We propose an index-based organization of the point cloud such that we can efficiently store start and end indexes of points inside every octant and (2) exploiting this representation, we can use pruning of irrelevant subtrees in the traversal to facilitate highly efficient radius neighbor search. We show significant runtime improvements of our proposed octree representation over state-of-the-art neighbor search implementations on three different urban datasets.


title: Annotation of Online Shopping Images without Labeled Training Examples

abstract: We are interested in the task of image annotation using noisy natural text as training data. An image and its caption convey different information, but are generated by the same underlying concepts. In this paper, we learn latent mixtures of topics that generate image and product descriptions on shopping websites by adapting a topic model for multilingual data (Mimno et al., 2009). We use the trained model to annotate test images without corresponding text. We capture visual properties such as color, texture, shape, and orientation by computing low-level image features, and measure the contribution of each type of visual feature towards the accuracy of the model. Our model significantly outperforms both a competitive baseline and a previous topic model-based system.


title: A Network Based IMSI Catcher Detection

abstract: The weaknesses of 2G combined with the need for backward compatibility make all the mobile networks 2G, 3G and 4G vulnerable to Man-in-the- Middle attacks using fake base stations ("IMSI Catchers"). Such equipment used to be quite expensive and unaffordable for most people. However, with the fall of hardware prices and the emergence of open source software, it is possible to build an IMSI catcher for less than two thousand dollars. The user privacy has never before been threatened as today. The existing detection systems is based on software applications installed on smartphones or special dedicated stand-alone systems. These systems are both expensive and have a limited range of detection. We propose a new detection method that uses existing GSM infrastructure to identify and detect IMSI Catchers. The benefits of this method include the protection of all users on total coverage of the operator's network and the cost saving since no extra equipment is required.


title: An ultrasensitive rapid immunocytotoxicity assay for detecting Clostridium difficile toxins.

abstract: We describe a novel ultrasensitive cell-based immunocytotoxicity assay for detecting Clostridium difficile toxin A and B. The assay is simple to perform with a turnaround time of approximately 3 h . It is particularly sensitive in detecting TcdA at a level less then 1 pg/ml. Using this assay, we were able to detect the presence of C. difficile toxins in the fecal and serum specimens of experimentally infected piglets.


title: Distributed stochastic optimization via matrix exponential learning

abstract: In this paper, we investigate a distributed learning scheme for a broad class of stochastic optimization problems and games that arise in signal processing and wireless communications. The proposed algorithm relies on the method of matrix exponential learning (MXL) and only requires locally computable gradient observations that are possibly imperfect and/or obsolete. To analyze it, we introduce the notion of a stable Nash equilibrium and we show that the algorithm is globally convergent to such equilibria - or locally convergent when an equilibrium is only locally stable. We also derive an explicit linear bound for the algorithm's convergence speed, which remains valid under measurement errors and uncertainty of arbitrarily high variance. To validate our theoretical analysis, we test the algorithm in realistic multi-carrier/multiple-antenna wireless scenarios where several users seek to maximize their energy efficiency. Our results show that learning allows users to attain a net increase between 100% and 500% in energy efficiency, even under very high uncertainty.


title: k-Pattern Set Mining under Constraints

abstract: We introduce the problem of k-pattern set mining, concerned with finding a set of k related patterns under constraints. This contrasts to regular pattern mining, where one searches for many individual patterns. The k-pattern set mining problem is a very general problem that can be instantiated to a wide variety of well-known mining tasks including concept-learning, rule-learning, redescription mining, conceptual clustering and tiling. To this end, we formulate a large number of constraints for use in k-pattern set mining, both at the local level, that is, on individual patterns, and on the global level, that is, on the overall pattern set. Building general solvers for the pattern set mining problem remains a challenge. Here, we investigate to what extent constraint programming (CP) can be used as a general solution strategy. We present a mapping of pattern set constraints to constraints currently available in CP. This allows us to investigate a large number of settings within a unified framework and to gain insight in the possibilities and limitations of these solvers. This is important as it allows us to create guidelines in how to model new problems successfully and how to model existing problems more efficiently. It also opens up the way for other solver technologies.


title: Variables Associated With Achievement in Higher Education: A Systematic Review of Meta-Analyses

abstract: The last 2 decades witnessed a surge in empirical studies on the variables associated with achievement in higher education. A number of meta-analyses synthesized these findings. In our systematic literature review, we included 38 meta-analyses investigating 105 correlates of achievement, based on 3,330 effect sizes from almost 2 million students. We provide a list of the 105 variables, ordered by the effect size, and summary statistics for central research topics. The results highlight the close relation between social interaction in courses and achievement. Achievement is also strongly associated with the stimulation of meaningful learning by presenting information in a clear way, relating it to the students, and using conceptually demanding learning tasks. Instruction and communication technology has comparably weak effect sizes, which did not increase over time. Strong moderator effects are found for almost all instructional methods, indicating that how a method is implemented in detail strongly affects achievement. Teachers with high-achieving students invest time and effort in designing the microstructure of their courses, establish clear learning goals, and employ feedback practices. This emphasizes the importance of teacher training in higher education. Students with high achievement are characterized by high self-efficacy, high prior achievement and intelligence, conscientiousness, and the goal-directed use of learning strategies. Barring the paucity of controlled experiments and the lack of meta-analyses on recent educational innovations, the variables associated with achievement in higher education are generally well investigated and well understood. By using these findings, teachers, university administrators, and policymakers can increase the effectivity of higher education.


title: Development of a Fast-Swimming Dolphin Robot Capable of Leaping

abstract: It remains a great challenge for a biomimetic dolphin robot to leap out of the water by reason of requirements for very high speed and exquisite motion control. In this paper, we estimate the minimum exit speed that allows the dolphin to completely leap out of the water, and for the first time, we build a self-contained leaping dolphin robot with commercially available actuators and power supply. To quantify the possible impact of the body length on the minimum exit speed during the leap, we employ a rigid body model rather than a particle model to numerically evaluate the leaping process. Furthermore, a robotic prototype intended for leap motions is created, with particular emphasis on streamlining and high-thrust tail propulsive mechanism designs in conjunction with a passive control strategy for the dorsoventral propulsion. Underwater tests on the untethered dolphin robot demonstrate the effectiveness of the proposed methods and mechatronic designs. We found that the dolphin robot successfully performed leaps with a length-specific speed of over 2.3 body lengths per second, and an emergence angle ranging between 35 and 60 ${}^{\circ}$ .


title: Usability guidelines for designing mobile learning portals

abstract: Mobile learning presumes the use of mobile Internet technology to facilitate the learning process. The growth and rapid evolution of the wireless technology have created new opportunities for anytime and anywhere learning paradigm. As a result, numerous mobile learning portals have been developed to gain the advantages of it. Nonetheless, there is little research and exploration has been initiated in proposing usability guidelines in designing mobile learning portals to achieve efficiency, effectiveness and satisfaction of learning. Thus, this paper seeks to present usability guidelines by grounding the user interface on usability theoretical framework, possible constraints, and unique properties of mobile computing. Three categories of usability have been formulated: user analysis, interaction and interface design. Ten golden usability guidelines have been suggested which aims for designing highly efficacious, user friendly and usable mobile interface to support dynamicity of mobile and handheld devices. Moreover, Mobile Learning Course Manager (MLCM) portal has been developed to demonstrate and exemplify the usability guidelines proposed.


title: The usage and adoption of cloud computing by small and medium businesses

abstract: Abstract Cloud computing has become the buzzword in the industry today. Though, it is not an entirely new concept but in today's digital age, it has become ubiquitous due to the proliferation of Internet, broadband, mobile devices, better bandwidth and mobility requirements for end-users (be it consumers, SMEs or enterprises). In this paper, the focus is on the perceived inclination of micro and small businesses (SMEs or SMBs) toward cloud computing and the benefits reaped by them. This paper presents five factors influencing the cloud usage by this business community, whose needs and business requirements are very different from large enterprises. Firstly, ease of use and convenience is the biggest favorable factor followed by security and privacy and then comes the cost reduction. The fourth factor reliability is ignored as SMEs do not consider cloud as reliable. Lastly but not the least, SMEs do not want to use cloud for sharing and collaboration and prefer their old conventional methods for sharing and collaborating with their stakeholders.


title: Improving Human-Machine Cooperative Visual Search With Soft Highlighting

abstract: Advances in machine learning have produced systems that attain human-level performance on certain visual tasks, e.g., object identification. Nonetheless, other tasks requiring visual expertise are unlikely to be entrusted to machines for some time, e.g., satellite and medical imagery analysis. We describe a human-machine cooperative approach to visual search, the aim of which is to outperform either human or machine acting alone. The traditional route to augmenting human performance with automatic classifiers is to draw boxes around regions of an image deemed likely to contain a target. Human experts typically reject this type of hard highlighting. We propose instead a soft highlighting technique in which the saliency of regions of the visual field is modulated in a graded fashion based on classifier confidence level. We report on experiments with both synthetic and natural images showing that soft highlighting achieves a performance synergy surpassing that attained by hard highlighting.


title: Scalable knowledge harvesting with high precision and high recall

abstract: Harvesting relational facts from Web sources has received great attention for automatically constructing large knowledge bases. Stateof-the-art approaches combine pattern-based gathering of fact candidates with constraint-based reasoning. However, they still face major challenges regarding the trade-offs between precision, recall, and scalability. Techniques that scale well are susceptible to noisy patterns that degrade precision, while techniques that employ deep reasoning for high precision cannot cope with Web-scale data. This paper presents a scalable system, called PROSPERA, for high-quality knowledge harvesting. We propose a new notion of ngram-itemsets for richer patterns, and use MaxSat-based constraint reasoning on both the quality of patterns and the validity of fact candidates.We compute pattern-occurrence statistics for two benefits: they serve to prune the hypotheses space and to derive informative weights of clauses for the reasoner. The paper shows how to incorporate these building blocks into a scalable architecture that can parallelize all phases on a Hadoop-based distributed platform. Our experiments with the ClueWeb09 corpus include comparisons to the recent ReadTheWeb experiment. We substantially outperform these prior results in terms of recall, with the same precision, while having low run-times.


title: DSP embeded hardware for non-contact bio-radar heart and respiration rate monitoring system

abstract: In this paper, we designed embedded-type noncontact bio-radar heart and respiration rate monitoring system. We implemented the hardware for arctangent combining, filtering and rate finding algorithm into the embedded system. We tested the hardware and compared it with the reference ECG measurements. Obtained results agree well with the ECG measurements. We propose arctangent demodulation for quadrature channel combining. Using Texas Instrument C6000 series DSP and external 12Bit ADC, we implemented elliptic digital filter and detection algorithm for robust commercial hand held device.


title: A Game Theoretical Method for Cost-Benefit Analysis of Malware Dissemination Prevention

abstract: Literature in malware proliferation focuses on modeling and analyzing its spread dynamics. Epidemiology models, which are inspired by the characteristics of biological disease spread in human populations, have been used against this threat to analyze the way malware spreads in a network. This work presents a modified version of the commonly used epidemiology models Susceptible Infected Recovered SIR and Susceptible Infected Susceptible SIS, which incorporates the ability to capture the relationships between nodes within a network, along with their effect on malware dissemination process. Drawing upon a model that illustrates the network’s behavior based on the attacker’s and the defender’s choices, we use game theory to compute optimal strategies for the defender to minimize the effect of malware spread, at the same time minimizing the security cost. We consider three defense mechanisms: patch, removal, and patch and removal, which correspond to the defender’s strategy and use probabilistically with a certain rate. The attacker chooses the type of attack according to its effectiveness and cost. Through the interaction between the two opponents we infer the optimal strategy for both players, known as Nash Equilibrium, evaluating the related payoffs. Hence, our model provides a cost-benefit risk management framework for managing malware spread in computer networks.


title: Automatic classification of trees using a UAV onboard camera and deep learning

abstract: Automatic classification of trees using remotely sensed data has been a dream of many scientists and land use managers. Recently, Unmanned aerial vehicles (UAV) has been expected to be an easy-to-use, cost-effective tool for remote sensing of forests, and deep learning has attracted attention for its ability concerning machine vision. In this study, using a commercially available UAV and a publicly available package for deep learning, we constructed a machine vision system for the automatic classification of trees. In our method, we segmented a UAV photography image of forest into individual tree crowns and carried out object-based deep learning. As a result, the system was able to classify 7 tree types at 89.0% accuracy. This performance is notable because we only used basic RGB images from a standard UAV. In contrast, most of previous studies used expensive hardware such as multispectral imagers to improve the performance. This result means that our method has the potential to classify individual trees in a cost-effective manner. This can be a usable tool for many forest researchers and managements.


title: Bitcoin Spread Prediction Using Social and Web Search Media

abstract: In the last decade, Web 2.0 services such as blogs, tweets, forums, chats, email etc. have been widely used as communication media, with very good results. Sharing knowledge is an important part of learning and enhancing skills. Furthermore, emotions may affect decisionmaking and individual behavior. Bitcoin, a decentralized electronic currency system, represents a radical change in financial systems, attracting a large number of users and a lot of media attention. In this work, we investigated if the spread of the Bitcoin’s price is related to the volumes of tweets or Web Search media results. We compared trends of price with Google Trends data, volume of tweets and particularly with those that express a positive sentiment. We found significant cross correlation values, especially between Bitcoin price and Google Trends data, arguing our initial idea based on studies about trends in stock and goods market.


title: V-edge: Fast Self-constructive Power Modeling of Smartphones Based on Battery Voltage Dynamics

abstract: System power models are important for power management and optimization on smartphones. However, existing approaches for power modeling have several limitations. Some require external power meters, which is not convenient for people to use. Other approaches either rely on the battery current sensing capability, which is not available on many smartphones, or take a long time to generate the power model. To overcome these limitations, we propose a new way of generating power models from battery voltage dynamics, called V-edge. V-edge is self-constructive and does not require current-sensing. Most importantly, it is fast in model building. Our implementation supports both component level power models and per-application energy accounting. Evaluation results using various benchmarks and applications show that the V-edge approach achieves high power modeling accuracy, and is two orders of magnitude faster than existing self-modeling approaches requiring no current-sensing.


title: Big data implementation for small and medium enterprises

abstract: Big data provides an opportunity for organizations to gain insights from their data in order to make better decision. Large companies in several sectors are achieving enormous improvements by applying big data analytics. Small and medium enterprises (SMEs), which have smaller size and less revenue, can also benefit from big data to add value to their business. This paper presents the capabilities, readiness, and challenges for SMEs to implement big data. The qualitative research is conducted by observation and interview of 40 SMEs whose businesses are in service or production sector in Thailand. According to the analysis, SMEs are divided into three categories, and the approach for implementing big data is proposed considering these categories. The results can be a guideline for big data implementation for SMEs to achieve competitive advantages.


title: Parenting style as context: An integrative model.

abstract: Despite broad consensus about the effects of parenting practices on child development, many questions about the construct parenting style remain unanswered. Particularly pressing issues are the variability in the effects of parenting style as a function of the child's cultural background, the processes through which parenting style influences the child's development, and the operationalization of parenting style. Drawing on historical review, the authors present a model that integrates two traditions in socialization research, the study of specific parenting practices and the study of global parent characteristics. They propose that parenting style is best conceptualized as a context that moderates the influence of specific parenting practices on the child. It is argued that only by maintaining the distinction between parenting style and parenting practice can researchers address questions concerning socialization processes. During the past 25 years, research based on Baumrind's conceptualization of parenting style has produced a remarkably consistent picture of the type of parenting conducive to the successful socialization of children into the dominant culture of the United States. Authoritativeness—a constellation of parent attributes that includes emotional support, high standards, appropriate autonomy granting, and clear, bidirectional communication—has been shown to help children and adolescents develop an instrumental competence characterized by the balancing of societal and individual needs and responsibilities. Among the indicators of instrumental competence are responsible independence, cooperation with adults and peers, psychosocial maturity, and academic success (for reviews, see Baumrind, 1989,199 la). This work on authoritative ness and its beneficial effects builds on half a century of research on parenting and parenting style. Yet, despite some impressive consistencies in the socialization literature, important questions remain unanswered. As researchers have expanded beyond samples of White, predominantly middle-class families, it has become increasingly clear that the influence of authoritativeness, as well as other styles of parenting, varies depending on the social milieu in which the family is embedded. For example, Baumrind (1972) reported that authoritarian parenting, which is associated with fearful, timid behavior and behavioral compliance among EuropeanAmerican children, is associated with assertiveness among African-American girls. Furthermore, recent studies in which the effects of authoritativeness have been compared across ethnic groups have consistently shown that authoritative parenting is most strongly associated with academic achievement among


title: Mapping controllers from the s-domain to the z-domain using magnitude invariance and phase invariance methods.

abstract: Thesis (M.S)-- Wichita State University, College of Engineering, Dept. of Electrical and Computer Engineering.


title: A natural language bipolar argumentation approach to support users in online debate interactions

abstract: With the growing use of the Social Web, an increasing number of applications for exchanging opinions with other people are becoming available online. These applications are widely adopted with the consequence that the number of opinions about the debated issues increases. In order to cut in on a debate, the participants need first to evaluate the opinions of the other users to detect whether they are in favour or against the debated issue. Bipolar argumentation proposes algorithms and semantics to evaluate the set of accepted arguments, given the support and the attack relations among them. Two main problems arise. First, an automated framework to detect the relations among the arguments represented by the natural language (NL) formulation of the users’ opinions is needed. Our paper addresses this open issue by proposing and evaluating the use of NL techniques to identify the arguments and their relations. In particular, we adopt the textual entailment (TE) approach, a generic framework for applied semant...


title: A New Read-Disturb Failure Mechanism Caused by Boosting Hot-Carrier Injection Effect in MLC NAND Flash Memory

abstract: In this paper, we have reported a new failure phenomenon of read-disturb in MLC NAND flash memory caused by boosting hot-carrier injection effect. (1) The read-disturb failure occurred on unselected WL (WLn+1) after the adjacent selected WL (WLn) was performed with more than 1K read cycles. (2) The read-disturb failure of WLn+1 depends on WLn cell's Vth and its applied voltage. (3) The mechanism of this kind of failure can be explained by hot carrier injection that is generated by discharging from boosting voltage in unselected cell area (Drain of WLn) to ground (Source of WLn).


title: Imaging the effects of annealing on the polymorphic phases of poly(vinylidene fluoride).

abstract: The effect of annealing on the phase transformation and the dielectric properties of poly(vinylidene fluoride) (PVDF) is explored using quasi-single molecule (quasi-SM) microscopy. The solvatochromic properties of nile red (NR) are employed to measure the spatial distribution of the local dielectric constant (ε) in ∼30 μm thick PVDF films before and after annealing at 90 °C. The results presented here demonstrate that nonannealed films exhibit much larger ε distributions, both in terms of magnitude and distribution, when compared to annealed films. The polymorphic phase of PVDF before and after annealing is also confirmed using X-ray diffraction. Nonannealed films are found to be in the γ-phase with annealing promoting the transition to primarily β-phase. Combining these results, we conclude that the decrease in ε with annealing time is due to the phase transformation from γ- to β-phase. Using quasi-SM imaging techniques one can readily visualize the ε environments present within different polymorphic phases of PVDF.