title: LTCC Multilayered Substrate-Integrated Waveguide Filter With Enhanced Frequency Selectivity for System-in-Package Applications

abstract: This paper presents a cross-coupled bandpass filter with stacked substrate-integrated waveguide cavities on low-temperature cofired ceramic substrates. The proposed filter has a local multipoint distribution service band with a novel same-side-feed input/output structure. Conventionally, a cross-coupled structure generates only a single pair of transmission zeros. The proposed filter can generate two pairs of transmission zeros beside the passband, thereby providing an excellent cutoff rate in the stopband and improved frequency selectivity. The additional pair of transmission zeros is created by the same-side-feed structure, which constructs an additional source-load coupling path without increasing the circuit size. A multipath coupling diagram is used to illustrate the conformation of the second pair of transmission zeros and predict its behavior. The experimental filter exhibits responses centered at 27.95 GHz with an insertion loss of -2.8 dB, and a bandwidth of 9%. Two pairs of transmission zeros (at 26.3 and 29.6 GHz, and at 23.2 and 37 GHz) around the passband were obtained, achieving excellent selectivity and a wide stopband.


The Three Layers Evaluation Model for Computer-Generated Plots


title: Signal-Quality Indices for the Electrocardiogram and Photoplethysmogram: Derivation and Applications to Wireless Monitoring

abstract: The identification of invalid data in recordings obtained using wearable sensors is of particular importance since data obtained from mobile patients is, in general, noisier than data obtained from nonmobile patients. In this paper, we present a signal quality index (SQI), which is intended to assess whether reliable heart rates (HRs) can be obtained from electrocardiogram (ECG) and photoplethysmogram (PPG) signals collected using wearable sensors. The algorithms were validated on manually labeled data. Sensitivities and specificities of 94% and 97% were achieved for the ECG and 91% and 95% for the PPG. Additionally, we propose two applications of the SQI. First, we demonstrate that, by using the SQI as a trigger for a power-saving strategy, it is possible to reduce the recording time by up to 94% for the ECG and 93% for the PPG with only minimal loss of valid vital-sign data. Second, we demonstrate how an SQI can be used to reduce the error in the estimation of respiratory rate (RR) from the PPG. The performance of the two applications was assessed on data collected from a clinical study on hospital patients who were able to walk unassisted.


title: Segmentation- and Annotation-Free License Plate Recognition With Deep Localization and Failure Identification

abstract: Automated license plate recognition (ALPR) is essential in several roadway imaging applications. For ALPR systems deployed in the United States, variation between jurisdictions on character width, spacing, and the existence of noise sources (e.g., heavy shadows, non-uniform illumination, various optical geometries, poor contrast, and so on) present in LP images makes it challenging for the recognition accuracy and scalability of ALPR systems. Font and plate-layout variation across jurisdictions further adds to the difficulty of proper character segmentation and increases the level of manual annotation required for training classifiers for each state, which can result in excessive operational overhead and cost. In this paper, we propose a new ALPR workflow that includes novel methods for segmentation- and annotation-free ALPR, as well as improved plate localization and automation for failure identification. Our proposed workflow begins with localizing the LP region in the captured image using a two-stage approach that first extracts a set of candidate regions using a weak sparse network of winnows classifier and then filters them using a strong convolutional neural network (CNN) classifier in the second stage. Images that fail a primary confidence test for plate localization are further classified to identify localization failures, such as LP not present, LP too bright, LP too dark, or no vehicle found. In the localized plate region, we perform segmentation and optical character recognition (OCR) jointly by using a probabilistic inference method based on hidden Markov models (HMMs) where the most likely code sequence is determined by applying the Viterbi algorithm. In order to reduce manual annotation required for training classifiers for OCR, we propose the use of either artificially generated synthetic LP images or character samples acquired by trained ALPR systems already operating in other sites. The performance gap due to differences between training and target domain distributions is minimized using an unsupervised domain adaptation. We evaluated the performance of our proposed methods on LP images captured in several US jurisdictions under realistic conditions.


title: Association of Facebook Use With Compromised Well-Being: A Longitudinal Study

abstract: Face-to-face social interactions enhance well-being. With the ubiquity of social media, important questions have arisen about the impact of online social interactions. In the present study, we assessed the associations of both online and offline social networks with several subjective measures of well-being. We used 3 waves (2013, 2014, and 2015) of data from 5,208 subjects in the nationally representative Gallup Panel Social Network Study survey, including social network measures, in combination with objective measures of Facebook use. We investigated the associations of Facebook activity and real-world social network activity with self-reported physical health, self-reported mental health, self-reported life satisfaction, and body mass index. Our results showed that overall, the use of Facebook was negatively associated with well-being. For example, a 1-standard-deviation increase in "likes clicked" (clicking "like" on someone else's content), "links clicked" (clicking a link to another site or article), or "status updates" (updating one's own Facebook status) was associated with a decrease of 5%-8% of a standard deviation in self-reported mental health. These associations were robust to multivariate cross-sectional analyses, as well as to 2-wave prospective analyses. The negative associations of Facebook use were comparable to or greater in magnitude than the positive impact of offline interactions, which suggests a possible tradeoff between offline and online relationships.


title: BCTrust: A decentralized authentication blockchain-based mechanism

abstract: Internet of Things becomes a major part of our lives, billions of autonomous devices are connected and communicate with each other. This revolutionary paradigm creates a new dimension that removes the boundaries between the real and the virtual worlds. The Wireless Sensor Networks are a masterpiece of the success of this technology, using limited capacity sensors and actuators, industrial, medical, agricultural and many other environments can be covered and managed automatically. This autonomous interacting things should authenticate each other, and communicate securely. Otherwise malicious users can cause serious damages on such systems. In this paper we propose a robust, transparent, flexible and energy efficient blockchain-based authentication mechanism called BCTrust, which is designed especially for devices with computational, storage and energy consumption constraints. In order to evaluate our approach, we realized a real implementation with C programming language, and Ethereum Blockchain.


title: Gas permeation of polymer blends. IV. Poly(vinyl chloride) (PVC)/acrylonitrile–butadiene–styrene (ABS) terpolymer

abstract: The transport behavior of He, O2, N2, and CO2 in membranes of poly(vinyl chloride) (PVC)/acrylonitrile–butadiene–styrene (ABS) blends has been studied at 25°C. The blends were further characterized by dynamic mechanical measurements, differential thermal analysis (DTA), density measurements, and x-ray diffraction. The equilibrium sorption of CO2 and N2 was measured directly at atmospheric pressure using an electromicrobalance and compared with sorption values obtained as P/D ratios from permeation measurements. The rates of permeation (P) and diffusion (D) increase with increasing ABS content in the blends. The P and D values are not additive, and only slight indications of phase inversion in the blends are observed at 5–10 wt-% ABS in the blends. Experimental densities of the blends are higher than calculated densities assuming volume additivity. The data are interpreted to mean that the PVC/ABS blends form a two-phase system composed of a soft polybutadiene (rubber) phase and a rigid PVC/styrene–acrylonitrile copolymer (SAN) phase of mutually compatible components. DTA and dynamic mechanical measurements also show a two-phase system. Sorption values of CO2 and N2 by equilibrium sorption measurements increase with increasing ABS content in the blends without the large fluctuations which have been observed for the sorption values obtained from the time lag method. Comparison of the two types of sorption values (from direct measurements and from P/D ratios) show larger deviations for CO2 than for N2. This suggests that the time lag method is not valid for permeants with polar character in heterogeneous two-phase systems where chemical immobilizing effect on the permeant molecules occurs.


title: The evolutionary basis of risky adolescent behavior: Implications for science, policy, and practice.

abstract: This article proposes an evolutionary model of risky behavior in adolescence and contrasts it with the prevailing developmental psychopathology model. The evolutionary model contends that understanding the evolutionary functions of adolescence is critical to explaining why adolescents engage in risky behavior and that successful intervention depends on working with, instead of against, adolescent goals and motivations. The current article articulates 5 key evolutionary insights into risky adolescent behavior: (a) The adolescent transition is an inflection point in development of social status and reproductive trajectories; (b) interventions need to address the adaptive functions of risky and aggressive behaviors like bullying; (c) risky adolescent behavior adaptively calibrates over development to match both harsh and unpredictable environmental conditions; (d) understanding evolved sex differences is critical for understanding the psychology of risky behavior; and (e) mismatches between current and past environments can dysregulate adolescent behavior, as demonstrated by age-segregated social groupings. The evolutionary model has broad implications for designing interventions for high-risk youth and suggests new directions for research that have not been forthcoming from other perspectives.


title: A Panel Data Analysis of the Brain Gain

abstract: In this paper, we revisit the impact of skilled emigration on human capital accumulation using new panel data covering 147 countries during the period 1975–2000. We derive testable predictions from a stylized theoretical model and test them in dynamic regression models. Our empirical analysis predicts conditional convergence of human capital indicators. Our findings also reveal that skilled migration prospects foster human capital accumulation in low-income countries. In these countries, a net brain gain can be obtained if the skilled emigration rate is not too large (i.e., it does not exceed 20–30% depending on other country characteristics). In contrast, we find no evidence of a significant incentive mechanism in middle-income, and not surprisingly, high-income countries.


title: Processor Architecture Design Using 3D Integration Technology

abstract: The emerging three-dimensional (3D) chip architectures, with their intrinsic capability of reducing the wire length, is one of the promising solutions to mitigate the interconnect problem in modern microprocessor designs. 3D memory stacking also enables much higher memory bandwidth for future chip-multiprocessor design, mitigating the ``memory wall" problem. In addition, heterogenous integration enabled by 3D technology can also result in innovation designs for future microprocessors. This paper serves as a survey of various approaches to design future 3D microprocessors, leveraging the benefits of fast latency, higher bandwidth, and heterogeneous integration capability that are offered by 3D technology.


title: Life Satisfaction and Student Engagement in Adolescents

abstract: Situated within a positive psychology perspective, this study explored linkages between adolescent students’ positive subjective well-being and their levels of engagement in schooling. Specifically, using structural equation modeling techniques, we evaluated the nature and directionality of longitudinal relationships between life satisfaction and student engagement variables. It was hypothesized that adolescents’ life satisfaction and student engagement variables would show bidirectional relationships. To test this hypothesis, 779 students (53% female, 62% Caucasian) in a Southeastern US middle school completed a measure of global life satisfaction and measures of cognitive, emotional, and behavioral engagement at two time points, 5 months apart. A statistically significant bidirectional relationship between life satisfaction and cognitive engagement was found; however, non-significant relationships were found between life satisfaction and emotional and behavioral student engagement. The findings provide important evidence of the role of early adolescents’ life satisfaction in their engagement in schooling during the important transition grades between elementary and high school. The findings also help extend the positive psychology perspective to the relatively neglected context of education.


title: Performance improvement of character recognition in industrial applications using prior knowledge for more reliable segmentation

abstract: In industrial applications optical character recognition with smart cameras becomes more and more popular. Since these applications mostly have challenging environments for the systems it is most important to have very reliable character segmentation and classification algorithms. The investigations of several algorithms have shown that character segmentation is one if not the main bottleneck of character recognition. Furthermore, the requirements of robust and fast algorithms related to skew angle estimation and line segmentation, as well as tilt angle estimation, and character segmentation are high. This is the reason for introducing such algorithms that are specifically adapted to industrial applications. Additionally, a method is proposed that is based on the Bayes theorem to take account of prior knowledge for line and character segmentation. The main focus of the investigations of the character recognition system is recognition performance and speed, since real-time constraints are very hard in industrial application. Both requirements are evaluated on an image series captured with a smart camera in an industrial application.


title: LTZVisor: TrustZone is the Key

abstract: Virtualization technology starts becoming more and more widespread in the embedded systems arena, driven by the upward trend for integrating multiple environments into the same hardware platform. The penalties incurred by standard software-based virtualization, altogether with the strict timing requirements imposed by real-time virtualization are pushing research towards hardware-assisted solutions. Among existing commercial off-the-shelf (COTS) technologies, ARM TrustZone promises to be a game-changer for virtualization, despite of this technology still being seen with a lot of obscurity and scepticism. ::: In this paper we present a Lightweight TrustZone-assisted Hypervisor (LTZVisor) as a tool to understand, evaluate and discuss the benefits and limitations of using TrustZone hardware to assist virtualization. We demonstrate how TrustZone can be adequately exploited for meeting the real-time needs, while presenting a low performance cost on running unmodified rich operating systems. While ARM continues to spread TrustZone technology from the applications processors to the smallest of microcontrollers, it is undeniable that this technology is gaining an increasing relevance. Our intent is to encourage research and drive the next generation of TrustZone-assisted virtualization solutions.


title: A Randomized, Controlled Trial of Virtual Reality-Graded Exposure Therapy for Post-Traumatic Stress Disorder in Active Duty Service Members with Combat-Related Post-Traumatic Stress Disorder

abstract: Abstract Virtual reality (VR)-based therapy has emerged as a potentially useful means to treat post-traumatic stress disorder (PTSD), but randomized studies have been lacking for Service Members from Iraq or Afghanistan. This study documents a small, randomized, controlled trial of VR-graded exposure therapy (VR-GET) versus treatment as usual (TAU) for PTSD in Active Duty military personnel with combat-related PTSD. Success was gauged according to whether treatment resulted in a 30 percent or greater improvement in the PTSD symptom severity as assessed by the Clinician Administered PTSD Scale (CAPS) after 10 weeks of treatment. Seven of 10 participants improved by 30 percent or greater while in VR-GET, whereas only 1 of the 9 returning participants in TAU showed similar improvement. This is a clinically and statistically significant result (χ2 = 6.74, p < 0.01, relative risk 3.2). Participants in VR-GET improved an average of 35 points on the CAPS, whereas those in TAU averaged a 9-point improvement (p < ...


title: Relaxing the Rule of Ten Events per Variable in Logistic and Cox Regression

abstract: The rule of thumb that logistic and Cox models should be used with a minimum of 10 outcome events per predictor variable (EPV), based on two simulation studies, may be too conservative. The authors conducted a large simulation study of other influences on confidence interval coverage, type I error, relative bias, and other model performance measures. They found a range of circumstances in which coverage and bias were within acceptable levels despite less than 10 EPV, as well as other factors that were as influential as or more influential than EPV. They conclude that this rule can be relaxed, in particular for sensitivity analyses undertaken to demonstrate adequate control of confounding. bias (epidemiology); coverage probability; event history analysis; model adequacy; type I error; variable selection


title: JAM: a BDI-theoretic mobile agent architecture

abstract: JAM is a hybrid intelligent agent architecture that draws upon the theories and ideas of the Procedural Reasoning System (PRS), Structured Circuit Semantics (SCS), and Act plan interlhtgua. Furthermore, JAM draws upon the implementation pragmatics of the University of Michigan’s and SRI Internatlonal’s implementation of PRS (UMPRS and PRS-CL, respectively). JAM provides rich and extensive plan and procedural representations, metalevel and utility-based reasoning over multiple simultaneous goals, and goal-driven and event-driven behavior that are an amalgam of all of the sources listed above. The JAM agent architecture also provides an agentGo primitive function utilizing Java’s object serialization mechanism to provide widely-supported mobility capabilities.


title: AutoCaption: Automatic caption generation for personal photos

abstract: AutoCaption is a system that helps a smartphone user generate a caption for their photos. It operates by uploading the photo to a cloud service where a number of parallel modules are applied to recognize a variety of entities and relations. The outputs of the modules are combined to generate a large set of candidate captions, which are returned to the phone. The phone client includes a convenient user interface that allows users to select their favorite caption, reorder, add, or delete words to obtain the grammatical style they prefer. The user can also select from multiple candidates returned by the recognition modules.


title: Gas Sensors Based on Electrospun Nanofibers

abstract: Nanofibers fabricated via electrospinning have specific surface approximately one to two orders of the magnitude larger than flat films, making them excellent candidates for potential applications in sensors. This review is an attempt to give an overview on gas sensors using electrospun nanofibers comprising polyelectrolytes, conducting polymer composites, and semiconductors based on various sensing techniques such as acoustic wave, resistive, photoelectric, and optical techniques. The results of sensing experiments indicate that the nanofiber-based sensors showed much higher sensitivity and quicker responses to target gases, compared with sensors based on flat films.


title: Nested counters in bit-parallel string matching

abstract: Many algorithms, e.g. in the field of string matching, are based on handling many counters, which can be performed in parallel, even on a sequential machine, using bit-parallelism. The recently presented technique of nested counters (Matryoshka counters ) [1] is to handle small counters most of the time, and refer to larger counters periodically, when the small counters may get full, to prevent overflow. In this work, we present several non-trivial applications of Matryoshka counters in string matching algorithms, improving their worst- or average-case time complexities. The set of problems comprises (Δ ,α )-matching, matching with k insertions, episode matching, and matching under Levenshtein distance.


title: Speed or accuracy? a study in evaluation of simultaneous speech translation.

abstract: Simultaneous speech translation is a technology that attempts to reduce the delay inherent in speech translation by beginning translation before the end of explicit sentence boundaries. Despite best efforts, there is still often a trade-off between speed and accuracy in these systems, with systems with less delay also achieving lower accuracy. However, somewhat surprisingly, there is no previous work examining the relative importance of speed and accuracy, and thus given two systems with various speeds and accuracies, it is difficult to say with certainty which is better. In this paper, we make the first steps towards evaluation of simultaneous speech translation systems in consideration of both speed and accuracy. We collect user evaluations of speech translation results with different levels of accuracy and delay, and using this data to learn the parameters of an evaluation measure that can judge the trade-off between these two factors. Based on these results, we find that considering both accuracy and delay in the evaluation of speech translation results helps improve correlations with human judgements, and that users placed higher relative importance on reducing delay when results were presented through text, rather than speech.


title: 3-D MIMO: How Much Does It Meet Our Expectations Observed From Channel Measurements?

abstract: By taking advantage of the elevation domain, three-dimensional (3-D) multiple input and multiple output (MIMO) with massive antenna elements is considered as a promising and practical technique for the fifth Generation mobile communication system. So far, 3-D MIMO is mostly studied by simulation and a few field trials have been launched recently. It still remains unknown how much does the 3-D MIMO meet our expectations in versatile scenarios. In this paper, we answer this based on measurements with $56\times 32$ antenna elements at 3.5 GHz with 100-MHz bandwidth in three typical deployment scenarios, including outdoor to indoor (O2I), urban microcell (UMi), and urban macrocell (UMa). Each scenario contains two different site locations and 2–5 test routes under the same configuration. Based on the measured data, both elevation and azimuth angles are extracted and their stochastic behaviors are investigated. Then, we reconstruct two-dimensional and 3-D MIMO channels based on the measured data, and compare the capacity and eigenvalues distribution. It is observed that 3-D MIMO channel which fully utilizes the elevation domain does improve capacity and also enhance the contributing eigenvalue number. However, this gain varies from scenario to scenario in reality, O2I is the most beneficial scenario, then followed by UMi and UMa scenarios. More results of multiuser capacity varying with the scenario, antenna number and user number can provide the experimental insights for the efficient utilization of 3-D MIMO in future.


title: Stock Trading System based on the Multi-objective Particle Swarm Optimization of Technical Indicators on End-of-day Market Data

abstract: Stock traders consider several factors or objectives in making decisions. Moreover, they differ in the importance they attach to each of these objectives. This requires a tool that can provide an optimal tradeoff among different objectives, a problem aptly solved by a multi-objective optimization (MOO) system. This paper aims to investigate the application of multi-objective optimization to end-of-day historical stock trading. We present a stock trading system that uses multi-objective particle swarm optimization (MOPSO) of financial technical indicators. Using end-of-day market data, the system optimizes the weights of several technical indicators over two objective functions, namely, percent profit and Sharpe ratio. The performance of the system was compared to the performance of the technical indicators, the performance of the market, and the performance of another stock trading system which was optimized with the NSGA-II algorithm, a genetic algorithm-based MOO method. The results show that the system performed well on both training and out-of-sample data. In terms of percent profit, the system outperformed most, if not all, of the indicators under study, and, in some instances, it even outperformed the market itself. In terms of Sharpe ratio, the system consistently performed significantly better than all the technical indicators. The proposed MOPSO system also performed far better than the system optimized by NSGA-II. The proposed system provided a diversity of solutions for the two objective functions and is found to be robust and fast. These results show the potential of the system as a tool for making stock trading decisions.


title: A Self-propelled Catheter Capable of Generating Travelling Waves with Steering Function by Mono-Line Drive

abstract: This paper discusses the design method of a self-propelled catheter with thin diameter of 2.6mm, aimed for moving and steering in the bronchi. As the basic configuration, the proposed catheter is composed of an inner tube and McKibben artificial muscles. It can propel by generating travelling waves by a single supply line driven by pneumatics. Since the inner tube equips with orifices of different size, several McKibben chambers can deform with the time delay. We also propose the method to steer the direction at the branch pipe, whose angle can be adjusted by PWM control of pressuring. Besides, we also introduce the design method of how to deal with the pipes with different diameters. The validity of these proposed methods are verified by the prototype through the experiments in the bronchial model.


title: Mobile Assistive Technologies for Visual Impaired Users: A Survey

abstract: In this article, we summarize some of the recent advancements in assistive technologies that are designed for people with visual impairments (VI) and blind people. Present technology enables applications to be actively disseminated and to efficiently operate on handheld mobile devices. These applications include also those that require high computational requirements. As a consequent, digital travel aids, visual sensing modules, text-to-speech applications, navigational assistance tools, and the combination with diverse assistive haptic devices are becoming consolidated with typical mobile devices. This direction has opened diversity of new perspectives for practical training and rehabilitation of people with VI. The aim of this article is to give an overview about the recent developments of assistive applications designed for people with VI. In conclusion, we recommend designing a unified robust system for people with VI which provides the support of the different kind of services.


DeepInsight: Multi-Task Multi-Scale Deep Learning for Mental Disorder Diagnosis


title: Towards Deep and Representation Learning for Talent Search at LinkedIn

abstract: Talent search and recommendation systems at LinkedIn strive to match the potential candidates to the hiring needs of a recruiter or a hiring manager expressed in terms of a search query or a job posting. Recent work in this domain has mainly focused on linear models, which do not take complex relationships between features into account, as well as ensemble tree models, which introduce non-linearity but are still insufficient for exploring all the potential feature interactions, and strictly separate feature generation from modeling. In this paper, we present the results of our application of deep and representation learning models on LinkedIn Recruiter. Our key contributions include: (i) Learning semantic representations of sparse entities within the talent search domain, such as recruiter ids, candidate ids, and skill entity ids, for which we utilize neural network models that take advantage of LinkedIn Economic Graph, and (ii) Deep models for learning recruiter engagement and candidate response in talent search applications. We also explore learning to rank approaches applied to deep models, and show the benefits for the talent search use case. Finally, we present offline and online evaluation results for LinkedIn talent search and recommendation systems, and discuss potential challenges along the path to a fully deep model architecture. The challenges and approaches discussed generalize to any multi-faceted search engine.


title: Deep learning based Nucleus Classification in pancreas histological images

abstract: Tumor specimens contain a variety of healthy cells as well as cancerous cells, and this heterogeneity underlies resistance to various cancer therapies. But this problem has not been thoroughly investigated until recently. Meanwhile, technological breakthroughs in imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples, and modern machine learning approaches including deep learning have been shown to produce encouraging results by finding hidden structures and make accurate predictions. In this paper, we propose a Deep learning based Nucleus Classification (DeepNC) approach using paired histopathology and immunofluorescence images (for label), and demonstrate its classification prediction power. This method can solve current issue on discrepancy between genomic- or transcriptomic-based and pathology-based tumor purity estimates by improving histological evaluation. We also explain challenges in training a deep learning model for huge dataset.


title: Effective Building Block Design for Deep Convolutional Neural Networks using Search

abstract: Deep learning has shown promising results on many machine learning tasks but DL models are often complex networks with large number of neurons and layers, and recently, complex layer structures known as building blocks. Finding the best deep model requires a combination of finding both the right architecture and the correct set of parameters appropriate for that architecture. In addition, this complexity (in terms of layer types, number of neurons, and number of layers) also present problems with generalization since larger networks are easier to overfit to the data. In this paper, we propose a search framework for finding effective architectural building blocks for convolutional neural networks (CNN). Our approach is much faster at finding models that are close to state-of-the-art in performance. In addition, the models discovered by our approach are also smaller than models discovered by similar techniques. We achieve these twin advantages by designing our search space in such a way that it searches over a reduced set of state-of-the-art building blocks for CNNs including residual block, inception block, inception-residual block, ResNeXt block and many others. We apply this technique to generate models for multiple image datasets and show that these models achieve performance comparable to state-of-the-art (and even surpassing the state-of-the-art in one case). We also show that learned models are transferable between datasets.


title: "Purging my friends list. Good luck making the cut": Perceptions of narcissism on Facebook

abstract: Examined the perceptions of narcissistic statements on Facebook.Narcissistic status updates viewed negatively relative to neutral updates.Narcissism and gender of the perceiver played a role in ratings of status updates. Social media sites such as Facebook are thought to act as a platform for narcissistic behavior, such as posting self-promoting status updates or attractive photos. Narcissism is identifiable via social media, and it has been associated with negative peer appraisals. This article presents three studies with undergraduate students that examined the interpersonal implications of narcissism on Facebook in light of gender and perceiver narcissism. Results indicated that hypothetical targets who posted narcissistic status updates were perceived as less likeable, less successful, and less worthy of friendship than those who posted neutral status updates. Across the three studies, perceiver narcissism and target gender had some apparent influences on ratings. Implications regarding social media behavior and the interpersonal consequences of narcissism are discussed.


title: Cost-Effective Siting of Electric Vehicle Charging Infrastructure With Agent-Based Modeling

abstract: Plug-in electric vehicles (PEVs) represent a significant opportunity for governments to reduce emissions of both air pollutants and greenhouse gases, in addition to reduce their dependency on foreign sources of energy. Comprehensive planning analysis prior to the rollout of electric vehicle charging stations (EVCS) can ensure that charging stations are effectively sited, providing the best returns on investment while also meeting critical service requirements. We present a detailed description of the PEV infrastructure (PEVI) model, a spatially explicit agent-based microsimulation model that represents charging infrastructure, charging behavior, competition for scarce EVCS, and driver adaptation. A differential evolution and a heuristic optimization scheme are compared in their ability to find a cost-effective distribution of EVCS. In addition, several key assumptions of the model are tested for their impact on critical outcomes. Results are presented from a case study in Delhi, India, that highlight the spatial distribution of chargers of different levels, the impact of several technical and policy trends on the need for EVCS, the spatiotemporal distribution of charging demand, and the technical potential for load shifting PEV demand.


title: Online content-aware video condensation

abstract: Explosive growth of surveillance video data presents formidable challenges to its browsing, retrieval and storage. Video synopsis, an innovation proposed by Peleg and his colleagues, is aimed for fast browsing by shortening the video into a synopsis while keeping activities in video captured by a camera. However, the current techniques are offline methods requiring that all the video data be ready for the processing, and are expensive in time and space. In this paper, we propose an online and efficient solution, and its supporting algorithms to overcome the problems. The method adopts an online content-aware approach in a step-wise manner, hence applicable to endless video, with less computational cost. Moreover, we propose a novel tracking method, called sticky tracking, to achieve high-quality visualization. The system can achieve a faster-than-real-time speed with a multi-core CPU implementation. The advantages are demonstrated by extensive experiments with a wide variety of videos. The proposed solution and algorithms could be integrated with surveillance cameras, and impact the way that surveillance videos are recorded.


title: A multiresolution symbolic representation of time series

abstract: Efficiently and accurately searching for similarities among time series and discovering interesting patterns is an important and non-trivial problem. In this paper, we introduce a new representation of time series, the multiresolution vector quantized (MVQ) approximation, along with a new distance function. The novelty of MVQ is that it keeps both local and global information about the original time series in a hierarchical mechanism, processing the original time series at multiple resolutions. Moreover, the proposed representation is symbolic employing key subsequences and potentially allows the application of text-based retrieval techniques into the similarity analysis of time series. The proposed method is fast and scales linearly with the size of database and the dimensionality. Contrary to the vast majority in the literature that uses the Euclidean distance, MVQ uses a multi-resolution/hierarchical distance function. We performed experiments with real and synthetic data. The proposed distance function consistently outperforms all the major competitors (Euclidean, dynamic time warping, piecewise aggregate approximation) achieving up to 20% better precision/recall and clustering accuracy on the tested datasets.


title: Planning and certifying software system reliability

abstract: An approach to software reliability and certification is presented that is based on the use of three mathematical models: the sampling, component, and certification models. The approach helps reduce reliability analysis to a problem that can be evaluated and manipulated through a series of spreadsheets. This approach was motivated by interest in applying the cleanroom software-engineering method in environments that require extensive code reuse. >


title: Disaster risk, climate change and international development: scope for, and challenges to, integration

abstract: Reducing losses to weather-related disasters, meeting the Millennium Development Goals and wider human development objectives, and implementing a successful response to climate change are aims that can only be accomplished if they are undertaken in an integrated manner. Currently, policy responses to address each of these independently may be redundant or, at worst, conflicting. We believe that this conflict can be attributed primarily to a lack of interaction and institutional overlap among the three communities of practice. Differences in language, method and political relevance may also contribute to the intellectual divide. Thus, this paper seeks to review the theoretical and policy linkages among disaster risk reduction, climate change and development. It finds that not only does action within one realm affect capacity for action in the others, but also that there is much that can be learnt and shared between realms in order to ensure a move towards a path of integrated and more sustainable development.


title: Age Differences in Personality Traits From 10 to 65: Big Five Domains and Facets in a Large Cross-Sectional Sample

abstract: Hypotheses about mean-level age differences in the Big Five personality domains, as well as 10 more specific facet traits within those domains, were tested in a very large cross-sectional sample (N 1,267,218) of children, adolescents, and adults (ages 10–65) assessed over the World Wide Web. The results supported several conclusions. First, late childhood and adolescence were key periods. Across these years, age trends for some traits (a) were especially pronounced, (b) were in a direction different from the corresponding adult trends, or (c) first indicated the presence of gender differences. Second, there were some negative trends in psychosocial maturity from late childhood into adolescence, whereas adult trends were overwhelmingly in the direction of greater maturity and adjustment. Third, the related but distinguishable facet traits within each broad Big Five domain often showed distinct age trends, highlighting the importance of facet-level research for understanding life span age differences in personality.


title: The k-Nearest Neighbor Algorithm Using MapReduce Paradigm

abstract: Data in any form is a valuable resource but more often than not data collected in the real world is completely random and unstructured. Hence, to utilize the true potential of data as a resource we must transform it in such a manner so as to retrieve meaningful information from it. Data mining fulfills this need. Today there is not only a need for efficient data mining techniques to process large volume of data but also a need for a means to meet the computational requirements to process such huge volume of data. In this paper we implement an effective data mining technique known as the k-Nearest Neighbor method on a distributed computing environment running Apache Hadoop that uses the MapReduce paradigm to process high volume data.


title: Reverse Engineering of Dynamic Parallel Program Behavior from Execution Traces

abstract: Trace-driven simulation has been used widely for architectural exploitation in electronic system-level (ESL) designs for complex SoCs (system-on-chips). Most often the trace files are provided by third parties without source code. This makes it difficult to process and manipulate the trace files, e.g. to identify the region of interest (ROI), for efficient and more focused simulations. It is thus necessary to deduce high-level structures and patterns in the trace to facilitate such manipulations. This can be viewed as a reverse engineering process to derive the dynamic control flow of the original program from its execution trace. Furthermore, after the high-level structure is manipulated, it is also important to generate a new trace from the resultant structure that preserves the characteristics of the original trace for effective trace-driven simulations. The problem becomes even more difficult if the trace was generated from a parallel program because of the complex interactions between the multiple threads of executions. In this paper, a novel scheme to reverse-engineering the program execution trace is proposed, which can obtain the high-level dynamic control structure of the original parallel program as well as regenerate a similar trace from the derived structure, all without referencing the source code. The effectiveness of the proposed scheme is evaluated and verified with extensive experiments.


title: The Parable of Google Flu: Traps in Big Data Analysis

abstract: In February 2013, Google Flu Trends (GFT) made headlines but not for a reason that Google executives or the creators of the flu tracking system would have hoped. Nature reported that GFT was predicting more than double the proportion of doctor visits for influenza-like illness (ILI) than the Centers for Disease Control and Prevention (CDC), which bases its estimates on surveillance reports from laboratories across the United States ( 1 , 2 ). This happened despite the fact that GFT was built to predict CDC reports. Given that GFT is often held up as an exemplary use of big data ( 3 , 4 ), what lessons can we draw from this error?


Web enabled spatio-temporal semantic analysis of traffic noise using CityGML


title: Array Data Flow Analysis for Load-Store Optimizations in Fine-Grain Architectures

abstract: The performence of scientific programs on modern processors can be significantly degraded by memory references that frequently arise due to load and store operations associated with array references. We have developed techniques for optimally allocating registers to array elements whose values are repeatedly referenced over one or more loop iterations. The resulting placement of loads and stores is optimal in that number of loads and stores encoutered along each path through the loop is minimal for the given program branching structure. To place load, store, and register-to-register shift operations without introducing fully/partially redundant and dead memory operations, a detailed value flow analysis of array references is required. We present an analysis framework to efficiently solve various data flow problems required by array load-store optimizations. The framework determines the collective behavior of recurrent references spread over multiple loop iterations. We also demonstrate how our algorithms can be adapted for various fine-grain architectures.


title: The Effect of Capital Structure and Ownership Structure on Financial Performance and Dividend Policy on Manufacturing Companies in the Indonesia Stock Exchange

abstract: This study aimed to examine the effect of capital structure and ownership structure on financial performance and dividend policy. The study involved the manufacturing companies listed on IDX from 2012 to 2016. The data were analyzed by applying PLS (Partial Least Square). Research method used in this research was explanatory research using purposive sampling. The results of this study were: Capital Structure had a positive significant effect on Financial Performance, Capital Structure had non-significant effect on Dividend Policy, Ownership Structure had a positive significant effect on Financial Performance, Ownership Structure had non-significant effect on Dividend Policy, and Financial Performance had a positive significant effect on Dividend Policy.


X-ray analysis and the structure of the components of plant cell walls


title: Bulk Current Injection in Twisted Wire Pairs With Not Perfectly Balanced Terminations

abstract: In this paper, common mode (CM) and differential mode (DM) voltages induced by bulk current injection at the terminations of a differential interconnection are predicted and correlated to the degree of unbalance of the terminal units. This is done by extending a previously derived lumped-parameter representation of the injection probe and by combining modal analysis with a multiconductor transmission line theory. Under nonrestrictive assumptions on termination unbalance, it is shown that CM and DM voltages can be readily predicted by two equivalent circuits. The circuit for CM prediction is independent on termination unbalance and directly excited by the injection probe. The circuit for DM prediction does not involve any of the probe model parameters, and is driven by two voltage sources related to the induced CM voltages through the common-mode rejection ratio of each termination. Model accuracy was proven by measurements carried out on a test bench setup according to the BCI Standards.


title: Rational Trust Modeling

abstract: Trust models are widely used in various computer science disciplines. The main purpose of a trust model is to continuously measure trustworthiness of a set of entities based on their behaviors. In this article, the novel notion of "rational trust modeling" is introduced by bridging trust management and game theory. Note that trust models/reputation systems have been used in game theory (e.g., repeated games) for a long time, however, game theory has not been utilized in the process of trust model construction; this is where the novelty of our approach comes from. In our proposed setting, the designer of a trust model assumes that the players who intend to utilize the model are rational/selfish, i.e., they decide to become trustworthy or untrustworthy based on the utility that they can gain. In other words, the players are incentivized (or penalized) by the model itself to act properly. The problem of trust management can be then approached by game theoretical analyses and solution concepts such as Nash equilibrium. Although rationality might be built-in in some existing trust models, we intend to formalize the notion of rational trust modeling from the designer's perspective. This approach will result in two fascinating outcomes. First of all, the designer of a trust model can incentivise trustworthiness in the first place by incorporating proper parameters into the trust function, which can be later utilized among selfish players in strategic trust-based interactions (e.g., e-commerce scenarios). Furthermore, using a rational trust model, we can prevent many well-known attacks on trust models. These two prominent properties also help us to predict behavior of the players in subsequent steps by game theoretical analyses.


title: A Current Fed Two-Inductor Boost Converter With an Integrated Magnetic Structure and Passive Lossless Snubbers for Photovoltaic Module Integrated Converter Applications

abstract: In this paper, a photovoltaic (PV) module integrated converter is implemented with a current fed two-inductor boost converter cascaded with a line frequency unfolder. The current source is a sinusoidally modulated two-phase buck converter with an interphase transformer. The boost cell operates at a fixed duty ratio and has an integrated magnetic structure. The two inductors and the transformer are integrated into one magnetic core. Passive lossless snubbers are employed to recover the energy trapped in the transformer leakage inductance and to minimize the switching losses. The two-inductor boost converter output interfaces with the mains via an unfolding stage, where the MOSFETs are driven by the PV gate drivers. Experimental results are provided for a 100-W converter developing a single phase 240-V 50-Hz output


title: DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf Devices

abstract: In this paper, we present DREAMER, a multimodal database consisting of electroencephalogram (EEG) and electrocardiogram (ECG) signals recorded during affect elicitation by means of audio-visual stimuli. Signals from 23 participants were recorded along with the participants self-assessment of their affective state after each stimuli, in terms of valence, arousal, and dominance. All the signals were captured using portable, wearable, wireless, low-cost, and off-the-shelf equipment that has the potential to allow the use of affective computing methods in everyday applications. A baseline for participant-wise affect recognition using EEG and ECG-based features, as well as their fusion, was established through supervised classification experiments using support vector machines (SVMs). The self-assessment of the participants was evaluated through comparison with the self-assessments from another study using the same audio-visual stimuli. Classification results for valence, arousal, and dominance of the proposed database are comparable to the ones achieved for other databases that use nonportable, expensive, medical grade devices. These results indicate the prospects of using low-cost devices for affect recognition applications. The proposed database will be made publicly available in order to allow researchers to achieve a more thorough evaluation of the suitability of these capturing devices for affect recognition applications.


title: Incrementality In Deterministic Dependency Parsing

abstract: Deterministic dependency parsing is a robust and efficient approach to syntactic parsing of unrestricted natural language text. In this paper, we analyze its potential for incremental processing and conclude that strict incrementality is not achievable within this framework. However, we also show that it is possible to minimize the number of structures that require non-incremental processing by choosing an optimal parsing algorithm. This claim is substantiated with experimental evidence showing that the algorithm achieves incremental parsing for 68.9% of the input when tested on a random sample of Swedish text. When restricted to sentences that are accepted by the parser, the degree of incrementality increases to 87.9%.


title: Discourse Segmentation Of Multi-Party Conversation

abstract: We present a domain-independent topic segmentation algorithm for multi-party speech. Our feature-based algorithm combines knowledge about content using a text-based algorithm as a feature and about form using linguistic and acoustic cues about topic shifts extracted from speech. This segmentation algorithm uses automatically induced decision rules to combine the different features. The embedded text-based algorithm builds on lexical cohesion and has performance comparable to state-of-the-art algorithms based on lexical information. A significant error reduction is obtained by combining the two knowledge sources.


title: Real-Time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs

abstract: Precision farming robots, which target to reduce the amount of herbicides that need to be brought out in the fields, must have the ability to identify crops and weeds in real time to trigger weeding actions. In this paper, we address the problem of CNN-based semantic segmentation of crop fields separating sugar beet plants, weeds, and background solely based on RGB data. We propose a CNN that exploits existing vegetation indexes and provides a classification in real time. Furthermore, it can be effectively re-trained to so far unseen fields with a comparably small amount of training data. We implemented and thoroughly evaluated our system on a real agricultural robot operating in different fields in Germany and Switzerland. The results show that our system generalizes well, can operate at around 20Hz, and is suitable for online operation in the fields.


Document Ranking with Citation Information and Oversampling Sentence Classification in the LUIMA Framework.


title: Pinocchio: Nearly Practical Verifiable Computation

abstract: To instill greater confidence in computations outsourced to the cloud, clients should be able to verify the correctness of the results returned. To this end, we introduce Pinocchio, a built system for efficiently verifying general computations while relying only on cryptographic assumptions. With Pinocchio, the client creates a public evaluation key to describe her computation; this setup is proportional to evaluating the computation once. The worker then evaluates the computation on a particular input and uses the evaluation key to produce a proof of correctness. The proof is only 288 bytes, regardless of the computation performed or the size of the inputs and outputs. Anyone can use a public verification key to check the proof. Crucially, our evaluation on seven applications demonstrates that Pinocchio is efficient in practice too. Pinocchio's verification time is typically 10ms: 5-7 orders of magnitude less than previous work; indeed Pinocchio is the first general-purpose system to demonstrate verification cheaper than native execution (for some apps). Pinocchio also reduces the worker's proof effort by an additional 19-60x. As an additional feature, Pinocchio generalizes to zero-knowledge proofs at a negligible cost over the base protocol. Finally, to aid development, Pinocchio provides an end-to-end toolchain that compiles a subset of C into programs that implement the verifiable computation protocol.


title: Crypto-Nets: Neural Networks over Encrypted Data

abstract: The problem we address is the following: how can a user employ a predictive model that is held by a third party, without compromising private information. For example, a hospital may wish to use a cloud service to predict the readmission risk of a patient. However, due to regulations, the patient's medical files cannot be revealed. The goal is to make an inference using the model, without jeopardizing the accuracy of the prediction or the privacy of the data. ::: To achieve high accuracy, we use neural networks, which have been shown to outperform other learning models for many tasks. To achieve the privacy requirements, we use homomorphic encryption in the following protocol: the data owner encrypts the data and sends the ciphertexts to the third party to obtain a prediction from a trained model. The model operates on these ciphertexts and sends back the encrypted prediction. In this protocol, not only the data remains private, even the values predicted are available only to the data owner. ::: Using homomorphic encryption and modifications to the activation functions and training algorithms of neural networks, we show that it is protocol is possible and may be feasible. This method paves the way to build a secure cloud-based neural network prediction services without invading users' privacy.


title: Talos: no more ransomware victims with formal methods

abstract: Ransomware is a very effective form of malware that is recently spreading out on an impressive number of workstations and smartphones. This malware blocks the access to the infected machine or to the files located in the infected machine. The attackers will restore the machine and files only after the payment of a certain amount of money, usually given in the form of bitcoins. Commercial solutions are still ineffective to recognize the last variants of ransomware, and the problem has been poorly investigated in literature. In this paper we discuss a methodology based on formal methods for detecting ransomware malware on Android devices. We have implemented our method in a tool named Talos. We evaluate the method, and the obtained results show that Talos is very effective in recognizing ransomware (accuracy of 0.99) even when it is obfuscated (accuracy still remains at 0.99).


title: Review of Control Techniques for HVAC Systems—Nonlinearity Approaches Based on Fuzzy Cognitive Maps

abstract: Heating, Ventilating, and Air Conditioning (HVAC) systems are the major energy-consuming devices in buildings. Nowadays, due to the high demand for HVAC system installation in buildings, designing an effective controller in order to decrease the energy consumption of the devices while meeting the thermal comfort demands in buildings are the most important goals of control designers. The purpose of this article is to investigate the different control methods for Heating, Ventilating, and Air Conditioning and Refrigeration (HVAC & R) systems. The advantages and disadvantages of each control method are discussed and finally the Fuzzy Cognitive Map (FCM) method is introduced as a new strategy for HVAC systems. The FCM method is an intelligent and advanced control technique to address the nonlinearity, Multiple-Input and Multiple-Output (MIMO), complexity and coupling effect features of the systems. The significance of this method and improvements by this method are compared with other methods.


title: Introduction to the Special Issue on Machine Learning for Multiple Modalities in Interactive Systems and Robots

abstract: This special issue highlights research articles that apply machine learning to robots and other systems that interact with users through more than one modality, such as speech, gestures, and vision. For example, a robot may coordinate its speech with its actions, taking into account (audio-)visual feedback during their execution. Machine learning provides interactive systems with opportunities to improve performance not only of individual components but also of the system as a whole. However, machine learning methods that encompass multiple modalities of an interactive system are still relatively hard to find. The articles in this special issue represent examples that contribute to filling this gap.


title: Applying an extended model of deterrence across cultures: An investigation of information systems misuse in the U.S. and South Korea

abstract: Intentional employee misuse of IS is a global problem. Research suggests that security countermeasures can deter misuse by increasing the perceived certainty and severity of punishment for such behavior. However, the value of generalizing this work beyond Western cultures is open to question. In our study, we examined whether national culture influenced the deterrent capabilities of security policies, security education, training, and awareness programs and computer monitoring. Using U.S. and Korean samples, we found evidence that the deterrent effect of certain security countermeasures varied between the two countries, as did the influence of age and gender. The results have implications for information security management practices in global businesses.


title: Diversified Sensitivity-Based Undersampling for Imbalance Classification Problems

abstract: Undersampling is a widely adopted method to deal with imbalance pattern classification problems. Current methods mainly depend on either random resampling on the majority class or resampling at the decision boundary. Random-based undersampling fails to take into consideration informative samples in the data while resampling at the decision boundary is sensitive to class overlapping. Both techniques ignore the distribution information of the training dataset. In this paper, we propose a diversified sensitivity-based undersampling method. Samples of the majority class are clustered to capture the distribution information and enhance the diversity of the resampling. A stochastic sensitivity measure is applied to select samples from both clusters of the majority class and the minority class. By iteratively clustering and sampling, a balanced set of samples yielding high classifier sensitivity is selected. The proposed method yields a good generalization capability for 14 UCI datasets.


title: Joint Dimension Reduction and Metric Learning for Person Re-identification

abstract: Person re-identification is an important technique towards automatic search of a person's presence in a surveillance video. Among various methods developed for person re-identification, the Mahalanobis metric learning approaches have attracted much attention due to their impressive performance. In practice, many previous papers have applied the Principle Component Analysis (PCA) for dimension reduction before metric learning. However, this may not be the optimal way for metric learning in low dimensional space. In this paper, we propose to jointly learn the discriminant low dimensional subspace and the distance metric. This is achieved by learning a projection matrix and a Restricted Quadratic Discriminant Analysis (RQDA) model. We show that the problem can be formulated as a Generalized Rayleigh Quotient, and a closed-form solution can be obtained by the generalized eigenvalue decomposition. We also present a practical computation method for RQDA, as well as its regularization. For the application of person re-identification, we propose a Retinex and maximum occurrence based feature representation method, which is robust to both illumination and viewpoint changes. Experiments on two challenging public databases, VIPeR and QMUL GRID, show that the performance of the proposed method is comparable to the state of the art.


Adaptive Co-attention Network for Named Entity Recognition in Tweets


title: Embodied narratives: a performative co-design technique

abstract: This paper describes a new co-design technique for early stages of the design process called Embodied Narratives. The technique leverages children's natural playfulness to inspire and motivate the design process, and it uses technology as a part of the process to encourage exploration and improvisation in familiar settings. Its outcome is not the final design but valuable material for further co-design activities. The paper expands the set of available techniques that can be used for co-designing with children, and contributes to growing research on the use of performances in design through empirical studies conducted with 36 children from age 10 to 11.


P 03 CMOS terahertz imaging pixel with a small on-chip antenna


title: Small-molecule inhibitors in myeloproliferative neoplasms: are we aiming for the right targets?

abstract: The ATP-binding pocket of the kinase domain of JAK2 is the major target of the present treatment of myeloproliferative neoplasms. Several inhibitors of JAK2 that are ATP competitive have been developed, but they do not discriminate between wild-type and mutant JAK2. These inhibitors have been used in myelofibrosis and, for the first time, treatment induced a reduction in spleen size and in constitutional symptoms. However, no dramatic effects on BM fibrosis, allele burden, or peripheral blast numbers were observed. These data indicate that other avenues should be explored that would either target mutant molecules (JAKs or receptors) more specifically and spare wild-type JAK2 or that would address other pathways that contribute to the malignant proliferation. Future success in treating myeloproliferative neoplasms will depend on advances of the understanding of JAK-STAT signaling and also on a better understanding of the disease pathogenesis, especially the role that mutants in spliceosome factors and epigenetic regulators play in the phenotype of the disease and the precise mechanism of fibrosis development.


title: Full-Duplex Communication in Cognitive Radio Networks: A Survey

abstract: Wireless networks with their ubiquitous applications have become an indispensable part of our daily lives. Wireless networks demand more and more spectral resources to support the ever increasing numbers of users. According to network engineers, the current spectrum crunch can be addressed with the introduction of cognitive radio networks (CRNs). In half-duplex (HD) CRNs, the secondary users (SUs) can either only sense the spectrum or transmit at a given time. This HD operation limits the SU throughput, because the SUs cannot transmit during the spectrum sensing. However, with the advances in self-interference suppression (SIS), full-duplex (FD) CRNs allow for simultaneous spectrum sensing and transmission on a given channel. This FD operation increases the throughput and reduces collisions as compared with HD-CRNs. In this paper, we present a comprehensive survey of FD-CRN communications. We cover the supporting network architectures and the various transmit and receive antenna designs. We classify the different SIS approaches in FD-CRNs. We survey the spectrum sensing approaches and security requirements for FD-CRNs. We also survey major advances in FD medium access control protocols as well as open issues, challenges, and future research directions to support the FD operation in CRNs.


title: How Data Breaches Ruin Firm Reputation on Social Media! - Insights from a Sentiment-based Event Study

abstract: Data breach events are heavily discussed in social media. Data breaches, which imply the loss of personal sensitive data, have negative consequences on the affected firms such as loss of market value, loss of customers and reputational damage. In the digital era, wherein ensuring information security is extremely demanding and the dissemination of information occurs at a very high speed, protecting corporate reputation has become a challenging task. While several studies have provided empirical evidence of the financial consequences of data breaches, little attention has been dedicated to the link between data breaches and reputational risk. To address this research gap, we have measured the reputational effect of data breaches based on social media content by applying a novel approach, the sentiment-based event study. The empirical results provide strong evidence that data breach events deteriorate the reputation of companies involved in such incidents.


Financial Time Series Forecasting - A Machine Learning Approach


title: Strategies for alveolar ridge reconstruction and preservation for implant therapy

abstract: Abstract Purpose In dental implant treatment, ridge preservation and immediate or early implant placement are recommended to minimize bone resorption after tooth extraction and achieve esthetic outcomes. However, there is no consensus concerning the efficacy of this surgical method. There is also no consensus on the efficacy of bone and soft tissue grafts and surgical methods for alveolar ridge reconstruction. Study selection This paper reports ridge alteration in the anterior maxilla after tooth extraction, and summarizes the efficacy of various ridge preservation methods and immediate or early implant placement as alveolar ridge preservation methods to minimize bone resorption after tooth extraction. The advantages and complications of alveolar ridge reconstruction methods, and the efficacy and surgical method of soft tissue graft are reviewed. Results The anterior maxilla is in the esthetic zone, and the thickness of the bone on the labial side around the natural tooth is less than 1mm in many cases. Therefore, it is impossible to prevent bone resorption completely, even if ridge preservation and immediate or early implant placement are performed after tooth extraction. It is necessary to obtain stable and long-term esthetics by combining connective tissue and free gingival grafts, in addition to hard tissue augmentation. Conclusions It is important to consider the burden and level of satisfaction of patients, such as in terms of donor site morbidity in hard and soft tissue grafting, and to pay attention to appropriate indications to avoid overtreatment.


LCL filter design for photovoltaic grid connected systems


title: Plagiarism Detection based on studying correlation between Author , Title and Content

abstract: Recently, the problem of plagiarism is becoming an important issue in many debates in the fields of Education and Technology. The wide use and availability of electronic resources makes it easy for students, authors and even academic people to access and use any piece of information and embed it into his/ her own work without proper citation. The problem is raising in an exponential manner the thing which puts the education process under threat. Several tools are presented to solve the problem of automating plagiarism detection each of which has its own good and bad features, but still the traditional way of plagiarism detection through free text search using search engines is considered an accurate and free way to detect plagiarism with the only disadvantage of being a time consuming method. This research intends to present an alternative to plagiarism detection tools by automating the traditional free search process on search engines to detect plagiarism by intelligently extracting selective parts of text from the file subject to check and pass them to search engine in different forms and processing results in order to come up with a decision of committing plagiarism in a certain degree. The approach used in this paper is to make string comparison of the text with the global www, which makes it more comprehensive compared to other plagiarism tools that depend on specific databases.


title: Opportunities and constraints: the current struggle with BPMN

abstract: Purpose – The business process modeling notation (BPMN) is an increasingly important standard for process modeling and has enjoyed high levels of attention in business practice. In this paper, experiences are shared from several research projects investigating the uptake and user acceptance of BPMN by analysts world‐wide. This personal viewpoint aims to offer a number of implications for business process management (BPM) practice and seeks to stimulate and guide further research and other developments in this area.Design/methodology/approach – This paper offers a personal viewpoint based on the experiences and findings gathered from survey research and interviews on the use of BPMN. While details on research execution are mostly omitted, references are provided to guide the interested reader to the methodology used in the original studies.Findings – First, statistics are provided on the usage of BPMN by process modelers world‐wide. Amongst others, it is shown that the high interest in BPMN has created a m...


title: Implicit Authentication through Learning User Behavior

abstract: Users are increasingly dependent on mobile devices. However, current authentication methods like password entry are significantly more frustrating and difficult to perform on these devices, leading users to create and reuse shorter passwords and pins, or no authentication at all. We present implicit authentication - authenticating users based on behavior patterns. We describe our model for performing implicit authentication and assess our techniques using more than two weeks of collected data from over 50 subjects.


title: An On-Chip Learning Neuromorphic Autoencoder With Current-Mode Transposable Memory Read and Virtual Lookup Table

abstract: This paper presents an IC implementation of on-chip learning neuromorphic autoencoder unit in a form of rate-based spiking neural network. With a current-mode signaling scheme embedded in a 500 × 500 6b SRAM-based memory, the proposed architecture achieves simultaneous processing of multiplications and accumulations. In addition, a transposable memory read for both forward and backward propagations and a virtual lookup table are also proposed to perform an unsupervised learning of restricted Boltzmann machine. The IC is fabricated using 28-nm CMOS process and is verified in a three-layer network of encoder-decoder pair for training and recovery of images with two-dimensional pixels. With a dataset of 50 digits, the IC shows a normalized root mean square error of 0.078. Measured energy efficiencies are 4.46 pJ per synaptic operation for inference and 19.26 pJ per synaptic weight update for learning, respectively. The learning performance is also estimated by simulations if the proposed hardware architecture is extended to apply to a batch training of 60 000 MNIST datasets.


title: Minimal Coflow Routing and Scheduling in OpenFlow-Based Cloud Storage Area Networks

abstract: Researches affirm that coflow scheduling/routing substantially shortens the average application inner communication time in data center networks(DCNs). The commonly desirable critical features of existing coflow scheduling/routing framework includes (1) coflow scheduling, (2) coflow routing, and (3) per-flow rate-limiting. However, to provide the 3 features, existing frameworks require customized computing frameworks, customized operating systems, or specific external commercial monitoring frameworks on software-defined networking(SDN) switches. These requirements defer or even prohibit the deployment of coflow scheduling/routing in production DCNs. In this paper, we design a coflow scheduling and routing framework, MinCOF which has minimal requirements on hosts and switches for cloud storage area networks(SANs) based on OpenFlow SDN. MinCOF accommodates all critical features of coflow scheduling/routing from previous works. The deployability in production environment is especially taken into consideration. The OpenFlow architecture is capable of processing the traffic load in a cloud SAN. Not necessary requirements for hosts from existing frameworks are migrated to the mature commodity OpenFlow 1.3 Switch and our coflow scheduler. Transfer applications on hosts only need slight enhancements on their existing connection establishment and progress reporting functions. Evaluations reveal that MinCOF decreases the average coflow completion time (CCT) by 12.94% compared to the latest OpenFlow-based coflow scheduling and routing framework.


title: How do antidepressants work? New perspectives for refining future treatment approaches

abstract: Summary Most currently available antidepressants target monoamine neurotransmitter function. However, a purely neurotransmitter-based explanation for antidepressant drug action is challenged by the delayed clinical onset of most agents and the need to explain how neurochemical changes reverse the many different symptoms of depression. Novel approaches to understanding of antidepressant drug action include a focus on early changes in emotional and social processing and the role of neural plasticity. In this Review, we discuss the ways in which these two different theories reflect different or complementary approaches, and how they might be integrated to offer novel solutions for people with depression. We consider the predictions made by these mechanistic approaches for the stratification and development of new therapeutics for depression, and the next steps that need to be made to facilitate this translation of science to the clinic.


A Linear Fleshy Corrugated Plaque on the Right Vulva: A Diagnostic Surprise


title: DehazeNet: An End-to-End System for Single Image Haze Removal

abstract: Single image haze removal is a challenging ill-posed problem. Existing methods use various constraints/priors to get plausible dehazing solutions. The key to achieve haze removal is to estimate a medium transmission map for an input hazy image. In this paper, we propose a trainable end-to-end system called DehazeNet, for medium transmission estimation. DehazeNet takes a hazy image as input, and outputs its medium transmission map that is subsequently used to recover a haze-free image via atmospheric scattering model. DehazeNet adopts convolutional neural network-based deep architecture, whose layers are specially designed to embody the established assumptions/priors in image dehazing. Specifically, the layers of Maxout units are used for feature extraction, which can generate almost all haze-relevant features. We also propose a novel nonlinear activation function in DehazeNet, called bilateral rectified linear unit, which is able to improve the quality of recovered haze-free image. We establish connections between the components of the proposed DehazeNet and those used in existing methods. Experiments on benchmark images show that DehazeNet achieves superior performance over existing methods, yet keeps efficient and easy to use.


title: Personality and music: Can traits explain how people use music in everyday life?

abstract: This paper reports the results of a study on the relationship between individual differences and specific uses of music, referring to why and how people use music in everyday life. Questionnaire data from 341 respondents showed that open and intellectually engaged individuals, and those with higher IQ scores, tended to use music in a rational/cognitive way, while neurotic, introverted and non-conscientious individuals were all more likely to use music for emotional regulation (e.g. change or enhance moods). Results suggest that individual differences in personality and cognitive ability may partly determine the way in which we experience music. Limitations and suggestions for future studies are discussed.


title: Design of fractional notch filter with asymmetric slopes and large values of notch magnitude

abstract: In this paper, we propose a 2nd order fractional band reject filter (FBRF) transfer function. It is based on the concept of introducing a fractional capacitor (Cα) having impedance, Zα = 1 over sαCα where 0 <; α <; 1. It is possible to achieve asymmetric slopes and large values of notch magnitude up to -60 dB using this filter. In addition, unlike integer order filter, independent control of slopes above and below the notch frequency can be achieved by simply changing the values of α. The operation of this filter is verified by plotting PSPICE simulation results for three different values of α = 0.1; 0.5; 0.8 and showing its comparison with experimental results.


Experimentally Reducing Partisan Incivility on Twitter


title: HIVE: Home Automation System for Intrusion Detection

abstract: Home intrusion plays a role and requires a system to notify and activate homeowners to be alert and protect their home assets in time. HIVE is proposed for home intrusion detection and devised in three parts. First, sensor manager is composed of sensor manager acting as ZigBee coordinator communicating with ZigBee sensors and actuators. Three different sensor types are passive infrared for detecting motion, magnetic switch for opening/closing of door detection, and load cell for the weight gauge from pressing steps on the floor. Two actuators, composing of piezo alarm and LED, are activated by sensor manager. Second, Firebase is developed as cloud database and user authentication service. Third, an android application is deployed for end-user for monitoring, configuring, and being notified remotely. As a result, sensor and actuator modules can join the ZigBee network automatically. Sensor Manager sends updated sensors' and actuators' data to Firebase, consequently, home users can be notified intrusion events via the android application, and remotely configure events, sensors, and actuators anywhere anytime via the Internet. Therefore, this system can enhance security and safety for home assets.


title: A Finger-Vein Based Cancellable Bio-cryptosystem

abstract: Irrevocability is one major issue in existing bio-cryptosystems. In this paper, we proposed a cancellable bio-cryptosystem by taking the full advantage of cancellable and non-invertible properties of bio-hashing biometrics. Specifically, two transformed templates are generated by using the bio-hashing algorithm and applied into two different secure sketches, fuzzy commitment sketch and fuzzy vault sketch, respectively. These two secure sketches can be fused in two different ways: AND fusion and OR fusion, so as to emphasis either on the recognition accuracy or the security level of the system. Experimental results and security analysis show the validity of the proposed scheme.


title: Comparison of Operating Systems TinyOS and Contiki

abstract: Wireless sensor networks can be used in a lot of di↵erent application areas. Since such networks were first proposed, many di↵erent node platforms both in regard to hardware and software were introduced. In this paper we present operating systems for wireless sensor nodes in general and two of the best known operating systems, TinyOS and Contiki with their most notable di↵erences and similarities. Both have strengths and weaknesses which are important for various requirements of wireless sensor networks.


title: Real versus imagined locomotion: A [18F]-FDG PET-fMRI comparison

abstract: Abstract The cortical, cerebellar and brainstem BOLD-signal changes have been identified with fMRI in humans during mental imagery of walking. In this study the whole brain activation and deactivation pattern during real locomotion was investigated by [ 18 F]-FDG-PET and compared to BOLD-signal changes during imagined locomotion in the same subjects using fMRI. Sixteen healthy subjects were scanned at locomotion and rest with [ 18 F]-FDG-PET. In the locomotion paradigm subjects walked at constant velocity for 10 min. Then [ 18 F]-FDG was injected intravenously while subjects continued walking for another 10 min. For comparison fMRI was performed in the same subjects during imagined walking. During real and imagined locomotion a basic locomotion network including activations in the frontal cortex, cerebellum, pontomesencephalic tegmentum, parahippocampal, fusiform and occipital gyri, and deactivations in the multisensory vestibular cortices (esp. superior temporal gyrus, inferior parietal lobule) was shown. As a difference, the primary motor and somatosensory cortices were activated during real locomotion as distinct to the supplementary motor cortex and basal ganglia during imagined locomotion. Activations of the brainstem locomotor centers were more prominent in imagined locomotion. In conclusion, basic activation and deactivation patterns of real locomotion correspond to that of imagined locomotion. The differences may be due to distinct patterns of locomotion tested. Contrary to constant velocity real locomotion (10 min) in [ 18 F]-FDG-PET, mental imagery of locomotion over repeated 20-s periods includes gait initiation and velocity changes. Real steady-state locomotion seems to use a direct pathway via the primary motor cortex, whereas imagined modulatory locomotion an indirect pathway via a supplementary motor cortex and basal ganglia loop.


RIFO revisited: Detecting relaxed irrelevance


title: In Vitro Analysis of α-Amanitin-Resistant Transcription from the rRNA, Procyclic Acidic Repetitive Protein, and Variant Surface Glycoprotein Gene Promoters inTrypanosoma brucei

abstract: In Trypanosoma brucei, transcription resistant to the mushroom toxin α-amanitin is not restricted to the rRNA genes (rDNA), as in higher eukaryotes, but extends to genes encoding the major cell surface proteins variant surface glycoprotein (VSG) and procyclin or procyclic acidic repetitive protein (PARP). Here, we report the development of a homologous cell extract from procyclic T. brucei cells in which rDNA and PARP A and VSG gene promoters drive efficient, accurate, and α-amanitin-resistant transcription. A comparative analysis revealed that transcription from the three promoters generally required identical reaction conditions for maximal efficiency. Nevertheless, PARP promoter transcription proved to be exceptional by its high efficiency, its lag phase, a high template DNA concentration optimum, and its tolerance to increasing concentrations of Mn2+. Mutational analysis for both the PARP and rDNA promoters showed that the proximal and distal core elements were essential for efficient transcription in vitro. Deletion of the upstream control regions (UCRs), however, had a different effect. Whereas PARP UCR deletion reduced transcription efficiency almost 10-fold, deletion of the rDNA UCR had only a minor effect on transcription efficiency.


E-procurement for increasing agility of Business Processes : A Case study & Critical Research Reviews


title: Modeling and Experiments of Untethered Quadrupedal Running with a Bounding Gait: The Scout II Robot

abstract: In this paper we compare models and experiments involving Scout II, an untethered four-legged running robot with only one actuator per compliant leg. Scout II achieves dynamically stable running of up to 1.3 m s-1 on flat ground via a bounding gait. Energetics analysis reveals a highly efficient system with a specific resistance of only 1.4. The running controller requires no task-level or body-state feedback, and relies on the passive dynamics of the mechanical system. These results contribute to the increasing evidence that apparently complex dynamically dexterous tasks may be controlled via simple control laws. We discuss general modeling issues for dynamically stable legged robots. Two simulation models are compared with experimental data to test the validity of common simplifying assumptions. The need for including motor saturation and non-rigid torque transmission characteristics in simulation models is demonstrated. Similar issues are likely to be important in other dynamically stable legged robots as well. An extensive suite of experimental results documents the robot's performance and the validity of the proposed models.


title: A highly efficient 1-Watt broadband class-J SiGe power amplifier at 700MHz

abstract: In this paper, the design and measurement results of a highly efficient 1-Watt broadband class J SiGe power amplifier (PA) at 700 MHz are reported. Comparisons between a class J PA and a traditional class AB/B PA have been made, first through theoretical analysis in terms of load network, efficiency and bandwidth behavior, and secondly by bench measurement data. A single-ended power cell is designed and fabricated in the 0.35 μm IBM 5PAe SiGe BiCMOS technology with through-wafer-vias (TWVs). Watt-level output power with greater than 50% efficiency is achieved on bench across a wide bandwidth of 500 MHz to 900 MHz for the class J PA (i.e., >57% bandwidth at the center frequency of 700 MHz). P sat of 30.9 dBm with 62% collector efficiency (CE) at 700 MHz is measured while the highest efficiency of 68.9% occurs at 650 MHz using a 4.2 V supply. Load network of this class J PA is realized with lumped passive components on a FR4 printed circuit board (PCB). A narrow-band class AB PA counterpart is also designed and fabricated for comparison. The data suggests that the broadband class J SiGe PA can be promising for future multi-band wireless applications.


title: Fractures of the mandibular condyle--Open versus closed--A treatment dilemma.

abstract: PURPOSE ::: The aim of this prospective randomized controlled study was to evaluate the superiority of open method of treatment of mandibular condylar fracture over closed method of treatment by comparing them. ::: ::: ::: METHODS AND PATIENTS ::: Out of a total of 50 randomized patients fractures of the mandibular condylar process completed the study and were evaluated. All fractures were displaced, being either angulated between 10° and 45°. The follow-up examinations 1st day, 2nd day, 1st week, 2nd week, 6th week, and 6 months following treatment included clinical evaluation of functional and subjective parameters including visual analogue scale for pain and the interincisal mouth opening, malocclusion, deviation of mouth, range of motion, radiographic measurements were done. ::: ::: ::: RESULTS ::: No significant difference was found between the two groups in the maximal interincisal opening, range of movements and TMJ pain. However statistically significant difference was seen in the anatomic reduction of condyle, shortening of ascending ramus, occlusal status and deviation on mouth opening on immediate postoperative phase. ::: ::: ::: CONCLUSION ::: Both treatment options for condylar fractures of the mandible yielded acceptable results. However, a statistically significant difference was seen in the anatomic reduction of the condyle and there was no deviation on maximum mouth opening in patients treated with open reduction and internal fixation which suggests its superiority over closed method.


title: Isotropic Remeshing of Surfaces: A Local Parameterization Approach

abstract: We present a method for isotropic remeshing of arbitrary genus surfaces. The method is based on a mesh adaptation process, namely, a sequence of local modifications performed on a copy of the original mesh, while referring to the original mesh geometry. The algorithm has three stages. In the first stage the required number or vertices are generated by iterative simplification or refinement. The second stage performs an initial vertex partition using an area-based relaxation method. The third stage achieves precise isotropic vertex sampling prescribed by a given density function on the mesh. We use a modification of Lloyd's relaxation method to construct a weighted centroidal Voronoi tessellation of the mesh. We apply these iterations locally on small patches of the mesh that are parameterized into the 2D plane. This allows us to handle arbitrary complex meshes with any genus and any number of boundaries. The efficiency and the accuracy of the remeshing process is achieved using a patch-wise parameterization technique.


title: Multilinear Spatial Discriminant Analysis for Dimensionality Reduction

abstract: In the last few years, great efforts have been made to extend the linear projection technique (LPT) for multidimensional data (i.e., tensor), generally referred to as the multilinear projection technique (MPT). The vectorized nature of LPT requires high-dimensional data to be converted into vector, and hence may lose spatial neighborhood information of raw data. MPT well addresses this problem by encoding multidimensional data as general tensors of a second or even higher order. In this paper, we propose a novel multilinear projection technique, called multilinear spatial discriminant analysis (MSDA), to identify the underlying manifold of high-order tensor data. MSDA considers both the nonlocal structure and the local structure of data in the transform domain, seeking to learn the projection matrices from all directions of tensor data that simultaneously maximize the nonlocal structure and minimize the local structure. Different from multilinear principal component analysis (MPCA) that aims to preserve the global structure and tensor locality preserving projection (TLPP) that is in favor of preserving the local structure, MSDA seeks a tradeoff between the nonlocal (global) and local structures so as to drive its discriminant information from the range of the non-local structure and the range of the local structure. This spatial discriminant characteristic makes MSDA have more powerful manifold preserving ability than TLPP and MPCA. Theoretical analysis shows that traditional MPTs, such as multilinear linear discriminant analysis, TLPP, MPCA, and tensor maximum margin criterion, could be derived from the MSDA model by setting different graphs and constraints. Extensive experiments on face databases (ORL, CMU PIE, and the extended Yale-B) and the Weizmann action database demonstrate the effectiveness of the proposed MSDA method.


title: Differentially Private K-Means Clustering

abstract: There are two broad approaches for differentially private data analysis. The interactive approach aims at developing customized differentially private algorithms for various data mining tasks. The non-interactive approach aims at developing differentially private algorithms that can output a synopsis of the input dataset, which can then be used to support various data mining tasks. In this paper we study the effectiveness of the two approaches on differentially private k-means clustering. We develop techniques to analyze the empirical error behaviors of the existing interactive and non-interactive approaches. Based on the analysis, we propose an improvement of DPLloyd which is a differentially private version of the Lloyd algorithm. We also propose a non-interactive approach EUGkM which publishes a differentially private synopsis for k-means clustering. Results from extensive and systematic experiments support our analysis and demonstrate the effectiveness of our improvement on DPLloyd and the proposed EUGkM algorithm.


title: Design principles of a human mimetic humanoid: Humanoid platform to study human intelligence and internal body system

abstract: Many systems and mechanisms in the human body are not fully understood, such as the principles of muscle control, the sensory nervous system that connects the brain and the body, learning in the brain, and the human walking motion. To address this knowledge deficit, we propose a human mimetic humanoid with an unprecedented degree of anatomical fidelity to the human musculoskeletal structure. The fundamental concept underlying our design is to consider the human mechanism, which contrasts with the conventional engineering approach used in the design of existing humanoids. We believe that the proposed human mimetic humanoid can be used to provide new opportunities in science, for instance, to quantitatively analyze the internal data of a human body in movement. We describe the principles and development of human mimetic humanoids, Kenshiro and Kengoro, and compare their anatomical fidelity with humans in terms of body proportions, skeletal structures, muscle arrangement, and joint performance. To demonstrate the potential of human mimetic humanoids, Kenshiro and Kengoro performed several typical human motions.


title: Amylin Selectively Signals Onto POMC Neurons in the Arcuate Nucleus of the Hypothalamus.

abstract: Amylin phosphorylates ERK (p-ERK) in the area postrema to reduce eating and synergizes with leptin to phosphorylate STAT3 in the arcuate (ARC) and ventromedial (VMN) hypothalamic nuclei to reduce food intake and body weight. The current studies assessed potential amylin and amylin-leptin ARC/VMN interactions on ERK signaling and their roles in postnatal hypothalamic pathway development. In amylin knockout mice, the density of agouti-related protein (AgRP)-immunoreactive (IR) fibers in the hypothalamic paraventricular nucleus (PVN) was increased, while the density of α-melanocyte-stimulating hormone (αMSH) fibers was decreased. In mice deficient of the amylin receptor components RAMP1/3, both AgRP and αMSH-IR fiber densities were decreased, while only αMSH-IR fiber density was decreased in rats injected neonatally in the ARC/VMN with an adeno-associated virus short hairpin RNA against the amylin core receptor. Amylin induced p-ERK in ARC neurons, 60% of which was present in POMC-expressing neurons, with none in NPY neurons. An amylin-leptin interaction was shown by an additive effect on ARC ERK signaling in neonatal rats and a 44% decrease in amylin-induced p-ERK in the ARC of leptin receptor-deficient and of ob/ob mice. Together, these results suggest that amylin directly acts, through a p-ERK-mediated process, on POMC neurons to enhance ARC-PVN αMSH pathway development.


title: Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models

abstract: In recent years, deep neural network approaches have been widely adopted for machine learning tasks, including classification. However, they were shown to be vulnerable to adversarial perturbations: carefully crafted small perturbations can cause misclassification of legitimate images. We propose Defense-GAN, a new framework leveraging the expressive capability of generative models to defend deep neural networks against such attacks. Defense-GAN is trained to model the distribution of unperturbed images and, at inference time, finds a close output to a given image. This output will not contain the adversarial changes and is fed to the classifier. Our proposed method can be used with any classification model and does not modify the classifier structure or training procedure. It can also be used as a defense against any attack as it does not assume knowledge of the process for generating the adversarial examples. We empirically show that Defense-GAN is consistently effective against different attack methods and improves on existing defense strategies.