title: Storage-Less and Converter-Less Photovoltaic Energy Harvesting With Maximum Power Point Tracking for Internet of Things

abstract: Energy harvesting from natural environment gives range of benefits for the Internet of things. Scavenging energy from photovoltaic (PV) cells is one of the most practical solutions in terms of power density among existing energy harvesting sources. PV power systems mandate the maximum power point tracking (MPPT) to scavenge the maximum possible solar energy. In general, a switching-mode power converter, an MPPT charger, controls the charging current to the energy storage element (a battery or equivalent), and the energy storage element provides power to the load device. The mismatch between the maximum power point (MPP) current and the load current is managed by the energy storage element. However, such architecture causes significant energy loss (typically over 20%) and a significant weight/volume and a high cost due to the cascaded power converters and the energy storage element. This paper pioneers a converter-less PV power system with the MPPT that directly supplies power to the load without the power converters or the energy storage element. The proposed system uses a nonvolatile microprocessor to enable an extremely fine-grain dynamic power management in a few hundred microseconds. This makes it possible to match the load current with the MPP current. We present detailed modeling, simulation, and optimization of the proposed energy harvesting system including the radio frequency transceiver. Experiments show that the proposed setup achieves an 87.1% of overall system efficiency during a day, 30.6% higher than the conventional MPPT methods in actual measurements, and thus a significantly higher duty cycle under a weak solar irradiance.


Platform Thinking in Incumbent Firms: From Concept to Capability


title: Intelligent control battery equalization for series connected lithium-ion battery strings

abstract: An intelligent battery equalization scheme based on fuzzy logic control is presented to adaptively control the equalizing process of series-connected lithium-ion batteries. The proposed battery equalization scheme is a bidirectional dc-dc converter with energy transferring capacitor that can be used to design the bidirectional nondissipative equalizer for a battery balancing system. Furthermore, it can be designed as a ripple-free converter for improving the input current distortion of the battery charge supply power system. A fuzzy-logic-controlled strategy is constructed with a set of membership functions to prescribe the cells equalizing behavior within a safe equalizing region for rapid cell voltage balancing. The simulation and experimental results show the advantage of the predicted equalizing performance of the lithium-ion battery stacks. The proposed fuzzy logic control battery equalization controller can abridge the equalization time about 32%. The proposed method maintains safe operation during the charge/discharge state in each lithium-ion cell of the battery strings.


title: Conglobation in the Pill Bug, Armadillidium vulgare, as a Water Conservation Mechanism

abstract: Water balance of the terrestrial isopod, Armadillidium vulgare, was investigated during conglobation (rolling-up behavior). Water loss and metabolic rates were measured at 18 +/- 1 degrees C in dry air using flow-through respirometry. Water-loss rates decreased 34.8% when specimens were in their conglobated form, while CO2 release decreased by 37.1%. Water loss was also measured gravimetrically at humidities ranging from 6 to 75 %RH. Conglobation was associated with a decrease in water-loss rates up to 53 %RH, but no significant differences were observed at higher humidities. Our findings suggest that conglobation behavior may help to conserve water, in addition to its demonstrated role in protection from predation.


title: ASAGI: A Parallel Server for Adaptive Geoinformation

abstract: We present ASAGI, an open-source library with a simple interface to access Cartesian material and geographic datasets in massively parallel simulations with dynamically adaptive mesh refinement (AMR). ASAGI distributes geographic datasets over all compute nodes storing only a portion of the dataset on each node. An automatic replication mechanism copies the data between nodes to assure fast local access even after load migration in the application. We demonstrate ASAGI's preparedness for up-to-petascale simulations in three use cases. We simulate a Tsunami on 512 cores and a porous media flow on up to 8,192 cores of SuperMUC with the AMR framework sam(oa)2. We also run an earthquake simulation with SeiSol on 65,536 cores. For all applications, ASAGI provides large complex 3D material datasets required for the realistic scenarios. The NUMA-awareness of ASAGI turned out to be especially useful for the hybrid MPI+OpenMP parallelization of both codes.


title: Does Asymmetric Information Drive Capital Structure Decisions?

abstract: Using a novel information asymmetry index based on measures of adverse selection developed by the market microstructure literature, we test whether information asymmetry is an important determinant of capital structure decisions, as suggested by the pecking order theory. Our index relies exclusively on measures of the market's assessment of adverse selection risk rather than on ex ante firm characteristics. We find that information asymmetry does affect the capital structure decisions of U.S. firms over the sample period 1973--2002. Our findings are robust to controlling for conventional leverage factors (size, tangibility, Q ratio, profitability), the sources of firms' financing needs, and such firm attributes as stock return volatility, stock turnover, and intensity of insider trading. For example, we estimate that on average, for every dollar of financing deficit to cover, firms in the highest adverse selection decile issue 30 cents of debt more than firms in the lowest decile. Overall, this evidence explains why the pecking order theory is only partially successful in explaining all of firms' capital structure decisions. It also suggests that the theory finds support when its basic assumptions hold in the data, as should reasonably be expected of any theory. The Author 2008. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org, Oxford University Press.


title: ZVS Soft Switching Boundaries for Dual Active Bridge DC–DC Converters Using Frequency Domain Analysis

abstract: Dual active bridge (DAB) converters offer an unmatched capability to transfer energy in either direction between two dc sources, while also providing galvanic isolation and high conversion efficiency. However, to operate at higher efficiencies, the bridges must operate with zero voltage switching (ZVS) over as wide an operating range as possible. The conventional approach to determine ZVS operation uses time domain analysis with ideal ac coupling inductances, which only approximately identifies the ZVS boundaries. This paper proposes a new approach using harmonic decomposition of the bridge switching patterns, which gives an explicit theoretical solution under all operating conditions, while also accommodating more complex ac coupling structures, practical impedance nonidealities, and the switching impact of dead-time and device capacitance. The methodology is confirmed by matching analytical predictions with experimental results for selected DAB systems.


title: Towards Proving the Adversarial Robustness of Deep Neural Networks

abstract: Autonomous vehicles are highly complex systems, required to function reliably in a wide variety of situations. Manually crafting software controllers for these vehicles is difficult, but there has been some success in using deep neural networks generated using machine-learning. However, deep neural networks are opaque to human engineers, rendering their correctness very difficult to prove manually; and existing automated techniques, which were not designed to operate on neural networks, fail to scale to large systems. This paper focuses on proving the adversarial robustness of deep neural networks, i.e. proving that small perturbations to a correctly-classified input to the network cannot cause it to be misclassified. We describe some of our recent and ongoing work on verifying the adversarial robustness of networks, and discuss some of the open questions we have encountered and how they might be addressed.


title: Evolutionary Competition in Platform Ecosystems

abstract: Intraplatform competition has received scant attention in prior studies, which predominantly study interplatform competition. We develop a middle-range theory of how complementarity between input control and a platform extension's modularization-by inducing evolution-influences its performance in a platform market. Primary and archival data spanning five years from 342 Firefox extensions show that such complementarity fosters performance by accelerating an extension's perpetual evolution.


title: Graphene Photonics and Optoelectronics

abstract: The richness of optical and electronic properties of graphene attracts enormous interest. Graphene has high mobility and optical transparency, in addition to flexibility, robustness and environmental stability. So far, the main focus has been on fundamental physics and electronic devices. However, we believe its true potential to be in photonics and optoelectronics, where the combination of its unique optical and electronic properties can be fully exploited, even in the absence of a bandgap, and the linear dispersion of the Dirac electrons enables ultra-wide-band tunability. The rise of graphene in photonics and optoelectronics is shown by several recent results, ranging from solar cells and light emitting devices, to touch screens, photodetectors and ultrafast lasers. Here we review the state of the art in this emerging field.


title: Enabling improved power management in multicore processors through clustered DVFS

abstract: In recent years, chip multiprocessors (CMP) have emerged as a solution for high-speed computing demands. However, power dissipation in CMPs can be high if numerous cores are simultaneously active. Dynamic voltage and frequency scaling (DVFS) is widely used to reduce the active power, but its effectiveness and cost depends on the granularity at which it is applied. Per-core DVFS allows the greatest flexibility in controlling power, but incurs the expense of an unrealistically large number of on-chip voltage regulators. Per-chip DVFS, where all cores are controlled by a single regulator overcomes this problem at the expense of greatly reduced flexibility. This work considers the problem of building an intermediate solution, clustering the cores of a multicore processor into DVFS domains and implementing DVFS on a per-cluster basis. Based on a typical workload, we propose a scheme to find similarity among the cores and cluster them based on this similarity. We also provide an algorithm to implement DVFS for the clusters, and evaluate the effectiveness of per-cluster DVFS in power reduction.


title: Post-click conversion modeling and analysis for non-guaranteed delivery display advertising

abstract: In on-line search and display advertising, the click-trough rate (CTR) has been traditionally a key measure of ad/campaign effectiveness. More recently, the market has gained interest in more direct measures of profitability, one early alternative is the conversion rate (CVR). CVRs measure the proportion of certain users who take a predefined, desirable action, such as a purchase, registration, download, etc.; as compared to simply page browsing. We provide a detailed analysis of conversion rates in the context of non-guaranteed delivery targeted advertising. In particular we focus on the post-click conversion (PCC) problem or the analysis of conversions after a user click on a referring ad. The key elements we study are the probability of a conversion given a click in a user/page context, P(conversion | click, context). We provide various fundamental properties of this process based on contextual information, formalize the problem of predicting PCC, and propose an approach for measuring attribute relevance when the underlying attribute distribution is non-stationary. We provide experimental analyses based on logged events from a large-scale advertising platform.


title: Uncovering social spammers: social honeypots + machine learning

abstract: Web-based social systems enable new community-based opportunities for participants to engage, share, and interact. This community value and related services like search and advertising are threatened by spammers, content polluters, and malware disseminators. In an effort to preserve community value and ensure longterm success, we propose and evaluate a honeypot-based approach for uncovering social spammers in online social systems. Two of the key components of the proposed approach are: (1) The deployment of social honeypots for harvesting deceptive spam profiles from social networking communities; and (2) Statistical analysis of the properties of these spam profiles for creating spam classifiers to actively filter out existing and new spammers. We describe the conceptual framework and design considerations of the proposed approach, and we present concrete observations from the deployment of social honeypots in MySpace and Twitter. We find that the deployed social honeypots identify social spammers with low false positive rates and that the harvested spam data contains signals that are strongly correlated with observable profile features (e.g., content, friend information, posting patterns, etc.). Based on these profile features, we develop machine learning based classifiers for identifying previously unknown spammers with high precision and a low rate of false positives.


title: World explorer: visualizing aggregate data from unstructured text in geo-referenced collections

abstract: The availability of map interfaces and location-aware devices makes a growing amount of unstructured, geo-referenced information available on the Web. This type of information can be valuable not only for browsing, finding and making sense of individual items, but also in aggregate form to help understand data trends and features. In particular, over twenty million geo-referenced photos are now available on Flickr, a photo-sharing website - the first major collection of its kind. These photos are often associated with user-entered unstructured text labels (i.e., tags). We show how we analyze the tags associated with the geo-referenced Flickr images to generate aggregate knowledge in the form of "representative tags" for arbitrary areas in the world. We use these tags to create a visualization tool, World Explorer, tha tcan help expose the content of the data, using a map interface to display the derived tags and the original photo items. We perform a qualitative evaluation of World Explorer that outlines the visualization's benefits in browsing this type of content. We provide insights regarding the aggregate versus individual-item requirements in browsing digital geo-referenced material.


title: Estimating 3D egomotion from perspective image sequence

abstract: The computation of sensor motion from sets of displacement vectors obtained from consecutive pairs of images is discussed. The problem is investigated with emphasis on its application to autonomous robots and land vehicles. The effects of 3D camera rotation and translation upon the observed image are discussed, particularly the concept of the focus of expansion (FOE). It is shown that locating the FOE precisely is difficult when displacement vectors are corrupted by noise and errors. A more robust performance can be achieved by computing a 2D region of possible FOE locations (termed the fuzzy FOE) instead of looking for a single-point FOE. The shape of this FOE region is an explicit indicator of the accuracy of the result. It has been shown elsewhere that given the fuzzy FOE, a number of powerful inferences about the 3D sense structure and motion become possible. Aspects of computing the fuzzy FOE are emphasized, and the performance of a particular algorithm on real motion sequences taken from a moving autonomous land vehicle is shown. >


title: Detailed and applied anatomy for improved rectal cancer treatment

abstract: Rectal anatomy is one of the most challenging concepts of visceral anatomy, even though currently there are more than 23,000 papers indexed in PubMed regarding this topic. Nonetheless, even though there is a plethora of information meant to assist clinicians to achieve a better practice, there is no universal understanding of its complexity. This in turn increases the morbidity rates due to iatrogenic causes, as mistakes that could be avoided are repeated. For this reason, this review attempts to gather current knowledge regarding the detailed anatomy of the rectum and to organize and present it in a manner that focuses on its clinical implications, not only for the colorectal surgeon, but most importantly for all colorectal cancer-related specialties.


title: ‘Finish your soup’: Counterproductive effects of pressuring children to eat on intake and affect

abstract: The authors examined whether pressuring preschoolers to eat would affect food intake and preferences, using a repeated-measures experimental design. In the experimental condition, children were pressured to eat by a request to finish their food. We collected intake data, heights and weights, child-feeding practices data, and children's comments about the food. Children consumed significantly more food when they were not pressured to eat and they made overwhelmingly fewer negative comments. Children who were pressured to eat at home had lower body mass index percentile scores and were less affected by the pressure in the lab setting than children who were not pressured at home. These data provide experimental evidence supporting previous correlational research indicating that pressure can have negative effects on children's affective responses to and intake of healthy foods.


title: Automatically Composing Representation Transformations as a Means for Generalization

abstract: How can we build a learner that can capture the essence of what makes a hard problem more complex than a simple one, break the hard problem along characteristic lines into smaller problems it knows how to solve, and sequentially solve the smaller problems until the larger one is solved? To work towards this goal, we focus on learning to generalize in a particular family of problems that exhibit compositional and recursive structure: their solutions can be found by composing in sequence a set of reusable partial solutions. Our key idea is to recast the problem of generalization as a problem of learning algorithmic procedures: we can formulate a solution to this family as a sequential decision-making process over transformations between representations. Our formulation enables the learner to learn the structure and parameters of its own computation graph with sparse supervision, make analogies between problems by transforming one problem representation to another, and exploit modularity and reuse to scale to problems of varying complexity. Experiments on solving a variety of multilingual arithmetic problems demonstrate that our method discovers the hierarchical decomposition of a problem into its subproblems, generalizes out of distribution to unseen problem classes, and extrapolates to harder versions of the same problem, yielding a 10-fold reduction in sample complexity compared to a monolithic recurrent neural network.


title: Transnational Affect and Emotion in Migration Research

abstract: Transnational migrants characteristically participate in an array of activities—mediated by flows of material objects and symbolic ties—to reproduce their transnational social fields. Scholars of transnationalism have generally understood it to be motivated by questions of identity, belonging, social memory, and sociality. However, in our own research, we have found the affective and emotional dimensions of transnational practice and believe that this represents a productive new theoretical and methodological approach that can advance our collective understanding of what motivates, compels, and structures transnational actors’ participation in transnational social fields, and reproduce emotional communities across borders and more broadly in the area of migration research. In this paper, we develop the notion of transnational affect and emotion to describe this emergent field of research. We argue that an array of affects such as shame, honour, pride, guilt, and obligation structure inter-subjective relat...


A Software Test Data Generation Tool for Unit Testing Of C + + Programs Using Control Flow Graph


title: High-power GaN MMIC PA Over 40–4000MHz

abstract: We report a high-performance GaN MMIC power amplifier operating from 40MHz to 4,000MHz. The MMIC achieved 80W pulsed (100us pulse width and 10% duty cycle) output power (P5dB) with 54% efficiency at 40MHz, 50W with about 30% efficiency across most of the mid band, and gradually decreases to 30W with 22% efficiency at 4000MHz. Power gain is 25dB across the 40-4000MHz band. This ultra wideband performance is achieved by both tailoring the device output impedance, and using a unique wide-band, circuit-matching topology. Detailed design techniques of both the device and the matching circuit will be presented.


title: What we talk about when we talk about context

abstract: The emergence of ubiquitous computing as a new design paradigm poses significant challenges for human-computer interaction (HCI) and interaction design. Traditionally, HCI has taken place within a constrained and well-understood domain of experience—single users sitting at desks and interacting with conventionally-designed computers employing screens, keyboards and mice for interaction. New opportunities have engendered considerable interest in “context-aware computing”—computational systems that can sense and respond to aspects of the settings in which they are used. However, considerable confusion surrounds the notion of “context”—what it means, what it includes and what role it plays in interactive systems. This paper suggests that the representational stance implied by conventional interpretations of “context” misinterprets the role of context in everyday human activity, and proposes an alternative model that suggests different directions for design.


title: Parallel matrix factorization for low-rank tensor completion

abstract: Higher-order low-rank tensors naturally arise in many applications including hyperspectral data recovery, video inpainting, seismic data reconstruction, and so on. ::: We propose a new model to recover a low-rank tensor by simultaneously performing low-rank matrix factorizations to the all-mode matricizations of the underlying tensor. An alternating minimization algorithm is applied to solve the model, along with two adaptive rank-adjusting strategies when the exact rank is not known. ::: Phase transition plots reveal that our algorithm can recover a variety of synthetic low-rank tensors from significantly fewer samples than the compared methods, which include a matrix completion method applied to tensor recovery and two state-of-the-art tensor completion methods. Further tests on real-world data show similar advantages. Although our model is non-convex, our algorithm performs consistently throughout the tests and gives better results than the compared methods, some of which are based on convex models. In addition, subsequence convergence of our algorithm can be established in the sense that any limit point of the iterates satisfies the KKT condtions.


title: Automatic mining of specifications from invocation traces and method invariants

abstract: Software library documentation often describes individual methods' APIs, but not the intended protocols and method interactions. This can lead to library misuse, and restrict runtime detection of protocol violations and automated verification of software that uses the library. Specification mining, if accurate, can help mitigate these issues, which has led to significant research into new model-inference techniques that produce FSM-based models from program invariants and execution traces. However, there is currently a lack of empirical studies that, in a principled way, measure the impact of the inference strategies on model quality. To this end, we identify four such strategies and systematically study the quality of the models they produce for nine off-the-shelf libraries. We find that (1) using invariants to infer an initial model significantly improves model quality, increasing precision by 4% and recall by 41%, on average; (2) effective invariant filtering is crucial for quality and scalability of strategies that use invariants; and (3) using traces in combination with invariants greatly improves robustness to input noise. We present our empirical evaluation, implement new and extend existing model-inference techniques, and make public our implementations, ground-truth models, and experimental data. Our work can lead to higher-quality model inference, and directly improve the techniques and tools that rely on model inference.


title: A virtual coach for upper-extremity myoelectric prosthetic rehabilitation

abstract: Successful prosthesis use is largely dependent on providing patients with high-quality, individualized pre-prosthetic training, ideally completed under the supervision of a trained therapist. Computer-based training systems, or ‘virtual coaches,’ designed to augment rehabilitation training protocols are an emerging area of research and could be a convenient and low-cost alternative to supplement the therapy received by the patient. In this contribution we completed an iterative needs focus group to determine important design elements required for an effective virtual coach software package.


title: Protein subcellular localization prediction based on compartment-specific features and structure conservation

abstract: BackgroundProtein subcellular localization is crucial for genome annotation, protein function prediction, and drug discovery. Determination of subcellular localization using experimental approaches is time-consuming; thus, computational approaches become highly desirable. Extensive studies of localization prediction have led to the development of several methods including composition-based and homology-based methods. However, their performance might be significantly degraded if homologous sequences are not detected. Moreover, methods that integrate various features could suffer from the problem of low coverage in high-throughput proteomic analyses due to the lack of information to characterize unknown proteins.ResultsWe propose a hybrid prediction method for Gram-negative bacteria that combines a one-versus-one support vector machines (SVM) model and a structural homology approach. The SVM model comprises a number of binary classifiers, in which biological features derived from Gram-negative bacteria translocation pathways are incorporated. In the structural homology approach, we employ secondary structure alignment for structural similarity comparison and assign the known localization of the top-ranked protein as the predicted localization of a query protein. The hybrid method achieves overall accuracy of 93.7% and 93.2% using ten-fold cross-validation on the benchmark data sets. In the assessment of the evaluation data sets, our method also attains accurate prediction accuracy of 84.0%, especially when testing on sequences with a low level of homology to the training data. A three-way data split procedure is also incorporated to prevent overestimation of the predictive performance. In addition, we show that the prediction accuracy should be approximately 85% for non-redundant data sets of sequence identity less than 30%.ConclusionOur results demonstrate that biological features derived from Gram-negative bacteria translocation pathways yield a significant improvement. The biological features are interpretable and can be applied in advanced analyses and experimental designs. Moreover, the overall accuracy of combining the structural homology approach is further improved, which suggests that structural conservation could be a useful indicator for inferring localization in addition to sequence homology. The proposed method can be used in large-scale analyses of proteomes.


title: Passive photometric stereo from motion

abstract: We introduce an iterative algorithm for shape reconstruction from multiple images of a moving (Lambertian) object illuminated by distant (and possibly time varying) lighting. Starting with an initial piecewise linear surface, the algorithm iteratively estimates a new surface based on the previous surface estimate and the photometric information available from the input image sequence. During each iteration, standard photometric stereo techniques are applied to estimate the surface normals up to an unknown generalized bas-relief transform, and a new surface is computed by integrating the estimated normals. The algorithm essentially consists of a sequence of matrix factorizations (of intensity values) followed by minimization using gradient descent (integration of the normals). Conceptually, the algorithm admits a clear geometric interpretation, which is used to provide a qualitative analysis of the algorithm's convergence. Implementation-wise, it is straightforward being based on several established photometric stereo and structure from motion algorithms. We demonstrate experimentally the effectiveness of our algorithm using several videos of hand-held objects moving in front of a fixed light and camera


title: 2DFQ: Two-Dimensional Fair Queuing for Multi-Tenant Cloud Services

abstract: In many important cloud services, different tenants execute their requests in the thread pool of the same process, requiring fair sharing of resources. However, using fair queue schedulers to provide fairness in this context is difficult because of high execution concurrency, and because request costs are unknown and have high variance. Using fair schedulers like WFQ and WF²Q in such settings leads to bursty schedules, where large requests block small ones for long periods of time. In this paper, we propose Two-Dimensional Fair Queueing (2DFQ), which spreads requests of different costs across di erent threads and minimizes the impact of tenants with unpredictable requests. In evaluation on production workloads from Azure Storage, a large-scale cloud system at Microsoft, we show that 2DFQ reduces the burstiness of service by 1-2 orders of magnitude. On workloads where many large requests compete with small ones, 2DFQ improves 99th percentile latencies by up to 2 orders of magnitude.


title: An Adaptation of the Vector-Space Model for Ontology-Based Information Retrieval

abstract: Semantic search has been one of the motivations of the semantic Web since it was envisioned. We propose a model for the exploitation of ontology-based knowledge bases to improve search over large document repositories. In our view of information retrieval on the semantic Web, a search engine returns documents rather than, or in addition to, exact values in response to user queries. For this purpose, our approach includes an ontology-based scheme for the semiautomatic annotation of documents and a retrieval system. The retrieval model is based on an adaptation of the classic vector-space model, including an annotation weighting algorithm, and a ranking algorithm. Semantic search is combined with conventional keyword-based retrieval to achieve tolerance to knowledge base incompleteness. Experiments are shown where our approach is tested on corpora of significant scale, showing clear improvements with respect to keyword-based search


title: Autonomous drifting using simulation-aided reinforcement learning

abstract: We introduce a framework that combines simple and complex continuous state-action simulators with a real-world robot to efficiently find good control policies, while minimizing the number of samples needed from the physical robot. The framework combines the strengths of various simulation levels by first finding optimal policies in a simple model, and then using that solution to initialize a gradient-based learner in a more complex simulation. The policy and transition dynamics from the complex simulation are in turn used to guide the learning in the physical world. A method is developed for transferring information gathered in the physical world back to the learning agent in the simulation. The new information is used to re-evaluate whether the original simulated policy is still optimal given the updated knowledge from the real-world. This reverse transfer is critical to minimizing samples from the physical world. The new framework is demonstrated on a robotic car learning to perform controlled drifting maneuvers. A video of the car's performance can be found at https: //youtu.be/opsmd5yuBF0.


title: Interactive Task Learning

abstract: This article presents a new research area called interactive task learning (ITL), in which an agent actively tries to learn not just how to perform a task better but the actual definition of a task through natural interaction with a human instructor while attempting to perform the task. The authors provide an analysis of desiderata for ITL systems, a review of related work, and a discussion of possible application areas for ITL systems.


title: Model-based and learned semantic object labeling in 3D point cloud maps of kitchen environments

abstract: We report on our experiences regarding the acquisition of hybrid Semantic 3D Object Maps for indoor household environments, in particular kitchens, out of sensed 3D point cloud data. Our proposed approach includes a processing pipeline, including geometric mapping and learning, for processing large input datasets and for extracting relevant objects useful for a personal robotic assistant to perform complex manipulation tasks. The type of objects modeled are objects which perform utilitarian functions in the environment such as kitchen appliances, cupboards, tables, and drawers. The resulted model is accurate enough to use it in physics-based simulations, where doors of 3D containers can be opened based on their hinge position. The resulted map is represented as a hybrid concept and is comprised of both the hierarchically classified objects and triangular meshes used for collision avoidance in manipulation routines.


title: Review of Big Data Storage Based on DNA Computing

abstract: There is a need of noteworthy scaling down in the information approached may be saved in the most recent decade. Delicate and advanced version hard paper duplicate which helps in two ways that they increased the effectiveness from claiming data management but also improved the distribution of entrance of information. On engineered DNA, it may be a chance to view the late improvement on the possibility about data capacity. Similarly in this way we have figured out how leap forward engineering could dramatically change the lifestyle out of our information capacity. This topic ' Big Data Storage based DNA' is described from the first research to newer one, their advantages and disadvantages, their techniques and how it will become a practice in the future. We also propose an approach is proposed as simple method to store data into DNA. The experiment work is done to validate the proposed approach result clearly show advantages merits of proposed method.


title: Powering MEMS portable devices — a review of non-regenerative and regenerative power supply systems with special emphasis on piezoelectric energy harvesting systems

abstract: Power consumption is forecast by the International Technology Roadmap of Semiconductors (ITRS) to pose long-term technical challenges for the semiconductor industry. The purpose of this paper is threefold: (1) to provide an overview of strategies for powering MEMS via non-regenerative and regenerative power supplies; (2) to review the fundamentals of piezoelectric energy harvesting, along with recent advancements, and (3) to discuss future trends and applications for piezoelectric energy harvesting technology. The paper concludes with a discussion of research needs that are critical for the enhancement of piezoelectric energy harvesting devices.


title: WHY DOES REFLECTOR ENHANCE THE GAIN OF HELICAL ANTENNAS?

abstract: We investigate uniformly-wound helical antennas above ground conductors of various shapes, with aim to determine factors that improve the antenna radiation pattern and gain. We show that the characteristics of helical antennas with large spillover fields can be improved using ground conductors (reflectors). This is achieved by intercepting unwanted radiation and redirecting it favorably. (8 pages)


title: Pediatric Obesity: Etiology and Treatment

abstract: This paper reviews factors that contribute to excessive weight gain in children and outlines current knowledge regarding approaches for treating pediatric obesity. Virtually all of the known genetic causes of obesity primarily increase energy intake. Genes regulating the leptin signaling pathway are particularly important for human energy homeostasis. Obesity is a chronic disorder that requires long-term strategies for management. The foundation for all treatments for pediatric obesity remains restriction of energy intake with lifestyle modification. There are few long-term studies of pharmacotherapeutic interventions for pediatric obesity. Bariatric surgical approaches are the most efficacious obesity treatments but, because of their potential risks, are reserved for those with the most significant complications of obesity.


title: Cross-domain recommendation without shared users or items by sharing latent vector distributions

abstract: We propose a cross-domain recommendation method for predicting the ratings of items in different domains, where neither users nor items are shared across domains. The proposed method is based on matrix factorization, which learns a latent vector for each user and each item. Matrix factorization techniques for a single-domain fail in the cross-domain recommendation task because the learned latent vectors are not aligned over different domains. The proposed method assumes that latent vectors in different domains are generated from a common Gaussian distribution with a full covariance matrix. By inferring the mean and covariance of the common Gaussian from given crossdomain rating matrices, the latent factors are aligned, which enables us to predict ratings in different domains. Experiments conducted on rating datasets from a wide variety of domains, e.g., movie, books and electronics, demonstrate that the proposed method achieves higher performance for predicting cross-domain ratings than existing methods.


Authentication Test-Based the RFID Authentication Protocol with Security Analysis


title: Knowledge Transfer, Knowledge Sharing and Knowledge Barriers - Three Blurry Terms in KM

abstract: In the knowledge management world there are many different terms flying around. Some are more important and frequently used than others. In this paper, we present and discuss the development and views of three terms: knowledge transfer, knowledge sharing and knowledge barriers. Knowledge transfer and knowledge sharing are sometimes used synonymously or are considered to have overlapping content. Several authors have pointed out this confusion while other authors have attempted to clarify the differences and define the terms. Knowledge barriers as a term seem to have a partly more obvious content although the borders between knowledge barriers and connecting terms, such as „barriers to knowledge sharing‟, seem to blur discussions and views. The aim in this paper is to make a contribution in finding appropriate demarcations between these concepts. After reviewing some Knowledge Management literature, it seems that the three terms, knowledge transfer, knowledge sharing and knowledge barriers, are somewhat unclear and has different meanings depending on the authors views. For knowledge transfer and knowledge sharing, the blurriness is linked mainly to the fact that the analytical level each term is related to has come and gone and come back again. For knowledge barriers, the blurriness comes from the development of the term. The mere existence of the many different categorizations of knowledge barriers implies that the concept itself is blurry. The concept seems clear cut and focuses on knowledge although it is also broad and later sources have included much more than knowledge. This paper concludes by highlighting the effects on the terms when two different knowledge perspectives, knowledge as an object (or the K-O view) and knowledge as a subjective contextual construction (or the K-SCC view) are applied. The clarifications are supported by examples from companies in different industries (such as Cargotec and IKEA) and emergency services.


title: Practical Tracking Control of Linear Motor With Adaptive Fractional Order Terminal Sliding Mode Control

abstract: This paper proposes a practical adaptive fractional order (FO) terminal sliding mode control (SMC) strategy for tracking control of the linear motor. Compared with conventional fast nonsingular SMC, the proposed approach, with a FO integral sliding surface and the adaptive switching input, can obtain higher convergence precision, even though the motion control system suffers from system uncertainties. The adaptive term is designed to guarantee finite-time high-precision convergence of the sliding mode variable, and meanwhile to degenerate the effect of uncertainties by selecting the proper adaptive gain. Moreover, continuous input due to cancelling the sign term ensures that the motion control system is chattering-free. Finally, to further improve precision, we introduce the super-twisting sliding mode disturbance observer for reducing unknown bounded disturbance, i.e., the quantization noise caused by velocity estimation. Experimental results indicate that the control system with the proposed controller is easily implemented, and has higher tracking precision and considerable robustness to uncertainties compared with the existing controllers.


title: Photovoltaic Power Conditioning System With Line Connection

abstract: A photovoltaic (PV) power conditioning system (PCS) with line connection is proposed. Using the power slope versus voltage of the PV array, the maximum power point tracking (MPPT) controller that produces a smooth transition to the maximum power point is proposed. The dc current of the PV array is estimated without using a dc current sensor. A current controller is suggested to provide power to the line with an almost-unity power factor that is derived using the feedback linearization concept. The disturbance of the line voltage is detected using a fast sensing technique. All control functions are implemented in software with a single-chip microcontroller. Experimental results obtained on a 2-kW prototype show high performance such as an almost-unity power factor, a power efficiency of 94%, and a total harmonic distortion (THD) of 3.6%


title: A review on stress inducement stimuli for assessing human stress using physiological signals

abstract: Assessing human stress in real-time is more difficult and challenging today. The present review deals about the measurement of stress in laboratory environment using different stress inducement stimuli by the help of physiological signals. Previous researchers have been used different stress inducement stimuli such as stroop colour word test (CWT), mental arithmetic test, public speaking task, cold pressor test, computer games and works used to induce the stress. Most of the researchers have been analyzed stress using questionnaire based approach and physiological signals. The several physiological signals like Electrocardiogram (ECG), Electromyogram (EMG), Galvanic Skin Response (GSR), Blood Pressure (BP), Skin Temperature (ST), Blood Volume Pulse (BVP), respiration rate (RIP) and Electroencephalogram (EEG) were briefly investigated to identify the stress. Different statistical methods like Analysis of variance (ANOVA), two-way ANOVA, Multivariate analysis of variance (MANOVA), t-test, paired t-tests and student t-tests have used to describe the correlation between stress inducement stimuli, subjective parameters (age, gender and etc.,) and physiological signals. This present works aims to find the most appropriate stress inducement stimuli, physiological signals and statistical method to efficiently asses the human stress.


The Propagation of Lies : Impeding the Spread of Misinformation by Identifying and Invading Echo Chambers in Networks Introduction and objectives


A Combination Method for Improving Text Summarization


title: Nanostructured materials for water desalination.

abstract: Desalination of seawater and brackish water is becoming an increasingly important means to address the scarcity of fresh water resources in the world. Decreasing the energy requirements and infrastructure costs of existing desalination technologies remains a challenge. By enabling the manipulation of matter and control of transport at nanometer length scales, the emergence of nanotechnology offers new opportunities to advance water desalination technologies. This review focuses on nanostructured materials that are directly involved in the separation of water from salt as opposed to mitigating issues such as fouling. We discuss separation mechanisms and novel transport phenomena in materials including zeolites, carbon nanotubes, and graphene with potential applications to reverse osmosis, capacitive deionization, and multi-stage flash, among others. Such nanostructured materials can potentially enable the development of next-generation desalination systems with increased efficiency and capacity.


title: Kilobot: A low cost robot with scalable operations designed for collective behaviors

abstract: Abstract In current robotics research there is a vast body of work on algorithms and control methods for groups of decentralized cooperating robots, called a swarm or collective. These algorithms are generally meant to control collectives of hundreds or even thousands of robots; however, for reasons of cost, time, or complexity, they are generally validated in simulation only, or on a group of a few tens of robots. To address this issue, this paper presents Kilobot, an open-source, low cost robot designed to make testing collective algorithms on hundreds or thousands of robots accessible to robotics researchers. To enable the possibility of large Kilobot collectives where the number of robots is an order of magnitude larger than the largest that exist today, each robot is made with only $14 worth of parts and takes 5 min to assemble. Furthermore, the robot design allows a single user to easily operate a large Kilobot collective, such as programming, powering on, and charging all robots, which would be difficult or impossible to do with many existing robotic systems. We demonstrate the capabilities of the Kilobot as a collective robot, by using a small robot test collective to implement four popular swarm behaviors: foraging, formation control, phototaxis, and synchronization.


title: Dual Temporal Scale Convolutional Neural Network for Micro-Expression Recognition

abstract: Facial micro-expression is a brief involuntary facial movement and can reveal the genuine emotion that people try to conceal. Traditional methods of spontaneous micro-expression recognition rely excessively on sophisticated hand-crafted feature design and the recognition rate is not high enough for its practical application. In this paper, we proposed a Dual Temporal Scale Convolutional Neural Network (DTSCNN) for spontaneous micro-expressions recognition. The DTSCNN is a two-stream network. Different of stream of DTSCNN is used to adapt to different frame rate of micro-expression video clips. Each stream of DTSCNN consists of an independent shallow network for avoiding the overfitting problem. Meanwhile, we fed the networks with optical-flow sequences to ensure that the shallow networks can further acquire higher-level features. Experimental results on spontaneous micro-expression databases (CASME I/II) showed that our method can achieve a recognition rate almost 10% higher than what some state-of-the-art method can achieve.


title: Learning Algorithms for Active Learning

abstract: We introduce a model that learns active learning algorithms via metalearning. For a distribution of related tasks, our model jointly learns: a data representation, an item selection heuristic, and a method for constructing prediction functions from labeled training sets. Our model uses the item selection heuristic to gather labeled training sets from which to construct prediction functions. Using the Omniglot and MovieLens datasets, we test our model in synthetic and practical settings.


title: Optimized contrast enhancement for real-time image and video dehazing

abstract: A fast and optimized dehazing algorithm for hazy images and videos is proposed in this work. Based on the observation that a hazy image exhibits low contrast in general, we restore the hazy image by enhancing its contrast. However, the overcompensation of the degraded contrast may truncate pixel values and cause information loss. Therefore, we formulate a cost function that consists of the contrast term and the information loss term. By minimizing the cost function, the proposed algorithm enhances the contrast and preserves the information optimally. Moreover, we extend the static image dehazing algorithm to real-time video dehazing. We reduce flickering artifacts in a dehazed video sequence by making transmission values temporally coherent. Experimental results show that the proposed algorithm effectively removes haze and is sufficiently fast for real-time dehazing applications.


title: The Use of Facebook in National Election Campaigns: Politics as Usual?

abstract: The uptake of online media in election campaigning is leading to speculations about the transformation of politics and cyber-democracy. Politicians running for seats in Parliament are increasingly using online media to disseminate information to potential voters and building dynamic, online communities. Drawing on an online survey of the Facebook networks of the two top candidates running for seats in the 2007 Danish Parliament election, this study suggests that the online sphere is primarily populated by users who already know the candidates through the traditional channels of party organizations, and that they do not expect to influence the policy of their candidates. Instead, users view Facebook mainly as an information channel and as a means to gain social prestige.


title: Action recognition based on a mixture of RGB and depth based skeleton

abstract: In this paper, we study how different skeleton extraction methods affect the performance of action recognition. As shown in previous work skeleton information can be exploited for action recognition. Nevertheless, skeleton detection problem is already hard and very often it is difficult to obtain reliable skeleton information from videos. In this paper, we compare two skeleton detection methods: the depth-map based method used with Kinect camera and RGB based method that uses Deep Convolutional Neural Networks. In order to balance the pros and cons of mentioned skeleton detection methods w.r.t. action recognition task, we propose a fusion of classifiers trained based on each skeleton detection method. Such fusion lead to performance improvement. We validate our approach on CAD-60 and MSRDailyActivity3D, achieving state-of-the-art results.


title: A meta-analytic study of social desirability distortion in computer-administered questionnaires, traditional questionnaires, and interviews

abstract: A meta-analysis of social desirability distortion compared computer questionnaires with traditional paper-and-pencil questionnaires and face-to-face interviews in 61 studies (1967-1997; 673 effect sizes). Controlling for correlated observations, a near-zero overall effect size was obtained for computer versus paper-and-pencil questionnaires. With moderators, there was less distortion on computerized measures of social desirability responding than on the paper-and-pencil measures, especially when respondents were alone and could backtrack. There was more distortion on the computer on other scales, but distortion was small when respondents were alone, anonymous, and could backtrack. There was less distortion on computerized versions of interviews than on face-to-face interviews. Research is needed on nonlinear patterns of distortion, and on the effects of context and interface on privacy perceptions and on responses to sensitive questions.


Coordination in Distributed Agile Software Development: A Systematic Review


title: One-against-all multi-class SVM classification using reliability measures

abstract: Support vector machines (SVM) is originally designed for binary classification. To extend it to multi-class scenario, a typical conventional way is to decompose an M-class problem into a series of two-class problems, for which one-against-all is the earliest and one of the most widely used implementations. However, certain theoretical analysis reveals a drawback, i.e., the competence of each classifier is totally neglected when the results of classification from the multiple classifiers are combined for the final decision. To overcome this limitation, this paper introduces reliability measures into the multi-class framework. Two measures are designed: static reliability measure (SRM) and dynamic reliability measure (DRM). SRM works on a collective basis and yields a constant value regardless of the location of the test sample. DRM, on the other hand, accounts for the spatial variation of the classifier's performance. Based on these two reliability measures, a new decision strategy for the one-against-all method is proposed, which is tested on benchmark data sets and demonstrates its effectiveness.


title: The effects of presentation method and simulation fidelity on psychomotor education in a bimanual metrology training simulation

abstract: In this study, we empirically evaluated the effects of presentation method and simulation fidelity on task performance and psychomotor skills acquisition in an immersive bimanual simulation towards precision metrology education. In a 2 × 2 experiment design, we investigated a large-screen immersive display (LSID) with a head-mounted display (HMD), and the presence versus absence of gravity. Advantages of the HMD include interacting with the simulation in a more natural manner as compared to using a large-screen immersive display due to the similarities between the interactions afforded in the virtual compared to the real-world task. Suspending the laws of physics may have an effect on usability and in turn could affect learning outcomes. Our dependent variables consisted of a pre and post cognition questionnaire, quantitative performance measures, perceived workload and system usefulness, and a psychomotor assessment to measure to what extent transfer of learning took place from the virtual to the real world. Results indicate that the HMD condition was preferable to the immersive display in several metrics while the no-gravity condition resulted in users adopting strategies that were not advantageous for task performance.


User Privacy in the Public Bitcoin Blockchain


title: Using context- and content-based trust policies on the semantic web

abstract: The current discussion about a future Semantic Web trust architecture is focused on reputational trust mechanisms based on explicit trust ratings. What is often overlooked is the fact that, besides of ratings, huge parts of the application-specific data published on the Semantic Web are also trust relevant and therefore can be used for flexible, fine-grained trust evaluations. In this poster we propose the usage of context- and content-based trust mechanisms and outline a trust architecture which allows the formulation of subjective and task-specific trust policies as a combination of reputation-, context- and content-based trust mechanisms.


title: An adaptive learning rate for stochastic variational inference

abstract: Stochastic variational inference finds good posterior approximations of probabilistic models with very large data sets. It optimizes the variational objective with stochastic optimization, following noisy estimates of the natural gradient. Operationally, stochastic inference iteratively subsamples from the data, analyzes the subsample, and updates parameters with a decreasing learning rate. However, the algorithm is sensitive to that rate, which usually requires hand-tuning to each application. We solve this problem by developing an adaptive learning rate for stochastic variational inference. Our method requires no tuning and is easily implemented with computations already made in the algorithm. We demonstrate our approach with latent Dirichlet allocation applied to three large text corpora. Inference with the adaptive learning rate converges faster and to a better approximation than the best settings of hand-tuned rates.


title: An Evaluation of Naïve Bayesian Anti-Spam Filtering Techniques

abstract: An efficient anti-spam filter that would block all spam, without blocking any legitimate messages is a growing need. To address this problem, we examine the effectiveness of statistically-based approaches Naive Bayesian anti-spam filters, as it is content-based and self-learning (adaptive) in nature. Additionally, we designed a derivative filter based on relative numbers of tokens. We train the filters using a large corpus of legitimate messages and spam and we test the filter using new incoming personal messages. More specifically, four filtering techniques available for a Naive Bayesian filter are evaluated. We look at the effectiveness of the technique, and we evaluate different threshold values in order to find an optimal anti-spam filter configuration. Based on cost-sensitive measures, we conclude that additional safety precautions are needed for a Bayesian anti-spam filter to be put into practice. However, our technique can make a positive contribution as a first pass filter.


title: Robust Gait Recognition by Integrating Inertial and RGBD Sensors

abstract: Gait has been considered as a promising and unique biometric for person identification. Traditionally, gait data are collected using either color sensors, such as a CCD camera, depth sensors, such as a Microsoft Kinect, or inertial sensors, such as an accelerometer. However, a single type of sensors may only capture part of the dynamic gait features and make the gait recognition sensitive to complex covariate conditions, leading to fragile gait-based person identification systems. In this paper, we propose to combine all three types of sensors for gait data collection and gait recognition, which can be used for important identification applications, such as identity recognition to access a restricted building or area. We propose two new algorithms, namely EigenGait and TrajGait, to extract gait features from the inertial data and the RGBD (color and depth) data, respectively. Specifically, EigenGait extracts general gait dynamics from the accelerometer readings in the eigenspace and TrajGait extracts more detailed sub-dynamics by analyzing 3D dense trajectories. Finally, both extracted features are fed into a supervised classifier for gait recognition and person identification. Experiments on 50 subjects, with comparisons to several other state-of-the-art gait-recognition approaches, show that the proposed approach can achieve higher recognition accuracy and robustness.


title: Lightweight User-Space Record And Replay

abstract: The ability to record and replay program executions with low overhead enables many applications, such as reverse-execution debugging, debugging of hard-to-reproduce test failures, and "black box" forensic analysis of failures in deployed systems. Existing record-and-replay approaches rely on recording an entire virtual machine (which is heavyweight), modifying the OS kernel (which adds deployment and maintenance costs), or pervasive code instrumentation (which imposes significant performance and complexity overhead). We investigated whether it is possible to build a practical record-and-replay system avoiding all these issues. The answer turns out to be yes --- if the CPU and operating system meet certain non-obvious constraints. Fortunately modern Intel CPUs, Linux kernels and user-space frameworks meet these constraints, although this has only become true recently. With some novel optimizations, our system RR records and replays real-world workloads with low overhead with an entirely user-space implementation running on stock hardware and operating systems. RR forms the basis of an open-source reverse-execution debugger seeing significant use in practice. We present the design and implementation of RR, describe its performance on a variety of workloads, and identify constraints on hardware and operating system design required to support our approach.


title: Building A Discourse-Tagged Corpus In The Framework Of Rhetorical Structure Theory

abstract: We describe our experience in developing a discourse-annotated corpus for community-wide use. Working in the framework of Rhetorical Structure Theory, we were able to create a large annotated resource with very high consistency, using a well-defined methodology and protocol. This resource is made publicly available through the Linguistic Data Consortium to enable researchers to develop empirically grounded, discourse-specific applications.


title: 3D point cloud generation from 2D depth camera images using successive triangulation

abstract: Depth cameras are low-cost, plug & play solution to generate point cloud. 3D depth camera yields depth images which do not convey the actual distance. A 3D camera driver does not support raw depth data output, these are usually filtered and calibrated as per the sensor specifications and hence a method is required to map every pixel back to its original point in 3D space. This paper demonstrates the method to triangulate a pixel from the 2D depth image back to its actual position in 3D space. Further this method illustrates the independence of this mapping operation, which facilitates parallel computing. Triangulation method and ratios between the pixel positions and camera parameters are used to estimate the true position in 3D space. The algorithm performance can be increased by 70% by the usage of TPL libraries. This performance differs from processor to processor


title: Delta Operation Language for Model Difference Representation.

abstract: Software models evolve over time undergoing various changes and resulting in several versions during their lifetime. Models are differentiated during the evolution process and the differences between subsequent model versions are represented in differences documents for further analysis and manipulations as history information. Software models have rich data structures which differ from the code of software systems. That is why, a representation approach for the model differences has to provide effective handling and management of difference information. Furthermore, the model differences represented in the differences documents have to be easy to access and reuse. This paper introduces the Delta Operations Language (DOL), a meta-model independent and operation-based approach to model difference representations. The approach represents the model differences in terms of DOL and provides several DOL Services to access and reuse the DOL-based differences for further analysis and manipulations. To explain ideas behind the approach, it is applied to UML activity diagrams as a running example.


title: Online Fall Detection using Recurrent Neural Networks

abstract: Unintentional falls can cause severe injuries and even death, especially if no immediate assistance is given. The aim of Fall Detection Systems (FDSs) is to detect an occurring fall. This information can be used to trigger the necessary assistance in case of injury. This can be done by using either ambient-based sensors, e.g. cameras, or wearable devices. The aim of this work is to study the technical aspects of FDSs based on wearable devices and artificial intelligence techniques, in particular Deep Learning (DL), to implement an effective algorithm for on-line fall detection. The proposed classifier is based on a Recurrent Neural Network (RNN) model with underlying Long Short-Term Memory (LSTM) blocks. The method is tested on the publicly available SisFall dataset, with extended annotation, and compared with the results obtained by the SisFall authors.


title: Prevalence and Characteristics of Vibrator Use by Women in the United States: Results from a Nationally Representative Study

abstract: INTRODUCTION ::: Although vibrators are commonly recommended by clinicians as adjunct to treatment for female sexual dysfunction, and for sexual enhancement, little is known about their prevalence or correlates of use. ::: ::: ::: AIM ::: The aim of this study was to determine the lifetime and recent prevalence of women's vibrator use during masturbation and partnered sex, and the correlates of use related to sociodemographic variables, health behaviors, and sexual function. ::: ::: ::: METHODS ::: A nationally representative sample of 3,800 women aged 18-60 years were invited to participate in a cross-sectional Internet-based survey; 2,056 (54.1%) participated. ::: ::: ::: MAIN OUTCOME MEASURES ::: The prevalence of vibrator use, the relationship between vibrator use and physical and psychological well-being (as assessed by the Centers for Disease Control and Prevention [CDC] Healthy Days measure) and health-promoting behaviors, the relationship between vibrator use and women's scores on the Female Sexual Function Index, and an assessment of the frequency and severity of side effects potentially associated with vibrator use. ::: ::: ::: RESULTS ::: The prevalence of women's vibrator use was found to be 52.5% (95% CI 50.3-54.7%). Vibrator users were significantly more likely to have had a gynecologic exam during the past year (P < 0.001) and to have performed genital self-examination during the previous month (P < 0.001). Vibrator use was significantly related to several aspects of sexual function (i.e., desire, arousal, lubrication, orgasm, pain, overall function) with recent vibrator users scoring higher on most sexual function domains, indicating more positive sexual function. Most women (71.5%) reported having never experienced genital symptoms associated with vibrator use. There were no significant associations between vibrator use and participants' scores on the CDC Healthy Days Measures. ::: ::: ::: CONCLUSIONS ::: Vibrator use among women is common, associated with health-promoting behaviors and positive sexual function, and rarely associated with side effects. Clinicians may find these data useful in responding to patients' sexual issues and recommending vibrator use to improve sexual function. Further research on the relationships between vibrator use and sexual health is warranted.


title: Modeling and broadening temporal user interest in personalized news recommendation

abstract: An experimental study on user interest evolution in real-world recommender systems.Integrating the long-term and short-term reading preferences of users.Selecting news from the user-item affinity graph using absorbing random walk model.Extensive empirical experiments on news data obtained from popular news websites. User profiling is an important step for solving the problem of personalized news recommendation. Traditional user profiling techniques often construct profiles of users based on static historical data accessed by users. However, due to the frequent updating of news repository, it is possible that a user's fine-grained reading preference would evolve over time while his/her long-term interest remains stable. Therefore, it is imperative to reason on such preference evaluation for user profiling in news recommenders. Besides, in content-based news recommenders, a user's preference tends to be stable due to the mechanism of selecting similar content-wise news articles with respect to the user's profile. To activate users' reading motivations, a successful recommender needs to introduce "somewhat novel" articles to users.In this paper, we initially provide an experimental study on the evolution of user interests in real-world news recommender systems, and then propose a novel recommendation approach, in which the long-term and short-term reading preferences of users are seamlessly integrated when recommending news items. Given a hierarchy of newly-published news articles, news groups that a user might prefer are differentiated using the long-term profile, and then in each selected news group, a list of news items are chosen as the recommended candidates based on the short-term user profile. We further propose to select news items from the user-item affinity graph using absorbing random walk model to increase the diversity of the recommended news list. Extensive empirical experiments on a collection of news data obtained from various popular news websites demonstrate the effectiveness of our method.


title: Behavioral intention formation in knowledge sharing: examining the roles of extrinsic motivators, social-psychological factors, and organizational climate

abstract: Individuals' knowledge does not transform easily into organizational knowledge even with the implementation of knowledge repositories. Rather, individuals tend to hoard knowledge for various reasons. The aim of this study is to develop an integrative understanding of the factors supporting or inhibiting individuals' knowledge-sharing intentions. We employ as our theoretical framework the theory of reasoned action (TRA), and augment it with extrinsic motivators, social-psychological forces and organizational climate factors that are believed to influence individuals' knowledge- sharing intentions. ::: ::: Through a field survey of 154 managers from 27 Korean organizations, we confirm our hypothesis that attitudes toward and subjective norms with regard to knowledge sharing as well as organizational climate affect individuals' intentions to share knowledge. Additionally, we find that anticipated reciprocal relationships affect individuals' attitudes toward knowledge sharing while both sense of self-worth and organizational climate affect subjective norms. Contrary to common belief, we find anticipated extrinsic rewards exert a negative effect on individuals' knowledge-sharing attitudes.


title: A METHOD FOR ESTIMATING FRACTAL DIMENSION OF TREE CROWNS FROM DIGITAL IMAGES

abstract: A new method for estimating fractal dimension of tree crowns from digital images is presented. Three species of trees, Japanese yew (Taxus cuspidata Sieb & Zucc), Hicks yew (Taxus × media), and eastern white pine (Pinus strobus L.), were studied. Fractal dimensions of Japanese yew and Hicks yew range from 2.26 to 2.70. Fractal dimension of eastern white pine range from 2.14 to 2.43. The difference in fractal dimension between Japanese yew and eastern white pine was statistically significant at 0.05 significance level as was the difference in fractal dimension between Hicks yew and eastern white pine. On average, the greater fractal dimensions of Japanese yew and Hicks yew were possibly related to uniform foliage distribution within their tree crowns. Therefore, fractal dimension may be useful for tree crown structure classification and for indexing tree images.


title: An Enhanced Domination Based Evolutionary Algorithm for Multi-objective Problems

abstract: We proposed a new evolutionary algorithm for multiobjective optimization problems. The influence of constraints on search space and Pareto front are analyzed first. According to the analysis, a new clustering method based on domination is proposed, in which the infeasible solutions are employed. Then, aiming to converge to Pareto fronts of the multiobjective problems quickly, a differential evolution based crossover operator is designed. In the designed crossover operator, uniform design method was used. At last, a square search method is employed to update the feasible nondominated solutions to improve the precision. Experiments on 10 selected test problems and comparisons with NSGA-II are made. Simulation results indicate that our proposal is effective and sound, and our proposal outperforms NSGA-II on the selected test problems.


title: Distributed graph processing: an approach based on overlay composition

abstract: In this day and age, human being is generating an unprecedented amount of data coming from multiple and heterogeneous sources, ranging from scientific devices to business transactions. In many contexts such data is modelled as a graph, however, due to its size, it is often infeasible to process it on a single machine. As a consequence, the adoption of parallel and distributed computing frameworks is the natural approach to face this problem. This paper proposes an high-level approach for large graphs processing. Our approach takes inspiration from overlay networks, a widely exploited approach for information dissemination, aggregation and computing orchestration in highly distributed systems. We propose Telos, a programming framework supporting the definition of computations on graphs as a composition of overlays, each devoted to a specific aim. Telos is implemented on the top of Apache Spark. A set of experimental results are presented to give a preliminary evidence of the effectiveness of our approach.


title: Agnostically learning decision trees

abstract: We give a query algorithm for agnostically learning decision trees with respect to the uniform distribution on inputs. Given black-box access to an *arbitrary* binary function f on the n-dimensional hypercube, our algorithm finds a function that agrees with f on almost (within an epsilon fraction) as many inputs as the best size-t decision tree, in time poly(n,t,1e). This is the first polynomial-time algorithm for learning decision trees in a harsh noise model. We also give a *proper* agnostic learning algorithm for juntas, a sub-class of decision trees, again using membership queries. Conceptually, the present paper parallels recent work towards agnostic learning of halfspaces (Kalai et al, 2005); algorithmically, it is more challenging. The core of our learning algorithm is a procedure to implicitly solve a convex optimization problem over the L1 ball in 2n dimensions using an approximate gradient projection method.


PLANT LEAF RECOGNITION


title: The Gator Tech Smart House: a programmable pervasive space

abstract: Research groups in both academia and industry have developed prototype systems to demonstrate the benefits of pervasive computing in various application domains. Unfortunately, many first-generation pervasive computing systems lack the ability to evolve as new technologies emerge or as an application domain matures. To address this limitation, the University of Florida's Mobile and Pervasive Computing Laboratory is developing programmable pervasive spaces in which a smart space exists as both a runtime environment and a software library. Service discovery and gateway protocols automatically integrate system components using generic middleware that maintains a service definition for each sensor and actuator in the space. The Gator Tech Smart House in Gainesville, Florida, is the culmination of more than five years of research in pervasive and mobile computing. The project's goal is to create assistive environments such as homes that can sense themselves and their residents and enact mappings between the physical world and remote monitoring and intervention services.


title: Data Driven Governing Equations Approximation Using Deep Neural Networks

abstract: Abstract We present a numerical framework for approximating unknown governing equations using observation data and deep neural networks (DNN). In particular, we propose to use residual network (ResNet) as the basic building block for equation approximation. We demonstrate that the ResNet block can be considered as a one-step method that is exact in temporal integration. We then present two multi-step methods, recurrent ResNet (RT-ResNet) method and recursive ReNet (RS-ResNet) method. The RT-ResNet is a multi-step method on uniform time steps, whereas the RS-ResNet is an adaptive multi-step method using variable time steps. All three methods presented here are based on integral form of the underlying dynamical system. As a result, they do not require time derivative data for equation recovery and can cope with relatively coarsely distributed trajectory data. Several numerical examples are presented to demonstrate the performance of the methods.


title: A Review of Human Activity Recognition Methods

abstract: Recognizing human activities from video sequences or still images is a challenging task due to problems such as background clutter, partial occlusion, changes in scale, viewpoint, lighting, and appearance. Many applications, including video surveillance systems, human-computer interaction, and robotics for human behavior characterization, require a multiple activity recognition system. In this work, we provide a detailed review of recent and state-of-the-art research advances in the field of human activity classification. We propose a categorization of human activity methodologies and discuss their advantages and limitations. In particular, we divide human activity classification methods into two large categories according to whether they use data from different modalities or not. Then, each of these categories is further analyzed into sub-categories, which reflect how they model human activities and what type of activities they are interested in. Moreover, we provide a comprehensive analysis of the existing, publicly available human activity classification datasets and examine the requirements for an ideal human activity recognition dataset. Finally, we report the characteristics of future research directions and present some open issues on human activity recognition.


title: Wireless Sensor Network Based Smart Grid Communications: Cyber Attacks, Intrusion Detection System and Topology Control

abstract: The existing power grid is going through a massive transformation. Smart grid technology is a radical approach for improvisation in prevailing power grid. Integration of electrical and communication infrastructure is inevitable for the deployment of Smart grid network. Smart grid technology is characterized by full duplex communication, automatic metering infrastructure, renewable energy integration, distribution automation and complete monitoring and control of entire power grid. Wireless sensor networks (WSNs) are small micro electrical mechanical systems that are deployed to collect and communicate the data from surroundings. WSNs can be used for monitoring and control of smart grid assets. Security of wireless sensor based communication network is a major concern for researchers and developers. The limited processing capabilities of wireless sensor networks make them more vulnerable to cyber-attacks. The countermeasures against cyber-attacks must be less complex with an ability to offer confidentiality, data readiness and integrity. The address oriented design and development approach for usual communication network requires a paradigm shift to design data oriented WSN architecture. WSN security is an inevitable part of smart grid cyber security. This paper is expected to serve as a comprehensive assessment and analysis of communication standards, cyber security issues and solutions for WSN based smart grid infrastructure.


title: Understanding and Mitigating the Security Risks of Voice-Controlled Third-Party Skills on Amazon Alexa and Google Home

abstract: Virtual personal assistants (VPA) (e.g., Amazon Alexa and Google Assistant) today mostly rely on the voice channel to communicate with their users, which however is known to be vulnerable, lacking proper authentication. The rapid growth of VPA skill markets opens a new attack avenue, potentially allowing a remote adversary to publish attack skills to attack a large number of VPA users through popular IoT devices such as Amazon Echo and Google Home. In this paper, we report a study that concludes such remote, large-scale attacks are indeed realistic. More specifically, we implemented two new attacks: voice squatting in which the adversary exploits the way a skill is invoked (e.g.,"open capital one"), using a malicious skill with similarly pronounced name (e.g.,"capital won") or paraphrased name (e.g.,"capital one please") to hijack the voice command meant for a different skill, and voice masquerading in which a malicious skill impersonates the VPA service or a legitimate skill to steal the user's data or eavesdrop on her conversations. These attacks aim at the way VPAs work or the user's mis-conceptions about their functionalities, and are found to pose a realistic threat by our experiments (including user studies and real-world deployments) on Amazon Echo and Google Home. The significance of our findings have already been acknowledged by Amazon and Google, and further evidenced by the risky skills discovered on Alexa and Google markets by the new detection systems we built. We further developed techniques for automatic detection of these attacks, which already capture real-world skills likely to pose such threats.


title: Challenges of ICT and artificial intelligence in smart grids

abstract: This paper provides an overview of the actual developments on smart grids, particularly analysing the efforts on research and development in the different regions in the world. The paper also discusses the roles of Artificial Intelligence (AI) and Information & Communication Technologies (ICT) in the future development of smart grids. Particularly, it highlights the importance of power simulation tools in developing control strategies and discusses how AI techniques can work with these tools to provide simulation solutions that are closer to a real implementation.


title: Influence of a variable reluctance resolver on an E-motor-system

abstract: In this paper the fundamental knowledge of a variable reluctance resolver is presented and an analytical model is demonstrated. With the simulation results are calculated and validated by measurements on a sensor test bench. Based on the introduced model, mechanical and electrical failures of any variable reluctance sensor can be analyzed. The model based simulation is compared to the measurement results and future prospects are given.


title: Hough Networks for Head Pose Estimation and Facial Feature Localization.

abstract: We present Hough Networks (HNs), a novel method that combines the idea of Hough Forests (HFs) [12] with Convolutional Neural Networks (CNNs) [18]. Similar to HFs we perform a simultaneous classification and regression on densely extracted image patches. But instead of a Random Forest (RF) we utilize a CNN which is able to learn higherorder feature representations and does not rely on any handcrafted features. Applying a CNN on a patch level has the advantage of reasoning about more image details and additionally allows to segment the image into foreground and background. Furthermore, the structure of a CNN supports efficient inference of patches extracted from a regular grid. We evaluate HNs on two computer vision tasks: head pose estimation and facial feature localization. Our method achieves at least state-of-the-art performance without sacrificing versatility which allows extension to many other applications.


title: A Component Model for Augmented/Mixed Reality Applications with Reconfigurable Data-flow

abstract: In this paper, we will introduce a new componentbased framework for mixed and augmented reality applications. We will see the developed framework intends to meet several requirements such as portability, variable component granularity, scalability, and a high level of abstraction for end-users. The provided framework, at runtime, is composed of a set of several libraries where components are stored, an XML file in which configuration and communications between components are described and a runtime program that deals with the two previous parts of the system. Our component model can also deal with reconfigurable data-flows by the use of a finite state-machine, using several component configurations within an application.


title: Recent Techniques of Clustering of Time Series Data: A Survey

abstract: Time-Series clustering is one of the important concepts of data mining that is used to gain insight into the mechanism that generate the time-series and predicting the future values of the given time-series. Time-series data are frequently very large and elements of these kinds of data have temporal ordering. The clustering of time series is organized into three groups depending upon whether they work directly on raw data either in frequency or time domain, indirectly with the features extracted from the raw data or with model built from raw data. In this paper, we have shown the survey and summarization of previous work that investigated the clustering of time series in various application domains ranging from science, engineering, business, finance, economic, health care, to government.


title: Exploring the value of purchasing online game items

abstract: The aims of this paper are to: (1) modify the theory of consumption values in order to investigate online game users' perceived value of purchasable game items, and (2) develop a new construct - the ''integrated value of purchasing game items'' - based on the modified theory of consumption values. We found that the enjoyment, character competency, visual authority, and monetary values are appropriate for describing how online game users perceive the value of game items. Utilizing second order analysis, the ''integrated value of purchasing game items'' was developed. To show the validity of the new construct, we developed a research model and tested it using the results of 327 valid questionnaires. Results revealed that the new construct is statistically significant in affecting users' intention to purchase game items.


title: PGA: Using Graphs to Express and Automatically Reconcile Network Policies

abstract: Software Defined Networking (SDN) and cloud automation enable a large number of diverse parties (network operators, application admins, tenants/end-users) and control programs (SDN Apps, network services) to generate network policies independently and dynamically. Yet existing policy abstractions and frameworks do not support natural expression and automatic composition of high-level policies from diverse sources. We tackle the open problem of automatic, correct and fast composition of multiple independently specified network policies. We first develop a high-level Policy Graph Abstraction (PGA) that allows network policies to be expressed simply and independently, and leverage the graph structure to detect and resolve policy conflicts efficiently. Besides supporting ACL policies, PGA also models and composes service chaining policies, i.e., the sequence of middleboxes to be traversed, by merging multiple service chain requirements into conflict-free composed chains. Our system validation using a large enterprise network policy dataset demonstrates practical composition times even for very large inputs, with only sub-millisecond runtime latencies.


title: Collaboration decision making based on AHP method in Tr-OrBAC model: Case study

abstract: In the collaborative systems, in order to establish successful and protected collaboration, the trust management is first established between the participating entities and the trust score of each entity is calculated. This score is obtained based on the three trust criteria (satisfaction, reputation and recommendation) that are redefined and reused in our model: Tr-OrBAC. The evaluation of these criteria permits to select and choose the appropriate type of access to be assigned to a requester entity in the collaborative system according to its reliability. Each access type will be presented by OrBAC security rules in which we add the trust score as a new variable. The main goal of this paper is to exploit the AHP (Analytic Hierarchy Process) method for multi-criteria analyses in order to evaluate the trust criteria and to select the correct access type for the collaborating entities. In order to illustrate the feasibility of using AHP in Tr-OrBAC for taking the collaboration decision, we apply this solution in the Electrical Grid as a Critical Infrastructure case study.


title: Personal Recommendation as Link Prediction using a Bipartite Graph Kernel

abstract: We implement a personal recommendation system on the Yelp academic dataset using a novel algorithm proposed by [Li and Chen., 2012]. By constructing a bipartite userbusiness graph where positive reviews are edges between nodes, we pose the recommendation problem as link prediction. More specifically, we construct a kernel which combines the information from both the node features and local graph structure. This kernel is then put into a one-class support vector machine, which generates an ordering of likely user-business links. We downsample the graph and tune the parameters of our algorithm (via cross validation). In what follows we provide a summary of the Yelp data, describe the recommendation algorithm in detail, and present the results of our implementation.


title: FireFly: a reconfigurable wireless data center fabric using free-space optics

abstract: Conventional static datacenter (DC) network designs offer extreme cost vs. performance tradeoffs---simple leaf-spine networks are cost-effective but oversubscribed, while "fat tree"-like solutions offer good worst-case performance but are expensive. Recent results make a promising case for augmenting an oversubscribed network with reconfigurable inter-rack wireless or optical links. Inspired by the promise of reconfigurability, this paper presents FireFly, an inter-rack network solution that pushes DC network design to the extreme on three key fronts: (1) all links are reconfigurable; (2) all links are wireless; and (3) non top-of-rack switches are eliminated altogether. This vision, if realized, can offer significant benefits in terms of increased flexibility, reduced equipment cost, and minimal cabling complexity. In order to achieve this vision, we need to look beyond traditional RF wireless solutions due to their interference footprint which limits range and data rates. Thus, we make the case for using free-space optics (FSO). We demonstrate the viability of this architecture by (a) building a proof-of-concept prototype of a steerable small form factor FSO device using commodity components and (b) developing practical heuristics to address algorithmic and system-level challenges in network design and management.


title: Parametric Optimization for Practical Control Systems Design

abstract: In this contribution, calculation machinery and code to compute different performance indices, which are necessary for the parametric optimization, are proposed. Moreover, a short review about parametric optimization is carried out. It is highlighted that normally only the ISE (Integral Square Error) performance index is used for control system design. One reason for that is the existence of several numerical algorithms to compute it along the infinite trajectory at once. Finally, an optimal design of a PI controller is used to illustrate and compare the new algorithms.


title: Reports of Repetitive Penile-Genital Penetration Often Have No Definitive Evidence of Penetration

abstract: Objectives: The goals were to evaluate the association of definitive hymenal findings with the number of reported episodes of penile-genital penetration, pain, bleeding, dysuria, and time since assault for girls presenting for nonacute, sexual assault examinations. Methods: Charts of all girls 5 to 17 of age who provided a history of nonacute, penile-genital, penetrative abuse were reviewed. Interviews and examinations occurred over a 4-year period at a children's advocacy center. Characteristics of the histories provided by the subjects were examined for associations with definitive findings of penetrative trauma. Results: Five hundred six patients were included in the study. Of the 56 children with definitive examination results, 52 had no history of consensual penile-vaginal intercourse and all were >/=10 years of age. Analysis was unable to detect an association between the number of reported penile-genital penetrative events and definitive genital findings. Eighty-seven percent of victims who provided a history of >10 penetrative events had no definitive evidence of penetration. A history of bleeding with abuse was more than twice as likely for subjects with definitive findings. Children 10 penetrative events, although none had definitive findings on examination. Conclusions: Most victims who reported repetitive penile-genital contact that involved some degree of perceived penetration had no definitive evidence of penetration on examination of the hymen. Similar results were seen for victims of repetitive assaults involving perceived penetration over long periods of time, as well as victims with a history of consensual sex. Language: en


title: Algorithm 887: CHOLMOD, Supernodal Sparse Cholesky Factorization and Update/Downdate

abstract: CHOLMOD is a set of routines for factorizing sparse symmetric positive definite matrices of the form A or AAT, updating/downdating a sparse Cholesky factorization, solving linear systems, updating/downdating the solution to the triangular system Lx = b, and many other sparse matrix functions for both symmetric and unsymmetric matrices. Its supernodal Cholesky factorization relies on LAPACK and the Level-3 BLAS, and obtains a substantial fraction of the peak performance of the BLAS. Both real and complex matrices are supported. CHOLMOD is written in ANSI/ISO C, with both C and MATLABTM interfaces. It appears in MATLAB 7.2 as x = A\b when A is sparse symmetric positive definite, as well as in several other sparse matrix functions.


title: The Need for Design Thinking in Business Schools

abstract: The demands placed on today's organizations and their managers suggest that we have to develop pedagogies combining analytic reasoning with a more exploratory skill set that design practitioners have embraced and business schools have traditionally neglected. Design thinking is an iterative, exploratory process involving visualizing, experimenting, creating, and prototyping of models, and gathering feedback. It is a particularly apt method for addressing innovation and messy, ill-structured situations. We discuss key characteristics of design thinking, link design-thinking characteristics to recent studies of cognition, and note how the repertoire of skills and methods that embody design thinking can address deficits in business school education.


title: Visual Tracking: An Experimental Survey

abstract: There is a large variety of trackers, which have been proposed in the literature during the last two decades with some mixed success. Object tracking in realistic scenarios is a difficult problem, therefore, it remains a most active area of research in computer vision. A good tracker should perform well in a large number of videos involving illumination changes, occlusion, clutter, camera motion, low contrast, specularities, and at least six more aspects. However, the performance of proposed trackers have been evaluated typically on less than ten videos, or on the special purpose datasets. In this paper, we aim to evaluate trackers systematically and experimentally on 315 video fragments covering above aspects. We selected a set of nineteen trackers to include a wide variety of algorithms often cited in literature, supplemented with trackers appearing in 2010 and 2011 for which the code was publicly available. We demonstrate that trackers can be evaluated objectively by survival curves, Kaplan Meier statistics, and Grubs testing. We find that in the evaluation practice the F-score is as effective as the object tracking accuracy (OTA) score. The analysis under a large variety of circumstances provides objective insight into the strengths and weaknesses of trackers.


title: The Membrane QmoABC Complex Interacts Directly with the Dissimilatory Adenosine 5′-Phosphosulfate Reductase in Sulfate Reducing Bacteria

abstract: The adenosine 5’-phosphosulfate reductase (AprAB) is the enzyme responsible for the reduction of adenosine 5’-phosphosulfate (APS) to sulfite in the biological process of dissimilatory sulfate reduction, which is carried out by a ubiquitous group of sulfate reducing prokaryotes. The electron donor for AprAB has not been clearly identified, but was proposed to be the QmoABC membrane complex, since an aprBA-qmoABC gene cluster is found in many sulfate-reducing and sulfur-oxidising bacteria. The QmoABC complex is essential for sulfate reduction, but electron transfer between QmoABC and AprAB has not been reported. In this work we provide the first direct evidence that QmoABC and AprAB interact in Desulfovibrio spp., using co-immunoprecipitation, cross-linking Far-Western blot, tag-affinity purification and surface plasmon resonance studies. This showed that the QmoABC-AprAB complex has a strong steady-state affinity (KD = 90 ± 3 nM), but has a transient character due to a fast dissociation rate. Far-Western blot identified QmoA as the Qmo subunit most involved in the interaction. Nevertheless, electron transfer from menaquinol analogues to APS through anaerobically purified QmoABC and AprAB could not be detected. We propose that this reaction requires the involvement of a third partner to allow electron flow driven by a reverse electron bifurcation process i.e. electron confurcation. This process is deemed essential to allow coupling of APS reduction to chemiosmotic energy conservation.