title: Impact of Relational Coordination on Nurse Job Satisfaction, Work Engagement and Burnout: Achieving the Quadruple Aim

abstract: AIM ::: To explore how relational coordination, known to enhance quality and efficiency outcomes for patients and hospitals, impacts direct care nurse outcomes such as burnout, work engagement, and job satisfaction, addressing the "Quadruple Aim," to improve the experience of providing care. ::: ::: ::: BACKGROUND ::: Hospitals are complex organizations in which multiple providers work interdependently, under conditions of uncertainty and time constraints, to deliver safe quality care despite differences in specialization, training, and status. Relational coordination-communicating and relating for the purpose of task integration-is known to improve quality, safety, and efficiency under these conditions, but less is known about its impact on the well-being of direct care providers themselves. ::: ::: ::: METHODS ::: Surveys measuring relational coordination among nurses and other types of providers as well as job-related outcomes in 5 acute care community hospitals were completed by direct care RNs. ::: ::: ::: RESULTS ::: Relational coordination was significantly related to increased job satisfaction, increased work engagement, and reduced burnout. ::: ::: ::: CONCLUSIONS ::: Relational coordination contributes to the well-being of direct care nurses, addressing the Quadruple Aim by improving the experience of providing care.


title: To catch a fake: Curbing deceptive Yelp ratings and venues

abstract: The popularity and influence of reviews, make sites like Yelp ideal targets for malicious behaviors. We present Marco, a novel system that exploits the unique combination of social, spatial and temporal signals gleaned from Yelp, to detect venues whose ratings are impacted by fraudulent reviews. Marco increases the cost and complexity of attacks, by imposing a tradeoff on fraudsters, between their ability to impact venue ratings and their ability to remain undetected. We contribute a new dataset to the community, which consists of both ground truth and gold standard data. We show that Marco significantly outperforms state-of-the-art approaches, by achieving 94% accuracy in classifying reviews as fraudulent or genuine, and 95.8% accuracy in classifying venues as deceptive or legitimate. Marco successfully flagged 244 deceptive venues from our large dataset with 7,435 venues, 270,121 reviews and 195,417 users. Furthermore, we use Marco to evaluate the impact of Yelp events, organized for elite reviewers, on the hosting venues. We collect data from 149 Yelp elite events throughout the US. We show that two weeks after an event, twice as many hosting venues experience a significant rating boost rather than a negative impact.


title: A Comprehensive Online Database of Machine-Readable Lead-Sheets for Jazz Standards

abstract: Jazz standards are songs representative of a body of musical knowledge shared by most professional jazz musicians. As such, the corpus of jazz standards constitutes a unique opportunity to study a musical genre with a “closed-world” approach, since most jazz composers are no longer in activity today. Although many scores for jazz standards can be found on the Internet, no effort, to our knowledge, has been dedicated so far to building a comprehensive database of machine-readable scores for jazz standards. This paper reports on the rationale, design and population of such a database, containing harmonic (chord progressions) as well as melodic and structural information. The database can be used to feed both analysis and generation systems. We report on preliminary results in this vein. We get around the tricky and often unclear copyright issues imposed by the publishing industry, by providing only statistical information about songs. The completeness of such a database should benefit many research experiments in MIR and opens up novel and exciting applications in music generation exploiting symbolic information, notably in style modeling.


title: Scale-invariant heat kernel signatures for non-rigid shape recognition

abstract: One of the biggest challenges in non-rigid shape retrieval and comparison is the design of a shape descriptor that would maintain invariance under a wide class of transformations the shape can undergo. Recently, heat kernel signature was introduced as an intrinsic local shape descriptor based on diffusion scale-space analysis. In this paper, we develop a scale-invariant version of the heat kernel descriptor. Our construction is based on a logarithmically sampled scale-space in which shape scaling corresponds, up to a multiplicative constant, to a translation. This translation is undone using the magnitude of the Fourier transform. The proposed scale-invariant local descriptors can be used in the bag-of-features framework for shape retrieval in the presence of transformations such as isometric deformations, missing data, topological noise, and global and local scaling. We get significant performance improvement over state-of-the-art algorithms on recently established non-rigid shape retrieval benchmarks.


title: Bipartite Ranking through Minimization of Univariate Loss

abstract: Minimization of the rank loss or, equivalently, maximization of the AUC in bipartite ranking calls for minimizing the number of disagreements between pairs of instances. Since the complexity of this problem is inherently quadratic in the number of training examples, it is tempting to ask how much is actually lost by minimizing a simple univariate loss function, as done by standard classification methods, as a surrogate. In this paper, we first note that minimization of 0/1 loss is not an option, as it may yield an arbitrarily high rank loss. We show, however, that better results can be achieved by means of a weighted (cost-sensitive) version of 0/1 loss. Yet, the real gain is obtained through margin-based loss functions, for which we are able to derive proper bounds, not only for rank risk but, more importantly, also for rank regret. The paper is completed with an experimental study in which we address specific questions raised by our theoretical analysis.


title: Realtime Dynamic 3D Facial Reconstruction for Monocular Video In-the-Wild

abstract: With the increasing amount of videos recorded using 2D mobile cameras, the technique for recovering the 3D dynamic facial models from these monocular videos has become a necessity for many image and video editing applications. While methods based parametric 3D facial models can reconstruct the 3D shape in dynamic environment, large structural changes are ignored. Structure-from-motion methods can reconstruct these changes but assume the object to be static. To address this problem we present a novel method for realtime dynamic 3D facial tracking and reconstruction from videos captured in uncontrolled environments. Our method can track the deforming facial geometry and reconstruct external objects that protrude from the face such as glasses and hair. It also allows users to move around, perform facial expressions freely without degrading the reconstruction quality.


title: Modeling Interference for Apache Spark Jobs

abstract: To maximize resource utilization and system throughput, hardware resources are often shared across multiple Apache Spark jobs through virtualization techniques in cloud platforms. However, while the performance of these jobs running in virtualized environment can be negatively affected due to interference caused by resource contention, it is nontrivial to predict the effect of interference on job performance in such settings, which is critical for efficient scheduling of such jobs and performance troubleshooting. To address this challenge, in this paper, we develop analytical models to estimate the effect of interference among multiple Apache Spark jobs running concurrently on job execution time in virtualized cloud environment. We evaluated the accuracy of our models using four real-life applications (e.g., Page rank, K-means, Logistic regression, and Word count) on a 6 node cluster while running up to four jobs concurrently. Our experimental results show that the model can achieve high prediction accuracy, and ranges between 86% to 99% when the number of concurrent jobs are four and all start simultaneously, and ranges between 71% to 99% when the number of concurrent jobs are four and start at different times.


title: A computational model for periodic pattern perception based on frieze and wallpaper groups

abstract: We present a computational model for periodic pattern perception based on the mathematical theory of crystallographic groups. In each N-dimensional Euclidean space, a finite number of symmetry groups can characterize the structures of an infinite variety of periodic patterns. In 2D space, there are seven frieze groups describing monochrome patterns that repeat along one direction and 17 wallpaper groups for patterns that repeat along two linearly independent directions to tile the plane. We develop a set of computer algorithms that "understand" a given periodic pattern by automatically finding its underlying lattice, identifying its symmetry group, and extracting its representative motifs. We also extend this computational model for near-periodic patterns using geometric AIC. Applications of such a computational model include pattern indexing, texture synthesis, image compression, and gait analysis.


title: CRISP: an interruption management algorithm based on collaborative filtering

abstract: Interruptions can have a significant impact on users working to complete a task. When people are collaborating, either with other users or with systems, coordinating interruptions is an important factor in maintaining efficiency and preventing information overload. Computer systems can observe user behavior, model it, and use this to optimize the interruptions to minimize disruption. However, current techniques often require long training periods that make them unsuitable for online collaborative environments where new users frequently participate. ::: In this paper, we present a novel synthesis between Collaborative Filtering methods and machine learning classification algorithms to create a fast learning algorithm, CRISP. CRISP exploits the similarities between users in order to apply data from known users to new users, therefore requiring less information on each person. Results from user studies indicate the algorithm significantly improves users' performances in completing the task and their perception of how long it took to complete each task.


title: Identifying users of portable devices from gait pattern with accelerometers

abstract: Identifying users of portable devices from gait signals acquired with three-dimensional accelerometers was studied. Three approaches, correlation, frequency domain and data distribution statistics, were used. Test subjects (N=36) walked with fast, normal and slow walking speeds in enrolment and test sessions on separate days wearing the accelerometer device on their belt, at back. It was shown to be possible to identify users with this novel gait recognition method. Best equal error rate (EER=7%) was achieved with the signal correlation method, while the frequency domain method and two variations of the data distribution statistics method produced EER of 10%, 18% and 19%, respectively.


title: Infrastructure Implications and Cost Comparisons

abstract: Alternatives to petroleum-based fuels for transportation are sought to address concerns over climate change and energy security. Key semiconductor, software, and battery technologies have sufficiently progressed over the past few decades to enable a mass-market-viable plug-in electric vehicle (PEV) alternative. In this paper, the various PEV architectures are described, including market availability, technologies and trends, practical ranges, battery replacement and power costs, implications for grid operations, and other developments. Manufacturers’ recently announced prices and EPA standardized test data are used (where available) to increase the accuracy of cost comparisons for competing vehicles. Results indicate that in relatively low fuel-cost regions, like the U.S., PEVs enjoy a positive discounted net present value, thanks to tax credits and assuming that the original battery does not need replacement by owner. Even without the tax credits, PEVs offer financial payback for those residing in higher fuel-cost regions, as long as their batteries last the vehicle’s lifetime or are replaced by manufacturers (under warranty).


title: The information systems identity crisis: focusing on high-visibility and high-impact research

abstract: This paper presents an alternative view of the Information Systems identity crisis described recently by Benbasat and Zmud (2003). We agree with many of their observations, but we are con cerned with their prescription for IS research. We critique their discussion of errors of inclusion and exclusion in IS research and highlight the potential misinterpretations that are possible from a literal 1Ron Weber was the accepting senior editor for this paper. Iris Vessey and Rudy Hirschheim served as reviewers. reading of their comments. Our conclusion is that following Benbasat and Zmud's nomological net will result in a micro focus for IS research. The results of such a focus are potentially dangerous for the field. They could result in the elimination of IS from many academic programs. We present an alternative set of heuristics that can be used to assess what lies within the domain of IS scholar ship. We argue that the IS community has a powerful story to tell about the transformational impact of information technology. We believe that a significant portion of our research should be macro studies of the impact of IT. It is important for academic colleagues, deans, and managers to understand the transformational power of the technology. As IS researchers with deep knowl edge of the underlying artifact, we are best positioned to do such research.


title: Experimental and Theoretical Data on the Mechanism by Which Red Wine Anthocyanins Are Transported through a Human MKN-28 Gastric Cell Model

abstract: The gastric absorption of red wine anthocyanins was evaluated using a gastric MKN-28 cell barrier model. Anthocyanin transport was not affected by the presence of 4% ethanol and decreased with the increase of pH. Gastric cells pretreated with anthocyanins were found to increase anthocyanin transport. The presence of d-(+)-glucose was found to decrease anthocyanin uptake, suggesting the involvement of glucose transporters. RT-PCR assays revealed that GLUT1, GLUT3, and MCT1 transporters were expressed in MKN-28 cells. Computational studies were performed to provide a structural characterization of the binding site of hGLUT1 to glucose or different anthocyanins under different forms. Docking results demonstrated that anthocyanins can bind to glucose transporters from both intracellular and extracellular sides. Anthocyanins seem to enter into the transporter by two main conformations: B ring or glucose. From MD simulations, hGLUT1 was found to form complexes with all anthocyanins tested in the different proto...


title: Low Voltage CMOS Current and Voltage References without Resistors

abstract: The authors describe low voltage current and voltage references which only use MOSFETs in strong inversion and pnp substrate transistors; both the references exhibit very good performance in terms of power supply rejection and do not require compensation capacitances; the minimum supply voltage is about 0.8V for the current reference and 1.2V for the voltage reference.


title: Overlapping Data Transfers with Computation on GPU with Tiles

abstract: GPUs are employed to accelerate scientific applications however they require much more programming effort from the programmers particularly because of the disjoint address spaces between the host and the device. OpenACC and OpenMP 4.0 provide directive based programming solutions to alleviate the programming burden however synchronous data movement can create a performance bottleneck in fully taking advantage of GPUs. We propose a tiling based programming model and its library that simplifies the development of GPU programs and overlaps the data movement with computation. The programming model decomposes the data and computation into tiles and treats them as the main data transfer and execution units, which enables pipelining the transfers to hide the transfer latency. Moreover, partitioning application data into tiles allows the programmer to still take advantage of GPU even though application data cannot fit into the device memory. The library leverages C++ lambda functions, OpenACC directives, CUDA streams and tiling API from TiDA to support both productivity and performance. We show the performance of the library on a data transfer-intensive and a compute-intensive kernels and compare its speedup against OpenACC and CUDA. The results indicate that the library can hide the transfer latency, handle the cases where there is no sufficient device memory, and achieves reasonable performance.


title: A novel disintegrin protein from Naja naja venom induces cytotoxicity and apoptosis in human cancer cell lines in vitro

abstract: Abstract Animal venoms and toxins are potential bioresources that have been known to mankind as a therapeutic tool for more than a century through folk and traditional medicine. The purified “disintegrin protein” (64 kDa) from the venom of the Indian cobra snake ( Naja naja ) exhibited cytotoxic effects of various types of human cancer cell lines such as breast cancer (MCF-7), lung cancer (A549) and liver cancer (HepG2). In vitro cytotoxicity, DNA fragmentation, an apoptotic assay and a cell cycle analysis were performed to evaluate the anticancer activity of disintegrin against the above cell lines. The IC 50 value of disintegrin was determined to be 2.5 ± 0.5 μg/mL, 3.5 ± 0.5 μg/mL, and 3 ± 0.5 μg/mL for the MCF-7, A549 and HepG2 cell lines respectively. Moreover, the increased distribution of G0/G1 and S phase led to decreased populations of cells in the G2/M phase of MCF-7, HepG2 and A549 cells.


title: Why Cancer and Inflammation?

abstract: Central to the development of cancer are genetic changes that endow these "cancer cells" with many of the hallmarks of cancer, such as self-sufficient growth and resistance to anti-growth and pro-death signals. However, while the genetic changes that occur within cancer cells themselves, such as activated oncogenes or dysfunctional tumor suppressors, are responsible for many aspects of cancer development, they are not sufficient. Tumor promotion and progression are dependent on ancillary processes provided by cells of the tumor environment but that are not necessarily cancerous themselves. Inflammation has long been associated with the development of cancer. This review will discuss the reflexive relationship between cancer and inflammation with particular focus on how considering the role of inflammation in physiologic processes such as the maintenance of tissue homeostasis and repair may provide a logical framework for understanding the connection between the inflammatory response and cancer.


title: Unsupervised Video Object Segmentation with Motion-Based Bilateral Networks

abstract: In this work, we study the unsupervised video object segmentation problem where moving objects are segmented without prior knowledge of these objects. First, we propose a motion-based bilateral network to estimate the background based on the motion pattern of non-object regions. The bilateral network reduces false positive regions by accurately identifying background objects. Then, we integrate the background estimate from the bilateral network with instance embeddings into a graph, which allows multiple frame reasoning with graph edges linking pixels from different frames. We classify graph nodes by defining and minimizing a cost function, and segment the video frames based on the node labels. The proposed method outperforms previous state-of-the-art unsupervised video object segmentation methods against the DAVIS 2016 and the FBMS-59 datasets.


title: SECOND: Sparsely Embedded Convolutional Detection

abstract: LiDAR-based or RGB-D-based object detection is used in numerous applications, ranging from autonomous driving to robot vision. Voxel-based 3D convolutional networks have been used for some time to enhance the retention of information when processing point cloud LiDAR data. However, problems remain, including a slow inference speed and low orientation estimation performance. We therefore investigate an improved sparse convolution method for such networks, which significantly increases the speed of both training and inference. We also introduce a new form of angle loss regression to improve the orientation estimation performance and a new data augmentation approach that can enhance the convergence speed and performance. The proposed network produces state-of-the-art results on the KITTI 3D object detection benchmarks while maintaining a fast inference speed.


Medications and Myasthenia Gravis ( A Reference for Health Care Professionals )


title: The Obstetric Consequences of Female Genital Mutilation/Cutting: A Systematic Review and Meta-Analysis

abstract: Various forms of female genital mutilation/cutting (FGM/C) have been performed for millennia and continue to be prevalent in parts of Africa. Although the health consequences following FGM/C have been broadly investigated, divergent study results have called into question whether FGM/C is associated with obstetric consequences. To clarify the present state of empirical research, we conducted a systematic review of the scientific literature and quantitative meta-analyses of the obstetric consequences of FGM/C. We included 44 primary studies, of which 28 were comparative, involving almost 3 million participants. The methodological study quality was generally low, but several studies reported the same outcome and were sufficiently similar to warrant pooling of effect sizes in meta-analyses. The meta-analyses results showed that prolonged labor, obstetric lacerations, instrumental delivery, obstetric hemorrhage, and difficult delivery are markedly associated with FGM/C, indicating that FGM/C is a factor in their occurrence and significantly increases the risk of delivery complications. There was no significant difference in risk with respect to cesarean section and episiotomy. These results can make up the background documentation for health promotion and health care decisions that inform work to reduce the prevalence of FGM/C and improve the quality of services related to the consequences of FGM/C.


title: Cigarette smoking as a risk factor for stroke. The Framingham Study.

abstract: The impact of cigarette smoking on stroke incidence was assessed in the Framingham Heart Study cohort of 4255 men and women who were aged 36 to 68 years and free of stroke and transient ischemic attacks. During 26 years of follow-up, 459 strokes occurred. Regardless of smoking status and in each sex, hypertensive subjects had twice the incidence of stroke. Using the Cox proportional hazard regression method, smoking was significantly related to stroke after age and hypertension were taken into account. Even after pertinent cardiovascular disease risk factors were added to the Cox model, cigarette smoking continued to make a significant independent contribution to the risk of stroke generally and brain infarction specifically. The risk of stroke increased as the number of cigarettes smoked increased. The relative risk of stroke in heavy smokers (>40 cigarettes per day) was twice that of light smokers (fewer than ten cigarettes per day). Lapsed smokers developed stroke at the same level as nonsmokers soon after stopping. Stroke risk decreased significantly by two years and was at the level of nonsmokers by five years after cessation of cigarette smoking. ( JAMA 1988;259:1025-1029)


title: CopperDroid: Automatic Reconstruction of Android Malware Behaviors

abstract: Mobile devices and their application marketplaces drive the entire economy of the today's mobile landscape. Android platforms alone have produced staggering revenues, exceeding five billion USD, which has attracted cybercriminals and increased malware in Android markets at an alarming rate. To better understand this slew of threats, we present CopperDroid, an automatic VMI-based dynamic analysis system to reconstruct the behaviors of Android malware. The novelty of CopperDroid lies in its agnostic approach to identify interesting OS- and high-level Android-specific behaviors. It reconstructs these behaviors by observing and dissecting system calls and, therefore, is resistant to the multitude of alterations the Android runtime is subjected to over its life-cycle. CopperDroid automatically and accurately reconstructs events of interest that describe, not only well-known process-OS interactions (e.g., file and process creation), but also complex intra- and inter-process communications (e.g., SMS reception), whose semantics are typically contextualized through complex Android objects. Because CopperDroid's reconstruction mechanisms are agnostic to the underlying action invocation methods, it is able to capture actions initiated both from Java and native code execution. CopperDroid's analysis generates detailed behavioral profiles that abstract a large stream of low-level—often uninteresting—events into concise, high-level semantics, which are well-suited to provide insightful behavioral traits and open the possibility to further research directions. We carried out an extensive evaluation to assess the capabilities and performance of CopperDroid on more than 2,900 Android malware samples. Our experiments show that CopperDroid faithfully reconstructs OS- and Android-specific behaviors. Additionally, we demonstrate how CopperDroid can be leveraged to disclose additional behaviors through the use of a simple, yet effective, app stimulation technique. Using this technique, we successfully triggered and disclosed additional behaviors on more than 60% of the analyzed malware samples. This qualitatively demonstrates the versatility of CopperDroid's ability to improve dynamic-based code coverage.


title: $K$-Embeddings: Learning Conceptual Embeddings for Words using Context

abstract: We describe a technique for adding contextual distinctions to word embeddings by extending the usual embedding process — into two phases. The first phase resembles existing methods, but also constructs K classifications of concepts. The second phase uses these classifications in developing refined K embeddings for words, namely word K-embeddings. The technique is iterative, scalable, and can be combined with other methods (including Word2Vec) in achieving still more expressive representations. Experimental results show consistently large performance gains on a Semantic-Syntactic Word Relationship test set for different K settings. For example, an overall gain of 20% is recorded at K = 5. In addition, we demonstrate that an iterative process can further tune the embeddings and gain an extra 1% (K = 10 in 3 iterations) on the same benchmark. The examples also show that polysemous concepts are meaningfully embedded in our K different conceptual embeddings for words.


title: Ten Simple Rules for Reproducible Research in Jupyter Notebooks

abstract: Reproducibility of computational studies is a hallmark of scientific methodology. It enables researchers to build with confidence on the methods and findings of others, reuse and extend computational pipelines, and thereby drive scientific progress. Since many experimental studies rely on computational analyses, biologists need guidance on how to set up and document reproducible data analyses or simulations. ::: In this paper, we address several questions about reproducibility. For example, what are the technical and non-technical barriers to reproducible computational studies? What opportunities and challenges do computational notebooks offer to overcome some of these barriers? What tools are available and how can they be used effectively? ::: We have developed a set of rules to serve as a guide to scientists with a specific focus on computational notebook systems, such as Jupyter Notebooks, which have become a tool of choice for many applications. Notebooks combine detailed workflows with narrative text and visualization of results. Combined with software repositories and open source licensing, notebooks are powerful tools for transparent, collaborative, reproducible, and reusable data analyses.


title: Creatism: A deep-learning photographer capable of creating professional work

abstract: Machine-learning excels in many areas with well-defined goals. However, a clear goal is usually not available in art forms, such as photography. The success of a photograph is measured by its aesthetic value, a very subjective concept. This adds to the challenge for a machine learning approach. ::: We introduce Creatism, a deep-learning system for artistic content creation. In our system, we break down aesthetics into multiple aspects, each can be learned individually from a shared dataset of professional examples. Each aspect corresponds to an image operation that can be optimized efficiently. A novel editing tool, dramatic mask, is introduced as one operation that improves dramatic lighting for a photo. Our training does not require a dataset with before/after image pairs, or any additional labels to indicate different aspects in aesthetics. ::: Using our system, we mimic the workflow of a landscape photographer, from framing for the best composition to carrying out various post-processing operations. The environment for our virtual photographer is simulated by a collection of panorama images from Google Street View. We design a "Turing-test"-like experiment to objectively measure quality of its creations, where professional photographers rate a mixture of photographs from different sources blindly. Experiments show that a portion of our robot's creation can be confused with professional work.


title: Gender Estimation Based on Smile-Dynamics

abstract: Automated gender estimation has numerous applications, including video surveillance, human–computer interaction, anonymous customized advertisement, and image retrieval. Most commonly, the underlying algorithms analyze the facial appearance for clues of gender. In this paper, we propose a novel method for gender estimation, which exploits dynamic features gleaned from smiles and we proceed to show that: a) facial dynamics incorporate clues for gender dimorphism and b) while for adult individuals appearance features are more accurate than dynamic features, for subjects under 18 years facial dynamics can outperform appearance features. In addition, we fuse proposed dynamics-based approach with state-of-the-art appearance-based algorithms, predominantly improving performance of the latter. Results show that smile-dynamics include pertinent and complementary to appearance gender information.


title: Degree of nonlinearity (DoN) measure for target tracking in videos

abstract: Performance evaluation of tracking methods includes methods of relative and absolute performance. Absolute tracking performance is the robust end result presented to a user which determines the product solution for real world analysis. However, to achieve robust performance, the tracking method is subject to the sensor data, filtering performance, and associated models, which requires relative performance evaluation. In this paper, we highlight the efficacy of using DoN measure in evaluating video tracking capabilities. Three developments are presented as to the real-world issues associated with the nonlinear video-based tracking: (1) challenges of performance evaluation with real data, (2) approaches to utilize DoN as improvements for relative track evaluation, and (3) operational implementations lessons associated from user-defined operating picture (UDOP) plugins. Results are presented using relevant data with a highlighted tracker.


title: Voltage control of magnetism in multiferroic heterostructures

abstract: Electrical tuning of magnetism is of great fundamental and technical importance for fast, compact and ultra-low power electronic devices. Multiferroics, simultaneously exhibiting ferroelectricity and ferromagnetism, have attracted much interest owing to the capability of controlling magnetism by an electric field through magnetoelectric (ME) coupling. In particular, strong strain-mediated ME interaction observed in layered multiferroic heterostructures makes it practically possible for realizing electrically reconfigurable microwave devices, ultra-low power electronics and magnetoelectric random access memories (MERAMs). In this review, we demonstrate this remarkable E-field manipulation of magnetism in various multiferroic composite systems, aiming at the creation of novel compact, lightweight, energy-efficient and tunable electronic and microwave devices. First of all, tunable microwave devices are demonstrated based on ferrite/ferroelectric and magnetic-metal/ferroelectric composites, showing giant ferromagnetic resonance (FMR) tunability with narrow FMR linewidth. Then, E-field manipulation of magnetoresistance in multiferroic anisotropic magnetoresistance and giant magnetoresistance devices for achieving low-power electronic devices is discussed. Finally, E-field control of exchange-bias and deterministic magnetization switching is demonstrated in exchange-coupled antiferromagnetic/ferromagnetic/ferroelectric multiferroic hetero-structures at room temperature, indicating an important step towards MERAMs. In addition, recent progress in electrically non-volatile tuning of magnetic states is also presented. These tunable multiferroic heterostructures and devices provide great opportunities for next-generation reconfigurable radio frequency/microwave communication systems and radars, spintronics, sensors and memories.


title: High-Speed (MHz) Series Resonant Converter (SRC) Using Multilayered Coreless Printed Circuit Board (PCB) Step-Down Power Transformer

abstract: In this paper, design and analysis of an isolated low-profile, series resonant converter (SRC) using multilayered coreless printed circuit board (PCB) power transformer was presented. For obtaining the stringent height switch mode power supplies, a multilayered coreless PCB power transformer of approximately 4:1 turn's ratio was designed in a four-layered PCB laminate that can be operated in megahertz switching frequency. The outermost radius of the transformer is 10 mm with achieved power density of 16 W/cm2. The energy efficiency of the power transformer is found to be in the range of 87-96% with the output power level of 0.1-50 W operated at a frequency of 2.6 MHz. This designed step-down transformer was utilized in the SRC and evaluated. The supply voltage of the converter is varied from 60-120 VDC with a nominal input voltage of 90 V and has been tested up to the power level of 34.5 W. The energy efficiency of the converter under zero-voltage switching condition is found to be in the range of 80-86.5% with the switching frequency range of 2.4-2.75 MHz. By using a constant off-time frequency modulation technique, the converter was regulated to 20 VDC for different load conditions. Thermal profile with converter loss at nominal voltage is presented.


title: Are we ready for sex robots?

abstract: Sex robots are gaining a remarkable amount of attention in current discussions about technology and the future of human relationships. To help understand what kinds of relationships people will have with these robots, empirical data about people's views of sex robots is a necessary contribution to these debates. We report the results of the first systematic survey that asks about the appropriateness and value of sex robots, acceptable forms they can take, and the degree to which using them counts as sex. The results show a consistent difference in the uses for which women and men found sex robots to be appropriate, with women less and men more inclined to consider them socially useful. We also found convergences on what sex robots are like and how sex with them is to be classified, suggesting that larger views about relationships and society, not just understandings of sex robots themselves, should be a matter for more research and thus frame future work on the ethics of sex robots.


title: Transaction-Cost Economics: The Governance of Contractual Relations

abstract: THE new institutional economics is preoccupied with the origins, incidence, and ramifications of transaction costs. Indeed, if transaction costs are negligible, the organization of economic activity is irrelevant, since any advantages one mode of organization appears to hold over another will simply be eliminated by costless contracting. But despite the growing realization that transaction costs are central to the study of economics,' skeptics remain. Stanley Fischer's complaint is typical: "Transaction costs have a well-deserved bad name as a theoretical device ... [partly] because there is a suspicion that almost anything can be rationalized by invoking suitably specified transaction costs."2 Put differently, there are too many degrees of freedom; the concept wants for definition.


title: Signal-to-interference ratio improvement by using a phased array antenna of switched-beam elements

abstract: A phased array antenna of switched-beam elements is proposed to improve the signal-to-interference ratio (SIR). By using 1-bit phase shifters, the main beam of the array is switched to desired signal direction, and with the switched-beam patch antenna elements the null patterns are changeable. The results of radiation patterns show that the interference suppression can be improved. Moreover, the SIR measurement is also set up in multipath environment to ensure the array performance. It is found that the SIR is significantly affected by the multipath environment; however, the SIR improvement of more than 10 dB can be obtained by changing the null patterns.


title: Global Second-Order Pooling Convolutional Networks

abstract: Deep Convolutional Networks (ConvNets) are fundamental to, besides large-scale visual recognition, a lot of vision tasks. As the primary goal of the ConvNets is to characterize complex boundaries of thousands of classes in a high-dimensional space, it is critical to learn higher-order representations for enhancing non-linear modeling capability. Recently, Global Second-order Pooling (GSoP), plugged at the end of networks, has attracted increasing attentions, achieving much better performance than classical, first-order networks in a variety of vision tasks. However, how to effectively introduce higher-order representation in earlier layers for improving non-linear capability of ConvNets is still an open problem. In this paper, we propose a novel network model introducing GSoP across from lower to higher layers for exploiting holistic image information throughout a network. Given an input 3D tensor outputted by some previous convolutional layer, we perform GSoP to obtain a covariance matrix which, after nonlinear transformation, is used for tensor scaling along channel dimension. Similarly, we can perform GSoP along spatial dimension for tensor scaling as well. In this way, we can make full use of the second-order statistics of the holistic image throughout a network. The proposed networks are thoroughly evaluated on large-scale ImageNet-1K, and experiments have shown that they outperform non-trivially the counterparts while achieving state-of-the-art results.


title: Classification of cities of Isfahan province in view point of tourism infrastructure by using TOPSIS and AHP models.

abstract: Extended abstract 1Introduction The hierarchical tourism areas, in fact, are criteria for determining the centralization and adjusting the in equality between different regions. Little has been published, up to 1990s, in terms of doing hierarchy of tourism areas. But during last decades, investigators have paid more attention to make areal, national, ultra national hierarchy of tourism industry. Today, with advances in statistical and digital systems and by using different indices in various fields, geographical studies have facilitated the process of tourism areal hierarchy. These studies help the most to arrange the hierarchy, providing a useful form to distribute tourists around the province as an appropriate service leading to desired function. The purpose of this research is to study the effective factors in tourist


title: BrainHex: A neurobiological gamer typology survey☆

abstract: This paper briefly presents a player satisfaction model called BrainHex, which was based on insights from neurobiological findings as well as the results from earlier demographic game design models (DGD1 and DGD2). The model presents seven different archetypes of players: Seeker, Survivor, Daredevil, Mastermind, Conqueror, Socialiser, and Achiever. We explain how each of these player archetypes relates to older player typologies (such as Myers-Briggs), and how each archetype characterizes a specific playing style. We conducted a survey among more than 50,000 players using the BrainHex model as a personality type motivator to gather and compare demographic data to the different BrainHex archetypes. We discuss some results from this survey with a focus on psychometric orientation of respondents, to establish relationships between personality types and BrainHex archetypes.


Image Analysis Applications in Plant Growth and Health Assessment


Foreground Extraction using graph cut


title: Measuring intrusion detection capability: an information-theoretic approach

abstract: A fundamental problem in intrusion detection is what metric(s) can be used to objectively evaluate an intrusion detection system (IDS) in terms of its ability to correctly classify events as normal or intrusive. Traditional metrics (e.g., true positive rate and false positive rate) measure different aspects, but no single metric seems sufficient to measure the capability of intrusion detection systems. The lack of a single unified metric makes it difficult to fine-tune and evaluate an IDS. In this paper, we provide an in-depth analysis of existing metrics. Specifically, we analyze a typical cost-based scheme [6], and demonstrate that this approach is very confusing and ineffective when the cost factor is not carefully selected. In addition, we provide a novel information-theoretic analysis of IDS and propose a new metric that highly complements cost-based analysis. When examining the intrusion detection process from an information-theoretic point of view, intuitively, we should have less uncertainty about the input (event data) given the IDS output (alarm data). Thus, our new metric, C I D (Intrusion Detection Capability), is defined as the ratio of the mutual information between the IDS input and output to the entropy of the input. C I D has the desired property that: (1) It takes into account all the important aspects of detection capability naturally, i.e., true positive rate, false positive rate, positive predictive value, negative predictive value, and base rate; (2) it objectively provides an intrinsic measure of intrusion detection capability; and (3) it is sensitive to IDS operation parameters such as true positive rate and false positive rate, which can demonstrate the effect of the subtle changes of intrusion detection systems. We propose C I D as an appropriate performance measure to maximize when fine-tuning an IDS. The obtained operation point is the best that can be achieved by the IDS in terms of its intrinsic ability to classify input data. We use numerical examples as well as experiments of actual IDSs on various data sets to show that by using C I D , we can choose the best (optimal) operating point for an IDS and objectively compare different IDSs.


title: A new approach to developing and implementing eager database replication protocols

abstract: Database replication is traditionally seen as a way to increase the availability and performance of distributed databases. Although a large number of protocols providing data consistency and fault-tolerance have been proposed, few of these ideas have ever been used in commercial products due to their complexity and performance implications. Instead, current products allow inconsistencies and often resort to centralized approaches which eliminates some of the advantages of replication. As an alternative, we propose a suite of replication protocols that addresses the main problems related to database replication. On the one hand, our protocols maintain data consistency and the same transactional semantics found in centralized systems. On the other hand, they provide flexibility and reasonable performance. To do so, our protocols take advantage of the rich semantics of group communication primitives and the relaxed isolation guarantees provided by most databases. This allows us to eliminate the possibility of deadlocks, reduce the message overhead and increase performance. A detailed simulation study shows the feasibility of the approach and the flexibility with which different types of bottlenecks can be circumvented.


title: Trustworthiness Attributes and Metrics for Engineering Trusted Internet-Based Software Systems

abstract: Trustworthiness of Internet-based software systems, apps, services and platform is a key success factor for their use and acceptance by organizations and end-users. The notion of trustworthiness, though, is subject to individual interpretation and preference, e.g., organizations require confidence about how their business critical data is handled whereas end-users may be more concerned about usability. As one main contribution, we present an extensive list of software quality attributes that contribute to trustworthiness. Those software quality attributes have been identified by a systematic review of the research literature and by analyzing two real-world use cases. As a second contribution, we sketch an approach for systematically deriving metrics to measure the trustworthiness of software system. Our work thereby contributes to better understanding which software quality attributes should be considered and assured when engineering trustworthy Internet-based software systems.


title: A Model Relating Pupil Diameter to Mental Workload and Lighting Conditions

abstract: In this paper, we present a proof-of-concept approach to estimating mental workload by measuring the user's pupil diameter under various controlled lighting conditions. Knowing the user's mental workload is desirable for many application scenarios, ranging from driving a car, to adaptive workplace setups. Typically, physiological sensors allow inferring mental workload, but these sensors might be rather uncomfortable to wear. Measuring pupil diameter through remote eye-tracking instead is an unobtrusive method. However, a practical eye-tracking-based system must also account for pupil changes due to variable lighting conditions. Based on the results of a study with tasks of varying mental demand and six different lighting conditions, we built a simple model that is able to infer the workload independently of the lighting condition in 75% of the tested conditions.


title: Survey of multi-agent systems for microgrid control

abstract: Multi-agent systems (MAS) consist of multiple intelligent agents that interact to solve problems that may be beyond the capabilities of a single agent or system. For many years, conceptual MAS designs and architectures have been proposed for applications in power systems and power engineering. With the increasing use and modeling of distributed energy resources for microgrid applications, MAS are well suited to manage the size and complexity of these energy systems. The purpose of this paper is to survey applications of MAS in the control and operation of microgrids. The paper will review MAS concepts, architectures, develop platforms and processes, provide example applications, and discuss limitations.


title: Automatic TV logo detection and classification in broadcast videos

abstract: In this study1, we present a fully automatic TV logo identification system. TV logos are detected in static regions given by time-averaged edges subjected to post-processing operations. Once the region of interest of a logo candidate is established, TV logos are recognized via their subspace features. Comparative analysis of features has indicated that ICA-II architecture yields the most discriminative with an accuracy of 99.2% in a database of 3040 logo images (152 varieties). Online tests for both detection and recognition on running videos have achieved 96.0% average accuracy. A more reliable logo identifier will be feasible by improving the accuracy of the extracted logo mask.


title: User model interoperability: a survey

abstract: Nowadays a large number of user-adaptive systems has been developed. Commonly, the effort to build user models is repeated across applications and domains, due to the lack of interoperability and synchronization among user-adaptive systems. There is a strong need for the next generation of user models to be interoperable, i.e. to be able to exchange user model portions and to use the information that has been exchanged to enrich the user experience. This paper presents an overview of the well-established literature dealing with user model interoperability, discussing the most representative work which has provided valuable solutions to face interoperability issues. Based on a detailed decomposition and a deep analysis of the selected work, we have isolated a set of dimensions characterizing the user model interoperability process along which the work has been classified. Starting from this analysis, the paper presents some open issues and possible future deployments in the area.


title: Analytical investigation of mutual interference between automotive FMCW radar sensors

abstract: Radar sensors are key components of modern driver assistance systems. Mutual interference was identified as a problem of increased importance because of the appearance of safety functions and the increasing rate of vehicles equipped with radar sensors. This paper describes mutual interference between automotive FMCW radar sensors. Analytical formulas were derived to be able to calculate the probability for ghost targets and the interference power per frequency bin. The results of the analytical calculations are compared with simulation results on the basis of a simple interference scenario with interference from an oncoming vehicle.


title: Unsupervised Approaches for Automatic Keyword Extraction Using Meeting Transcripts

abstract: This paper explores several unsupervised approaches to automatic keyword extraction using meeting transcripts. In the TFIDF (term frequency, inverse document frequency) weighting framework, we incorporated part-of-speech (POS) information, word clustering, and sentence salience score. We also evaluated a graph-based approach that measures the importance of a word based on its connection with other sentences or words. The system performance is evaluated in different ways, including comparison to human annotated keywords using F-measure and a weighted score relative to the oracle system performance, as well as a novel alternative human evaluation. Our results have shown that the simple unsupervised TFIDF approach performs reasonably well, and the additional information from POS and sentence score helps keyword extraction. However, the graph method is less effective for this domain. Experiments were also performed using speech recognition output and we observed degradation and different patterns compared to human transcripts.


title: Wrinkle Detection Using Hessian Line Tracking

abstract: Wrinkles play an important role in the face-based analysis. They have been widely used in applications, such as facial retouching, facial expression recognition, and face age estimation. Although a few techniques for a wrinkle analysis have been explored in the literature, poor detection limits the accuracy and reliability of wrinkle segmentation. Therefore, an automated wrinkle detection method is crucial to maintain consistency and reduce human error. In this paper, we propose Hessian line tracking (HLT) to overcome the detection problem. HLT is composed of Hessian seeding and directional line tracking. It is an extension of a Hessian filter; however, it significantly increases the accuracy of wrinkle localization when compared with existing methods. In the experimental phase, three coders were instructed to annotate wrinkles manually. To assess the manual annotation, both intrareliability and interreliability were measured, with an accuracy of 94% or above. The experimental results show that the proposed method is capable of tracking hidden pixels; thus, it increases connectivity of detection between wrinkles, allowing some fine wrinkles to be detected. In comparison to the state-of-the-art methods such as the Cula Method, Frangi Filter, and Hybrid Hessian Filter, the proposed HLT yields better results, with an accuracy of 84%. This paper demonstrates that the HLT is a remarkably strong detector of forehead wrinkles in 2-D images.


title: A weakly supervised road extraction approach via deep convolutional nets based image segmentation

abstract: Extracting road information from remote sensing images plays an import role for many practical areas. In this paper, an approach for road extraction is proposed, in order to obtain standard road region with high accuracy. By utilizing the road design and construction specifications made by the transportation industry, the road objects are assigned into different classes. Then the corresponding task is considered as an image segmentation approach, and deep convolutional network is applied to perform pixel-level estimation to predict the ownership probability of different classes. Besides, a modification processing approach is presented to exploit the segmentation result and obtain formal road network by connecting the missing or unsmooth road subsections. Experiments on remote sensing images are performed, and show that the method is efficient for acquiring multi-type roads from complex situations.


title: Real-time optimization of Viola -Jones face detection for mobile platforms

abstract: Face detection algorithms based on the Viola-Jones object detection approach is widely adopted in digital camera products. Due to the computational complexity of these algorithms, often a hardware coprocessor is used for their real-time operation. This paper discusses how to achieve real-time software-based implementation of these algorithms on mobile devices that have relatively limited processing and memory capabilities. Various optimization techniques are discussed and an example implementation outcome on an actual mobile platform is presented.


title: Model-checking continuous-time Markov chains

abstract: We present a logical formalism for expressing properties of continuous-time Markov chains. The semantics for such properties arise as a natural extension of previous work on discrete-time Markov chains to continuous time. The major result is that the verification problem is decidable; this is shown using results in algebraic and transcendental number theory.


title: Entity linking at web scale

abstract: This paper investigates entity linking over millions of high-precision extractions from a corpus of 500 million Web documents, toward the goal of creating a useful knowledge base of general facts. This paper is the first to report on entity linking over this many extractions, and describes new opportunities (such as corpus-level features) and challenges we found when entity linking at Web scale. We present several techniques that we developed and also lessons that we learned. We envision a future where information extraction and entity linking are paired to automatically generate knowledge bases with billions of assertions over millions of linked entities.


title: Attention-Aware Generative Adversarial Networks (ATA-GANs)

abstract: In this work, we present a novel approach for training Generative Adversarial Networks (GANs). Using the attention maps produced by a Teacher- Network we are able to improve the quality of the generated images as well as perform weakly object localization on the generated images. To this end, we generate images of HEp-2 cells captured with Indirect Imunofluoresence (IIF) and study the ability of our network to perform a weakly localization of the cell. Firstly, we demonstrate that whilst GANs can learn the mapping between the input domain and the target distribution efficiently, the discriminator network is not able to detect the regions of interest. Secondly, we present a novel attention transfer mechanism which allows us to enforce the discriminator to put emphasis on the regions of interest via transfer learning. Thirdly, we show that this leads to more realistic images, as the discriminator learns to put emphasis on the area of interest. Fourthly, the proposed method allows one to generate both images as well as attention maps which can be useful for data annotation e.g in object detection.


title: Speaking Plant Approach for Automatic Fertigation System in Greenhouse

abstract: Nowadays, many vegetables are grown inside greenhouses in which environment is controlled and nutrition can be supplied through water supply using electrical pump, namely fertigation. Dosage of nutrition in water for many vegetable plants are also known so that by controlling water supply all the needs for the plants to grow are available. Furthermore, water supply can be controlled using electrical pump which is activated according to the plants conditionin relation with water supply. In order to supply water and nutrition in the right amount and time, plants condition can be observed using a CCD camera attached to image processing facilities to develop a speaking plant approach. In this study, plants development during their growing period are observed using image processing. Three populations of tomato plants, with less, enough, and exceeded nutrition in water, are captured using a CCD camera every three days, and the images were analyzed using a developed computer program for the height of plants. The results showed that the development of the plants can be monitored using this method. After that, the response of plant growth in the same condition was monitored, and the response was used as input for the fertigation system to turn electrical pump automatically on and off, so the fertigation system could maintain the growth of the plants.


title: INFORMATION TECHNOLOGY SERVICE MANAGEMENT (ITSM): CONTRIBUTING FACTORS TO IT SERVICE DISRUPTIONS – A CASE OF MALAYSIA PUBLIC SERVICE AGENCIES

abstract: Researchers have highlighted that the main factors that contribute to IT service failure are the people, process and technology. However, relatively few empirical studies examine to what degree these factors contribute to service disruptions in the public sector organizations. This study explores the IT service management (ITSM) at eight (8) Front-end Agencies, four (4) Ministries and six (6) Departments in Malaysian public service to identify the level of contribution of each factor to the public IT service disruptions. This study was undertaken using questionnaires via stratified sampling. The empirical results reveal that human action, decision, management, error and failure are the major causes to the IT service disruptions followed by an improper process or procedures and technology failure. In addition, we can conclude that human is an important factor and need to give more attention by the management since human is the creator, who uses, manages and maintains the technology and process to enable the delivery of services as specified in the objectives, vision and mission of the organization. Although the literature states that human failure was due to knowledge, skill, attitude and behavior of an individual and the organization environment, but no literature was found studies on what characteristics of human and environmental organizations that make up the resilience service delivery and the creation of an organization that is resilient. Future research on what characteristics on human and organization environmental that contribute to organizational and business resilience is suggested at the end of the paper. However, this paper only covers literature that discussed in depth the type of human failure and the cause of failure. Nevertheless, it is believed that the findings provide a valuable understanding of the current situation in this research field.


title: Interventional pain management skills competency in pain medicine fellows: a method for development and assessment.

abstract: The purposes of this project were to propose an educational module to instruct pain medicine fellows in the appropriate performance of interventional pain management techniques and to verify procedural competency through objective evaluation methodology. Eight board-certified pain medicine physicians spanning two fellowship programs trained seven fellows using a standardized competency-based module. Assessment tools address the basic competencies outlined by the Accreditation Council for Graduate Medical Education (American Board of Anesthesiology Pain Medicine Content Outline). The seven fellows demonstrated proficiency in every segment of the evaluation module. Objective measures compared the fellows' performance on standardized procedure checklists administered 9 mos into training; fellows in the 2012-2013 academic year also received testing at the 3-mo mark. Support for the assessment module is demonstrated by appropriate performance of interventional procedures, with improvement noted from 3-mo to 9-mo testing, successful completion of chart-stimulated oral examinations, proper performance of relevant physical examination maneuvers, and completion of program-specific medical knowledge written tests. The fellows were evaluated via patient surveys and 360-degree global rating scales, maintained procedure logs, and completed two patient-care reports; these were reviewed by program directors to ensure adequate completion. The standardized educational module and evaluation methodology presented provide a potential framework for the definition of baseline competency in the clinical skill area of interventional pain management.


title: Understanding the Elusive Black Box of Dynamic Capabilities

abstract: A major challenge for managers in turbulent environments is to make sound decisions quickly. Dynamic capabilities have been proposed as a means for addressing turbulent environments by helping managers extend, modify, and reconfigure existing operational capabilities into new ones that better match the environment. However, because dynamic capabilities have been viewed as an elusive black box, it is difficult for managers to make sound decisions in turbulent environments if they cannot effectively measure dynamic capabilities. Therefore, we first seek to propose a measurable model of dynamic capabilities by conceptualizing, operationalizing, and measuring dynamic capabilities. Specifically, drawing upon the dynamic capabilities literature, we identify a set of capabilities — sensing the environment, learning, coordinating, and integrating — that help reconfigure existing operational capabilities into new ones that better match the environment. Second, we propose a structural model where dynamic capabilities influence performance by reconfiguring existing operational capabilities in the context of new product development (NPD). Data from 180 NPD units support both the measurable model of dynamic capabilities and also the structural model by which dynamic capabilities influence performance in NPD by reconfiguring operational capabilities, particularly in higher levels of environmental turbulence. The study’s implications for managerial decision making in turbulent environments by capturing the elusive black box of dynamic capabilities are discussed.


title: A Widely-Tunable High-SMSR Narrow-Linewidth Laser Heterogeneously Integrated on Silicon

abstract: A heterogeneously-integrated widely-tunable semiconductor laser utilizing unbalanced MZI filter assisted Vernier ring resonators is demonstrated. A wavelength tuning range of 55 nm, SMSR greater than 50 dB and spectral linewidth of 50–85 kHz over this range was achieved.


title: Hybrid Digital and Analog Beamforming Design for Large-Scale Antenna Arrays

abstract: The potential of using of millimeter wave (mmWave) frequency for future wireless cellular communication systems has motivated the study of large-scale antenna arrays for achieving highly directional beamforming. However, the conventional fully digital beamforming methods which require one radio frequency (RF) chain per antenna element is not viable for large-scale antenna arrays due to the high cost and high power consumption of RF chain components in high frequencies. To address the challenge of this hardware limitation, this paper considers a hybrid beamforming architecture in which the overall beamformer consists of a low-dimensional digital beamformer followed by an RF beamformer implemented using analog phase shifters. Our aim is to show that such an architecture can approach the performance of a fully digital scheme with much fewer number of RF chains. Specifically, this paper establishes that if the number of RF chains is twice the total number of data streams, the hybrid beamforming structure can realize any fully digital beamformer exactly, regardless of the number of antenna elements. For cases with fewer number of RF chains, this paper further considers the hybrid beamforming design problem for both the transmission scenario of a point-to-point multiple-input multiple-output (MIMO) system and a downlink multi-user multiple-input single-output (MU-MISO) system. For each scenario, we propose a heuristic hybrid beamforming design that achieves a performance close to the performance of the fully digital beamforming baseline. Finally, the proposed algorithms are modified for the more practical setting in which only finite resolution phase shifters are available. Numerical simulations show that the proposed schemes are effective even when phase shifters with very low resolution are used.


title: Deep Supervised Hashing with Triplet Labels

abstract: Hashing is one of the most popular and powerful approximate nearest neighbor search techniques for large-scale image retrieval. Most traditional hashing methods first represent images as off-the-shelf visual features and then produce hashing codes in a separate stage. However, off-the-shelf visual features may not be optimally compatible with the hash code learning procedure, which may result in sub-optimal hash codes. Recently, deep hashing methods have been proposed to simultaneously learn image features and hash codes using deep neural networks and have shown superior performance over traditional hashing methods. Most deep hashing methods are given supervised information in the form of pairwise labels or triplet labels. The current state-of-the-art deep hashing method DPSH [1], which is based on pairwise labels, performs image feature learning and hash code learning simultaneously by maximizing the likelihood of pairwise similarities. Inspired by DPSH [1], we propose a triplet label based deep hashing method which aims to maximize the likelihood of the given triplet labels. Experimental results show that our method outperforms all the baselines on CIFAR-10 and NUS-WIDE datasets, including the state-of-the-art method DPSH [1] and all the previous triplet label based deep hashing methods.


title: A Formalization Of Metaphors And Image-Schemas In User Interfaces

abstract: Sound engineering approaches to user interface design require the formalization of key interaction concepts, one of them being metaphor. Work on interface metaphors has, however, been largely non-formal so far. The few existing formal theories of metaphor have been developed in the context of natural language understanding, learning, or reasoning. We propose to formalize interface metaphors by algebraic specifications. This approach provides a comprehensive formalization for the essential aspects of metaphorical user interfaces. Specifically, metaphor domains are being formalized by algebras, metaphorical mappings by morphisms, and image-schemas by categories. The paper explains these concepts and the approach, using examples of spatial and spatializing metaphors.


title: A comprehensive characterization of NLP techniques for identifying equivalent requirements

abstract: Though very important in software engineering, linking artifacts of the same type (clone detection) or of different types (traceability recovery) is extremely tedious, error-prone and requires significant effort. Past research focused on supporting analysts with mechanisms based on Natural Language Processing (NLP) to identify candidate links. Because a plethora of NLP techniques exists, and their performances vary among contexts, it is important to characterize them according to the provided level of support. The aim of this paper is to characterize a comprehensive set of NLP techniques according to the provided level of support to human analysts in detecting equivalent requirements. The characterization consists on a case study, featuring real requirements, in the context of an Italian company in the defense and aerospace domain. The major result from the case study is that simple NLP are more precise than complex ones.


title: Bidirectional converter for vehicle to grid (V2G) reactive power operation

abstract: In this paper, a bidirectional converter with a uniform controller for Vehicle to grid (V2G) application is designed. The bidirectional converter consists of two stages one is ac-dc converter and second is dc-dc converter. For ac-dc converter bipolar modulation is used. Two separate controller systems are designed for converters which follow active and reactive power commands from grid. Uniform controller provides reactive power support to the grid. The charger operates in two quadrants I and IV. There are three modes of operation viz. charging only operation, charging-capacitive operation and charging-inductive operation. During operation under these three operating modes vehicle's battery is not affected. The whole system is tested using MATLAB/SIMULINK.


title: PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts

abstract: We present PubMed 200k RCT, a new dataset based on PubMed for sequential sentence classification. The dataset consists of approximately 200,000 abstracts of randomized controlled trials, totaling 2.3 million sentences. Each sentence of each abstract is labeled with their role in the abstract using one of the following classes: background, objective, method, result, or conclusion. The purpose of releasing this dataset is twofold. First, the majority of datasets for sequential short-text classification (i.e., classification of short texts that appear in sequences) are small: we hope that releasing a new large dataset will help develop more accurate algorithms for this task. Second, from an application perspective, researchers need better tools to efficiently skim through the literature. Automatically classifying each sentence in an abstract would help researchers read abstracts more efficiently, especially in fields where abstracts may be long, such as the medical field.


title: Mental imagery in the motor context

abstract: Abstract The working hypothesis of the paper is that motor images are endowed with the same properties as those of the (corresponding) motor representations, and therefore have the same functional relationship to the imagined or represented movement and the same causal role in the generation of this movement. The fact that the timing of simulated movements follows the same constraints as that of actually executed movements is consistent with this hypothesis. Accordingly, many neural mechanisms are activated during motor imagery, as revealed by a sharp increase in tendinous reflexes in the limb imagined to move, and by vegetative changes which correlate with the level of mental effort. At the cortical level, a specific pattern of activation, that closely resembles that of action execution, is observed in areas devoted to motor control. This activation might be the substrate for the effects of mental training. A hierarchical model of the organization of action is proposed: this model implies a short-term memory storage of a ‘copy’ of the various representational steps. These memories are erased when an action corresponding to the represented goal takes place. By contrast, if the action is incompletely or not executed, the whole system remains activated, and the content of the representation is rehearsed. This mechanism would be the substrate for conscious access to this content during motor imagery and mental training.


title: Dispersion simulation and visualization for urban security

abstract: We present a system for simulating and visualizing the propagation of dispersive contaminants with an application to urban security. In particular, we simulate airborne contaminant propagation in open environments characterised by sky-scrapers and deep urban canyons. Our approach is based on the multiple relaxation time lattice Boltzmann model (MRTLBM), which can efficiently handle complex boundary conditions such as buildings. In addition, we model thermal effects on the flow field using the hybrid thermal MRTLBM. Our approach can also accommodate readings from various sensors distributed in the environment and adapt the simulation accordingly. We accelerate the computation and efficiently render many buildings with small textures on the GPU. We render streamlines and the contaminant smoke with self-shadowing composited with the textured buildings.


Feature extraction for classifying students based on their academic performance


title: Takeover Requests in Simulated Partially Autonomous Vehicles Considering Human Factors

abstract: In the development of autonomous vehicles, the main focus of sensor research has been in relation to environmental perception, and only minimal work has focused on the human–vehicle interaction perspective. However, human factors need to be considered to ensure the safe operation of partially autonomous vehicles. This study briefly introduces a design methodology for the takeover request (TOR) time in National Highway Traffic Safety Administration Level 3 vehicles and compares four different TORs in a simulated environment based on human-in-the-loop experiments with various driving scenarios. A total of 30 drivers participated in the study, and the quantitative/qualitative data obtained show statistically significant differences between the four TOR thresholds. This study shows that the timing involved in the takeover can be obtained by using a performance-based approach considering human factors.


title: Development of an Underwater Manipulator and Its Free-Floating Autonomous Operation

abstract: This paper addresses the novel design of an underwater manipulator with a lightweight multilink structure and its free-floating autonomous operation. The concept design reduces the coupling between the manipulator and the vehicle efficiently, even in the case where the vehicle weight in air is not significantly greater than the manipulator weight. The specific implementation of the mechanical structure is elaborated. Moreover, a closed-loop control system based on binocular vision is proposed for underwater manipulation. In the end, experimental results demonstrate that the conceived underwater manipulator can accomplish the autonomous operation quickly.


title: Evolution of Power Converter Topologies and Technical Considerations of Power Electronic Transformer-Based Rolling Stock Architectures

abstract: Power electronic transformer (PET) technology is one of the promising technology for medium/high power conversion systems. With the cutting-edge improvements in the power electronics and magnetics, makes it possible to substitute conventional line frequency transformer traction (LFTT) technology with the PET technology. Over the past years, research and field trial studies are conducted to explore the technical challenges associated with the operation, functionalities, and control of PET-based traction systems. This paper aims to review the essential requirements, technical challenges, and the existing state of the art of PET traction system architectures. Finally, this paper discusses technical considerations and introduces the new research possibilities especially in the power conversion stages, PET design, and the power switching devices.


title: Software-Defined Networks with Mobile Edge Computing and Caching for Smart Cities: A Big Data Deep Reinforcement Learning Approach

abstract: Recent advances in networking, caching, and computing have significant impacts on the developments of smart cities. Nevertheless, these important enabling technologies have traditionally been studied separately in the existing works on smart cities. In this article, we propose an integrated framework that can enable dynamic orchestration of networking, caching, and computing resources to improve the performance of applications for smart cities. Then we present a novel big data deep reinforcement learning approach. Simulation results with different system parameters are presented to show the effectiveness of the proposed scheme.


title: How good is good enough? Re-evaluating the bar for energy disaggregation

abstract: Since the early 1980s, the research community has developed ever more sophisticated algorithms for the problem of energy disaggregation, but despite decades of research, there is still a dearth of applications with demonstrated value. In this work, we explore a question that is highly pertinent to this research community: how good does energy disaggregation need to be in order to infer characteristics of a household? We present novel techniques that use unsupervised energy disaggregation to predict both household occupancy and static properties of the household such as size of the home and number of occupants. Results show that basic disaggregation approaches performs up to 30% better at occupancy estimation than using aggregate power data alone, and are up to 10% better at estimating static household characteristics. These results show that even rudimentary energy disaggregation techniques are sufficient for improved inference of household characteristics. To conclude, we re-evaluate the bar set by the community for energy disaggregation accuracy and try to answer the question"how good is good enough?"


title: Affective characterization of movie scenes based on content analysis and physiological changes

abstract: In this paper, we propose an approach for affective characterization of movie scenes based on the emotions that are actually felt by spectators. Such a representation can be used to characterize the emotional content of video clips in application areas such as affective video indexing and retrieval, and neuromarketing studies. A dataset of 64 different scenes from eight movies was shown to eight participants. While watching these scenes, their physiological responses were recorded. The participants were asked to self-assess their felt emotional arousal and valence for each scene. In addition, content-based audio- and video-based features were extracted from the movie scenes in order to characterize each scene. Degrees of arousal and valence were estimated by a linear combination of features from physiological signals, as well as by a linear combination of content-based features. We showed that a significant correlation exists between valence-arousal provided by the spectator's self-assessments, and affective grades obtained automatically from either physiological responses or from audio-video features. By means of an analysis of variance (ANOVA), the variation of different participants' self assessments and different gender groups self assessments for both valence and arousal were shown to be significant (p-values lower than 0.005). These affective characterization results demonstrate the ability of using multimedia features and physiological responses to predict the expected affect of the user in response to the emotional video content.


title: FlexStore: A Software Defined, Energy Adaptive Distributed Storage Framework

abstract: In this paper we propose a flexible and scalable distributed storage framework called flex Store that can adapt to variations in available or consumable power and demonstrate its performance in the context of reduplicated virtual machine disks. We propose and investigate smart control techniques in order to cope with the power constraints either introduced as a result of increasing node density in the storage arrays (consumable power constraints) or introduced when a mix of renewable (green) and conventional (brown) energy sources are used to power the data enter. The key component in the proposed storage framework is the policy engine which is a software layer that provides interfaces to define performance requirements of the applications (and also energy related policies). The policy engine enforces those policies in the storage system by adjusting the allocation of storage resources. The experimental results demonstrate the ability of the framework to dynamically adapt to the changes in workload and power constraints and minimize performance impacts. Our evaluation of the prototype shows that the adaptive replication mechanisms can reduce the IO latencies by around 65% during energy plenty situations and the impact of adaptation actions on IO latencies during energy constrained situations is reduced by more than 40% compared to the case without the adaptive replication and optimized adaptation mechanisms.


title: Knowing when you're wrong: building fast and reliable approximate query processing systems

abstract: Modern data analytics applications typically process massive amounts of data on clusters of tens, hundreds, or thousands of machines to support near-real-time decisions.The quantity of data and limitations of disk and memory bandwidth often make it infeasible to deliver answers at interactive speeds. However, it has been widely observed that many applications can tolerate some degree of inaccuracy. This is especially true for exploratory queries on data, where users are satisfied with "close-enough" answers if they can come quickly. A popular technique for speeding up queries at the cost of accuracy is to execute each query on a sample of data, rather than the whole dataset. To ensure that the returned result is not too inaccurate, past work on approximate query processing has used statistical techniques to estimate "error bars" on returned results. However, existing work in the sampling-based approximate query processing (S-AQP) community has not validated whether these techniques actually generate accurate error bars for real query workloads. In fact, we find that error bar estimation often fails on real world production workloads. Fortunately, it is possible to quickly and accurately diagnose the failure of error estimation for a query. In this paper, we show that it is possible to implement a query approximation pipeline that produces approximate answers and reliable error bars at interactive speeds.


title: Hand Gesture Recognition Based on Perceptual Shape Decomposition with a Kinect Camera

abstract: SUMMARY In this paper, we propose the Perceptual Shape Decompo-sition (PSD) to detect ﬁngers for a Kinect-based hand gesture recognitionsystem. The PSD is formulated as a discrete optimization problem by re-moving all negative minima with minimum cost. Experiments show thatour PSD is perceptually relevant and robust against distortion and handvariations, and thus improves the recognition system performance. key words: Kinect camerta, hand gesture recognition, perceptual decom-position, ﬁnger detection 1. Introduction Hand gesture recognition is important for human-computerinteraction (HCI) in diﬀerent areas, such as virtual reality,signlanguagerecognition,andcomputergames[1]. Usuallythe shape feature is suﬃcient for successful recognition[2].However, due to the nature of optical sensor, the quality ofcaptured images is sensitive to the lighting conditions andcluttered backgrounds, which makes it very diﬃcult to ob-tain hand shapes[3].Thanks to the recent advent of the Kinect depth cam-era[4], new opportunities for hand gesture recognitionemerge. Ren et al. were the ﬁrst to develop a real-lifehand gesture recognition system with a Kinect sensor[5],as shown in Fig.1. With the assistant of depth cue, theirsystem can segment hands robustly. However, due to thelow-resolutionoftheKinectdepthmapandthesmallsizeofhand image, the segmentation of hand is usually inaccurateand noisy, which signiﬁcantly aﬀects the recognition per-formance. In order to address this problem, they proposedthe Finger-Earth Mover’s Distance (FEMD), which onlymatches the ﬁngers rather than the whole hand[5]. Thus,the ﬁnger detection has a signiﬁcant impact upon robust-ness, accuracy and eﬃciency of their hand gesture recogni-tion system.Ren et al. presented two shape decomposition meth-ods for ﬁnger detection. The ﬁrst one is the Threshold-ing Decomposition (TD)[6]. This method decomposes out-stretched ﬁngers using a circle that is concentric with themaximal inscribed circle of the hand shape and of radiusa speciﬁed threshold value. Despite the implementation


title: Compassionate mind training for people with high shame and self-criticism: Overview and pilot study of a group of therapy approach

abstract: Compassionate mind training (CMT) was developed for people with high shame and self-criticism, whose problems tend to be chronic, and who find self-warmth and self-acceptance difficult and/or frightening. This paper offers a short overview of the role of shame and self-criticism in psychological difficulties, the importance of considering different types of affect system (activating versus soothing) and the theory and therapy process of CMT. The paper explores patient acceptability, understanding, abilities to utilize and practice compassion focused processes and the effectiveness of CMT from an uncontrolled trial. Six patients attending a cognitive–behavioural-based day centre for chronic difficulties completed 12 two-hour sessions in compassionate mind training. They were advised that this was part of a research programme to look at the process and effectiveness of CMT and to become active collaborators, advising the researchers on what was helpful and what was not. Results showed significant reductions in depression, anxiety, self-criticism, shame, inferiority and submissive behaviour. There was also a significant increase in the participants' ability to be self-soothing and focus on feelings of warmth and reassurance for the self. Compassionate mind training may be a useful addition for some patients with chronic difficulties, especially those from traumatic backgrounds, who may lack a sense of inner warmth or abilities to be self-soothing. Copyright © 2006 John Wiley & Sons, Ltd.


title: Autocorrelation and partial autocorrelation functions to improve neural networks models on univariate time series forecasting

abstract: This paper proposes the autocorrelation function (acf) and partial autocorrelation function (pacf) as tools to help and improve the construction of the input layer for univariate time series artificial neural network (ANN) models, as used in classical time series analysis. Especially reducing the number of input layer neurons, and also helping the user to understand the behaviour of the series. Although the acf and pacf are considered linear functions, this paper shows that they can be used even in non linear time series. The ANNs used in this work are the Incremental Gaussian Mixture Network (IGMN), because it is a deterministic model, and the multilayer perceptron (MLP), the most used ANN model for time series forecasting.


title: Efficient Privacy-Preserving Outsourcing of Large-Scale Convex Separable Programming for Smart Cities

abstract: One of the most salient features of smart city is to utilize big data to make our lives more convenient and more intelligent. This is usually achieved through solving a series of large-scale common and fundamental problems such as linear systems of equations, linear programs, etc. However, it is a very challenging task for resource-limited clients and small companies to solve such problems as the data volume keeps increasing. With cloud computing, an alternative is to solve complex problems by outsourcing them to the cloud. Nonetheless, data privacy is one of the main concerns. Many previous works on privacy-preserving outsourcing are based on cryptographic techniques like homomorphic encryption and have very high computational complexity, which may not be practical for big data applications. In this paper, we design an efficient privacy-preserving outsourcing algorithm based on arithmetic operations only for large-scale convex separable programming problems. Specifically, we first develop an efficient transformation scheme to preserve data privacy. Then we linearize the convex functions with arbitrary accuracy and solve the problem by outsourcing it to the cloud. The client can efficiently verify the correctness of the returned results to prevent any malicious behavior of the cloud. Implementations on Amazon Elastic Compute Cloud (EC2) platform show that the proposed scheme provides significant time savings.


abstract: Data-centric abstractions and execution strategies are needed to exploit parallelism in large-scale graph analytics.


title: Research on the architecture and key technology of Internet of Things (IoT) applied on smart grid

abstract: Advances in the areas of embedded systems, computing, and networking are leading to an infrastructure composed of millions of heterogeneous devices. These devices will not simply convey information but process it in transit, connect peer to peer, and form advanced collaborations. This “Internet of Things (IoT)” infrastructure will be strongly integrated with the environment. This paper focuses on researching on the architecture and key technology of Internet of Things. Moreover, the applications of Internet of Things are interpreted in this paper. Especially, the application of IoT in smart grid is emphasized. The work presented here proposes the principal characteristics for an effective integration of the Internet of Things in smart grid.


title: Practical Eavesdropping and Skimming Attacks on High-Frequency RFID Tokens

abstract: RFID systems often use near-field magnetic coupling to implement communication channels. The advertised operational range of these channels is less than 10 cm and therefore several implemented systems assume that the communication channel is location limited and therefore relatively secure. Nevertheless, there have been repeated questions raised about the vulnerability of these near-field systems against eavesdropping and skimming attacks. In this paper we revisit the topic of RFID eavesdropping and skimming attacks, surveying previous work and explaining why the feasibility of practical attacks is still a relevant and novel research topic. We present a brief overview of the radio characteristics for popular HF RFID standards and present some practical results for eavesdropping experiments against tokens adhering to the ISO 14443 and ISO 15693 standards. We also discuss how an attacker could construct a low-cost eavesdropping device using easy to obtain parts and reference designs. Finally, we present results for skimming experiments against ISO 14443 tokens.


title: Flexible Technologies for Self-Powered Wearable Health and Environmental Sensing

abstract: This article provides the latest advances from the NSF Advanced Self-powered Systems of Integrated sensors and Technologies (ASSIST) center. The work in the center addresses the key challenges in wearable health and environmental sys- tems by exploring technologies that enable ultra-long battery lifetime, user comfort and wearability, robust medically vali- dated sensor data with value added from multimodal sensing, and access to open architecture data streams. The vison of the ASSIST center is to use nanotechnology to build miniature, self- powered, wearable, and wireless sensing devices that can ena- ble monitoring of personal health and personal environmental exposure and enable correlation of multimodal sensors. These devices can empower patients and doctors to transition from managing illness to managing wellness and create a paradigm shift in improving healthcare outcomes. This article presents the latest advances in high-efficiency nanostructured energy harvesters and storage capacitors, new sensing modalities that consume less power, low power computation, and communi- cation strategies, and novel flexible materials that provide form, function, and comfort. These technologies span a spatial scale ranging from underlying materials at the nanoscale to body worn structures, and the challenge is to integrate them into a unified device designed to revolutionize wearable health applications.


title: Multilingual Modal Sense Classification using a Convolutional Neural Network

abstract: Modal sense classification (MSC) is a special WSD task that depends on the meaning of the proposition in the modal's scope. We explore a CNN architecture for classifying modal sense in English and German. We show that CNNs are superior to manually designed feature-based classifiers and a standard NN classifier. We analyze the feature maps learned by the CNN and identify known and previously unattested linguistic features. We benchmark the CNN on a standard WSD task, where it compares favorably to models using sense-disambiguated target vectors.


title: Point-and-Shoot Memories The Influence of Taking Photos on Memory for a Museum Tour

abstract: Two studies examined whether photographing objects impacts what is remembered about them. Participants were led on a guided tour of an art museum and were directed to observe some objects and to photograph others. Results showed a photo-taking-impairment effect: If participants took a photo of each object as a whole, they remembered fewer objects and remembered fewer details about the objects and the objects’ locations in the museum than if they instead only observed the objects and did not photograph them. However, when participants zoomed in to photograph a specific part of the object, their subsequent recognition and detail memory was not impaired, and, in fact, memory for features that were not zoomed in on was just as strong as memory for features that were zoomed in on. This finding highlights key differences between people’s memory and the camera’s “memory” and suggests that the additional attentional and cognitive processes engaged by this focused activity can eliminate the photo-taking-impairment...


title: Compile-time path expansion in Lore

abstract: Semistructured data usually is modeled as labeled directed graphs, and query languages are based on declarative path expressions that specify traversals through the graphs. Regular (or generalized) path expressions use regular expression operators to specify traversal patterns. Regular path expressions typically are evaluated at run-time by exploring the database graph. However, if the database includes a structural summary such as a DataGuide, then an alternative approach is to expand regular path expressions at compile-time using the structural summary, reducing the run-time overhead of database exploration. This paper describes algorithms for compile-time regular path expression expansion in the context of the Lorel query language for semistructured data, and reports on performance results conducted on the Lore system illustrating the benefits of compile-time expansion


title: Detection of multiple change–points in multivariate time series

abstract: We consider the multiple change-point problem for multivariate time series, including strongly dependent processes, with an unknown number of change-points. We assume that the covariance structure of the series changes abruptly at some unknown common change-point times. The proposed adaptive method is able to detect changes in multivariate i.i.d., weakly and strongly dependent series. This adaptive method outperforms the Schwarz criteria, mainly for the case of weakly dependent data. We consider applications to multivariate series of daily stock indices returns and series generated by an artificial financial market.


title: Design and Development of Non-Contact Bio-Potential Electrodes for Pervasive Health Monitoring Applications

abstract: For the advent of pervasive bio-potential monitoring, it will be necessary to utilize a combination of cheap, quick to apply, low-noise electrodes and compact electronics with wireless technologies. Once available, all electrical activity resulting from the processes of the human body could be actively and constantly monitored without the need for cumbersome application and maintenance. This could significantly improve the early diagnosis of a range of different conditions in high-risk individuals, opening the possibility for new treatments and interventions as conditions develop. This paper presents the design and implementation of compact, non-contact capacitive bio-potential electrodes utilising a low impedance current-to-voltage configuration and a bootstrapped voltage follower, demonstrating results applicable to research applications for capacitive electrocardiography and capacitive electromyography. The presented electrodes use few components, have a small surface area and are capable of acquiring a range of bio-potential signals.