title: Realization of Function Approximation Based on Matlab

abstract: Three means to realize function approach such as the interpolation approach,fitting approach as well as the neural network approach are discussed based on Matlab to meet the demand of data processing in engineering application.Based on basic principle of the introduction,realization methods to piecewise linear interpolation,Hermite interpolation,cubic spline interpolation and least squares curve fitting method are researched using interpolation function and fitting function in Matlab with example.BP neural network to proximate nonlinear functions is designed,desired efect is achieved through the training and simulation of network.All results indicate that different methods are adopted according to specific problems,and function approach is realized simply and effectively in Matlab.


title: A Planar Light Probe

abstract: We develop a novel technique for measuring lighting that exploits the interaction of light with a set of custom BRDFs. This enables the construction of a planar light probe with certain advantages over existing methods for measuring lighting. To facilitate the construction of our light probe, we derive a new class of bi-directional reflectance functions based on the interaction of light through two planar surfaces separated by a transparent medium. Under certain assumptions and proper selection of the two surfaces, we show how to recover Fourier series coefficients of the incident lighting parameterized over the plane. The results are experimentally validated by imaging a sheet of glass with spatially varying patterns printed on either side.


title: A 61.5dB SNDR pipelined ADC using simple highly-scalable ring amplifiers

abstract: A ring amplifier based pipelined ADC is presented that uses simple cells constructed from small inverters and capacitors to perform amplification. The basic ring amplifier structure is characterized and demonstrated to be highly scalable, power efficient, and compression-immune (inherent rail-to-rail output swing). The prototype 10.5-bit ADC, fabricated in 0.18µm CMOS technology, achieves 61.5dB SNDR at a 30MHz sampling rate and consumes 2.6mW, resulting in a FoM of 90fJ/conversion-step.


title: The load slice core microarchitecture

abstract: Driven by the motivation to expose instruction-level parallelism (ILP), microprocessor cores have evolved from simple, in-order pipelines into complex, superscalar out-of-order designs. By extracting ILP, these processors also enable parallel cache and memory operations as a useful side-effect. Today, however, the growing off-chip memory wall and complex cache hierarchies of many-core processors make cache and memory accesses ever more costly. This increases the importance of extracting memory hierarchy parallelism (MHP), while reducing the net impact of more general, yet complex and power-hungry ILP-extraction techniques. In addition, for multi-core processors operating in power- and energy-constrained environments, energy-efficiency has largely replaced single-thread performance as the primary concern. Based on this observation, we propose a core microarchitecture that is aimed squarely at generating parallel accesses to the memory hierarchy while maximizing energy efficiency. The Load Slice Core extends the efficient in-order, stall-on-use core with a second in-order pipeline that enables memory accesses and address-generating instructions to bypass stalled instructions in the main pipeline. Backward program slices containing address-generating instructions leading up to loads and stores are extracted automatically by the hardware, using a novel iterative algorithm that requires no software support or recompilation. On average, the Load Slice Core improves performance over a baseline in-order processor by 53% with overheads of only 15% in area and 22% in power, leading to an increase in energy efficiency (MIPS/Watt) over in-order and out-of-order designs by 43% and over 4.7×, respectively. In addition, for a power- and area-constrained many-core design, the Load Slice Core outperforms both in-order and out-of-order designs, achieving a 53% and 95% higher performance, respectively, thus providing an alternative direction for future many-core processors.


title: 4 GHz 3D-printed balun-fed bowtie antenna with finite ground plane for gain and impedance matching enhancement

abstract: A 3D-printed balun-fed bowtie antenna structure with a ground plane for unidirectional radiation is presented. The 4 GHz antenna is fabricated using Acrylonitrile Butadiene Styrene (ABS) for the substrate and packaging, and DuPont CB028 Ag paste for the conductors. For comparison, a conventional microstrip bowtie has also been designed using a 32 mil Rogers 4003C substrate. The antennas are fed with a Klopfenstein taper microstrip balun designed on a 32 mil Rogers 4003C substrate. The 3D and microstrip printed bowtie designs exhibit ∼4 dB gain, while the 3D-printed bowtie design with a finite ground plane exhibits 7.3 dB gain with no degradation in return loss (RL) bandwidth. The RL bandwidth is ∼35%.


title: Culture and Change Blindness

abstract: Research on perception and cognition suggests that whereas East Asians view the world holistically, attending to the entire field and relations among objects, Westerners view the world analytically, focusing on the attributes of salient objects. These propositions were examined in the change-blindness paradigm. Research in that paradigm finds American participants to be more sensitive to changes in focal objects than to changes in the periphery or context. We anticipated that this would be less true for East Asians and that they would be more sensitive to context changes than would Americans. We presented participants with still photos and with animated vignettes having changes in focal object information and contextual information. Compared to Americans, East Asians were more sensitive to contextual changes than to focal object changes. These results suggest that there can be cultural variation in what may seem to be basic perceptual processes.


title: Design of compact microstrip diplexer with high selectivity

abstract: A novel design of compact microstrip diplexer, using coupled dual-mode open-loop resonators, is proposed. The main advantage in using dual-mode open-loop resonators is that a compact narrowband bandpass filter with high selective asymmetrical frequency response is easily realized. This property is explored to design diplexer for a system with two extremely close bands. A prototype diplexer operating in the 2.07/2.3 GHz band, having compact size (44 mm × 11 mm) and high isolation (>−40 dB), is designed for demonstration.


title: The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing

abstract: Unbounded, unordered, global-scale datasets are increasingly common in day-to-day business (e.g. Web logs, mobile usage statistics, and sensor networks). At the same time, consumers of these datasets have evolved sophisticated requirements, such as event-time ordering and windowing by features of the data themselves, in addition to an insatiable hunger for faster answers. Meanwhile, practicality dictates that one can never fully optimize along all dimensions of correctness, latency, and cost for these types of input. As a result, data processing practitioners are left with the quandary of how to reconcile the tensions between these seemingly competing propositions, often resulting in disparate implementations and systems. ::: ::: We propose that a fundamental shift of approach is necessary to deal with these evolved requirements in modern data processing. We as a field must stop trying to groom unbounded datasets into finite pools of information that eventually become complete, and instead live and breathe under the assumption that we will never know if or when we have seen all of our data, only that new data will arrive, old data may be retracted, and the only way to make this problem tractable is via principled abstractions that allow the practitioner the choice of appropriate tradeoffs along the axes of interest: correctness, latency, and cost. ::: ::: In this paper, we present one such approach, the Dataflow Model, along with a detailed examination of the semantics it enables, an overview of the core principles that guided its design, and a validation of the model itself via the real-world experiences that led to its development.


title: Activation of influenza viruses by proteases from host cells and bacteria in the human airway epithelium

abstract: Influenza is an acute infection of the respiratory tract, which affects each year millions of people. Influenza virus infection is initiated by the surface glycoprotein hemagglutinin (HA) through receptor binding and fusion of viral and endosomal membranes. HA is synthesized as a precursor protein and requires cleavage by host cell proteases to gain its fusion capacity. Although cleavage of HA is crucial for virus infectivity, little was known about relevant proteases in the human airways for a long time. Recent progress in the identification and characterization of HA-activating host cell proteases has been considerable however and supports the idea of targeting HA cleavage as a novel approach for influenza treatment. Interestingly, certain bacteria have been demonstrated to support HA activation either by secreting proteases that cleave HA or due to activation of cellular proteases and thereby may contribute to virus spread and enhanced pathogenicity. In this review, we give an overview on activation of influenza viruses by proteases from host cells and bacteria with the main focus on recent progress on HA cleavage by proteases HAT and TMPRSS2 in the human airway epithelium. In addition, we outline investigations of HA-activating proteases as potential drug targets for influenza treatment.


title: Predictors of Relationship Satisfaction in Online Romantic Relationships

abstract: Based on traditional theories of interpersonal relationship development and on the hyperpersonal communication theory, this study examined predictors of relationship satisfaction for individuals involved in online romantic relationships. One hundred-fourteen individuals (N = 114) involved in online romantic relationships, and who had only engaged in computer-mediated communication (CMC) with their partners, completed an online questionnaire about their relationships. Intimacy, trust, and communication satisfaction were found to be the strongest predictors of relationship satisfaction for individuals involved in online romances. Additionally, perceptions of relationship variables differed depending on relationship length and time spent communicating. Implications for interpersonal and hyperpersonal communication theories, and future investigation of online relationships, are discussed.


title: Latent Factor Representations for Cold-Start Video Recommendation

abstract: Recommending items that have rarely/never been viewed by users is a bottleneck for collaborative filtering (CF) based recommendation algorithms. To alleviate this problem, item content representation (mostly in textual form) has been used as auxiliary information for learning latent factor representations. In this work we present a novel method for learning latent factor representation for videos based on modelling the emotional connection between user and item. First of all we present a comparative analysis of state-of-the art emotion modelling approaches that brings out a surprising finding regarding the efficacy of latent factor representations in modelling emotion in video content. Based on this finding we present a method visual-CLiMF for learning latent factor representations for cold start videos based on implicit feedback. Visual-CLiMF is based on the popular collaborative less-is-more approach but demonstrates how emotional aspects of items could be used as auxiliary information to improve MRR performance. Experiments on a new data set and the Amazon products data set demonstrate the effectiveness of visual-CLiMF which outperforms existing CF methods with or without content information.


title: Automated Test Input Generation for Android: Towards Getting There in an Industrial Case

abstract: Monkey, a random testing tool from Google, has been popularly used in industrial practices for automatic test input generation for Android due to its applicability to a variety of application settings, e.g., ease of use and compatibility with different Android platforms. Recently, Monkey has been under the spotlight of the research community: recent studies found out that none of the studied tools from the academia were actually better than Monkey when applied on a set of open source Android apps. Our recent efforts performed the first case study of applying Monkey on WeChat, a popular messenger app with over 800 million monthly active users, and revealed many limitations of Monkey along with developing our improved approach to alleviate some of these limitations. In this paper, we explore two optimization techniques to improve the effectiveness and efficiency of our previous approach. We also conduct manual categorization of not-covered activities and two automatic coverage-analysis techniques to provide insightful information about the not-covered code entities. Lastly, we present findings of our empirical studies of conducting automatic random testing on WeChat with the preceding techniques.


title: Offset Reduction of CMOS Based DynamicComparator by using Charge Storage Techniques- A Comparative Study

abstract: Comparator is one of the most widely used building block for analog and mixed signal systems. For the implementation of high-performance CMOS A/D converters, low offset comparators are essential. In this paper, dynamic comparator offset is calculated to the extent of high accuracy. The offset so calculated has been reduced by the charge storage techniques to achieve an efficient design. In addition to the offset, propagation delay and power dissipation, being the important parameter of the comparator, has been analyzed. It is observed that offset voltage in the comparator has been reduced to 350μV for output offset storage technique and 400μV for input offset storage techniques from 91mV. In this paper, BPTM model has been used in analyze the dynamic comparator.


title: An artificial neural network (p,d,q) model for timeseries forecasting

abstract: Artificial neural networks (ANNs) are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. However, despite all advantages cited for artificial neural networks, their performance for some real time series is not satisfactory. Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing forecasters. Both theoretical and empirical findings have indicated that integration of different models can be an effective way of improving upon their predictive performance, especially when the models in the ensemble are quite different. In this paper, a novel hybrid model of artificial neural networks is proposed using auto-regressive integrated moving average (ARIMA) models in order to yield a more accurate forecasting model than artificial neural networks. The empirical results with three well-known real data sets indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by artificial neural networks. Therefore, it can be used as an appropriate alternative model for forecasting task, especially when higher forecasting accuracy is needed.


title: On the importance of accurate weak classifier learning for boosted weak classifiers

abstract: Recent work has shown that improving model learning for weak classifiers can yield significant gains in the overall accuracy of a boosted classifier. However, most published classifier boosting research relies only on rudimentary learning techniques for weak classifiers. So while it is known that improving the model learning can greatly improve the accuracy of the resulting strong classifier, it remains to be shown how much can yet be gained by further improving the model learning at the weak classifier level. This paper derives a very accurate model learning method for weak classifiers based on the popular Haar-like features and presents an investigation of its usefulness compared to the standard and recent approaches. The accuracy of the new method is shown by demonstrating the new models ability to predict ROC performance on validation data. A discussion of the problems in learning accurate weak hypotheses is given, along with example solutions. It is also shown that a previous simpler method can be further improved. Lastly, we show that improving model accuracy does not continue to yield improved overall classification beyond a certain point. At this point the learning technique, in this case RealBoost, is unable to make gains from the improved model data. The method has been tested on pedestrian detection tasks using classifiers boosted using the RealBoost boosting algorithm. A subset of our most interesting results is shown to demonstrate the value of method.


title: SmartStore: a new metadata organization paradigm with semantic-awareness for next-generation file systems

abstract: Existing storage systems using hierarchical directory tree do not meet scalability and functionality requirements for exponentially growing datasets and increasingly complex queries in Exabyte-level systems with billions of files. This paper proposes semantic-aware organization, called SmartStore, which exploits metadata semantics of files to judiciously aggregate correlated files into semantica-ware groups by using information retrieval tools. Decentralized design improves system scalability and reduces query latency for complex queries (range and top-k queries), which is conducive to constructing semantic-aware caching, and conventional filename-based query. SmartStore limits search scope of complex query to a single or a minimal number of semantically related groups and avoids or alleviates brute-force search in entire system. Extensive experiments using real-world traces show that SmartStore improves system scalability and reduces query latency over basic database approaches by one thousand times. To the best of our knowledge, this is the first study implementing complex queries in large-scale file systems.


title: Determining the Demographic impacts on the Organizational Commitment of Academicians in the HEIs of DCs like Pakistan

abstract: In the contemporary age where the globalization is taking place in every sphere of life, theacademic institutions occupy their place at the helm. The dynamism in the academicinstitutions in general and the universities in specific is directly proportional to thecompetence of their employees. The success of the universities depends upon solidfunctioning, faithfulness and the involved academicians regarding high level of teachingand research. Provision of workforce to the different sectors countrywide is possible onlythrough the universities with high level of educational standards and OrganizationalCommitment on the part of the staff members especially the academicians. Unlike thedeveloped countries, this role becomes more emphasizing in the developing countries likePakistan. The sole objective of this ‘Review Article’ is to investigate the impact ofdemographics (gender, qualification, designation, age, and marital status) onOrganizational Commitment (Emotional, Cost-benefited and Obligatory) of employees,working in the public sector universities. Up to some extent, it is a complicated task tocome across and deal with the people who are physically, linguistically, culturally,educationally and ethnically diverse. In the light of current research we construct a‘Theoretical Framework’ on the topic which will subsequently be used as a ‘ResearchModel’ for the upcoming ‘experimental-study’, using the study of workforce from theuniversities of Khyber Pakhtunkhwa, Pakistan. It is acknowledged from the present studythat the demographics have the significant impact on the organizational commitment


title: Using Data Mining Methods to Predict Personally Identifiable Information in Emails

abstract: Private information management and compliance are important issues nowadays for most of organizations. As a major communication tool for organizations, email is one of the many potential sources for privacy leaks. Information extraction methods have been applied to detect private information in text files. However, since email messages usually consist of low quality text, information extraction methods for private information detection may not achieve good performance. In this paper, we address the problem of predicting the presence of private information in email using data mining and text mining methods. Two prediction models are proposed. The first model is based on association rules that predict one type of private information based on other types of private information identified in emails. The second model is based on classification models that predict private information according to the content of the emails. Experiments on the Enron email dataset show promising results.


eview he impact of cognitive training and mental stimulation on cognitive nd everyday functioning of healthy older adults : A systematic review nd meta-analysis


title: A Low-Noise CMOS Image Sensor With Digital Correlated Multiple Sampling

abstract: This paper presents a low noise CMOS image sensor using conventional 3T active pixel with Nwell/Psub diode as photo detector. Both fixed pattern noise (FPN) and temporal noise are suppressed by the proposed digital correlated multiple sampling (DCMS) technology. FPN and temporal noise from pixel, buffer circuit, and column-parallel ADC are analyzed in detail, and the total noise with DCMS is derived. General expression of 1/f noise with correlated multiple sampling is given, illustrating impact of delay time in DCMS. Output noise of image sensor, frame rate, power, and area are affected by order and oversampling ratio of sigma–delta ADC, which are discussed for practical design. A prototype CMOS image sensor with $800\times 600$ pixel array and second-order incremental sigma–delta ADCs is implemented with the 0.35- $\mu \text{m}$ standard CMOS process. Measurement results of the implemented image sensor show a column FPN of 0.009%, an input referred noise of 3.5 $\text{e}^{-}_{\mathrm {\mathbf {rms}}}$ , and a dynamic range of 84 dB with oversampling ratio of 255. This indicates that image sensor with low noise can be achieved by DCMS without the CIS process and column amplification.


title: Attribute-Based Detection of Unfamiliar Classes with Humans in the Loop

abstract: Recent work in computer vision has addressed zero-shot learning or unseen class detection, which involves categorizing objects without observing any training examples. However, these problems assume that attributes or defining characteristics of these unobserved classes are known, leveraging this information at test time to detect an unseen class. We address the more realistic problem of detecting categories that do not appear in the dataset in any form. We denote such a category as an unfamiliar class, it is neither observed at train time, nor do we possess any knowledge regarding its relationships to attributes. This problem is one that has received limited attention within the computer vision community. In this work, we propose a novel approach to the unfamiliar class detection task that builds on attribute-based classification methods, and we empirically demonstrate how classification accuracy is impacted by attribute noise and dataset "difficulty," as quantified by the separation of classes in the attribute space. We also present a method for incorporating human users to overcome deficiencies in attribute detection. We demonstrate results superior to existing methods on the challenging CUB-200-2011 dataset.


Annotated Corpus for Named Entity Recognition


title: Fluoroquinolones: Synthesis and Application

abstract: The data on 6-fluorо-1,4-dihydroquinolin-4-oxo-3-carboxylic acids and their structural analogues accumulated in the literature for the last 10–15 years are reviewed. Synthetic approaches to the quinolone system, as well as all kind of structural modifications by incorporating substituents into 1–8 positions or by means of annelation have been discussed. The “structure-activity” relationships for antibacterial fluoroquinolones, as well as the data on other types of biological activity for the family of bi- and polycyclic fluoroquinolones are presented. The formation of complexes of fluoroquinolones with metals and their applications have been considered. The bibliography – 377 references.


title: Ocean acidification causes structural deformities in juvenile coral skeletons

abstract: Rising atmospheric CO2 is causing the oceans to both warm and acidify, which could reduce the calcification rates of corals globally. Successful coral recruitment and high rates of juvenile calcification are critical to the replenishment and ultimate viability of coral reef ecosystems. Although elevated Pco2 (partial pressure of CO2) has been shown to reduce the skeletal weight of coral recruits, the structural changes caused by acidification during initial skeletal deposition are unknown. We show, using high-resolution three-dimensional x-ray microscopy, that ocean acidification (Pco2 ~900 μatm, pH ~7.7) not only causes reduced overall mineral deposition but also a deformed and porous skeletal structure in newly settled coral recruits. In contrast, elevated temperature (+3°C) had little effect on skeletal formation except to partially mitigate the effects of elevated Pco2. The striking structural deformities we observed show that new recruits are at significant risk, being unable to effectively build their skeletons in the Pco2 conditions predicted to occur for open ocean surface waters under a “business-as-usual” emissions scenario [RCP (representative concentration pathway) 8.5] by the year 2100.


title: Detecting pedestrians and vehicles in traffic scene based on boosted HOG features and SVM

abstract: This paper presents a popular method called boosted hog features to detect pedestrians and vehicles in static images. We compared the differences and similarities of detecting pedestrians and vehicles, then we selected boosted hog features to get an satisfying result. In the part of detecting pedestrians, Histograms of Oriented Gradients (HOG) feature is applied as the basic feature due to its good performance in various kinds of background. On that basis, we create a new feature with boosting algorithm to obtain more accurate results. In the part of detecting vehicle, we select the shadow underneath vehicle as the feature, so we can utilize it to detect vehicles in daytime. The shadow is an important feature for vehicles in traffic scenes. The region under vehicle is usually darker than other objects or backgrounds and can be segmented by setting a threshold. Finally, experimental results show that the detection of pedestrians and vehicles using boosted hog feature and linear svm combines the advantages of hog feature and adaboost classifier, and can achieve better detection results than the detector using conditional HOG features. At the end, the paper shows its efficiency and effectiveness using to application in real situations.


title: Fast-Converging Conditional Generative Adversarial Networks for Image Synthesis

abstract: Building on top of the success of generative adversarial networks (GANs), conditional GANs attempt to better direct the data generation process by conditioning with certain additional information. Inspired by the most recent AC-GAN, in this paper we propose a fast-converging conditional GAN (FC-GAN). In addition to the real/fake classifier used in vanilla GANs, our discriminator has an advanced auxiliary classifier which distinguishes each real class from an extra ‘fake’ class. The ‘fake’ class avoids mixing generated data with real data, which can potentially confuse the classification of real data as AC-GAN does, and makes the advanced auxiliary classifier behave as another real/fake classifier. As a result, FC-GAN can accelerate the process of differentiation of all classes, thus boost the convergence speed. Experimental results on image synthesis demonstrate our model is competitive in the quality of images generated while achieving a faster convergence rate.


title: Decoupled Planar WWAN Antennas With T-Shaped Protruded Ground for Smartphone Applications

abstract: A planar antenna array composed of two decoupled multiband wireless wide area network (WWAN) antenna elements for slim mobile phone application is presented and studied in this letter. A wide T-shaped protruded ground is deliberately designed to enhance the impedance matching and decouple the two closely deposed antenna elements. The integrity of the proposed T-shaped decoupling structure allows the reuse of space in between the antenna elements in the real applications. With small size occupation, the proposed antenna array covers two wide operating bands including 824-960 and 1710-2170 MHz, and within a short separating distance it achieves isolation better than 12 dB in the lower band and 15 dB in upper band. A prototype is fabricated, and the array's overall performances are measured and compared to simulated results to validate the proposed solution.


title: A low cost stair climbing wheelchair

abstract: There are around 68 millions of wheelchair users in the world. And 49.2% must use steps to enter or exit their homes, in the USA. But only a few successful stair climbing wheelchair models or middle scale robots have been built. A low cos stair climbing wheelchair is here proposed. The wheelchair is powered by a system which enables it to have smooth motion on flat surfaces and has the capability of climbing stairs and traversing obstacles by local and dynamic conditions and not by an external command and so, only two motors for locomotion are necessary. Experimental tests show its functionality. The additional cost of a stair climbing wheelchair as proposed here is 20% of a conventional wheelchair of the economic class.


title: Humanoid Robots as Cooperative Partners for People

abstract: Received Accepted This paper presents an overview of our work towards building socially intelligent, cooperative humanoid robots that can work and learn in partnership with people. People understand each other in social terms, allowing us to engage others in a variety of complex social interactions including communication, social learning, and cooperation. We present our theoretical framework that is a novel combination of Joint Intention Theory and Situated Learning Theory and demonstrate how it can be applied to develop our sociable humanoid robot, Leonardo. We demonstrate the robot’s ability to learn quickly and effectively from natural human instruction using gesture and dialog, and then cooperate to perform a learned task jointly with a person. Such issues must be addressed to enable many new and exciting applications for robots that require them to play a long-term role in people’s daily lives.


title: Suicide by Asphyxiation Due to Helium Inhalation

abstract: Suicide by asphyxiation using helium is the most widely promoted method of "self-deliverance" by right-to-die advocates. However, little is known about persons committing such suicides or the circumstances and manner in which they are completed. Prior reports of suicides by asphyxia- tion involving helium were reviewed and deaths determined by the North Carolina Office of the Chief Medical Examiner to be helium-associated asphyxial suicides occurring between January 1, 2000 and December 31, 2008 were included in a new case series examined in this article. The 10 asphyxial suicides involving helium identified in North Carolina tended to occur almost exclusively in non-Hispanic, white men who were relatively young (M age 41.1 11.6). In 6 of 10 cases, decedents suffered from significant psychiatric dysfunction; in 3 of these 6 cases, psychiatric disor- ders were present comorbidly with substance abuse. In none these cases were decedents suffering from terminal illness. Most persons committing suicide with helium were free of terminal illness but suffered from psychiatric and/or substance use disorders.


title: 4H-SiC Trench MOSFET With Floating/Grounded Junction Barrier-controlled Gate Structure

abstract: A novel silicon carbide (SiC) trench MOSFET with floating/grounded junction barrier-controlled gate structure (FJB-MOS/GJB-MOS) is presented and investigated utilizing Sentaurus TCAD simulations. The split P+ region introduced beneath the trench could better shield the gate oxide from the high electric field in the blocking mode, leading to an enhancement in the breakdown voltage while without significant degradation of other characteristics. As a result, the FJB-MOS with floating P+ shielding exhibits a higher figure of merit related to the breakdown voltage and the specific on-resistance ( ${V}_{\textsf {BR}}^{{\,\textsf {2}}}/{R}_{ \mathrm{\scriptscriptstyle ON},\textsf {sp}}$ ), which is improved by 15% and 49%, respectively, with comparison to those of the state-of-the-art double-trench MOSFET and L-shaped gate trench MOSFET. In terms of the GJB-MOS with grounded P+ shielding, it shows great advantage in reducing the switching losses thanks to the lower specific gate–drain charge ${Q}_{{\:\textsf {gd,sp}}}$ and is more conductive to high frequency applications. Additionally, the formation of the P+ region is aided by the Sentaurus Process and the processing implementation of the proposed structure is discussed.


title: Modified Social-Spider Optimization Algorithm Applied to Electromagnetic Optimization

abstract: Social-spider optimization (SSO) is a new nature-inspired algorithm of the swarm intelligence field to global optimization applications, which is based on the simulation of cooperative behavior of social spiders. To enhance the performance of the standard SSO, a modified SSO (MSSO) approach based on beta distribution and natural gradient local search was proposed in this paper. In order to verify the performance of the MSSO, tests using Loney’s solenoid benchmark and a brushless direct current motor benchmark are realized comparing the effectiveness of the SSO and the proposed MSSO. The results of this paper demonstrated that the MSSO performance is promising in electromagnetics optimization.


title: A new optimization method: Big Bang-Big Crunch

abstract: Nature is the principal source for proposing new optimization methods such as genetic algorithms (GA) and simulated annealing (SA) methods. All traditional evolutionary algorithms are heuristic population-based search procedures that incorporate random variation and selection. The main contribution of this study is that it proposes a novel optimization method that relies on one of the theories of the evolution of the universe; namely, the Big Bang and Big Crunch Theory. In the Big Bang phase, energy dissipation produces disorder and randomness is the main feature of this phase; whereas, in the Big Crunch phase, randomly distributed particles are drawn into an order. Inspired by this theory, an optimization algorithm is constructed, which will be called the Big Bang-Big Crunch (BB-BC) method that generates random points in the Big Bang phase and shrinks those points to a single representative point via a center of mass or minimal cost approach in the Big Crunch phase. It is shown that the performance of the new (BB-BC) method demonstrates superiority over an improved and enhanced genetic search algorithm also developed by the authors of this study, and outperforms the classical genetic algorithm (GA) for many benchmark test functions.


title: A Resource-Based View of Strategic IT Alignment: How Knowledge Sharing Creates Competitive Advantage

abstract: A critical decision problem for top management, and the focus of this study, is whether the CEO (chief executive officer) and CIO (chief information officer) should commit their time to formal planning with the expectation of producing an information technology (IT)-based competitive advantage. Using the perspective of the resource-based view, a model is presented that examines how strategic IT alignment can produce enhanced organizational strategies that yield competitive advantage. One hundred sixty-one CIOs provided data using a postal survey. Results supported seven of the eight hypotheses. They showed that information intensity is an important antecedent to strategic IT alignment, that strategic IT alignment is best explained by multiple constructs which operationalize both process and content measures, and that alignment between the IT plan and the business plan is significantly related to the use of IT for competitive advantage. Study results raise questions about the effect of CEO participation, which appears to be the weak link in the process, and also about the perception of the CIO on the importance of CEO involvement. The paper contributes to our understanding of how knowledge sharing in the alignment process contributes to the creation of superior organizational strategies, provides a framework of the alignment-performance relationship, and furnishes several new constructs.


title: Procedural Modeling Using Autoencoder Networks

abstract: Procedural modeling systems allow users to create high quality content through parametric, conditional or stochastic rule sets. While such approaches create an abstraction layer by freeing the user from direct geometry editing, the nonlinear nature and the high number of parameters associated with such design spaces result in arduous modeling experiences for non-expert users. We propose a method to enable intuitive exploration of such high dimensional procedural modeling spaces within a lower dimensional space learned through autoencoder network training. Our method automatically generates a representative training dataset from the procedural modeling rule set based on shape similarity features. We then leverage the samples in this dataset to train an autoencoder neural network, while also structuring the learned lower dimensional space for continuous exploration with respect to shape features. We demonstrate the efficacy our method with user studies where designers create content with more than 10-fold faster speeds using our system compared to the classic procedural modeling interface.


title: Cognitive ALE for HF Radios

abstract: This paper presents a novel technique for reducing time delay and improving the performance of Automatic Link Establishment (ALE) in High Frequency (HF)radios. The techniques proposed for Listen Before Transmit(LBT) based on cognitive radio sensing techniques such as energy detection, matched filter detection and cyclostationary feature detection. ALE is a de facto standard for HF radios through which communication is to be carried out by automatically selecting the channel. Current LBT techniques for ALE work fine for good quality channels but it performs pitiable when the channel quality becomes worst.


title: Hyperbolic Graph Generator

abstract: Abstract Networks representing many complex systems in nature and society share some common structural properties like heterogeneous degree distributions and strong clustering. Recent research on network geometry has shown that those real networks can be adequately modeled as random geometric graphs in hyperbolic spaces. In this paper, we present a computer program to generate such graphs. Besides real-world-like networks, the program can generate random graphs from other well-known graph ensembles, such as the soft configuration model, random geometric graphs on a circle, or Erdős–Renyi random graphs. The simulations show a good match between the expected values of different network structural properties and the corresponding empirical values measured in generated graphs, confirming the accurate behavior of the program. Program summary Program title: Hyperbolic graph generator Catalogue identifier: AEXC_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEXC_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: GNU General Public License, version 3 No. of lines in distributed program, including test data, etc.: 101190 No. of bytes in distributed program, including test data, etc.: 771660 Distribution format: tar.gz Programming language: C++. Computer: Any. Operating system: Any. Classification: 6.3, 4.13, 23. Nature of problem: Generation of graphs in hyperbolic spaces. Solution method: Implementation based on analytical equations. Additional comments: Can be used as a command-line tool or installed as a library to support more complex software. Running time: Depends on the number of nodes. A few seconds for the graph in the example provided.


title: Preliminary results on S-Band LFM Radar Development for submarine surveillance

abstract: In this paper, a preliminary results on S-Band Radar Development for submarine surveillance is described. The proposed submarine radar system is designed to be operated in bistatic configuration. As the radar signal, LFM-CW waveform operating at 2.4 GHz is implemented. Performance of the designed submarine radar to detect of a target is evaluated through a simulation system. Target detection is conducted by using stretch processing and cross-correlation signal processing. From this evaluation, the result of target detection using cross-correlation still shows better accuracy than the ohers. Target distance detection accuracy is 100 % for maximum target distance up to its maximum unambiguous range.


title: A Review of Technology Standards and Patent Portfolios for Enabling Cyber-Physical Systems in Advanced Manufacturing

abstract: Cyber-physical systems (CPS) are a collection of transformative technologies for managing interconnected physical and computational capabilities. Recent developments in technology are increasing the availability and affordability of sensors, data acquisition systems, and computer networks. The competitive nature of industry requires manufacturers to implement new methodologies. CPS is a broad area of engineering which supports applications across industries, such as manufacturing, healthcare, electric power grids, agriculture, and transportation. In particular, CPS is the core technology enabling the transition from Industry 3.0 to Industry 4.0 (I 4.0) and is transforming global advanced manufacturing. This paper provides a consolidated review of the latest CPS literature, a complete review of international standards, and a complete analysis of patent portfolios related to the 5C’s CPS architecture model by Lee et al. The critical evaluation of international standards and the intellectual property contained in CPS patents is unaddressed by the previous research and will benefit both academic scholars and industry practitioners. The analysis provides a basis for predicting research and development future trends and helps policy makers manage technology changes that will result from CPS in I 4.0. This paper covers the emerging I 4.0 standards from the International Organization for Standardization, the International Electrotechnical Commission, and China’s Guobiao standards followed by a patent analysis covering global patents issued in the U.S., Europe, China, and the World Intellectual Property Organization.


title: Monte Carlo smoothing for non-linear time series

abstract: We develop methods for performing smoothing computations in general state-space models. The methods rely on a particle representation of the filtering distributions, and their evolution through time using sequential importance sampling and resampling ideas. In particular, novel techniques are presented for generation of sample realizations of historical state sequences. This is carried out in a forward-filtering backward-smoothing procedure that can be viewed as the nonlinear, non-Gaussian counterpart of standard Kalman filter-based simulation smoothers in the linear Gaussian case. Convergence in the mean squared error sense of the smoothed trajectories is proved, showing the validity of our proposed method. The methods are tested in a substantial application for the processing of speech signals represented by a time-varying autoregression and parameterized in terms of time-varying partial correlation coefficients, comparing the results of our algorithm with those from a simple smoother based on the filte...


title: The information lens: an intelligent system for information sharing in organizations

abstract: This paper describes an intelligent system to help people share and filter information communicated by computer-based messaging systems. The system exploits concepts from artificial intelligence such as frames, production rules, and inheritance networks, but it avoids the unsolved problems of natural language understanding by providing users with a rich set of semi-structured message templates. A consistent set of “direct manipulation” editors simplifies the use of the system by individuals, and an incremental enhancement path simplifies the adoption of the system by groups. One of the key problems that arises when any group of people cooperates to solve problems or make decisions is how to share information. Thus one of the central goals of designing good “organizational interfaces” (Malone, 1985) should be to help people share information in groups and organizations. In this paper, we will describe a prototype system, called the Information Lens, that focuses on one aspect of this problem: how to help people share the many diverse kinds of qualitative information that are communicated via electronic messaging systems. It is already a common experience in mature computer-based messaging communities for people to feel flooded with large quantities of electronic “junk mail” (Denning, 1982; Palme, 1984; Wilson, 1984; Hiltz & Turoff, 1985), and the widespread availability of inexpensive communication capability has the potential to overwhelm people with even more messages that are of little or no value to them. At the same time, it is also a common experience for people to be ignorant of facts that would facilitate their work and that are known elsewhere in their organization. The system we will describe helps solve both these problems: it helps people filter, sort, and prioritize messages that are already addressed to them, and it also helps them find useful messages they would not otherwise have received. The most common previous approach to structuring information sharing in electronic messaging environments is to let users implicitly specify their general areas of interest by associating themselves with centralized distribution lists or conference topics related to particular subjects (e.g., Hiltz & Turoff, 1978). Since these methods of disseminating information are often targeted for relatively large audiences, however, it is usually impossible for all the information distributed to be of interest to all recipients. The Information Lens system uses much more detailed representations of message contents and receivers' interests to provide more sophisticated filtering possibilities. One of the key ideas behind this system is that many of the unsolved problems of natural language understanding can be avoided by using semi-structured templates (or frames) for different types of messages. These templates are used by the senders of messages to facilitate composing messages in the first place. Then, the same templates are used by the receivers of messages to facilitate constructing a set of rules to be used for filtering and categorizing messages of different types.


title: Learning Semantically and Additively Compositional Distributional Representations

abstract: This paper connects a vector-based composition model to a formal semantics, the Dependency-based Compositional Semantics (DCS). We show theoretical evidence that the vector compositions in our model conform to the logic of DCS. Experimentally, we show that vector-based composition brings a strong ability to calculate similar phrases as similar vectors, achieving near state-of-the-art on a wide range of phrase similarity tasks and relation classification; meanwhile, DCS can guide building vectors for structured queries that can be directly executed. We evaluate this utility on sentence completion task and report a new state-of-the-art.


title: Three-Stage CMOS OTA for Large Capacitive Loads With Efficient Frequency Compensation Scheme

abstract: A simple compensation strategy, which employs passive components only, is adopted to design a three-stage operational transconductance amplifier (OTA) suitable for driving high capacitive loads. Compared to the classical nested Miller compensation technique, the new solution exploits two additional resistors and allows a reduction in the values of the compensation capacitors of about an order of magnitude. The OTA was fabricated using 0.35-mum CMOS technology and exhibits a 1.4-MHz gain-bandwidth with a load of 500 pF


title: Edge curvature and convexity based ellipse detection method

abstract: In this paper, we propose a novel ellipse detection method for real images. The proposed method uses the information of edge curvature and their convexity in relation to other edge contours as clues for identifying edge contours that can be grouped together. A search region is computed for every edge contour that contains other edge contours eligible for grouping with the current edge contour. A two-dimensional Hough transform is performed in an intermediate step, in which we use a new 'relationship score' for ranking the edge contours in a group, instead of the conventional histogram count. The score is found to be more selective and thus more efficient. In addition, we use three novel saliency criteria, that are non-heuristic and consider various aspects for quantifying the goodness of the detected elliptic hypotheses and finally selecting good elliptic hypotheses. The thresholds for selection of elliptic hypotheses are determined by the detected hypotheses themselves, such that the selection is free from human intervention. The method requires a few seconds in most cases. So, it is suitable for practical applications. The performance of the proposed ellipse detection method has been tested on a dataset containing 1200 synthetic images and the Caltech 256 dataset containing real images. In both cases, the results show that the proposed ellipse detection method performs far better than existing methods and is close to the ideal results, with precision, recall, and F-measure, all very close to 1. Further, the method is robust to the increase in the complexity of the images (such as overlapping ellipses, occluded ellipses), while the performance of the contemporary methods deteriorates significantly.


title: Using Unmanned Aerial Vehicles (UAV) for High-Resolution Reconstruction of Topography: The Structure from Motion Approach on Coastal Environments

abstract: The availability of high-resolution Digital Surface Models of coastal environments is of increasing interest for scientists involved in the study of the coastal system processes. Among the range of terrestrial and aerial methods available to produce such a dataset, this study tests the utility of the Structure from Motion (SfM) approach to low-altitude aerial imageries collected by Unmanned Aerial Vehicle (UAV). The SfM image-based approach was selected whilst searching for a rapid, inexpensive, and highly automated method, able to produce 3D information from unstructured aerial images. In particular, it was used to generate a dense point cloud and successively a high-resolution Digital Surface Models (DSM) of a beach dune system in Marina di Ravenna (Italy). The quality of the elevation dataset produced by the UAV-SfM was initially evaluated by comparison with point cloud generated by a Terrestrial Laser Scanning (TLS) surveys. Such a comparison served to highlight an average difference in the vertical values of 0.05 m (RMS = 0.19 m). However, although the points cloud comparison is the best approach to investigate the absolute or relative correspondence between UAV and TLS methods, the assessment of geomorphic features is usually based on multi-temporal surfaces analysis, where an interpolation process is required. DSMs were therefore generated from UAV and TLS points clouds and vertical absolute accuracies assessed by comparison with a Global Navigation Satellite System (GNSS) survey. The vertical comparison of UAV and TLS DSMs with respect to GNSS measurements pointed out an average distance at cm-level (RMS = 0.011 m). The successive point by point direct comparison between UAV and TLS elevations show a very small average distance, 0.015 m, with RMS = 0.220 m. Larger values are encountered in areas where sudden changes in topography are present. The UAV-based approach was demonstrated to be a straightforward one and accuracy of the vertical dataset was comparable with results obtained by TLS technology.


title: Credit scoring using the hybrid neural discriminant technique

abstract: Abstract Credit scoring has become a very important task as the credit industry has been experiencing double-digit growth rate during the past few decades. The artificial neural network is becoming a very popular alternative in credit scoring models due to its associated memory characteristic and generalization capability. However, the decision of network's topology, importance of potential input variables and the long training process has often long been criticized and hence limited its application in handling credit scoring problems. The objective of the proposed study is to explore the performance of credit scoring by integrating the backpropagation neural networks with traditional discriminant analysis approach. To demonstrate the inclusion of the credit scoring result from discriminant analysis would simplify the network structure and improve the credit scoring accuracy of the designed neural network model, credit scoring tasks are performed on one bank credit card data set. As the results reveal, the proposed hybrid approach converges much faster than the conventional neural networks model. Moreover, the credit scoring accuracies increase in terms of the proposed methodology and outperform traditional discriminant analysis and logistic regression approaches.


title: Pelvic floor muscle and transversus abdominis activation in abdominal hypopressive technique through surface electromyography.

abstract: AIMS ::: The abdominal hypopressive technique (AHT) is performed mainly via transversus abdominis (TrA) activation and has been indicated for pelvic floor muscle (PFM) disorders. In some European countries, this technique has become widely used. This study aimed to investigate PFM and TrA activation during the AHT through surface electromyography. ::: ::: ::: METHODS ::: Thirty-four nulliparous physical therapists in good general health were asked to participate in the trial. To ascertain a correct PFM contraction, each of the participants was assessed by inspection and digital palpation. Ability to contract the TrA was assessed by surface electromyography and AHT training was given before analysis. TrA and PFM activity was recorded using surface electromyography. ::: ::: ::: RESULTS ::: The mean age of the volunteers was 28.1 (±6.0) years and the mean body mass index was 23.7 (±3.3) kg/m(2) . More than half of the volunteers were physically active (61.3%) and regularly performed PFM training (52.9%) but not TrA exercises (58.8%). Although the AHT activates the PFM when compared to resting tone (P < 0.001), this method is significantly less effective than PFM alone (P < 0.001). Additionally, the combination of the two techniques (AHT + PFM) was similarly effective as isolated PFM contraction (P = 0.586). Although the AHT activated the TrA significantly more than did PFM alone (P = 0.002), the addition of PFM contraction to the AHT significantly increased the amount of TrA activation (P < 0.001). ::: ::: ::: CONCLUSIONS ::: Based on our results, the AHT is less effective than PFM contraction alone, and adding PFM contraction to AHT also improves the TrA contraction.


title: A Minimally Supervised Approach for Synonym Extraction with Word Embeddings

abstract: In this paper we present a novel approach to minimally supervised synonym extraction. The approach is based on the word embeddings and aims at presenting a method for synonym extraction that is extensible to various languages. We report experiments with word vectors trained by using both the continuous bag-of-words model (CBoW) and the skip-gram model (SG) investigating the effects of different settings with respect to the contextual window size, the number of dimensions and the type of word vectors. We analyze the word categories that are (cosine) similar in the vector space, showing that cosine similarity on its own is a bad indicator to determine if two words are synonymous. In this context, we propose a new measure, relative cosine similarity, for calculating similarity relative to other cosine-similar words in the corpus. We show that calculating similarity relative to other words boosts the precision of the extraction. We also experiment with combining similarity scores from differently-trained vectors and explore the advantages of using a part-of-speech tagger as a way of introducing some light supervision, thus aiding extraction. We perform both intrinsic and extrinsic evaluation on our final system: intrinsic evaluation is carried out manually by two human evaluators and we use the output of our system in a machine translation task for extrinsic evaluation, showing that the extracted synonyms improve the evaluation metric.


title: Object Co-segmentation via Graph Optimized-Flexible Manifold Ranking

abstract: Aiming at automatically discovering the common objects contained in a set of relevant images and segmenting them as foreground simultaneously, object co-segmentation has become an active research topic in recent years. Although a number of approaches have been proposed to address this problem, many of them are designed with the misleading assumption, unscalable prior, or low flexibility and thus still suffer from certain limitations, which reduces their capability in the real-world scenarios. To alleviate these limitations, we propose a novel two-stage co-segmentation framework, which introduces the weak background prior to establish a globally close-loop graph to represent the common object and union background separately. Then a novel graph optimized-flexible manifold ranking algorithm is proposed to flexibly optimize the graph connection and node labels to co-segment the common objects. Experiments on three image datasets demonstrate that our method outperforms other state-of-the-art methods.


title: Ellipsis and discourse coherence

abstract: VP ellipsis generally requires a syntactically matching antecedent. However, many documented examples exist where the antecedent is not appropriate. Kehler (2000, Linguistics and philosophy23(6), 533–575. 2002, Coherence, Reference and the Theory of Grammer, CSLI Publications. Stanford.) proposed an elegant theory which predicts a syntactic antecedent for an elided VP is required only for a certain discourse coherence relation (resemblance), not for cause-effect relations. Most of the data Kehler used to motivate his theory come from corpus studies and thus do not consist of true minimal pairs. We report five experiments testing predictions of the coherence theory, using standard minimal pair materials. The results raise questions about the empirical basis for coherence theory because a syntactically-matching antecedent is preferred for all coherence relations, not just resemblance relations. Further, strict identity readings, which should not be available when a syntactic antecedent is required, are influenced by parallelism per se, holding the discourse coherence relation constant. This draws into question the causal role of coherence relations in processing VP ellipsis.


title: A Survey of Statistical Methods and Computing for Big Data

abstract: Big data are data on a massive scale in terms of volume, intensity, and complexity that exceed the capacity of standard software tools. They present opportunities as well as challenges to statisticians. The role of computational statisticians in scientific discovery from big data analyses has been under-recognized even by peer statisticians. This article reviews recent methodological and software developments in statistics that address the big data challenges. Methodologies are grouped into three classes: subsampling-based, divide and conquer, and sequential updating for stream data. Software review focuses on the open source R and R packages, covering recent tools that help break the barriers of computer memory and computing power. Some of the tools are illustrated in a case study with a logistic regression for the chance of airline delay.


title: Comparative analysis of MCDM methods for pipe material selection in sugar industry

abstract: The material plays an important role in an engineering design process. The suitable material selection for a particular product is one of the vital tasks for the designers. In order to fulfil the product's end requirements, designers need to analyze the performance of various materials and spot suitable materials with precise functionalities. Due to the presence of large number of materials with diverse properties, the material selection process is complicated and time consuming task. There is a necessity of systematic and efficient approach towards material selection to choose best alternative material for a product. The aim of this paper is to describe the application of four Multi Criteria Decision Making methods for solving pipes material selection problem in sugar industry. FAHP-TOPSIS, FAHP-VIKOR, FAHP-ELECTRE, FAHP-PROMTHEE are the four methods used to choose the best alternative among the various materials. The ranking performance of various MCDM methods is also compared with each other and exploring the effectiveness and flexibility of VIKOR method. Five stainless steel grades such as J4, JSLAUS, J204Cu, 409M, 304 and seven evaluation criteria such as yield strength, ultimate tensile strength, percentage of elongation, hardness, cost, corrosion rate and wear rate are focussed in this study to choose the suitable material.


title: Accurate Sum and Dot Product

abstract: Algorithms for summation and dot product of floating-point numbers are presented which are fast in terms of measured computing time. We show that the computed results are as accurate as if computed in twice or K-fold working precision, $K\ge 3$. For twice the working precision our algorithms for summation and dot product are some 40% faster than the corresponding XBLAS routines while sharing similar error estimates. Our algorithms are widely applicable because they require only addition, subtraction, and multiplication of floating-point numbers in the same working precision as the given data. Higher precision is unnecessary, algorithms are straight loops without branch, and no access to mantissa or exponent is necessary.


title: EXPERT AND EXCEPTIONAL PERFORMANCE: Evidence of Maximal Adaptation to Task Constraints

abstract: ▪ Abstract Expert and exceptional performance are shown to be mediated by cognitive and perceptual-motor skills and by domain-specific physiological and anatomical adaptations. The highest levels of human performance in different domains can only be attained after around ten years of extended, daily amounts of deliberate practice activities. Laboratory analyses of expert performance in many domains such as chess, medicine, auditing, computer programming, bridge, physics, sports, typing, juggling, dance, and music reveal maximal adaptations of experts to domain-specific constraints. For example, acquired anticipatory skills circumvent general limits on reaction time, and distinctive memory skills allow a domain-specific expansion of working memory capacity to support planning, reasoning, and evaluation. Many of the mechanisms of superior expert performance serve the dual purpose of mediating experts' current performance and of allowing continued improvement of this performance in response to informative fe...


title: Semi-supervised learning with induced word senses for state of the art word sense disambiguation

abstract: Word Sense Disambiguation (WSD) aims to determine the meaning of a word in context, and successful approaches are known to benefit many applications in Natural Language Processing. Although supervised learning has been shown to provide superior WSD performance, current sense-annotated corpora do not contain a sufficient number of instances per word type to train supervised systems for all words. While unsupervised techniques have been proposed to overcome this data sparsity problem, such techniques have not outperformed supervised methods. In this paper, we propose a new approach to building semi-supervised WSD systems that combines a small amount of sense-annotated data with information from Word Sense Induction, a fully-unsupervised technique that automatically learns the different senses of a word based on how it is used. In three experiments, we show how sense induction models may be effectively combined to ultimately produce high-performance semi-supervised WSD systems that exceed the performance of state-of-the-art supervised WSD techniques trained on the same sense-annotated data. We anticipate that our results and released software will also benefit evaluation practices for sense induction systems and those working in low-resource languages by demonstrating how to quickly produce accurate WSD systems with minimal annotation effort.


title: Goal commitment and the goal-setting process: conceptual clarification and empirical synthesis.

abstract: Goals are central to current treatments of work motivation, and goal commitment is a critical construct in understanding the relationship between goals and task performance. Despite this importance, there is confusion about the role of goal commitment and only recently has this key construct received the empirical attention it warrants. This meta-analysis, based on 83 independent samples, updates the goal commitment literature by summarizing the accumulated evidence on the antecedents and consequences of goal commitment. Using this aggregate empirical evidence, the role of goal commitment in the goal-setting process is clarified and key areas for future research are identified.


title: TPD: Travel Prediction-based Data Forwarding for light-traffic vehicular networks

abstract: This paper proposes Travel Prediction-based Data forwarding (TPD), tailored and optimized for multihop vehicle-to-vehicle communications. The previous schemes forward data packets mostly utilizing statistical information about road network traffic, which becomes much less accurate when vehicles travel in a light-traffic vehicular network. In this light-traffic vehicular network, highly dynamic vehicle mobility can introduce a large variance for the traffic statistics used in the data forwarding process. However, with the popularity of GPS navigation systems, vehicle trajectories become available and can be utilized to significantly reduce this uncertainty in the road traffic statistics. Our TPD takes advantage of these vehicle trajectories for a better data forwarding in light-traffic vehicular networks. Our idea is that with the trajectory information of vehicles in a target road network, a vehicle encounter graph is constructed to predict vehicle encounter events (i.e., timing for two vehicles to exchange data packets in communication range). With this encounter graph, TPD optimizes data forwarding process for minimal data delivery delay under a specific delivery ratio threshold. Through extensive simulations, we demonstrate that our TPD significantly outperforms existing legacy schemes in a variety of road network settings.


title: Using ISO/IEC 12207 to analyze open source software development processes: an e-learning case study

abstract: To date, there is no comprehensive study of open source software development process (OSSDP) carried out for open source (OS) e-learning systems. This paper presents the work which objectively analyzes the open source software development (OSSD) practices carried out by e-learning systems development communities and their results are represented using DEMO models. These results are compared using ISO/IEC 12207:2008. The comparison of DEMO models with ISO/IEC 12207 is a useful contribution; as it provides deeper understanding to-wards the OS e-learning system development.


title: Characterization of a Novel Nine-Transistor SRAM Cell

abstract: Data stability of SRAM cells has become an important issue with the scaling of CMOS technology. Memory banks are also important sources of leakage since the majority of transistors are utilized for on-chip caches in today's high performance microprocessors. A new nine-transistor (9T) SRAM cell is proposed in this paper for simultaneously reducing leakage power and enhancing data stability. The proposed 9T SRAM cell completely isolates the data from the bit lines during a read operation. The read static-noise-margin of the proposed circuit is thereby enhanced by 2 X as compared to a conventional six-transistor (6T) SRAM cell. The idle 9T SRAM cells are placed into a super cutoff sleep mode, thereby reducing the leakage power consumption by 22.9% as compared to the standard 6T SRAM cells in a 65-nm CMOS technology. The leakage power reduction and read stability enhancement provided with the new circuit technique are also verified under process parameter variations.


title: A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects

abstract: Recently, Minimum Cost Multicut Formulations have been proposed and proven to be successful in both motion trajectory segmentation and multi-target tracking scenarios. Both tasks benefit from decomposing a graphical model into an optimal number of connected components based on attractive and repulsive pairwise terms. The two tasks are formulated on different levels of granularity and, accordingly, leverage mostly local information for motion segmentation and mostly high-level information for multi-target tracking. In this paper we argue that point trajectories and their local relationships can contribute to the high-level task of multi-target tracking and also argue that high-level cues from object detection and tracking are helpful to solve motion segmentation. We propose a joint graphical model for point trajectories and object detections whose Multicuts are solutions to motion segmentation {\it and} multi-target tracking problems at once. Results on the FBMS59 motion segmentation benchmark as well as on pedestrian tracking sequences from the 2D MOT 2015 benchmark demonstrate the promise of this joint approach.


title: A new method to assess the risk of local and widespread flooding on rivers and coasts

abstract: To date, national- and regional-scale flood risk assessments have provided valuable information about the annual expected consequences of flooding, but not the exposure to widespread concurrent flooding that could have damaging consequences for people and the economy. We present a new method for flood risk assessment that accommodates the risk of widespread flooding. It is based on a statistical conditional exceedance model, which is fitted to gauged data and describes the joint probability of extreme river flows or sea levels at multiple locations. The method can be applied together with data from models for flood defence systems and economic damages to calculate a risk profile describing the probability distribution of economic losses or other consequences aggregated over a region. The method has the potential to augment national or regional risk assessments of expected annual damage with new information about the likelihoods, extent and impacts of events that could contribute to the risk.


title: Light fidelity (LI-FI) based indoor communication system

abstract: Indoor wireless communication is an essential part of next generation wireless communication system.For an indoor communication number of users and their device are increasing very rapidly so as a result capacity of frequency spectrum to accommodate further users in future is limited and also it would be difficult for service providers to provide more user reliable and high speed communication so this short come can be solve in future by using Li-Fi based indoor communication system. Li-Fi which is an emerging branch of optical wireless communication can be useful in future as a replacement and backup of Wireless Fidelity (Wi-Fi)for indoor communication because it can provide high data rate of transmission along with high capacity to utilize more users as its spectrum bandwidth is much broader than the radio spectrum. In this paper we will look at the different aspects of the Li-Fi based indoor communication system,summarizes some of the research conducted so far and we will also proposed a Li-Fi based communication model keeping in mind coverage area for multiple user and evaluate its performance under different scenarios .


title: Human machine interface in the Internet of Things (IoT)

abstract: Humans would play a central role in IoT systems. The human operator represents one of the most vulnerable in the IoT system, one who can easily be overlooked. In the present study, we sought to accentuate human-in-the-loop issues in System of Systems, and in particular IoT systems through reviewing relevant literature from various perspectives. Some of the issues we highlighted include information visualization, cognition and human trust in intelligent systems. We also explained how neuroergonomics approaches may be employed together with traditional behavioral and subjective measures to improve human-IoT device interactions.


title: What is compassion and how can we measure it? A review of definitions and measures

abstract: The importance of compassion is widely recognized and it is receiving increasing research attention. Yet, there is lack of consensus on definition and a paucity of psychometrically robust measures of this construct. Without an agreed definition and adequate measures, we cannot study compassion, measure compassion or evaluate whether interventions designed to enhance compassion are effective. In response, this paper proposes a definition of compassion and offers a systematic review of self- and observer-rated measures. Following consolidation of existing definitions, we propose that compassion consists of five elements: recognizing suffering, understanding the universality of human suffering, feeling for the person suffering, tolerating uncomfortable feelings, and motivation to act/acting to alleviate suffering. Three databases were searched (Web of Science, PsycInfo, and Medline) and nine measures included and rated for quality. Quality ratings ranged from 2 to 7 out of 14 with low ratings due to poor internal consistency for subscales, insufficient evidence for factor structure and/or failure to examine floor/ceiling effects, test–retest reliability, and discriminant validity. We call our five-element definition, and if supported, the development of a measure of compassion based on this operational definition, and which demonstrates adequate psychometric properties.


title: Process Discovery using Integer Linear Programming J.M.E.M. van der Werf, B.F. van Dongen, K.M. van Hee,

abstract: The research domain of process discovery aims at constructing a process model (e.g. a Petri net) which is an abstract representation of an execution log. Such a model should (1) be able to reproduce the log under consideration and (2) be independent of the number of cases in the log. In this paper, we present a process discovery algorithm where we use concepts taken from the language-based theory of regions, a well-known Petri net research area. We identify a number of shortcomings of this theory from the process discovery perspective, and we provide solutions based on integer linear programming.


The computational linguistics summarization pilot task


title: Information seeking: an overview of web tracking and the criteria for tracking software

abstract: Purpose – The purpose of this article is to alert researchers to software for web tracking of information seeking behaviour, and to offer a list of criteria that will make it easier to select software. A selection of research projects based on web tracking as well as the benefits and disadvantages of web tracking are also explored.Design/methodology/approach – An overview of the literature, including clarification of key concepts, a brief overview of studies of web information seeking behaviour based on web tracking, identification of software used, as well as the strengths and short‐comings noted for web tracking is used as a background to the identification of criteria for the selection of web tracking software.Findings – Web tracking can offer very valuable information for the development of websites, portals, digital libraries, etc. It, however, needs to be supplemented by qualitative studies, and researchers need to ensure that the tracking software will collect the data required.Research limitations...


title: Video text detection and recognition: Dataset and benchmark

abstract: This paper focuses on the problem of text detection and recognition in videos. Even though text detection and recognition in images has seen much progress in recent years, relatively little work has been done to extend these solutions to the video domain. In this work, we extend an existing end-to-end solution for text recognition in natural images to video. We explore a variety of methods for training local character models and explore methods to capitalize on the temporal redundancy of text in video. We present detection performance using the Video Analysis and Content Extraction (VACE) benchmarking framework on the ICDAR 2013 Robust Reading Challenge 3 video dataset and on a new video text dataset. We also propose a new performance metric based on precision-recall curves to measure the performance of text recognition in videos. Using this metric, we provide early video text recognition results on the above mentioned datasets.


title: Identifying Meaningful Citations

abstract: We introduce the novel task of identifying important citations in scholarly literature, i.e., citations that indicate that the cited work is used or extended in the new effort. We believe this task is a crucial component in algorithms that detect and follow research topics and in methods that measure the quality of publications.We model this task as a supervised classification problem at two levels of detail: a coarse one with classes (important vs. non-important), and a more detailed one with four importance classes. We annotate a dataset of approximately 450 citations with this information, and release it publicly. We propose a supervised classification approach that addresses this task with a battery of features that range from citation counts to where the citation appears in the body of the paper, and show that,our approach achieves a precision of 65% for a recall of 90%.


title: A capacitive ice layer detection system suitable for autonomous inspection of runways using an ROV

abstract: This paper presents a simple and efficient non-contact ice layer detection system. The proposed scheme is based on a capacitive sensing principle. Many accidents have been reported in the winter due slippery roads and runways. Presence of ice layers on the surface of runways and roads is one of the important reasons for slippery conditions. Continuous inspection of the runway will be required in such conditions to avoid accidents. Frequent human inspections may be inefficient and may fail to see thin transparent layers of ice. A capacitive sensor that can be integrated into an ROV for automated inspection of ice layers in important locations like runways is proposed in this paper. The sensor is mounted to the bottom part of the body of an ROV. Relative permittivity of ice is a function of excitation frequency. A multi-frequency excitation is employed to exploit this property and detect presence of ice layers reliably. A prototype of capacitive ice detector has been developed and integrated into an ROV. Prototype has been tested on various types of floors with ice layer, in a climatic chamber. Tests were also performed on roadways. From the test results, it has been observed that the developed system successfully detected ice layers as thin as 1 mm.


title: RT-Fall: A Real-Time and Contactless Fall Detection System with Commodity WiFi Devices

abstract: This paper presents the design and implementation of RT-Fall, a real-time, contactless, low-cost yet accurate indoor fall detection system using the commodity WiFi devices. RT-Fall exploits the phase and amplitude of the fine-grained Channel State Information (CSI) accessible in commodity WiFi devices, and for the first time fulfills the goal of segmenting and detecting the falls automatically in real-time, which allows users to perform daily activities naturally and continuously without wearing any devices on the body. This work makes two key technical contributions. First, we find that the CSI phase difference over two antennas is a more sensitive base signal than amplitude for activity recognition, which can enable very reliable segmentation of fall and fall-like activities. Second, we discover the sharp power profile decline pattern of the fall in the time-frequency domain and further exploit the insight for new feature extraction and accurate fall segmentation/detection. Experimental results in four indoor scenarios demonstrate that RT-fall consistently outperforms the state-of-the-art approach WiFall with 14 percent higher sensitivity and 10 percent higher specificity on average.


title: Pseudo-Traveling-Wave Resonator With Magnetically Tunable Phase Gradient of Fields and Its Applications to Beam-Steering Antennas

abstract: A pseudo-traveling-wave resonator with magnetically tunable phase gradient of field distribution is investigated, and a new type of beam-steering antenna based on the resonator is experimentally demonstrated for the first time. It is a short-ended transmission-line resonator and is composed of a nonreciprocal phase-shift composite right/left-handed transmission line using a polycrystalline yttrium-iron-garnet rod. The resonator operates as zeroth-order resonator if there is no dc magnetic field, and the radiation beam directs to broadside. By increasing an externally applied dc magnetic field normal to the substrate, the effective dc magnetization in the ferrite increases under the unsaturated regime. The phase gradient of the field distribution along the resonator is then continuously increased. As a result, the radiation beam direction changes obliquely with respect to broadside. Continuous backfire-to-endfire beam steering with more than 40° was achieved with almost constant gain of 5 dBi. In addition, numerical simulation results show considerably high radiation efficiency of 85%-95%, and the measured beam angle and gain were found almost constant within the relative bandwidth of 2%.


title: Systematic literature review (SLR) automation: a systematic literature review

abstract: Context: A systematic literature review(SLR) is a methodology used to find and aggregate all relevant studies about a specific research question or topic of interest. Most of the SLR processes are manually conducted. Automating these processes can reduce the workload and time consumed by human. Method: we use SLR as a methodology to survey the literature about the technologies used to automate SLR processes. Result: from the collected data we found many work done to automate the study selection process but there is no evidence about automation of the planning and reporting process. Most of the authors use machine learning classifiers to automate the study selection process. From our survey, there are processes that are similar to the SLR process for which there are automatic techniques to perform them. Conclusion: Because of these results, we concluded that there should be more research done on the planning, reporting, data extraction and synthesizing processes of SLR.


title: Active Power Factor Correction (PFC) Circuit With Resistor-Free Zero-Current Detection

abstract: An active power-factor correction (PFC) circuit is presented that employs a newly proposed resistor-free zero-current detection (ZCD). While the conventional ZCD requires either a sensing resistor or auxiliary transformer, the proposed ZCD requires only one OFF-chip capacitor. The active PFC circuit with the proposed resistor-free ZCD has been implemented in a 0.35-μm BCDMOS process and the power factor is improved up to 9% from the one employing the conventional ZCD. The proposed resistor-free ZCD scheme can be applied to any type of switch-mode dc-dc power converter.


title: Potential guided directional-RRT* for accelerated motion planning in cluttered environments

abstract: Recently proposed Rapidly Exploring Random Tree Star (RRT*) algorithm which is an extension of Rapidly Exploring Random Tree (RRT) provides collision free asymptotically optimal path regardless of obstacle's geometry in a given environment. However, the drawback of this technique is a slow processing rate. This paper presents our proposed Potential Guided Directional-RRT* which addresses this problem and provides accelerated processing rate by incorporating Artificial Potential Fields Algorithm into RRT*. Artificial Potential Field algorithm directs the random samples toward the goal which leads to an increase in the speed of RRT*. We have presented simulation results of our technique and their comparison with results of RRT* under different environmental conditions to demonstrate apace execution rate of our novel idea.


title: Exploring Precision Farming Scenarios Using Fuzzy Cognitive Maps

abstract: One of the major problems confronted in precision agriculture is uncertainty about how exactly would yield in a certain area respond to decreased application of certain nutrients. One way to deal with this type of uncertainty is the use of scenarios as a method to explore future projections from current objectives and constraints. In the absence of data, soft computing techniques can be used as effective semi-quantitative methods to produce scenario simulations, based on a consistent set of conditions. In this work, we propose a dynamic rule-based Fuzzy Cognitive Map variant to perform simulations, where the novelty resides in an enhanced forward inference algorithm with reasoning that is characterized by magnitudes of change and effects. The proposed method leverages expert knowledge to provide an estimation of crop yield, and hence it can enable farmers to gain insights about how yield varies across a field, so they can determine how to adapt fertilizer application accordingly. It allows also producing simulations that can be used by managers to identify effects of increasing or decreasing fertilizers on yield, and hence it can facilitate the adoption of precision agriculture regulations by farmers. We present an illustrative example to predict cotton yield change, as a response to stimulated management options using proactive scenarios, based on decreasing Phosphorus, Potassium and Nitrogen. The results of the case study revealed that decreasing the three nutrients by half does not decrease yield by more than 10%.


title: Is there a link between the qualitative descriptors and the quantitative perception of dyspnea in asthma

abstract: Background There is no obvious link between qualitative descriptors and overall intensity of dyspnea during bronchoconstriction in patients with asthma. Aims To determine whether qualitative and quantitative perception of methacholine-induced bronchoconstriction independently contribute to characterizing clinically stable asthma. Material and methods We assessed changes in inspiratory capacity, and quantitative (by Borg scale) and qualitative (by a panel of eight dyspnea descriptors) sensations of dyspnea at 20 to 30% fall in FEV 1 during methacholine inhalation in 49 asthmatics. Furthermore, we calculated the level of perception of bronchoconstriction at 20% fall in FEV 1 (PB 20 ). Results Descriptors selected by patients during methacholine inhalation allowed us to define three language subgroups: (1) chest tightness (subgroup A, n=21); (2) work/effort (subgroup B, n=7); and (3) both descriptors (subgroup C, n=13). Eight of the 49 patients (subgroup D) were not able to make a clear-cut distinction among descriptors. The subgroups exhibited similar function at baseline and during methacholine inhalation. Most importantly, patients selected chest tightness to a greater extent (42.85%), and work/effort (14.3%) and both descriptors (26.5%) to a lesser extent at the lowest level of bronchoconstriction (FEV 1 fall 1 . Thirty-two patients were normoperceivers (PB 20 ≥ 1.4 to 20 ≥ 5 au), and 10 patients were hypoperceivers (PB 20 Conclusions In patients with clinically stable asthma, PB 20 and language of dyspnea independently contribute to defining the condition of the disease. However, the possibility that this independence may be due to a β-error should be taken into account.


title: Evaluation of saturation and cross-magnetization effects in interior permanent magnet synchronous motor

abstract: This paper presents the evaluation of saturation and cross-magnetization effects in an interior permanent magnet synchronous motor (IPMSM) over the entire range of direct- and quadrature-axis excitations. The conventional two-axis machine model is modified in order to include the influence of saturation and cross-coupling effects on the variation of self- and cross-cou- pling inductances in the direct and the quadrature axis. The two-axis machine model parameters are evaluated by experiments performed on an IPMSM using a controlled voltage-source in- verter and are compared with parameter values evaluated by the finite-element method. The evaluation of two-axis machine model parameters reveals significant saturation and cross-magnetization effects in both axes, especially in the flux-weakening regime. Index Terms—Cross-magnetization, interior permanent magnet synchronous motor (IPMSM), saturation, two-axis machine model.


title: High quality information provisioning and data pricing

abstract: This paper presents ideas on how to advance the research on high quality information provisioning and information pricing. To this end, the current state of the art in combining data curation and information provisioning as well as in regard to data pricing is reviewed. Based on that, open issues, such as tailoring data to a user's need and determining a market value of data, are identified. As preliminary solutions, it is proposed to investigate the identified problems in an integrated manner.


Training Neural Nets to Aggregate Crowdsourced Responses


title: Compact eight MIMO antennas for 5G smartphones and their MIMO capacity verification

abstract: The handset or smartphone antenna has evolved from the external antenna before the year 2000 to the internal antenna and casing-integrated antenna for 2G/3G/4G communications till now. For the fifth-generation (5G) communications, it is expected that the Massive MIMO system is very promising for applications and a large number of MIMO antennas will be attractive to be deployed in the smartphone to effectively increase the channel capacity. In this paper, promising compact eight MIMO antennas in the smartphone are presented. The MIMO antennas are operated in the 3.5-GHz band (3400∼3600 MHz), which has been recently identified in WRC-15 for global mobile broadband services in the future. The achievable MIMO channel capacities for the proposed compact eight MIMO antennas are calculated and verified by MIMO OTA (over-the-air) testing in the open space. Results are presented and discussed.


title: The Durability Measures of the Lining of Newly Built Tunnels from Lining Disease of the Existing Tunnels

abstract: The 15 tunnels located in Line Chengkun, Baocheng and Dacheng were investigated by the authors. The tunnel disease was inducted and summarized, and the main disease forms and diseases reasons were analyzed.The five measures ( improve the compactness, control and reduce the initial crack, improve the toughness, reduce the second stiffness and mist curing) are proposed, and can improve the durability of the tunnel lining proved by large number indoor concrete test. The authors hope the results of this paper benefit design and construction.


title: Multi-View Image Generation from a Single-View

abstract: This paper addresses a challenging problem -- how to generate multi-view cloth images from only a single view input. To generate realistic-looking images with different views from the input, we propose a new image generation model termed VariGANs that combines the strengths of the variational inference and the Generative Adversarial Networks (GANs). Our proposed VariGANs model generates the target image in a coarse-to-fine manner instead of a single pass which suffers from severe artifacts. It first performs variational inference to model global appearance of the object (e.g., shape and color) and produce a coarse image with a different view. Conditioned on the generated low resolution images, it then proceeds to perform adversarial learning to fill details and generate images of consistent details with the input. Extensive experiments conducted on two clothing datasets, MVC and DeepFashion, have demonstrated that images of a novel view generated by our model are more plausible than those generated by existing approaches, in terms of more consistent global appearance as well as richer and sharper details.


title: Automated P.falciparum Detection System for Post-Treatment Malaria Diagnosis Using Modified Annular Ring Ratio Method

abstract: The gametocytes of the malaria parasite Plasmodium falciparum are highly resistant to antimalarial drugs. Its presence in the blood can be detected even after a successful malaria treatment. This paper explains a modified Annular Ring Ratio method which successfully locates and differentiates gametocytes of P. falciparum species in thin blood film images. The method can be used as an efficient tool for gametocyte detection for post-treatment malaria diagnosis. It also identifies the presence of any White Blood Cells (WBCs) in the image, and discards other artifacts and non infected cells. It utilizes the information based on structure, color and geometry of the cells and does not require any segmentation or non-illumination correction techniques that are commonly used for cell detection.


title: Improving Word Embeddings with Convolutional Feature Learning and Subword Information

abstract: We present a novel approach to learning word embeddings by exploring subword information (character n-gram, root/affix and inflections) and capturing the structural information of their context with convolutional feature learning. Specifically, we introduce a convolutional neural network architecture that allows us to measure structural information of context words and incorporate subword features conveying semantic, syntactic and morphological information related to the words. To assess the effectiveness of our model, we conduct extensive experiments on the standard word similarity and word analogy tasks. We showed improvements over existing state-of-the-art methods for learning word embeddings, including skipgram, GloVe, char n-gram and DSSM.


title: Q-Learning-Based Vulnerability Analysis of Smart Grid Against Sequential Topology Attacks

abstract: Recent studies on sequential attack schemes revealed new smart grid vulnerability that can be exploited by attacks on the network topology. Traditional power systems contingency analysis needs to be expanded to handle the complex risk of cyber-physical attacks. To analyze the transmission grid vulnerability under sequential topology attacks, this paper proposes a Q-learning-based approach to identify critical attack sequences with consideration of physical system behaviors. A realistic power flow cascading outage model is used to simulate the system behavior, where attacker can use the Q-learning to improve the damage of sequential topology attack toward system failures with the least attack efforts. Case studies based on three IEEE test systems have demonstrated the learning ability and effectiveness of Q-learning-based vulnerability analysis.


title: User requirements and design of a visualization for intrusion detection analysis

abstract: This paper reports on the user requirements gathering activities and design of an information visualization tool for analyzing network data for intrusion detection (ID). User-centered design methods have been widely used for many years. However, innovative visualization displays are often developed with limited consideration of user needs in the context of real-life problems. While it can be argued that this is required to generate creative new solutions, the resulting tools may not fully support actual users in their daily work. We studied ID analysts' activities in order to understand their work practices. This resulted in a simple task model of ID work and guidelines for visualization support. Noting the lack of current visualization support for the analysis ID task and grounded in the actual needs of ID analysts, we designed a visualization prototype for investigating network traffic.


title: Research on counter-terrorism based on big data

abstract: In the area of big data, people have a new perspective on counter-terrorism research. In this paper, we have carried out a systematic research on the applications of big data in counter-terrorism field by using quantitative analysis method. And then we have demonstrated effect of big data on counter-terrorism research from data collection and preprocessing, data mining and analysis, monitoring and warning three aspects. After that, we have used all the data of The Times and The New York Times about WUC in 2012 for analysis and argument. Finally, it concludes the deficiencies of the research on the territory of counter-terrorism by using big data and the problems worth studying in the future.