title: A meta-analysis of the impact of trust on technology acceptance model: Investigation of moderating influence of subject and context type

abstract: The technology acceptance model (TAM) has proven to be one of the most powerful theories to explain user's technology adoption. Among many external variables incorporated into TAM, trust is considered to be an important factor that influences the user's online behavior, especially in the e-commerce context. This study conducts a meta-analysis based on the previous TAM studies in an attempt to make well-grounded statements on the role of trust. Furthermore, the paper examines those TAM studies by considering moderating effects of subject type (students or non-students) and context type (commercial or non-commercial). Results indicate a significant influence of trust on TAM constructs. Moderating effects are found for most pair-wise relationships. The findings yield implications for both researchers and service providers.


title: A Framework for Evaluating the Effectiveness of Information Systems at Jordan Banks: An Empirical Study

abstract: Banks and other financial institutions in Jordan are one of the largest investors in the fields of information systems (IS), and there are many indications that these trends to continue in the future. However, there is a concern among CEOs and top managers that the IS investments are not yielding the anticipated outcomes. This paper investigates the investment of information systems at Jordan banks and reports the results of an empirical study that evaluates the contribution of IS in the effectiveness of banks operations. The paper measures the factors which determine information systems effectiveness at Jordan main banks. These variables are presumably system decision performance, system usage and user satisfaction among others that are considered the most effective variables in banks performance.


title: MOS operational amplifier design-a tutorial overview

abstract: Presents an overview of current design techniques for operational amplifiers implemented in CMOS and NMOS technology at a tutorial level. Primary emphasis is placed on CMOS amplifiers because of their more widespread use. Factors affecting voltage gain, input noise, offsets, common mode and power supply rejection, power dissipation, and transient response are considered for the traditional bipolar-derived two-stage architecture. Alternative circuit approaches for optimization of particular performance aspects are summarized, and examples are given.


title: Permittivity and tan delta characteristics of epoxy nanocomposites in the frequency range of 1 MHz-1 GHz

abstract: To achieve a compact and reliable design of electrical equipment for the present day requirements, there is an urgent need for better and smart insulating materials and in this respect, the reported enhancements in dielectric properties obtained for polymer nanocomposites seems to be very encouraging. To further understand the dielectric behavior of polymer nanocomposites, this experimental work reports the trends of dielectric permittivities and tan delta (loss tangent) of epoxy nanocomposites with single nano-fillers of Al2O3 and TiO2at low filler concentrations (0.1%, 0.5%, 1% & 5%) over a frequency range of 1 MHz-1 GHz. Results show that the nanocomposites demonstrate some very different dielectric characteristics when compared to those for polymer microcomposites. Unlike the usual expectations of increasing permittivity with increasing filler concentration in polymer microcomposites, it has been seen that up to a certain nano-filler concentration and depending on the permittivity of the nano-filler, the permittivities of the epoxy nanocomposites are less than that of the unfilled epoxy at all the measured frequencies. This suggests that there is a very strong dependence of the filler concentration and nano-filler permittivity on the final permittivity of the nanocomposites at all these frequencies. But, in the case of tan delta behavior in nanocomposites, significant effects of filler concentrations were not observed with both Al2O3 and TiO2 fillers. Tan delta values in nanocomposites with Al2O3 fillers are found to be marginally lower at all filler concentrations when compared with the value for unfilled epoxy. But, in TiO2Oepoxy nanocomposites, although the variations in tan delta are not significant with respect to unfilled epoxy, some interesting trends are observed with respect to the frequencies of measurement.


title: Replicating a Trading Strategy by Means of LSTM for Financial Industry Applications

abstract: This paper investigates the possibility of learning a trading rule looking at the relationship between market indicators and decisions undertaken regarding entering or quitting a position. As means to achieve this objective, we employ a long short-term memory machine, due its capability to relate past and recent events. Our solution is a first step in the direction of building a model-free robot, based on deep learning, able to identify the logic that links the market mood given by technical indicators to the undertaken investment decisions. Although preliminary, experimental results show that the proposed solution is viable and promising.


title: Affective Representations for Sarcasm Detection

abstract: Sarcasm detection from text has gained increasing attention. While one thread of research has emphasized the importance of affective content in sarcasm detection, another avenue of research has explored the effectiveness of word representations. In this paper, we introduce a novel model for automated sarcasm detection in text, called Affective Word Embeddings for Sarcasm (AWES), which incorporates affective information into word representations. Extensive evaluation on sarcasm detection on six datasets across three domains of text (tweets, reviews and forum posts) demonstrates the effectiveness of the proposed model. The experimental results indicate that while sentiment affective representations yield best results on datasets comprising of short length text such as tweets, richer representations derived from fine-grained emotions are more suitable for detecting sarcasm from longer length documents such as product reviews and discussion forum posts.


title: Benefits and Pitfalls of Multimedia and Interactive Features in Technology-Enhanced Storybooks

abstract: A meta-analysis was conducted on the effects of technology-enhanced stories for young children’s literacy development when compared to listening to stories in more traditional settings like storybook reading. A small but significant additional benefit of technology was found for story comprehension (g+ = 0.17) and expressive vocabulary (g+ = 0.20), based on data from 2,147 children in 43 studies. When investigating the different characteristics of technology-enhanced stories, multimedia features like animated pictures, music, and sound effects were found beneficial. In contrast, interactive elements like hotspots, games, and dictionaries were found to be distracting. Especially for children disadvantaged because of less stimulating family environments, multimedia features were helpful and interactive features were detrimental. Findings are discussed from the perspective of cognitive processing theories.


title: Adaptive Resource Allocation and Provisioning in Multi-Service Cloud Environments

abstract: In the current cloud business environment, the cloud provider (CP) can provide a means for offering the required quality of service (QoS) for multiple classes of clients. We consider the cloud market where various resources such as CPUs, memory, and storage in the form of Virtual Machine (VM) instances can be provisioned and then leased to clients with QoS guarantees. Unlike existing works, we propose a novel Service Level Agreement (SLA) framework for cloud computing, in which a price control parameter is used to meet QoS demands for all classes in the market. The framework uses reinforcement learning (RL) to derive a VM hiring policy that can adapt to changes in the system to guarantee the QoS for all client classes. These changes include: service cost, system capacity, and the demand for service. In exhibiting solutions, when the CP leases more VMs to a class of clients, the QoS is degraded for other classes due to an inadequate number of VMs. However, our approach integrates computing resources adaptation with service admission control based on the RL model. To the best of our knowledge, this study is the first attempt that facilitates this integration to enhance the CP's profit and avoid SLA violation. Numerical analysis stresses the ability of our approach to avoid SLA violation while maximizing the CP's profit under varying cloud environment conditions.


title: Compressing Deep Convolutional Networks using Vector Quantization

abstract: Deep convolutional neural networks (CNN) has become the most promising method for object recognition, repeatedly demonstrating record breaking results for image classification and object detection in recent years. However, a very deep CNN generally involves many layers with millions of parameters, making the storage of the network model to be extremely large. This prohibits the usage of deep CNNs on resource limited hardware, especially cell phones or other embedded devices. In this paper, we tackle this model storage issue by investigating information theoretical vector quantization methods for compressing the parameters of CNNs. In particular, we have found in terms of compressing the most storage demanding dense connected layers, vector quantization methods have a clear gain over existing matrix factorization methods. Simply applying k-means clustering to the weights or conducting product quantization can lead to a very good balance between model size and recognition accuracy. For the 1000-category classification task in the ImageNet challenge, we are able to achieve 16-24 times compression of the network with only 1% loss of classification accuracy using the state-of-the-art CNN.


title: Compulsive mobile application usage and technostress: the role of personality traits

abstract: Purpose ::: ::: ::: ::: ::: Compulsive usage of mobile applications may have a negative effect on people’s health and social interaction. Past studies have indicated that personality traits were related to compulsive usage of technologies, but most of them have explored the factors from the system and interface design perspectives, specifically. The purpose of this paper is to explore how the Big Five personality traits, materialism, and external locus of control affect compulsive mobile application usage, and examines how compulsive usage impacts technostress. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: The present study proposes a framework based on Big Five personality traits and related literature. The author collected a total of 546 valid responses to the online survey, and the author examined the 18 proposed hypotheses using SmartPLS software. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The results show that neuroticism, extraversion, materialism, and external locus of control have significant effects on compulsive usage of mobile social applications. In addition, agreeableness, materialism, and external locus of control significantly influence compulsive usage of mobile game applications. Compulsive usage (of both mobile social apps and mobile games), materialism, and external locus of control all have a positive, direct impact on technostress. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: ::: This study offers mobile app design companies and educational institutions a understanding of the social problems caused by the misuse of mobile devices, helping them to prevent the increase of the negative influences of such misuse. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: The primary value of this paper lies in providing a better understanding of the influence of personality traits on the compulsive use of mobile apps and technostress.


title: Broadband Self-Compensating Phase Shifter Combining Delay Line and Equal-Length Unequal-Width Phaser

abstract: In this paper, a broadband self-compensating phase shifter is presented and developed on the basis of substrate integrated waveguide (SIW) technology. Since the SIW is a dispersive guided-wave structure, the effective bandwidth of SIW phase shifter is usually narrow. Phase shifts generated by two different structures, namely delay line and equal-length unequal-width phase shifter, are discovered in this work to present interesting opposite tendencies versus frequency. Therefore, an appropriate combination of them will make the phase shift almost constant over a very wide band. Design equations and process are given following a mathematical analysis. To demonstrate the interesting and useful features of the proposed technique, a 90° and a 45° self-compensating phase shifters are designed as showcases with standard printed circuit board process. For the 90° version, the measured amplitude and phase imbalance between the two paths are within 0.2 dB and 2.5° , respectively, within the frequency band from 25.11 to 39.75 GHz, or around 49% bandwidth. The return loss is found to be better than 12 dB within the frequency band of interest. The 45° one has the similar excellent performance. The measurements demonstrate that this type of SIW phase shifter is superior to all of its counterparts.


title: Are "non-functional" requirements really non-functional?: an investigation of non-functional requirements in practice

abstract: Non-functional requirements (NFRs) are commonly distinguished from functional requirements by differentiating how the system shall do something in contrast to what the system shall do. This distinction is not only prevalent in research, but also influences how requirements are handled in practice. NFRs are usually documented separately from functional requirements, without quantitative measures, and with relatively vague descriptions. As a result, they remain difficult to analyze and test. Several authors argue, however, that many so-called NFRs actually describe behavioral properties and may be treated the same way as functional requirements. In this paper, we empirically investigate this point of view and aim to increase our understanding on the nature of NFRs addressing system properties. We report on the classification of 530 NFRs extracted from 11 industrial requirements specifications and analyze to which extent these NFRs describe system behavior. Our results suggest that most "non-functional" requirements are not non-functional as they describe behavior of a system. Consequently, we argue that many so-called NFRs can be handled similarly to functional requirements.


title: Factors influencing the Cloud Computing adoption in Higher Education Institutions of Punjab, Pakistan

abstract: Cloud Computing is one of the most important trend and newest area in the field of information technology in which resources (e.g. CPU and storage) can be leased and released by customers through the Internet in an on-demand basis. The adoption of Cloud Computing in Education and developing countries is real an opportunity. Although Cloud computing has gained popularity in Pakistan especially in education and industry, but its impact in Pakistan is still unexplored especially in Higher Education Department. Already published work investigated in respect of factors influencing on adoption of cloud computing but very few investigated said analysis in developing countries. The Higher Education Institutions (HEIs) of Punjab, Pakistan are still not focused to discover cloud adoption factors. In this study, we prepared cloud adoption model for Higher Education Institutions (HEIs) of Punjab, a survey was carried out from 900 students all over Punjab. The survey was designed based upon literature and after discussion and opinions of academicians. In this paper, 34 hypothesis were developed that affect the cloud computing adoption in HEIs and tested by using powerful statistical analysis tools i.e. SPSS and SmartPLS. Statistical findings shows that 84.44% of students voted in the favor of cloud computing adoption in their colleges, while 99% supported Reduce Cost as most important factor in cloud adoption.


title: A Brand-level Ranking System with the Customized Attention-GRU Model

abstract: In e-commerce websites like Taobao, brand is playing a more important role in influencing users' decision of click/purchase, partly because users are now attaching more importance to the quality of products and brand is an indicator of quality. However, existing ranking systems are not specifically designed to satisfy this kind of demand. Some design tricks may partially alleviate this problem, but still cannot provide satisfactory results or may create additional interaction cost. In this paper, we design the first brand-level ranking system to address this problem. The key challenge of this system is how to sufficiently exploit users' rich behavior in e-commerce websites to rank the brands. In our solution, we firstly conduct the feature engineering specifically tailored for the personalized brand ranking problem and then rank the brands by an adapted Attention-GRU model containing three important modifications. Note that our proposed modifications can also apply to many other machine learning models on various tasks. We conduct a series of experiments to evaluate the effectiveness of our proposed ranking model and test the response to the brand-level ranking system from real users on a large-scale e-commerce platform, i.e. Taobao.


title: HYPOTHESIZING THE APTNESS OF SOCIAL MEDIA AND THE INFORMATION RICHNESS REQUIREMENTS OF DISASTER MANAGEMENT

abstract: In this article, the author first analyzes the social presence theory, media richness theory and taskmedia fit to investigate the suitability of various types of Social Media in disaster management. Then, on the basis of this analysis, use of social media is proposed to facilitate the communication tasks involved in the interaction between disaster management agencies and communities during disaster management. Next the author adapt a conceptual framework that integrates three types of communication (involving disaster management agencies and communities). The framework is further used as a springboard to develop a number of hypotheses to predict the aptness of rich and lean types of Social Media against the media richness requirements of disaster management tasks.


title: QFN based packaging concepts for millimeter-wave transceivers

abstract: This paper introduces the idea of a low-cost surface-mountable millimeter-wave radar sensor. Different packaging topologies are compared with the potential of integrating the whole radar frontend together with the antennas into a single QFN package. The pros and cons of the different packaging concepts are discussed and finally, a 120 GHz off-chip antenna design for integration into a QFN package and its measurements are presented.


title: A Review of Feature Reduction Techniques in Neuroimaging

abstract: Machine learning techniques are increasingly being used in making relevant predictions and inferences on individual subjects neuroimaging scan data. Previous studies have mostly focused on categorical discrimination of patients and matched healthy controls and more recently, on prediction of individual continuous variables such as clinical scores or age. However, these studies are greatly hampered by the large number of predictor variables (voxels) and low observations (subjects) also known as the curse-of-dimensionality or small-n-large-p problem. As a result, feature reduction techniques such as feature subset selection and dimensionality reduction are used to remove redundant predictor variables and experimental noise, a process which mitigates the curse-of-dimensionality and small-n-large-p effects. Feature reduction is an essential step before training a machine learning model to avoid overfitting and therefore improving model prediction accuracy and generalization ability. In this review, we discuss feature reduction techniques used with machine learning in neuroimaging studies.


title: Highly Available Transactions: Virtues and Limitations

abstract: To minimize network latency and remain online during server failures and network partitions, many modern distributed data storage systems eschew transactional functionality, which provides strong semantic guarantees for groups of multiple operations over multiple data items. In this work, we consider the problem of providing Highly Available Transactions (HATs): transactional guarantees that do not suffer unavailability during system partitions or incur high network latency. We introduce a taxonomy of highly available systems and analyze existing ACID isolation and distributed data consistency guarantees to identify which can and cannot be achieved in HAT systems. This unifies the literature on weak transactional isolation, replica consistency, and highly available systems. We analytically and experimentally quantify the availability and performance benefits of HATs---often two to three orders of magnitude over wide-area networks---and discuss their necessary semantic compromises.


title: Efficient techniques for dynamic vehicle detection

abstract: Fast detection of moving vehicles is crucial for safe autonomous urban driving. We present the vehicle detection algorithm developed for our entry in the Urban Grand Challenge, an autonomous driving race organized by the U.S. Government in 2007. The algorithm provides reliable detection of moving vehicles from a high-speed moving platform using laser range finders. We present the notion of motion evidence, which allows us to overcome the low signal-to-noise ratio that arises during rapid detection of moving vehicles in noisy urban environments. We also present and evaluate an array of optimization techniques that enable accurate detection in real time. Experimental results show empirical validation on data from the most challenging situations presented at the Urban Grand Challenge as well as other urban settings.


title: Modeling Visual Problem Solving as Analogical Reasoning

abstract: We present a computational model of visual problem solving, designed to solve problems from the Raven's Progressive Matrices intelligence test. The model builds on the claim that analogical reasoning lies at the heart of visual problem solving, and intelligence more broadly. Images are compared via structure mapping, aligning the common relational structure in 2 images to identify commonalities and differences. These commonalities or differences can themselves be reified and used as the input for future comparisons. When images fail to align, the model dynamically rerepresents them to facilitate the comparison. In our analysis, we find that the model matches adult human performance on the Standard Progressive Matrices test, and that problems which are difficult for the model are also difficult for people. Furthermore, we show that model operations involving abstraction and rerepresentation are particularly difficult for people, suggesting that these operations may be critical for performing visual problem solving, and reasoning more generally, at the highest level. (PsycINFO Database Record


title: Distributed denial of service (DDoS) resilience in cloud

abstract: Despite the increasing popularity of cloud services, ensuring the security and availability of data, resources and services remains an ongoing research challenge. Distributed denial of service (DDoS) attacks are not a new threat, but remain a major security challenge and are a topic of ongoing research interest. Mitigating DDoS attack in cloud presents a new dimension to solutions proffered in traditional computing due to its architecture and features. This paper reviews 96 publications on DDoS attack and defense approaches in cloud computing published between January 2009 and December 2015, and discusses existing research trends. A taxonomy and a conceptual cloud DDoS mitigation framework based on change point detection are presented. Future research directions are also outlined.


title: Modeling and Design Considerations for Substrate Integrated Waveguide Circuits and Components

abstract: This paper presents an overview of the recent achievements in the held of substrate integrated waveguides (SIW) technology, with particular emphasis on the modeling strategy and design considerations of millimeter-wave integrated circuits as well as the physical interpretation of the operation principles and loss mechanisms of these structures. The most common numerical methods for modeling both SIW interconnects and circuits are presented. Some considerations and guidelines for designing SIW structures, interconnects and circuits are discussed, along with the physical interpretation of the major issues related to radiation leakage and losses. Examples of SIW circuits and components operating in the microwave and millimeter wave bands are also reported, with numerical and experimental results.


title: Women’s Autonomy and Unintended Pregnancy Among Currently Pregnant Women in Bangladesh

abstract: This paper examines the net effect of women’s autonomy on their pregnancy intention status among currently pregnant Bangladeshi women. This study is based on data from the Bangladesh Demographic Health Survey, 2007 (BDHS). A subset of interviews from currently pregnant women (718) were extracted from 10,146 married women of reproductive age. The BDHS 2007 used a pre-tested, structured questionnaire to collect sociodemographic, women’s empowerment, and pregnancy information. Associations between unintended pregnancy and explanatory variables were assessed using bivariate analysis. Logistic regression was used to assess the net effect of women’s autonomy on current pregnancy intention status after controlling for other variables. Results indicate that women’s autonomy is a significant predictor of unintended pregnancy after adjusting for other factors. A unit increase in the autonomy scale decreases the odds of unintended pregnancy by 16%. Besides autonomy, our results also indicate that current age, number of children ever born, age at marriage, religion, media access, and contraceptive use exert strong influences over unintended pregnancy. Women who have ever used contraceptives are 82% more likely to classify their current pregnancies as unintended compared with women who are non-users of contraceptives. Improvement in women’s autonomy and effective and efficient use of contraceptives may reduce unintended pregnancies as well as improve reproductive health outcomes.


title: Causality in Economics

abstract: Formal analysis of causal relations using graphical methods has become increasingly popular in the natural and social sciences, but has been used much less in economics. The reason may be that heretofore graphical methods have been based on models that are structural in the sense that the equality symbol is taken to denote causation directly. We argue that current economic models are not structural in this sense. This paper proposes a formal analysis of causality that applies to economic models and develops its properties. Among other results, it is shown that Granger causality can be connected to causality only under very strong assumptions. Also, it is shown that graphical methods are not likely to Þnd much application in determining causal orderings as deÞned in this paper in economic models. Formal analysis of causal relations using graphical methods has become increasingly popular in the natural and social sciences. Pearl [22] is a good sample of this work. Graphical methods are a development of analytical techniques that originated in economic theory–speciÞcally, they take as their basis structural economic models as deÞned by the Cowles group half a century ago. The standard reference on causation is Simon [26]. The term “structural” was was given several distinct, though related, meanings by the Cowles economists, as has frequently been observed. At a minimum, the term refers to the distinction between the structural form and the reduced form of a model. In the structural form each internal variable is expressed as a function of some other internal variables and some external variables, whereas the reduced form referred to the solution of a model, in which each internal variable is expressed as a function of the external variables alone. As the term implies, the structural form was viewed as more fundamental than the reduced form. It was seen as containing information not


title: Object Based Fast Motion Estimation and Compensation Algorithm for Surveillance Video Compression

abstract: In surveillance systems, the storage requirements for video archival are a major concern because of recording of videos continuously for long periods of time, resulting in large amounts of data. Therefore, it is essential to apply efficient compression techniques for compressing surveillance video. The techniques used for the general video compression may not be the efficient technique for the compression of surveillance video because of the use of static camera as compared to moving camera in general purpose videos. Generally surveillance video consist of multiple objects, smaller in size as compared to the background and they have frequents occlusion with each other. In this paper a new object based motion estimation and compensation technique for surveillance video compression is proposed. Background differencing and summing technique (BDST) is used for the segmentation of the moving objects. This technique not only identifies moving object but also the maximum distance moved by the object in given group of frames. A bonding box is created based on the movement of the object in order to segment the moving objects. For exploiting the temporal redundancy, the motion estimation and compensation is carried out for the bonding box region only. The multiresolution property of discrete wavelet transform is used for the motion estimation and compensation. Experimental results show that the approach achieves high compression ratios compared to MPEG-2 compression.


title: Bayesian Compression for Deep Learning

abstract: Compression and computational efficiency in deep learning have become a problem of great significance. In this work, we argue that the most principled and effective way to attack this problem is by taking a Bayesian point of view, where through sparsity inducing priors we prune large parts of the network. We introduce two novelties in this paper: 1) we use hierarchical priors to prune nodes instead of individual weights, and 2) we use the posterior uncertainties to determine the optimal fixed point precision to encode the weights. Both factors significantly contribute to achieving the state of the art in terms of compression rates, while still staying competitive with methods designed to optimize for speed or energy efficiency.


title: Linux block IO: introducing multi-queue SSD access on multi-core systems

abstract: The IO performance of storage devices has accelerated from hundreds of IOPS five years ago, to hundreds of thousands of IOPS today, and tens of millions of IOPS projected in five years. This sharp evolution is primarily due to the introduction of NAND-flash devices and their data parallel design. In this work, we demonstrate that the block layer within the operating system, originally designed to handle thousands of IOPS, has become a bottleneck to overall storage system performance, specially on the high NUMA-factor processors systems that are becoming commonplace. We describe the design of a next generation block layer that is capable of handling tens of millions of IOPS on a multi-core system equipped with a single storage device. Our experiments show that our design scales graciously with the number of cores, even on NUMA systems with multiple sockets.


title: Anomaly Based Web Phishing Page Detection

abstract: Many anti-phishing schemes have recently been proposed in literature. Despite all those efforts, the threat of phishing attacks is not mitigated. One of the main reasons is that phishing attackers have the adaptability to change their tactics with little cost. In this paper, we propose a novel approach, which is independent of any specific phishing implementation. Our idea is to examine the anomalies in web pages, in particular, the discrepancy between a web site?s identity and its structural features and HTTP transactions. It demands neither user expertise nor prior knowledge of the website. The evasion of our phishing detection entails high cost to the adversary. As shown by the experiments, our phishing detector functions with low miss rate and low false-positive rate.


title: A Media-Rich Curriculum for Modeling and Simulation

abstract: We discuss a novel approach for teaching Modeling and Simulation (M&S) using a rich, multi-media focus with an emphasis on student construction and creative representation of simulations for dynamic systems. The increased use of M&S to enhance understanding and analyze problems across an ever-widening range of disciplines means that the diversity and number of professionals who will work with simulations is also increasing. Therefore, it is important that expanded opportunities exist for a wide range of students to take M&S courses as part of their secondary or post-secondary education. In addition to introducing M&S to new audiences, it is important to consider how professionals from these diverse disciplines can enhance and improve the quality and effectiveness of simulations. Courses, which target non-traditional simulation students, can broaden the diversity of expertise within the M&S community. We introduce an M&S course that uses Max/MSP software, which is familiar for many multimedia students and faculty. The course targets a mixed class of graduate students from Art and Technology, Computer Science, and Engineering programs.


title: A novel H6-type transformerless inverter for grid-connected photovoltaic application

abstract: A novel H6-type inverter is proposed for the trade-off solution of common-mode current (leakage current) and conversion efficiency in transformerless photovoltaic (PV) grid-connected energy generation system. A direct power passing path is introduced into the H5 topology to ensure the current flows through less power switches than that in the H5 topology during the operational mode of direct power transfer from the input to output sides. So the conduction loss in the proposed inverter is lower than the H5 topology. The operational principle is analyzed in detail, and some extended topologies with the same idea are given as well. The proposed topology features a fine trade-off between the power loss and the common-mode performance, compared with the famous H5 and Heric topologies. Comparative experimental results of the three topologies are presented with a universal prototype.


title: Probabilistic Multimodal Map Matching With Rich Smartphone Data

abstract: The conventional map matching algorithms identify from a GPS trace the true uni-modal path, which is a path with a single transport mode. There are also studies on detecting transport modes from GPS data. A recent study on probabilistic map matching shows that knowing the transport modes helps in identifying the true paths. It is also obvious that knowing the physical paths improves the detection of transport modes. E.g. known that the GPS points are on train tracks, it is overwhelmingly likely that the corresponding transport mode is train. In this paper, we aim at developing a model for identifying multi-modal paths. In this model, the transport modes and physical paths are detected simultaneously in order to take advantage of the correlation between them. In this model, many kinds of data from smartphone are fused to improve the accuracy of the detection result. Those data include GPS, nearby Bluetooth and acceleration. The model doesn’t require pre-processing of raw data into segments with single transport modes. The problem is modeled in a probabilistic manner such that error in measurements can be accounted for. The modeling methodology is to simulate the process of the smartphone user traveling on a path while the smartphone is recording the measurements by using travel models to capture the traveling and measurement models to capture the recording.


title: From Machine Learning to Explainable AI

abstract: The success of statistical machine learning (ML) methods made the field of Artificial Intelligence (AI) so popular again, after the last AI winter. Meanwhile deep learning approaches even exceed human performance in particular tasks. However, such approaches have some disadvantages besides of needing big quality data, much computational power and engineering effort; those approaches are becoming increasingly opaque, and even if we understand the underlying mathematical principles of such models they still lack explicit declarative knowledge. For example, words are mapped to high-dimensional vectors, making them unintelligible to humans. What we need in the future are context-adaptive procedures, i.e. systems that construct contextual explanatory models for classes of real-world phenomena. This is the goal of explainable AI, which is not a new field; rather, the problem of explainability is as old as AI itself. While rule-based approaches of early AI were comprehensible “glass-box” approaches at least in narrow domains, their weakness was in dealing with uncertainties of the real world. Maybe one step further is in linking probabilistic learning methods with large knowledge representations (ontologies) and logical approaches, thus making results re-traceable, explainable and comprehensible on demand.


title: Market Model Benchmark Suite for Machine Learning Techniques

abstract: Recent developments in deep-reinforcement learning have yielded promising results in artificial games and test domains. To explore opportunities and evaluate the performance of these machine learning techniques, various benchmark suites are available, such as the Arcade Learning Environment, rllab, OpenAI Gym, and the StarCraft II Learning Environment. This set of benchmark suites is extended with the open business simulation model described here, which helps to promote the use of machine learning techniques as valueadding tools in the context of strategic decision making and economic model calibration and harmonization. The benchmark suite extends the current state-of-the-art problems for deep-reinforcement learning by offering an infinite state and action space for multiple players in a non-zero-sum game environment of imperfect information. It provides a model that can be characterized as both a credit assignment problem and an optimization problem. Experiments with this suite?s deep-reinforcement learning algorithms, which yield remarkable results for various artificial games, highlight that stylized market behavior can be replicated, but the infinite action space, simultaneous decision making, and imperfect information pose a computational challenge. With the directions provided, the benchmark suite can be used to explore new solutions in machine learning for strategic decision making and model calibration.


title: TA4REC: Recurrent Neural Networks with Time Attention Factors for Session-based Recommendations

abstract: Recommender systems show increasingly importance with the development of E-commerce, news and multimedia applications. Traditional recommendation algorithms such as collaborative-filtering-based methods and graph-based methods mainly use items’ original attributes and relationships between items and users, ignoring items’ chronological order in browsing sessions. In recent years, RNN-based methods show their superiority when dealing with the sequential data, and some modified RNN models have been proposed. However, these RNN models only use the sequence order of items and neglect items’ browsing time information. It is widely accepted that users tend to spend more time on their interested items, and these interested items are always closely related to users’ current target. Based on the above view, items’ browsing time is an important feature in recommendations. In this paper, we propose a modified RNN-based recommender system called TA4Rec, which can recommend the probable item that may be clicked in the next moment. Our main contribution is to introduce a method to calculate the time-attention factors from browsing items’ duration time and add time-attention factors to the RNN-based model. We conduct experiments on RecSys Challenge 2015 dataset and the result shows that TA4REC model has gained obvious improvement on session-based recommendations than the classic session-based recommender method.


title: PhyCloak: obfuscating sensing from communication signals

abstract: Recognition of human activities and gestures using preexisting WiFi signals has been shown to be feasible in recent studies. Given the pervasiveness of WiFi signals, this emerging sort of sensing poses a serious privacy threat. This paper is the first to counter the threat of unwanted or even malicious communication based sensing: it proposes a blackbox sensor obfuscation technique PhyCloak which distorts only the physical information in the communication signal that leaks privacy. The data in the communication signal is preserved and, in fact, the throughput of the link is increased with careful design. Moreover, the design allows coupling of the Phy-Cloak module with legitimate sensors, so that their sensing is preserved, while that of illegitimate sensors is obfuscated. The effectiveness of the design is validated via a prototype implementation on an SDR platform.


title: Water harvest via dewing.

abstract: Harvesting water from humid air via dewing can provide a viable solution to a water shortage problem where liquid-phase water is not available. Here we experimentally quantify the effects of wettability and geometry of the condensation substrate on the water harvest efficiency. Uniformly hydrophilic surfaces are found to exhibit higher rates of water condensation and collection than surfaces with lower wettability. This is in contrast to a fog basking method where the most efficient surface consists of hydrophilic islands surrounded by hydrophobic background. A thin drainage path in the lower portion of the condensation substrate is revealed to greatly enhance the water collection efficiency. The optimal surface conditions found in this work can be used to design a practical device that harvests water as its biological counterpart, a green tree frog, Litoria caerulea , does during the dry season in tropical northern Australia.


title: A multiagent reinforcement learning algorithm using extended optimal response

abstract: Stochastic games provides a theoretical framework to multiagent reinforcement learning. Based on the framework, a multiagent reinforcement learning algorithm for zero-sum stochastic games was proposed by Littman and it was extended to general-sum games by Hu and Wellman. Given a stochastic game, if all agents learn with their algorithm, we can expect that the policies of the agents converge to a Nash equilibrium. However, agents with their algorithm always try to converge to a Nash equilibrium independent of the policies used by the other agents. In addition, in case there are multiple Nash equilibria, agents must agree on the equilibrium where they want to reach. Thus, their algorithm lacks adaptability in a sense. In this paper, we propose a multiagent reinforcement learning algorithm. The algorithm uses the extended optimal response which we introduce in this paper. It will converge to a Nash equilibrium when other agents are adaptable, otherwise it will make an optimal response. We also provide some empirical results in three simple stochastic games, which show that the algorithm can realize what we intend.


QUALITY, DIVERSITY AND INNOVATION: THEIR ROLE IN THE ECONOMIC FUNCTIONING OF THE MEDIA INDUSTRIES Researching Diversity of Content in a Multi-platform Context


title: Vision-Based Fallen Person Detection for the Elderly

abstract: Falls are serious and costly for elderly people. The Centers for Disease Control and Prevention of the US reports that millions of older people, 65 and older, fall each year at least once. Serious injuries such as; hip fractures, broken bones or head injury, are caused by 20% of the falls. The time it takes to respond and treat a fallen person is crucial. With this paper we present a new , non-invasive system for fallen people detection. Our approach uses only stereo camera data for passively sensing the environment. The key novelty is a human fall detector which uses a CNN based human pose estimator in combination with stereo data to reconstruct the human pose in 3D and estimate the ground plane in 3D. Furthermore, our system consists of a reasoning module which formulates a number of measures to reason whether a person is fallen. We have tested our approach in different scenarios covering most activities elderly people might encounter living at home. Based on our extensive evaluations, our systems shows high accuracy and almost no miss-classification. To reproduce our results, the implementation is publicly available to the scientific community.


title: Hate Speech Detection: A Solved Problem? The Challenging Case of Long Tail on Twitter

abstract: In recent years, the increasing propagation of hate speech on social media and the urgent need for effective counter-measures have drawn significant investment from governments, companies, and researchers. A large number of methods have been developed for automated hate speech detection online. This aims to classify textual content into non-hate or hate speech, in which case the method may also identify the targeting characteristics (i.e., types of hate, such as race, and religion) in the hate speech. However, we notice significant difference between the performance of the two (i.e., non-hate v.s. hate). In this work, we argue for a focus on the latter problem for practical reasons. We show that it is a much more challenging task, as our analysis of the language in the typical datasets shows that hate speech lacks unique, discriminative features and therefore is found in the 'long tail' in a dataset that is difficult to discover. We then propose Deep Neural Network structures serving as feature extractors that are particularly effective for capturing the semantics of hate speech. Our methods are evaluated on the largest collection of hate speech datasets based on Twitter, and are shown to be able to outperform the best performing method by up to 5 percentage points in macro-average F1, or 8 percentage points in the more challenging case of identifying hateful content.


title: Doppelgänger: a cache for approximate computing

abstract: Modern processors contain large last level caches (LLCs) that consume substantial energy and area yet are imperative for high performance. Cache designs have improved dramatically by considering reference locality. Data values are also a source of optimization. Compression and deduplication exploit data values to use cache storage more efficiently resulting in smaller caches without sacrificing performance. In multi-megabyte LLCs, many identical or similar values may be cached across multiple blocks simultaneously. This redundancy effectively wastes cache capacity. We observe that a large fraction of cache values exhibit approximate similarity. More specifically, values across cache blocks are not identical but are similar. Coupled with approximate computing which observes that some applications can tolerate error or inexactness, we leverage approximate similarity to design a novel LLC architecture: the Doppelganger cache. The Doppelganger cache associates the tags of multiple similar blocks with a single data array entry to reduce the amount of data stored. Our design achieves 1.55×, 2.55× and 1.41 × reductions in LLC area, dynamic energy and leakage energy without harming performance nor incurring high application error.


title: Application of artificial intelligence to accounting, tax, and audit services: Research at Brigham Young University

abstract: Abstract The domains of accounting, tax, and audit services have become much more complex during recent years. This development has generated a need for leveraging professional expertise and judgment processes. In an attempt to meet this need, there are a number of projects in the Marriott School of Management, Brigham Young University (BYU) that apply artificial intelligence techniques and methods to augment professional expertise. Much of the development and application of artificial intelligence in accounting, tax, and audit services has been in the form of expert systems. This paper examines some of the early expert system work by BYU faculty and provides a framework to guide current and future efforts.


title: Holistic DDoS mitigation using NFV

abstract: Distributed Denial of Service (DDoS) is a sophisticated cyber-attack due to its variety of types and techniques. The traditional mitigation method of this attack is to deploy dedicated security appliances such as firewall, load balancer, etc. However, due to the limited capacity of the hardware and the potential high volume of DDoS traffic, it may not be able to defend all the attacks. Therefore, cloud-based DDoS protection services were introduced to allow the organizations to redirect their traffic to the scrubbing centers in the cloud for filtering. This solution has some drawbacks such as privacy violation and latency. More recently, Network Functions Virtualization (NFV) and edge computing have been proposed as new networking service models. In this paper, we design a framework that leverages NFV and edge computing for DDoS mitigation through two-stage processes.


title: Augmentative and Alternative Communication for People with Progressive Neuromuscular Disease

abstract: Individuals with progressive neuromuscular disease often experience complex communication needs and consequently find that interaction using their natural speech may not sufficiently meet their daily needs. Increasingly, assistive technology advances provide accommodations for and/or access to communication. Assistive technology related to communication is referred to as augmentative and alternative communication (AAC). The nature of communication challenges in progressive neuromuscular diseases can be as varied as the AAC options currently available. AAC systems continue to be designed and implemented to provide targeted assistance based on an individual's changing needs.


title: A Capacitorless LDO Regulator With Fast Feedback Technique and Low-Quiescent Current Error Amplifier

abstract: This brief presents an ultralow quiescent class-AB error amplifier (ERR AMP) of low dropout (LDO) and a slew-rate (SR) enhancement circuit to minimize compensation capacitance and speed up transient response designed in the 0.11-μm 1-poly 6-metal CMOS process. In order to increase the current capability with a low standby quiescent current under large-signal operation, the proposed scheme has a class-AB-operation operational transconductance amplifier (OTA) that acts as an ERR AMP. As a result, the new OTA achieved a higher dc gain and faster settling time than conventional OTAs, demonstrating a dc gain improvement of 15.8 dB and a settling time six times faster than that of a conventional OTA. The proposed additional SR enhancement circuit improved the response based on voltage-spike detection when the voltage dramatically changed at the output node.


title: A Review of the Volumetric Torque Density of Rotary Magnetic Gear Designs

abstract: This paper provides a review of the current and past developments in the area of rotary magnetic gear technology. The performance is investigated with respect to torque density and gear ratio. The aim of the paper is to present a snap-shot of the current state-of the art in this rapidly developing area of research.


title: Generative adversarial networks and adversarial methods in biomedical image analysis

abstract: Generative adversarial networks (GANs) and other adversarial methods are based on a game-theoretical perspective on joint optimization of two neural networks as players in a game. Adversarial techniques have been extensively used to synthesize and analyze biomedical images. We provide an introduction to GANs and adversarial methods, with an overview of biomedical image analysis tasks that have benefited from such methods. We conclude with a discussion of strengths and limitations of adversarial methods in biomedical image analysis, and propose potential future research directions.


title: Structural Fat Grafting: More Than a Permanent Filler

abstract: Grafted fat has many attributes of an ideal filler, but the results, like those of any procedure, are technique dependent. Fat grafting remains shrouded in the stigma of variable results experienced by most plastic surgeons when they first graft fat. However, many who originally reported failure eventually report success after altering their methods of harvesting, refinement, and placement. Many surgeons have refined their techniques to obtain long-term survival and volume replacement with grafted fat. They have observed that transplanted fat not only adjusts facial and body proportion but also improves surrounding tissues into which the fat is placed. They have noted not only the improvement in the quality of aging skin and scars but also a remarkable improvement in conditions such as radiation damage, chronic ulceration, breast capsular contracture, and damaged vocal cords. The mechanism of fat graft survival is not clear, and the role of adipose-derived stem cells and preadipocytes in fat survival remains to be determined. Early research has indicated the possible involvement of more undifferentiated cells in some of the observed effects of fat grafting on surrounding tissues. Of particular interest is the research that has pointed to the use of stem cells to repair and even to become bone, cartilage, muscle, blood vessels, nerves, and skin. Further studies are essential to understand grafted fat tissue.


title: A Blockchain-based Infrastructure for Reliable and Cost-effective IoT-aided Smart Grids

abstract: One of the main trends in the evolution of smart grids is transactive energy, where distributed energy resources, e.g. smart meters, develop towards Internet-of-Things (IoT) devices enabling prosumers to trade energy directly among each other, without the need of involving any centralised third party. The expected advantages in terms of cost-effectiveness would be significant, indeed technical solutions are being investigated and large-scale deployment are planned by major utilities companies. However, introducing transactive energy in the smart grid entails new security threats, such as forging energy transactions. This paper introduces an infrastructure to support reliable and cost-effective transactive energy, based on blockchain and smart contracts, where functionalities are implemented as fully decentralised applications. Energy transactions are stored in the blockchain, whose high replication level ensures stronger guarantees against tampering. Energy auctions are carried out according to transparent rules implemented as smart contracts, hence visible to all involved actors. Threats deriving from known vulnerabilities of smart meters are mitigated by temporarily keeping out exposed prosumers and updating their devices as soon as security patches become available.


title: Osteoimmunology and the influence of pro-inflammatory cytokines on osteoclasts

abstract: Bone and immune system are functionally interconnected. Immune and bone cells derive from same progenitors in the bone marrow, they share a common microenvironment and are being influenced by similar mediators. The evidence on increased bone resorption associated with inappropriate activation of T cells such as during inflammation, is well established. However, the molecular mechanisms beyond this clinical observation have begun to be intensively studied with the advancement of osteoimmunology. Now days, we have firm evidence on the influence of numerous proinflammatory cytokines on bone cells, with the majority of data focused on osteoclasts, the bone resorbing cells. It has been shown that some proinflammatory cytokines could possess osteoclastogenic and/or anti-osteoclastogenic properties and can target osteoclasts directly or via receptor activator of nuclear factor κB (RANK)/RANK ligand(RANKL)/osteoprotegerin (OPG) system. Several studies have reported opposing data regarding (anti)osteoclastogenic properties of these cytokines. ::: ::: Therefore, the first part of this review is summarizing current evidence on the influence of pro-inflammatory cytokines on osteoclasts and thus on bone resorption. In the second part, the evidence on the role of pro-inflammatory cytokines in osteoporosis and osteoarthritis is reviewed to show that unravelling the mechanisms beyond such complex bone diseases, is almost impossible without considering skeletal and immune systems as an indivisible integrated system.


title: A Parallel Graph Coloring Heuristic

abstract: The problem of computing good graph colorings arises in many diverse applications, such as in the estimation of sparse Jacobians and in the development of efficient, parallel iterative methods for solving sparse linear systems. This paper presents an asynchronous graph coloring heuristic well suited to distributed memory parallel computers. Experimental results obtained on an Intel iPSC/860 are presented, which demonstrate that, for graphs arising from finite element applications, the heuristic exhibits scalable performance and generates colorings usually within three or four colors of the best-known linear time sequential heuristics. For bounded degree graphs, it is shown that the expected running time of the heuristic under the P-RAM computation model is bounded by $EO(\log (n)/\log \log (n))$. This bound is an improvement over the previously known best upper bound for the expected running time of a random heuristic for the graph coloring problem.


title: Ripple: Overview and Outlook

abstract: Ripple is a payment system and a digital currency which evolved completely independently of Bitcoin. Although Ripple holds the second highest market cap after Bitcoin, there are surprisingly no studies which analyze the provisions of Ripple.


title: Adaptive sampling for environmental field estimation using robotic sensors

abstract: Monitoring environmental phenomena by distributed sensor sampling confronts the challenge of unpredictable variability in the spatial distribution of phenomena often coupled with demands for a high spatial sampling rate. The introduction of actuation-enabled robotics sensors permits a system to optimize the sampling distribution through runtime adaptation. However, such systems must efficiently dispense sampling points or otherwise suffer from poor temporal response. In this paper, we propose and characterize an active modeling system. In our approach, as the robotic sensor acquires measurement samples of the environment, it builds a model of the phenomenon. Our algorithm is based on an incremental optimization process where the robot supports a continuous, iterative process of 1) collecting samples with maximal coverage in the design space; 2) building the environmental model; 3) predicting sampling point locations that contribute the greatest certainty regarding the phenomenon; and 4) sampling the environment based on a combined measure of information gain and navigation and sampling cost. This can provide significant reductions in the magnitude of field estimation error with a modest navigational trajectory time. We evaluate our algorithm through a simulation, using a combination of static and mobile sensors sampling light illumination field.


title: POP: Privacy-Preserving Outsourced Photo Sharing and Searching for Mobile Devices

abstract: Facing a large number of personal photos and limited resource of mobile devices, cloud plays an important role in photo storing, sharing and searching. Meanwhile, some recent reputation damage and stalk events caused by photo leakage increase people's concern about photo privacy. Though most would agree that photo search function and privacy are both valuable, few cloud system supports both of them simultaneously. The center of such an ideal system is privacy-preserving outsourced image similarity measurement, which is extremely challenging when the cloud is untrusted and a high extra overhead is disliked. In this work, we introduce a framework POP, which enables privacy-seeking mobile device users to outsource burdensome photo sharing and searching safely to untrusted servers. Unauthorized parties, including the server, learn nothing about photos or search queries. This is achieved by our carefully designed architecture and novel non-interactive privacy-preserving protocols for image similarity computation. Our framework is compatible with the state-of-the-art image search techniques, and it requires few changes to existing cloud systems. For efficiency and good user experience, our framework allows users to define personalized private content by a simple check-box configuration and then enjoy the sharing and searching services as usual. All privacy protection modules are transparent to users. The evaluation of our prototype implementation with 31,772 real-life images shows little extra communication and computation overhead caused by our system.


title: The Healthy Heart: Lessons from Nature's Elite Athletes.

abstract: The incidence of cardiovascular disease in humans is more than three times that of many wild and domestic mammals despite nearly identical heart morphologies and responses to exercise. A survey of mammalian species from 0.002-kg shrews to 43,000-kg whales shows that the human heart is more dog-like than cat-like and that neither body size nor longevity accounts for the relative vulnerability to cardiovascular disease. Rather, a major difference is daily activity patterns, which may underlie the comparatively healthy hearts of wild mammals.


title: Relaying Protocols for Wireless Energy Harvesting and Information Processing

abstract: An emerging solution for prolonging the lifetime of energy constrained relay nodes in wireless networks is to avail the ambient radio-frequency (RF) signal and to simultaneously harvest energy and process information. In this paper, an amplify-and-forward (AF) relaying network is considered, where an energy constrained relay node harvests energy from the received RF signal and uses that harvested energy to forward the source information to the destination. Based on the time switching and power splitting receiver architectures, two relaying protocols, namely, i) time switching-based relaying (TSR) protocol and ii) power splitting-based relaying (PSR) protocol are proposed to enable energy harvesting and information processing at the relay. In order to determine the throughput, analytical expressions for the outage probability and the ergodic capacity are derived for delay-limited and delay-tolerant transmission modes, respectively. The numerical analysis provides practical insights into the effect of various system parameters, such as energy harvesting time, power splitting ratio, source transmission rate, source to relay distance, noise power, and energy harvesting efficiency, on the performance of wireless energy harvesting and information processing using AF relay nodes. In particular, the TSR protocol outperforms the PSR protocol in terms of throughput at relatively low signal-to-noise-ratios and high transmission rate.


title: Statistical evaluation of no-reference image visual quality metrics

abstract: A task of no-reference visual quality metric verification is considered. A test set that contains 500 JPEG format images having different distortions is created. The results of experiments carried out by 316 volunteer observers to evaluate visual quality of images are presented. The experiments allowed obtaining mean opinion scores (MOS) based on averaging the evaluations. Several non-reference image visual quality metrics have been analyzed. Spearman and Kendall correlations between MOS and metrics values are calculated. It is shown that all analyzed metrics have not enough correspondence to human perception.


title: Twelve tips to stimulate intrinsic motivation in students through autonomy-supportive classroom teaching derived from Self-Determination Theory

abstract: Background: Self-Determination Theory (SDT) of motivations distinguishes between intrinsic and extrinsic motivations. Intrinsic motivation is observed when one engages in an activity out of genuine interest and is truly self-determined. Intrinsic motivation is the desired type of motivation for study as it is associated with deep learning, better performance and positive well-being in comparison to extrinsic motivation. It is dependent on the fulfilment of three basic psychological needs described by SDT. These are the needs for autonomy, competence and relatedness. According to SDT, autonomy-supportive teaching is important, because it makes students feel autonomous and competent in their learning and also supported (relatedness) by their teachers.Aim: The concept of autonomy-supportive teaching is relevant to medical education, but less known. Through this article, we aim to make this concept understood and practically used by medical teachers.Methods: We used SDT literature as a basis to formulate thes...


title: Age-Related Changes in 1/f Neural Electrophysiological Noise

abstract: Aging is associated with performance decrements across multiple cognitive domains. The neural noise hypothesis, a dominant view of the basis of this decline, posits that aging is accompanied by an increase in spontaneous, noisy baseline neural activity. Here we analyze data from two different groups of human subjects: intracranial electrocorticography from 15 participants over a 38 year age range (15–53 years) and scalp EEG data from healthy younger (20–30 years) and older (60–70 years) adults to test the neural noise hypothesis from a 1/f noise perspective. Many natural phenomena, including electrophysiology, are characterized by 1/f noise. The defining characteristic of 1/f is that the power of the signal frequency content decreases rapidly as a function of the frequency (f) itself. The slope of this decay, the noise exponent (χ), is often SIGNIFICANCE STATEMENT Understanding the neurobiological origins of age-related cognitive decline is of critical scientific, medical, and public health importance, especially considering the rapid aging of the world9s population. We find, in two separate human studies, that 1/f electrophysiological noise increases with aging. In addition, we observe that this age-related 1/f noise statistically mediates age-related working memory decline. These results significantly add to this understanding and contextualize a long-standing problem in cognition by encapsulating age-related cognitive decline within a neurocomputational model of 1/f noise-induced deficits in neural communication.


title: Employees’ Perceptions of Corporate Social Responsibility and Job Performance: A Sequential Mediation Model

abstract: In spite of the increasing importance of corporate social responsibility (CSR) and employee job performance, little is still known about the links between the socially responsible actions of organizations and the job performance of their members. In order to explain how employees’ perceptions of CSR influence their job performance, this study first examines the relationships between perceived CSR, organizational identification, job satisfaction, and job performance, and then develops a sequential mediation model by fully integrating these links. The results of structural equation modeling analyses conducted for 250 employees at hotels in South Korea offered strong support for the proposed model. We found that perceived CSR was indirectly and positively associated with job performance sequentially mediated first through organizational identification and then job satisfaction. This study theoretically contributes to the CSR literature by revealing the sequential mechanism through which employees’ perceptions of CSR affect their job performance, and offers practical implications by stressing the importance of employees’ perceptions of CSR. Limitations of this study and future research directions are discussed.


title: A case for ICN usage in IoT environments

abstract: Information-Centric Networking (ICN) has recently emerged as a promising Future Internet architecture that aims to cope with the increasing demand for highly scalable and efficient distribution of content. Moving away from the Internet communication model based in addressable hosts, ICN leverages in-network storage for caching, multi-party communication through replication, and interaction models that decouple senders and receivers. This novel networking approach has the potential to outperform IP in several dimensions, besides just content dissemination. Concretely, the rise of the Internet of Things (IoT), with its rich set of challenges and requirements placed over the current Internet, provide an interesting ground for showcasing the contribution and performance of ICN mechanisms. This work analyses how the in-network caching mechanisms associated to ICN, particularly those implemented in the Content-Centric Networking (CCN) architecture, contribute in IoT environments, particularly in terms of energy consumption and bandwidth usage. A simulation comparing IP and the CCN architecture (an instantiation of ICN) in IoT environments demonstrated that CCN leads to a considerable reduction of the energy consumed by the information producers and to a reduction of bandwidth requirements, as well as highlighted the flexibility for adapting current ICN caching mechanisms to target specific requirements of IoT.


title: Semantic Parsing as Machine Translation

abstract: Semantic parsing is the problem of deriving a structured meaning representation from a natural language utterance. Here we approach it as a straightforward machine translation task, and demonstrate that standard machine translation components can be adapted into a semantic parser. In experiments on the multilingual GeoQuery corpus we find that our parser is competitive with the state of the art, and in some cases achieves higher accuracy than recently proposed purpose-built systems. These results support the use of machine translation methods as an informative baseline in semantic parsing evaluations, and suggest that research in semantic parsing could benefit from advances in machine translation.


Cloud-based Online Portal and Mobile Friendly Application for the Holy Qur ’ an


Rennick Concept Map of Adult Perspectives Psychosocial Experiences Associated With Confirmed and Self-Identified Dyslexia : A Participant-Driven


title: Lateral Crural Tensioning for Refinement of the Nasal Tip and Increasing Alar Stability: A Case Series

abstract: The objective of this study was to discuss the technical details and our experiences with lateral crural tensioning (LCT) in both functional and aesthetic rhinoplasties. A retrospective medical review was completed for all patients who underwent rhinoplasty with LCT from the years 2011 to 2014. The indications for LCT included correction of lateral crural convexity, boxy tip geometry, and dynamic collapse of the external nasal valve. The details of the rhinoplasty procedure and complications were evaluated. A total of 114 LCT rhinoplasty cases were included in this series. The most common adjunctive maneuvers included placement of spreader grafts (92% of cases) and alar rim grafts (78% of cases). Conventional classic cephalic trim was not performed in any subjects and conservative paradomal cephalic trim was performed in 48% of cases. As experience with the technique progressed, the use of onlay tip grafts decreased and the use of articulated rim grafts increased. Indications for revision were dissatisfaction with cosmetic outcome (4.4% of cases) and nasal obstruction (0.9% of cases). LCT combines traditional lateral crural steal with the use of a caudal septal extension graft to refine the broad tip and increase stability of the alar lobule. This maneuver is essentially cartilage sparing and does not rely on extensive grafting maneuvers that can reduce airway area.


title: A performance spectrum for parallel computational frameworks that solve PDEs

abstract: Important computational physics problems are often large-scale in nature, and it is highly desirable to have robust and high performing computational frameworks that can quickly address these problems. However, it is no trivial task to determine whether a computational framework is performing efficiently or is scalable. The aim of this paper is to present various strategies for better understanding the performance of any parallel computational frameworks for solving PDEs. Important performance issues that negatively impact time-to-solution are discussed, and we propose a performance spectrum analysis that can enhance one's understanding of critical aforementioned performance issues. As proof of concept, we examine commonly used finite element simulation packages and software and apply the performance spectrum to quickly analyze the performance and scalability across various hardware platforms, software implementations, and numerical discretizations. It is shown that the proposed performance spectrum is a versatile performance model that is not only extendable to more complex PDEs such as hydrostatic ice sheet flow equations, but also useful for understanding hardware performance in a massively parallel computing environment. Potential applications and future extensions of this work are also discussed.


title: A big data application to predict depression in the university based on the reading habits

abstract: This study deeply mines the correlation between reading habits and depressive tendency of university students based on the data set from university library records and mental health questionnaires results. This paper compares different text categorization algorithms including kNN, SVM and naive Bayesian classifier on accuracy and time-consuming under different sample sizes. Ultimately, we construct a book classifier using naive Bayesian classification algorithm based on polynomial model, and the accuracy of the classifier reaches to 0.823. To build a psychological prediction model, this study uses linear regression algorithm and logistic regression algorithm respectively and the prediction accuracy of the models are compared under different error criteria, which turns out the logistic model behaves better.


title: The Delphi Method for Graduate Research

abstract: Introduction It continues to be an exciting time to be a researcher in the information systems discipline; there seems to be a plethora of interesting and pressing research topics suitable for research at the masters or PhD level. Researchers may want to look forward to see what will be the key information systems issues in a wireless world, the ethical dilemmas in social network analysis, and the lessons early adopters learn. Practitioners may be interested in what others think about the strengths and weaknesses of an existing information system, or the effectiveness of a newly implemented information system. The Delphi method can help to uncover data in these research directions. The Delphi method is an iterative process used to collect and distill the judgments of experts using a series of questionnaires interspersed with feedback. The questionnaires are designed to focus on problems, opportunities, solutions, or forecasts. Each subsequent questionnaire is developed based on the results of the previous questionnaire. The process stops when the research question is answered: for example, when consensus is reached, theoretical saturation is achieved, or when sufficient information has been exchanged. The Delphi method has its origins in the American business community, and has since been widely accepted throughout the world in many industry sectors including health care, defense, business, education, information technology, transportation and engineering. The Delphi method's flexibility is evident in how it has been used. It is a method for structuring a group communication process to facilitate group problem solving and to structure models (Linstone & Turloff, 1975). The method can also be used as a judgment, decision-aiding or forecasting tool (Rowe & Wright, 1999), and can be applied to program planning and administration (Delbeq, Van de Ven, & Gustafson, 1975). The Delphi method can be used when there is incomplete knowledge about a problem or phenomena (Adler & Ziglio, 1996; Delbeq et al., 1975). The method can be applied to problems that do not lend themselves to precise analytical techniques but rather could benefit from the subjective judgments of individuals on a collective basis (Adler & Ziglio, 1996) and to focus their collective human intelligence on the problem at hand (Linstone & Turloff, 1975). Also, the Delphi is used to investigate what does not yet exist (Czinkota & Ronkainen, 1997; Halal, Kull, & Leffmann, 1997; Skulmoski & Hartman 2002). The Delphi method is a mature and a very adaptable research method used in many research arenas by researchers across the globe. To better understand its diversity in application, one needs to consider the origins of the Delphi method. The Classical Delphi The original Delphi method was developed by Norman Dalkey of the RAND Corporation in the 1950's for a U.S. sponsored military project. Dalkey states that the goal of the project was "to solicit expert opinion to the selection, from the point of view of a Soviet strategic planner, of an optimal U.S. industrial target system and to the estimation of the number of A-bombs required to reduce the munitions output by a prescribed amount," (Dalkey & Helmer, 1963, p. 458). Rowe and Wright (1999) characterize the classical Delphi method by four key features: 1. Anonymity of Delphi participants: allows the participants to freely express their opinions without undue social pressures to conform from others in the group. Decisions are evaluated on their merit, rather than who has proposed the idea. 2. Iteration: allows the participants to refine their views in light of the progress of the group's work from round to round. 3. Controlled feedback: informs the participants of the other participant's perspectives, and provides the opportunity for Delphi participants to clarify or change their views. 4. Statistical aggregation of group response: allows for a quantitative analysis and interpretation of data. …


title: Local Ambient Occlusion in Direct Volume Rendering

abstract: This paper presents a novel technique to efficiently compute illumination for Direct Volume Rendering using a local approximation of ambient occlusion to integrate the intensity of incident light for each voxel. An advantage with this local approach is that fully shadowed regions are avoided, a desirable feature in many applications of volume rendering such as medical visualization. Additional transfer function interactions are also presented, for instance, to highlight specific structures with luminous tissue effects and create an improved context for semitransparent tissues with a separate absorption control for the illumination settings. Multiresolution volume management and GPU-based computation are used to accelerate the calculations and support large data sets. The scheme yields interactive frame rates with an adaptive sampling approach for incrementally refined illumination under arbitrary transfer function changes. The illumination effects can give a better understanding of the shape and density of tissues and so has the potential to increase the diagnostic value of medical volume rendering. Since the proposed method is gradient-free, it is especially beneficial at the borders of clip planes, where gradients are undefined, and for noisy data sets.


title: Automatic generation of destination maps

abstract: Destination maps are navigational aids designed to show anyone within a region how to reach a location (the destination). Hand-designed destination maps include only the most important roads in the region and are non-uniformly scaled to ensure that all of the important roads from the highways to the residential streets are visible. We present the first automated system for creating such destination maps based on the design principles used by mapmakers. Our system includes novel algorithms for selecting the important roads based on mental representations of road networks, and for laying out the roads based on a non-linear optimization procedure. The final layouts are labeled and rendered in a variety of styles ranging from informal to more formal map styles. The system has been used to generate over 57,000 destination maps by thousands of users. We report feedback from both a formal and informal user study, as well as provide quantitative measures of success.


title: Ferroelectric gate tunnel field-effect transistors with low-power steep turn-on

abstract: Using a ferroelectric PbZrTiO3 gate stack, the range of the steep subthreshold swing in tunnel field-effect transistors was extended by 3.5 orders of magnitude demonstrating an improvement in the swing (by approximately double the slope). The drain conductance (gd) shows only 16% enhancement with large V DS (∼−1.5V) indicates internal voltage amplification with ferroelectric negative capacitance effect beneficial to small lateral drain-source bias voltages (−0.1 V). The concept of coupling the ferroelectric polarization is proposed. The power consumption is also discussed in low-power applications of steep subthreshold slope devices.


Cable-Driven Parallel Robots: Theory and Application


title: Simulation of nonlinear models of modified BPSK Costas loop for non sinusoidal waveforms in MatLab Simulink

abstract: A modification of BPSK Costas loop, where low pass filters are applied separately from the loop, is considered. Such modification of the loop can work with non-sinusoidal signals or with some special phase detectors instead of multipliers. In this paper a general approach to the analytical computation of phase detector characteristic of the loop for general periodic signal waveforms is demonstrated and an effective simulation of the loop in signal's phase space is considered.


title: Managing Employee Compliance with Information Security Policies: The Critical Role of Top Management and Organizational Culture

abstract: We develop an individual behavioral model that integrates the role of top management and organizational culture into the theory of planned behavior in an attempt to better understand how top management can influence security compliance behavior of employees. Using survey data and structural equation modeling, we test hypotheses on the relationships among top management participation, organizational culture, and key determinants of employee compliance with information security policies. We find that top management participation in information security initiatives has significant direct and indirect influences on employees’ attitudes towards, subjective norm of, and perceived behavioral control over compliance with information security policies. We also find that the top management participation strongly influences organizational culture which in turn impacts employees’ attitudes towards and perceived behavioral control over compliance with information security policies. Furthermore, we find that the effects of top management participation and organizational culture on employee behavioral intentions are fully mediated by employee cognitive beliefs about compliance with information security policies. Our findings extend information security research literature by showing how top management can play a proactive role in shaping employee compliance behavior in addition to the deterrence oriented remedies advocated in the extant literature. Our findings also refine the theories about the role of organizational culture in shaping employee compliance behavior. Significant theoretical and practical implications of these findings are discussed.


title: Pedestrian Detection Based on YOLO Network Model

abstract: After going through the deep network, there will be some loss of pedestrian information, which will cause the disappearance of gradients, causing inaccurate pedestrian detection. This paper improves the network structure of YOLO algorithm and proposes a new network structure YOLO-R. First, three Passthrough layers were added to the original YOLO network. The Passthrough layer consists of the Route layer and the Reorg layer. Its role is to connect the shallow layer pedestrian features to the deep layer pedestrian features and link the high and low resolution pedestrian features. The role of the Route layer is to pass the pedestrian characteristic information of the specified layer to the current layer, and then use the Reorg layer to reorganize the feature map so that the currently-introduced Route layer feature can be matched with the feature map of the next layer. The three Passthrough layers added in this algorithm can well transfer the network's shallow pedestrian fine-grained features to the deep network, enabling the network to better learn shallow pedestrian feature information. This paper also changes the layer number of the Passthrough layer connection in the original YOLO algorithm from Layer 16 to Layer 12 to increase the ability of the network to extract the information of the shallow pedestrian features. The improvement was tested on the INRIA pedestrian dataset. The experimental results show that this method can effectively improve the detection accuracy of pedestrians, while reducing the false detection rate and the missed detection rate, and the detection speed can reach 25 frames per second.


title: Large margin hierarchical classification

abstract: We present an algorithmic framework for supervised classification learning where the set of labels is organized in a predefined hierarchical structure. This structure is encoded by a rooted tree which induces a metric over the label set. Our approach combines ideas from large margin kernel methods and Bayesian analysis. Following the large margin principle, we associate a prototype with each label in the tree and formulate the learning task as an optimization problem with varying margin constraints. In the spirit of Bayesian methods, we impose similarity requirements between the prototypes corresponding to adjacent labels in the hierarchy. We describe new online and batch algorithms for solving the constrained optimization problem. We derive a worst case loss-bound for the online algorithm and provide generalization analysis for its batch counterpart. We demonstrate the merits of our approach with a series of experiments on synthetic, text and speech data.


title: Deployment of a Tensegrity Footbridge

abstract: Deployable structures are structures that transform their shape from a compact state to an extended in-service position. Structures composed of tension elements that surround compression elements in equilibrium are called tensegrity structures. Tensegrities are good candidates for deployable structures since shape transformations occur by changing lengths of elements at low energy costs. Although the tensegrity concept was first introduced in 1948, few full-scale tensegrity-based structures have been built. Previous work has demonstrated that a tensegrity ring topology is potentially a viable system for a deployable footbridge. This paper describes a study of a near-full-scale deployable tensegrity footbridge. The study has been carried out both numerically and experimentally. The deployment of two modules (one half of the footbridge) is achieved through changing the length of five active cables. Deployment is aided by energy stored in low stiffness spring elements. Self-weight significantly influences deployment, and deployment is not reproducible using the same sequence of cable- length changes. Active control is thus required for accurate positioning of front nodes in order to complete deployment through joining both sides at center span. Additionally, testing and numerical analyses have revealed that the deployment behavior of the structure is nonlinear with respect to cable-length changes. Finally, modeling the behavior of the structure cannot be done accurately using friction-free and dimension- less joints. Similar deployable tensegrity structures of class two and higher are expected to require simulation models that include joint dimensions for accurate prediction of nodal positions. DOI: 10.1061/(ASCE)ST.1943-541X.0001260 . © 2015 American Society of Civil Engineers.


title: Injuries in primary care practices.

abstract: A study of injuries among 0-15 year old children attending primary care clinics in a low social class and a middle class neighbourhood was carried out during a period of one year. Data were collected in a structured form by the physicians while the patient was in the clinic. The rate of injuries was 121.3/1000 children per year in the clinic from the lower social class neighbourhood and 70.7/1000 children per year in the clinic from the middle class neighbourhood. Injuries in playgrounds, and on pavements and streets occurred in higher proportions in the low social class than in the middle class neighbourhood clinics. The most frequent causes of injuries were falls and being struck and injured by cutting/piercing instruments; these caused mostly contusions and lacerations. The clinic was the first place of treatment in 65% of the cases in the middle class area and in 45% in the lower social class area; 28% and 10% respectively were referred to the hospital for additional treatment. Data from primary care clinics should be considered when estimating the incidence of injuries in the community, in planning intervention programmes, and future research.


title: Single phase inverter control with capacitor current feedback

abstract: In this paper, single phase sinusoidal pulse width modulated (SPWM) inverter control with modified unipolar switching is presented. The two loops namely, capacitor current feedback as inner loop and output voltage feedback as outer loop were implemented. The mathematical model and state space analysis of power stage is derived and used to design the feedback compensators. The compensators for the two feedback loops are designed and verified using MATLAB and Simulink. The simulation results for a 1kVA SPWM inverter are presented. With the designed PI compensators, the system exhibits stable output voltage and good transient response for linear and non linear loads. For 700W load step, the output voltage recovers in a time less than 0.5ms.


Inside-Outside and Forward-Backward Algorithms Are Just Backprop (tutorial paper)


title: Robot motor skill coordination with EM-based Reinforcement Learning

abstract: We present an approach allowing a robot to acquire new motor skills by learning the couplings across motor control variables. The demonstrated skill is first encoded in a compact form through a modified version of Dynamic Movement Primitives (DMP) which encapsulates correlation information. Expectation-Maximization based Reinforcement Learning is then used to modulate the mixture of dynamical systems initialized from the user's demonstration. The approach is evaluated on a torque-controlled 7 DOFs Barrett WAM robotic arm. Two skill learning experiments are conducted: a reaching task where the robot needs to adapt the learned movement to avoid an obstacle, and a dynamic pancake-flipping task.


title: How Hemostatic Agents Interact With the Coagulation Cascade

abstract: Abstract Hemostasis is a critical component of the preservation of hemodynamic stability and operative visibility during surgery. Initially, hemostasis is achieved via the careful application of direct pressure to allow time for the coagulation cascade to create a fibrin and platelet plug. Other first-line methods of hemostasis in surgery include repair or ligation of the bleeding vessel with sutures, clips, or staples and coagulation of the bleeding site with a thermal energy-based device. When these methods are insufficient to provide adequate hemostasis, topical hemostatic agents can be used to augment the creation of a clot during surgery. A basic understanding of how and where these products interact with the coagulation cascade is essential to achieving optimal hemostasis outcomes.


title: Nectar and Pollen Production and Insect Visitation on Ornamentals from the Genus Hosta Tratt. (Asparagaceae)

abstract: Abstract Properly arranged ornamental gardens in both urban and agricultural landscapes can be of a benefit to bees. In this study, we observed the flowering phenology, nectar, and pollen production of the ornamental Hosta species and varieties (H. sieboldiana Engler, H. capitata Nakai, H. crispula Maekawa, H. fluctuans Maekawa, syn. H. sieboldiana var. fluctuans hort., H. undulata var. univittata Miquel (Hylander), syn. H. univittata). Our experiment was conducted in the 2012 - 2014 time period, at the UMCS Botanical Garden in Lublin, Poland (51° 14’ N, 21° 34’ E). The total sugar yield varied almost 5-fold among Hosta ornamentals; the lowest amount was calculated for H. fluctuans (2.31 g per 10 m2) and the highest for H. capitata (11.80 g per 10 m2). The average pollen yield was from 0.24 g per 10 m2 (H. undulata var. univittata) to 9.53 g per 10 m2 (H. capitata). Pollen grains were bilaterally symmetrical, and large-sized. In polar view, they were prolatum (shape index 1.33 - 1.61), while in equatorial view, oblatum (shape index 0.5 - 0.7). Hosta species can complete the summer pasture mainly for bumblebees. In four of the five Hosta representatives, bumblebees accounted for 60 - 70% of the total visits. The honeybee predominated only on the flowers of H. capitata (54 - 71%). Solitary bees were rarely observed (3 - 9% of visits). The phenotypic traits of Hosta flowers (the accumulation of nectar in the deep and narrow perianth tube) reduced the access to nectar reward and restricted an array of insect visitors.


title: Smart City as a Service (SCaaS): A Future Roadmap for E-Government Smart City Cloud Computing Initiatives

abstract: The concept of a smart city has been identified as not only representing a crucible for technological innovation, a medium for realizing global integration but also as an exemplar response for addressing current and impending global issues (societal, environmental economic and governance). There is now general consensus that emerging information communication technologies (ICT) such as cloud computing can be deployed as a vehicle for catalyzing smart city innovation. There is also evidence to suggest that cloud computing has become a strategic direction for many e-government initiatives as evidenced by the emergence of global government clouds (G-Cloud). In this paper, we delineate the concept of a smart city and explore the propitious potential of cloud computing to enable the development of smart cities. We review the current state of the art pertaining to a selection of cloud schemes currently being operationalized by international governments aimed at capitalizing on the nascent innovation capabilities of the technological paradigm, and present a future research roadmap for cloud facilitated smart city development.


title: Personalized Video Recommendation through Graph Propagation

abstract: The rapid growth of the number of videos on the Internet provides enormous potential for users to find content of interest. However, the vast quantity of videos also turns the finding process into a difficult task. In this article, we address the problem of providing personalized video recommendation for users. Rather than only exploring the user-video bipartite graph that is formulated using click information, we first combine the clicks and queries information to build a tripartite graph. In the tripartite graph, the query nodes act as bridges to connect user nodes and video nodes. Then, to further enrich the connections between users and videos, three subgraphs between the same kinds of nodes are added to the tripartite graph by exploring content-based information (video tags and textual queries). We propose an iterative propagation algorithm over the enhanced graph to compute the preference information of each user. Experiments conducted on a dataset with 1,369 users, 8,765 queries, and 17,712 videos collected from a commercial video search engine demonstrate the effectiveness of the proposed method.


title: Bayesian Foreground and Shadow Detection in Uncertain Frame Rate Surveillance Videos

abstract: In in this paper, we propose a new model regarding foreground and shadow detection in video sequences. The model works without detailed a priori object-shape information, and it is also appropriate for low and unstable frame rate video sources. Contribution is presented in three key issues: 1) we propose a novel adaptive shadow model, and show the improvements versus previous approaches in scenes with difficult lighting and coloring effects; 2) we give a novel description for the foreground based on spatial statistics of the neighboring pixel values, which enhances the detection of background or shadow-colored object parts; 3) we show how microstructure analysis can be used in the proposed framework as additional feature components improving the results. Finally, a Markov random field model is used to enhance the accuracy of the separation. We validate our method on outdoor and indoor sequences including real surveillance videos and well-known benchmark test sets.