An Energy-Efficient Micropower Neural Recording Amplifier

This paper describes an ultralow-power neural recording amplifier. The amplifier appears to be the lowest power and most energy-efficient neural recording amplifier reported to date. We describe low-noise design techniques that help the neural amplifier achieve input-referred noise that is near the theoretical limit of any amplifier using a differential pair as an input stage. Since neural amplifiers must include differential input pairs in practice to allow robust rejection of common-mode and power supply noise, our design appears to be near the optimum allowed by theory. The bandwidth of the amplifier can be adjusted for recording either neural spikes or local field potentials (LFPs). When configured for recording neural spikes, the amplifier yielded a midband gain of 40.8 dB and a -3-dB bandwidth from 45 Hz to 5.32 kHz; the amplifier's input-referred noise was measured to be 3.06 muVrms while consuming 7.56 muW of power from a 2.8-V supply corresponding to a noise efficiency factor (NEF) of 2.67 with the theoretical limit being 2.02. When configured for recording LFPs, the amplifier achieved a midband gain of 40.9 dB and a -3-dB bandwidth from 392 mHz to 295 Hz; the input-referred noise was 1.66 muVrms while consuming 2.08 muW from a 2.8-V supply corresponding to an NEF of 3.21. The amplifier was fabricated in AMI's 0.5-mum CMOS process and occupies 0.16 mm2 of chip area. We obtained successful recordings of action potentials from the robust nucleus of the arcopallium (RA) of an anesthesized zebra finch brain with the amplifier. Our experimental measurements of the amplifier's performance including its noise were in good accord with theory and circuit simulations.


A Survey of Algorithmic Shapes

In the context of computer-aided design, computer graphics and geometry processing, the idea of generative modeling is to allow the generation of highly complex objects based on a set of formal construction rules. Using these construction rules, a shape is described by a sequence of processing steps, rather than just by the result of all applied operations: shape design becomes rule design. Due to its very general nature, this approach can be applied to any domain and to any shape representation that provides a set of generating functions. The aim of this survey is to give an overview of the concepts and techniques of procedural and generative modeling, as well as their applications with a special focus on archeology and architecture.


Trust in leadership: meta-analytic findings and implications for research and practice.

In this study, the authors examined the findings and implications of the research on trust in leadership that has been conducted during the past 4 decades. First, the study provides estimates of the primary relationships between trust in leadership and key outcomes, antecedents, and correlates (k = 106). Second, the study explores how specifying the construct with alternative leadership referents (direct leaders vs. organizational leadership) and definitions (types of trust) results in systematically different relationships between trust in leadership and outcomes and antecedents. Direct leaders (e.g., supervisors) appear to be a particularly important referent of trust. Last, a theoretical framework is offered to provide parsimony to the expansive literature and to clarify the different perspectives on the construct of trust in leadership and its operation.


An Empirical Assessment of Organizational Commitment and Organizational Effectiveness

Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.


Social Exchange in Organizations : Perceived Organizational Support , Leader-Member Exchange , and Employee Reciprocity

Social exchange (P. Blau, 1964) and the norm of reciprocity (A. W. Gouldner, 1960) have been used to explain the relationship of perceived organizational support and leader-member exchange with employee attitudes and behavior. Recent empirical research suggests that individuals engage in different reciprocation efforts depending on the exchange partner (e.g., B. L. McNeely & B. M. Meglino, 1994). The purpose of the present study was to further investigate these relationships by examining the relative contribution of indicators of employee-organization exchange and subordinate-supervisor exchange. Structural equation modeling was used to compare nested models. Results indicate that perceived organizational support is associated with organizational commitment, whereas leader-member exchange is associated with citizenship and in-role behavior.


What we know about leadership. Effectiveness and personality.

Although psychologists know a great deal about leadership, persons who make decisions about real leaders seem largely to ignore their accumulated wisdom. In an effort to make past research more accessible, interpretable, and relevant to decision makers, this article defines leadership and then answers nine questions that routinely come up when practical decisions are made about leadership (e.g., whom to appoint, how to evaluate them, when to terminate them.


Justice at the millennium: a meta-analytic review of 25 years of organizational justice research.

The field of organizational justice continues to be marked by several important research questions, including the size of relationships among justice dimensions, the relative importance of different justice criteria, and the unique effects of justice dimensions on key outcomes. To address such questions, the authors conducted a meta-analytic review of 183 justice studies. The results suggest that although different justice dimensions are moderately to highly related, they contribute incremental variance explained in fairness perceptions. The results also illustrate the overall and unique relationships among distributive, procedural, interpersonal, and informational justice and several organizational outcomes (e.g., job satisfaction, organizational commitment, evaluation of authority, organizational citizenship behavior, withdrawal, performance). These findings are reviewed in terms of their implications for future research on organizational justice.


Comparison of Feedback Control Techniques for Torque-Vectoring Control of Fully Electric Vehicles

Fully electric vehicles (FEVs) with individually controlled powertrains can significantly enhance vehicle response to steering-wheel inputs in both steady-state and transient conditions, thereby improving vehicle handling and, thus, active safety and the fun-to-drive element. This paper presents a comparison between different torque-vectoring control structures for the yaw moment control of FEVs. Two second-order sliding-mode controllers are evaluated against a feedforward controller combined with either a conventional or an adaptive proportional-integral-derivative (PID) controller. Furthermore, the potential performance and robustness benefits arising from the integration of a body sideslip controller with the yaw rate feedback control system are assessed. The results show that all the evaluated controllers are able to significantly change the understeer behavior with respect to the baseline vehicle. The PID-based controllers achieve very good vehicle performance in steady-state and transient conditions, whereas the controllers based on the sliding-mode approach demonstrate a high level of robustness against variations in the vehicle parameters. The integrated sideslip controller effectively maintains the sideslip angle within acceptable limits in the case of an erroneous estimation of the tire-road friction coefficient.


Using the Delphi method

The reliability and validity of the selected research method are subjects to which every researcher is bound to address himself when representing the findings and conclusions of his/her work. In this study we will discuss the reliability, validity and philosophical aspects of the Delphi method, first with a small literature review and then by representing two different surveys conducted using the Delphi method. The point of view in our report is the usability of Delphi in collecting qualitative data for software engineering research. The most significant features of the Delphi method are its recursion and the possibility to get immediate feedback and evaluate one's own answer. Although there are many forms of Delphi techniques, these features exist in one form or the other in all Delphi variations. In Delphi-based surveys, the minimum number of participants is smaller and participants are not selected at random but because of their particular expertise. Among the traditional research methods this is seen to cause the risk of bias and endangering both reliability and validity. In the Delphi method, the recursion produced by three or more rounds, the expertise and - in most cases - anonymity of participants, and the opportunity to evaluate and argue one's own answer after having seen the other opinions and arguments are thought to guarantee the quality of well-planned and well-conducted research.


Analysis on 2-element insertion sort algorithm

This paper proposes a 2-element insertion sort algorithm, an improved algorithm on the direct insertion sort, gives the algorithm design idea and the description in C. After analyzes comparatively the time complexity and space complexity of the algorithms, the paper summarizes the pros and cons of the 2-element insertion sort algorithm compared with the original one, so as to giving a theoretical basis for the direct insertion sort algorithm optimization, and playing a guiding role in teaching the relevant chapters in “Data Structure” curriculum.


OPTIMAL PATHS FOR A CAR THAT GOES BOTH FORWARDS AND BACKWARDS

The path taken by a car with a given minimum turning radius has a lower bound on its radius of curvature at each point, but the path has cusps if the car shifts into or out of reverse gear. What is the shortest such path a car can travel between two points if its starting and ending directions are specified? One need consider only paths with at most 2 cusps or reversals. We give a set of paths which is sufficient in the sense that it always contains a shortest path and small in the sense that there are at most 68, but usually many fewer paths in the set for any pair of endpoints and directions. We give these paths by explicit formula. Calculating the length of each of these paths and selecting the (not necessarily unique) path with smallest length yields a simple algorithm for a shortest path in each case. These optimal paths or geodesies may be described as follows: If C is an arc of a circle of the minimal turning radius and S is a line segment, then it is sufficient to consider only certain paths of the form CCSCC where arcs and segments fit smoothly, one or more of the arcs or segments may vanish, and where reversals, or equivalently cusps, between arcs or segments are allowed. This contrasts with the case when cusps are not allowed, where Dubins (1957) has shown that paths of the form CCC and CSC suffice.


Improving classification with latent variable models by sequential constraint optimization

In this paper we propose a method to use multiple generative models with latent variables for classi cation tasks. The standard approach to use generative models for classi cation is to train a separate model for each class. A novel data point is then classi ed by the model that attributes the highest probability. The algorithm we propose modi es the parameters of the models to improve the classi cation accuracy. Our approach is made computationally tractable by assuming that each of the models is deterministic, by which we mean that a data-point is associated to only a single latent state. The resulting algorithm is a variant of the support vector machine learning algorithm and in a limiting case the method is similar to the standard perceptron learning algorithm. We apply the method to two types of latent variable models. The rst has a discrete latent state space and the second, principal component analysis, has a continuous latent state space. We compare the e6ectiveness of both approaches on a handwritten digit recognition problem and on a satellite image recognition problem. c © 2003 Elsevier B.V. All rights reserved.


Personalized PageRank Based Multi-document Summarization

This paper presents a novel multi-document summarization approach based on personalized pagerank (PPRSum). In this algorithm, we uniformly integrate various kinds of information in the corpus. At first, we train a salience model of sentence global features based on Naive Bayes Model. Secondly, we generate a relevance model for each corpus utilizing the query of it. Then, we compute the personalized prior probability for each sentence in the corpus utilizing the salience model and the relevance model both. With the help of personalized prior probability, a Personalized PageRank ranking process is performed depending on the relationships among all sentences in the corpus. Additionally, the redundancy penalty is imposed on each sentence. The summary is produced by choosing the sentences with both high query-focused information richness and high information novelty. Experiments on DUC2007 are performed and the ROUGE evaluation results show that PPRSum ranks between the 1st and the 2nd systems on DUC2007 main task.


A Foundation for Multi-dimensional Databases

We present a multi-dimensional database model, which we believe can serve as a conceptual model for On-Line Analytical Processing (OLAP)-based applications. Apart from providing the functionalities necessary for OLAP-based applications, the main feature of the model we propose is a clear separation between structural aspects and the contents. This separation of concerns allows us to define data manipulation languages in a reasonably simple, transparent way. In particular, we show that the data cube operator can be expressed easily. Concretely, we define an algebra and a calculus and show them to be equivalent. We conclude by comparing our approach to related work. The conceptual multi-dimensional database model developed here is orthogonal to its implementation, which is not a subject of the present paper.


Regional differences in synaptogenesis in human cerebral cortex.

The formation of synaptic contacts in human cerebral cortex was compared in two cortical regions: auditory cortex (Heschl's gyrus) and prefrontal cortex (middle frontal gyrus). Synapse formation in both cortical regions begins in the fetus, before conceptual age 27 weeks. Synaptic density increases more rapidly in auditory cortex, where the maximum is reached near postnatal age 3 months. Maximum synaptic density in middle frontal gyrus is not reached until after age 15 months. Synaptogenesis occurs concurrently with dendritic and axonal growth and with myelination of the subcortical white matter. A phase of net synapse elimination occurs late in childhood, earlier in auditory cortex, where it has ended by age 12 years, than in prefrontal cortex, where it extends to midadolescence. Synaptogenesis and synapse elimination in humans appear to be heterochronous in different cortical regions and, in that respect, appears to differ from the rhesus monkey, where they are concurrent. In other respects, including overproduction of synaptic contacts in infancy, persistence of high levels of synaptic density to late childhood or adolescence, the absolute values of maximum and adult synaptic density, and layer specific differences, findings in the human resemble those in rhesus monkeys.


On shared understanding in software engineering: an essay

Shared understanding is essential for efficient software engineering when the risk of unsatisfactory out-come and rework of project results shall be low. Today, however, shared understanding is used mostly in an unreflected, ad-hoc way. This affects the quality of the engineered software solutions and generates re-work once the quality problems are discovered. In this article, we investigate the role, value, and usage of shared understanding in software engineering. We contribute a reflected analysis of the problem, in particular of how to rely on shared understanding that is implicit, rather than explicit. After an overview of the state of the art we discuss forms and value of shared understanding in software engineering, survey enablers and obstacles, compile existing practices for dealing with shared understanding, and present a roadmap for improving knowledge and practice in this area.


Too Much of a Good Thing? The Relationship Between Number of Friends and Interpersonal Impressions on Facebook

A central feature of the online social networking system, Facebook, is the connection to and links among friends. The sum of the number of one’s friends is a feature displayed on users’ profiles as a vestige of the friend connections a user has accrued. In contrast to offline social networks, individuals in online network systems frequently accrue friends numbering several hundred. The uncertain meaning of friend status in these systems raises questions about whether and how sociometric popularity conveys attractiveness in non-traditional, non-linear ways. An experiment examined the relationship between the number of friends a Facebook profile featured and observers’ ratings of attractiveness and extraversion. A curvilinear effect of sociometric popularity and social attractiveness emerged, as did a quartic relationship between friend count and perceived extraversion. These results suggest that an overabundance of friend connections raises doubts about Facebook users’ popularity and desirability. Zu viel des Guten? Zur Beziehung zwischen der Anzahl der Freunde und interpersonalen Eindrücken bei Facebook Eine zentrale Eigenschaft des sozialen Online-Netzwerks Facebook ist die Verbindung von Freunden. Die Gesamtanzahl der Freunde eines Nutzers wird als Merkmal im Benutzerprofil angezeigt und dient als eine Statistik der Freundeverbindungen, die ein Nutzer gesammelt hat. Im Gegensatz zu Offline-Netzwerken, haben Personen in OnlineNetzwerken oft mehrere Hundert Freunde. Die unklare Bedeutung des Freundestatus in diesem System wirft die Frage auf, ob und wie soziometrische Popularität die Attraktivität auf nicht-traditionelle, nichtlineare Weise ausdrückt. In einem Experiment wurde die Beziehung zwischen der Anzahl der Freunde im Facebook-Profil und der Einschätzung von Attraktivität und Extraversion durch den Beobachter untersucht. Es zeigten sich ein kurvilinearer Effekt von soziometrischer Popularität und sozialer Attraktivität, sowie eine biquatratische Beziehung zwischen der Anzahl der Freunde und wahrgenommener Extraversion. Diese Ergebnisse deuten an, dass eine übermäßig hohe Zahl an Freunden Zweifel an der Popularität und Attraktivität des Facebook-Nutzers


Crossbar RRAM Arrays: Selector Device Requirements During Write Operation

A comprehensive analysis of write operations (SET and RESET) in a resistance-change memory (resistive random access memory) crossbar array is carried out. Three types of resistive switching memory cells-nonlinear, rectifying-SET, and rectifying-RESET-are compared with each other in terms of voltage delivery, current delivery, and power consumption. Two different write schemes, V/2 and V/3, were considered, and the V/2 write scheme is preferred due to much lower power consumption. A simple numerical method was developed that simulates entire current flows and node voltages within a crossbar array and provides a quantitative tool for the accurate analysis of crossbar arrays and guidelines for developing reliable write operation.


Segmentation quality evaluation based on multi-scale convolutional neural networks

Segmentation quality evaluation is an important task in image segmentation. The existing evaluation methods formulate segmentation quality as regression model, and recent Convolutional Neural Network (CNN) based evaluation methods show superior performance. However, designing efficient CNN-based segmentation evaluation model is still under exploited. In this paper, we propose two types of CNN structures such as double-net and multi-scale network for segmentation quality evaluation. We observe that learning the local and global information and considering multi-scale image are useful for segmentation quality evaluation. To train and verify the proposed networks, we construct a novel objective segmentation quality evaluation dataset with large amount of data by combining several proposal generation methods. The experimental results demonstrate that the proposed method obtains larger Linear Correlation Coefficient (LCC) value than several state-of-art segmentation quality evaluation methods.


Exploring Hypergraph Representation on Face Anti-spoofing Beyond 2D Attacks

Face anti-spoofing plays a crucial role in protecting face recognition systems from various attacks. Previous modelbased and deep learning approaches achieve satisfactory performance for 2D face spoofs, but remain limited for more advanced 3D attacks such as vivid masks. In this paper, we address 3D face anti-spoofing via the proposed Hypergraph Convolutional Neural Networks (HGCNN). Firstly, we construct a computation-efficient and posture-invariant face representation with only a few key points on hypergraphs. The hypergraph representation is then fed into the designed HGCNN with hypergraph convolution for feature extraction, while the depth auxiliary is also exploited for 3D mask anti-spoofing. Further, we build a 3D face attack database with color, depth and infrared light information to overcome the deficiency of 3D face anti-spoofing data. Experiments show that our method achieves the state-of-theart performance over widely used 3D and 2D databases as well as the proposed one under various tests.


A review of eye-tracking applications as tools for training

Substantial literature exists regarding how eye-tracking systems can be used to measure cognitive load and how these measurements can be useful for adapting training in real time. Much of the published literature discusses the applications and limitations of the research and typically provides recommendations for improvement. This review assesses these articles collectively to provide a clearer solution for implementing eye-tracking systems into a training environment. Although limitations exist for using eye tracking as an interface tool, gaze and pupillary response have been successfully used to reflect changes in cognitive load and are starting to be incorporated into adaptive training systems, although issues are still present with differentiating pupil responses from simultaneous psychological effects. Additionally, current eye-tracking systems and data analysis software have proven accurate enough for general use, but issues including system cost and software integration prevent this technology from becoming commercialized for use in common instructional settings.


Waveguide slot antennas for circularly polarized radiated field

We present a new waveguide slot configuration able to radiate a circular polarization with a very low axial ratio. This configuration is mainly intended as a circularly polarized radiating element to be used in an array. Both left-hand and right-hand circular polarization can be independently obtained and therefore this configuration could be used to realize a polarization agile antenna. The proposed configuration consists of two very closely spaced radiating slots, so a specialized method of moments code has been developed to analyze, in a very effective way, the near-field interaction between such slots. Our method of moments code has been validated against a commercial finite-element method code, and then used to design a number of different circularly polarized radiating elements.


Evaluating the Readability of Text Simplification Output for Readers with Cognitive Disabilities

This paper presents an approach for automatic evaluation of the readability of text simplification output for readers with cognitive disabilities. First, we present our work towards the development of the EasyRead corpus, which contains easy-to-read documents created especially for people with cognitive disabilities. We then compare the EasyRead corpus to the simplified output contained in the LocalNews corpus (Feng, 2009), the accessibility of which has been evaluated through reading comprehension experiments including 20 adults with mild intellectual disability. This comparison is made on the basis of 13 disability-specific linguistic features. The comparison reveals that there are no major differences between the two corpora, which shows that the EasyRead corpus is to a similar reading level as the user-evaluated texts. We also discuss the role of Simple Wikipedia (Zhu et al., 2010) as a widely-used accessibility benchmark, in light of our finding that it is significantly more complex than both the EasyRead and the LocalNews corpora.


Wafer-scale VLSI implementations of pulse coupled neural networks

In this paper, we present a system architecture currently under development that will allow very large (>10 neurons, >10 synapses) reconfigurable networks to be built, in the form of interlinked dies on a single wafer. Reconfigurable routing and complex adaptation/plasticity across several timescales in neurons and synapses allow for the implementation of large-scale biologically realistic neuronal networks and behaviors. Compared to biology the network dynamics will be about 10 to 10 times faster, so that developmental plasticity can be studied in detail. Keywords—neural VLSI hardware, system simulation, mapping algorithms, parallelization.


Escher sphere construction kit

M.C. Escher created a myriad of amazing planar tessellations, yet only a few three-dimensional ones such as his wooden fish ball and dodecahedral flower. We have developed an interactive program to design and manufacture “Escher Spheres” sets of tiles that can be assembled into spherical balls. The user chooses from a set of predefined symmetry groups and then deforms the boundaries of the basic domain tile; all corresponding points based on the chosen symmetry class move concurrently, instantly showing the overall result. The interior of the tile can be embellished with a bas-relief. Finally the tile is radially extruded and output as a solid model suitable for free-form fabrication.


Comparative study of algorithms for Atrial Fibrillation detection

Automatic detection of Atrial Fibrillation (AF) is necessary for the long-term monitoring of patients who are suspected to have AF. Several methods for AF detection exist in the literature. These methods are mainly based on two different characteristics of AF ECGs: the irregularity of RR intervals (RRI) and the fibrillatory electrical Atrial Activity (AA). The electrical AA is characterized by the absence of the P-wave (PWA) and special frequency properties (FSA). Nine AF detection algorithms were selected from literature and evaluated with the same protocol in order to study their performance under different conditions. Results showed that the highest sensitivity (Se=97.64%) and specificity (Sp=96.08%) was achieved with methods based on analysis of irregularity of RR interval, while combining RR and atrial activity analysis gave the highest positive predictive value (PPV=92.75%). Algorithms based on RR irregularity were also the most robust against noise (Se=85.79% and Sp=81.90% for SNR=0dB; and Se=82.52% and Sp=40.47% for SNR=−5dB).


Resource management in a changing and uncertain climate

© The Ecological Society of America www.frontiersinecology.org C change has already had important effects on ecological systems (Parmesan 2006; Root and Schneider 2006; IPCC 2007a; Rosenzweig et al. 2008). Projected changes in climate for the coming century are all greater than the climatic changes the Earth has experienced in the past 100 years (IPCC 2007b). Consequently, future changes in climate are likely to result in even more dramatic ecological responses, including declines in particularly sensitive species (eg corals), continued shifts in species distributions, and substantial changes in ecosystem processes (IPCC 2007a). Changes in hydrologic and fire regimes will fundamentally alter ecological systems. Sea-level rise, in particular, will have dramatic effects on coastal systems (Watson et al. 1996). Changes in phenology will affect the delicate relationships between pollinators and plants, parasites and hosts, foragers and forage, and predators and prey (eg Memmott et al. 2007). Despite the pervasiveness of climate change, most land, water, and resource managers are still following management plans that were developed before there was a scientific consensus that climate-change impacts were both real and substantial (Pyke et al. 2008). Climate change poses difficult challenges for many already overstretched natural resource managers, who must deal with day-to-day crises and who have little access to climate experts. Much of the widely available information about climate change focuses on global or regional scales that are often too broad to fully inform the management of specific nature reserves or regional forests. Moreover, much of the information that is available has a high level of uncertainty, and thus can be difficult to interpret. Finally, although climate change is having impacts today, the largest impacts are still decades in the future. Envisioning these impacts and acting well in advance of their realization will require a new level of proactive management. Several articles have provided general recommendations for managing particular systems in a changing climate (Noss 2001; Hannah et al. 2002; West and Salm REVIEWS REVIEWS REVIEWS


A Smart Fridge with an Ability to Enhance Health and Enable Better Nutrition

Intelligent appliances with multimedia capability have been emerging into our daily life, thanks to the fast advance of computing technology and the wide use of the Interne. Smart home is one of the most prominent areas of intelligent appliances. Kitchen is one of the places where such intelligent appliances have been used. Since modern life style is driving people spending less time on cooking healthy food at home, an enjoyable and healthy life style can be assisted with an intelligent kitchenware such as a smart fridge. In this paper we introduce a novel idea of the smart fridge which can enable better nutrition and enhance health. It is designed for managing items stored in it and advising its users with cooking methods depending on what kind of food is stored. More importantly, it can perform other actions such as dietary control, nutrition monitoring, eating habit analysis, etc. The characteristics, functions and design details of the smart fridge are presented in details. We are confident that such a smart fridge will be an important component in future smart home.


Review: A Review of Culture in Information Systems Research: Toward a Theory of Information Technology Culture Conflict

An understanding of culture is important to the study of information technologies in that culture at various levels, including national, organizational, and group, can influence the successful implementation and use of information technology. Culture also plays a role in managerial processes that may directly, or indirectly, influence IT. Culture is a challenging variable to research, in part because of the multiple divergent definitions and measures of culture. Notwithstanding, a wide body of literature has emerged that sheds light on the relationship of IT and culture. This paper sets out to provide a review of this literature in order to lend insights into our understanding of the linkages between IT and culture. We begin by conceptualizing culture and laying the groundwork for a values-based approach to the examination of IT and culture. Using this approach, we then provide a comprehensive review of the organizational and cross-cultural IT literature that conceptually links these two traditionally separate streams of research. From our analysis, we develop six themes of IT-culture research emphasizing culture's impact on IT, IT's impact on culture, and IT culture. Building upon these themes, we then develop a theory of IT, values, and conflict. Based upon the theory, we develop propositions concerning three types of cultural conflict and the results of these conflicts. Ultimately, the theory suggests that the reconciliation of these conflicts results in a reorientation of values. We conclude with the particular research challenges posed in this line of inquiry.


Multivariate data analysis of NMR data.

Multivariate methods based on principal components (PCA and PLS) have been used to reduce NMR spectral information, to predict NMR parameters of complicated structures, and to relate shift data sets to dependent descriptors of biological significance. Noise reduction and elimination of instrumental artifacts are easily performed on 2D NMR data. Configurational classification of triterpenes and shift predictions in disubstituted benzenes can be obtained using PCA and PLS analysis. Finally, the shift predictions of tripeptides from descriptors of amino acids open the possibility of automatic analysis of multidimensional data of complex structures.


IT Consumerization: When Gadgets Turn Into Enterprise IT Tools


Preserving differential privacy in convolutional deep belief networks

The remarkable development of deep learning in medicine and healthcare domain presents obvious privacy issues, when deep neural networks are built on users’ personal and highly sensitive data, e.g., clinical records, user profiles, biomedical images, etc. However, only a few scientific studies on preserving privacy in deep learning have been conducted. In this paper, we focus on developing a private convolutional deep belief network (pCDBN), which essentially is a convolutional deep belief network (CDBN) under differential privacy. Our main idea of enforcing $$\epsilon $$ ϵ -differential privacy is to leverage the functional mechanism to perturb the energy-based objective functions of traditional CDBNs, rather than their results. One key contribution of this work is that we propose the use of Chebyshev expansion to derive the approximate polynomial representation of objective functions. Our theoretical analysis shows that we can further derive the sensitivity and error bounds of the approximate polynomial representation. As a result, preserving differential privacy in CDBNs is feasible. We applied our model in a health social network, i.e., YesiWell data, and in a handwriting digit dataset, i.e., MNIST data, for human behavior prediction, human behavior classification, and handwriting digit recognition tasks. Theoretical analysis and rigorous experimental evaluations show that the pCDBN is highly effective. It significantly outperforms existing solutions.


Gate sizing: finFETs vs 32nm bulk MOSFETs

FinFET devices promise to replace traditional MOSFETs because of superior ability in controlling leakage and minimizing short channel effects while delivering a strong drive current. We investigate in this paper gate sizing of finFET devices, and we provide a comparison with 32nm bulk CMOS. Wider finFET devices are built utilizing multiple parallel fins between the source and drain. Independent gating of the finFET's double gates allows significant reduction in leakage current. We perform temperature-aware circuit optimization by modeling delay using temperature-dependent parameters, and by imposing constraints that limit the maximum allowable number of parallel fins. We show that finFET circuits are superior in performance and produce less static power when compared to 32nm circuits.


An evolutionary artificial neural networks approach for breast cancer diagnosis

This paper presents an evolutionary artificial neural network (EANN) approach based on the pareto-differential evolution (PDE) algorithm augmented with local search for the prediction of breast cancer. The approach is named memetic pareto artificial neural network (MPANN). Artificial neural networks (ANNs) could be used to improve the work of medical practitioners in the diagnosis of breast cancer. Their abilities to approximate nonlinear functions and capture complex relationships in the data are instrumental abilities which could support the medical domain. We compare our results against an evolutionary programming approach and standard backpropagation (BP), and we show experimentally that MPANN has better generalization and much lower computational cost.


pypet: A Python Toolkit for Data Management of Parameter Explorations

pypet (Python parameter exploration toolkit) is a new multi-platform Python toolkit for managing numerical simulations. Sampling the space of model parameters is a key aspect of simulations and numerical experiments. pypet is designed to allow easy and arbitrary sampling of trajectories through a parameter space beyond simple grid searches. pypet collects and stores both simulation parameters and results in a single HDF5 file. This collective storage allows fast and convenient loading of data for further analyses. pypet provides various additional features such as multiprocessing and parallelization of simulations, dynamic loading of data, integration of git version control, and supervision of experiments via the electronic lab notebook Sumatra. pypet supports a rich set of data formats, including native Python types, Numpy and Scipy data, Pandas DataFrames, and BRIAN(2) quantities. Besides these formats, users can easily extend the toolkit to allow customized data types. pypet is a flexible tool suited for both short Python scripts and large scale projects. pypet's various features, especially the tight link between parameters and results, promote reproducible research in computational neuroscience and simulation-based disciplines.


Foreground object detection from videos containing complex background

This paper proposes a novel method for detection and segmentation of foreground objects from a video which contains both stationary and moving background objects and undergoes both gradual and sudden "once-off" changes. A Bayes decision rule for classification of background and foreground from selected feature vectors is formulated. Under this rule, different types of background objects will be classified from foreground objects by choosing a proper feature vector. The stationary background object is described by the color feature, and the moving background object is represented by the color co-occurrence feature. Foreground objects are extracted by fusing the classification results from both stationary and moving pixels. Learning strategies for the gradual and sudden "once-off" background changes are proposed to adapt to various changes in background through the video. The convergence of the learning process is proved and a formula to select a proper learning rate is also derived. Experiments have shown promising results in extracting foreground objects from many complex backgrounds including wavering tree branches, flickering screens and water surfaces, moving escalators, opening and closing doors, switching lights and shadows of moving objects.


Amorphous silicon thin film transistor circuit integration for organic LED displays on glass and plastic

This paper presents design considerations along with measurement results pertinent to hydrogenated amorphous silicon (a-Si:H) thin film transistor (TFT) drive circuits for active matrix organic light emitting diode (AMOLED) displays. We describe both pixel architectures and TFT circuit topologies that are amenable for vertically integrated, high aperture ratio pixels. Here, the OLED layer is integrated directly above the TFT circuit layer, to provide an active pixel area that is at least 90% of the total pixel area with an aperture ratio that remains virtually independent of scaling. Both voltage-programmed and current-programmed drive circuits are considered. The latter provides compensation for shifts in device characteristics due to metastable shifts in the threshold voltage of the TFT. Various drive circuits on glass and plastic were fabricated and tested. Integration of on-panel gate drivers is also discussed where we present the architecture of an a-Si:H based gate de-multiplexer that is threshold voltage shift invariant. In addition, a programmable current mirror with good linearity and stability is presented. Programmable current sources are an essential requirement in the design of source driver output stages.


Nonreciprocal Phase-Shift Composite Right/Left Handed Microstrip Lines Using Ferrite-Rod-Embedded Substrate

Nonreciprocal phase-shift composite right/left handed transmission lines with small nonreciprocity in the amplitude of the transmission coefficients are proposed. The present structures are composed of a microstrip line periodically loaded with series capacitance and shunt inductance, and a ferrite rectangular rod is embedded in the dielectric substrate just below the center strip. The balanced composite right/left handed transmission line with nonreciprocal phase characteristics is designed. The numerical simulation results validate that the nonreciprocity in magnitude of the transmission coefficients is negligibly small whereas the nonreciprocity in the phase is still kept relatively large for the present configuration.


EXPLORING THE CONSUMERS ’ ACCEPTANCE OF ELECTRONIC RETAILING USING TECHNOLOGY ACCEPTANCE MODEL

Following the approach of Pavlou (2003), we investigate key drivers for consumers̀ electronic retailing (e-tailing) acceptance that are integrated into TAM (technology acceptance model). TAM is one of the better known models for explaining the intention to use technology and it integrates two perceptions: usefulness and ease of use. Th e main purpose of this paper is to explore the relationship between perceived ease of use and perceived usefulness in accepting e-tailing among Croatian consumers. In particular, the focus of the paper is on the areas which infl uenced customer commitment and loyalty in e-tailing. Th e results of the quantitative study among Croatian consumers show that electronic retailing, e.g. purchasing via the Internet, would enhance consumers̀ eff ectiveness in getting product information and their eff ectiveness in purchasing products.


Large-scale learning of word relatedness with constraints

Prior work on computing semantic relatedness of words focused on representing their meaning in isolation, effectively disregarding inter-word affinities. We propose a large-scale data mining approach to learning word-word relatedness, where known pairs of related words impose constraints on the learning process. We learn for each word a low-dimensional representation, which strives to maximize the likelihood of a word given the contexts in which it appears. Our method, called CLEAR, is shown to significantly outperform previously published approaches. The proposed method is based on first principles, and is generic enough to exploit diverse types of text corpora, while having the flexibility to impose constraints on the derived word similarities. We also make publicly available a new labeled dataset for evaluating word relatedness algorithms, which we believe to be the largest such dataset to date.


The Use of Information Retrieval Techniques for Intrusion Detection

Intrusion detection is a broad problem, and we need a greater range of tools than is currently available. In this article, we report a new approach. We have applied information retrieval techniques to index audit trails. These indexes can be extremely e cient at detecting attacks whose signature is an unusual combination of events, and they may consume only a very small additional amount of storage. This approach allows the intrusion detection community to adopt a wide range of techniques developed in applications ranging from library science to web search engines.


Image-based ghostings for single layer occlusions in augmented reality

In augmented reality displays, X-Ray visualization techniques make hidden objects visible through combining the physical view with an artificial rendering of the hidden information. An important step in X-Ray visualization is to decide which parts of the physical scene should be kept and which should be replaced by overlays. The combination should provide users with essential perceptual cues to understand the relationship of depth between hidden information and the physical scene. In this paper we present an approach that addresses this decision in unknown environments by analyzing camera images of the physical scene and using the extracted information for occlusion management. Pixels are grouped into perceptually coherent image regions and a set of parameters is determined for each region. The parameters change the X-Ray visualization for either preserving existing structures or generating synthetic structures. Finally, users can customize the overall opacity of foreground regions to adapt the visualization.


Twin gate rectangular recessed channel (TG-RRC) MOSFET for digital-logic applications

In this paper, twin gate rectangular recessed channel (TG-RRC) MOSFET with independent gate control is used to realize its application in digital electronics by using it as two input logic. The input logic is controlled by the independent gates which have different work functions (Φ1 for gate 1 and Φ2 gate 2) which are separated by oxide layer of 2 nm, thus controlling various electrical parameters such as current density, potential etc. across the channel in a swift manner and also increasing the switching characteristics of the device. It can give the full functionality of “AND” and “NAND” gate. We have shown the structure and functioning of the gates with a simulation on Silvaco TCAD. Since it is one of the fundamental gates has much applicability in digital logics starting from basic Flip-flops to sequential circuits in SRAM or DRAM cells.


Robot path planning : an object-oriented approach

Path planning has important applications in many areas, for example industrial robotics, autonomous systems, virtual prototyping, and computer-aided drug design. This thesis presents a new framework for developing and evaluating path planning algorithms. The framework is named CoPP (Components for Path Planning). It consists of loosely coupled and reusable components that are useful for building path planning applications. The framework is especially designed to make it easy to do fair comparisons between different path planning algorithms. CoPP is also designed to allow almost any user-defined moving system. The default type of moving system is a robot class, which is capable of describing tree-like kinematic chains. Additional features of this robot class are: joint couplings, numerical or closed-form inverse kinematics, and hierarchical robot representations. The last feature is useful when planning for complex systems like a mobile platform equipped with an arm and a hand. During the last six years, Rapidly-exploring Random Trees (RRTs) have become a popular framework for developing randomized path planning algorithms. This thesis presents a method for augmenting bidirectional RRT-planners with local trees. For problems where the solution trajectory has to pass through several narrow passages, local trees help to reduce the required planning time. To reduce the work needed for programming of industrial robots, it is desirable to allow task specifications at a very high level, leaving it up to the robot system to figure out what to do. Here we present a fast and flexible pick-and-place planner. Given an object that has to be moved to another position, the planner chooses a suitable grasp of the object and finds motions that bring the object to the desired position. The planner can also handle constraints on, e.g., the orientation of the manipulated object. For planning of pick-and-place tasks it is necessary to choose a grasp suitable to the task. Unless the grasp is given, some sort of grasp planning has to be performed. This thesis presents a fast grasp planner for a threefingered robot hand. The grasp planner could be used in an industrial setting, where a robot is to pick up irregularly shaped objects from a conveyor belt. In conjunction with grasp planning, a new method for evaluating grasp stability is presented.


A virtual reality based exercise system for hand rehabilitation post-stroke: transfer to function

This paper presents preliminary results from a virtual reality (VR)-based system for hand rehabilitation that uses a CyberGlove and a Rutgers Master II-ND haptic glove. This computerized system trains finger range of motion, finger flexion speed, independence of finger motion, and finger strength using specific VR simulation exercises. A remote Web-based monitoring station was developed to allow telerehabilitation interventions. The remote therapist observes simplified versions of the patient exercises that are updated in real time. Patient data is stored transparently in an Oracle database, which is also Web accessible through a portal GUI. Thus the remote therapist or attending physician can graph exercise outcomes and thus evaluate patient outcomes at a distance. Data from the VR simulations is complemented by clinical measurements of hand function and strength. Eight chronic post-stroke subjects participated in a pilot study of the above system. In keeping with variability in both their lesion size and site and in their initial upper extremity function, each subject showed improvement on a unique combination of movement parameters in VR training. Importantly, these improvements transferred to gains on clinical tests, as well as to significant reductions in task-completion times for the prehension of real objects. These results are indicative of the potential feasibility of this exercise system for rehabilitation in patients with hand dysfunction resulting from neurological impairment.


Research challenges in legal-rule and QoS-aware cloud service brokerage

The ICT industry and specifically critical sectors, such as healthcare, transportation, energy and government, require as mandatory the compliance of ICT systems and services with legislation and regulation, as well as with standards. In the era of cloud computing, this compliance management issue is exacerbated by the distributed nature of the system and by the limited control that customers have on the services. Today, the cloud industry is aware of this problem (as evidenced by the compliance program of many cloud service providers), and the research community is addressing the many facets of the legal-rule compliance checking and quality assurance problem. Cloud service brokerage plays an important role in legislation compliance and QoS management of cloud services. In this paper we discuss our experience in designing a legal-rule and QoS-aware cloud service broker, and we explore relate research issues. Specifically we provide three main contributions to the literature: first, we describe the detailed design architecture of the legal-rule and QoS-aware broker. Second, we discuss our design choices which rely on the state of the art solutions available in literature. We cover four main research areas: cloud broker service deployment, seamless cloud service migration, cloud service monitoring, and legal rule compliance checking. Finally, from the literature review in these research areas, we identify and discuss research challenges.


Arc Flash Calculations for a 1.3-MW Photovoltaic System

When planning for the installation of a 1.3-MW photovoltaic (PV) system and its integration into an existing facility's electrical system, an arc flash study was performed to determine the best protective device settings to minimize the arc flash energy in the PV system and to determine the effect of the additional short-circuit current on arc flash calculations for the existing system. The existing system has distribution at 13.8 kV and two 10-MVA transformers to a 2.4-kV feeder system. The PV system adds the hazard of 500-Vdc subarray collection wiring and terminations. The dc arc flash exposure of PV systems, including those in the inverter enclosures, can be complicated, and this paper outlines the basic data collected for the study, the steps taken to complete the calculations, the methodology used, the results, and the team's learnings.


Joint Optimization of Constellation With Mapping Matrix for SCMA Codebook Design

Sparse code multiple access (SCMA) is being considered as a promising multiple access solution for 5G systems. A distinguishing feature of SCMA is that it combines the procedures of bit to constellation symbol mapping and subsequent spreading using multidimensional codebooks differentiated by users. Such codebooks dominate the system implementation as a main source of not only performance gain but also design complexity. This letter presents a joint constellation with mapping matrix design for SCMA codebooks, which formulates the constellations optimization as a nonconvex quadratically constrained quadratic programming problem based on a set of well-constructed mapping matrices. We elaborately solve the problem to achieve outperformance over existing SCMA design in terms of bit error rate (BER). For improving practicality, an approximate approach is further proposed to reduce the complexity significantly with a limited BER loss.


Exploring the Interconnectedness of Cryptocurrencies using Correlation Networks

Correlation networks were used to detect characteristics which, although fixed over time, have an important influence on the evolution of prices over time. Potentially important features were identified using the websites and whitepapers of cryptocurrencies with the largest userbases. These were assessed using two datasets to enhance robustness: one with fourteen cryptocurrencies beginning from 9 November 2017, and a subset with nine cryptocurrencies starting 9 September 2016, both ending 6 March 2018. Separately analysing the subset of cryptocurrencies raised the number of data points from 115 to 537, and improved robustness to changes in relationships over time. Excluding USD Tether, the results showed a positive association between different cryptocurrencies that was statistically significant. Robust, strong positive associations were observed for six cryptocurrencies where one was a fork of the other; Bitcoin / Bitcoin Cash was an exception. There was evidence for the existence of a group of cryptocurrencies particularly associated with Cardano, and a separate group correlated with Ethereum. The data was not consistent with a token’s functionality or creation mechanism being the dominant determinants of the evolution of prices over time but did suggest that factors other than speculation contributed to the price.


Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality Tightening

We propose a novel training algorithm for reinforcement learning which combines the strength of deep Q-learning with a constrained optimization approach to tighten optimality and encourage faster reward propagation. Our novel technique makes deep reinforcement learning more practical by drastically reducing the training time. We evaluate the performance of our approach on the 49 games of the challenging Arcade Learning Environment, and report significant improvements in both training time and accuracy.


Ako: Decentralised Deep Learning with Partial Gradient Exchange

Distributed systems for the training of deep neural networks (DNNs) with large amounts of data have vastly improved the accuracy of machine learning models for image and speech recognition. DNN systems scale to large cluster deployments by having worker nodes train many model replicas in parallel; to ensure model convergence, parameter servers periodically synchronise the replicas. This raises the challenge of how to split resources between workers and parameter servers so that the cluster CPU and network resources are fully utilised without introducing bottlenecks. In practice, this requires manual tuning for each model configuration or hardware type.
 We describe Ako, a decentralised dataflow-based DNN system without parameter servers that is designed to saturate cluster resources. All nodes execute workers that fully use the CPU resources to update model replicas. To synchronise replicas as often as possible subject to the available network bandwidth, workers exchange partitioned gradient updates directly with each other. The number of partitions is chosen so that the used network bandwidth remains constant, independently of cluster size. Since workers eventually receive all gradient partitions after several rounds, convergence is unaffected. For the ImageNet benchmark on a 64-node cluster, Ako does not require any resource allocation decisions, yet converges faster than deployments with parameter servers.


Positional asphyxia or diabetic ketoacidosis? A case report.

We describe an autopsy case in which a patient with diabetic ketoacidosis (DKA) was found in a head-down position. A female in her late 70s was found dead in her home in a supine position on the kitchen floor. The upper part of her body was hanging down over the edge of the kitchen floor to the backyard through the open window. External examination revealed congestion of the head and upper region of the face and neck. There were numerous petechiae on the superior palpebral conjunctivae and upper part of the oral mucosa. On internal examination, extensive hemorrhages in the subcutaneous fat tissues and muscles were observed at the upper part of the neck, although there were no external injuries on the neck. Histopathological examination revealed that hemorrhages were accompanied with infiltration of polymorphonuclear leukocytes both within and around the hemorrhages on the neck skin. Nodular glomerulosclerosis and many fat droplets in the cytoplasm of proximal tubule cells were found in the kidney. Postmortem blood analysis showed acetone (204.2 μg/ml), HbA1c (10.8%), acetoacetate (<2.0 μmol/l), 3-hydroxybutyrate (11,844 μmol/l), blood urea nitrogen (128.9 mg/dl), and creatinine (3.11 mg/dl). The glucose and acetone levels in the urine were 876.7 mg/dl and 201.4 μg/ml, respectively, suggesting that she suffered severe DKA. However, since hemorrhage of the neck could have developed only when she was still alive, asphyxia should have arisen antemortem. Based on these findings, we concluded that the direct cause of her death is positional asphyxia, which was resulted from DKA. It is difficult to diagnose the cause of death when the victim is in an unusual posture. To confirm a suspicion of positional asphyxia, photographs of the undisturbed scene are useful in addition to a precise autopsy and accurate examinations.


The impact of staff turnover on software projects: the importance of understanding what makes software practitioners tick

In this paper we investigate the impact of staff turnover on software projects. In particular we investigate whether high staff turnover damages project success. We analyse data from an empirical study of 89 software practitioners to show that projects with high staff turnover are less successful. Furthermore our empirical data suggests a relationship between high staff turnover on projects and low staff motivation levels. We discuss factors which have been previously found to improve motivation levels and conclude that improving motivation levels can reduce staff turnover, which in turn increases project success.


Fast Global Registration

We present an algorithm for fast global registration of partially overlapping 3D surfaces. The algorithm operates on candidate matches that cover the surfaces. A single objective is optimized to align the surfaces and disable false matches. The objective is defined densely over the surfaces and the optimization achieves tight alignment with no initialization. No correspondence updates or closest-point queries are performed in the inner loop. An extension of the algorithm can perform joint global registration of many partially overlapping surfaces. Extensive experiments demonstrate that the presented approach matches or exceeds the accuracy of state-of-the-art global registration pipelines, while being at least an order of magnitude faster. Remarkably, the presented approach is also faster than local refinement algorithms such as ICP. It provides the accuracy achieved by well-initialized local refinement algorithms, without requiring an initialization and at lower computational cost.


Surfel-Based Next Best View Planning

Next best view (NBV) planning is a central task for automated three-dimensional (3-D) reconstruction in robotics. The most expensive phase of NBV computation is the view simulation step, where the information gain of a large number of candidate sensor poses are estimated. Usually, information gain is related to the visibility of unknown space from the simulated viewpoint. A well-established technique is to adopt a volumetric representation of the environment and to compute the NBV from ray casting by maximizing the number of unknown visible voxels. This letter explores a novel approach for NBV planning based on surfel representation of the environment. Surfels are oriented surface elements, such as circular disks, without explicit connectivity. A new kind of surfel is introduced to represent the frontier between empty and unknown space. Surfels are extracted during 3-D reconstruction, with minimal overhead, from a KinectFusion volumetric representation. Surfel rendering is used to generate images from each simulated sensor pose. Experiments in a real robot setup are reported. The proposed approach achieves better performance than volumetric algorithms based on ray casting implemented on graphics processing unit (GPU), with comparable results in terms of reconstruction quality. Moreover, surfel-based NBV planning can be applied in larger environments as a volumetric representation is limited by GPU memory.


Green Internet of Things for Smart World

Smart world is envisioned as an era in which objects (e.g., watches, mobile phones, computers, cars, buses, and trains) can automatically and intelligently serve people in a collaborative manner. Paving the way for smart world, Internet of Things (IoT) connects everything in the smart world. Motivated by achieving a sustainable smart world, this paper discusses various technologies and issues regarding green IoT, which further reduces the energy consumption of IoT. Particularly, an overview regarding IoT and green IoT is performed first. Then, the hot green information and communications technologies (ICTs) (e.g., green radio-frequency identification, green wireless sensor network, green cloud computing, green machine to machine, and green data center) enabling green IoT are studied, and general green ICT principles are summarized. Furthermore, the latest developments and future vision about sensor cloud, which is a novel paradigm in green IoT, are reviewed and introduced, respectively. Finally, future research directions and open problems about green IoT are presented. Our work targets to be an enlightening and latest guidance for research with respect to green IoT and smart world.


Learning patterns for discovering domain-oriented opinion words

Sentiment analysis is a challenging task that attracted increasing interest during the last years. The availability of online data along with the business interest to keep up with consumer feedback generates a constant demand for online analysis of user-generated content. A key role to this task plays the utilization of domain-specific lexicons of opinion words that enables algorithms to classify short snippets of text into sentiment classes (positive, negative). This process is known as dictionary-based sentiment analysis. The related work tends to solve this lexicon identification problem by either exploiting a corpus and a thesaurus or by manually defining a set of patterns that will extract opinion words. In this work, we propose an unsupervised approach for discovering patterns that will extract domain-specific dictionary. Our approach (DidaxTo) utilizes opinion modifiers, sentiment consistency theories, polarity assignment graphs and pattern similarity metrics. The outcome is compared against lexicons extracted by the state-of-the-art approaches on a sentiment analysis task. Experiments on user reviews coming from a diverse set of products demonstrate the utility of the proposed method. An implementation of the proposed approach in an easy to use application for extracting opinion words from any domain and evaluate their quality is also presented.


Relativity of remembering: why the laws of memory vanished.

For 120 years, cognitive psychologists have sought general laws of learning and memory. In this review I conclude that none has stood the test of time. No empirical law withstands manipulation across the four sets of factors that Jenkins (1979) identified as critical to memory experiments: types of subjects, kinds of events to be remembered, manipulation of encoding conditions, and variations in test conditions. Another factor affecting many phenomena is whether a manipulation of conditions occurs in randomized, within-subjects designs rather than between-subjects (or within-subject, blocked) designs. The fact that simple laws do not hold reveals the complex, interactive nature of memory phenomena. Nonetheless, the science of memory is robust, with most findings easily replicated under the same conditions as originally used, but when other variables are manipulated, effects may disappear or reverse. These same points are probably true of psychological research in most, if not all, domains.


Decision quality and satisfaction: the effects of online information sources and self-efficacy

Purpose – Digital libraries and social media are two sources of online information with different characteristics. This study integrates self-efficacy into the analysis of the relationship between information sources and decision-making, with the aim of exploring the effect of self-efficacy on decision-making, as well as the interacting effect of self-efficacy and information sources on decision-making. Design/methodology/approach –Survey data were collected and the partial least squares (PLS) structural equation modelling (SEM) was employed to verify the research model. Findings – The effect of digital library usage for acquiring information on perceived decision quality is larger than that of social media usage for acquiring information on perceived decision quality. Self-efficacy in acquiring information stands out as the key determinant for perceived decision quality. The effect of social media usage for acquiring information on perceived decision quality is positively moderated by self-efficacy in acquiring information. Practical implications – Decision-making is a fundamental activity for individuals, but human decision-making is often subject to biases. The findings of this study provide useful insights into decision quality improvement, highlighting the importance of self-efficacy in acquiring information in the face of information overload. Originality/value – This study integrates self-efficacy into the analysis of the relationship between information sources and decision-making, presenting a new perspective for decision-making research and practice alike.


BTS guidelines for the insertion of a chest drain.

D Laws, E Neville, J Duffy, on behalf of the British Thoracic Society Pleural Disease Group, a subgroup of the British Thoracic Society Standards of Care Committee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .


LOCOMO satcom terminal: A switchable RHCP/LHCP Array Antenna for on-the-move applications in Ka-band

This paper describes the main features of a multilayer antenna panel for a low cost Ka-band Array Antenna for on-the-move applications. The LOCOMO satcom terminal is based on a dual-polarized low-profile antenna with trasmit/receive and RHCP/LHCP switching capability.


Multi-labeled Relation Extraction with Attentive Capsule Network

To disclose overlapped multiple relations from a sentence still keeps challenging. Most current works in terms of neural models inconveniently assuming that each sentence is explicitly mapped to a relation label, cannot handle multiple relations properly as the overlapped features of the relations are either ignored or very difficult to identify. To tackle with the new issue, we propose a novel approach for multi-labeled relation extraction with capsule network which acts considerably better than current convolutional or recurrent net in identifying the highly overlapped relations within an individual sentence. To better cluster the features and precisely extract the relations, we further devise attention-based routing algorithm and sliding-margin loss function, and embed them into our capsule network. The experimental results show that the proposed approach can indeed extract the highly overlapped features and achieve significant performance improvement for relation extraction comparing to the state-of-the-art works. Introduction Relation extraction plays a crucial role in many natural language processing (NLP) tasks. It aims to identify relation facts for pairs of entities in a sentence to construct triples like [Arthur Lee, place born, Memphis]. Relation extraction has received renewed interest in the neural network era, when neural models are effective to extract semantic meanings of relations. Compared with traditional approaches which focus on manually designed features, neural methods such as Convolutional Neural Network (CNN) (Liu et al. 2013; Zeng et al. 2014) and Recurrent Neural Network (RNN) (Zhang and Wang 2015; Zhou et al. 2016) have achieved significant improvement in relation classification. However, previous neural models are unlikely to scale in the scenario where a sentence has multiple relation labels and face the challenges in extracting highly overlapped and discrete relation features due to the following two drawbacks. First, one entity pair can express multiple relations in a sentence, which will confuse relation extractor seriously. For example, as in Figure 1, the entity pair [Arthur Lee, Memphis] keeps three possible relations which are place birth, ∗Corresponding authors: Weijia Jia, Hai Zhao, {jia-wj, zhaohai}@cs.sjtu.edu.cn Copyright c © 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. place death and place lived. The sentence S1 and S2 can both express two relations, and the sentence S3 represents another two relations. These sentences contain multiple kinds of relation features which are difficult to be identified clearly. The existing neural models tendentiously merge low-level semantic meanings to one high-level relation representation vector with methods such as max-pooling (Zeng et al. 2014; Zhang, Zhao, and Qin 2016) and word-level attention (Zhou et al. 2016). However, one high-level relation vector is still insufficient to express multiple relations precisely. Second, current methods are neglecting of the discretization of relation features. For instance, as shown in Figure 1, all the sentences express their relations with a few significant words (labeled italic in the figure) distributed discretely in the sentences. However, common neural methods handle sentences with fixed structures, which are difficult to gather relation features of different positions. For example, being spatially sensitive, CNNs adopt convolutional feature detectors to extract local patterns from a sliding window of vector sequences and use the max-pooling to select the prominent ones. Besides, the feature distribution of “no relation (NA, others)” in a dataset is different from that of definite relations. A sentence can be classified to “no relation” only when it does not contain any features of other relations. In this paper, to extract overlapped and discrete relation features, we propose a novel approach for multi-labeled relation extraction with an attentive capsule network. As shown in Figure 1, the relation extractor of the proposed method is constructed with three major layers that are feature extracting, feature clustering and relation predicting. The first one extracts low-level semantic meanings. The second layer clusters low-level features to high-level relation representations, and the final one predicts relation types for each relation representation. The low-level features are extracted with traditional neural models such as Bidirectional Long ShortTerm Memory (Bi-LSTM) and CNN. For the feature clustering layer, we utilize an attentive capsule network inspired by Sabour, Frosst, and Hinton (2017). Capsule (vector) is a small group of neurons used to express features. Its overall length indicates the significance of features, and the direction of a capsule suggests the specific property of the feature. The low-level semantic meanings from the first layer are embedded to amounts of low-level capsules, which will ar X iv :1 81 1. 04 35 4v 1 [ cs .C L ] 1 1 N ov 2 01 8 ID Instances Relations S1 [Arthur Lee], the leader of Love, died on Thursday in [Memphis]. person/place_death


A design for an electronically-steerable holographic antenna with polarization control

We present a design for an electronic-steerable holographic antenna with polarization control composed of a radial array of Ku-band, electronically-steerable, surface-wave waveguide (SWG) artificial-impedance-surface antennas (AISA). The antenna operates by launching surface waves into each of the SWGs via a central feed network. The surface-wave impedance is electronically controlled with varactor-tuned impedance patches. The impedance is adjusted to scan the antenna in elevation, azimuth and polarization. The radial symmetry allows for 360° azimuthal steering. If constructed with previously-demonstrated SWG AISAs, it is capable of scanning in elevation from -75° to 75° with gain variation of less than 3 dB. The polarization can be switched among V-Pol, H-Pol, LHCP and RHCP at will.


Progressive Visual Analytics: User-Driven Visual Exploration of In-Progress Analytics

As datasets grow and analytic algorithms become more complex, the typical workflow of analysts launching an analytic, waiting for it to complete, inspecting the results, and then re-Iaunching the computation with adjusted parameters is not realistic for many real-world tasks. This paper presents an alternative workflow, progressive visual analytics, which enables an analyst to inspect partial results of an algorithm as they become available and interact with the algorithm to prioritize subspaces of interest. Progressive visual analytics depends on adapting analytical algorithms to produce meaningful partial results and enable analyst intervention without sacrificing computational speed. The paradigm also depends on adapting information visualization techniques to incorporate the constantly refining results without overwhelming analysts and provide interactions to support an analyst directing the analytic. The contributions of this paper include: a description of the progressive visual analytics paradigm; design goals for both the algorithms and visualizations in progressive visual analytics systems; an example progressive visual analytics system (Progressive Insights) for analyzing common patterns in a collection of event sequences; and an evaluation of Progressive Insights and the progressive visual analytics paradigm by clinical researchers analyzing electronic medical records.


ℓ<ce:inf loc=post>1</ce:inf>-penalized linear mixed-effects models for high dimensional data with application to BCI

Recently, a novel statistical model has been proposed to estimate population effects and individual variability between subgroups simultaneously, by extending Lasso methods. We will for the first time apply this so-called ℓ(1)-penalized linear regression mixed-effects model for a large scale real world problem: we study a large set of brain computer interface data and through the novel estimator are able to obtain a subject-independent classifier that compares favorably with prior zero-training algorithms. This unifying model inherently compensates shifts in the input space attributed to the individuality of a subject. In particular we are now for the first time able to differentiate within-subject and between-subject variability. Thus a deeper understanding both of the underlying statistical and physiological structures of the data is gained.


BotCop: An Online Botnet Traffic Classifier

A botnet is a network of compromised computers infected with malicious code that can be controlled remotely under a common command and control (C&C) channel. As one the most serious security threats to the Internet, a botnet cannot only be implemented with existing network applications (e.g. IRC, HTTP, or Peer-to-Peer) but also can be constructed by unknown or creative applications, thus making the botnet detection a challenging problem. In this paper, we propose a new online botnet traffic classification system, called BotCop, in which the network traffic are fully classified into different application communities by using payload signatures and a novel decision tree model, and then on each obtained application community, the temporal-frequent characteristic of flows is studied and analyzed to differentiate the malicious communication traffic created by bots from normal traffic generated by human beings. We evaluate our approach with about 30 million flows collected over one day on a large-scale WiFi ISP network and results show that the proposed approach successfully detects an IRC botnet from about 30 million flows with a high detection rate and a low false alarm rate.


On the compression of recurrent neural networks with an application to LVCSR acoustic modeling for embedded speech recognition

We study the problem of compressing recurrent neural networks (RNNs). In particular, we focus on the compression of RNN acoustic models, which are motivated by the goal of building compact and accurate speech recognition systems which can be run efficiently on mobile devices. In this work, we present a technique for general recurrent model compression that jointly compresses both recurrent and non-recurrent inter-layer weight matrices. We find that the proposed technique allows us to reduce the size of our Long Short-Term Memory (LSTM) acoustic model to a third of its original size with negligible loss in accuracy.


Efficient Object Instance Search Using Fuzzy Objects Matching

Recently, global features aggregated from local convolutional features of the convolutional neural network have shown to be much more effective in comparison with hand-crafted features for image retrieval. However, the global feature might not effectively capture the relevance between the query object and reference images in the object instance search task, especially when the query object is relatively small and there exist multiple types of objects in reference images. Moreover, the object instance search requires to localize the object in the reference image, which may not be achieved through global representations. In this paper, we propose a Fuzzy Objects Matching (FOM) framework to effectively and efficiently capture the relevance between the query object and reference images in the dataset. In the proposed FOM scheme, object proposals are utilized to detect the potential regions of the query object in reference images. To achieve high search efficiency, we factorize the feature matrix of all the object proposals from one reference image into the product of a set of fuzzy objects and sparse codes. In addition, we refine the feature of the generated fuzzy objects according to its neighborhood in the feature space to generate more robust representation. The experimental results demonstrate that the proposed FOM framework significantly outperforms the state-of-theart methods in precision with less memory and computational cost on three public datasets. The task of object instance search, is to retrieve all the images containing a specific object query and localize the query object in the reference images. It has received a sustained attention over the last decade, leading to many object instance search systems (Meng et al. 2010; Jiang, Meng, and Yuan 2012; Jiang et al. 2015; Tolias, Avrithis, and Jégou 2013; Tao et al. 2014; Razavian et al. 2014a; 2014b; Tolias, Sicre, and Jégou 2016; Meng et al. 2016; Bhattacharjee et al. 2016b; 2016a; Mohedano et al. 2016; Cao et al. 2016; Wu et al. 2016). Since the query object only occupies a small part of an image, the global representation may not be effective to capture the relevance between the query object with reference image. Therefore, the relevance between the query object and one reference image is not determined by the overall similarity between the query and the reference image. Copyright c © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. ...


Parsing English into Abstract Meaning Representation Using Syntax-Based Machine Translation

We present a parser for Abstract Meaning Representation (AMR). We treat Englishto-AMR conversion within the framework of string-to-tree, syntax-based machine translation (SBMT). To make this work, we transform the AMR structure into a form suitable for the mechanics of SBMT and useful for modeling. We introduce an AMR-specific language model and add data and features drawn from semantic resources. Our resulting AMR parser significantly improves upon state-of-the-art results.


An image encryption scheme based on elliptic curve pseudo random and Advanced Encryption System

Elliptic curve cryptography (ECC) has proven to be an effective cryptography. ECC has its own advantages such as efficient key size compared to other public key infrastructures. This paper exploits the Elliptic curve random generator defined by National Institute of Standards and Technology (NIST) to generate a sequence of arbitrary numbers based on curves. The random generation phase is based on public shared key and a changing point G, which is a generator of a curve to obtain random sequences. Then, Advanced Encryption System is applied to these sequences acquiring arbitrary keys for encrypting image. Using AES alongside well distributed randoms provides a prominent encryption technique. Our experiments show that the proposed method fulfills the basics of cryptography including simpleness and correctness. Moreover, the results of the evaluation prove the effectiveness and security of the proposed method.


Black Box Testing: Techniques for Functional Testing of Software and Systems

Editor: Peter Anderson Rochester Institute of Technology 1 Lomb Memorial Dr. Rochester, NY 14623 pga@cs.rit.edu Each technique receives similar coverage, including a description of its new and prerequisite vocabulary, its model, the bug assumptlons that underlie its effectiveness, typical applications, examples (almost all based on standard IRS tax forms), caveats and limitations, automation and tools for the technique, a self-evaluation quiz, and exercises. Common threads in the presentation show the role


Introduction to Neurolinguistics

attitude is lacking in patients with anomia (inability to name), since they cannot categorize, and in patients with agrammatism (difficulties using grammatical morphemes and function words), since they cannot use elements that “have no meaning in isolation.” In his description of abstract attitude, Goldstein mixes psychological factors, such as the ability to separate figure and ground, with physiological factors, like spreading and activation (i.e., the fact that stimuli can activate an abnormally large area in the cortex if thresholds of activation for neurons are changed by brain damage, which makes the patient unable to detect, for example, exactly where somebody is touching his skin). An abstract attitude is required for the fully efficient use of language and therefore the linguistic disorder arises secondarily. Words lose their function to organize the external world. Goldstein assumed different positions with respect to localization, but he was mainly an anti-localist. He claimed that the cortex could be separated into a “peripheral part” with structural localization and a “central part” which is equipotential and constructs “dynamic structures” against a “dynamic background.” Lesions in the central part cause a loss of abstract attitude. It was Karl Lashley (1933, 1950), a physiologist, who presented the theory of cerebral equipotentiality and mass effect. Equipotentiality means that all parts of the cortex have the same “ability” and mass effect means that functions are dependent on how much of the cortex is used. Lashley claimed that studies of humans were too unreliable, due to humans’ complex behavior, and therefore concentrated on experiments with rats in mazes. He accepted specified functions only for motor and sensory projection areas and objected to attempts to localize mental functions. He emphasized that psychological unity does not guarantee neurological unity; for example, many structures participate in intentional movements. Lashley tried to identify different integrative mechanisms, such as intensity, spatial orientation and serial ordering. Inspired by Marie, Bay (1962) suggested that speech is dependent on a large number of “extralinguistic” factors, which are disturbed by brain damage. In aphasia, the transmission of information is disturbed. This disorder always involves a reduced intellect. The aphasic patient’s concepts become pathologically distorted, which can be seen in tasks like drawing or sculpting. The same defect is noticed in speech: concepts become fuzzy, deformed, and poorly differentiated. Jason Brown’s theory of aphasia is inspired by Jackson, von Monakow, and Freud. It contains, among other things, evolutionary aspects and a regression hypothesis combined with a functional view of aphasia. He raises the connections between emotions, degree of consciousness and intentionality, creativity, and language. Brown’s theory is presented in several books, including Mind, brain and consciousness from 1977. (See also Chapter 10.) Chapter 2. The development of theories about brain and language 27 Localism and associationism Localism continued to survive but it did not attract much attention during the first half of the 20th century. Some examples are mentioned below. Swedish neurologist Salomon Henschen (1926) formulated a detailed model on the basis of all the clinical pathological cases of aphasia that had been described to that point. The model contained a large number of separate centers for different aspects of language function. Korbinian Brodmann (1909), another neurologist, divided the cortex into numerous histologically defined areas, each of which had a determined physiological function. His numbering system is still used to give exact references to areas of the brain (see Figure 14.6 in Chapter 14). Karl Kleist (1916) investigated war injuries from the First World War and tried to find a psychological explanation for each cytoand myeloarchitectonic (related to cellular patterns and myelinated nerve cell patterns, respectively) area. Speech and language were localized according to tradition. Johannes Nielsen (1947) devoted himself to the anatomical-physiological classification of cortical functions. He distinguished between receptive and afferent signals, and between primary identification through one sense and secondary identification via associations of different senses at a higher level. According to Nielsen, recognition means receiving impressions and comparing them to engrams (physical alterations of nerve tissue in response to stimuli). Remembering, on the other hand, is starting from the concept and identifying it with an engram, which takes place at a higher level. Two types of aphasia can then occur: (a) a type that changes the connection between language and recognition, and (b) a type that changes the “signification” (what Head called “semantic aphasia”). The latter is not localized in specific areas; rather, any lesion can affect the whole system. The destruction of a projection area causes a total loss of function, while the destruction of an adjacent zone causes agnosia (inability to recognize) and the destruction of association zones that are not localized results in memory disorders. Thus, this theory is strongly inspired by associationism. Associationism is rediscovered by Geschwind Associationism continues to be very influential today. It was Norman Geschwind who rediscovered associationism and made it known as “connectionism,” or the neoclassical school of neurolinguistics, in his 1965 book Disconnection syndromes in animals and man. (For more information, see Chapter 3.) Introduction to Neurolinguistics 28 Dynamic localization of function: Pavlov, Vygotsky, and Luria Dynamic localization of function is a tradition originating in Russia which developed during the 20th century in parallel with the other traditions. Today, it also has considerable influence in Western Europe and the U.S. The well-known physiologist Ivan Pavlov (1949) claimed that complex behavior could not be handled by isolated and fixed brain structures, but must emerge during the ontogenetic development of dynamic systems. These systems were said to consist of connected structures in different places within the nervous system. Psychologist Lev Vygotsky emphasized that it is necessary to first investigate what is to be localized, before asking the question where. Functions must be analyzed with respect to ontogenetic development. Vygotsky (1970) saw a function as a complex activity whereby the whole organism adapts to a task. The activity can be performed in different ways, because of the cooperation of several organs. This dynamic cooperation is controlled by neural structures that monitor different organs and are localized in different places. Vygotsky also formulated a theory about the development of language and thinking that became very influential. The noted psychologist and aphasiologist Alexander Luria studied aphasia within the framework of the theory of dynamic localization of function. Luria’s work has been extremely influential in aphasia research, and especially in clinical work. (It will be further discussed in Chapter 3.) The test psychological tradition Theodore Weisenburg and Katherine McBride investigated cognitive factors in persons with aphasia, persons with right-hemisphere lesions, and control subjects, using a large test battery of verbal and nonverbal tasks. Their methodology became the model for a large number of studies. The results showed that persons with aphasia varied a great deal cognitively. In general, aphasia was associated with a cognitive disorder, but there was no necessary correlation. Certain persons with severe aphasia had excellent results on nonverbal tests. Thus, there was no support for Head’s theories. Weisenburg and McBride claimed that the importance of language for thinking must differ in different individuals and that the intellect before the lesion must be determinant. Many psychologists were inspired by Weisenburg and McBride and by Goldstein and developed experiments for testing “symbolic thinking” in aphasics. Subjects with aphasia performed classification tasks, Raven’s matrices (analogy tests), and similar tests. The results most often showed considerable variability, and the general conclusion was that the cognitive disorder, where such a disorder existed, could not be attributed to the linguistic disorder. Nevertheless, certain types of cognitive disorders were often found in persons with aphasia, such as difficulties in discriminating faces and Chapter 2. The development of theories about brain and language 29 interpreting mimed actions, as well as construction apraxia (i.e., problems performing spatial construction tasks). Linguists and the linguistic influence on aphasiology Aphasia research was traditionally an area for neurologists, but philosophers and psychologists have also had considerable influence on the area. In the middle of the 20th century, linguists joined this group. They saw studies of aphasia as a means of testing general theories about language. By functionally analyzing different disorders using the descriptive methods of linguistics, they aimed to gain better understanding of the role of the brain in normal speech. The term neurolinguistics was eventually adopted for these studies. In 1941, Roman Jakobson published the book Kindersprache, Aphasie und allgemeine Lautgesetze (“Child language, aphasia and phonological universals”), in which he pointed out parallels between linguistic universals, children’s language development, and symptoms of aphasia. At this point, he was the only linguist to systematically describe aphasia within the structuralist framework. Jakobson applied Ferdinand de Saussure’s principle of paradigmatic and syntagmatic relations in language. If language consisted of two types of operations — choice of units and combination of units — then two types of aphasia should be possible: (a) a


Análisis de la Simplificación de Expresiones Numéricas en Español mediante un Estudio Empírico

In this paper we present the results of an empirical study carried out on a parallel corpus of original and manually simplified texts in Spanish and a subsequent survey, with the aim of targeting simplification operations concerning numerical expressions. For the purpose of the study, a “numerical expression” is understood as any phrase expressing quantity possibly modified with a numerical hedge, such as almost a quarter. Data is analyzed both in context and in isolation, and attention is paid to the difference the target reader makes to simplification. Our future work aims at computational implementation of the transformation rules extracted so far.


Sistema de Conversión Texto a Voz de Código Abierto Para Lenguas Ibéricas

This paper presents a text-to-speech system based on statistical synthesis which, for the first time, allows generating speech in any of the four official languages of Spain as well as English. Using the AhoTTS system already developed for Spanish and Basque as a starting point, we have added support for Catalan, Galician and English using the code of available open-source modules. The resulting system, named multilingual AhoTTS, has also been released as open-source and it is already being used in real applications.


The RB and p 53 pathways in cancer

The life history of cancer cells encompasses a series of genetic missteps in which normal cells are progressively transformed into tumor cells that invade surrounding tissues and become malignant. Most prominent among the regulators disrupted in cancer cells are two tumor suppressors, the retinoblastoma protein (RB) and the p53 transcription factor. Here, we discuss interconnecting signaling pathways controlled by RB and p53, attempting to explain their potentially universal involvement in the etiology of cancer. Pinpointing the various ways by which the functions of RB and p53 are subverted in individual tumors should provide a rational basis for developing more refined tumor-specific therapies.


Creativity, the Turing Test, and the (Better) Lovelace Test

The Turing Test (TT) is claimed by many to be a way to test for the presence, in computers, of such ``deep'' phenomena as thought and consciousness. Unfortunately, attempts to build computational systems able to pass TT (or at least restricted versions of this test) have devolved into shallow symbol manipulation designed to, by hook or by crook, trick. The human creators of such systems know all too well that they have merely tried to fool those people who interact with their systems into believing that these systems really have minds. And the problem is fundamental: the structure of the TT is such as to cultivate tricksters. A better test is one that insists on a certain restrictive epistemic relation between an artificial agent (or system) A, its output o, and the human architect H of A – a relation which, roughly speaking, obtains when H cannot account for how A produced o. We call this test the ``Lovelace Test'' in honor of Lady Lovelace, who believed that only when computers originate things should they be believed to have minds.


Social network analysis: a powerful strategy, also for the information sciences

Social network analysis (SNA) is not a formal theory in sociology but rather a strategy for investigating social structures. As it is an idea that can be applied in many fields, we study, in particular, its influence in the information sciences. Information scientists study publication, citation and co-citation networks, collaboration structures and other forms of social interaction networks. Moreover, the Internet represents a social network of an unprecedented scale. We are convinced that in all these studies social network analysis can successfully be applied. SNA is further related to recent theories concerning the free market economy, geography and transport networks. The growth of SNA is documented and a co-author network of SNA is drawn. Centrality measures of this network are calculated. * To whom all correspondence should be addressed.


Classifying the Semantic Relations in Noun Compounds via a Domain-Specific Lexical Hierarchy

We are developing corpus-based techniques for identifying semantic relations at an intermediate level of description (more specific than those used in case frames, but more general than those used in traditional knowledge representation systems). In this paper we describe a classification algorithm for identifying relationships between two-word noun compounds. We find that a very simple approach using a machine learning algorithm and a domain-specific lexical hierarchy successfully generalizes from training instances, performing better on previously unseen words than a baseline consisting of training on the words themselves.