title: Graph databases: A survey

abstract: In the era of big data, data analytics, business intelligence database management plays a vital role from technical business management and research point of view. Over many decades, database management has been a topic of active research. There are different type of database management system have been proposed over a period of time but Relational Database Management System (RDBMS) is the one which has been most popularly used in academic research as well as industrial setup[1]. In recent years, graph databases regained interest among the researchers for certain obvious reasons. One of the most important reasons for such an interest in a graph database is because of the inherent property of graphs as a graph structure. Graphs are present everywhere in the data structure, which represents the strong connectivity within the data. Most of the graph database models are defined in which data-structure for schema and instances are modeled as graph or generalization of a graph. In such graph database models, data manipulations are expressed by graph-oriented operations and type constructors [9]. Now days, most of the real world applications can be modeled as a graph and one of the best real world examples is social or biological network. This paper gives an overview of the different type of graph databases, applications, and comparison between their models based on some properties.


title: Stable Supersaturated Aqueous Solutions of Silatecan 7-t-Butyldimethylsilyl-10-Hydroxycamptothecin via Chemical Conversion in the Presence of a Chemically Modified β-Cyclodextrin

abstract: Purpose. A method for obtaining clear supersaturated aqueous solutions for parenteral administration of the poorly soluble experimental anti-cancer drug silatecan 7-t-butyldimethylsilyl-10-hydroxycamptothecin (DB-67) has been developed.


title: 1. Introduction Beyond the Manin obstruction

abstract: We construct a (smooth, projective) surface over the field of rational numbers, which is a counterexample to the Hasse principle not accounted for by the Manin obstruction. The construction relies on the classical 4-descent on elliptic curves. By combining the Manin obstruction with descent we define a refinement of the Manin obstruction which for surfaces of the type considered suffices to explain the absence of a rational point.


title: Human Activity Recognition Based on Silhouette Directionality

abstract: Recent advances in computer vision and pattern recognition have fueled numerous initiatives that aim to intelligently recognize human activities. In this paper, we propose an algorithm for nonintrusive human activity recognition. We use an adaptive background-foreground separation technique to extract motion information and generate silhouettes (foreground) from the input videos. We then derive directionality-based feature vectors (directional vectors) from the silhouette contours and use the distinct data distribution of directional vectors in a vector space for clustering and recognition. We also exploit the dynamic characteristic of human motion in order to smooth decisions over time and reduce errors in activity recognition. Our approach is monocular, tolerant to moderate view changes, and can be applied to both frontal and lateral views of most activities. Experiments with short and long video sequences show robust recognition under conditions of varying view angles, zoom depths, backgrounds, and frame rates.


title: Action selection for hammer shots in curling

abstract: Curling is an adversarial two-player game with a continuous state and action space, and stochastic transitions. This paper focuses on one aspect of the full game, namely, finding the optimal "hammer shot", which is the last action taken before a score is tallied. We survey existing methods for finding an optimal action in a continuous, low-dimensional space with stochastic outcomes, and adapt a method based on Delaunay triangulation to our application. Experiments using our curling physics simulator show that the adapted Delaunay triangulation's shot selection outperforms other algorithms, and with some caveats, exceeds Olympic-level human performance.


title: A Sequential Neural Encoder With Latent Structured Description for Modeling Sentences

abstract: In this paper, we propose a sequential neural encoder with latent structured description (SNELSD) for modeling sentences. This model introduces latent chunk-level representations into conventional sequential neural encoders, i.e., recurrent neural networks with long short-term memory (LSTM) units, to consider the compositionality of languages in semantic modeling. An SNELSD model has a hierarchical structure that includes a detection layer and a description layer. The detection layer predicts the boundaries of latent word chunks in an input sentence and derives a chunk-level vector for each word. The description layer utilizes modified LSTM units to process these chunk-level vectors in a recurrent manner and produces sequential encoding outputs. These output vectors are further concatenated with word vectors or the outputs of a chain LSTM encoder to obtain the final sentence representation. All the model parameters are learned in an end-to-end manner without a dependency on additional text chunking or syntax parsing. A natural language inference task and a sentiment analysis task are adopted to evaluate the performance of our proposed model. The experimental results demonstrate the effectiveness of the proposed SNELSD model on exploring task-dependent chunking patterns during the semantic modeling of sentences. Furthermore, the proposed method achieves better performance than conventional chain LSTMs and tree-structured LSTMs on both tasks.


title: Capacity maximizing MMSE-optimal pilots for wireless OFDM over frequency-selective block Rayleigh-fading channels

abstract: The location, number, and power of pilot symbols embedded in multicarrier block transmissions over rapidly fading channels, are important design parameters affecting not only channel estimation performance, but also channel capacity. Considering orthogonal frequency-division multiplexing (OFDM) systems with decoupled information-bearing symbols from pilot symbols transmitted over wireless frequency-selective Rayleigh-fading channels, we show that equispaced and equipowered pilot symbols are optimal in terms of minimizing the mean-square channel estimation error. We also design the number of pilots, and the power distributed between information bearing and pilot symbols, using as criterion a lower bound on the average capacity. Numerical results corroborate our theoretical findings.


title: The transsexual brain – A review of findings on the neural basis of transsexualism

abstract: Transsexualism describes the condition when a person's psychological gender differs from his or her biological sex and is commonly thought to arise from a discrepant cerebral and genital sexual differentiation. This review intends to give an extensive overview of structural and functional neurobiological correlates of transsexualism and their course under cross-sex hormonal treatment. Research in this field enables insight into the stability or variability of gender differences and their relation to hormonal status. For a number of sexually dimorphic brain structures or processes, signs of feminisation or masculinisation are observable in transsexual individuals, which, during hormonal treatment, partly seem to further adjust to characteristics of the desired sex. Still, it appears the data are quite inhomogeneous, mostly not replicated and in many cases available for male-to-female transsexuals only. As the prevalence of homosexuality is markedly higher among transsexuals than among the general population, disentangling correlates of sexual orientation and gender identity is a major problem. To resolve such deficiencies, the implementation of specific research standards is proposed.


title: A reference gene catalogue of the pig gut microbiome

abstract: 7.7 million non-redundant genes have been documented in the pig gut microbiome gene catalogue, revealing a 96% similarity in functional pathways to the human catalogue and influences from sex, age, host genetics and antibiotic treatments.


title: Hepatitis B and D viruses exploit sodium taurocholate co-transporting polypeptide for species-specific entry into hepatocytes.

abstract: BACKGROUND & AIMS ::: Hepatitis B and D viruses (HBV and HDV) are human pathogens with restricted host ranges and high selectivity for hepatocytes; the HBV L-envelope protein interacts specifically with a receptor on these cells. We aimed to identify this receptor and analyze whether it is the recently described sodium-taurocholate co-transporter polypeptide (NTCP), encoded by the SLC10A1 gene. ::: ::: ::: METHODS ::: To identify receptor candidates, we compared gene expression patterns between differentiated HepaRG cells, which express the receptor, and naïve cells, which do not. Receptor candidates were evaluated by small hairpin RNA silencing in HepaRG cells; the ability of receptor expression to confer binding and infection were tested in transduced hepatoma cell lines. We used interspecies domain swapping to identify motifs for receptor-mediated host discrimination of HBV and HDV binding and infection. ::: ::: ::: RESULTS ::: Bioinformatic analyses of comparative expression arrays confirmed that NTCP, which was previously identified through a biochemical approach is a bona fide receptor for HBV and HDV. NTCPs from rat, mouse, and human bound Myrcludex B, a peptide ligand derived from the HBV L-protein. Myrcludex B blocked NTCP transport of bile salts; small hairpin RNA-mediated knockdown of NTCP in HepaRG cells prevented their infection by HBV or HDV. Expression of human but not mouse NTCP in HepG2 and HuH7 cells conferred a limited cell-type-related and virus-dependent susceptibility to infection; these limitations were overcome when cells were cultured with dimethyl sulfoxide. We identified 2 short-sequence motifs in human NTCP that were required for species-specific binding and infection by HBV and HDV. ::: ::: ::: CONCLUSIONS ::: Human NTCP is a specific receptor for HBV and HDV. NTCP-expressing cell lines can be efficiently infected with these viruses, and might be used in basic research and high-throughput screening studies. Mapping of motifs in NTCPs have increased our understanding of the species specificities of HBV and HDV, and could lead to small animal models for studies of viral infection and replication.


Gated Self-Matching Networks for Reading Comprehension and Question Answering


FACE RECOGNITION USING SOFT COMPUTING TOOLS : A SURVEY


title: Three phase grid interfaced solar water pumping system

abstract: This paper presents a new topology for a three phase grid interfaced solar water pumping system. An induction motor driven pump is fed from the solar PV array and a three phase grid using a Vienna converter. The novelty of the proposed system lies in the operation of the pump at rated water discharge when it is operated with the support of the grid. The Vienna converter serves the purpose of power flow from the grid. Moreover, it also improves the power quality at the AC mains. The proposed system is well suited for the areas where the grid support is available. The induction motor is controlled using the open loop scalar (volts/Hertz) control on account of its simple implementation with reduced sensors. Based on the availability of the power source, the modes of operation are identified and performance of the system at starting, in steady state and dynamic conditions are simulated. Simulated results show the satisfactory performance of the proposed system.


title: Structured embedding via pairwise relations and long-range interactions in knowledge base

abstract: We consider the problem of embedding entities and relations of knowledge bases into low-dimensional continuous vector spaces (distributed representations). Unlike most existing approaches, which are primarily efficient for modelling pairwise relations between entities, we attempt to explicitly model both pairwise relations and long-range interactions between entities, by interpreting them as linear operators on the low-dimensional embeddings of the entities. Therefore, in this paper we introduces path ranking to capture the long-range interactions of knowledge graph and at the same time preserve the pairwise relations of knowledge graph; we call it structured embedding via pairwise relation and long-range interactions (referred to as SePLi). Comparing with the-state-of-the-art models, SePLi achieves better performances of embeddings.


title: Speech Recognition: Keyword Spotting Through Image Recognition

abstract: The problem of identifying voice commands has always been a challenge due to the presence of noise and variability in speed, pitch, etc. We will compare the efficacies of several neural network architectures for the speech recognition problem. In particular, we will build a model to determine whether a one second audio clip contains a particular word (out of a set of 10), an unknown word, or silence. The models to be implemented are a CNN recommended by the Tensorflow Speech Recognition tutorial, a low-latency CNN, and an adversarially trained CNN. The result is a demonstration of how to convert a problem in audio recognition to the better-studied domain of image classification, where the powerful techniques of convolutional neural networks are fully developed. Additionally, we demonstrate the applicability of the technique of Virtual Adversarial Training (VAT) to this problem domain, functioning as a powerful regularizer with promising potential future applications.


title: Robot Vision: Obstacle-Avoidance Techniques for Unmanned Aerial Vehicles

abstract: In this article, a vision-based technique for obstacle avoidance and target identification is combined with haptic feedback to develop a new teleoperated navigation system for underactuated aerial vehicles in unknown environments. A three-dimensional (3-D) map of the surrounding environment is built by matching the keypoints among several images, which are acquired by an onboard camera and stored in a buffer together with the corresponding estimated odometry. Hence, based on the 3-D map, a visual identification algorithm is employed to localize both obstacles and the desired target to build a virtual field accordingly. A bilateral control system has been developed such that an operator can safely navigate in an unknown environment and perceive it by means of a vision-based haptic force-feedback device. Experimental tests in an indoor environment verify the effectiveness of the proposed teleoperated control.


Analysis of Foreign Language Teaching Methods: An Automatic Readability Approach.


title: A Robust Matching Method for Distorted Fingerprints

abstract: This paper proposes a robust method to match distorted fingerprints. We first aligned the query and template fingerprints. To cope with the deformation brought by distorted fingerprints, we trained the distribution of orientation difference at a series of concentric circles around the corresponding pairs, and calculate the posterior probability of matched class using Bayesian method. We compute the score of unmatched absolute reliable minutiae by doubling the size of bounding box. Finally, we fuse the both scores with the score of matched minutiae using the multiplication rule. The experiments have been conducted on the public fingerprint collection, DB3 in FVC2002, and the results present that our method outperforms the conventional matching algorithm.


title: Innovation and practice of continuous auditing

abstract: The traditional audit paradigm is outdated in the real time economy. Innovation of the traditional audit process is necessary to support real time assurance. Practitioners and academics are exploring continuous auditing as a potential successor to the traditional audit paradigm. Using technology and automation, continuous auditing methodology enhances the efficiency and effectiveness of the audit process to support real time assurance. This paper defines how continuous auditing methodology introduces innovation to practice in seven dimensions and proposes a four-stage paradigm to advance future research. In addition, we formulate a set of methodological propositions concerning the future of assurance for practitioners and academic researchers.


title: On Updating Problems in Latent Semantic Indexing

abstract: We develop new SVD-updating algorithms for three types of updating problems arising from latent semantic indexing (LSI) for information retrieval to deal with rapidly changing text document collections. We also provide theoretical justification for using a reduced-dimension representation of the original document collection in the updating process. Numerical experiments using several standard text document collections show that the new algorithms give higher (interpolated) average precisions than the existing algorithms, and the retrieval accuracy is comparable to that obtained using the complete document collection.


title: A survey on electric vehicle powertrain systems

abstract: This study covers various aspects of powertrain systems for battery electric vehicles with main focus on front axes driven vehicles. A brief comparison of conventional and electrical powertrain regarding the driveability issues is presented in order to indicate the performance benefits of electrical machines into the vehicle applications. System level differences between internal combustion engine and electrical machine are given. As the most commonly used electrical machine types, a comparison of induction machine and interior permanent magnet synchronous machine is given. Main features of motor drives in industrial and electric vehicle applications are compared and system integration issues are discussed. As case study, electric version of FIAT New Doblo is presented including the test results on acceleration performance. Impact of vehicle weight on traction power demand is analysed both analytically and experimentally. Important research and development fields for motor drives in electric vehicles are highlighted considering the cost reduction, efficiency and reliability issues in automotive.


title: Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

abstract: In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or words' embedding, represented by graphs. We present a formulation of CNNs in the context of spectral graph theory, which provides the necessary mathematical background and efficient numerical schemes to design fast localized convolutional filters on graphs. Importantly, the proposed technique offers the same linear computational complexity and constant learning complexity as classical CNNs, while being universal to any graph structure. Experiments on MNIST and 20NEWS demonstrate the ability of this novel deep learning system to learn local, stationary, and compositional features on graphs.


Optimizing Regular Path Expressions Using Graph Schemas


title: An Automated Tool for MC/DC Test Data Generation

abstract: Structural testing is often the most common sought criteria for exercising aspects of control flow (i.e. such as statement, branch and path coverage). In many cases, criteria based on statement, decision and path coverage appears sufficiently effective for testing (in terms of selecting the appropriate test cases for testing consideration) the various parts of the software implementation. In other cases involving complex predicates, criteria based on statement, branch, and path coverage appear problematic owing to the problem of masking (where one variable is “masking” the effects of other variables). Addressing this issue, this paper discusses the design and implementation of an automatic test data generation called MC/DC GEN for structural testing based on Multiple Condition/Decision Coverage (MC/DC). In doing so, this paper also highlights the possible adoption of MC/DC GEN for practical use.


title: ERP implementation: a compilation and analysis of critical success factors

abstract: Purpose – To explore the current literature base of critical success factors (CSFs) of ERP implementations, prepare a compilation, and identify any gaps that might exist.Design/methodology/approach – Hundreds of journals were searched using key terms identified in a preliminary literature review. Successive rounds of article abstract reviews resulted in 45 articles being selected for the compilation. CSF constructs were then identified using content analysis methodology and an inductive coding technique. A subsequent critical analysis identified gaps in the literature base.Findings – The most significant finding is the lack of research that has focused on the identification of CSFs from the perspectives of key stakeholders. Additionally, there appears to be much variance with respect to what exactly is encompassed by change management, one of the most widely cited CSFs, and little detail of specific implementation tactics.Research limitations/implications – There is a need to focus future research efforts...


title: The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy

abstract: What is the role of social interactions in the creation of price bubbles? Answering this question requires obtaining collective behavioural traces generated by the activity of a large number of actors. Digital currencies offer a unique possibility to measure socio-economic signals from such digital traces. Here, we focus on Bitcoin, the most popular cryptocurrency. Bitcoin has experienced periods of rapid increase in exchange rates (price) followed by sharp decline; we hypothesise that these fluctuations are largely driven by the interplay between different social phenomena. We thus quantify four socio-economic signals about Bitcoin from large data sets: price on on-line exchanges, volume of word-of-mouth communication in on-line social media, volume of information search, and user base growth. By using vector autoregression, we identify two positive feedback loops that lead to price bubbles in the absence of exogenous stimuli: one driven by word of mouth, and the other by new Bitcoin adopters. We also observe that spikes in information search, presumably linked to external events, precede drastic price declines. Understanding the interplay between the socio-economic signals we measured can lead to applications beyond cryptocurrencies to other phenomena which leave digital footprints, such as on-line social network usage.


title: Secure and Reliable Routing Protocols for Heterogeneous Multihop Wireless Networks

abstract: In this paper, we propose E-STAR for establishing stable and reliable routes in heterogeneous multihop wireless networks. E-STAR combines payment and trust systems with a trust-based and energy-aware routing protocol. The payment system rewards the nodes that relay others’ packets and charges those that send packets. The trust system evaluates the nodes’ competence and reliability in relaying packets in terms of multi-dimensional trust values. The trust values are attached to the nodes’ public-key certificates to be used in making routing decisions. We develop two routing protocols to direct traffic through those highly-trusted nodes having sufficient energy to minimize the probability of breaking the route. By this way, E-STAR can stimulate the nodes not only to relay packets, but also to maintain route stability and report correct battery energy capability. This is because any loss of trust will result in loss of future earnings. Moreover, for the efficient implementation of the trust system, the trust values are computed by processing the payment receipts. Analytical results demonstrate that E-STAR can secure the payment and trust calculation without false accusations. Simulation results demonstrate that our routing protocols can improve the packet delivery ratio and route stability.


An Automatic Method for Image Matting Based on Saliency Detection ⋆


abstract: Untyped dependency parsing can be viewed as the problem of finding maximum spanning trees (MSTs) in directed graphs. Using this representation, the Eisner (1996) parsing algorithm is sufficient for searching the space of projective trees. More importantly, the representation is extended naturally to non-projective parsing using Chu-Liu-Edmonds (Chu and Liu, 1965; Edmonds, 1967) MST algorithm. These efficient parse search methods support large-margin discriminative training methods for learning dependency parsers. We evaluate these methods experimentally on the English and Czech treebanks. Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-06-11. This technical report is available at ScholarlyCommons: http://repository.upenn.edu/cis_reports/55 Spanning Tree Methods for Discriminative Training of Dependency Parsers Ryan McDonald Koby Crammer Fernando Pereira Department of Computer and Information Science University of Pennsylvania Philadelphia, PA, 19107 {ryantm,crammer,pereira}@cis.upenn.edu


Optimizing handover performance in LTE networks containing relays


title: Deep convolutional neural network for latent fingerprint enhancement

abstract: Abstract In this work, we propose a novel latent fingerprint enhancement method based on FingerNet inspired by recent development of Convolutional Neural Network (CNN). Although CNN is achieving superior performance in many computer vision tasks from low-level image processing to high-level semantic understanding, limited attention has been paid in fingerprint community. The proposed FingerNet has three major parts: one common convolution part shared by two different deconvolution parts, which are the enhancement branch and the orientation branch. The convolution part is to extract fingerprint features particularly for enhancement purpose. The enhancement deconvolution branch is employed to remove structured noise and enhance fingerprints as its task. The orientation deconvolution branch performs the task of guiding enhancement through a multi-task learning strategy. The network is trained in the manner of pixels-to-pixels and end-to-end learning, that can directly enhance latent fingerprint as the output. We also study some implementation details such as single-task learning, multi-task learning, and the residual learning. Experimental results of the FingerNet system on latent fingerprint dataset NIST SD27 demonstrate effectiveness and robustness of the proposed method.


title: Towards an open-domain conversational system fully based on natural language processing

abstract: This paper proposes an architecture for an open-domain conversational system and evaluates an implemented system. The proposed architecture is fully composed of modules based on natural language processing techniques. Experimental results using human subjects show that our architecture achieves significantly better naturalness than a retrieval-based baseline and that its naturalness is close to that of a rule-based system using 149K hand-crafted rules.


title: Improving relative depth judgments in augmented reality with auxiliary augmentations

abstract: Significant depth judgment errors are common in augmented reality. This study presents a visualization approach for improving relative depth judgments in augmented reality. The approach uses auxiliary augmented objects in addition to the main augmentation to support ordinal and interval depth judgment tasks. The auxiliary augmentations are positioned spatially near real-world objects, and the location of the main augmentation can be deduced based on the relative depth cues between the augmented objects. In the experimental part, the visualization approach was tested in the “X-ray” visualization case with a video see-through system. Two relative depth cues, in addition to motion parallax, were used between graphical objects: relative size and binocular disparity. The results show that the presence of auxiliary objects significantly reduced errors in depth judgment. Errors in judging the ordinal location with respect to a wall (front, at, or behind) and judging depth intervals were reduced. In addition to reduced errors, the presence of auxiliary augmentation increased the confidence in depth judgments, and it was subjectively preferred. The visualization approach did not have an effect on the viewing time.


title: Anomaly detection in aviation data using extreme learning machines

abstract: We develop fast anomaly detection algorithms using extreme learning machines (ELM) to discover operationally significant anomalies in large aviation data sets. Anomaly detection (aka one-class classification or outlier detection) is an active area of research to identify safety risks in aviation. Aviation data is characterized by high dimensionality, heterogeneity (continuous and categorical variables), multimodality and temporality. To address these challenges, NASA Ames has developed several anomaly detection algorithms including MKAD, the present state of the art [1]. MKAD's computational complexity is quadratic with respect to the number of training examples which makes it time consuming (and sometimes infeasible) for mining very large data sets. In this paper, we utilize ELM's fast training and good generalization properties to develop scalable anomaly detection algorithms for very large data sets. We adapt unsupervised ELM algorithms such as the autoencoder and embedding models to perform anomaly detection. The unsupervised models capture the nominal data distribution and by choosing a desired strength of detection that defines the upper bound of outliers in the training data, the anomaly decision boundary is determined. The autoencoder model detects anomalies as the ones that have a large reconstruction error while the embedding model detects anomalies as the ones that lie outside a hypersphere in the embedded space. The proposed algorithms are applied to a real aviation safety benchmark problem and the results show that the ELM based algorithms are comparable to MKAD in detection while training is made faster by two orders of magnitude.


title: Comparison of Three Evolutionary Algorithms: GA, PSO, and DE

abstract: This paper focuses on three very similar evolutionary algorithms: genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE). While GA is more suitable for discrete optimization, PSO and DE are more natural for continuous optimization. The paper first gives a brief introduction to the three EA techniques to highlight the common computational procedures. The general observations on the similarities and differences among the three algorithms based on computational steps are discussed, contrasting the basic performances of algorithms. Summary of relevant literatures is given on job shop, flexible job shop, vehicle routing, location-allocation, and multimode resource constrained project scheduling problems.


title: Uniform Random Number Generation

abstract: In typical stochastic simulations, randomness is produced by generating a sequence of independent uniform variates (usually real-valued between 0 and 1, or integer-valued in some interval) and transforming them in an appropriate way. In this paper, we examine practical ways of generating (deterministic approximations to) such uniform variates on a computer. We compare them in terms of ease of implementation, efficiency, theoretical support, and statistical robustness. We look in particular at several classes of generators, such as linear congruential, multiple recursive, digital multistep, Tausworthe, lagged-Fibonacci, generalized feedback shift register, matrix, linear congruential over fields of formal series, and combined generators, and show how all of them can be analyzed in terms of their lattice structure. We also mention other classes of generators, like non-linear generators, discuss other kinds of theoretical and empirical statistical tests, and give a bibliographic survey of recent papers on the subject.


title: SCD based IEC 61850 traffic estimation for substation automation networks

abstract: In Substation Automation System(SAS), communication network plays a critical role in the real time operation of the substation. IEC 61850 is a communication standard for SAS for the data transmission within and between station level, bay level and process level. The standard defines both the information model and the services used for communication between Intelligent Electronic Devices (IEDs) in a substation. The information model can be mapped to application protocols like MMS, GOOSE, SV of which GOOSE and SV messages are time critical and use Layer 2 of Open Systems Interconnection (OSI) model. IEC 61850 also defines substation configuration description (SCD), which embodies the substation model, the IED equipment model and the communication network model. With the increase in size and complexity of SAS network, it is desirable to properly segment multicast flows in order to avoid overloading at IEDs with unsubscribed messages. Knowledge of data flows and traffic patterns allow detecting network bottlenecks and aid in systematic network segmentation, filtering and segregation of the traffic. Hence there is a need to optimize the traffic segmentation and generate appropriate traffic filters which will be configured in network switch. Furthermore, by analyzing the application data and logical dataflow information present in the SCD file, it is possible to estimate the network bandwidth and flow latency. This would validate the network configuration for the fulfillment of the performance requirements based on the dataflow information in SCD file. So, there is a need to develop models that will estimate the GOOSE/SV traffic from the SCD file data. This paper proposes a method to optimally segment the Layer 2 network traffic and also a model to estimate GOOSE/SV traffic in SAS Network based on the information extracted from SCD file. This estimation would aid in analyzing the required network performance during the deployment and also better planning of SAS networks.


title: An Application Layer Protocol for Energy-Efficient Bandwidth Aggregation with Guaranteed Quality-of-Experience

abstract: WiFi and cellular networks are pervasively provided for mobile Internet access. Although most existing mobile devices are equipped with both WiFi and cellular network interfaces, concurrent data transmissions over these interfaces for improved throughput are not provided. In this paper, a bandwidth aggregation prototype, named Application Layer Protocol based Aggregation (ALP-A), is developed for easy use by simply installing an application in mobile devices without modifying their operating systems or drivers. It provides desired quality-of-experience (QoE), i.e., acceptable response delay to users, learned from application characteristics and users behaviors. Furthermore, we propose an online algorithm of traffic scheduling over WiFi and cellular interfaces with the objective of minimizing energy consumption while guaranteeing the QoE. Over the prototype implemented on Andriod-based smartphones, we conduct extensive experiments to show that ALP-A outperforms existing schemes significantly.


title: A Heuristic Search Algorithm Using the Stability of Learning Algorithms in Certain Scenarios as the Fitness Function: An Artificial General Intelligence Engineering Approach

abstract: This paper presents a non-manual design engineering method based on heuristic search algorithm to search for candidate agents in the solution space which formed by artificial intelligence agents modeled on the base of bionics.Compared with the artificial design method represented by meta-learning and the bionics method represented by the neural architecture chip,this method is more feasible for realizing artificial general intelligence,and it has a much better interaction with cognitive neuroscience;at the same time,the engineering method is based on the theoretical hypothesis that the final learning algorithm is stable in certain scenarios,and has generalization ability in various scenarios.The paper discusses the theory preliminarily and proposes the possible correlation between the theory and the fixed-point theorem in the field of mathematics.Limited by the author's knowledge level,this correlation is proposed only as a kind of conjecture.


title: Design of a 28-GHz low noise amplifier using 0.15-um InGaAs pHEMT E-mode technology

abstract: We present a 28-GHz low-noise amplifier (LNA) using 0.15-um InGaAs pHEMT enhancement-mode (E-mode) technology. The 28-GHz LNA exhibits a gain of 22.2 dB and a 3-dB bandwidth of 14.1 GHz (22.7–36.8 GHz) under 3-V supply with 38.5-mA current consumption. The simulation results of the LNA show a noise figure (NF) of 2.13 dB and a P1dB of 10.8 dBm. The chip size is 1488 um × 796 um.


Genetic Programming Exploratory Power and the Discovery of Functions


title: An empirical evaluation of internet latency expansion

abstract: The Internet's latency expansion determines the asymptotic performance of large-scale distributed systems (such as Peer-to-Peer systems), but previous studies on the Internet have defined expansion in terms of router-level hops. In this paper, we empirically determine the Internet's latency expansion characteristics using measurements from two different Internet topology datasets. Our results show that the Internet router-level topology exhibits a power-law latency expansion, in contrast to its exponential expansion rate in terms of hops.


Music and the Brain


title: A New Trapdoor-indistinguishable Public Key Encryption with Keyword Search

abstract: The public key encryption with keyword search (PEKS) provides a way for users to search data which are encrypted under the users’ public key on a storage system. However, the original schemes are based on the unrealistic assumption of a secure channel between the receiver and the server. Baek et al. [1] first proposed a secure channel-free public key encryption with keyword search (SCF-PEKS) to remove the assumption. However, Rhee et al. [2] point out that the SCF-PEKS scheme suffers from the keyword-guessing attack and proposed a scheme which satisfies the property of trapdoor indistinguishability without using an additional secure channel. In this paper, we construct a new efficient trapdoor-indistinguishable public key encryption with keyword search. Moreover, we prove that the proposed scheme can achieve the desired security properties.


Adopting the Word-Pair-Dependency-Triplets with Individual Comparison for Natural Language Inference


title: Semantic Interoperability in Digital Library Systems

abstract: This report is a state-of-the-art overview of activities and research being undertaken in areas relating to semantic interoperability in digital library systems. It has been undertaken as part of the cluster activity of WP5: Knowledge Extraction and Semantic Interoperability (KESI). The authors and contributors draw on the research expertise and experience of a number of organisations (UKOLN, ICS-FORTH, NETLAB, TUC-MUSIC, University of Glamorgan) as well as several work-packages (WP5: Knowledge Extraction and Semantic Interoperability; WP3: Audio-Visual and Non-traditional Objects) within the DELOS2 NoE. In addition, a workshop was held [KESI Workshop Sept. 2004] (co-located with ECDL 2004) in order to provide a forum for the discussion of issues relevant to the topic of this report. We are grateful to those who participated in the forum and for their valuable comments, which have helped to shape this report. Definitions of interoperability, syntactic interoperability and semantic interoperability are presented noting that semantic interoperability is very much about matching concepts as a basis. The NSF Post Digital Libraries Futures Workshop: Wave of the Future [NSF Workshop] has identified semantic interoperability as being of primary importance in digital library research.


title: A visual tool for ontology alignment to enable geospatial interoperability $

abstract: In distributed geospatial applications with heterogeneous databases, an ontology-driven approach to data integration relies on the alignment of the concepts of a global ontology that describe the domain, with the concepts of the ontologies that describe the data in the distributed databases. Once the alignment between the global ontology and each distributed ontology is established, agreements that encode a variety of mappings between concepts are derived. In this way, users can potentially query hundreds of geospatial databases using a single query. Using our approach, querying can be easily extended to new data sources and, therefore, to new regions. In this paper, we describe the AgreementMaker, a tool that displays the ontologies, supports several mapping layers visually, presents automatically generated mappings, and finally produces the agreements.


title: Single-chip microprocessor that communicates directly using light

abstract: Data transport across short electrical wires is limited by both bandwidth and power density, which creates a performance bottleneck for semiconductor microchips in modern computer systems—from mobile phones to large-scale data centres. These limitations can be overcome1, 2, 3 by using optical communications based on chip-scale electronic–photonic systems4, 5, 6, 7 enabled by silicon-based nanophotonic devices8. However, combining electronics and photonics on the same chip has proved challenging, owing to microchip manufacturing conflicts between electronics and photonics. Consequently, current electronic–photonic chips9, 10, 11 are limited to niche manufacturing processes and include only a few optical devices alongside simple circuits. Here we report an electronic–photonic system on a single chip integrating over 70 million transistors and 850 photonic components that work together to provide logic, memory, and interconnect functions. This system is a realization of a microprocessor that uses on-chip photonic devices to directly communicate with other chips using light. To integrate electronics and photonics at the scale of a microprocessor chip, we adopt a ‘zero-change’ approach to the integration of photonics. Instead of developing a custom process to enable the fabrication of photonics12, which would complicate or eliminate the possibility of integration with state-of-the-art transistors at large scale and at high yield, we design optical devices using a standard microelectronics foundry process that is used for modern microprocessors13, 14, 15, 16. This demonstration could represent the beginning of an era of chip-scale electronic–photonic systems with the potential to transform computing system architectures, enabling more powerful computers, from network infrastructure to data centres and supercomputers.


title: A turnstile junction waveguide orthomode transducer

abstract: An orthomode transducer with a circular waveguide input and two rectangular waveguide outputs is described. The design utilizes a turnstile junction and two E-plane power combiners. A K-band version of this device has been constructed and tested. From 18 to 26 GHz, the input reflection coefficient was less than -19 dB, the cross-polarization was less than -48 dB, and the transmission loss was ~0.15 dB. We estimate the mechanical tolerances that would be required to scale this device to the 200-270-GHz band for use in a dual-polarization radio-astronomy receiver


title: A SOA-Based e-Government Data Integration

abstract: Data Integration presents a core issue in the Palestinian e-Government Technical Framework. The currently used data integration model relies on the Integrated Central Database which lacks quality attributes such as: interoperability and flexibility. We purpose a SOA-based approach for data integration that achieves the above attributes. We present and analyze the current architecture and implementation of the Palestinian e-Government Integrated Central Database model. We transform the current model into a SOA framework that is realized using Enterprise Service Bus (ESB) and Web Services. The proposed framework offers database replication and connectivity functionalities for the Central Database. The proposed framework is evaluated using a scenario-based software architecture evaluation method and proves that it achieves the framework goals of quality attributes: interoperability and flexibility. Moreover, a prototype of the framework is implemented and validates the framework correctness. A specific usage is presented and further proves that the framework accomplishes its functionality and quality attributes.


title: Multiple Access Technologies for cellular M2M Communications: An Overview

abstract: This paper reviews the multiple access techniques for machine-to-machine (M2M) communications in future wireless cellular networks. M2M communications aims at providing te communication infrastructure for the emerging Internet of Things (IoT), which will revolutionize the way we interact with our surrounding physical environment. We provide an overview of the multiple access strategies and explain their limitations when used for M2M communications. We show the throughput efficiency of different multiple access techniques when used in coordinated and uncoordinated scenarios. Non-orthogonal multiple access is also shown to support a larger number of devices compared to orthogonal multiple access techniques, especially in uncoordinated scenarios. We also detail the issues and challenges of different multiple access techniques to be used for M2M applications in cellular networks.


title: Network Density Estimation: A GIS Approach for Analysing Point Patterns in a Network Space

abstract: Human activities and more generally the phenomena related to human behaviour take place in a network-constrained subset of the geographical space. These phenomena can be expressed as locations having their positions configured by a road network, as address points with street numbers. Although these events are considered as points on a network, point pattern analysis and the techniques implemented in a GIS environment generally consider events as taking place in a uniform space, with distance expressed as Euclidean and over a homogeneous and isotropic space. Network-spatial analysis has developed as a research agenda where the attention is drawn towards point pattern analytical techniques applied to a space constrained by a road network. Little attention has been put on first order properties of a point pattern (i.e. density) in a network space, while mainly second order analysis such as nearest neighbour and K -functions have been implemented for network configurations of the geographical space. In this article, a method for examining clusters of human-related events on a network, called Network Density Estimation (NDE), is implemented using spatial statistical tools and GIS packages. The method is presented and compared to conventional first order spatial analytical techniques such as Kernel Density Estimation (KDE). Network Density Estimation is tested using the locations of a sample of central, urban activities associated with bank and insurance company branches in the central areas of two midsize European cities, Trieste (Italy) and Swindon (UK).


Detecting annotation noise in automatically labelled data.


title: Automated and Scalable Mutation Testing

abstract: Symbolic execution and mutation testing have been demonstrated to be effective in revealing program defects. Despite their potential, their application cost, scalability and robust operation are obstacles to their practical use. Symbolic execution has the problem of path explosion, which results from the vast number of program paths, while mutation testing involves a large number of program variants to be analysed and executed. Both these problems result in scalability issues when applied in real world programs. These problems are to by addressed by the present research. The key point to be addressed is the design of few but effective mutations. These will be selected based on symbolic execution. The present research will 1) evaluate the level of con?dence or the guarantees that can be established by mutation testing, 2) design a technique to effectively detect useful mutants, 3) automate the whole mutation testing process.


title: Free matroidal structure of covering-based rough sets

abstract: Matroids were defined as abstract generation of graphs and matrices to try to capture abstractly essence of (in)dependence, it has wide application in electrical and computer science, mechanical engineering. Rough set theory is a new tool to deal with various types of data, covering-based rough set is a generalization of traditional rough set theory. This paper concerns the relationships between covering-based rough sets and matroid. Firstly, we establish the free matroidal structure of covering-based rough sets. Any subset of a universe can generate a free matroid, therefore any covering of a universe can be characterized by a family of free matroids. On the other hand, a family of free matroids can be used to generate a covering. Secondly, reducible element is to remove the redundant element in covering-based rough sets, reducible matroid is to remove redundant matroid in a family of matroids. We mainly research the relationship between reducible element and the reducible matroid in the family of free matroids induced by covering. Actually they are equivalent, the reducible element of a covering is always consistent with the reducible matroid induced by the covering. These results enrich covering-based rough set theory and matroid theory.


title: Integrating Technology Addiction and Adoption: An Empirical Investigation of Online Auction Websites

abstract: Technology addiction is a relatively new, but important, concept that has not yet been well integrated into mainstream IS models. This study bridges this gap, and integrates technology addiction and technology adoption in the context of online auction websites. The findings of an empirical study of 132 eBay users indicate that addiction to online auction websites augments user perceptions of the usefulness and enjoyment attributed to the technology, which in turn influence future usage. Post-hoc analysis further indicates that addiction is not a proximal concept, and as such, it affects future usage through key system-referenced perceptions. Implications of the findings are discussed.


Diagnosis Of Heart Disease Using Datamining Algorithm


On then Use of Windows for Harmonic Analysis with the Discrete Fourier Transform


title: X-band isoflux pattern antenna for SAR data transmission

abstract: In this paper, a design scheme for isoflux pattern antenna suitable for SAR data transmission is presented. An isoflux pattern is suitable for SAR data transmission of LEO satellite requiring wide angle coverage of the earth for the longer time visibility. Also, it is advantageous to provide uniform power density over the earth and constant power transmission to the ground station during the passage on the orbit. Based on the principle of generating isoflux pattern, we design basic isoflux pattern antenna which shows required isoflux characteristics. For improved performance, the basic antenna is optimized with genetic algorithm. The optimized isoflux pattern antenna is designed and implemented and shows rapid skirt characteristics, low side lobe, and low back lobe level.


title: Autism From 2 to 9 Years of Age

abstract: Context Autism represents an unusual pattern of development beginning in the infant and toddler years. Objectives To examine the stability of autism spectrum diagnoses made at ages 2 through 9 years and identify features that predicted later diagnosis. Design Prospective study of diagnostic classifications from standardized instruments including a parent interview (Autism Diagnostic Interview–Revised [ADI-R]), an observational scale (Pre-Linguistic Autism Diagnostic Observation Schedule/Autism Diagnostic Observation Schedule [ADOS]), and independent clinical diagnoses made at ages 2 and 9 years compared with a clinical research team's criterion standard diagnoses. Setting Three inception cohorts: consecutive referrals for autism assessment to (1) state-funded community autism centers, (2) a private university autism clinic, and (3) case controls with developmental delay from community clinics. Participants At 2 years of age, 192 autism referrals and 22 developmentally delayed case controls; 172 children seen at 9 years of age. Main Outcome Measures Consensus best-estimate diagnoses at 9 years of age. Results Percentage agreement between best-estimate diagnoses at 2 and 9 years of age was 67, with a weighted κ of 0.72. Diagnostic change was primarily accounted for by movement from pervasive developmental disorder not otherwise specified to autism. Each measure at age 2 years was strongly prognostic for autism at age 9 years, with odds ratios of 6.6 for parent interview, 6.8 for observation, and 12.8 for clinical judgment. Once verbal IQ ( P = .001) was taken into account at age 2 years, the ADI-R repetitive domain ( P = .02) and the ADOS social ( P = .05) and repetitive domains ( P = .005) significantly predicted autism at age 9 years. Conclusions Diagnostic stability at age 9 years was very high for autism at age 2 years and less strong for pervasive developmental disorder not otherwise specified. Judgment of experienced clinicians, trained on standard instruments, consistently added to information available from parent interview and standardized observation.


title: Intersection collision avoidance system using infrastructure communication

abstract: Nearly one third of all documented vehicle collisions are reported at urban and rural intersections, and this number continues to rise in spite of technological innovations for vehicle safety. Most collision avoidance systems currently under investigation are based on road-vehicle or inter-vehicle communication. However, reliability remains an issue, as high mobility often leads to message collisions and link breakage. In this paper reliable, real-time intersection collision avoidance (ICA) system based solely on infrastructure communication is proposed. ICA utilizes telematics and wireless sensor networks (WSN) to monitor approaching traffic, detect possible collision, and then transfer information to drivers, warning them of high collision probability.


Crash to not crash: Playing video games to predict vehicle collisions


title: Heterogeneity in Mobile Cloud Computing: Taxonomy and Open Challenges

abstract: The unabated flurry of research activities to augment various mobile devices by leveraging heterogeneous cloud resources has created a new research domain called Mobile Cloud Computing (MCC). In the core of such a non-uniform environment, facilitating interoperability, portability, and integration among heterogeneous platforms is nontrivial. Building such facilitators in MCC requires investigations to understand heterogeneity and its challenges over the roots. Although there are many research studies in mobile computing and cloud computing, convergence of these two areas grants further academic efforts towards flourishing MCC. In this paper, we define MCC, explain its major challenges, discuss heterogeneity in convergent computing (i.e. mobile computing and cloud computing) and networking (wired and wireless networks), and divide it into two dimensions, namely vertical and horizontal. Heterogeneity roots are analyzed and taxonomized as hardware, platform, feature, API, and network. Multidimensional heterogeneity in MCC results in application and code fragmentation problems that impede development of cross-platform mobile applications which is mathematically described. The impacts of heterogeneity in MCC are investigated, related opportunities and challenges are identified, and predominant heterogeneity handling approaches like virtualization, middleware, and service oriented architecture (SOA) are discussed. We outline open issues that help in identifying new research directions in MCC.


Broadband power dividers based on waveguide T-junctions at Ka-band


Perception and Production of Mandarin Tones by German Speakers


title: Obstacle avoidance for industrial AGVs

abstract: The paper deals with the obstacle avoidance problem for Automated Guided Vehicles (AGVs). We propose an automatic algorithm to build new obstacle-free trajectories that leave the original roadmap. The new path has to also comply with the dynamic and kinematic constraints of the AGV. The new paths are generated by means of polar spline curves, lanechange maneuver curves and line segments. The proposed method is validated by means of simulations.


title: The embodiment of sexualized virtual selves: The Proteus effect and experiences of self-objectification via avatars

abstract: Research has indicated that many video games and virtual worlds are populated by unrealistic, hypersexualized representations of women, but the effects of embodying these representations remains understudied. The Proteus effect proposed by Yee and Bailenson (2007) suggests that embodiment may lead to shifts in self-perception both online and offline based on the avatar's features or behaviors. A 2x2 experiment, the first of its kind, examined how self-perception and attitudes changed after women (N=86) entered a fully immersive virtual environment and embodied sexualized or nonsexualized avatars which featured either the participant's face or the face of an unknown other. Findings supported the Proteus effect. Participants who wore sexualized avatars internalized the avatar's appearance and self-objectified, reporting more body-related thoughts than those wearing nonsexualized avatars. Participants who saw their own faces, particularly on sexualized avatars, expressed more rape myth acceptance than those in other conditions. Implications for both online and offline consequences of using sexualized avatars are discussed.


title: From Pixels to Actions: Learning to Drive a Car with Deep Neural Networks

abstract: The promise of self-driving cars promotes several advantages, e.g. they have the ability to outperform human drivers while being safer. Here we take a deeper look into some aspects from algorithms aimed at making this promise a reality. More specifically, we analyze an end-to-end neural network to predict a car's steering actions on a highway based on images taken from a single car-mounted camera. We focus our analysis on several aspects which could have a significant impact on the performance of the system. These aspects are: the input data format, the temporal dependencies between consecutive inputs, and the origin of the data. We show that, for the task at hand, regression networks outperform their classifier counterparts. In addition, there seems to be a small difference between networks that use coloured images and ones that use grayscale images as input. For the second aspect, by feeding the network three concatenated images, we get a significant decrease of 30% in mean squared error. For the third aspect, by using simulation data we are able to train networks that have a performance comparable to networks trained on real-life datasets. We also qualitatively demonstrate that the standard metrics that are used to evaluate networks do not necessarily accurately reflect a system's driving behaviour. We show that a promising confusion matrix may result in poor driving behaviour while a very ill-looking confusion matrix may result in good driving behaviour.


The Book of Noodles; Stories of Simpletons; Or, Fools and Their Follies


title: Conversations in the Eye of the Storm: At-Scale Features of Conversational Structure in a High-Tempo, High-Stakes Microblogging Environment

abstract: This work propels social media research beyond the single post as the unit of analysis toward fuller treatment of interaction by making the construct of the conversation analytically available. We offer a method for constructing @reply conversations in Twitter to apprehend social media conversational features at scale. We apply this method to the high-tempo, high-stakes environment of 2012's Hurricane Sandy, with its high volume of online talk by affected locals and distinct disaster-stage phasing by which to consider interactional difference. We investigate the temporality of conversations; the relationality of who speaks to whom; the number and kind of conversationalists; and how content affects temporal features. The analysis reveals that, during the height of the emergency, people expand conversations both in number and kind of conversational partners-just as their information search intensifies. This expansion contributes to longer, slower-paced conversations in the high-emergency period, suggesting reliance on online relationships during times of greatest uncertainty.


title: moDNN: Memory optimal DNN training on GPUs

abstract: Graphics processing units (GPUs) are widely adopted to accelerate the training of deep neural networks (DNNs). However, the limited GPU memory size restricts the maximum scale of DNNs that can be trained on GPUs, which presents serious challenges. This paper proposes an moDNN framework to optimize the memory usage in DNN training. moDNN supports automatic tuning of DNN training code to match any given memory budget (not smaller than the theoretical lower bound). By taking full advantage of overlapping computations and data transfers, we have developed heuristics to judiciously schedule data offloading and prefetching, together with training algorithm selection, to optimize the memory usage. We further introduce a new sub-batch size selection method which also greatly reduces the memory usage. moDNN can save the memory usage up to 50 χ, compared with the ideal case which assumes that the GPU memory is sufficient to hold all data. When executing moDNN on a GPU with 12GB memory, the performance loss is only 8%, which is much lower than that caused by the best known existing approach, vDNN. moDNN is also applicable to multiple GPUs and attains 1.84 χ average speedup on two GPUs.


title: Ubiquitous Human Upper-Limb Motion Estimation using Wearable Sensors

abstract: Human motion capture technologies have been widely used in a wide spectrum of applications, including interactive game and learning, animation, film special effects, health care, navigation, and so on. The existing human motion capture techniques, which use structured multiple high-resolution cameras in a dedicated studio, are complicated and expensive. With the rapid development of microsensors-on-chip, human motion capture using wearable microsensors has become an active research topic. Because of the agility in movement, upper-limb motion estimation has been regarded as the most difficult problem in human motion capture. In this paper, we take the upper limb as our research subject and propose a novel ubiquitous upper-limb motion estimation algorithm, which concentrates on modeling the relationship between upper-arm movement and forearm movement. A link structure with 5 degrees of freedom (DOF) is proposed to model the human upper-limb skeleton structure. Parameters are defined according to Denavit-Hartenberg convention, forward kinematics equations are derived, and an unscented Kalman filter is deployed to estimate the defined parameters. The experimental results have shown that the proposed upper-limb motion capture and analysis algorithm outperforms other fusion methods and provides accurate results in comparison to the BTS optical motion tracker.


title: Eye Tracking Reveals Abnormal Visual Preference for Geometric Images as an Early Biomarker of an Autism Spectrum Disorder Subtype Associated With Increased Symptom Severity

abstract: Abstract Background Clinically and biologically, autism spectrum disorder (ASD) is heterogeneous. Unusual patterns of visual preference as indexed by eye tracking are hallmarks; however, whether they can be used to define an early biomarker of ASD as a whole or leveraged to define a subtype is unclear. To begin to examine this issue, large cohorts are required. Methods A sample of 334 toddlers from six distinct groups (115 toddlers with ASD, 20 toddlers with ASD features, 57 toddlers with developmental delay, 53 toddlers with other conditions [e.g., premature birth, prenatal drug exposure], 64 toddlers with typical development, and 25 unaffected toddlers with siblings with ASD) was studied. Toddlers watched a movie containing geometric and social images. Fixation duration and number of saccades within each area of interest and validation statistics for this independent sample were computed. Next, to maximize power, data from our previous study ( n = 110) were added for a total of 444 subjects. A subset of toddlers repeated the eye-tracking procedure. Results As in the original study, a subset of toddlers with ASD fixated on geometric images >69% of the time. Using this cutoff, sensitivity for ASD was 21%, specificity was 98%, and positive predictive value was 86%. Toddlers with ASD who strongly preferred geometric images had 1) worse cognitive, language, and social skills relative to toddlers with ASD who strongly preferred social images and 2) fewer saccades when viewing geometric images. Unaffected siblings of ASD probands did not show evidence of heightened preference for geometric images. Test-retest reliability was good. Examination of age effects suggested that this test may not be appropriate with children >4 years old. Conclusions Enhanced visual preference for geometric repetition may be an early developmental biomarker of an ASD subtype with more severe symptoms.


title: COMBINE: leveraging the power of wireless peers through collaborative downloading

abstract: Mobile devices are increasingly equipped with multiple network interfaces: Wireless Local Area Network (WLAN) interfaces for local connectivity and Wireless Wide Area Network (WWAN) interfaces for wide-area connectivity. The WWAN typically provides much wider coverage but much lower speeds than the WLAN. To address this dichotomy, we present COMBINE, a system for collaborative downloading wherein devices that are within WLAN range pool together their WWAN links, significantly increasing the effective speed available to them. COMBINE makes a number of novel contributions overprior work in this area, including: (a) a framework of incentives for collaboration that addresses several practical issues including the unification of monetary and energy costs, and on-the-fly estimation of the energy cost of communication in a system in operation; (b) a protocol for collaborative group formation and workload distribution that is energy efficient and adaptive to fluctuations in network conditions; and (c) an application-level striping procedure that eases deployment by avoiding the need for special-purpose proxies in the infrastructure. We present experimental results based on the prototype we have implemented that showen couraging speeds-ups with COMBINE.


title: An Energy-Efficient Clustering Solution for Wireless Sensor Networks

abstract: Hot spots in a wireless sensor network emerge as locations under heavy traffic load. Nodes in such areas quickly deplete energy resources, leading to disruption in network services. This problem is common for data collection scenarios in which Cluster Heads (CH) have a heavy burden of gathering and relaying information. The relay load on CHs especially intensifies as the distance to the sink decreases. To balance the traffic load and the energy consumption in the network, the CH role should be rotated among all nodes and the cluster sizes should be carefully determined at different parts of the network. This paper proposes a distributed clustering algorithm, Energy-efficient Clustering (EC), that determines suitable cluster sizes depending on the hop distance to the data sink, while achieving approximate equalization of node lifetimes and reduced energy consumption levels. We additionally propose a simple energy-efficient multihop data collection protocol to evaluate the effectiveness of EC and calculate the end-to-end energy consumption of this protocol; yet EC is suitable for any data collection protocol that focuses on energy conservation. Performance results demonstrate that EC extends network lifetime and achieves energy equalization more effectively than two well-known clustering algorithms, HEED and UCR.


title: A millimeter wave software defined radio platform with phased arrays: poster

abstract: Recently, there has been significant interest in performing research on millimeter wave (mmWave) communications. However, there do not exist any mmWave radio platforms with phased arrays available to the networking community. All existing mmWave platforms use horn antennas which require mechanical steering and are not suitable for non-static links or multi-user networks. We have built MiRa: a full-fledged mmWave radio with phased arrays capable of beam steering. MiRa operates as a daughterboard for the USRP software radio which enables easy manipulation of mmWave signals using standard GNU-radio software. With its reconfigurable architecture, steerable phased arrays and open SDR platform, MiRa can help advance mmWave research in the mobile and networking community.


title: Designing four channel nano cavities coupled photonic crystal based bio-sensor for detection in water bactria

abstract: A biosensor is a device which is works on the principle of binding mechanism whose aim is to search the analyte. A 2-D photonic crystal based biosensor has been proposed for the detection of bacteria in polluted water. The transmission is change by changing a refractive index of an analyte. The refractive index of the polluted water bacteria is varied according to the sample. In the layout of biosensor, hexagonal symmetry has four channels with nanocavity. The bandgap range of structure is 2049 nm to 1260 nm and 1550 nm Gaussian modulated continuous wave is used in this design. The average quality factor of biosensor is 328. The finite difference time domain (FDTD) method is use to analyzed the result. Plane wave expansion method is being used for bandgap calculation.


title: Measurement and Analysis of Weather Phenomena with K-Band Rain Radar

abstract: An integrated multiple-input and multiple-output (MIMO) antenna system covering Long Term Evolution (LTE) and vehicle to vehicle (V2V) communications was proposed in this research. The collinear array antenna with the phase reversal section was used to obtain an omnidirectional radiation pattern for vehicular communications. Also, the planar inverted-F antennas (PIFAs) were used as MIMO LTE antennas. The whole antennas were designed to fit in a shark-fin case. Antenna characteristics such as return loss, isolation, and radiation pattern were measured and communication tests were conducted in a real environment.


title: Difference engine: harnessing memory redundancy in virtual machines

abstract: Virtual machine monitors (VMMs) are a popular platform for Internet hosting centers and cloud-based compute services. By multiplexing hardware resources among virtual machines (VMs) running commodity operating systems, VMMs decrease both the capital outlay and management overhead of hosting centers. Appropriate placement and migration policies can take advantage of statistical multiplexing to effectively utilize available processors. However, main memory is not amenable to such multiplexing and is often the primary bottleneck in achieving higher degrees of consolidation. Previous efforts have shown that content-based page sharing provides modest decreases in the memory footprint of VMs running similar operating systems and applications. Our studies show that significant additional gains can be had by leveraging both subpage level sharing (through page patching) and incore memory compression. We build Difference Engine, an extension to the Xen VMM, to support each of these---in addition to standard copy-on-write full-page sharing---and demonstrate substantial savings across VMs running disparate workloads (up to 65%). In head-to-head memory-savings comparisons, Difference Engine outperforms VMware ESX server by a factor 1.6--2.5 for heterogeneous workloads. In all cases, the performance overhead of Difference Engine is less than 7%.


title: A Case for Protecting Computer Games With SGX

abstract: The integrity and confidentiality of computer games has long been a concern of game developers, both in preventing players from cheating and from obtaining unlicensed copies of the software. Recently, Intel released SGX, which can provide new security guarantees for software developers to achieve an unprecedented level of software integrity and confidentiality. To explore how SGX can protect a computer game in practice, in this paper we make a first step of exploring new ways to protect the integrity and confidentiality of game code and data, and in doing so we have developed a framework and design principles for integrating games with SGX. We have applied our framework to demonstrate how it can be used to protect a real world computer game.


title: Dynamic matching and scheduling of a class of independent tasks onto heterogeneous computing systems

abstract: Dynamic mapping (matching and scheduling) heuristics for a class of independent tasks using heterogeneous distributed computing systems are studied. Two types of mapping heuristics are considered: on-line and batch mode heuristics. Three new heuristics, one for batch and two for on-line, are introduced as part of this research. Simulation studies are performed to compare these heuristics with some existing ones. In total, five on-line heuristics and three batch heuristics are examined. The on-line heuristics consider; to varying degrees and in different ways, task affinity for different machines and machine ready times. The batch heuristics consider these factors, as well as aging of tasks waiting to execute. The simulation results reveal that the choice of mapping heuristic depends on parameters such as: (a) the structure of the heterogeneity among tasks and machines, (b) the optimization requirements, and (c) the arrival rate of the tasks.


title: A wideband Noise-Canceling CMOS LNA exploiting a transformer

abstract: An image forming apparatus includes a photoconductor drum on whose surface a toner image is formed, a transfer drum, including a dielectric layer, a pressure conductive layer whose volume resistivity falls by the application of pressure, and a conductive layer stacked in this order from a surface of the transfer drum, for transferring the toner image formed on the photoconductor drum onto a transfer sheet by electrically attracting and holding the transfer sheet onto a surface of the dielectric layer and by bringing the transfer sheet into contact with the photoconductor drum, power source section for applying a voltage to the conductive layer, and a grounded ground roller for coming into contact with the surface of the dielectric layer via the transfer sheet. This makes it possible to always maintain the amount of electric charge injected to the transfer material at an optimum value by adjusting a contact pressure between the transfer section and the contact and charge member. As a result, it is possible to electrostatically attract the transfer material onto the transfer section stably, regardless of a kind of the transfer material.\!


title: Firmware-only implementation of time-to-digital converter (TDC) in field-programmable gate array (FPGA)

abstract: A Time-to-Digital Converter (TDC) implemented in general purpose field-programmable gate array, (FPGA) for the Fermilab CKM experiment will be presented. The TDC uses a delay chain and register array structure to produce lower bits in addition to higher bits from a clock counter. Lacking the direct controls custom chips, the FPGA implementation of the delay chain and register array structure had to address two major problems: (1) The logic elements used for the delay chain and register array structure must be placed and routed by the FPGA compiler in a predictable manner, to assure uniformity of the TDC binning and short-term stability. (2) The delay variation due to temperature and power supply voltage must he compensated for to assure long-term stability. We used the chain structures in the existing FPGAs that the venders designed for general purpose such as carry algorithm or logic expansion to solve the first problem. To compensate for delay variations, we studied several digital compensation strategies that can be implemented in the same FPGA device. Some bench-top test results will also be presented in this document.


Lung Nodule Detection Using Convolutional Neural Networks


title: A tutorial on partially observable Markov decision processes

abstract: Abstract The partially observable Markov decision process (POMDP) model of environments was first explored in the engineering and operations research communities 40 years ago. More recently, the model has been embraced by researchers in artificial intelligence and machine learning, leading to a flurry of solution algorithms that can identify optimal or near-optimal behavior in many environments represented as POMDPs. The purpose of this article is to introduce the POMDP model to behavioral scientists who may wish to apply the framework to the problem of understanding normative behavior in experimental settings. The article includes concrete examples using a publicly-available POMDP solution package.


title: ASSESSMENT OF GERMPLASM USING MULTIVARIATE ANALYSIS FOR GRAIN YIELD AND QUALITY TRAITS IN SPRING WHEAT

abstract: Present study was designed to find diversity patterns among fourteen lines of hexaploid wheat (Triticum aestivum L.). Nine quantitative traits were determined phenotypically. Moreover the grain properties were also determined by laboratory procedures. Cluster analysis categorized cultivars into four groups. Based on Principal Component Analysis (PCA), the first seven components explained over 88% of genetic variation. Cluster analysis based on PCA using the first seven principal components indicated four separate groups of genotypes, with the maximum genetic distance observed between the genotypes in each cluster. Grain diameter, grain weight, gluten content, protein content, fiber, fat and starch contributed towards significant principal components (PCs). The genotypes 88146, Fareed-06, 6317 and 88132 were concluded as more diverse parents. Diverse parents from various clusters are helpful in planning and broadening the breeding programme by planning the crosses and increased use of heterosis and genetic diversity especially for grain quality in Pakistan.


title: Public and Private Sector IT Governance: Identifying Contextual Differences

abstract: This paper highlights systemic contextual differences and the unique IT Governance issues that might arise in public and private sector organizations. Public sector organizations constitute a significant component of economic activity in most countries. Like their private sector counterparts, many public sector agencies are struggling to cope with reduced or inadequate IT budgets and are continuously looking for ways to extract maximum value from IT resources. While both sectors face similar managerial-level IT issues and challenges, we argue that there are systemic differences between private and public sector organizations suggesting that a one size fits all approach to IT Governance may not apply.


title: Sub 50-nm FinFET: PMOS

abstract: High performance PMOSFETs with gate length as short as 18-nm are reported. A self-aligned double-gate MOSFET structure (FinFET) is used to suppress the short channel effect. A 45 nm gate-length PMOS FinEET has an I/sub dsat/ of 410 /spl mu/A//spl mu/m (or 820 /spl mu/A//spl mu/m depending on the definition of the width of a double-gate device) at Vd=Vg=1.2 V and Tox=2.5 nm. The quasi-planar nature of this variant of the double-gate MOSFETs makes device fabrication relatively easy using the conventional planar MOSFET process technologies. Simulation shows possible scaling to 10-nm gate length.


title: Feature detector and descriptor evaluation in human action recognition

abstract: In this paper, we evaluate and compare different feature detection and feature description methods for part-based approaches in human action recognition. Different methods have been proposed in the literature for both feature detection of space-time interest points and description of local video patches. It is however unclear which method performs better in the field of human action recognition. We compare, in the feature detection section, Dollar's method [18], Laptev's method [22], a bank of 3D-Gabor filters [6] and a method based on Space-Time Differences of Gaussians. We also compare and evaluate different descriptors such as Gradient [18], HOG-HOF [22], 3D SIFT [24] and an enhanced version of LBP-TOP [15]. We show the combination of Dollar's detection method and the improved LBP-TOP descriptor to be computationally efficient and to reach the best recognition accuracy on the KTH database.


title: Noise Adaptive Stream Weighting in Audio-Visual Speech Recognition

abstract: It has been shown that integration of acoustic and visual information especially in noisy conditions yields improved speech recognition results. This raises the question of how to weight the two modalities in different noise conditions. Throughout this paper we develop a weighting process adaptive to various background noise situations. In the presented recognition system, audio and video data are combined following a Separate Integration (SI) architecture. A hybrid Artificial Neural Network/Hidden Markov Model (ANN/HMM) system is used for the experiments. The neural networks were in all cases trained on clean data. Firstly, we evaluate the performance of different weighting schemes in a manually controlled recognition task with different types of noise. Next, we compare different criteria to estimate the reliability of the audio stream. Based on this, a mapping between the measurements and the free parameter of the fusion process is derived and its applicability is demonstrated. Finally, the possibilities and limitations of adaptive weighting are compared and discussed.


title: Network In Network

abstract: We propose a novel deep network structure called "Network In Network" (NIN) to enhance model discriminability for local patches within the receptive field. The conventional convolutional layer uses linear filters followed by a nonlinear activation function to scan the input. Instead, we build micro neural networks with more complex structures to abstract the data within the receptive field. We instantiate the micro neural network with a multilayer perceptron, which is a potent function approximator. The feature maps are obtained by sliding the micro networks over the input in a similar manner as CNN; they are then fed into the next layer. Deep NIN can be implemented by stacking mutiple of the above described structure. With enhanced local modeling via the micro network, we are able to utilize global average pooling over feature maps in the classification layer, which is easier to interpret and less prone to overfitting than traditional fully connected layers. We demonstrated the state-of-the-art classification performances with NIN on CIFAR-10 and CIFAR-100, and reasonable performances on SVHN and MNIST datasets.


Model selection and estimation in regression with grouped variables


title: Hydrochloric acid-catalyzed levulinic acid formation from cellulose: data and kinetic model to maximize yields

abstract: In this study, the kinetics of the acid catalyzed hydrolysis of microcrystalline cellulose (Avicel PH101) to levulinic (LA) and formic (FA) acids was investigated in a batch reactor over the following range of conditions: 160–200°C, hydrochloric acid concentrations of 0.309–0.927 M (11.3–33.8 g/l), cellulose concentrations of 49.8–149 mM (8.06–24.1 g/l), and residence times of 0–50 min. The maximum LA yield of around 60% of theoretical was achieved for an initial cellulose concentration of 99.6 mM, acid concentration 0.927 M, and 180–200°C. A mathematical model and its analytical solution were developed to predict conversion of cellulose to LA and FA through glucose and hydroxymethyl-2-furfural based on an irreversible pseudo-first order reaction. Rate analysis of each reaction indicated that the rate-controlling step shifted from LA formation initially to HMF formation later. © 2011 American Institute of Chemical Engineers AIChE J, 2012


title: Towards cloud based mobile ad hoc network simulation

abstract: Simulations are considered as the most convenient approach for validating any research proposal. A simulator attempts to model a real-world setting in order to analyze the behavior of any algorithm. A large number of simulation software for ad hoc network has been proposed in literature. These simulators are based on execution of simulation on standalone computers. This paper introduces the notion of ‘simulation as a service’ for mobile ad hoc network's simulation based on cloud computing. Cloud computing is an emerging discipline that enables outsourcing computing task remotely based on pay-as-you-go model. The paper provides a generalized architecture to perform ad hoc network simulations on cloud platforms. Similar to different service models provided by cloud, some service models have been introduced for cloud based simulation. The proposed approach provides different advantages such as dynamic service provisioning to support large scale simulation, sharing of simulation models and reduced cost for simulation etc. The proposed tool has been implemented on Heroku platform that demonstrates the scalability of proposed approach.


title: Ontology-Based Meta-Mining of Knowledge Discovery Workflows

abstract: This chapter describes a principled approach to meta-learning that has three distinctive features. First, whereas most previous work on meta-learning focused exclusively on the learning task, our approach applies meta-learning to the full knowledge discovery process and is thus more aptly referred to as meta-mining. Second, traditional meta-learning regards learning algorithms as black boxes and essentially correlates properties of their input (data) with the performance of their output (learned model). We propose to tear open the black box and analyse algorithms in terms of their core components, their underlying assumptions, the cost functions and optimization strategies they use, and the models and decision boundaries they generate. Third, to ground meta-mining on a declarative representation of the data mining (dm) process and its components, we built a DM ontology and knowledge base using the Web Ontology Language (owl).


title: Learning Face Representation from Scratch

abstract: Pushing by big data and deep convolutional neural network (CNN), the performance of face recognition is becoming comparable to human. Using private large scale training datasets, several groups achieve very high performance on LFW, i.e., 97% to 99%. While there are many open source implementations of CNN, none of large scale face dataset is publicly available. The current situation in the field of face recognition is that data is more important than algorithm. To solve this problem, this paper proposes a semi-automatical way to collect face images from Internet and builds a large scale dataset containing about 10,000 subjects and 500,000 images, called CASIAWebFace. Based on the database, we use a 11-layer CNN to learn discriminative representation and obtain state-of-theart accuracy on LFW and YTF. The publication of CASIAWebFace will attract more research groups entering this field and accelerate the development of face recognition in the wild.


title: A Baseline for General Music Object Detection with Deep Learning

abstract: Deep learning is bringing breakthroughs to many computer vision subfields including Optical Music Recognition (OMR), which has seen a series of improvements to musical symbol detection achieved by using generic deep learning models. However, so far, each such proposal has been based on a specific dataset and different evaluation criteria, which made it difficult to quantify the new deep learning-based state-of-the-art and assess the relative merits of these detection models on music scores. In this paper, a baseline for general detection of musical symbols with deep learning is presented. We consider three datasets of heterogeneous typology but with the same annotation format, three neural models of different nature, and establish their performance in terms of a common evaluation standard. The experimental results confirm that the direct music object detection with deep learning is indeed promising, but at the same time illustrates some of the domain-specific shortcomings of the general detectors. A qualitative comparison then suggests avenues for OMR improvement, based both on properties of the detection model and how the datasets are defined. To the best of our knowledge, this is the first time that competing music object detection systems from the machine learning paradigm are directly compared to each other. We hope that this work will serve as a reference to measure the progress of future developments of OMR in music object detection.


title: Deep learning UI design patterns of mobile apps

abstract: User interface (UI) is one of the most important components of a mobile app and strongly influences users' perception of the app. However, UI design tasks are typically manual and time-consuming. This paper proposes a novel approach to (semi)-automate those tasks. Our key idea is to develop and deploy advanced deep learning models based on recurrent neural networks (RNN) and generative adversarial networks (GAN) to learn UI design patterns from millions of currently available mobile apps. Once trained, those models can be used to search for UI design samples given user-provided descriptions written in natural language and generate professional-looking UI designs from simpler, less elegant design drafts.


title: Learning with personalized recommender systems: A psychological view

abstract: This paper explores the potentials of recommender systems for learning from a psychological point of view. It is argued that main features of recommender systems (collective responsibility, collective intelligence, user control, guidance, personalization) fit very well to principles in the learning sciences. However, recommender systems should not be transferred from commercial to educational contexts on a one-to-one basis, but rather need adaptations in order to facilitate learning. Potential adaptations are discussed both with regard to learners as recipients of information and learners as producers of data. Moreover, it is distinguished between system-centered adaptations that enable proper functioning in educational contexts, and social adaptations that address typical information processing biases. Implications for the design of educational recommender systems and for research on educational recommender systems are discussed.


title: A review of adaptive feature extraction and classification methods for EEG-based brain-computer interfaces

abstract: A brain-computer interface (BCI) is a system that allows its users to control external devices which are independent of peripheral nerves and muscles with brain activities. Electroencephalogram (EEG) signals are electrical signals collected from the scalp. They are frequently used in brain-computer interaction. However, EEG signals which change over time are highly non-stationary. One major challenge in current BCI research is how to extract features of time-varying EEG signals and classify the signals as accurately as possible. An effective BCI should be robust against and adaptive to the dynamic variations of brain activities. Adaptive learning in a BCI system, a rapidly developing application of machine learning, would be an effective approach to conquer the challenge. This paper reviews representative adaptive feature extraction and classification methods for EEG-based BCIs and further discusses some important open problems which can hopefully be useful to promote the research of the BCIs.


title: e-health first impressions and visual evaluations: key design principles for attention and appeal

abstract: Design plays a critical role in the development of e-health, greatly impacting the outreach potential for pertinent health communication. Design influences viewers' initial evaluations of electronic displays of health information, as well as directly impacting the likelihood one will attend to and favorably evaluate the information, essential actions for processing the health concepts presented. Individuals with low health literacy, representing a hard-to-reach audience susceptible to worsened health outcomes, will benefit greatly from the application of theory-based design principles. Design principles that have been shown to appeal and engage audiences are the necessary first step for effective message delivery. Design principles, which directly impact increased attention, favorable evaluations, and greater information processing abilities, include: web aesthetics, visual complexity, affordances, prototypicality, and persuasive imagery. These areas of theory-driven design research should guide scholars in e-health investigation with research goals of broader outreach, reduction of disparities, and potential avenues for reduced health care costs. Improving design by working with this hard-to-reach audience will simultaneously improve practice, as the applications of key design principles through theory-driven design research will allow practitioners to create effective e-health that will benefit people more broadly.


A comparison of interactivity in the Linux 2.6 scheduler and an MLFQ scheduler


title: Multi-scale Patch Aggregation (MPA) for Simultaneous Detection and Segmentation

abstract: Aiming at simultaneous detection and segmentation (SD-S), we propose a proposal-free framework, which detect and segment object instances via mid-level patches. We design a unified trainable network on patches, which is followed by a fast and effective patch aggregation algorithm to infer object instances. Our method benefits from end-to-end training. Without object proposal generation, computation time can also be reduced. In experiments, our method yields results 62.1% and 61.8% in terms of mAPr on VOC2012 segmentation val and VOC2012 SDS val, which are state-of-the-art at the time of submission. We also report results on Microsoft COCO test-std/test-dev dataset in this paper.


title: successful suicide: national long term cohort study

abstract: OBJECTIVE ::: To study the association between method of attempted suicide and risk of subsequent successful suicide. ::: ::: ::: DESIGN ::: Cohort study with follow-up for 21-31 years. ::: ::: ::: SETTING ::: Swedish national register linkage study. ::: ::: ::: PARTICIPANTS ::: 48,649 individuals admitted to hospital in 1973-82 after attempted suicide. ::: ::: ::: MAIN OUTCOME MEASURE ::: Completed suicide, 1973-2003. Multiple Cox regression modelling was conducted for each method at the index (first) attempt, with poisoning as the reference category. Relative risks were expressed as hazard ratios with 95% confidence intervals. ::: ::: ::: RESULTS ::: 5740 individuals (12%) committed suicide during follow-up. The risk of successful suicide varied substantially according to the method used at the index attempt. Individuals who had attempted suicide by hanging, strangulation, or suffocation had the worst prognosis. In this group, 258 (54%) men and 125 (57%) women later successfully committed suicide (hazard ratio 6.2, 95% confidence interval 5.5 to 6.9, after adjustment for age, sex, education, immigrant status, and co-occurring psychiatric morbidity), and 333 (87%) did so with a year after the index attempt. For other methods (gassing, jumping from a height, using a firearm or explosive, or drowning), risks were significantly lower than for hanging but still raised at 1.8 to 4.0. Cutting, other methods, and late effect of suicide attempt or other self inflicted harm conferred risks at levels similar to that for the reference category of poisoning (used by 84%). Most of those who successfully committed suicide used the same method as they did at the index attempt-for example, >90% for hanging in men and women. ::: ::: ::: CONCLUSION ::: The method used at an unsuccessful suicide attempt predicts later completed suicide, after adjustment for sociodemographic confounding and psychiatric disorder. Intensified aftercare is warranted after suicide attempts involving hanging, drowning, firearms or explosives, jumping from a height, or gassing.


Road Condition Detection Using Smartphone Sensors : A Survey


The Customer Relationship Management Process : Its Measurement and Impact on Performance Author ( s ) :


title: Hierarchical Structure of Microgrids Control System

abstract: Advanced control strategies are vital components for realization of microgrids. This paper reviews the status of hierarchical control strategies applied to microgrids and discusses the future trends. This hierarchical control structure consists of primary, secondary, and tertiary levels, and is a versatile tool in managing stationary and dynamic performance of microgrids while incorporating economical aspects. Various control approaches are compared and their respective advantages are highlighted. In addition, the coordination among different control hierarchies is discussed.