title: Brain–machine interfaces to restore motor function and probe neural circuits

abstract: Recent studies have shown that it is possible to create functional, bidirectional, real-time interfaces between living brain tissue and artificial devices. It is reasonable to predict that further research on brain–machine interfaces will lead to the development of a new generation of neuroprosthetic devices aimed at restoring motor functions in severely paralysed patients. In addition, I propose that such interfaces can become the core of a new experimental approach with which to investigate the operation of neural systems in behaving animals.


title: Salient Object Detection: A Benchmark

abstract: We extensively compare, qualitatively and quantitatively, 41 state-of-the-art models (29 salient object detection, 10 fixation prediction, 1 objectness, and 1 baseline) over seven challenging data sets for the purpose of benchmarking salient object detection and segmentation methods. From the results obtained so far, our evaluation shows a consistent rapid progress over the last few years in terms of both accuracy and running time. The top contenders in this benchmark significantly outperform the models identified as the best in the previous benchmark conducted three years ago. We find that the models designed specifically for salient object detection generally work better than models in closely related areas, which in turn provides a precise definition and suggests an appropriate treatment of this problem that distinguishes it from other problems. In particular, we analyze the influences of center bias and scene complexity in model performance, which, along with the hard cases for the state-of-the-art models, provide useful hints toward constructing more challenging large-scale data sets and better saliency models. Finally, we propose probable solutions for tackling several open problems, such as evaluation scores and data set bias, which also suggest future research directions in the rapidly growing field of salient object detection.


title: Factors analysis of urban transport system in Beijing: Based on system dynamics

abstract: The increasingly serious problem of road congestion in Beijing, involving many factors, is a structure of complex dynamic systems. As a “Policy and Planning Laboratory”, System Dynamics is quite suitable approach for studying complex structure involved function, behavior, et al. and the dynamic relations between these factors. In this paper, we shall first establish a system dynamics model of Beijing urban transport system. Then, based on data simulation, we will try to find out the main factors of urban traffic and its impact on urban transport.


title: Wide-Area Traffic Monitoring With the SAR/GMTI System PAMIR

abstract: This paper presents a wide area traffic monitoring experiment under real conditions, using the scan-MTI mode of the airborne radar sensor PAMIR. This flexible GMTI (Ground Moving Target Indication) mode was designed in order to rapidly monitor wide areas for moving targets. The scan operation enables the detection of targets from different aspect angles with a high revisit rate. The parameters (e.g., radial velocity, signal-to-noise ratio, and positioning accuracy) of the detected vehicles are investigated and compared to the expected theoretical GMTI performance. It will be shown that the scan-MTI mode is particularly adapted to perform an efficient wide-area traffic monitoring.


title: The development of a new mouse model of global ischemia: focus on the relationships between ischemia duration, anesthesia, cerebral vasculature, and neuronal injury following global ischemia in mice

abstract: A new model for mouse global ischemia is presented, and the relationship of ischemia duration, cerebral vasculature, and ischemic neuronal injury has been determined. CD-1 mice anesthetized by chloral hydrate were subjected to global ischemia by bilateral common carotid artery occlusion under controlled ventilation for 3, 5, and 10 min. After evaluating the patency of the posterior communicating artery (PcomA) as hypoplastic or normoplastic, neuronal injury was independently determined in the striatum, cortex, and hippocampus in each hemisphere. Ischemic injury was strongly correlated with not only ischemia duration, but also with the patency of the PcomAs. Furthermore, neuronal injury developed in a delayed fashion after 3-min ischemia, while it was maximized at 24 h after 10-min ischemia. Physiological studies showed the induction of slight hypotension as compared with inhalation anesthesia, and improvement of blood gas data relative to spontaneous respiration. These data demonstrate the usefulness of this method to induce selective vulnerability and delayed neuronal cell death in mice, and to provide a useful model to study the detailed mechanism of global ischemia using transgenic or knockout mutant mice.


title: A text-based decision support system for financial sequence prediction

abstract: Although most quantitative financial data are analyzed using traditional statistical, artificial intelligence or data mining techniques, the abundance of online electronic financial news articles has opened up new possibilities for intelligent systems that can extract and organize relevant knowledge automatically in a usable format. Most information extraction systems require a hand-built dictionary of templates and thus need continual modification to accommodate new patterns that are observed in the text. In this research, we propose a novel text-based decision support system (DSS) that (i) extracts event sequences from shallow text patterns, and (ii) predicts the likelihood of the occurrence of events using a classifier-based inference engine. The prediction relies on two major, but complementary, feature sets: adjacent events and a set of information-theoretic functions. In contrast to other approaches, the proposed text-based DSS gives explanatory hypotheses about its predictions from a coalition of intimations learned from the inference engine, while preserving robustness and without indulging in formalism. We investigate more than 2000 financial reports with 28,000 sentences. Experiments show that the prediction accuracy of our model outperforms similar statistical models by 7% for the seen data while significantly improving the prediction accuracy for the unseen data. Further comparisons substantiate the experimental findings.


title: Knowledge graph refinement: A survey of approaches and evaluation methods

abstract: In the recent years, different Web knowledge graphs, both free and commercial, have been created. While Google coined the term "Knowledge Graph" in 2012, there are also a few openly available knowledge graphs, with DBpedia, YAGO, and Freebase being among the most prominent ones. Those graphs are often constructed from semi-structured knowledge, such as Wikipedia, or harvested from the web with a combination of statistical and linguistic methods. The result are large-scale knowledge graphs that try to make a good trade-off between completeness and correctness. In order to further increase the utility of such knowledge graphs, various refinement methods have been proposed, which try to infer and add missing knowledge to the graph, or identify erroneous pieces of information. In this article, we provide a survey of such knowledge graph refinement approaches, with a dual look at both the methods being proposed as well as the evaluation methodologies used.


title: Mobile marketing research: The-state-of-the-art

abstract: Rapid proliferation in the business potential of mobile marketing attracts researchers from various fields to contribute to the growing body of knowledge on the phenomena. Although the literature on mobile marketing is accumulating, the stream of research is still in the development stage, hence is highly inconsistent and fragmented. This paper aims to organize and classify the literature on mobile marketing and assess the-state-of-the-art in order to facilitate future research. The review covers 255 peer-reviewed journal articles from 82 journals published between 2000 and 2008. The resulting framework summarizes the progress in mobile marketing research and provides future research directions.


title: The Mild Behavioral Impairment Checklist (MBI-C): A Rating Scale for Neuropsychiatric Symptoms in Pre-Dementia Populations

abstract: ZI is supported by the Hotchkiss Brain Institute via ::: the Alzheimer Society of Calgary. EES is supported ::: by the Katthy Taylor Chair in Vascular Dementia ::: and CIHR. DS is supported in part by the Department ::: ofVeterans Affairs.GSis supported by National ::: Institute of Health: AG038893 and AG041633. JC ::: acknowledges funding from the National Institute of ::: General Medical Sciences (Grant: P20GM109025) ::: and support from Keep Memory Alive. MEM is ::: supported by the Australian National Health and ::: Medical Research Council (NHMRC) and Australian ::: Research Council (ARC) Dementia Research Development ::: Fellowship #1102028. SG is supported by ::: CIHR and the Weston Brain Institute. KGL is supported ::: in part by NIH grant P50AG005146 for the ::: Johns Hopkins ADRC.


title: FPGA Implementation of Steinhart–Hart Equation for Accurate Thermistor Linearization

abstract: This paper presents fixed point operation-based Field Programmable Gate Arrays (FPGA) implementation of Steinhart–Hart Equation (SHHE) for thermistor linearization. FPGA implementation issues of SHHE are presented and their solutions are proposed and experimentally validated in a LabVIEWTM environment. Experimental temperature calibration, performed using a M/S Fluke drywell calibrator, revealed a lowest nonlinearity of 0.11% for an industrial grade thermistor in the input temperature range from −20 °C to 120 °C. Therefore, the main contribution of this work is to demonstrate the lowest nonlinearity for a wider temperature range. This work is expected to be very useful to instrumentation engineers as it employs a time-tested technique, for thermistor linearization in FPGA, leading to the lowest nonlinearity for a larger input temperature range.


title: A large-scale substrate integrated waveguide slot array antenna with good matching characteristic

abstract: An effective way to improve the matching characteristic of a large-scale substrate integrated waveguide (SIW) slot array antenna is proposed and demonstrated in this paper. Firstly, a SIW ferrite resonance isolator and a 32×32 slot array antenna are modeled and simulated by Ansoft HFSS, respectively. Then, these components are combined and integrated as a single one. The comparison between the designed 32×32 slot array antennas with and without isolator shows that the S11 is improved at least 5dB within 9.5-10.5GHz bandwidth and more than 30dB at 10GHz.


title: Extending the Implicit Association Test (IAT): Assessing Consumer Attitudes Based on Multi-Dimensional Implicit Associations

abstract: Background: The authors present a procedural extension of the popular Implicit Association Test (IAT; [1]) that allows for indirect measurement of attitudes on multiple dimensions (e.g., safe–unsafe; young–old; innovative–conventional, etc.) rather than on a single evaluative dimension only (e.g., good–bad). Methodology/Principal Findings: In two within-subjects studies, attitudes toward three automobile brands were measured on six attribute dimensions. Emphasis was placed on evaluating the methodological appropriateness of the new procedure, providing strong evidence for its reliability, validity, and sensitivity. Conclusions/Significance: This new procedure yields detailed information on the multifaceted nature of brand associations that can add up to a more abstract overall attitude. Just as the IAT, its multi-dimensional extension/application (dubbed md-IAT) is suited for reliably measuring attitudes consumers may not be consciously aware of, able to express, or willing to share with the researcher [2,3].


title: OpenFlow for Wireless Mesh Networks

abstract: everal protocols for routing and forwarding in Wireless Mesh Networks (WMN) have been proposed, such as AODV, OLSR or B.A.T.M.A.N. However, providing support for e.g. flow-based routing where flows of one source take different paths through the network is hard to implement in a unified way using traditional routing protocols. OpenFlow is an emerging technology which makes network elements such as routers or switches programmable via a standardized interface. By using virtualization and flow-based routing, OpenFlow enables a rapid deployment of novel packet forwarding and routing algorithms, focusing on fixed networks. We propose an architecture that integrates OpenFlow with WMNs and provides such flow-based routing and forwarding capabilities. To demonstrate the feasibility of our OpenFlow based approach, we have implemented a simple solution to solve the problem of client mobility in a WMN which handles the fast migration of client addresses (e.g. IP addresses) between Mesh Access Points and the interaction with re-routing without the need for tunneling. Measurements from a real mesh testbed (KAUMesh) demonstrate the feasibility of our approach based on the evaluation of forwarding performance, control traffic and rule activation time.


title: An End-to-End Architecture for Keyword Spotting and Voice Activity Detection

abstract: We propose a single neural network architecture for two tasks: on-line keyword spotting and voice activity detection. We develop novel inference algorithms for an end-to-end Recurrent Neural Network trained with the Connectionist Temporal Classification loss function which allow our model to achieve high accuracy on both keyword spotting and voice activity detection without retraining. In contrast to prior voice activity detection models, our architecture does not require aligned training data and uses the same parameters as the keyword spotting model. This allows us to deploy a high quality voice activity detector with no additional memory or maintenance requirements.


title: Flexible and Wearable Hand Exoskeleton and Its Application to Computer Mouse

abstract: This paper proposes a flexible and wearable hand exoskeleton which can be used as a computer mouse. The hand exoskeleton is developed based on a new concept of wearable mouse. The wearable mouse, which consists of flexible bend sensor, accelerometer and bluetooth, is designed for comfortable and supple usage. To demonstrate the effectiveness of the proposed wearable mouse, experiments are carried out for mouse operation consisting of click, cursor movement and wireless communication. The experimental results show that our wearable mouse is more accurate than a standard mouse.


title: Model-Based Dynamic Cost Estimation and Tracking Method for Agile Software Development

abstract: In this paper, we present a software cost estimation model for agile development which can help estimate concrete development costs for the desired features of a product and track the project progress dynamically. In general, existing cost estimation methods for agile developments used a story point. Because it is relative value, the estimation results tend to be easily fluctuated by the small variation of the baseline story point. For tracking project’s progress, the velocity was measured to check the progress and was used to make plan for the iteration and the releases of the project. The proposed method in this paper provides the systematic estimation and dynamic tracking methodology for agile projects. To estimate the effort of a project development, function points are used in addition to the story point. The function points are determined based on the user stories of desired features of the product. We adopt the Kalman filter algorithm for tracking project progress. The remaining function points at a certain point during the project are modeled as the state space model for the Kalman filter. The daily variation of the function point is observed and inputted to the Kalman Filter for providing concrete estimation and velocity. Moreover we validate the better performance of our model by comparing with traditional methods through a case study.


title: SemEval-2016 Task 13: Taxonomy Extraction Evaluation (TExEval-2)

abstract: This paper describes the second edition of the shared task on Taxonomy Extraction Evaluation organised as part of SemEval 2016. This task aims to extract hypernym-hyponym relations between a given list of domain-specific terms and then to construct a domain taxonomy based on them. TExEval-2 introduced a multilingual setting for this task, covering four different languages including English, Dutch, Italian and French from domains as diverse as environment, food and science. A total of 62 runs submitted by 5 different teams were evaluated using structural measures, by comparison with gold standard taxonomies and by manual quality assessment of novel relations.


title: Deep Learning Based Feature Selection for Remote Sensing Scene Classification

abstract: With the popular use of high-resolution satellite images, more and more research efforts have been placed on remote sensing scene classification/recognition. In scene classification, effective feature selection can significantly boost the final performance. In this letter, a novel deep-learning-based feature-selection method is proposed, which formulates the feature-selection problem as a feature reconstruction problem. Note that the popular deep-learning technique, i.e., the deep belief network (DBN), achieves feature abstraction by minimizing the reconstruction error over the whole feature set, and features with smaller reconstruction errors would hold more feature intrinsics for image representation. Therefore, the proposed method selects features that are more reconstructible as the discriminative features. Specifically, an iterative algorithm is developed to adapt the DBN to produce the inquired reconstruction weights. In the experiments, 2800 remote sensing scene images of seven categories are collected for performance evaluation. Experimental results demonstrate the effectiveness of the proposed method.


title: CEGAR for Qualitative Analysis of Probabilistic Systems

abstract: We consider Markov decision processes (MDPs) which are a standard model for probabilistic systems.We focus on qualitative properties forMDPs that can express that desired behaviors of the system arise almost-surely (with probability 1) or with positive probability. We introduce a new simulation relation to capture the refinement relation ofMDPs with respect to qualitative properties, and present discrete graph theoretic algorithms with quadratic complexity to compute the simulation relation.We present an automated technique for assume-guarantee style reasoning for compositional analysis ofMDPs with qualitative properties by giving a counterexample guided abstraction-refinement approach to compute our new simulation relation. We have implemented our algorithms and show that the compositional analysis leads to significant improvements.


title: Microtia repair with rib cartilage grafts: a review of personal experience with 1000 cases.

abstract: Surgical construction of the auricle with autogenous tissues is a unique marrying of science and art. Although the surgeon's facility with both sculpture and design is imperative, the surgical result is equally influenced by adherence to sound principles of plastic surgery and tissue transfer. The material reviewed in this article is derived from clinical experience with congenital microtia: 1094 completed ears in 1000 patients (94 cases were bilateral). This article focuses on total repair of major congenital ear defects, but includes relevant supplementary input from experience gained by managing more than 125 traumatic auricular deformities.


title: A Low-Profile Ultra-Wideband Modified Planar Inverted-F Antenna

abstract: A miniaturized modified planar inverted-F antenna (PIFA) is presented and experimentally studied. This antenna consists of a planar rectangular monopole top-loaded with a rectangular patch attached to two rectangular plates, one shorted to the ground and the other suspended, both placed at the optimum distance on each side of the planar monopole. The fabricated antenna prototype had a measured impedance bandwidth of 125%, covering 3 to 13 GHz for reflection coefficient better than -10 dB. The radiator size was 20×10×7.5 mm3, making it electrically small over most of the band and suitable for incorporation in mobile devices. The radiation patterns and gains of this antenna have been cross-validated numerically and experimentally and confirm that this antenna has adequate characteristics for short range ultra-wideband wireless applications.


title: An Inertial-Measurement-Unit-Based Pen With a Trajectory Reconstruction Algorithm and Its Applications

abstract: This paper presents an inertial-measurement-unit-based pen (IMUPEN) and its associated trajectory reconstruction algorithm for motion trajectory reconstruction and handwritten digit recognition applications. The IMUPEN is composed of a triaxial accelerometer, two gyroscopes, a microcontroller, and an RF wireless transmission module. Users can hold the IMUPEN to write numerals or draw simple symbols at normal speed. During writing or drawing movements, the inertial signals generated for the movements are transmitted to a computer via the wireless module. A trajectory reconstruction algorithm composed of the procedures of data collection, signal preprocessing, and trajectory reconstruction has been developed for reconstructing the trajectories of movements. In order to minimize the cumulative errors caused by the intrinsic noise/drift of sensors, we have developed an orientation error compensation method and a multiaxis dynamic switch. The advantages of the IMUPEN include the following: 1) It is portable and can be used anywhere without any external reference device or writing ambit limitations, and 2) its trajectory reconstruction algorithm can reduce orientation and integral errors effectively and thus can reconstruct the trajectories of movements accurately. Our experimental results on motion trajectory reconstruction and handwritten digit recognition have successfully validated the effectiveness of the IMUPEN and its trajectory reconstruction algorithm.


title: Emotional intelligence as a standard intelligence.

abstract: The authors have claimed that emotional intelligence (EI) meets traditional standards for an intelligence (J. D. Mayer, D. R. Caruso, & P. Salovey, 1999). R. D. Roberts, M. Zeidner, and G. Matthews (2001) questioned whether that claim was warranted. The central issue raised by Roberts et al. concerning Mayer et al. (1999) is whether there are correct answers to questions on tests purporting to measure EI as a set of abilities. To address this issue (and others), the present authors briefly restate their view of intelligence, emotion, and EI. They then present arguments for the reasonableness of measuring EI as an ability, indicate that correct answers exist, and summarize recent data suggesting that such measures are, indeed, reliable.


title: Evaluation of zero-speed sensor using NiFe/CoFe multilayer thin films and twisted FeCoV wires

abstract: The fabrication and characterization of magnetic sensors consisting of double magnetic layers are described. Both thin film based material and wire based materials were used for the double layers. The sensor elements were fabricated by patterning NiFe/CoFe multilayer thin films. This thin film based sensor exhibited a constant output voltage per excitation magnetic field at frequencies down to 0.1 Hz. The magnetic sensor using a twisted FeCoV wire, the conventional material for the Wiegand effect, had the disadvantage of an asymmetric output voltage generated by an alternating magnetic field. It was found that the magnetic wire whose ends were both slightly etched exhibited a symmetric output voltage.


title: Digital innovation and the fourth industrial revolution: epochal social changes?

abstract: ITC technologies have come to comprehensively represent images and expectations of the future. Hopes of ongoing progress, economic growth, skill upgrading and possibly also democratisation are attached to new ICTs as well as fears of totalitarian control, alienation, job loss and insecurity. Currently, with the terms "Industry 4.0." and ‘Fourth Industrial Revolution” (FIR), public institutions (such as the national governments of Germany, Us, Italy, France, and Hollande), private institutions (the World Economic Forum, Hedge Funds, commercial banks), and literature refer to the inchoate transformation of production of goods and services resulting from the application of a new wave of technological innovations: interconnected collaborative robots; machine learning; Artificial Intelligence; 3D printers connected to digital development software; simulation of interconnected machines; integration of the information flow along the value chain; multidirectional communication between manufacturing processes and products (Internet of Things). According to the main representations of Industry 4.0. by private and public institutions, its effects are expected to be mainly positive, for what regards productivity, economic opportunities and the future of work. The positive potentials now attributed to the new cycle of innovation evoke and expand those attributed to the previous waves of innovation linked to ITC technologies, and, even before, to the transition from Fordism to Post-Fordism. However, these transformations have so far not achieved any of the promises they raised. Improvements for workers in terms of work conditions, work performance and work relationships cannot be determined by any technical innovation in itself, being technological innovation always socially shaped.


title: Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data

abstract: In recent years, research in artificial neural networks has resurged, now under the deep-learning umbrella, and grown extremely popular. Recently reported success of DL techniques in crowd-sourced QSAR and predictive toxicology competitions has showcased these methods as powerful tools in drug-discovery and toxicology research. The aim of this work was dual, first large number of hyper-parameter configurations were explored to investigate how they affect the performance of DNNs and could act as starting points when tuning DNNs and second their performance was compared to popular methods widely employed in the field of cheminformatics namely Naive Bayes, k-nearest neighbor, random forest and support vector machines. Moreover, robustness of machine learning methods to different levels of artificially introduced noise was assessed. The open-source Caffe deep-learning framework and modern NVidia GPU units were utilized to carry out this study, allowing large number of DNN configurations to be explored. We show that feed-forward deep neural networks are capable of achieving strong classification performance and outperform shallow methods across diverse activity classes when optimized. Hyper-parameters that were found to play critical role are the activation function, dropout regularization, number hidden layers and number of neurons. When compared to the rest methods, tuned DNNs were found to statistically outperform, with p value <0.01 based on Wilcoxon statistical test. DNN achieved on average MCC units of 0.149 higher than NB, 0.092 than kNN, 0.052 than SVM with linear kernel, 0.021 than RF and finally 0.009 higher than SVM with radial basis function kernel. When exploring robustness to noise, non-linear methods were found to perform well when dealing with low levels of noise, lower than or equal to 20%, however when dealing with higher levels of noise, higher than 30%, the Naive Bayes method was found to perform well and even outperform at the highest level of noise 50% more sophisticated methods across several datasets.


title: Emulating emulation-resistant malware

abstract: The authors of malware attempt to frustrate reverse engineering and analysis by creating programs that crash or otherwise behave differently when executed on an emulated platform than when executed on real hardware. In order to defeat such techniques and facilitate automatic and semi-automatic dynamic analysis of malware, we propose an automated technique to dynamically modify the execution of a whole-system emulator to fool a malware sample's anti-emulation checks. Our approach uses a scalable trace matching algorithm to locate the point where emulated execution diverges, and then compares the states of the reference system and the emulator to create a dynamic state modification that repairs the difference. We evaluate our technique by building an implementation into an emulator used for in-depth malware analysis. On case studies that include real samples of malware collected in the wild and an attack that has not yet been exploited, our tool automatically ameliorates the malware sample's anti-emulation checks to enable analysis, and its modifications are robust to system changes.


title: High performance algorithm realization on FPGA for stepper motor controller

abstract: This paper focuses on studying the dynamic characteristics of stepper motors. Based on the relation of velocity and torque of stepper motors, a novel algorithm of velocity profile for stepper motor is proposed for straightforwardly realizing on field programmable gate array (FPGA). Missing steps have been a major issue while applying stepper motors on various critical situations. The reason of causing missing steps of stepper motors is due to the high velocity slewing. To alleviate this difficulty, a closed loop control may be applied, but introducing a complex design and more cost than open loop one. If open loop is chosen, the velocity profiles for various applications must be considered to avoid the missing step effect. There are three widespread used profiles: parabolic, trapezoidal, and S-curve profiles. For carrying out the operation of calculating the tedious profiles, a recursive velocity profile algorithm is presented along with a control parameter beta, where 0 < beta < 1, beta=1, and 1 < beta < 2, are corresponding to S-curve, trapezoidal, and parabolic profiles respectively, then this algorithm can be realized on a compact hardware. Thus, a high speed and high precision of stepper motor controller can be achieved by combining a system on chip (SOC) with this hardware.


title: The general employee scheduling problem: an integration of MS and AI

abstract: Abstract The general employee scheduling problem extends the standard shift scheduling problem by discarding key limitations such as employee homogeneity and the absence of connections across time period blocks. The resulting increased generality yields a scheduling model that applies to real world problems confronted in a wide variety of areas. The price of the increased generality is a marked increase in size and complexity over related models reported in the literature. The integer programming formulation for the general employee scheduling problem, arising in typical real world settings, contains from one million to over four million zero-one variables. By contrast, studies of special cases reported over the past decade have focused on problems involving between 100 and 500 variables. We characterize the relationship between the general employee scheduling problem and related problems, reporting computational results for a procedure that solves these more complex problems within 98–99% optimality and runs on a microcomputer. We view our approach as an integration of management science and artificial intelligence techniques. The benefits of such an integration are suggested by the fact that other zero-one scheduling implementations reported in the literature, including the one awarded the Lancaster Prize in 1984, have obtained comparable approximations of optimality only for problems from two to three orders of magnitude smaller, and then only by the use of large mainframe computers.


title: Lattice-Based Volumetric Global Illumination

abstract: We describe a novel volumetric global illumination framework based on the face-centered cubic (FCC) lattice. An FCC lattice has important advantages over a Cartesian lattice. It has higher packing density in the frequency domain, which translates to better sampling efficiency. Furthermore, it has the maximal possible kissing number (equivalent to the number of nearest neighbors of each site), which provides optimal 3D angular discretization among all lattices. We employ a new two-pass (illumination and rendering) global illumination scheme on an FCC lattice. This scheme exploits the angular discretization to greatly simplify the computation in multiple scattering and to minimize illumination information storage. The GPU has been utilized to further accelerate the rendering stage. We demonstrate our new framework with participating media and volume rendering with multiple scattering, where both are significantly faster than traditional techniques with comparable quality.


title: Distributed Secret Sharing Approach With Cheater Prevention Based on QR Code

abstract: QR barcodes are used extensively due to their beneficial properties, including small tag, large data capacity, reliability, and high-speed scanning. However, the private data of the QR barcode lacks adequate security protection. In this article, we design a secret QR sharing approach to protect the private QR data with a secure and reliable distributed system. The proposed approach differs from related QR code schemes in which it uses the QR characteristics to achieve secret sharing and can resist the print-and-scan operation. The secret can be split and conveyed with QR tags in the distribution application, and the system can retrieve the lossless secret when authorized participants cooperate. General browsers can read the original data from the marked QR tag via a barcode reader, and this helps reduce the security risk of the secret. Based on our experiments, the new approach is feasible and provides content readability, cheater detectability, and an adjustable secret payload of the QR barcode.


title: Feature-based decomposition of inductive proofs applied to real-time avionics software: an experience report

abstract: The hardware and software in modern aircraft control systems are good candidates for verification using formal methods: they are complex, safety-critical, and challenge the capabilities of test-based verification strategies. We have previously reported on our use of model checking to verify the time partitioning property of the Deos/spl trade/ real-time operating system for embedded avionics. The size and complexity of this system have limited us to analyzing only one configuration at a time. To overcome this limit and generalize our analysis to arbitrary configurations we have turned to theorem proving. This paper describes our use of the PVS theorem prover to analyze the Deos scheduler. In addition to our inductive proof of the time partitioning invariant, we present a feature-based technique for modeling state-transition systems and formulating inductive invariants. This technique facilitates an incremental approach to theorem proving that scales well to models of increasing complexity, and has the potential to be applicable to a wide range of problems.


title: Singularity Detection And Processing With Wavelets

abstract: The mathematical characterization of singularities with Lipschitz exponents is reviewed. Theorems that estimate local Lipschitz exponents of functions from the evolution across scales of their wavelet transform are reviewed. It is then proven that the local maxima of the wavelet transform modulus detect the locations of irregular structures and provide numerical procedures to compute their Lipschitz exponents. The wavelet transform of singularities with fast oscillations has a particular behavior that is studied separately. The local frequency of such oscillations is measured from the wavelet transform modulus maxima. It has been shown numerically that one- and two-dimensional signals can be reconstructed, with a good approximation, from the local maxima of their wavelet transform modulus. As an application, an algorithm is developed that removes white noises from signals by analyzing the evolution of the wavelet transform maxima across scales. In two dimensions, the wavelet transform maxima indicate the location of edges in images. >


title: Use of B-mode ultrasound for visceral fat mass evaluation: comparisons with magnetic resonance imaging.

abstract: The validity of the visceral fat evaluation based on B-mode ultrasonography was tested on 30 healthy young women (mean age 19.6 years). The mass of visceral fat (VFM) was estimated by subtracting the subcutaneous fat mass (SFM) from the total body fat mass. The SFM was calculated as the sum of segmental subcutaneous fat mass determined from the surface area and mean thickness of adipose tissue in six body segments (face and neck, upper arm, forearm, thigh, lower leg, and trunk). Reproducibility of the determination of VFM by the repeated measures of SFM and total fat mass was sufficiently high with the difference of 5.0%. Serial cross-sectional areas of visceral adipose tissue (VATarea) were measured by magnetic resonance imaging (MRI) at three different positions of the trunk (at umbilicus and at 3.5 cm upper and lower positions). The VFM correlated significantly to each VATarea (r = 0.75 to r = 0.78, P < 0.01). The present findings suggest that the VFM can be determined with the use of B-mode ultrasonography for the clinical assessment and field surveys.


title: Structured Convolution Matrices for Energy-efficient Deep learning

abstract: We derive a relationship between network representation in energy-efficient neuromorphic architectures and block Toplitz convolutional matrices. Inspired by this connection, we develop deep convolutional networks using a family of structured convolutional matrices and achieve state-of-the-art trade-off between energy efficiency and classification accuracy for well-known image recognition tasks. We also put forward a novel method to train binary convolutional networks by utilising an existing connection between noisy-rectified linear units and binary activations.


title: Efficient Bayesian Network Learning for System Optimization in Reliability Engineering

abstract: We present a new Bayesian network modeling that learns the behavior of an unknown system from real data and can be used for reliability engineering and optimization processes in industrial systems. The suggested approach relies on quantitative criteria for addressing the trade-off between the complexity of a learned model and its prediction accuracy. These criteria are based on measures from Information Theory as they predetermine both the accuracy as well as the complexity of the model. We illustrate the proposed method by a classical example of system reliability engineering. Using computer experiments, we show how in a targeted Bayesian network learning, a tremendous reduction in the model complexity can be accomplished, while maintaining most of the essential information for optimizing the system.


title: High payload secret hiding technology for QR codes

abstract: Quick response (QR) code has become one of the more popular two-dimensional barcodes because of its greater data capacity and higher damage resistance. The barcode scanners can easily extract the information hidden in the QR code while scanning the data modules. However, some sensitive data directly stored in QR codes are insecure in real-world QR applications, such as the e-ticket and e-coupon. To protect the sensitive data, this paper explores the characteristics of QR barcodes to design a secret hiding mechanism for the QR barcode with a higher payload compared to the past ones. For a normal scanner, a browser can only reveal the formal information from the marked QR code. The authorized user/scanner can further reveal the sensitive data from the marked QR tag. The experiments demonstrate a satisfactory secret payload and the feasibility of the proposed scheme.


title: UWB Antenna with 3.5-/5.5-GHz Dual Band-Notched Characteristics Using Two Radiating Stubs

abstract: in this letter, a new microstrip antenna for UWB applications is suggested. The antenna is able to filter both of the interference frequency bands of WiMAX and WLAN. The antenna consists of two radiating stubs, a partial ground, and a feed-line. In the configuration, by etching an inverted T- shaped slot on the ground plane, extra resonance is excited, thus broad impedance bandwidth can be obtained. The measured results exhibits that the antenna can cover the bandwidth from 2.7 to 13.0 GHz for VSWR≤ 2 excluding the rejected bands from 3.0 to 3.7 GHz and from 4.9 to 6.0 GHz.


The Ties that Blog: Examining the Relationship between Social Ties and Continued Participation in the Wallop Weblogging System


title: A Novel Blockchain-Based Product Ownership Management System (POMS) for Anti-Counterfeits in the Post Supply Chain

abstract: For more than a decade now, radio frequency identification (RFID) technology has been quite effective in providing anti-counterfeits measures in the supply chain. However, the genuineness of RFID tags cannot be guaranteed in the post supply chain, since these tags can be rather easily cloned in the public space. In this paper, we propose a novel product ownership management system (POMS) of RFID-attached products for anti-counterfeits that can be used in the post supply chain. For this purpose, we leverage the idea of Bitcoin ’s blockchain that anyone can check the proof of possession of balance. With the proposed POMS, a customer can reject the purchase of counterfeits even with genuine RFID tag information, if the seller does not possess their ownership. We have implemented a proof-of-concept experimental system employing a blockchain-based decentralized application platform, Ethereum , and evaluated its cost performance. Results have shown that, typically, the cost of managing the ownership of a product with up to six transfers is less than U.S. $1.


title: NEOFog: Nonvolatility-Exploiting Optimizations for Fog Computing

abstract: Nonvolatile processors have emerged as one of the promising solutions for energy harvesting scenarios, among which Wireless Sensor Networks (WSN) provide some of the most important applications. In a typical distributed sensing system, due to difference in location, energy harvester angles, power sources, etc. different nodes may have different amount of energy ready for use. While prior approaches have examined these challenges, they have not done so in the context of the features offered by nonvolatile computing approaches, which disrupt certain foundational assumptions. We propose a new set of nonvolatility-exploiting optimizations and embody them in the NEOFog system architecture. We discuss shifts in the tradeoffs in data and program distribution for nonvolatile processing-based WSNs, showing how non-volatile processing and non-volatile RF support alter the benefits of computation and communication-centric approaches. We also propose a new algorithm specific to nonvolatile sensing systems for load balancing both computation and communication demands. Collectively, the NV-aware optimizations in NEOFog increase the ability to perform in-fog processing by 4.2X and can increase this to 8X if virtualized nodes are 3X multiplexed.


title: Illumination and Pose Invariant Face Recognition: A Technical Review

abstract: Face recognition being the most important biometric trait it still faces many challenges, like pose variation, illumination variation etc. When such variations are present in both pose and illumination, all the algorithms are greatly affected by these variations and their performance gets degraded. In this paper we are presenting a detail survey on 2D face recognition under such uncontrolled conditions. Here we have explored different techniques proposed for illumination and pose problem in addition with the classifiers that have been successfully used for face recognition in general. The objective of this review paper is to summarize and compare some of the well-known methods for better understanding of reader.


title: Density-based place clustering in geo-social networks

abstract: Spatial clustering deals with the unsupervised grouping of places into clusters and finds important applications in urban planning and marketing. Current spatial clustering models disregard information about the people who are related to the clustered places. In this paper, we show how the density-based clustering paradigm can be extended to apply on places which are visited by users of a geo-social network. Our model considers both spatial information and the social relationships between users who visit the clustered places. After formally defining the model and the distance measure it relies on, we present efficient algorithms for its implementation, based on spatial indexing. We evaluate the effectiveness of our model via a case study on real data; in addition, we design two quantitative measures, called social entropy and community score to evaluate the quality of the discovered clusters. The results show that geo-social clusters have special properties and cannot be found by applying simple spatial clustering approaches. The efficiency of our index-based implementation is also evaluated experimentally.


title: Diffusion Kernels on Graphs and Other Discrete Input Spaces

abstract: The application of kernel-based learning algorithms has, so far, largely been confined to realvalued data and a few special data types, such as strings. In this paper we propose a general method of constructing natural families of kernels over discrete structures, based on the matrix exponentiation idea. In particular, we focus on generating kernels on graphs, for which we propose a special class of exponential kernels called diffusion kernels, which are based on the heat equation and can be regarded as the discretization of the familiar Gaussian kernel of Euclidean space.


title: HOPNET: A hybrid ant colony optimization routing algorithm for mobile ad hoc network

abstract: Mobile ad hoc network (MANET) is a group of mobile nodes which communicates with each other without any supporting infrastructure. Routing in MANET is extremely challenging because of MANETs dynamic features, its limited bandwidth and power energy. Nature-inspired algorithms (swarm intelligence) such as ant colony optimization (ACO) algorithms have shown to be a good technique for developing routing algorithms for MANETs. Swarm intelligence is a computational intelligence technique that involves collective behavior of autonomous agents that locally interact with each other in a distributed environment to solve a given problem in the hope of finding a global solution to the problem. In this paper, we propose a hybrid routing algorithm for MANETs based on ACO and zone routing framework of bordercasting. The algorithm, HOPNET, based on ants hopping from one zone to the next, consists of the local proactive route discovery within a node's neighborhood and reactive communication between the neighborhoods. The algorithm has features extracted from ZRP and DSR protocols and is simulated on GlomoSim and is compared to AODV routing protocol. The algorithm is also compared to the well known hybrid routing algorithm, AntHocNet, which is not based on zone routing framework. Results indicate that HOPNET is highly scalable for large networks compared to AntHocNet. The results also indicate that the selection of the zone radius has considerable impact on the delivery packet ratio and HOPNET performs significantly better than AntHocNet for high and low mobility. The algorithm has been compared to random way point model and random drunken model and the results show the efficiency and inefficiency of bordercasting. Finally, HOPNET is compared to ZRP and the strength of nature-inspired algorithm is shown.


title: Poster: Towards Encrypted Query Processing for the Internet of Things

abstract: The Internet of Things (IoT) is envisioned to digitize the physical world, resulting in a digital representation of our proximate living space. The possibility of inferring privacy violating information from IoT data necessitates adequate security measures regarding data storage and communication. To address these privacy and security concerns, we introduce our system that stores IoT data securely in the Cloud database while still allowing query processing over the encrypted data. We enable this by encrypting IoT data with a set of cryptographic schemes such as order-preserving and partially homomorphic encryptions. To achieve this on resource-limited devices, our system relies on optimized algorithms that accelerate partial homomorphic and order-preserving encryptions by 1 to 2 orders of magnitude. Our early results show the feasibility of our system on low-power devices. We envision our system as an enabler of secure IoT applications.


title: Impulsivity and prefrontal hypometabolism in borderline personality disorder

abstract: Abstract Prefrontal hypoperfusion and decreased glucose uptake in the prefrontal cortex (PFC) are found in violent criminal offenders, murderers and aggressive psychiatric patients. These abnormalities may be independent of diagnosis and associated with impulsive-aggression as a personality trait. Impulsive-aggression is a clinical characteristic of borderline personality disorder (BPD) where it is associated with assaultive and suicidal behaviors. We conducted FDG-PET studies in 13 non-depressed, impulsive female subjects with BPD and 9 healthy controls to look for abnormalities in glucose metabolism in areas of the PFC associated with regulation of impulsive behavior. Statistical Parametric Mapping-99 ( spm99) was used to analyze the PET data with Hamilton depression scores as covariate. Significant reductions in FDG uptake in BPD subjects relative to healthy controls were found bilaterally in medial orbital frontal cortex, including Brodmann's areas 9, 10 and 11. There were no significant areas of increased uptake in BPD subjects compared to control subjects. Covarying for measures of impulsivity or impulsive-aggression rendered insignificant the differences between groups. Decreased glucose uptake in medial orbital frontal cortex may be associated with diminished regulation of impulsive behavior in BPD.


The Current State of Business Intelligence in Academia


title: Visualizing Keyboard Pattern Passwords

abstract: Passwords are fundamental security vulnerabilities in many systems. Several researchers have investigated the trade-off between password memorability versus resiliency to cracking and have looked at alternative systems such as graphical passwords and biometrics. To create stronger passwords, many systems enforce rules regarding the required length and types of characters passwords must contain. Another suggested approach is to use passphrases to combat dictionary attacks. One common ‘trick’ used to remember passwords that conform to complex rules is to select a pattern of keys on the keyboard. Although appearing random, the pattern is easy to remember. The purpose of this research was to investigate how often patterns are used, whether patterns could be classified into common categories, and whether those categories could be used to attack and defeat pattern-based passwords. Visualization techniques were used to collect data and assist in pattern categorization. The approach successfully identified 2 out ...


title: Re-examining Machine Translation Metrics for Paraphrase Identification

abstract: We propose to re-examine the hypothesis that automated metrics developed for MT evaluation can prove useful for paraphrase identification in light of the significant work on the development of new MT metrics over the last 4 years. We show that a meta-classifier trained using nothing but recent MT metrics outperforms all previous paraphrase identification approaches on the Microsoft Research Paraphrase corpus. In addition, we apply our system to a second corpus developed for the task of plagiarism detection and obtain extremely positive results. Finally, we conduct extensive error analysis and uncover the top systematic sources of error for a paraphrase identification approach relying solely on MT metrics. We release both the new dataset and the error analysis annotations for use by the community.


title: TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning

abstract: TF.Learn is a high-level Python module for distributed machine learning inside TensorFlow. It provides an easy-to-use Scikit-learn style interface to simplify the process of creating, configuring, training, evaluating, and experimenting a machine learning model. TF.Learn integrates a wide range of state-of-art machine learning algorithms built on top of TensorFlow's low level APIs for small to large-scale supervised and unsupervised problems. This module focuses on bringing machine learning to non-specialists using a general-purpose high-level language as well as researchers who want to implement, benchmark, and compare their new methods in a structured environment. Emphasis is put on ease of use, performance, documentation, and API consistency.


Comparison of Symmetric and Asymmetric Cryptography with Existing Vulnerabilities and Countermeasures


title: Fungal naphtho-γ-pyrones—secondary metabolites of industrial interest

abstract: Naphtho-γ-pyrones (NGPs) are secondary metabolites mainly produced by filamentous fungi (Fusarium sp., Aspergillus sp.) that should be considered by industrials. Indeed, these natural biomolecules show various biological activities: anti-oxidant, anti-microbial, anti-cancer, anti-HIV, anti-hyperuricuric, anti-tubercular, or mammalian triacylglycerol synthesis inhibition which could be useful for pharmaceutical, cosmetic, and/or food industries. In this review, we draw an overview on the interest in studying fungal NGPs by presenting their biological activities and their potential values for industrials, their biochemical properties, and what is currently known on their biosynthetic pathway. Finally, we will present what remains to be discovered about NGPs.


title: Discontinuous Space Vector PWM Strategy of Neutral-Point-Clamped Three-Level Inverters for Output Current Ripple Reduction

abstract: In order to meet the high performance and efficiency requirements, a new space vector-based discontinuous modulation strategy for neutral-point clamped three-level inverter is presented in this paper. Different from the conventional discontinuous PWM (DPWM) strategies, a new clamping state is applied to synthesize the reference voltage under the condition of low modulation index. The rms value of output current ripple for different clamping states are calculated, and then the distribution of the new clamping state and the other two clamping states adopted in conventional DPWMs is optimized based on the calculation results. The switching sequences are redesigned according to the new distribution of the three clamping states. The output voltage and current, switching loss, neutral-point voltage ripple and the common mode voltage of the proposed DPWM strategy and DPWM0–DPWM3 are compared by simulation and experimental results. The results show that by the use of the proposed strategy, the values of total demand distortion of output current ( I TDD) and weighted total harmonic distortion of output voltage $({\boldsymbol{V}_{{\mathbf{WTHD}}}})$ are the lowest. Furthermore, the product of switching loss and $I_{\rm TDD}$ of the proposed strategy maintains the lowest within a large range of modulation index-load angle plane. It can be concluded that the utilization of the new clamping state and optimal distribution of different clamping states could enhance the output performance of the inverter. Moreover, the high-efficiency requirement can also be realized at the same time.


title: Genetic CNN

abstract: The deep Convolutional Neural Network (CNN) is the state-of-the-art solution for large-scale visual recognition. Following basic principles such as increasing the depth and constructing highway connections, researchers have manually designed a lot of fixed network structures and verified their effectiveness. In this paper, we discuss the possibility of learning deep network structures automatically. Note that the number of possible network structures increases exponentially with the number of layers in the network, which inspires us to adopt the genetic algorithm to efficiently traverse this large search space. We first propose an encoding method to represent each network structure in a fixed-length binary string, and initialize the genetic algorithm by generating a set of randomized individuals. In each generation, we define standard genetic operations, e.g., selection, mutation and crossover, to eliminate weak individuals and then generate more competitive ones. The competitiveness of each individual is defined as its recognition accuracy, which is obtained via training the network from scratch and evaluating it on a validation set. We run the genetic process on two small datasets, i.e., MNIST and CIFAR10, demonstrating its ability to evolve and find high-quality structures which are little studied before. These structures are also transferrable to the large-scale ILSVRC2012 dataset.


title: Peripheral neuropathy with a syndrome of continuous motor unit activity

abstract: A chronic alcoholic who had also been submitted to partial gastrectomy developed a syndrome of continuous motor unit activity responsive to phenytoin therapy. There were signs of minimal distal sensorimotor polyneuropathy. Symptoms of the syndrome of continuous motor unit activity were fasciculation, muscle stiffness, myokymia, impaired muscular relaxation and percussion myotonia. Electromyography at rest showed fasciculation, doublets, triplets, multiplets, trains of repetitive discharges and myotonic discharges. Trousseau's and Chvostek's signs were absent. No abnormality of serum potassium, calcium, magnesium, creatine kinase, alkaline phosphatase, arterial blood gases and pH were demonstrated, but the serum Vitamin B12 level was reduced. The electrophysiological findings and muscle biopsy were compatible with a mixed sensorimotor polyneuropathy. Tests of neuromuscular transmission showed a significant decrement in the amplitude of the evoked muscle action potential in the abductor digiti minimi on repetitive nerve stimulation. These findings suggest that hyperexcitability and hyperactivity of the peripheral motor axons underlie the syndrome of continuous motor unit activity in the present case.


title: Feature Construction for Reinforcement Learning in Hearts

abstract: Temporal difference (TD) learning has been used to learn strong evaluation functions in a variety of two-player games. TD-gammon illustrated how the combination of game tree search and learning methods can achieve grand-master level play in backgammon. In this work, we develop a player for the game of hearts, a 4-player game, based on stochastic linear regression and TD learning. Using a small set of basic game features we exhaustively combined features into a more expressive representation of the game state. We report initial results on learning with various combinations of features and training under self-play and against search-based players. Our simple learner was able to beat one of the best search-based hearts programs.


Transmitting Sport Values: The Importance of Parental Involvement in Children’s Sport Activity


title: Metabolic Approaches To Enhance Transdermal Drug Delivery. 1. Effect of Lipid Synthesis Inhibitors

abstract: The intercellular domains of the stratum corneum, which contain a mixture of cholesterol, free fatty acids, and ceramides, mediate both the epidermal permeability barrier and the transdermal delivery of both lipophilic and hydrophilic molecules. Prior studies have shown that each of the three key lipid classes is required for normal barrier function. For example, selective inhibition of either cholesterol, fatty acid, or ceramide synthesis in the epidermis delays barrier recovery rates after barrier perturbation of hairless mouse skin in vivo. In this study, we investigated the potential of certain inhibitors of lipid synthesis to enhance the transdermal delivery of lidocaine or caffeine as a result of their capacity to perturb barrier homeostasis. After acetone disruption of the barrier, the extent of lidocaine delivery and the degree of altered barrier function paralleled each other. Moreover, the further alteration in barrier function produced by either the fatty acid synthesis inhibitor 5-(tetradecyloxy)-2-furancarboxylic acid (TOFA), the cholesterol synthesis inhibitor fluvastatin (FLU), or cholesterol sulfate (CS) resulted in a further increase in lidocaine absorption. Furthermore, coapplications of TOFA and CS together caused an additive increase in lidocaine uptake. Finally, a comparable increase in drug delivery occurred when the barrier was disrupted initially with DMSO instead of acetone; coapplications of TOFA and FLU together again delayed barrier recovery and increased drug delivery by about 8-fold vs delivery from a standard enhancing vehicle. Whereas these metabolic inhibitors also variably increased the octanol/water partitioning of the drugs studied (perhaps via complexion or pH alterations), physicochemical effects of the inhibitors alone did not alter drug uptake in intact skin; i.e., passive mechanisms alone cannot account for the net increase in drug delivery. Our results show that modulations of epidermal lipid biosynthesis, following application of conventional, chemical penetration enhancers, cause a further boost in drug delivery, attributable to the ability of these agents to alter both permeability barrier homeostasis and thermodynamics. This biochemical/metabolic approach provides a novel means to enhance transdermal drug delivery in conjunction with the concurrent or prior use of chemical enhancers.


title: Unsupervised Eye Blink Artifact Denoising of EEG Data with Modified Multiscale Sample Entropy, Kurtosis, and Wavelet-ICA

abstract: Brain activities commonly recorded using the electroencephalogram (EEG) are contaminated with ocular artifacts. These activities can be suppressed using a robust independent component analysis (ICA) tool, but its efficiency relies on manual intervention to accurately identify the independent artifactual components. In this paper, we present a new unsupervised, robust, and computationally fast statistical algorithm that uses modified multiscale sample entropy (mMSE) and Kurtosis to automatically identify the independent eye blink artifactual components, and subsequently denoise these components using biorthogonal wavelet decomposition. A 95% two-sided confidence interval of the mean is used to determine the threshold for Kurtosis and mMSE to identify the blink related components in the ICA decomposed data. The algorithm preserves the persistent neural activity in the independent components and removes only the artifactual activity. Results have shown improved performance in the reconstructed EEG signals using the proposed unsupervised algorithm in terms of mutual information, correlation coefficient, and spectral coherence in comparison with conventional zeroing-ICA and wavelet enhanced ICA artifact removal techniques. The algorithm achieves an average sensitivity of 90% and an average specificity of 98%, with average execution time for the datasets ( $N = 7$ ) of 0.06 s ( ${\rm SD} = 0.021$ ) compared to the conventional wICA requiring 0.1078 s ( ${\rm SD} = 0.004$ ). The proposed algorithm neither requires manual identification for artifactual components nor additional electrooculographic channel. The algorithm was tested for 12 channels, but might be useful for dense EEG systems.


title: On the synergy of network science and artificial intelligence

abstract: Traditionally science is done using the reductionism paradigm. Artificial intelligence does not make an exception and it follows the same strategy. At the same time, network science tries to study complex systems as a whole. This Ph.D. research takes an alternative approach to the reductionism strategy, and tries to advance both fields, i.e. artificial intelligence and network science, by searching for the synergy between them, while not ignoring any other source of inspiration, e.g. neuroscience.


title: A balanced reciprocal translocation t(5;7)(q14;q32) associated with autistic disorder: Molecular analysis of the chromosome 7 breakpoint

abstract: Autism is a neuropsychiatric disorder characterized by impairments in social interaction, restricted and stereotypic pattern of interest with onset by 3 years of age. The results of genetic linkage studied for autistic disorder (AD) have suggested a susceptibility locus for the disease on the long arm of chromosome 7. We report a girl with AD and a balanced reciprocal translocation t(5;7)(q14;q32). The mother carries the translocation but do not express the disease. Fluorescent in situ hybridization (FISH) analysis with chromosome 7-specific YAC clones showed that the breakpoint coincides with the candidate region for AD. We identified a PAC clone that spans the translocation breakpoint and the breakpoint was mapped to a 2 kb region. Mutation screening of the genes SSBP and T2R3 located just centromeric to the breakpoint was performed in a set of 29 unrelated autistic sibling pairs who shared at least one chromosome 7 haplotype. We found no sequence variations, which predict amino acid alterations. Two single nucleotide polymorphisms were identified in the T2R3 gene, and associations between allele variants and AD in our population were not found. The methylation pattern of different chromosome 7 regions in the patient's genomic DNA appears normal. Here we report the clinical presentation of the patient with AD and the characterization of the genomic organization across the breakpoint at 7q32. The precise localization of the breakpoint on 7q32 may be relevant for further linkage studies and molecular analysis of AD in this region.


title: Role of the Amygdala in Decision-Making

abstract: Abstract: The somatic marker hypothesis proposes that both the amygdala and the orbitofrontal cortex are parts of a neural circuit critical for judgment and decision-making. Although both structures couple exteroceptive sensory information with interoceptive information concerning somatic/emotional states, they do so at different levels, thus making different contributions to the process. We define ‘primary inducers’ as stimuli that unconditionally, or through learning (e.g., conditioning and semantic knowledge), can (perceptually or subliminally) produce states that are pleasurable or aversive. Encountering a fear object (e.g., a snake), a stimulus predictive of a snake, or semantic information such as winning or losing a large sum of money are all examples of primary inducers. ‘Secondary inducers’ are entities generated by the recall of a personal or hypothetical emotional event or perceiving a primary inducer that generates ‘thoughts’ and ‘memories’ about the inducer, all of which, when they are brought to memory, elicit a somatic state. The episodic memory of encountering a snake, losing a large sum of money, imagining the gain of a large sum of money, or hearing or looking at primary inducers that bring to memory ‘thoughts’ pertaining to an emotional event are all examples of secondary inducers. We present evidence in support of the hypothesis that the amygdala is a critical substrate in the neural system necessary for triggering somatic states from primary inducers. The ventromedial cortex is a critical substrate in the neural system necessary for the triggering of somatic states from secondary inducers. The amygdala system is a priori a necessary step for the normal development of the orbitofrontal system for triggering somatic states from secondary inducers. However, once this orbitofrontal system is developed, the induction of somatic states by secondary inducers via the orbitofrontal system is less dependent on the amygdala system. Perhaps the amygdala is equivalent to the hippocampus with regard to emotions, that is, necessary for acquiring new emotional attributes (anterograde emotions), but not for retrieving old emotional attributes (retrograde emotions). Given the numerous lesion and functional neuroimaging studies illustrating the involvement of the amygdala in complex cognitive and behavioral functions, including ‘social cognition,’ we suggest that this involvement is a manifestation of a more fundamental function mediated by the amygdala, which is to couple stimuli/entities with their emotional attributes, that is, the processing of somatic states from primary inducers.


title: Low-Cost Semi-Z-source Inverter for Single-Phase Photovoltaic Systems

abstract: This paper presents several nonisolated semi-Z-source inverters for a single-phase photovoltaic (PV) system with low cost and doubly grounded features. These semi-Z-source inverters employ the Z-source/quasi-Z-source network and only two active switches to achieve the same output voltage as the traditional voltage-fed full-bridge inverter does. The two active switches of the semi-Z-source inverter are controlled complementarily. Different from the traditional single-phase Z-source/quasi-Z-source inverter, shoot-through zero state is not applicable to the semi-Z-source inverter. The input dc source and the output ac voltage of the semi-Z-source inverter share the same ground, thus leading to less leakage ground current advantages over other nondoubly grounded inverters, such as voltage-fed full-bridge inverter. This is a preferred feature for nonisolated grid-connected inverters, especially in PV application. A revised nonlinear sinusoid pulse width modulation method for a semi-Z-source inverter is also proposed. By using this method, desired duty cycle can be generated to output the sinusoidal voltage. Other dc-dc converters with similar voltage gain curve, which can also be used as a single-phase inverter, are also discussed and summarized. A single-phase semi-Z-source inverter prototype is built; experimental results are provided to verify and demonstrate the special features of the proposed circuit.


title: Dating human cultural capacity using phylogenetic principles

abstract: Humans have genetically based unique abilities making complex culture possible; an assemblage of traits which we term “cultural capacity”. The age of this capacity has for long been subject to controversy. We apply phylogenetic principles to date this capacity, integrating evidence from archaeology, genetics, paleoanthropology, and linguistics. We show that cultural capacity is older than the first split in the modern human lineage, and at least 170,000 years old, based on data on hyoid bone morphology, FOXP2 alleles, agreement between genetic and language trees, fire use, burials, and the early appearance of tools comparable to those of modern hunter-gatherers. We cannot exclude that Neanderthals had cultural capacity some 500,000 years ago. A capacity for complex culture, therefore, must have existed before complex culture itself. It may even originated long before. This seeming paradox is resolved by theoretical models suggesting that cultural evolution is exceedingly slow in its initial stages.


title: Scientific Article Recommendation: Exploiting Common Author Relations and Historical Preferences

abstract: Scientific article recommender systems are playing an increasingly important role for researchers in retrieving scientific articles of interest in the coming era of big scholarly data. Most existing studies have designed unified methods for all target researchers and hence the same algorithms are run to generate recommendations for all researchers no matter which situations they are in. However, different researchers may have their own features and there might be corresponding methods for them resulting in better recommendations. In this paper, we propose a novel recommendation method which incorporates information on common author relations between articles (i.e., two articles with the same author(s)). The rationale underlying our method is that researchers often search articles published by the same author(s). Since not all researchers have such author-based search patterns, we present two features, which are defined based on information about pairwise articles with common author relations and frequently appeared authors, to determine target researchers for recommendation. Extensive experiments we performed on a real-world dataset demonstrate that the defined features are effective to determine relevant target researchers and the proposed method generates more accurate recommendations for relevant researchers when compared to a Baseline method.


title: PERFORMANCE INVESTIGATIONS WITH ANTIPODAL LINEAR TAPERED SLOT ANTENNA ON 60 GHZ RADIO LINK IN A NARROW HALLWAY ENVIRONMENT

abstract: The performance of wireless communication systems is predominantly dependent on propagation environment and respective radiating antennas. Due to the shorter wavelength at Millimeter Wave (MmW) frequencies, the propagation loss through surroundings in indoor environments is typically very high. To improve the channel capacity and to reduce inter-user interference, a high gain directional antenna is desired at MmW frequencies. Traditional antennas used in MmW devices are not suitable for low-cost commercial devices due to their heavy, bulky and expensive configurations. This paper focuses on design and development of a very compact (44.61 mm × 9.93 mm × 0.381 mm) high gain Antipodal Linear Tapered Slot Antenna (ALTSA) utilizing Substrate Integrated Waveguide (SIW) technology at 60 GHz. Received signal strength (RSS), path loss (PL) and capacity are studied for MmW based wireless applications utilizing electromagnetic (EM) computations and Radio Frequency (RF) experiments in narrow hallway environment.


title: Validation of "Signs of Inflammation in Children that Kill" (SICK) score for immediate non-invasive assessment of severity of illness

abstract: ObjectiveTo validate the SICK scoring system's ability to differentiate between individuals with higher and lower probabilities of deathMethodWe performed a one year two-centre prospective evaluation of all children aged between one month and 12 years referred to the Paediatric team at St Stephens Hospital in Delhi and admitted to the Paediatric Department at West Middlesex University Hospital in London. We calculated SICK scores at presentation and correlated them with subsequent in-hospital mortality. We used discrimination by areas under receiver operating characteristic (ROC) curves to measure performance.ResultsWe prospectively evaluated 3895 children in Delhi and 1473 children in London. The areas under the ROC curves were 84.8% in Delhi, 81.0% in London and 84.1% (95% CI 77.4 - 90.8%) for combined data. Hosmer-Lemeshow goodness of fit for the combined data was good (Hosmer-Lemeshow Chi-square = 2.13 (p = 0.345).ConclusionsWe propose the SICK score as a useful triage tool at initial presentation and highlight its particular suitability for resource poor settings.


Online Video Game Addiction: A Review and an Information Systems Research Agenda.


title: Using Word Embedding to Enable Semantic Queries in Relational Databases

abstract: We investigate opportunities for exploiting Artificial Intelligence (AI) techniques for enhancing capabilities of relational databases. In particular, we explore applications of Natural Language Processing (NLP) techniques to endow relational databases with capabilities that were very hard to realize in practice. We apply an unsupervised neural-network based NLP idea, Distributed Representation via Word Embedding, to extract latent information from a relational table. The word embedding model is based on meaningful textual view of a relational database and captures inter-/intra-attribute relationships between database tokens. For each database token, the model includes a vector that encodes these contextual semantic relationships. These vectors enable processing a new class of SQL-based business intelligence queries called cognitive intelligence (CI) queries that use the generated vectors to analyze contextual semantic relationships between database tokens. The cognitive capabilities enable complex queries such as semantic matching, reasoning queries such as analogies, predictive queries using entities not present in a database, and using knowledge from external sources.


title: SemEval-2015 Task 2: Semantic Textual Similarity, English, Spanish and Pilot on Interpretability

abstract: In semantic textual similarity (STS), systems rate the degree of semantic equivalence between two text snippets. This year, the participants were challenged with new datasets in English and Spanish. The annotations for both subtasks leveraged crowdsourcing. The English subtask attracted 29 teams with 74 system runs, and the Spanish subtask engaged 7 teams participating with 16 system runs. In addition, this year we ran a pilot task on interpretable STS, where the systems needed to add an explanatory layer, that is, they had to align the chunks in the sentence pair, explicitly annotating the kind of relation and the score of the chunk pair. The train and test data were manually annotated by an expert, and included headline and image sentence pairs from previous years. 7 teams participated with 29 runs.


FORWARD-LOOKING QUANTITATIVE INFORMATION IN ITALIAN LISTED FIRMS. EMPIRICAL EVIDENCE IN 2006-2010


title: Towards a multi-foci approach to workplace aggression: A meta-analytic review of outcomes from different perpetrators

abstract: Summary Using meta-analysis, we compare three attitudinal outcomes (i.e., job satisfaction, affective commitment, and turnover intent), three behavioral outcomes (i.e., interpersonal deviance, organizational deviance, and work performance), and four health-related outcomes (i.e., general health, depression, emotional exhaustion, and physical well being) of workplace aggression from three different sources: Supervisors, co-workers, and outsiders. Results from 66 samples show that supervisor aggression has the strongest adverse effects across the attitudinal and behavioral outcomes. Co-worker aggression had stronger effects than outsider aggression on the attitudinal and behavioral outcomes, whereas there was no significant difference between supervisor, co-worker, and outsider aggression for the majority of the health-related outcomes. These results have implications for how workplace aggression is conceptualized and measured, and we propose new research questions that emphasize a multifoci approach. Copyright # 2009 John Wiley & Sons, Ltd.


title: Modeling and evaluating user behavior in exploratory visual analysis

abstract: Empirical evaluation methods for visualizations have traditionally focused on assessing the outcome of the visual analytic process as opposed to characterizing how that process unfolds. There are only a handful of methods that can be used to systematically study how people use visualizations, making it difficult for researchers to capture and characterize the subtlety of cognitive and interaction behaviors users exhibit during visual analysis. To validate and improve visualization design, it is important for researchers to be able to assess and understand how users interact with visualization systems under realistic scenarios. This article presents a methodology for modeling and evaluating the behavior of users in exploratory visual analysis. We model visual exploration using a Markov chain process comprising transitions between mental, interaction, and computational states. These states and the transitions between them can be deduced from a variety of sources, including verbal transcripts, videos and aud...


title: A Handheld Gun Detection using Faster R-CNN Deep Learning

abstract: Today's, most of the criminal activities are taken place using handheld arms particularly gun, pistol and revolver. Several surveys revealed that hand held gun is the foremost weapon used for diverse crimes like burglary, rape, etc. Therefore, automatic gun detection is a prime requirement in current scenario and this paper presents automatic gun detection from cluttered scene using Convolutional Neural Networks (CNN). We have used Deep Convolutional Network (DCN), a state-of-the-art Faster Region-based CNN model, through transfer learning, for automatic gun detection from cluttered scenes. We have evaluated our gun detection over Internet Movie Firearms Database (IMFDB), a benchmark gun database. For detecting the visual handheld gun, we got propitious performance of our system. Moreover, we demonstrate that, against the number of several training images, CNN model magnifies the classification accuracy, which is most advantageous in those practices where generous liberal is often not available.


title: Agent-based modelling of quality management effects on organizational productivity

abstract: This paper presents the application of agent-based simulation as a modelling metaphor for investigating the relationship between quality management (QM) and organizational productivity. The effects of QM on organizational productivity are traditionally researched by inductive reasoning through statistical models. Adopting a macro (system) level, top-down approach, statistical models fall short of providing an explanatory account of micro-level factors like individual’s problem-solving characteristics or customer requirements complexity, because organizations are considered as black boxes in such models and hence constructs of QM are defined at an organizational level. The question is how an explanatory, bottom-up account of QM effects can be provided. By virtue of the agent-based modelling paradigm, an innovative model, fundamentally different from the dominant statistical models is presented to fill this gap. Regarding individuals’ characteristics, results show that a well-balanced organization comprised of similar agents (in terms of agents’ problem-solving time and accuracy) outperforms other scenarios. Furthermore, from the results for varying complexity of customer requirements, it can be argued that more intricacy does not always lead to less productivity. Moreover, the usefulness of quality leadership represented as a reinforcement learning algorithm is reduced in comparison to a random algorithm when the complexity of customer requirements increases. The results have been validated by face validation and real data.


title: FOIL: A Midterm Report

abstract: FOIL is a learning system that constructs Horn clause programs from examples. This paper summarises the development of FOIL from 1989 up to early 1993 and evaluates its effectiveness on a non-trivial sequence of learning tasks taken from a Prolog programming text. Although many of these tasks are handled reasonably well, the experiment highlights some weaknesses of the current implementation. Areas for further research are identified.


title: Near-duplicate detection for images and videos

abstract: In this paper, we describe a system for detecting duplicate images and videos in a large collection of multimedia data. Our system consists of three major elements: Local-Difference-Pattern (LDP) as the unified feature to describe both images and videos, Locality-Sensitive-Hashing (LSH) as the core indexing structure to assure the most frequent data access occurred in the main memory, and multi-steps verification for queries to best exclude false positives and to increase the precision. The experimental results, validated on two public datasets, demonstrate that the proposed method is robust against the common image-processing tricks used to produce duplicates. In addition, the memory requirement has been addressed in our system to handle large-scale database.


title: Quaternionic Weber Local Descriptor of Color Images

abstract: This paper proposes a simple but effective framework named quaternionic Weber local descriptor (QWLD) for color image feature extraction. Integrating quaternionic representation (QR) of the color image and Weber’s law (WL), QWLD possesses both their superiorities. It uses QR to handle all color channels of the image in a holistic way while preserving their relations, and applies WL to ensure that the derived descriptors are robust and discriminative. Using the QWLD framework, we further develop the quaternionic-increment-based Weber descriptor and quaternionic-distance-based Weber descriptor in terms of different perspectives. Extensive experiments on different color image recognition problems demonstrate that the proposed framework and descriptors outperform state-of-the-art local descriptors.


title: Computer-based personality judgments are more accurate than those made by humans

abstract: Judging others’ personalities is an essential skill in successful social living, as personality is a key driver behind people’s interactions, behaviors, and emotions. Although accurate personality judgments stem from social-cognitive skills, developments in machine learning show that computer models can also make valid judgments. This study compares the accuracy of human and computer-based personality judgments, using a sample of 86,220 volunteers who completed a 100-item personality questionnaire. We show that (i) computer predictions based on a generic digital footprint (Facebook Likes) are more accurate (r = 0.56) than those made by the participants’ Facebook friends using a personality questionnaire (r = 0.49); (ii) computer models show higher interjudge agreement; and (iii) computer personality judgments have higher external validity when predicting life outcomes such as substance use, political attitudes, and physical health; for some outcomes, they even outperform the self-rated personality scores. Computers outpacing humans in personality judgment presents significant opportunities and challenges in the areas of psychological assessment, marketing, and privacy.


title: Manufacturing execution systems : A vision for managing software development

abstract: We describe manufacturing execution systems (MESs) as a vision for software development.We identify gaps between the MES vision and current software development practices.To narrow the gaps we prototype a Modularity Debt Management Decision Support System.We provide four case studies, realizing MES vision and illustrating its benefits. Software development suffers from a lack of predictability with respect to cost, time, and quality. Predictability is one of the major concerns addressed by modern manufacturing execution systems (MESs). A MES does not actually execute the manufacturing (e.g., controlling equipment and producing goods), but rather collects, analyzes, integrates, and presents the data generated in industrial production so that employees have better insights into processes and can react quickly, leading to predictable manufacturing processes. In this paper, we introduce the principles and functional areas of a MES. We then analyze the gaps between MES-vision-driven software development and current practices. These gaps include: (1) lack of a unified data collection infrastructure, (2) lack of integrated people data, (3) lack of common conceptual frameworks driving improvement loops from development data, and (4) lack of support for projection and simulation. Finally, we illustrate the feasibility of leveraging MES principles to manage software development, using a Modularity Debt Management Decision Support System prototype we developed. In this prototype we demonstrate that information integration in MES-vision-driven systems enables new types of analyses, not previously available, for software development decision support. We conclude with suggestions for moving current software development practices closer to the MES vision.


A New Quality Model for Natural Language Requirements Specifications


title: Fast visual object counting via example-based density estimation

abstract: Density estimation based visual object counting (DE-VOC) methods estimate the counts of an image by integrating over its predicted density map. They perform effectively but inefficiently. This paper proposes a fast DE-VOC method but maintains its effectiveness. Essentially, the feature space of image patches from VOC can be clustered into subspaces, and the examples of each subspace can be collected to learn its embedding. Also, it is assumed that the neighborhood embeddings of image patches and their corresponding density maps generated from training images are similar. With these principles, a closed form DE-VOC algorithm is derived, where the embedding and centroid of each neighborhood are precomputed by the training samples. Consequently, the density map of a given patch is estimated by simple classification and mapping. Experimental results show that our proposed method is comparable with mainstream ones on counting accuracy while running much faster in testing phase.


title: Domain-Specific Languages in Practice: A User Study on the Success Factors

abstract: Domain specific languages (DSLs) are languages in which the properties of a specific domain can be expressed. In this paper we present an empirical study on the use of a DSL in industry. This DSL encapsulates the details of services that communicate using Windows Communication Foundation (WCF). From definitions of the data contracts between clients and servers, WCF/C# code for service plumbing is generated. We conducted a survey amongst developers that use this DSL while developing applications for customers. The DSL has been used in about 30 projects all around the world. We describe the known success factors of the use of DSLs, such as improved maintainability and ease of re-use, and assert how well this DSL scores on all of them. The analysis of the results of this case study also shows which conditions should be fulfilled in order to increase the chances of success in using a DSL in a real life case.


title: A Concerted Model-driven and Pattern-based Framework for Developing User Interfaces of Interactive Ubiquitous Applications

abstract: Modeling and building interactive user interfaces ( UI) typically requires the skills of software developer s and HCI experts who cooperate with platform and marketing experts in order to arrive at solutions with the required s oftware quality, usability, and user experience. The combin ation of model-driven user interface development practices w ith pattern-based approaches that specify HCI- and soft warepatterns in a formalized way and respect emerging standards offers potentialities to facilitate and a t least partially automate the user interface development p rocess, therefore reduce the time-to-market and development costs, and lead to solutions that can easily be adapted to varying contexts and target devices. Such pattern-aided UI adaptation is not limited to design time decisions but can also be applied during runtime. This paper highligh ts the architecture and capabilities of the Pattern-Based Modeling and Generation of Interactive Systems (PaMGIS) framework to broadly support the construction and adaptation of user interface models. It is discusse d, how pattern descriptions that capture important parts o f the design knowledge should be organized in order to be automatically processed during the modeling process .


title: Representation Learning Using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval

abstract: Methods of deep neural networks (DNNs) have recently demonstrated superior performance on a number of natural language processing tasks. However, in most previous work, the models are learned based on either unsupervised objectives, which does not directly optimize the desired task, or singletask supervised objectives, which often suffer from insufficient training data. We develop a multi-task DNN for learning representations across multiple tasks, not only leveraging large amounts of cross-task data, but also benefiting from a regularization effect that leads to more general representations to help tasks in new domains. Our multi-task DNN approach combines tasks of multiple-domain classification (for query classification) and information retrieval (ranking for web search), and demonstrates significant gains over strong baselines in a comprehensive set of domain adaptation.


title: An automatic rebar splitting system based on two-level of the chain transmission

abstract: This paper proposed an automatic rebar splitting system counting by machine vision based on two-level transmission. Compared with other existing systems, this system significantly reduces the space requirements for installation, can use the space of rebar workshop more effectively. This system uses efficient algorithm to solve counting problems caused adhesion and sliding between rebar. By improving the sub-steel machine, this system uses the two-step rebar splitting and visual verification technology to split stacked rebar exactly. This system makes little change on production line and runs fast, accurately, easily to be maintained. It not only meets the needs of production but greatly improve the efficiency of the existing counting system, which is fit for space limited rebar workshop.


title: The production of Pleurotus sajor-caju in peach palm leaves (Bactris gasipaes) and evaluation of its use to enrich wheat flour

abstract: The aim of this study was to evaluate of Pleurotus sajor-caju production in peach palm leaves and the addition of different fractions of mushroom powder to wheat flour to increase its nutritional value without changing its characteristics. The best yield (48.4%), biologic efficiency (4.5%), and Pr (0.36 g/day) values were obtained using 20% inoculum fraction and 10% rice bran fraction. The Pleurotus sajor-caju fruiting body cultivated under these conditions had the following composition in 100 g: 29.91 g (carbohydrates), 42.92 g (proteins), 1.24 g (lipids), 15.93 g (fibers), 7.42 g (ashes), 1.6 g (phosphorus), 2.7 g (potassium), 8.73 mg (iron), 23.75 mg (sodium), 0.34 mg (thiamine), and 0.57 mg (riboflavin). The wheat flour with mushroom powder had reduced sugar content, but it did not have increased fat content. The fiber, protein, phosphorus, potassium, iron, and riboflavin contents were increased mainly when 10% mushroom powder was added to the wheat flour. Furthermore, this flour does not undergo drastic alterations in its physicochemical characteristics such as in moisture, wet gluten, color, and falling number.


title: DiffEqFlux.jl - A Julia Library for Neural Differential Equations

abstract: DiffEqFlux.jl is a library for fusing neural networks and differential equations. In this work we describe differential equations from the viewpoint of data science and discuss the complementary nature between machine learning models and differential equations. We demonstrate the ability to incorporate DifferentialEquations.jl-defined differential equation problems into a Flux-defined neural network, and vice versa. The advantages of being able to use the entire DifferentialEquations.jl suite for this purpose is demonstrated by counter examples where simple integration strategies fail, but the sophisticated integration strategies provided by the DifferentialEquations.jl library succeed. This is followed by a demonstration of delay differential equations and stochastic differential equations inside of neural networks. We show high-level functionality for defining neural ordinary differential equations (neural networks embedded into the differential equation) and describe the extra models in the Flux model zoo which includes neural stochastic differential equations. We conclude by discussing the various adjoint methods used for backpropogation of the differential equation solvers. DiffEqFlux.jl is an important contribution to the area, as it allows the full weight of the differential equation solvers developed from decades of research in the scientific computing field to be readily applied to the challenges posed by machine learning and data science.


Data-Driven News Generation for Automated Journalism


title: Building Theories from Case Study Research

abstract: This paper describes the process of inducting theory using case studies-from specifying the research questions to reaching closure. Some features of the process, such as problem definition and construct validation, are similar to hypothesis-testing research. Others, such as within-case analysis and replication logic, are unique to the inductive, case-oriented process. Overall, the process described here is highly iterative and tightly linked to data. This research approach is especially appropriate in new topic areas. The resultant theory is often novel, testable, and empirically valid. Finally, framebreaking insights, the tests of good theory (e.g., parsimony, logical coherence), and convincing grounding in the evidence are the key criteria for evaluating this type of research.


abstract: The physical design flow of any project depends upon the size of the design, the technology, the number of designers, the clock frequency, and the time to do the design. As technology advances and design-styles change, physical design flows are constantly reinvented as traditional phases are removed and new ones are added to accommodate changes in technology. Includes a personal perspective from Ralph Otten as he looks back on the major technical milestones in the history of physical design automation. Explore State-of-the-Art Techniques and TrendsHandbook of Algorithms for Physical Design Automation provides a detailed overview of VLSI physical design automation, emphasizing state-of-the-art techniques, trends and improvements that have emerged during the previous decade. After a brief introduction to the modern physical design problem, basic algorithmic techniques, and partitioning, the book discusses significant advances in floorplanning representations and describes recent formulations of the floorplanning problem. The text also addresses issues of placement, net layout and optimization, routing multiple signal nets, manufacturability, physical synthesis, special nets, and designing for specialized technologies. Highly-Focused Information for Next Generation Design Problems Although several books on this topic are currently available, most are either too broad or out of date. Alternatively, proceedings and journal articles are valuable resources for researchers in this area, but the material is widely dispersed in the literature. This handbook pulls together a broad variety of perspectives on the most challenging problems in the field, and focuses on emerging problems and research results.