title: ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression

abstract: We propose an efficient and unified framework, namely ThiNet, to simultaneously accelerate and compress CNN models in both training and inference stages. We focus on the filter level pruning, i.e., the whole filter would be discarded if it is less important. Our method does not change the original network structure, thus it can be perfectly supported by any off-the-shelf deep learning libraries. We formally establish filter pruning as an optimization problem, and reveal that we need to prune filters based on statistics information computed from its next layer, not the current layer, which differentiates ThiNet from existing methods. Experimental results demonstrate the effectiveness of this strategy, which has advanced the state-of-the-art. We also show the performance of ThiNet on ILSVRC-12 benchmark. ThiNet achieves 3.31$\times$ FLOPs reduction and 16.63$\times$ compression on VGG-16, with only 0.52$\%$ top-5 accuracy drop. Similar experiments with ResNet-50 reveal that even for a compact network, ThiNet can also reduce more than half of the parameters and FLOPs, at the cost of roughly 1$\%$ top-5 accuracy drop. Moreover, the original VGG-16 model can be further pruned into a very small model with only 5.05MB model size, preserving AlexNet level accuracy but showing much stronger generalization ability.


title: A Geometric take on Metric Learning

abstract: Multi-metric learning techniques learn local metric tensors in different parts of a feature space. With such an approach, even simple classifiers can be competitive with the state-of-the-art because the distance measure locally adapts to the structure of the data. The learned distance measure is, however, non-metric, which has prevented multi-metric learning from generalizing to tasks such as dimensionality reduction and regression in a principled way. We prove that, with appropriate changes, multi-metric learning corresponds to learning the structure of a Riemannian manifold. We then show that this structure gives us a principled way to perform dimensionality reduction and regression according to the learned metrics. Algorithmically, we provide the first practical algorithm for computing geodesics according to the learned metrics, as well as algorithms for computing exponential and logarithmic maps on the Riemannian manifold. Together, these tools let many Euclidean algorithms take advantage of multi-metric learning. We illustrate the approach on regression and dimensionality reduction tasks that involve predicting measurements of the human body from shape data.


title: Rolling Upgrade with Dynamic Batch Size for IaaS Cloud

abstract: Cloud systems are upgraded regularly to improve their performance, fix bugs, deploy new software versions, etc. Their hosted services, for example, telecommunication services, may have stringent non-functional requirements such that they do not tolerate more than five minutes of downtime in a year regardless whether they are provided by a cloud system or the outage is due to an upgrade. Such services must remain highly available under any circumstance, which imposes availability requirements on the provider cloud system. The dynamicity of the environment is one of the main challenges for maintaining High Availability (HA) in cloud deployments during upgrades. To maintain availability during upgrades, most cloud providers use rolling upgrade, and to avoid any unexpected interference between the upgrade process and the cloud's scaling mechanism, scaling is disabled for the time of the upgrade. In this paper, we propose a novel approach for rolling upgrades applicable to – among others – IaaS cloud systems to address HA. This approach mitigates the interference between the upgrade process, any failure handling and scaling by dynamically adjusting the upgrade process to the changes in the cloud environment. Accordingly, the upgrade process can start/resume only when the system has sufficient resources to perform an upgrade iteration and suspends the process when this is not the case. As a result, scaling does not need to be disabled during upgrades, rather the scaling operations regulate the pace of the upgrade.


title: Neuroticism and marital satisfaction: The mediating role played by the sexual relationship.

abstract: Why is Neuroticism so harmful to marriage and other intimate relationships? Given that such relationships generally involve a sexual component, the current longitudinal study explored whether the apparent negative impact of own and partner's Neuroticism on marriage could be explained by dissatisfaction with the sexual relationship. Just after their weddings, 72 couples reported their marital satisfaction, sexual satisfaction, and Neuroticism. One year later, they again reported their marital and sexual satisfaction. Own Neuroticism predicted lower levels of concurrent marital and sexual satisfaction among husbands and wives, declines in sexual satisfaction among husbands and wives, and declines in marital satisfaction among wives. Partner's Neuroticism predicted lower levels of concurrent marital satisfaction among husbands and wives, lower levels of concurrent sexual satisfaction among husbands, and declines in sexual satisfaction among husbands. Consistent with predictions, sexual satisfaction mediated every effect of own and partner Neuroticism on marital satisfaction. Results highlight the prominent role played by the sexual relationship in accounting for marital outcomes and thus suggest specific processes through which Neuroticism may affect the marriage.


title: A 5mA 0.6μm CMOS Miller-Compensated LDO Regulator with -27dB Worst-Case Power-Supply Rejection Using 60pF of On-Chip Capacitance

abstract: A 0.6mum 1.8V 5mA Miller-compensated SoC LDO regulator uses 60pF of capacitance to achieve a worst-case power-supply rejection of -27dB over 50MHz. The entire regulator is shielded from fluctuations in the supply using an NMOS cascode that is biased using a charge pump, voltage reference, and RC filters to maintain low dropout. The RC filter establishes a stable bias for the cascode without a significant impact on the efficiency or bandwidth of the LDO regulator.


title: Predicting the performance of online consumer reviews

abstract: Although online consumer reviews (OCRs) have helped consumers to know about the strengths and weaknesses of different products and find the ones that best suit their needs, they introduce a challenge for businesses to analyze them because of their volume, variety, velocity and veracity. This research investigates the predictors of readership and helpfulness of OCR using a sentiment mining approach for big data analytics. Our findings show that reviews with higher levels of positive sentiment in the title receive more readerships. Sentimental reviews with neutral polarity in the text are also perceived to be more helpful. The length and longevity of a review positively influence both its readership and helpfulness. Because the current methods used for sorting OCR may bias both their readership and helpfulness, the approach used in this study can be adopted by online vendors to develop scalable automated systems for sorting and classification of big OCR data which will benefit both vendors and consumers. Investigating the effect of review sentiment on readership and helpfulness of OCR.Providing a research model that predicts the performance of OCR.Reviews with higher levels of positive sentiment in the title receive more readerships.Reviews with neutral polarity in the text are perceived to be more helpful.Length and longevity of a review positively influence both its readership and helpfulness.


title: The iCub humanoid robot: an open platform for research in embodied cognition

abstract: We report about the iCub, a humanoid robot for research in embodied cognition. At 104 cm tall, the iCub has the size of a three and half year old child. It will be able to crawl on all fours and sit up to manipulate objects. Its hands have been designed to support sophisticate manipulation skills. The iCub is distributed as Open Source following the GPL/FDL licenses. The entire design is available for download from the project homepage and repository (http://www.robotcub.org). In the following, we will concentrate on the description of the hardware and software systems. The scientific objectives of the project and its philosophical underpinning are described extensively elsewhere [1].


title: The Relation between Breakfast Skipping and School Performance in Adolescents.

abstract: Boschloo, A., Ouwehand, C., Dekker, S., Lee, N., De Groot, R., Krabbendam, L., & Jolles, J. (2012). The relation between breakfast skipping and school performance in adolescents. Mind, Brain, and Education, 6(2), 81-88. doi:10.1111/j.1751-228x.2012.01138.x


title: An Efficient HOS-Based Gait Authentication of Accelerometer Data

abstract: We propose a novel efficient and reliable gait authentication approach. It is based on the analysis of accelerometer signals using higher order statistics. Gait patterns are obtained by transformation of acceleration data in feature space represented with higher order cumulants. The proposed approach is able to operate on multichannel and multisensor data by combining feature-level and sensor-level fusion. Evaluation of the proposed approach was performed using the largest currently available data set OU-ISIR containing inertial data of 744 subjects. Authentication was performed by cross-comparison of gallery and probe gait patterns transformed in feature space. In addition, the proposed approach was evaluated using data set collected by McGill University, containing long-sequence acceleration signals of 20 subjects acquired by smartphone during casual walking. The results have shown an average equal error rate of 6% to 12%, depending on the selected experimental parameters and setup. When compared with the latest state of the art, evaluated performance reveal the proposed approach as one of the most efficient and reliable of the currently available accelerometer-based gait authentication approaches.


title: LA-Credit: A Load-Awareness Scheduling Algorithm for Xen Virtualized Platforms

abstract: Xen is a powerful virtualization solution which enables multiple virtual machines to run in a single physical machine isolatedly. Since the hardware resources of a physical machine (e.g., the CPU) are limited and they have to be shared by multiple virtual machines, the performance of virtual machines might be degraded. How to allocate the underlying hardware resources to virtual machines appropriately has become an active research topic. In this paper, we are interested in CPU scheduling algorithm for a Xen virtualized system. The default CPU scheduling algorithm of Xen, called Credit scheduler, is a fairness strategy which provides proportional fair shared CPU time to each virtual machine based on its predefined weight value. However, the Credit scheduler might deteriorate the performance of a virtual machine when its predefined weight value cannot meet its current requirement of the physical CPU. It is because the dynamic workload of a virtual machine is unpredictable. In this paper, a credit-based CPU scheduling algorithm, called load-awareness credit (LA-Credit), is proposed to adjust the weight values of virtual machines dynamically such that the overall system performance could be improved. In particular, the LA-Credit algorithm monitors the current workload of each virtual machine and then it calculates and adjusts an appropriate weight for each virtual machine so that a virtual machine with heavy workload can obtain more CPU share. We have implemented the LA-Credit algorithm in Xen and evaluated the performance, for which some encouraging results were obtained.


The Effect of Note Taking Media and Preference on the Cognitive Processes Involved in Learning


title: No better ways to generate hard NP instances than picking uniformly at random

abstract: Distributed NP (DNP) problems are ones supplied with probability distributions of instances. It is shown that every DNP problem complete for P-time computable distributions is also complete for all distributions that can be sampled. This result makes the concept of average-case NP completeness robust and the question of the average-case complexity of complete DNP problems a natural alternative to P=?NP. Similar techniques yield a connection between cryptography and learning theory.


title: The Space of Transferable Adversarial Examples

abstract: Adversarial examples are maliciously perturbed inputs designed to mislead machine learning (ML) models at test-time. They often transfer: the same adversarial example fools more than one model. In this work, we propose novel methods for estimating the previously unknown dimensionality of the space of adversarial inputs. We find that adversarial examples span a contiguous subspace of large (~25) dimensionality. Adversarial subspaces with higher dimensionality are more likely to intersect. We find that for two different models, a significant fraction of their subspaces is shared, thus enabling transferability. In the first quantitative analysis of the similarity of different models' decision boundaries, we show that these boundaries are actually close in arbitrary directions, whether adversarial or benign. We conclude by formally studying the limits of transferability. We derive (1) sufficient conditions on the data distribution that imply transferability for simple model classes and (2) examples of scenarios in which transfer does not occur. These findings indicate that it may be possible to design defenses against transfer-based attacks, even for models that are vulnerable to direct attacks.


title: High performance pipelined architecture of Ghash

abstract: Ghash is the authentication function of GCM. In this paper, we propose a high performance pipelined architecture of Ghash. The pipelined architecture can boost the operating frequency of the hash function block that is the bottleneck for higher speed GCM hardware. The most attractive feature is our pipelined architecture based on divided-and-conquer algorithm. According to the architecture, the critical path of the multiply-adder used for Ghash is divided into 3-stage to obtain the higher throughput and reduce the time complexity. At the same, divided-and-conquer algorithm can efficiently reduce the space complexity. The proposed architecture is compared to previous work on resource consumption and throughput aspects, and evaluated by using Xilinx Virtex5. From the statistics of our analysis and implementation, the number of AND gates consumed reduces by 25%, the number of XOR gates consumed reduces by 21.4%, and the throughput increases by 12.556Gbps reaching to 43.32Gbps. This design makes it very easy to implement the low complexity and high speed architecture of Ghash, and the design adapts to high speed networks security for authentication function.


title: A brief report on some physiological parameters of streptozocin-diabetic rat

abstract: Several biological changes occur when streptozocin is given to experimental animals. The rat streptozocin (STZ) model is extensively used in diabetic experiments. In this brief report, the main physiological characteristics of rats injected with streptozocin are presented. These characteristics are manifested by weight loss, organ weight reduction, serum glucose elevation, decrease in serum insulin level, and other enzyme and hormonal changes. A collection of these parameters may be helpful in establishing a database to describe this model.


title: Implementing the Chinese Wall Security Model in Workflow Management Systems

abstract: The Chinese wall security model (CWSM) was designed to provide access controls that mitigate conflict of interest in commercial organizations, and is especially important for large-scale interenterprise workflow applications. This paper describes how to implement the CWSM in a WfMS. We first demonstrate situations in which the role-based access control model is not sufficient for this, and we then propose a security policy language to solve this problem, also providing support for the intrinsic dynamic access control mechanism defined in the CWSM (i.e., the dynamic binding of subjects and elements in the company data set). This language can also specify several requirements of the dynamic security policy that arise when applying the CWSM in WfMSs. Finally we discuss how to implement a run-time system to implement CWSM policies specified by this language in a WfMS.


DAMAGE DETECTION IN COMPOSITE SANDWICH PANELS USING NONLINEAR ACOUSTICS AND COINTEGRATION-BASED SIGNAL PROCESSING


title: AN INVESTIGATION OF FACTORS AFFECTING THE USE OF ICT FOR TEACHING IN THE WESTERN CAPE SCHOOLS

abstract: The Khanya project has been equipping schools and educators with ICT skills and equipment to be used in the curriculum delivery in South Africa. However, research and anecdotal evidence show that there is low adoption rate of ICT among educators in Khanya schools. This interpretive study sets out to analyse the factors which are preventing the educators from using the technology in their work. The perspective of limited access and/or use of ICT as deprivation of capabilities provides a conceptual base for this paper. We employed Sen’s Capability Approach as a conceptual lens to examine the educators’ situation regarding ICT for teaching and learning. Data was collected through in-depth interviews with fourteen educators and two Khanya personnel. The results of the study show that there are a number of factors (personal, social and environmental) which are preventing the educators from realising their potential capabilities from the ICT.


title: Control systems engineering in continuous pharmaceutical manufacturing. May 20-21, 2014 Continuous Manufacturing Symposium.

abstract: This white paper provides a perspective of the challenges, research needs, and future directions for control systems engineering in continuous pharmaceutical processing. The main motivation for writing this paper is to facilitate the development and deployment of control systems technologies so as to ensure quality of the drug product. Although the main focus is on small-molecule pharmaceutical products, most of the same statements apply to biological drug products. An introduction to continuous manufacturing and control systems is followed by a discussion of the current status and technical needs in process monitoring and control, systems integration, and risk analysis. Some key points are that: (1) the desired objective in continuous manufacturing should be the satisfaction of all critical quality attributes (CQAs), not for all variables to operate at steady-state values; (2) the design of start-up and shutdown procedures can significantly affect the economic operation of a continuous manufacturing process; (3) the traceability of material as it moves through the manufacturing facility is an important consideration that can at least in part be addressed using residence time distributions; and (4) the control systems technologies must assure quality in the presence of disturbances, dynamics, uncertainties, nonlinearities, and constraints. Direct measurement, first- principles and empirical model-based predictions, and design space approaches are described for ensuring that CQA specifications are met. Ways are discussed for universities, regulatory bodies, and industry to facilitate working around or through barriers to the development of control systems engineering technologies for continuous drug manufacturing. Industry and regulatory bodies should work with federal agencies to create federal funding mechanisms to attract faculty to this area. Universities should hire faculty interested in developing first- principles models and control systems technologies for drug manufacturing that are easily transportable to industry. Industry can facilitate the move to continuous manufacturing by working with universities on the conception of new continuous pharmaceutical manufacturing process unit operations that have the potential to make major improvements in product quality, controllability, or reduced capital and/or operating costs. Regulatory bodies should ensure that: (1) regulations and regulatory practices promote, and do not derail, the development and implementation of continuous manufacturing and control systems engineering approaches; (2) the individuals who approve specific regulatory filings are sufficiently trained to make good decisions regarding control systems approaches; (3) provide regulatory clarity and eliminate/reduce regulatory risks; (4) financially support the development of high-quality training materials for use of undergraduate students, graduate students, industrial employees, and regulatory staff; (5) enhance the training of their own technical staff by financially supporting joint research projects with universities in the development of continuous pharmaceutical manufacturing processes and the associated control systems engineering theory, numerical algorithms, and software; and (6) strongly encourage the federal agencies that support research to fund these research areas. C


title: Automated Security Analysis of Dynamic Web Applications through Symbolic Code Execution

abstract: The automatic identification of security vulnerabilities is a critical issue in the development of web-based applications. We present a methodology and tool for vulnerability identification based on symbolic code execution exploiting Static Taint Analysis to improve the efficiency of the analysis. The tool targets PHP web applications, and demonstrates the effectiveness of our approach in identifying cross-site scripting and SQL injection vulnerabilities on both NIST synthetic benchmarks and real world applications. It proves to be faster and more effective than its main competitors, both open source and commercial.


Magnetic Wall Climbing Robot for Thin Surfaces with Specific Obstacles


title: Development of novel quinoa-based yoghurt fermented with dextran producer Weissella cibaria MG1

abstract: The aim of this study was to develop a novel beverage fermented with Weissella cibaria MG1 based on aqueous extracts of wholemeal quinoa flour. The protein digestibility of quinoa based-milk was improved by applying complex proteolytic enzymes able to increase protein solubility by 54.58%. The growth and fermentation characteristics of Weissella cibaria MG1, including EPS production at the end of fermentation, were investigated. Fermented wholemeal quinoa milk using MG1 showed high viable cell counts (>109cfu/ml), a pH of 5.16, and significantly higher water holding capacity (WHC, 100%), viscosity (0.57mPas) and exopolysaccharide (EPS) amount (40mg/l) than the chemical acidified control. High EPS (dextran) concentration in quinoa milk caused earlier aggregation because more EPS occupy more space, and the chenopodin were forced to interact with each other. Microstructure observation indicated that the network structures of EPS-protein improve the texture of fermented quinoa milk. Overall, Weissella cibaria MG1 showed satisfactory technology properties and great potential for further possible application in the development of high viscosity fermented quinoa milk.


title: Hierarchical Disentangled Representations.

abstract: Deep latent-variable models learn representations of high-dimensional data in an unsupervised manner. A number of recent efforts have focused on learning representations that disentangle statistically independent axes of variation, often by introducing suitable modifications of the objective function. We synthesize this growing body of literature by formulating a generalization of the evidence lower bound that explicitly represents the trade-offs between sparsity of the latent code, bijectivity of representations, and coverage of the support of the empirical data distribution. Our objective is also suitable to learning hierarchical representations that disentangle blocks of variables whilst allowing for some degree of correlations within blocks. Experiments on a range of datasets demonstrate that learned representations contain interpretable features, are able to learn discrete attributes, and generalize to unseen combinations of factors.


title: Aspect extraction in sentiment analysis: comparative analysis and survey

abstract: Sentiment analysis (SA) has become one of the most active and progressively popular areas in information retrieval and text mining due to the expansion of the World Wide Web (WWW). SA deals with the computational treatment or the classification of user's sentiments, opinions and emotions hidden within the text. Aspect extraction is the most vital and extensively explored phase of SA to carry out the classification of sentiments in precise manners. During the last decade, enormous number of research has focused on identifying and extracting aspects. Therefore, in this survey, a comprehensive overview has been attempted for different aspect extraction techniques and approaches. These techniques have been categorized in accordance with the adopted approach. Despite being a traditional survey, a comprehensive comparative analysis is conducted among different approaches of aspect extraction, which not only elaborates the performance of any technique but also guides the reader to compare the accuracy with other state-of-the-art and most recent approaches.


title: A 0.45V CMOS relaxation oscillator with ±2.5% frequency stability from −55°C to 125°C

abstract: Low-power and low-voltage oscillators are critical for many autonomous systems. Ultra low-power and low-voltage operation can enable these systems to remain functional under extreme supply conditions while consuming very little energy. Stable operation under these conditions and over a wide temperature range can be very challenging. This paper1 presents the design of a relaxation oscillator that operates under 0.5V and consumes less than a 100nW and is thus suitable for many autonomous applications. Despite of the low supply voltage, simulation results shows a relaxation oscillator with a frequency stability better than ±2.5% from −55°C to 125°C.


title: A 2-Port Stable Negative Capacitance Circuit Design With Unilateral Gain Boosting Technique

abstract: Herein, we present a floating 2-port nonreciprocal negative capacitance (NCAP) circuit that employs a unilateral gain boosting technique by including an asymmetric cross-coupled differential cascode transistor pair. The proposed circuit provides an asymmetric gain characteristic. The forward gain ( $S_{21}$ ) is increased at the cost of the backward gain ( $S_{12}$ ). The technique not only provides a boosted forward gain but also stable operation resulting from this reduced backward gain. We further investigate the stability of the NCAP circuit in terms of the output resistances of the current sources in the circuit. The proposed NCAP circuit was implemented in IBM 7RF 180-nm CMOS technology and was designed to have a supply voltage of 3 V and a current of 10 mA. The S-parameter measurements showed a self-resonance frequency of 2.25 GHz. The maximum $S_{21}$ was −0.2 dB at 1 GHz. In the frequency range from 0.5 to 1.5 GHz, the capacitance was approximately −4 pF, and the difference between $S_{21}$ and $S_{12}$ was 8.7–9.6 dB. The size of the core chip was $150 \times 190~\mu \text{m}^{2}$ .


title: The neural substrates of procrastination: A voxel‐based morphometry study

abstract: Abstract Procrastination is a pervasive phenomenon across different cultures and brings about lots of serious consequences, including performance, subjective well-being, and even public policy. However, little is known about the neural substrates of procrastination. In order to shed light upon this question, we investigated the neuroanatomical substrates of procrastination across two independent samples using voxel-based morphometry (VBM) method. The whole-brain analysis showed procrastination was positively correlated with the gray matter (GM) volume of clusters in the parahippocampal gyrus (PHG) and the orbital frontal cortex (OFC), while negatively correlated with the GM volume of clusters in the inferior frontal gyrus (IFG) and the middle frontal gyrus (MFG) in sample one (151 participants). We further conducted a verification procedure on another sample (108 participants) using region-of-interest analysis to examine the reliability of these results. Results showed procrastination can be predicted by the GM volume of the OFC and the MFG. The present findings suggest that the MFG and OFC, which are the key regions of self-control and emotion regulation, may play an important role in procrastination.


Children's drawings. A preoperative assessment tool.


title: Balancing exploration and exploitation in listwise and pairwise online learning to rank for information retrieval

abstract: As retrieval systems become more complex, learning to rank approaches are being developed to automatically tune their parameters. Using online learning to rank, retrieval systems can learn directly from implicit feedback inferred from user interactions. In such an online setting, algorithms must obtain feedback for effective learning while simultaneously utilizing what has already been learned to produce high quality results. We formulate this challenge as an exploration---exploitation dilemma and propose two methods for addressing it. By adding mechanisms for balancing exploration and exploitation during learning, each method extends a state-of-the-art learning to rank method, one based on listwise learning and the other on pairwise learning. Using a recently developed simulation framework that allows assessment of online performance, we empirically evaluate both methods. Our results show that balancing exploration and exploitation can substantially and significantly improve the online retrieval performance of both listwise and pairwise approaches. In addition, the results demonstrate that such a balance affects the two approaches in different ways, especially when user feedback is noisy, yielding new insights relevant to making online learning to rank effective in practice.


title: Substrate integrated waveguide and its applications to leaky wave antennas

abstract: Substrate Integrated Waveguide has been an emerging technology for the realization of microwave and millimeter wave regions. It is the planar form of the conventional rectangular waveguide. It has profound applications at higher frequencies, since prevalent platforms like microstrip and coplanar waveguide have loss related issues. This paper discusses basic concepts of SIW, design aspects and their applications to leaky wave antennas. A brief overview of recent works on Substrate integrated Waveguide based Leaky Wave Antennas has been provided.


title: Inside YAGO2s: a transparent information extraction architecture

abstract: YAGO [9, 6] is one of the largest public ontologies constructed by information extraction. In a recent refactoring called YAGO2s, the system has been given a modular and completely transparent architecture. In this demo, users can see how more than 30 individual modules of YAGO work in parallel to extract facts, to check facts for their correctness, to deduce facts, and to merge facts from different sources. A GUI allows users to play with different input files, to trace the provenance of individual facts to their sources, to change deduction rules, and to run individual extractors. Users can see step by step how the extractors work together to combine the individual facts to the coherent whole of the YAGO ontology.


title: A Microstrip Antenna Array Formed by Microstrip Line Fed Tooth-Like-Slot Patches

abstract: A new 2 times 2 microstrip antenna array is presented. The element used in this array is a tooth-like-slot patch. This patch is electromagnetically coupled by a microstrip feed line. In addition to the easy feeding, the proposed structure possesses the advantages of being wide bandwidth, high gain and the low cross-polarization level. The patch element has the peak gain 10.2 dBi and the cross-polarization is lower than -23 dB within the impedance bandwidth. The measured VSWR of 2 times 2 array show that the impedance bandwidth is 34%. The gain of the array is 14-16.3 dBi at 4.8-6.1 GHz. And the cross-polarization level of the array is lower than -20 dB within the impedance bandwidth


title: CMOS-MEMS membrane for audio-frequency acoustic actuation

abstract: Using CMOS-MEMS micromachining techniques we have constructed a prototype earphone that is audible from 1 to 15 kHz. The fabrication of the acoustic membrane consists of only two steps in addition to the prior post-CMOS micromachining steps developed at CMU. The ability to build a membrane directly on a standard CMOS chip, integrating mechanical structures with signal processing electronics will enable a variety of applications including economical earphones, microphones, hearing aids, high-fidelity earphones, cellular phones and noise cancellation. The large compliance of the CMOS-MEMS membrane also promises application as a sensitive microphone and pressure sensor.


Extractive Summarization Using Multi-Task Learning with Document Classification


title: Adversarial learning: A critical review and active learning study

abstract: This papers consists of two parts. The first is a critical review of prior art on adversarial learning, i) identifying some significant limitations of previous works, which have focused mainly on attack exploits and ii) proposing novel defenses against adversarial attacks. The second part is an experimental study considering the adversarial active learning scenario and an investigation of the efficacy of a mixed sample selection strategy for combating an adversary who attempts to disrupt the classifier learning.


title: An Ontology-Driven Framework for Data Transformation in Scientific Workflows

abstract: Ecologists spend considerable effort integrating heterogeneous data for statistical analyses and simulations, for example, to run and test predictive models. Our research is focused on reducing this effort by providing data integration and transformation tools, allowing researchers to focus on “real science,” that is, discovering new knowledge through analysis and modeling. This paper defines a generic framework for transforming heterogeneous data within scientific workflows. Our approach relies on a formalized ontology, which serves as a simple, unstructured global schema. In the framework, inputs and outputs of services within scientific workflows can have structural types and separate semantic types (expressions of the target ontology). In addition, a registration mapping can be defined to relate input and output structural types to their corresponding semantic types. Using registration mappings, appropriate data transformations can then be generated for each desired service composition. Here, we describe our proposed framework and an initial implementation for services that consume and produce XML data.


title: Speed Control for PMSM Servo System Using Predictive Functional Control and Extended State Observer

abstract: The speed regulation problem for permanent magnet synchronous motor (PMSM) servo system is studied in this paper. In order to optimize the control performance of the PMSM servo system, the predictive functional control (PFC) method is introduced in the control design of speed loop. The PFC-based speed control design consists of two steps. A simplified model is employed to predict the future q -axis current of PMSM. Then, an optimal control law is obtained by minimizing a quadratic performance index. However, it is noted that the standard PFC method does not achieve a satisfying effect in the presence of strong disturbances. To this end, an improved PFC method, called the PFC+ESO method, is developed. It introduces extended state observer (ESO) to estimate the lumped disturbances and adds a feedforward compensation item based on the estimated disturbances to the PFC speed controller. Simulation and experiment comparisons are made for these PFC methods and proportional-integral method with antiwindup control method to verify the effectiveness of the proposed methods.


Learning attention for historical text normalization by learning to pronounce


title: Touch-less palm print biometrics: Novel design and implementation

abstract: In this paper, we propose an innovative touch-less palm print recognition system. This project is motivated by the public's demand for non-invasive and hygienic biometric technology. For various reasons, users are concerned about touching the biometric scanners. Therefore, we propose to use a low-resolution web camera to capture the user's hand at a distance for recognition. The users do not need to touch any device for their palm print to be acquired. A novel hand tracking and palm print region of interest (ROI) extraction technique are used to track and capture the user's palm in real-time video stream. The discriminative palm print features are extracted based on a new method that applies local binary pattern (LBP) texture descriptor on the palm print directional gradient responses. Experiments show promising result using the proposed method. Performance can be further improved when a modified probabilistic neural network (PNN) is used for feature matching. Verification can be performed in less than one second in the proposed system.


title: Optimizing feature selection to improve medical diagnosis

abstract: In this paper, we propose a new optimization framework for improving feature selection in medical data classification. We call this framework Support Feature Machine (SFM). The use of SFM in feature selection is to find the optimal group of features that show strong separability between two classes. The separability is measured in terms of inter-class and intra-class distances. The objective of SFM optimization model is to maximize the correctly classified data samples in the training set, whose intra-class distances are smaller than inter-class distances. This concept can be incorporated with the modified nearest neighbor rule for unbalanced data. In addition, a variation of SFM that provides the feature weights (prioritization) is also presented. The proposed SFM framework and its extensions were tested on 5 real medical datasets that are related to the diagnosis of epilepsy, breast cancer, heart disease, diabetes, and liver disorders. The classification performance of SFM is compared with those of support vector machine (SVM) classification and Logical Data Analysis (LAD), which is also an optimization-based feature selection technique. SFM gives very good classification results, yet uses far fewer features to make the decision than SVM and LAD. This result provides a very significant implication in diagnostic practice. The outcome of this study suggests that the SFM framework can be used as a quick decision-making tool in real clinical settings.


title: OPTIMISM BIAS IN MANAGING IT PROJECT RISKS: A CONSTRUAL LEVEL THEORY PERSPECTIVE

abstract: Prior research has shown that people have a tendency to be overly optimistic about future events (i.e., optimism bias) in a variety of settings. In this study, we suggest that optimism bias has significant implications for IT project risk management, as it may cause people to become overly optimistic that they can easily manage project risks. Drawing upon construal level theory (CLT), we investigate optimism bias in managing IT project risks. Based on an experiment with IT professionals, we found that a high-level construal of a project risk leads individuals to have a more optimistic perception about successfully managing the project risk, causes them to focus more on benefits over costs in choosing a risk management plan, and leads them to identify more pros than cons associated with a risk management plan relative to a low-level construal. Implications for both theory and practice are discussed.


Content Based Retrieval Of Kannada Document Image From Multilingual Document Image


title: The impact of design management and process management on quality: an empirical investigation

abstract: Design management and process management are two important elements of total quality management (TQM) implementation. They are drastically different in their targets of improvement, visibility, and techniques. In this paper, we establish a framework for identifying the synergistic linkages of design and process management to the operational quality outcomes during the manufacturing process (internal quality) and upon the field usage of the products (external quality). Through a study of quality practices in 418 manufacturing plants from multiple industries, we empirically demonstrate that both design and process management efforts have an equal positive impact on internal quality outcomes such as scrap, rework, defects, performance, and external quality outcomes such as complaints, warranty, litigation, market share. A detailed contingency analysis shows that the proposed model of synergies between design and process management holds true for large and small firms; for firms with different levels of TQM experience; and in different industries with varying levels of competition, logistical complexity of production, or production process characteristics. Finally, the results also suggest that organizational learning enables mature TQM firms to implement both design and process efforts more rigorously and their synergy helps these firms to attain better quality outcomes. These findings indicate that, to attain superior quality outcomes, firms need to balance their design and process management efforts and persevere with long-term implementation of these efforts. Because the study spans all of the manufacturing sectors (SIC 20 through 39), these conclusions should help firms in any industry revisit their priorities in terms of the relative efforts in design management and process management.


Knowledge Transfer in Intraorganizational Networks : Effects of Network Position and Absorptive Capacity on Business Unit Innovation and Performance


title: Learning to Parse and Translate Improves Neural Machine Translation

abstract: There has been relatively little attention to incorporating linguistic prior to neural machine translation. Much of the previous work was further constrained to considering linguistic prior on the source side. In this paper, we propose a hybrid model, called NMT+RG, that learns to parse and translate by combining the recurrent neural network grammar into the attention-based neural machine translation. Our approach encourages the neural machine translation model to incorporate linguistic prior during training, and lets it translate on its own afterward. Extensive experiments with four language pairs show the effectiveness of the proposed NMT+RG.


title: Automated X-ray Image Analysis for Cargo Security: Critical Review and Future Promise

abstract: We review the relatively immature field of automated image analysis for X-ray cargo imagery. There is increasing demand for automated analysis methods that can assist in the inspection and selection of containers, due to the ever-growing volumes of traded cargo and the increasing concerns that customs- and security-related threats are being smuggled across borders by organised crime and terrorist networks. We split the field into the classical pipeline of image preprocessing and image understanding. Preprocessing includes: image manipulation; quality improvement; Threat Image Projection (TIP); and material discrimination and segmentation. Image understanding includes: Automated Threat Detection (ATD); and Automated Contents Verification (ACV). We identify several gaps in the literature that need to be addressed and propose ideas for future research. Where the current literature is sparse we borrow from the single-view, multi-view, and CT X-ray baggage domains, which have some characteristics in common with X-ray cargo.


Snakemake - a scalable bioinformatics workflow engine


title: An Extension of the String-to-String Correction Problem

abstract: The string-to-string correction problem asks for a sequence S of "edit operations" of minimal cost such that ~(A) = B, for given strings A and B. The edit operations previously investi- gated allow changing one symbol of a string into another single symbol, deleting one symbol from a string, or inserting a single symbol into a string. This paper extends the set of allowable edit opera- tions to include the operation of interchanging the positions of two adjacent characters Under certain restrictions on edit-operation costs, it is shown that the extended problem can still be solved in time proportional to the product of the lengths of the given strings.


Exploring the link between player involvement and learning within digital games.


Developed Forward and Inverse Modelling of Vertical Electrical Sounding (VES) Using MATLAB Implementation


title: Simultaneous charging and discharging integrating EV for V2G and G2V

abstract: Synchronization of EV to distribution grid is gaining attention as the vehicle will coordinate to distribution grid ensuring good power quality. Reduce the charging time of EV is one of the key goals in making user-friendly environment for electric vehicles. In this context, fast DC charging gives an interesting opportunity. This paper develops a Simulink model of synchronized charging and discharging of EV to distribution grid and for V2G and G2V applications, two types of converter schemes are also discussed. For making EV battery charging, the Simulink model comprises of dq frame converter control which consists of DC-DC. With this work, charging of vehicle will be fast and due to discharging of EV battery to grid, owner of vehicle will get the revenue so that overall charging price of vehicle will be reduced. Also, Two types of DC-AC converter scheme for V2G and G2V applications to find out best one on the basis of current THD analysis, prosperity of synchronization and charging-discharging control which is shown using curves of load current, source(grid) current and inverter current. Different aspects of EV's integration to grid are evaluated and better converter scheme is used in charging of EV and the results are shown.


title: A Fully Private Pipeline for Deep Learning on Electronic Health Records

abstract: We introduce an end-to-end private deep learning framework, applied to the task of predicting 30-day readmission from electronic health records. By using differential privacy during training and homomorphic encryption during inference, we demonstrate that our proposed pipeline could maintain high performance while providing robust privacy guarantees against information leak from data transmission or attacks against the model. We also explore several techniques to address the privacy-utility trade-off in deploying neural networks with privacy mechanisms, improving the accuracy of differentially-private training and the computation cost of encrypted operations using ideas from both machine learning and cryptography.


Loss Functions for Discriminative Training of Energy-Based Models


DeepStereo Dense Depth Estimation from Stereo Image Pairs using Convolutional Neural Networks


title: IoT based smart home: Security challenges, security requirements and solutions

abstract: Internet of Thing (IoT) is going to make such a world where physical things (smart home appliances, and smart watches etc.) revolutionized the information networks and services providing systems which provide innovative and smart services to human. With smart home technology, our living area is becoming more comfortable and convenient. Smart home technology provides automated, intelligent, smart, innovative and ubiquitous services to residential users through Information Communication Technology (ICT). Due to internet-connected, dynamic and heterogeneous nature of smart home environment creates new security, authentication and privacy challenges. In this paper, we investigate security attacks in smart home and evaluate their impact on the overall system security. We identified security requirements and solutions in the smart home environment. Based on several scenarios, we suggest to set security goals for the smart home environment. Based on historical data, we forecast security attacks (like malware, virus etc.) and estimated that how many attacks are expected to be launched in coming years.


AN EVALUATION OF THE EFFECT OF SURFACE CHROMIUM CONCENTRATION ON THE OXIDATION OF A STAINLESS STEEL


Ranking-Based Emotion Recognition for Experimental Music


title: On the Evolution of Lehman’s Laws

abstract: In this brief paper, we honor the contributions of the late Prof. Manny Lehman to the study of software evolution. We do so by means of a kind of evolutionary case study: First, we discuss his background in engineering and explore how this helped to shape his views on software systems and their development; next, we discuss the laws of software evolution that he postulated based on his industrial experiences; and finally, we examine how the nature of software systems and their development are undergoing radical change, and we consider what this means for future evolutionary studies of software. Copyright © 2013 John Wiley & Sons, Ltd.


title: Air interface design and ray tracing study for 5G millimeter wave communications

abstract: To meet the explosive growth in traffic during the next twenty years, 5G systems using local area networks need to be developed. These systems will comprise of small cells and will use extreme cell densification. The use of millimeter wave (Mmwave) frequencies, in particular from 20 GHz to 90 GHz, will revolutionize wireless communications given the extreme amount of available bandwidth. However, the different propagation conditions and hardware constraints of Mmwave (e.g., the use of RF beamforming with very large arrays) require reconsidering the modulation methods for Mmwave compared to those used below 6 GHz. In this paper we present ray-tracing results, which, along with recent propagation measurements at Mmwave, all point to the fact that Mmwave frequencies are very appropriate for next generation, 5G, local area wireless communication systems. Next, we propose null cyclic prefix single carrier as the best candidate for Mmwave communications. Finally, systemlevel simulation results show that with the right access point deployment peak rates of over 15 Gbps are possible at Mmwave along with a cell edge experience in excess of 400 Mbps.


title: Perturb and observe MPPT algorithm for solar PV systems-modeling and simulation

abstract: The following paper validates an algorithm for Maximum Power Point Tracking using Perturb and Observe technique. The algorithm starts by setting the computed maximum power PMAX to an initial value (usually zero). Next the actual PV voltage and current are measured at specific intervals and the instantaneous value of PV power, PACT is computed. PMAX and PACT are compared. PMAX and PACT are compared. If PACT is greater than PMAX, it is set as the new value of PMAX. At every instant the PACT is calculated, and the comparison is continuously executed. Hence the final value of PMAX will be the point at which maximum power can be delivered to the load. For maximum power transfer across the load, the input impedance should be equal to the load impedance. Based on the mechanism of load matching the duty cycle of the converter is varied so that the output power will almost be equal to the input in practical systems. The system will be modelled with the help of MATLAB/SIMULINK.


title: Online Social Networking and Addiction—A Review of the Psychological Literature

abstract: Social Networking Sites (SNSs) are virtual communities where users can create individual public profiles, interact with real-life friends, and meet other people based on shared interests. They are seen as a ‘global consumer phenomenon’ with an exponential rise in usage within the last few years. Anecdotal case study evidence suggests that ‘addiction’ to social networks on the Internet may be a potential mental health problem for some users. However, the contemporary scientific literature addressing the addictive qualities of social networks on the Internet is scarce. Therefore, this literature review is intended to provide empirical and conceptual insight into the emerging phenomenon of addiction to SNSs by: (1) outlining SNS usage patterns, (2) examining motivations for SNS usage, (3) examining personalities of SNS users, (4) examining negative consequences of SNS usage, (5) exploring potential SNS addiction, and (6) exploring SNS addiction specificity and comorbidity. The findings indicate that SNSs are predominantly used for social purposes, mostly related to the maintenance of established offline networks. Moreover, extraverts appear to use social networking sites for social enhancement, whereas introverts use it for social compensation, each of which appears to be related to greater usage, as does low conscientiousness and high narcissism. Negative correlates of SNS usage include the decrease in real life social community participation and academic achievement, as well as relationship problems, each of which may be indicative of potential addiction.


title: Locked-In Syndrome: a Challenge to Standard Accounts of Selfhood and Personhood?

abstract: A point made repeatedly over the last few years is that the Locked-in Syndrome (LIS) offers unique real-life material for revisiting and challenging certain ingrained philosophical assumptions about the nature of personhood and personal identity. Indeed, the claim has been made that a closer study of LIS will call into question some of the traditional conceptions of personhood that primarily highlight the significance of consciousness, self-consciousness and autonomy and suggest the need for a more interpersonal account of the person. I am skeptical about these claims and will in the following argue that the theoretical relevance of LIS for an understanding of selfhood and personhood has been exaggerated.


title: SketchNet: Sketch Classification with Web Images

abstract: In this study, we present a weakly supervised approach that discovers the discriminative structures of sketch images, given pairs of sketch images and web images. In contrast to traditional approaches that use global appearance features or relay on keypoint features, our aim is to automatically learn the shared latent structures that exist between sketch images and real images, even when there are significant appearance differences across its relevant real images. To accomplish this, we propose a deep convolutional neural network, named SketchNet. We firstly develop a triplet composed of sketch, positive and negative real image as the input of our neural network. To discover the coherent visual structures between the sketch and its positive pairs, we introduce the softmax as the loss function. Then a ranking mechanism is introduced to make the positive pairs obtain a higher score comparing over negative ones to achieve robust representation. Finally, we formalize above-mentioned constrains into the unified objective function, and create an ensemble feature representation to describe the sketch images. Experiments on the TUBerlin sketch benchmark demonstrate the effectiveness of our model and show that deep feature representation brings substantial improvements over other state-of-the-art methods on sketch classification.


title: Variable selection using random forests

abstract: This paper proposes, focusing on random forests, the increasingly used statistical method for classification and regression problems introduced by Leo Breiman in 2001, to investigate two classical issues of variable selection. The first one is to find important variables for interpretation and the second one is more restrictive and try to design a good parsimonious prediction model. The main contribution is twofold: to provide some experimental insights about the behavior of the variable importance index based on random forests and to propose a strategy involving a ranking of explanatory variables using the random forests score of importance and a stepwise ascending variable introduction strategy.


title: On the Implicit and on the Artificial - Morphogenesis and Emergent Aesthetics in Autonomous Collective Systems

abstract: Imagine a "machine" where there is no pre-commitment to any particular representational scheme: the desired behaviour is distributed and roughly specified simultaneously among many parts, but there is minimal specification of the mechanism required to generate that behaviour, i.e. the global behaviour evolves from the many relations of multiple simple behaviours. A machine that lives to and from/with Synergy. An artificial super-organism that avoids specific constraints and emerges within multiple low-level implicit bio-inspired mechanisms. KEYWORDS: Complex Science, ArtSBots Project, Swarm Intelligence, Stigmergy, UnManned Art, Symbiotic Art, Swarm Paintings, Robot Paintings, Non-Human Art, Painting Emergence and Cooperation, Art and Complexity, ArtBots: The Robot Talent Show.


title: Compatibility of high-intensity strength and endurance training on hormonal and skeletal muscle adaptations

abstract: Thirty-five healthy men were matched and randomly assigned to one of four training groups that performed high-intensity strength and endurance training (C; n = 9), upper body only high-intensity strength and endurance training (UC; n = 9), high-intensity endurance training (E; n = 8), or high-intensity strength training (ST; n = 9). The C and ST groups significantly increased one-repetition maximum strength for all exercises (P < 0.05). Only the C, UC, and E groups demonstrated significant increases in treadmill maximal oxygen consumption. The ST group showed significant increases in power output. Hormonal responses to treadmill exercise demonstrated a differential response to the different training programs, indicating that the underlying physiological milieu differed with the training program. Significant changes in muscle fiber areas were as follows: types I, IIa, and IIc increased in the ST group; types I and IIc decreased in the E group; type IIa increased in the C group; and there were no changes in the UC group. Significant shifts in percentage from type IIb to type IIa were observed in all training groups, with the greatest shift in the groups in which resistance trained the thigh musculature. This investigation indicates that the combination of strength and endurance training results in an attenuation of the performance improvements and physiological adaptations typical of single-mode training.


title: IQ of Neural Networks

abstract: IQ tests are an accepted method for assessing human intelligence. The tests consist of several parts that must be solved under a time constraint. Of all the tested abilities, pattern recognition has been found to have the highest correlation with general intelligence. This is primarily because pattern recognition is the ability to find order in a noisy environment, a necessary skill for intelligent agents. In this paper, we propose a convolutional neural network (CNN) model for solving geometric pattern recognition problems. The CNN receives as input multiple ordered input images and outputs the next image according to the pattern. Our CNN is able to solve problems involving rotation, reflection, color, size and shape patterns and score within the top 5% of human performance.


title: Smoothing Class Frequencies for KNN Medical Article Classification

abstract: K-Nearest Neighbor (KNN) is one of the most popular algorithms for text classification. In many experiments researchers have found that the KNN algorithm accomplishes very good performance on different data sets. In a previous work [16], we proposed an algorithm, called lf-igf KNN, to classify medical articles using local from neighborhood and global from corpus class label frequencies to device a weighting scheme for ranking all data points in the training set. In this work, we modify this previous work going beyond simple counting, both smoothing class label frequencies and neighbor's distances. We provide by this way an alternative and more robust weighted scheme for KNN classification. The evaluation experiments on the collection of medical documents, called Ohsumed, show promising results and inspire us to use smoothing techniques to treat class occurrences in traditional KNN classification.


title: Fast and small nonlinear pseudorandom number generators for computer simulation

abstract: In this paper we present Tyche, a nonlinear pseudorandom number generator designed for computer simulation. Tyche has a small 128-bit state and an expected period length of 2127. Unlike most nonlinear generators, Tyche is consistently fast across architectures, due to its very simple iteration function derived from ChaCha, one of today's fastest stream ciphers. ::: ::: Tyche is especially amenable for the highly parallel environments we find today, in particular for Graphics Processing Units (GPUs), where it enables a very large number of uncorrelated parallel streams running independently. For example, 216 parallel independent streams are expected to generate about 296 pseudorandom numbers each, without overlaps. ::: ::: Additionally, we determine bounds for the period length and parallelism of our generators, and evaluate their statistical quality and performance. We compare Tyche and the variant Tyche-i to the XORWOW and TEA8 generators in CPUs and GPUs. Our comparisons show that Tyche and Tyche-i simultaneously achieve high performance and excellent statistical properties, particularly when compared to other nonlinear generators.


title: A Probabilistic Approach to Aggregating Anomalies for Unsupervised Anomaly Detection with Industrial Applications

abstract: This paper presents a novel, unsupervised approach to detecting anomalies at the collective level. The method probabilistically aggregates the contribution of the individual anomalies in order to detect significantly anomalous groups of cases. The approach is unsupervised in that as only input, it uses a list of cases ranked according to its individual anomaly score. Thus, any anomaly detection algorithm can be used for scoring individual anomalies, both supervised and unsupervised approaches. The applicability of the proposed approach is shown by applying it to an artificial data set and to two industrial data sets — detecting anomalously moving cranes (model-based detection) and anomalous fuel consumption (neighbour-based detection).


title: Nugget discovery in visual exploration environments by query consolidation

abstract: Queries issued by casual users or specialists exploring a dataset often point us to important subsets of the data, be it clusters, outliers or other meaningful features. Capturing and caching such queries (henceforth called nuggets) has many potential benefits, including the optimization of the system performance and the search experience of users. Unfortunately, current visual exploration systems have not yet tapped into this potential resource of identifying and sharing important queries. In this paper, we introduce a query consolidation strategy aimed at solving the general problem of isolating important queries from the potentially huge amount of queries submitted. Our solution clusters redundant queries caused by exploration-style query specification, which is prevalent in data exploration systems. To measure the similarity between queries, we designed an effective distance metric that incorporates both the query specification and the actual query result. To overcome its high complexity when comparing queries with large result sets, we designed an approximation method, which is efficient while still providing excellent accuracy. A user study conducted on multivariate data sets comparing our proposed technique to others in the literature confirms that the proposed distance metric indeed matches well with users' intuition. As proof of feasibility, we integrated our proposed query consolidation solution into the Nugget Management System (NMS) framework [22], which is based on a visual exploration system XmdvTool. A second user study indicates that both the efficiency and accuracy of users' visual exploration are enhanced when supported by NMS.


title: Embedded Real-Time Object Detection for a UAV Warning System

abstract: In this paper, we demonstrate and evaluate a method to perform real-time object detection on-board a UAV using the state of the art YOLOv2 object detection algorithm running on an NVIDIA Jetson TX2, an GPU platform targeted at power constrained mobile applications that use neural networks under the hood. This, as a result of comparing several cutting edge object detection algorithms. Multiple evaluations we present provide insights that help choose the optimal object detection configuration given certain frame rate and detection accuracy requirements. We propose how this setup running on-board a UAV can be used to process a video feed during emergencies in real-time, and feed a decision support warning system using the generated detections.


title: A Conceptual Framework for ERP Evaluation in Universities of Pakistan

abstract: The higher education has been greatly impacted by worldwide trends. In a result, the universities throughout the world are focusing to enhance performance and efficiency in their workings. Therefore, the higher education has moved their systems to Enterprise Resource Planning (ERP) systems to cope with the needs of changing environment. However, the literature review indicates that there is void on the evaluation of success or failure of ERP systems in higher education Institutes in Pakistan. In overall, ERP systems implementation in higher education of Pakistan has not been given appropriate research focus. Thus, in this paper the authors have attempted to develop a conceptual framework for ERP evaluation in Universities of Pakistan. This seeks to expand the knowledge on ERP in higher educational institutes of Pakistan and focuses on understanding the ERP related critical success factors.


title: Beyond Outlier Detection: LookOut for Pictorial Explanation

abstract: Why is a given point in a dataset marked as an outlier by an off-the-shelf detection algorithm? Which feature(s) explain it the best? What is the best way to convince a human analyst that the point is indeed an outlier? We provide succinct, interpretable, and simple pictorial explanations of outlying behavior in multi-dimensional real-valued datasets while respecting the limited attention of human analysts. Specifically, we propose to output a few focus-plots, i.e., pairwise feature plots, from a few, carefully chosen feature sub-spaces. The proposed LookOut makes four contributions: (a) problem formulation: we introduce an “analyst-centered” problem formulation for explaining outliers via focus-plots, (b) explanation algorithm: we propose a plot-selection objective and the LookOut algorithm to approximate it with optimality guarantees, (c) generality: our explanation algorithm is both domain- and detector-agnostic, and (d) scalability: LookOut scales linearly with the size of input outliers to explain and the explanation budget. Our experiments show that LookOut performs near-ideally in terms of maximizing explanation objective on several real datasets, while producing visually interpretable and intuitive results in explaining groundtruth outliers. Code related to this paper is available at: https://github.com/NikhilGupta1997/Lookout.


title: CPlaNet: Enhancing Image Geolocalization by Combinatorial Partitioning of Maps

abstract: Image geolocalization is the task of identifying the location depicted in a photo based only on its visual information. This task is inherently challenging since many photos have only few, possibly ambiguous cues to their geolocation. Recent work has cast this task as a classification problem by partitioning the earth into a set of discrete cells that correspond to geographic regions. The granularity of this partitioning presents a critical trade-off; using fewer but larger cells results in lower location accuracy while using more but smaller cells reduces the number of training examples per class and increases model size, making the model prone to overfitting. To tackle this issue, we propose a simple but effective algorithm, combinatorial partitioning, which generates a large number of fine-grained output classes by intersecting multiple coarse-grained partitionings of the earth. Each classifier votes for the fine-grained classes that overlap with their respective coarse-grained ones. This technique allows us to predict locations at a fine scale while maintaining sufficient training examples per class. Our algorithm achieves the state-of-the-art performance in location recognition on multiple benchmark datasets.


title: HOW TO RIGOROUSLY DEVELOP PROCESS THEORY USING

abstract: Dynamic phenomena are key concerns of IS researchers. However, the methodological approaches usually selected to investigate IS phenomena often rely on variance theory. Underlying factor models represent a rather static approach to the phenomenon by focusing on independent and dependent variables and explaining the degree of the relationships between them. Process theory has been suggested to overcome this problem. Process theory provides a complementary, dynamic perspective on IS phenomena by explaining how independent and dependent variables are linked in terms of event sequences. Although applying both approaches provides more complete pictures of IS phenomena, a lack of research methods focusing on process theories may hinder this goal. This article seeks to help closing this gap by examining how case research can be applied to develop process theory. We analyzed IS case research as well as process research literature and consolidated inputs from both sources toward a single methodology. The results highlight that the development of process theory benefits from a consistent methodology and quality measures that have been suggested in general case research. However, we also found that each step requires specific consideration of process theory characteristics in order to develop rigorous process theory.


title: Automatic classification of single facial images

abstract: We propose a method for automatically classifying facial images based on labeled elastic graph matching, a 2D Gabor wavelet representation, and linear discriminant analysis. Results of tests with three image sets are presented for the classification of sex, "race", and expression. A visual interpretation of the discriminant vectors is provided.


title: Specifying Multiple Representations of Design Objects in SORAC

abstract: There is a current need for the development of intelligent CAD systems to integrate all phases of a design product’s lifecycle. One challenge is to satisfy the requirements of many different representation methods needed for the support of specialized designers working on the same application. It is essential to provide a CAD system composed of a number of representations or views, possibly derived from the same unified model. It is very difficult to define these specialized views correctly so that changes made to one view do not cause inconsistencies in the rest of the representation. Here we describe an approach to view definition that is rooted in our work on active databases. In this paper, we present extensions to SORAC that allow specialized view representations to be correctly defined in the same manner. View derivations are modeled as semantic relationships, with transformations between view and underlying representation as the associated behaviors. This allows the SORAC analysis tools to be extended to identify problematic view definition and transformation rules. Since specialized representations for design are often derived from long established practices, problematic view definitions, once discovered, must still be permitted. Thus, we conclude with a discussion of a stage-based transaction paradigm in which problematic updates can be managed.


title: A weighted dominant color descriptor for content-based image retrieval

abstract: Color has been extensively used in the process of image retrieval. The dominant color descriptor (DCD) that was proposed by MPEG-7 is a famous case in point. It is based on compactly describing the prominent colors of an image or a region. However, this technique suffers from some shortcomings; especially with respect to object-based image retrieval. In this paper, a new semantic feature extracted from dominant colors (weight for each DC) is proposed. The newly proposed technique helps reduce the effect of image background on image matching decision where an object's colors receive much more focus. In addition, a modification to DC-based similarity measure is also proposed. Experimental results demonstrate that the proposed descriptor with the similarity measure modification performs better than the existing descriptor in content-based image retrieval application. The proposed descriptor considers as step forward to the object-based image retrieval.


title: Belief & evidence in empirical software engineering

abstract: Empirical software engineering has produced a steady stream of evidence-based results concerning the factors that affect important outcomes such as cost, quality, and interval. However, programmers often also have strongly-held a priori opinions about these issues. These opinions are important, since developers are highly-trained professionals whose beliefs would doubtless affect their practice. As in evidence-based medicine, disseminating empirical findings to developers is a key step in ensuring that the findings impact practice. In this paper, we describe a case study, on the prior beliefs of developers at Microsoft, and the relationship of these beliefs to actual empirical data on the projects in which these developers work. Our findings are that a) programmers do indeed have very strong beliefs on certain topics b) their beliefs are primarily formed based on personal experience, rather than on findings in empirical research and c) beliefs can vary with each project, but do not necessarily correspond with actual evidence in that project. Our findings suggest that more effort should be taken to disseminate empirical findings to developers and that more in-depth study the interplay of belief and evidence in software practice is needed.


title: A Systematic Mapping Study on Requirements Engineering in Software Ecosystems

abstract: Software ecosystems (SECOs) and open innovation processes have been claimed as a way forward for the software industry. A proper understanding of requirements is as important for these IT-systems as for more traditional ones. This paper presents a mapping study on the issues of requirements engineering and quality aspects in SECOs and analyzes emerging ideas. Our findings indicate that among the various phases or subtasks of requirements engineering, most of the SECO specific research has been accomplished on elicitation, analysis, and modeling. On the other hand, requirements selection, prioritization, verification, and traceability has attracted few published studies. Among the various quality attributes, most of the SECOs research has been performed on security, performance and testability. On the other hand, reliability, safety, maintainability, transparency, usability attracted few published studies. The paper provides a review of the academic literature about SECO-related requirements engineering activities, modeling approaches, and quality attributes, positions the source publications in a taxonomy of issues and identifies gaps where there has been little research.


Sensor Study for Monitoring Varroa Mites on Honey Bees ( Apis mellifera )


title: Deformable model for estimating clothed and naked human shapes from a single image

abstract: Estimation of human shape from images has numerous applications ranging from graphics to surveillance. A single image provides insufficient constraints (e.g. clothing), making human shape estimation more challenging. We propose a method to simultaneously estimate a person's clothed and naked shapes from a single image of that person wearing clothing. The key component of our method is a deformable model of clothed human shape. We learn our deformable model, which spans variations in pose, body, and clothes, from a training dataset. These variations are derived by the non-rigid surface deformation, and encoded in various low-dimension parameters. Our deformable model can be used to produce clothed 3D meshes for different people in different poses, which neither appears in the training dataset. Afterward, given an input image, our deformable model is initialized with a few user-specified 2D joints and contours of the person. We optimize the parameters of the deformable model by pose fitting and body fitting in an iterative way. Then the clothed and naked 3D shapes of the person can be obtained simultaneously. We illustrate our method for texture mapping and animation. The experimental results on real images demonstrate the effectiveness of our method.


title: Detection and Analysis of 2016 US Presidential Election Related Rumors on Twitter

abstract: The 2016 U.S. presidential election has witnessed the major role of Twitter in the year’s most important political event. Candidates used this social media platform extensively for online campaigns. Meanwhile, social media has been filled with rumors, which might have had huge impacts on voters’ decisions. In this paper, we present a thorough analysis of rumor tweets from the followers of two presidential candidates: Hillary Clinton and Donald Trump. To overcome the difficulty of labeling a large amount of tweets as training data, we detect rumor tweets by matching them with verified rumor articles. We analyze over 8 million tweets collected from the followers of the two candidates. Our results provide answers to several primary concerns about rumors in this election, including: which side of the followers posted the most rumors, who posted these rumors, what rumors they posted, and when they posted these rumors. The insights of this paper can help us understand the online rumor behaviors in American politics.


title: A multinomial logistic regression modeling approach for anomaly intrusion detection

abstract: Although researchers have long studied using statistical modeling techniques to detect anomaly intrusion and profile user behavior, the feasibility of applying multinomial logistic regression modeling to predict multi-attack types has not been addressed, and the risk factors associated with individual major attacks remain unclear. To address the gaps, this study used the KDD-cup 1999 data and bootstrap simulation method to fit 3000 multinomial logistic regression models with the most frequent attack types (probe, DoS, U2R, and R2L) as an unordered independent variable, and identified 13 risk factors that are statistically significantly associated with these attacks. These risk factors were then used to construct a final multinomial model that had an ROC area of 0.99 for detecting abnormal events. Compared with the top KDD-cup 1999 winning results that were based on a rule-based decision tree algorithm, the multinomial logistic model-based classification results had similar sensitivity values in detecting normal (98.3% vs. 99.5%), probe (85.6% vs. 83.3%), and DoS (97.2% vs. 97.1%); remarkably high sensitivity in U2R (25.9% vs. 13.2%) and R2L (11.2% vs. 8.4%); and a significantly lower overall misclassification rate (18.9% vs. 35.7%). The study emphasizes that the multinomial logistic regression modeling technique with the 13 risk factors provides a robust approach to detect anomaly intrusion.


title: Sparse Signal Recovery for Binary Compressed Sensing by Majority Voting Neural Networks

abstract: In this paper, we propose majority voting neural networks for sparse signal recovery in binary compressed sensing. The majority voting neural network is composed of several independently trained feedforward neural networks employing the sigmoid function as an activation function. Our empirical study shows that a choice of a loss function used in training processes for the network is of prime importance. We found a loss function suitable for sparse signal recovery, which includes a cross entropy-like term and an $L_1$ regularized term. From the experimental results, we observed that the majority voting neural network achieves excellent recovery performance, which is approaching the optimal performance as the number of component nets grows. The simple architecture of the majority voting neural networks would be beneficial for both software and hardware implementations.


title: Underwater Optical Image Processing: A Comprehensive Review

abstract: Underwater cameras are widely used to observe the sea floor. They are usually included in autonomous underwater vehicles (AUVs), unmanned underwater vehicles (UUVs), and in situ ocean sensor networks. Despite being an important sensor for monitoring underwater scenes, there exist many issues with recent underwater camera sensors. Because of light’s transportation characteristics in water and the biological activity at the sea floor, the acquired underwater images often suffer from scatters and large amounts of noise. Over the last five years, many methods have been proposed to overcome traditional underwater imaging problems. This paper aims to review the state-of-the-art techniques in underwater image processing by highlighting the contributions and challenges presented in over 40 papers. We present an overview of various underwater image-processing approaches, such as underwater image de-scattering, underwater image color restoration, and underwater image quality assessments. Finally, we summarize the future trends and challenges in designing and processing underwater imaging sensors.


title: Newborn footprint recognition using orientation feature

abstract: Newborn and infant personal authentication is a critical issue for hospital, birthing centers, and other institutions where multiple births occur, which has not been well studied in the past. In this paper, we propose a novel online newborn personal authentication system for this issue based on footprint recognition. Compared with traditional offline footprinting scheme, the proposed system can capture digital footprint images with high quality. We also develop a preprocessing method for orientation and scale normalization. In this way, a coordinate system is defined to align the images, and a region of interest (ROI) is cropped. In recognition stage, four orientation feature-based approaches, Ordinal Code, BOCV, Competitive Code, and Robust Line Orientation Code, are exploited for recognition. A newborn footprint database is established to examine the performance of the proposed system, and promising experimental results demonstrate the effectiveness of the proposed system.


title: Optimal Neural Network Feature Selection for Spatial-Temporal Forecasting

abstract: Neural networks, and in general machine learning techniques, have been widely employed in forecasting time series and more recently in predicting spatial–temporal signals. All of these approaches involve some kind of feature selection regarding what past data and what neighbor data to use for forecasting. In this article, we show extensive empirical evidence on how to independently construct the optimal feature selection or input representation used by the input layer of a feed forward neural network for the purpose of forecasting spatial–temporal signals. The approach is based on results from the dynamical systems theory, namely, nonlinear embedding theorems. We demonstrate it for a variety of spatial–temporal signals and show that the optimal input layer representation consists of a grid, with spatial–temporal lags determined by the minimum of the mutual information of the spatial–temporal signals and the number of points taken in space–time decided by the embedding dimension of the signal. We present evidence of this proposal by running a Monte Carlo simulation of several combinations of input layer feature designs and show that the one predicted by the nonlinear embedding theorems seems to be optimal or close to being optimal. In total, we show evidence in four unrelated systems: a series of coupled Henon maps, a series of coupled ordinary differential equations (Lorenz-96) phenomenologically modeling atmospheric dynamics, the Kuramoto–Sivashinsky equation, a partial differential equation used in studies of instabilities in laminar flame fronts, and finally real physical data from sunspot areas in the Sun (in latitude and time) from 1874 to 2015. These four examples cover the range from simple toy models to complex nonlinear dynamical simulations and real data. Finally, we also compare our proposal against alternative feature selection methods and show that it also works for other machine learning forecasting models.Neural networks, and in general machine learning techniques, have been widely employed in forecasting time series and more recently in predicting spatial–temporal signals. All of these approaches involve some kind of feature selection regarding what past data and what neighbor data to use for forecasting. In this article, we show extensive empirical evidence on how to independently construct the optimal feature selection or input representation used by the input layer of a feed forward neural network for the purpose of forecasting spatial–temporal signals. The approach is based on results from the dynamical systems theory, namely, nonlinear embedding theorems. We demonstrate it for a variety of spatial–temporal signals and show that the optimal input layer representation consists of a grid, with spatial–temporal lags determined by the minimum of the mutual information of the spatial–temporal signals and the number of points taken in space–time decided by the embedding dimension of the signal. We present e...


title: Collaborative Memory Network for Recommendation Systems

abstract: Recommendation systems play a vital role to keep users engaged with personalized content in modern online platforms. Deep learning has revolutionized many research fields and there is a recent surge of interest in applying it to collaborative filtering (CF). However, existing methods compose deep learning architectures with the latent factor model ignoring a major class of CF models, neighborhood or memory-based approaches. We propose Collaborative Memory Networks (CMN), a deep architecture to unify the two classes of CF models capitalizing on the strengths of the global structure of latent factor model and local neighborhood-based structure in a nonlinear fashion. Motivated by the success of Memory Networks, we fuse a memory component and neural attention mechanism as the neighborhood component. The associative addressing scheme with the user and item memories in the memory module encodes complex user-item relations coupled with the neural attention mechanism to learn a user-item specific neighborhood. Finally, the output module jointly exploits the neighborhood with the user and item memories to produce the ranking score. Stacking multiple memory modules together yield deeper architectures capturing increasingly complex user-item relations. Furthermore, we show strong connections between CMN components, memory networks and the three classes of CF models. Comprehensive experimental results demonstrate the effectiveness of CMN on three public datasets outperforming competitive baselines. Qualitative visualization of the attention weights provide insight into the model's recommendation process and suggest the presence of higher order interactions.


title: Business Model of a Botnet

abstract: Botnets continue to be an active threat against firms or companies and individuals worldwide. Previous research regarding botnets has unveiled information on how the system and their stakeholders operate, but an insight on the economic structure that supports these stakeholders is lacking. The objective of this research is to analyse the business model and determine the revenue stream of a botnet owner. We also study the botnet life-cycle and determine the costs associated with it on the basis of four case studies. We conclude that building a full scale cyber army from scratch is very expensive where as acquiring a previously developed botnet requires a little cost. We find that initial setup and monthly costs were minimal compared to total revenue.


title: Dissociating contributions of ACC and vmPFC in reward prediction, outcome, and choice

abstract: Acting in an uncertain environment requires estimating the probability and the value of potential outcomes. These computations are typically ascribed to various parts of the medial prefrontal cortex (mPFC), but the functional architecture of this region remains debated. The anterior cingulate cortex (ACC) encodes reward prediction and outcome (i.e. win vs lose, Silvetti, Seurinck, & Verguts, 2013. Cortex, 49(6), 1627–35. doi:10.1016/j.cortex.2012.05.008). An outcome-related value signal has also been reported in the ventromedial Prefrontal Cortex (vmPFC, Rangel & Hare, 2010. Current Opinion in Neurobiology, 20(2), 262–70. doi:10.1016/j.conb.2010.03.001). Whether a functional dissociation can be traced in these regions with respect to reward prediction and outcome has been suggested but not rigorously tested. Hence an fMRI study was designed to systematically examine the contribution of ACC and vmPFC to reward prediction and outcome. A striking dissociation was identified, with ACC coding for positive prediction errors and vmPFC responding to outcome, irrespective of probability. Moreover, ACC has been assigned a crucial role in the selection of intentional actions (decision-making) and computing the value associated to these actions (action-based value). Conversely, vmPFC seems to implement stimulus-based value processing (Rudebeck et al., 2008. Journal of Neuroscience, 28(51), 13775–85. doi:10.1523/JNEUROSCI.3541-08.2008; Rushworth, Behrens, Rudebeck, & Walton, 2007. Trends in Cognitive Sciences, 11(4), 168–76. doi:10.1016/j.tics.2007.01.004). Therefore, a decision-making factor (choice vs. no choice condition) was also implemented in the present paradigm to distinguish stimulus-based versus action-based value coding in the mPFC during both decision and outcome phase. We found that vmPFC was more activated during the outcome phase in the no-choice than in the choice condition, potentially confirming the role of this area in stimulus-based (more than action-based) value processing.


title: Auricular anthropometry of Hong Kong Chinese babies.

abstract: OBJECTIVES ::: To provide a database of the auricular measurements of Chinese infants born in Hong Kong. ::: ::: ::: DESIGN ::: Prospective cross-sectional study. ::: ::: ::: SETTING AND SAMPLE POPULATION ::: A total of 2384 healthy singleton, born consecutively at the Prince of Wales Hospital and the Union Hospital from October 1998 to September 2000, were included in the study. The range of gestation was 33-42 weeks. ::: ::: ::: MEASUREMENTS AND RESULTS ::: Measurements included ear width (EW), ear length (EL) and ear position (EP). The data show generally higher values for males in the parameters measured. When compared with previously published data for Caucasian and Jordanian term babies, Chinese babies have shorter EL. The ears were within normal position in nearly all our infants. ::: ::: ::: CONCLUSION ::: The human ear appears to grow in a remarkably constant fashion. This study establishes the first set of gestational age-specific standard of the ear parameters for Chinese new-borns, potentially enabling early syndromal diagnosis. There are significant inter-racial differences in these ear parameters.


title: “It's Just a Lot of Work”: Adolescents’ Self‐Presentation Norms and Practices on Facebook and Instagram

abstract: We explored adolescents' (12- to 18-year-olds; n = 51) awareness of their audience and subsequent self-presentation practices on Facebook and Instagram through focus group discussions. Findings suggest that teens, who are developmentally able to perceive a situation from the third-person perspective and who value peer approval, purposefully share content to appear interesting, well liked, and attractive. Some teens invested great effort into posting by these norms, even asking their friends to help; however, this was more common among girls. Older teens especially discussed taking the perspective of their audience when deciding what to post, which is consistent with the finding that perspective taking continues to develop throughout adolescence. These findings suggest that perspective taking skills and need for peer approval influence self-presentation online.


title: Design of Broadband High-Efficiency Power Amplifiers Based on a Series of Continuous Modes

abstract: In this letter, a series of continuous modes for designing broadband high-efficiency power amplifiers (PAs) are proposed, which extend the continuous mode concept. The impedance solutions space based on these modes offer a greater freedom for realizing broadband high-efficiency PAs. To verify this theory, a high-efficiency PA over a 54.5% bandwidth from 1.6 to 2.8 GHz is designed. The simulated results show that the fundamental impedances in this PA are found to be in good agreement with the calculated ones. Moreover, this PA delivers average output power of 14.2 W with an average drain efficiency of 75.7% in the target band.


Smart-Contract Value-Transfer Protocols on a Distributed Mobile Application Platform


title: Real-time lane detection in various conditions and night cases

abstract: In this paper, we propose a real-time lane detection algorithm based on a hyperbola-pair lane boundary model and an improved RANSAC paradigm. Instead of modeling each road boundary separately, we propose a model to describe the road boundary as a pair of parallel hyperbolas on the ground plane. A fuzzy measurement is introduced into the RANSAC paradigm to improve the accuracy and robustness of fitting the points on the boundaries into the model. Our method is able to deal with existence of partial occlusion, other traffic participants and markings, etc. Experiment in many different conditions, including various conditions of illumination, weather and road, demonstrates its high performance and accuracy


title: Scoring Hidden Markov Models

abstract: Motivation: Statistical sequence comparison techniques, such as hidden Markov models and generalized profiles, calculate the probability that a sequence was generated by a given model. Log-odds scoring is a means of evaluating this probability by comparing it to a null hypothesis, usually a simpler statistical model intended to represent the universe of sequences as a whole, rather than the group of interest. Such scoring leads to two immediate questions: what should the null model be, and what threshold of log-odds score should be deemed a match to the model. Results: This paper analyses these two issues experimentally. Within the context of the Sequence Alignment and Modeling software suite (SAM), we consider a variety of null models and suitable thresholds. Additionally, we consider HMMer's log-odds scoring and SAM's original Z-scoring method. Among the null model choices, a simple looping null model that emits characters according to the geometric mean of the character probabilities in the columns modeled by the hidden Markov model (HMM) performs well or best across all four discrimination experiments. Availability: Information on obtaining the SAM program suite (free for academic use), as well as a server interface, is available from http://www.cse.ucsc.edu/research/compbio/ sam.html. HMMer is freely available from http: I/genome.


title: 360° video stabilization

abstract: We present a hybrid 3D-2D algorithm for stabilizing 360° video using a deformable rotation motion model. Our algorithm uses 3D analysis to estimate the rotation between key frames that are appropriately spaced such that the right amount of motion has occurred to make that operation reliable. For the remaining frames, it uses 2D optimization to maximize the visual smoothness of feature point trajectories. A new low-dimensional flexible deformed rotation motion model enables handling small translational jitter, parallax, lens deformation, and rolling shutter wobble. Our 3D-2D architecture achieves better robustness, speed, and smoothing ability than either pure 2D or 3D methods can provide. Stabilizing a video with our method takes less time than playing it at normal speed. The results are sufficiently smooth to be played back at high speed-up factors; for this purpose we present a simple 360° hyperlapse algorithm that remaps the video frame time stamps to balance the apparent camera velocity.


title: Deep Residual Networks with Exponential Linear Unit

abstract: The depth of convolutional neural networks is a crucial ingredient for reduction in test errors on benchmarks like ImageNet and COCO. However, training a neural network becomes difficult with increasing depth. Problems like vanishing gradient and diminishing feature reuse are quite trivial in very deep convolutional neural networks. The notable recent contributions towards solving these problems and simplifying the training of very deep models are Residual and Highway Networks. These networks allow earlier representations (from the input or those learned in earlier layers) to flow unimpededly to later layers through skip connections. Such very deep models with hundreds or more layers have lead to a considerable decrease in test errors, on benchmarks like ImageNet and COCO. In this paper, we propose to replace the combination of ReLU and Batch Normalization with Exponential Linear Unit (ELU) in Residual Networks. Our experiments show that this not only speeds up the learning behavior in Residual Networks, but also improves the classification performance as the depth increases. Our model increases the accuracy on datasets like CIFAR-10 and CIFAR-100 by a significant margin.