Query: Scalable real-time volumetric surface reconstruction

Relevant passages:
KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera <=SEP=> KinectFusion enables a user holding and moving a standard Kinect camera to rapidly create detailed 3D reconstructions of an indoor scene. Only the depth data from Kinect is used to track the 3D pose of the sensor and reconstruct, geometrically precise, 3D models of the physical scene in real-time. The capabilities of KinectFusion, as well as the novel GPU-based pipeline are described in full. Uses of the core system for low-cost handheld scanning, and geometry-aware augmented reality and physics-based interactions are shown. Novel extensions to the core GPU pipeline demonstrate object segmentation and user interaction directly in front of the sensor, without degrading camera tracking or reconstruction. These extensions are used to enable real-time multi-touch interactions anywhere, allowing any planar or non-planar reconstructed physical surface to be appropriated for touch.

DTAM: Dense tracking and mapping in real-time <=SEP=> DTAM is a system for real-time camera tracking and reconstruction which relies not on feature extraction but dense, every pixel methods. As a single hand-held RGB camera flies over a static scene, we estimate detailed textured depth maps at selected keyframes to produce a surface patchwork with millions of vertices. We use the hundreds of images available in a video stream to improve the quality of a simple photometric data term, and minimise a global spatially regularised energy functional in a novel non-convex optimisation framework. Interleaved, we track the camera's 6DOF motion precisely by frame-rate whole image alignment against the entire dense model. Our algorithms are highly parallelisable throughout and DTAM achieves real-time performance using current commodity GPU hardware. We demonstrate that a dense model permits superior tracking performance under rapid motion compared to a state of the art method using features; and also show the additional usefulness of the dense model for real-time scene interaction in a physics-enhanced augmented reality application.

A Theory of Shape by Space Carving <=SEP=> In this paper we consider the problem of computing the 3D shape of an unknown, arbitrarily-shaped scene from multiple photographs taken at known but arbitrarily-distributed viewpoints. By studying the equivalence class of all 3D shapes that reproduce the input photographs, we prove the existence of a special member of this class, the photo hull, that (1) can be computed directly from photographs of the scene, and (2) subsumes all other members of this class. We then give a provably-correct algorithm, called Space Carving, for computing this shape and present experimental results on complex real-world scenes. The approach is designed to (1) capture photorealistic shapes that accurately model scene appearance from a wide range of viewpoints, and (2) account for the complex interactions between occlusion, parallax, shading, and their view-dependent effects on scene-appearance.

Parallel Tracking and Mapping for Small AR Workspaces <=SEP=> This paper presents a method of estimating camera pose in an unknown scene. While this has previously been attempted by adapting SLAM algorithms developed for robotic exploration, we propose a system specifically designed to track a hand-held camera in a small AR workspace. We propose to split tracking and mapping into two separate tasks, processed in parallel threads on a dual-core computer: one thread deals with the task of robustly tracking erratic hand-held motion, while the other produces a 3D map of point features from previously observed video frames. This allows the use of computationally expensive batch optimisation techniques not usually associated with real-time operation: The result is a system that produces detailed maps with thousands of landmarks which can be tracked at frame-rate, with an accuracy and robustness rivalling that of state-of-the-art model-based systems.

A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms <=SEP=> Stereo matching is one of the most active research areas in computer vision. While a large number of algorithms for stereo correspondence have been developed, relatively little work has been done on characterizing their performance. In this paper, we present a taxonomy of dense, two-frame stereo methods. Our taxonomy is designed to assess the different components and design decisions made in individual stereo algorithms. Using this taxonomy, we compare existing stereo methods and present experiments evaluating the performance of many different variants. In order to establish a common software platform and a collection of data sets for easy evaluation, we have designed a stand-alone, flexible C++ implementation that enables the evaluation of individual components and that can easily be extended to include new algorithms. We have also produced several new multi-frame stereo data sets with ground truth and are making both the code and data sets available on the Web. Finally, we include a comparative evaluation of a large set of today's best-performing stereo algorithms.

New types of acetate-oxidizing, sulfate-reducing Desulfobacter species, D. hydrogenophilus sp. nov., D. latus sp. nov., and D. curvatus sp. nov. <=SEP=> Sulfate-reducing bacteria with oval to rod-shaped cells (strains AcRS1, AcRS2) and vibrio-shaped cells (strains AcRM3, AcRM4, AcRM5) differing by size were isolated from anaerobic marine sediment with acetate as the only electron donor. A vibrio-shaped type (strain AcKo) was also isolated from freshwater sediment. Two strains (AcRS1, AcRM3) used ethanol and pyruvate in addition to acetate, and one strain (AcRS1) grew autotrophically with H2, sulfate and CO2. Higher fatty acids or lactate were never utilized. All isolates were able to grow in ammonia-free medium in the presence of N2. Nitrogenase activity under such conditions was demonstrated by the acetylene reduction test. The facultatively lithoautotrophic strain (AcRS1), a strain (AcRS2) with unusually large cells (2×5 μm), and a vibrio-shaped strain (AcRM3) are described as new Desulfobacter species, D. hydrogenophilus, D. latus, and D. curvatus, respectively.

Offline and Online calibration of Mobile Robot and SLAM Device for Navigation <=SEP=> Robot navigation technology is required to accomplish difficult tasks in various environments. In navigation, it is necessary to know the information of the external environments and the state of the robot under the environment. On the other hand, various studies have been done on SLAM technology, which is also used for navigation, but also applied to devices for Mixed Reality and the like. In this paper, we propose a robot-device calibration method for navigation with a device using SLAM technology on a robot. The calibration is performed by using the position and orientation information given by the robot and the device. In the calibration, the most efficient way of movement is clarified according to the restriction of the robot movement. Furthermore, we also show a method to dynamically correct the position and orientation of the robot so that the information of the external environment and the shape information of the robot maintain consistency in order to reduce the dynamic error occurring during navigation. Our method can be easily used for various kinds of robots and localization with sufficient precision for navigation is possible with offline calibration and online position correction. In the experiments, we confirm the parameters obtained by two types of offline calibration according to the degree of freedom of robot movement and validate the effectiveness of online correction method by plotting localized position error during robot’s intense movement. Finally, we show the demonstration of navigation using SLAM device.

Text Detection and Recognition in Imagery: A Survey <=SEP=> This paper analyzes, compares, and contrasts technical challenges, methods, and the performance of text detection and recognition research in color imagery. It summarizes the fundamental problems and enumerates factors that should be considered when addressing these problems. Existing techniques are categorized as either stepwise or integrated and sub-problems are highlighted including text localization, verification, segmentation and recognition. Special issues associated with the enhancement of degraded text and the processing of video text, multi-oriented, perspectively distorted and multilingual text are also addressed. The categories and sub-categories of text are illustrated, benchmark datasets are enumerated, and the performance of the most representative approaches is compared. This review provides a fundamental comparison and analysis of the remaining problems in the field.

A Neural Network Framework for Fair Classifier <=SEP=> Machine learning models are extensively being used in decision making, especially for prediction tasks. These models could be biased or unfair towards a specific sensitive group either of a specific race, gender or age. Researchers have put efforts into characterizing a particular definition of fairness and enforcing them into the models. In this work, mainly we are concerned with the following three definitions, Disparate Impact, Demographic Parity and Equalized Odds. Researchers have shown that Equalized Odds cannot be satisfied in calibrated classifiers unless the classifier is perfect. Hence the primary challenge is to ensure a degree of fairness while guaranteeing as much accuracy as possible. Fairness constraints are complex and need not be convex. Incorporating them into a machine learning algorithm is a significant challenge. Hence, many researchers have tried to come up with a surrogate loss which is convex in order to build fair classifiers. Besides, certain papers try to build fair representations by preprocessing the data, irrespective of the classifier used. Such methods, not only require a lot of unrealistic assumptions but also require human engineered analytical solutions to build a machine learning model. We instead propose an automated solution which is generalizable over any fairness constraint. We use a neural network which is trained on batches and directly enforces the fairness constraint as the loss function without modifying it further. We have also experimented with other complex performance measures such as H-mean loss, Q-mean-loss, F-measure; without the need for any surrogate loss functions. Our experiments prove that the network achieves similar performance as state of the art. Thus, one can just plug-in appropriate loss function as per required fairness constraint and performance measure of the classifier and train a neural network to achieve that.

Development of GaN HEMT based High Power High Efficiency Distributed Power Amplifier for Military Applications <=SEP=> Implementing wide bandgap GaN HEMT device into broadband distributed power amplifier creates a tremendous opportunity for RF designers to develop high power high efficiency very broadband power amplifiers for military applications. Several prototypes of 10-40 W GaN based distributed power amplifiers, including MMIC distributed PA, are currently under the development at Rockwell Collins, Inc. In this paper, we will discuss the results of a 10 W distributed power amplifier with the maximum power output of more than 40 dBm and a power-added efficiency of 30-70% over the bandwidth of 20-2000 MHz.

An Empirical Analysis of Deep Network Loss Surfaces <=SEP=> The training of deep neural networks is a high-dimension optimization problem with respect to the loss function of a model. Unfortunately, these functions are of high dimension and non-convex and hence difficult to characterize. In this paper, we empirically investigate the geometry of the loss functions for state-of-the-art networks with multiple stochastic optimization methods. We do this through several experiments that are visualized on polygons to understand how and when these stochastic optimization methods find local minima.

On Modeling and Predicting Individual Paper Citation Count over Time <=SEP=> Evaluating a scientist’s past and future potential impact is key in decision making concerning with recruitment and funding, and is increasingly linked to publication citation count. Meanwhile, timely identifying those valuable work with great potential before they receive wide recognition and become highly cited papers is both useful for readers and authors in many regards. We propose a method for predicting the citation counts of individual publications, over an arbitrary time period. Our approach explores paper-specific covariates, and a point process model to account for the aging effect and triggering role of recent citations, through which papers lose and gain their popularity, respectively. Empirical results on the Microsoft Academic Graph data suggests that our model can be useful for both prediction and interpretability.

Adaptive Knee Joint Exoskeleton Based on Biological Geometries <=SEP=> This paper presents a relatively complete analytical model of a knee joint interacting with a two-link exoskeleton for investigating the effects of different exoskeleton designs on the internal joint forces/torque in the knee. The closed kinematic chain formed by the leg and exoskeleton has a significant effect on the joint forces/torque in the knee. A bio-joint model is used to capture this effect by relaxing a commonly made assumption that approximates a knee joint as a perfect engineering pin-joint in designing an exoskeleton. Based on the knowledge of a knee-joint kinematics, an adaptive knee-joint exoskeleton has been designed to eliminate negative effects associated with the closed leg-exoskeleton kinematic chain on a human knee. For experimental validation, the flexion motion of an artificial human knee is investigated comparing the performances of five exoskeleton designs against the case with no exoskeleton. Analytical results that estimate internal forces/torque using the kinematic and dynamic models (based on the properties of a knee joint) agree well with data obtained experimentally. This investigation illustrates the applications of the analytical model for designing an adaptive exoskeleton that minimizes internal joint forces due to a knee-exoskeleton interaction.

Constrained Two-dimensional Cutting Stock Problems a Best-first Branch-and-bound Algorithm Constrained Two-dimensional Cutting Stock Problems a Best-first Branch-and-bound Algorithm <=SEP=> In this work, we develop a new version of the algorithm proposed in 17] for solving exactly some variants of (un)weighted constrained two-dimensional cutting stock problems. We introduce one-dimensional bounded knapsacks in order to obtain an improved initial lower bound for limiting initially the size of the search space, an improved upper bound at each internal node and a symmetry detection for neglecting some useless duplicate patterns. A new eecient data structure of the set representing the best subprob-lems is used to implement the algorithms over the BOB library. The performance of our algorithms is evaluated on some problem instances of the literature and on other randomly generated hard problem instances (a total of 27 problem instances). Les Probl emes de D ecoupe 2-Dimensions Contraints Un Algorithme de Branch-and-Bound meilleur d'abord R esum e : Dans ce travail, nous concevons une nouvelle version de l'algorithme pro-pos e dans 17] pour r esoudre de mani ere exacte des probl emes de d ecoupe en 2-dimensions contraints avec ou sans pond eration. Nous introduisons des sacs a dos unidimension-nels contraints aan d'obtenir une meilleure borne inf erieure initiale pour limiter la taille de l'espace de recherche, une meilleure borne sup erieure pour chaque nnud interne et une d etection de sym etries pour supprimer des conngurations identiques. Une nouvelle structure de donn ees eecace pour g erer les meilleurs sous-probl emes a et e employ ee pour l'impl ementation des algorithmes sur la biblioth eque BOB. La performance de nos algo-rithmes est evalu ee sur 27 instances de probl emes extraites pour certaines de la litt erature et d'autres diiciles, engendr ees al eatoirement.

Developing an intelligent data discriminating system of anti-money laundering based on SVM <=SEP=> Statistical learning theory (SLT) is introduced to improve the embarrassments of anti-money laundering (AML) intelligence collection. A set of unusual behavior detection algorithm is presented in this paper based on support vector machine (SVM) in order to take the place of traditional predefined-rule suspicious transaction data filtering system. It could efficiently surmount the worst forms of suspicious data analyzing and reporting mechanism among bank branches including enormous data volume, dimensionality disorder with massive variances and feature overload.

Mitigating Poisoning Attacks on Machine Learning Models: A Data Provenance Based Approach <=SEP=> The use of machine learning models has become ubiquitous. Their predictions are used to make decisions about healthcare, security, investments and many other critical applications. Given this pervasiveness, it is not surprising that adversaries have an incentive to manipulate machine learning models to their advantage. One way of manipulating a model is through a poisoning or causative attack in which the adversary feeds carefully crafted poisonous data points into the training set. Taking advantage of recently developed tamper-free provenance frameworks, we present a methodology that uses contextual information about the origin and transformation of data points in the training set to identify poisonous data, thereby enabling online and regularly re-trained machine learning applications to consume data sources in potentially adversarial environments. To the best of our knowledge, this is the first approach to incorporate provenance information as part of a filtering algorithm to detect causative attacks. We present two variations of the methodology - one tailored to partially trusted data sets and the other to fully untrusted data sets. Finally, we evaluate our methodology against existing methods to detect poison data and show an improvement in the detection rate.

Specific language impairment: a convenient label for whom? <=SEP=> BACKGROUND
The term 'specific language impairment' (SLI), in use since the 1980s, describes children with language impairment whose cognitive skills are within normal limits where there is no identifiable reason for the language impairment. SLI is determined by applying exclusionary criteria, so that it is defined by what it is not rather than by what it is. The recent decision to not include SLI in DSM-5 provoked much debate and concern from researchers and clinicians.


AIMS
To explore how the term 'specific language impairment' emerged, to consider how disorders, including SLI, are generally defined and to explore how societal changes might impact on use the term.


METHODS & PROCEDURES
We reviewed the literature to explore the origins of the term 'specific language impairment' and present published evidence, as well as new analyses of population data, to explore the validity of continuing to use the term.


OUTCOMES & RESULTS AND CONCLUSIONS & IMPLICATIONS
We support the decision to exclude the term 'specific language impairment' from DSM-5 and conclude that the term has been a convenient label for researchers, but that the current classification is unacceptably arbitrary. Furthermore, we argue there is no empirical evidence to support the continued use of the term SLI and limited evidence that it has provided any real benefits for children and their families. In fact, the term may be disadvantageous to some due to the use of exclusionary criteria to determine eligibility for and access to speech pathology services. We propose the following recommendations. First, that the word 'specific' be removed and the label 'language impairment' be used. Second, that the exclusionary criteria be relaxed and in their place inclusionary criteria be adopted that take into account the fluid nature of language development particularly in the preschool period. Building on the goodwill and collaborations between the clinical and research communities we propose the establishment of an international consensus panel to develop an agreed definition and set of criteria for language impairment. Given the rich data now available in population studies it is possible to test the validity of these definitions and criteria. Consultation with service users and policy-makers should be incorporated into the decision-making process.

The Optimality of Naive Bayes <=SEP=> Naive Bayes is one of the most efficient and effective inductive learning algorithms for machine learning and data mining. Its competitive performance in classification is surprising, because the conditional independence assumption on which it is based, is rarely true in realworld applications. An open question is: what is the true reason for the surprisingly good performance of naive Bayes in classification? In this paper, we propose a novel explanation on the superb classification performance of naive Bayes. We show that, essentially, the dependence distribution; i.e., how the local dependence of a node distributes in each class, evenly or unevenly, and how the local dependencies of all nodes work together, consistently (supporting a certain classification) or inconsistently (canceling each other out), plays a crucial role. Therefore, no matter how strong the dependences among attributes are, naive Bayes can still be optimal if the dependences distribute evenly in classes, or if the dependences cancel each other out. We propose and prove a sufficient and necessary conditions for the optimality of naive Bayes. Further, we investigate the optimality of naive Bayes under the Gaussian distribution. We present and prove a sufficient condition for the optimality of naive Bayes, in which the dependence between attributes do exist. This provides evidence that dependence among attributes may cancel out each other. In addition, we explore when naive Bayes works well. Naive Bayes and Augmented Naive Bayes Classification is a fundamental issue in machine learning and data mining. In classification, the goal of a learning algorithm is to construct a classifier given a set of training examples with class labels. Typically, an example E is represented by a tuple of attribute values (x1, x2, , · · · , xn), where xi is the value of attribute Xi. Let C represent the classification variable, and let c be the value of C. In this paper, we assume that there are only two classes: + (the positive class) or − (the negative class). A classifier is a function that assigns a class label to an example. From the probability perspective, according to Bayes Copyright c © 2004, American Association for Artificial Intelligence (www.aaai.org). All rights reserved. Rule, the probability of an example E = (x1, x2, · · · , xn) being class c is p(c|E) = p(E|c)p(c) p(E) . E is classified as the class C = + if and only if fb(E) = p(C = +|E) p(C = −|E) ≥ 1, (1) where fb(E) is called a Bayesian classifier. Assume that all attributes are independent given the value of the class variable; that is, p(E|c) = p(x1, x2, · · · , xn|c) = n ∏

Object detection in VHR optical remote sensing images via learning rotation-invariant HOG feature <=SEP=> Object detection in very high resolution (VHR) optical remote sensing images is one of the most fundamental but challenging problems in the field of remote sensing image analysis. As object detection is usually carried out in feature space, effective feature representation is very important to construct a high-performance object detection system. During the last decades, a great deal of effort has been made to develop various feature representations for the detection of different types of objects. Among various features developed for visual object detection, the histogram of oriented gradients (HOG) feature is maybe one of the most popular features that has been successfully applied to computer vision community. However, although the HOG feature has achieved great success in nature scene images, it is problematic to directly use it for object detection in optical remote sensing images because it is difficult to effectively handle the problem of object rotation variations. To explore a possible solution to the problem, this paper proposes a novel method to learn rotation-invariant HOG (RIHOG) features for object detection in optical remote sensing images. This is achieved by learning a rotation-invariant transformation model via optimizing a new objective function, which constrains the training samples before and after rotation to share the similar features to achieve rotation-invariance. In the experiments, we evaluate the proposed method on a publicly available 10-class VHR geospatial object detection dataset and comprehensive comparisons with state-of-the-arts demonstrate the effectiveness the proposed method.

Using Topic Models in Content-Based News Recommender Systems <=SEP=> We study content-based recommendation of Finnish news in a system with a very small group of users. We compare three standard methods, Naïve Bayes (NB), K-Nearest Neighbor (kNN) Regression and Regulairized Linear Regression in a novel online simulation setting and in a coldstart simulation. We also apply Latent Dirichlet Allocation (LDA) on the large corpus of news and compare the learned features to those found by Singular Value Decomposition (SVD). Our results indicate that Naïve Bayes is the worst of the three models. K-Nearest Neighbor performs consistently well across input features. Regularized Linear Regression performs generally worse than kNN, but reaches similar performance as kNN with some features. Regularized Linear Regression gains statistically significant improvements over the word-features with LDA both on the full data set and in the cold-start simulation. In the cold-start simulation we find that LDA gives statistically significant improvements for all the methods.

THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY By <=SEP=> Benjamini and Hochberg suggest that the false discovery rate may be the appropriate error rate to control in many applied multiple testing problems. A simple procedure was given there as an FDR controlling procedure for independent test statistics and was shown to be much more powerful than comparable procedures which control the traditional familywise error rate. We prove that this same procedure also controls the false discovery rate when the test statistics have positive regression dependency on each of the test statistics corresponding to the true null hypotheses. This condition for positive dependency is general enough to cover many problems of practical interest, including the comparisons of many treatments with a single control, multivariate normal test statistics with positive correlation matrix and multivariate t. Furthermore, the test statistics may be discrete, and the tested hypotheses composite without posing special difficulties. For all other forms of dependency, a simple conservative modification of the procedure controls the false discovery rate. Thus the range of problems for which a procedure with proven FDR control can be offered is greatly increased.

SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-Rays <=SEP=> Chest X-ray (CXR) is one of the most commonly prescribed medical imaging procedures, often with over 2– 10x more scans than other imaging modalities such as MRI, CT scan, and PET scans. These voluminous CXR scans place significant workloads on radiologists and medical practitioners. Organ segmentation is a crucial step to obtain effective computer-aided detection on CXR. In this work, we propose Structure Correcting Adversarial Network (SCAN) to segment lung fields and the heart in CXR images. SCAN incorporates a critic network to impose on the convolutional segmentation network the structural regularities emerging from human physiology. During training, the critic network learns to discriminate between the ground truth organ annotations from the masks synthesized by the segmentation network. Through this adversarial process the critic network learns the higher order structures and guides the segmentation model to achieve realistic segmentation outcomes. Extensive experiments show that our method produces highly accurate and natural segmentation. Using only very limited training data available, our model reaches human-level performance without relying on any existing trained model or dataset. Our method also generalizes well to CXR images from a different patient population and disease profiles, surpassing the current state-of-the-art.

Quantizing Convolutional Neural Networks for Low-Power High-Throughput Inference Engines <=SEP=> Deep learning as a means to inferencing has proliferated thanks to its versatility and ability to approach or exceed human-level accuracy. These computational models have seemingly insatiable appetites for computational resources not only while training, but also when deployed at scales ranging from data centers all the way down to embedded devices. As such, increasing consideration is being made to maximize the computational efficiency given limited hardware and energy resources and, as a result, inferencing with reduced precision has emerged as a viable alternative to the IEEE 754 Standard for Floating-Point Arithmetic. We propose a quantization scheme that allows inferencing to be carried out using arithmetic that is fundamentally more efficient when compared to even half-precision floating-point. Our quantization procedure is significant in that we determine our quantization scheme parameters by calibrating against its reference floating-point model using a single inference batch rather than (re)training and achieve end-to-end post quantization accuracies comparable to the reference model.

SCARE: Side-Channel Analysis Based Reverse Engineering for Post-Silicon Validation <=SEP=> Reverse Engineering (RE) has been historically considered as a powerful approach to understand electronic hardware in order to gain competitive intelligence or accomplish piracy. In recent years, it has also been looked at as a way to authenticate hardware intellectual properties in the court of law. In this paper, we propose a beneficial role of RE in post-silicon validation of integrated circuits (IC) with respect to IC functionality, reliability and integrity. Unlike traditional destructive RE approaches, we propose a fast non-destructive side-channel analysis approach that can hierarchically extract structural information from an IC through its transient current signature. Such a top-down side-channel analysis approach is capable of reliably identifying pipeline stages and functional blocks. It is also suitable to distinguish sequential elements from combinational gates. For extraction of random logic structures (e.g. control blocks and finite state machines) we combine side-channel analysis with logic testing based Boolean function extraction. The proposed approach is amenable to automation, scalable, and can be applied as part of post-silicon validation process to verify that each IC implements exclusively the functionality described in the specification and is free from malicious modification or Trojan attacks. Simulation results on a pipelined DLX processor demonstrate the effectiveness of the proposed approach.

Process models in the practice of distributed software development: A systematic review of the literature <=SEP=> 0950-5849/$ see front matter 2010 Elsevier B.V. A doi:10.1016/j.infsof.2010.03.009 * Corresponding author. E-mail addresses: rafaelp@pucrs.br (R. Prikladnicki) Context: Distributed Software Development (DSD) has recently become an active research area. Although considerable research effort has been made in this area, as yet, no agreement has been reached as to an appropriate process model for DSD. Purpose: This paper is intended to identify and synthesize papers that describe process models for distributed software development in the context of overseas outsourcing, i.e. ‘‘offshoring”. Method: We used a systematic review methodology to search seven digital libraries and one topic-specific conference. Results: We found 27 primary studies describing stage-related DSD process models. Only five of such studies looked into outsourcing to a subsidiary company (i.e. ‘‘internal offshoring”). Nineteen primary studies addressed the need for DSD process models. Eight primary studies and three literature surveys described stage-based DSD process models, but only three of such models were empirically evaluated. Conclusion: We need more research aimed at internal offshoring. Furthermore, proposed models need to be empirically validated. 2010 Elsevier B.V. All rights reserved.

An integrative imputation method based on multi-omics datasets <=SEP=> Integrative analysis of multi-omics data is becoming increasingly important to unravel functional mechanisms of complex diseases. However, the currently available multi-omics datasets inevitably suffer from missing values due to technical limitations and various constrains in experiments. These missing values severely hinder integrative analysis of multi-omics data. Current imputation methods mainly focus on using single omics data while ignoring biological interconnections and information imbedded in multi-omics data sets. In this study, a novel multi-omics imputation method was proposed to integrate multiple correlated omics datasets for improving the imputation accuracy. Our method was designed to: 1) combine the estimates of missing value from individual omics data itself as well as from other omics, and 2) simultaneously impute multiple missing omics datasets by an iterative algorithm. We compared our method with five imputation methods using single omics data at different noise levels, sample sizes and data missing rates. The results demonstrated the advantage and efficiency of our method, consistently in terms of the imputation error and the recovery of mRNA-miRNA network structure. We concluded that our proposed imputation method can utilize more biological information to minimize the imputation error and thus can improve the performance of downstream analysis such as genetic regulatory network construction.

Security behaviors of smartphone users <=SEP=> Purpose – This paper aims to report on the information security behaviors of smartphone users in an affluent economy of the Middle East. Design/methodology/approach – A model based on prior research, synthesized from a thorough literature review, is tested using survey data from 500 smartphone users representing three major mobile operating systems. Findings – The overall level of security behaviors is low. Regression coefficients indicate that the efficacy of security measures and the cost of adopting them are the main factors influencing smartphone security behaviors. At present, smartphone users are more worried about malware and data leakage than targeted information theft. Research limitations/implications – Threats and counter-measures co-evolve over time, and our findings, which describe the state of smartphone security at the current time, will need to be updated in the future. Practical implications – Measures to improve security practices of smartphone users are needed urgently. The findings indicate that such measures should be broadly effective and relatively costless for users to implement. Social implications – Personal smartphones are joining enterprise networks through the acceptance of Bring-Your-Own-Device computing. Users’ laxity about smartphone security thus puts organizations at risk. Originality/value – The paper highlights the key factors influencing smartphone security and compares the situation for the three leading operating systems in the smartphone market.

Money Laundering on the Internet <=SEP=> Electronic money (or e-money) is money that is represented digitally and can be exchanged by means of smart card from one party to another without the need for an intermediary. It is anticipated that emoney will work just like paper money. One of its potential key features is anonymity. The proceeds of crime that are in the form of e-money could be used, to buy foreign currency and high value goods to be resold. E-money may therefore be used to place dirty money without having to smuggle cash or conduct face to face transactions.

Reining in the Outliers in Map-Reduce Clusters using Mantri <=SEP=> Experience from an operational Map-Reduce cluster reveals that outliers signi cantly prolong job completion. e causes for outliers include run-time contention for processor, memory and other resources, disk failures, varying bandwidth and congestion along network paths and, imbalance in task workload. We present Mantri, a system that monitors tasks and culls outliers using causeand resource-aware techniques. Mantri’s strategies include restarting outliers, network-aware placement of tasks and protecting outputs of valuable tasks. Using real-time progress reports,Mantri detects and acts on outliers early in their lifetime. Early action frees up resources that can be used by subsequent tasks and expedites the job overall. Acting based on the causes and the resource and opportunity cost of actions lets Mantri improve over prior work that only duplicates the laggards. Deployment in Bing’s production clusters and trace-driven simulations show that Mantri improves job completion times by .

CellSDN : Software-Defined Cellular Networks <=SEP=> Existing cellular networks suffer from inflexible and expen sive equipment, complex control-plane protocols, and vend orspecific configuration interfaces. In this paper, we argue th at software defined networking (SDN) can simplify the design and management of cellular data networks, while enabling new services. However, supporting many subscribers, frequent mobility, fine-grained measurement and control, and real-time adaptation introduces new scalability challeng es that future SDN architectures should address. As a first step , we present a software-defined cellular network architectur that (i) allows controller applications to express policie s based on the attributes of subscribers, rather than network addre sses and locations, (ii) enables real-time, fine-grained contro l via a local agent on each switch, and (iii) extends switches to support features like deep packet inspection and header com pression to meet the needs of cellular data services, (iv) su pports flexible “slicing” of network resources based on the at tributes of subscribers, rather than the packet header field s, and flexible “slicing” of base stations and radio resources b y having the controller to handle radio resource management, admission control and mobility in each slice.