Query: On continuous deployment maturity in customer projects

Relevant passages:
Characterizing the software process: a maturity framework <=SEP=> A description is given of a software-process maturity framework that has been developed to provide the US Department of Defense with a means to characterize the capabilities of software-development organizations. This software-development process-maturity model reasonably represents the actual ways in which software-development organizations improve. It provides a framework for assessing these organizations and identifying the priority areas for immediate improvement. It also helps identify those places where advanced technology can be most valuable in improving the software-development process. The framework can be used by any software organization to assess its own capabilities and identify the most important areas for improvement.<<ETX>>

Capability Maturity Model for Software <=SEP=> This paper provides an overview of the latest version of the Capability Maturity Model for Software, CMM v1.1. Based on over six years of experience with software process improvement and the contributions of hundreds of reviewers, CMM v1.1 describes the software engineering and management practices that characterize organizations as they mature their processes for developing and maintaining software. This paper stresses the need for a process maturity framework to prioritize improvement actions, describes the process maturity framework of five maturity levels and the associated structural components, and discusses future directions for the CMM.

Mixed Methods Research : A Research Paradigm Whose Time Has Come <=SEP=> 14 The purposes of this article are to position mixed methods research (mixed research is a synonym) as the natural complement to traditional qualitative and quantitative research, to present pragmatism as offering an attractive philosophical partner for mixed methods research, and to provide a framework for designing and conducting mixed methods research. In doing this, we briefly review the paradigm “wars” and incompatibility thesis, we show some commonalities between quantitative and qualitative research, we explain the tenets of pragmatism, we explain the fundamental principle of mixed research and how to apply it, we provide specific sets of designs for the two major types of mixed methods research (mixed-model designs and mixed-method designs), and, finally, we explain mixed methods research as following (recursively) an eight-step process. A key feature of mixed methods research is its methodological pluralism or eclecticism, which frequently results in superior research (compared to monomethod research). Mixed methods research will be successful as more investigators study and help advance its concepts and as they regularly practice it.

Climbing the "Stairway to Heaven" -- A Mulitiple-Case Study Exploring Barriers in the Transition from Agile Development towards Continuous Deployment of Software <=SEP=> Agile software development is well-known for its focus on close customer collaboration and customer feedback. In emphasizing flexibility, efficiency and speed, agile practices have lead to a paradigm shift in how software is developed. However, while agile practices have succeeded in involving the customer in the development cycle, there is an urgent need to learn from customer usage of software also after delivering and deployment of the software product. The concept of continuous deployment, i.e. the ability to deliver software functionality frequently to customers and subsequently, the ability to continuously learn from real-time customer usage of software, has become attractive to companies realizing the potential in having even shorter feedback loops. However, the transition towards continuous deployment involves a number of barriers. This paper presents a multiple-case study in which we explore barriers associated with the transition towards continuous deployment. Based on interviews at four different software development companies we present key barriers in this transition as well as actions that need to be taken to address these.

Agile Software Development: The Business of Innovation <=SEP=> T he rise and fall of the dot-com-driven Internet economy shouldn't distract us from seeing that the business environment continues to change at a dramatically increasing pace. To thrive in this turbulent environment, we must confront the business need for relentless innovation and forge the future workforce culture. Agile software development approaches such as Extreme Programming , Crystal methods, Lean Development, Scrum, Adaptive Software Development (ASD), and others view change from a perspective that mirrors today's turbulent business and technology environment. In a recent study of more than 200 software development projects, QSM Associates' Michael Mah reported that the researchers couldn't find nearly half of the projects' original plans to measure against. Why? Conforming to plan was no longer the primary goal; instead, satisfying customers—at the time of delivery , not at project initiation—took precedence. In many projects we review, major changes in the requirements, scope, and technology that are outside the development team's control often occur within a project's life span. Accepting that Barry Boehm's life cycle cost differentials theory—the cost of change grows through the software's development life cycle—remains valid, the question today is not how to stop change early in a project but how to better handle inevitable changes throughout its life cycle. Traditional approaches assumed that if we just tried hard enough, we could anticipate the complete set of requirements early and reduce cost by eliminating change. Today, eliminating change early means being unresponsive to business con-ditions—in other words, business failure. Similarly, traditional process manage-ment—by continuous measurement, error identification, and process refine-ments—strove to drive variations out of processes. This approach assumes that variations are the result of errors. Today, while process problems certainly cause some errors, external environmental changes cause critical variations. Because we cannot eliminate these changes, driving down the cost of responding to them is the only viable strategy. Rather than eliminating rework, the new strategy is to reduce its cost. However, in not just accommodating change, but embracing it, we also must be careful to retain quality. Expectations have grown over the years. The market demands and expects innovative, high-quality software that meets its needs— and soon. Agile methods are a response to this expectation. Their strategy is to reduce the cost of change throughout a project. Extreme Programming (XP), for example, calls for the software development team to • produce the first delivery in weeks, to achieve an early win and rapid …

Photogrammetric Multi-View Stereo and Imaging Network Design <=SEP=> 

Addressing the Winograd Schema Challenge as a Sequence Ranking Task <=SEP=> The Winograd Schema Challenge targets pronominal anaphora resolution problems which require the application of cognitive inference in combination with world knowledge. These problems are easy to solve for humans but most difficult to solve for machines. Computational models that previously addressed this task rely on syntactic preprocessing and incorporation of external knowledge by manually crafted features. We address the Winograd Schema Challenge from a new perspective as a sequence ranking task, and design a Siamese neural sequence ranking model which performs significantly better than a random baseline, even when solely trained on sequences of words. We evaluate against a baseline and a state-of-the-art system on two data sets and show that anonymization of noun phrase candidates strongly helps our model to generalize.

A Multi-User Surface Visuo-Haptic Display Using Electrostatic Friction Modulation and Capacitive-Type Position Sensing <=SEP=> This paper proposes a visuo-haptic feedback system that can provide haptic feedback to multiple users on an LCD monitor using electrostatic adhesion and built-in capacitive sensors. The prototype system consists of a 40-inch LCD monitor, an ITO electrode sheet arranged on the monitor, and multiple contact pads for electrostatic adhesion. By applying low-frequency haptic voltage and high-frequency sensing voltage to each pad, the system realizes passive haptic feedback, as well as position sensing using the same components. The passive haptic feedback force exceeds 1 N at 300 V<inline-formula><tex-math notation="LaTeX">$_\mathrm{rms}$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="nakamura-ieq1-2556660.gif"/></alternatives></inline-formula>. The performance of the sensing system is investigated in terms of interference, which shows the haptic voltage and existence of multiple pads can affect the sensing and can shift the output by a few millimeters. A pilot application, virtual hockey game, successfully demonstrates the sensing and haptic rendering capability of the proposed system. The effect of the interference on the demonstration is discussed.

Music, Intelligence and Artificiality <=SEP=> The discipline of Music-AI is defined as that activity which seeks to program computers to perform musical tasks in an intelligent, which possibly means humanlike way. A brief historical survey of different approaches within the discipline is presented. Two particular issues arise: the explicit representation of knowledge; and symbolic and subsymbolic representation and processing. When attempting to give a precise definition of Music-AI, it is argued that all musical processes must make some reference to human behaviour, and so Music-AI is a central rather than a peripheral discipline for musical computing. However, it turns out that the goals of Music-AI as first expressed, the mimicking of human behaviour, are impossible to achieve in full, and that it is impossible, in principle, for computers to pass a musical version of the Turing test. In practice, however, computers are used for their non-human-like behaviour just as much as their human-like behaviour, so the real goal of Music-AI must be reformulated. Furthermore, it is argued that the non-holistic analysis of human behaviour which this reformulation entails is actually informative for our understanding of human behaviour. Music-AI could also be fruitfully concerned with developing musical intelligences which were explicitly not human. Music-AI is then seen to be as much a creative enterprise as a scientific one.

Right nulled GLR parsers <=SEP=> The right nulled generalized LR parsing algorithm is a new generalization of LR parsing which provides an elegant correction to, and extension of, Tomita's GLR methods whereby we extend the notion of a reduction in a shift-reduce parser to include right nulled items. The result is a parsing technique which runs in linear time on LR(1) grammars and whose performance degrades gracefully to a polynomial bound in the presence of nonLR(1) rules. Compared to other GLR-based techniques, our algorithm is simpler and faster.

Review of axial flux induction motor for automotive applications <=SEP=> Hybrid and electric vehicles have been the focus of many academic and industrial studies to reduce transport pollution; they are now established products. In hybrid and electric vehicles, the drive motor should have high torque density, high power density, high efficiency, strong physical structure and variable speed range. An axial flux induction motor is an interesting solution, where the motor is a double sided axial flux machine. This can significantly increase torque density. In this paper a review of the axial flux motor for automotive applications, and the different possible topologies for the axial field motor, are presented.

A 450 fs 65-nm CMOS Millimeter-Wave Time-to-Digital Converter Using Statistical Element Selection for All-Digital PLLs <=SEP=> This paper presents a time-to-digital converter (TDC) that operates with a 20–64 GHz input and underpins the phase digitization function in a millimeter-wave all-digital fractional-N frequency synthesizer. A self-calibrated inductor-less frequency divider using dynamic CML latches provides an eight-phase input to a 3-bit “coarse” TDC, which is interfaced to a 5-bit “fine” TDC through a sub-sampling coarse-fine interface circuit. A wide bandwidth low dropout (LDO) on-chip regulator is used to decrease the effect of supply noise on TDC performance. A synthesized digital engine implements calibration using statistical element selection with mean adaptation to alleviate TDC nonlinearity that results from random mismatches and PVT variations. The TDC is fabricated in 65-nm CMOS along with the divider and calibration circuits, and achieves 450-fs resolution. The measured DNL and INL of the TDC are 0.65 and 1.2 LSB, respectively. The TDC consumes 11 mA from 1-V supply voltage. The TDC features a figure-of-merit of 0.167 (0.47) pJ per conversion step without (with) the frequency divider. A single-shot experiment shows that the on-chip LDO reduces the effect of TDC noise by reducing the standard deviation from 0.856 to 0.167 LSB for constant input. The prototype occupies an active area of  $502\times 110~\mu \text{m}^{\mathbf {2}}$  excluding pads.

An analysis and critique of Research through Design: towards a formalization of a research approach <=SEP=> The field of HCI is experiencing a growing interest in Research through Design (RtD), a research approach that employs methods and processes from design practice as a legitimate method of inquiry. We are interested in expanding and formalizing this research approach, and understanding how knowledge, or theory, is generated from this type of design research. We conducted interviews with 12 leading HCI design researchers, asking them about design research, design theory, and RtD specifically. They were easily able to identify different types of design research and design theory from contemporary and historical design research efforts, and believed that RtD might be one of the most important contributions of design researchers to the larger research community. We further examined three historical RtD projects that were repeatedly mentioned in the interviews, and performed a critique of current RtD practices within the HCI research and interaction design communities. While our critique summarizes the problems, it also shows possible directions for further developments and refinements of the approach.

The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes? <=SEP=> A successful grasp requires careful balancing of the contact forces. Deducing whether a particular grasp will be successful from indirect measurements, such as vision, is therefore quite challenging, and direct sensing of contacts through touch sensing provides an appealing avenue toward more successful and consistent robotic grasping. However, in order to fully evaluate the value of touch sensing for grasp outcome prediction, we must understand how touch sensing can influence outcome prediction accuracy when combined with other modalities. Doing so using conventional model-based techniques is exceptionally difficult. In this work, we investigate the question of whether touch sensing aids in predicting grasp outcomes within a multimodal sensing framework that combines vision and touch. To that end, we collected more than 9,000 grasping trials using a two-finger gripper equipped with GelSight high-resolution tactile sensors on each finger, and evaluated visuo-tactile deep neural network models to directly predict grasp outcomes from either modality individually, and from both modalities together. Our experimental results indicate that incorporating tactile readings substantially improve grasping performance.

Presupposed Content and Entailments in Natural Language Inference <=SEP=> Previous work has presented an accurate natural logic model for natural language inference. Other work has demonstrated the effectiveness of computing presuppositions for solving natural language inference problems. We extend this work to create a system for correctly computing lexical presuppositions and their interactions within the natural logic framework. The combination allows our system to properly handle presupposition projection from the lexical to the sentential level while taking advantage of the accuracy and coverage of the natural logic system. To solve an inference problem, our system computes a sequence of edits from premise to hypothesis. For each edit the system computes an entailment relation and a presupposition entailment relation. The relations are then separately composed according to a syntactic tree and the semantic properties of its nodes. Presuppositions are projected based on the properties of their syntactic and semantic environment. The edits are then composed and the resulting entailment relations are combined with the presupposition relation to yield an answer to the inference problem.

Reactive power capability of the wind turbine with Doubly Fed Induction Generator <=SEP=> With the increasing integration into power grids, wind power plants play an important role in the power system. Many requirements for the wind power plants have been proposed in the grid codes. According to these grid codes, wind power plants should have the ability to perform voltage control and reactive power compensation at the point of common coupling (PCC). Besides the shunt flexible alternating current transmission system (FACTS) devices such as the static var compensator (SVC) and the static synchronous compensator (STATCOM), the wind turbine itself can also provide a certain amount of reactive power compensation, depending on the wind speed and the active power control strategy. This paper analyzes the reactive power capability of Doubly Fed Induction Generator (DFIG) based wind turbine, considering the rated stator current limit, the rated rotor current limit, the rated rotor voltage limit, and the reactive power capability of the grid side convertor (GSC). The boundaries of reactive power capability of DFIG based wind turbine are derived. The result was obtained using the software MATLAB.

Mycorrhizosphere interactions to improve plant fitness and soil quality <=SEP=> Arbuscular mycorrhizal fungi are key components of soil microbiota and obviously interact with other microorganisms in the rhizosphere, i.e. the zone of influence of plant roots on microbial populations and other soil constituents. Mycorrhiza formation changes several aspects of plant physiology and some nutritional and physical properties of the rhizospheric soil. These effects modify the colonization patterns of the root or mycorrhizas (mycorrhizosphere) by soil microorganisms. The rhizosphere of mycorrhizal plants, in practice a mycorrhizosphere, harbors a great array of microbial activities responsible for several key ecosystem processes. This paper summarizes the main conceptual principles and accepted statements on the microbial interactions between mycorrhizal fungi and other members of rhizosphere microbiota and discusses current developments and future trends concerning the following topics: (i) effect of soil microorganisms on mycorrhiza formation; (ii) mycorrhizosphere establishment; (iii) interactions involved in nutrient cycling and plant growth; (iv) interactions involved in the biological control of plant pathogens; and (v) interactions to improve soil quality. The main conclusion is that microbial interactions in the rhizosphere of mycorrhizal plants improve plant fitness and soil quality, critical issues for a sustainable agricultural development and ecosystem functioning.

A Hidden Markov Model for Vehicle Detection and Counting <=SEP=> To reduce roadway congestion and improve traffic safety, accurate traffic metrics, such as number of vehicles travelling through lane-ways, are required. Unfortunately most existing infrastructure, such as loop-detectors and many video detectors, do not feasibly provide accurate vehicle counts. Consequently, a novel method is proposed which models vehicle motion using hidden Markov models (HMM). The proposed method represents a specified small region of the roadway as 'empty', 'vehicle entering', 'vehicle inside', and 'vehicle exiting', and then applies a modified Viterbi algorithm to the HMM sequential estimation framework to initialize and track vehicles. Vehicle observations are obtained using an Adaboost trained Haar-like feature detector. When tested on 88 hours of video, from three distinct locations, the proposed method proved to be robust to changes in lighting conditions, moving shadows, and camera motion, and consistently out-performed Multiple Target Tracking (MTT) and Virtual Detection Line(VDL) implementations. The median vehicle count error of the proposed method is lower than MTT and VDL by 28%, and 70% respectively. As future work, this algorithm will be implemented to provide the traffic industry with improved automated vehicle counting, with the intent to eventually provide real-time counts.

Striving for the moral self: the effects of recalling past moral actions on future moral behavior. <=SEP=> People's desires to see themselves as moral actors can contribute to their striving for and achievement of a sense of self-completeness. The authors use self-completion theory to predict (and show) that recalling one's own (im)moral behavior leads to compensatory rather than consistent moral action as a way of completing the moral self. In three studies, people who recalled their immoral behavior reported greater participation in moral activities (Study 1), reported stronger prosocial intentions (Study 2), and showed less cheating (Study 3) than people who recalled their moral behavior. These compensatory effects were related to the moral magnitude of the recalled event, but they did not emerge when people recalled their own positive or negative nonmoral behavior (Study 2) or others' (im)moral behavior (Study 3). Thus, the authors extend self-completion theory to the moral domain and use it to integrate the research on moral cleansing (remunerative moral strivings) and moral licensing (relaxed moral strivings).

Transcriptional reprogramming in yeast using dCas9 and combinatorial gRNA strategies <=SEP=> BACKGROUND
Transcriptional reprogramming is a fundamental process of living cells in order to adapt to environmental and endogenous cues. In order to allow flexible and timely control over gene expression without the interference of native gene expression machinery, a large number of studies have focused on developing synthetic biology tools for orthogonal control of transcription. Most recently, the nuclease-deficient Cas9 (dCas9) has emerged as a flexible tool for controlling activation and repression of target genes, by the simple RNA-guided positioning of dCas9 in the vicinity of the target gene transcription start site.


RESULTS
In this study we compared two different systems of dCas9-mediated transcriptional reprogramming, and applied them to genes controlling two biosynthetic pathways for biobased production of isoprenoids and triacylglycerols (TAGs) in baker's yeast Saccharomyces cerevisiae. By testing 101 guide-RNA (gRNA) structures on a total of 14 different yeast promoters, we identified the best-performing combinations based on reporter assays. Though a larger number of gRNA-promoter combinations do not perturb gene expression, some gRNAs support expression perturbations up to ~threefold. The best-performing gRNAs were used for single and multiplex reprogramming strategies for redirecting flux related to isoprenoid production and optimization of TAG profiles. From these studies, we identified both constitutive and inducible multiplex reprogramming strategies enabling significant changes in isoprenoid production and increases in TAG.


CONCLUSION
Taken together, we show similar performance for a constitutive and an inducible dCas9 approach, and identify multiplex gRNA designs that can significantly perturb isoprenoid production and TAG profiles in yeast without editing the genomic context of the target genes. We also identify a large number of gRNA positions in 14 native yeast target pomoters that do not affect expression, suggesting the need for further optimization of gRNA design tools and dCas9 engineering.

Expertise-related deactivation of the right temporoparietal junction during musical improvisation <=SEP=> Musical training has been associated with structural changes in the brain as well as functional differences in brain activity when musicians are compared to nonmusicians on both perceptual and motor tasks. Previous neuroimaging comparisons of musicians and nonmusicians in the motor domain have used tasks involving prelearned motor sequences or synchronization with an auditorily presented sequence during the experiment. Here we use functional magnetic resonance imaging (fMRI) to examine expertise-related differences in brain activity between musicians and nonmusicians during improvisation--the generation of novel musical-motor sequences--using a paradigm that we previously used in musicians alone. Despite behaviorally matched performance, the two groups showed significant differences in functional brain activity during improvisation. Specifically, musicians deactivated the right temporoparietal junction (rTPJ) during melodic improvisation, while nonmusicians showed no change in activity in this region. The rTPJ is thought to be part of a ventral attentional network for bottom-up stimulus-driven processing, and it has been postulated that deactivation of this region occurs in order to inhibit attentional shifts toward task-irrelevant stimuli during top-down, goal-driven behavior. We propose that the musicians' deactivation of the rTPJ during melodic improvisation may represent a training-induced shift toward inhibition of stimulus-driven attention, allowing for a more goal-directed performance state that aids in creative thought.

With a flick of the wrist: stretch sensors as lightweight input for mobile devices <=SEP=> With WristFlicker, we detect wrist movement through sets of stretch sensors embedded in clothing. Our system supports wrist rotation (pronation/supination), and both wrist tilts (flexion/extension and ulnar/radial deviation). Each wrist movement is measured by two opposing stretch sensors, mimicking the counteracting movement of muscles. We discuss interaction techniques that allow a user to control a music player through this lightweight input.

Hierarchical Neural Language Models for Joint Representation of Streaming Documents and their Content <=SEP=> We consider the problem of learning distributed representations for documents in data streams. The documents are represented as low-dimensional vectors and are jointly learned with distributed vector representations of word tokens using a hierarchical framework with two embedded neural language models. In particular, we exploit the context of documents in streams and use one of the language models to model the document sequences, and the other to model word sequences within them. The models learn continuous vector representations for both word tokens and documents such that semantically similar documents and words are close in a common vector space. We discuss extensions to our model, which can be applied to personalized recommendation and social relationship mining by adding further user layers to the hierarchy, thus learning user-specific vectors to represent individual preferences. We validated the learned representations on a public movie rating data set from MovieLens, as well as on a large-scale Yahoo News data comprising three months of user activity logs collected on Yahoo servers. The results indicate that the proposed model can learn useful representations of both documents and word tokens, outperforming the current state-of-the-art by a large margin.

IEC 61850 substation configuration language as a basis for automated security and SDN configuration <=SEP=> IEC61850 has revolutionized the way substations are configured and maintained. Substation Configuration Language (SCL) defines the parameters needed to configure individual devices and combine them into a working system. Security is implemented by IEC62351 and there are potential vulnerabilities. Best practice recommendations are for defense in depth. SCL contains sufficient information to auto-configure network equipment, firewalls, IDS and SDN based networks.

Design of Organic TFT Pixel Electrode Circuit for Active-Matrix OLED Displays <=SEP=> A new current-programming pixel circuit for active-matrix organic light-emitting diode (AM-OLED) displays, composed of four organic thin-film transistors (OTFTs) and one capacitor, has been designed, simulated and evaluated. The most critical issue in realizing AMOLED displays with OTFTs is the variation and aging of the driving-TFTs and degradation of OLEDs. This can cause image sticking or degradation of image quality. These problems require compensation methods for high-quality display applications, and pixel level approach is considered to be one of the most important factors for improving display image quality. Our design shows that the current OLED and OTFT technology can be implemented for AMOLED displays, compensating the degradation of OTFT device characteristics.

Learning to Navigate for Fine-Grained Classification <=SEP=> Fine-grained classification is challenging due to the difficulty of finding discriminative features. Finding those subtle traits that fully characterize the object is not straightforward. To handle this circumstance, we propose a novel self-supervision mechanism to effectively localize informative regions without the need of bounding-box/part annotations. Our model, termed NTS-Net for Navigator-Teacher-Scrutinizer Network, consists of a Navigator agent, a Teacher agent and a Scrutinizer agent. In consideration of intrinsic consistency between informativeness of the regions and their probability being ground-truth class, we design a novel training paradigm, which enables Navigator to detect most informative regions under the guidance from Teacher. After that, the Scrutinizer scrutinizes the proposed regions from Navigator and makes predictions. Our model can be viewed as a multi-agent cooperation, wherein agents benefit from each other, and make progress together. NTS-Net can be trained end-to-end, while provides accurate fine-grained classification predictions as well as highly informative regions during inference. We achieve state-of-the-art performance in extensive benchmark datasets.

Understanding predictability and exploration in human mobility <=SEP=> Predictive models for human mobility have important applications in many fields including traffic control, ubiquitous computing, and contextual advertisement. The predictive performance of models in literature varies quite broadly, from over 90% to under 40%. In this work we study which underlying factors - in terms of modeling approaches and spatio-temporal characteristics of the data sources - have resulted in this remarkably broad span of performance reported in the literature. Specifically we investigate which factors influence the accuracy of next-place prediction, using a high-precision location dataset of more than 400 users observed for periods between 3 months and one year. We show that it is much easier to achieve high accuracy when predicting the time-bin location than when predicting the next place. Moreover, we demonstrate how the temporal and spatial resolution of the data have strong influence on the accuracy of prediction. Finally we reveal that the exploration of new locations is an important factor in human mobility, and we measure that on average 20-25% of transitions are to new places, and approx. 70% of locations are visited only once. We discuss how these mechanisms are important factors limiting our ability to predict human mobility.

Detection of hiding in the least significant bit <=SEP=> In this paper, we apply the theory of hypothesis testing to the steganalysis, or detection of hidden data, in the least significant bit (LSB) of a host image. The hiding rate (if data is hidden) and host probability mass function (PMF) are unknown. Our main results are as follows. a) Two types of tests are derived: a universal (over choices of host PMF) method that has certain asymptotic optimality properties and methods that are based on knowledge or estimation of the host PMF and, hence, an appropriate likelihood ratio (LR). b) For a known host PMF, it is shown that the composite hypothesis testing problem corresponding to an unknown hiding rate reduces to a worst-case simple hypothesis testing problem. c) Using the results for a known host PMF, practical tests based on the estimation of the host PMF are obtained. These are shown to be superior to the state of the art in terms of receiver operating characteristics as well as self-calibration across different host images. Estimators for the hiding rate are also developed.

An Efficient Algorithm of Frequent Itemsets Mining Based on MapReduce <=SEP=> Mainstream parallel algorithms for mining frequent itemsets (patterns) were designed by implementing FP-Growth or Apriori algorithms on MapReduce (MR) framework. Existing MR FP-Growth algorithms can not distribute data equally among nodes, and MR Apriori algorithms utilize multiple map/reduce procedures and generate too many key-value pairs with value of 1; these disadvantages hinder their performance. This paper proposes an algorithm FIMMR: it firstly mines local frequent itemsets for each data chunk as candidates, applies prune strategies to the candidates, and then identifies global frequent itemsets from candidates. Experimental results show that the time efficiency of FIMMR outperforms PFP and SPC significantly; and under small minimum support threshold, FIMMR can achieve one order of magnitude improvement than the other two algorithms; meanwhile, the speedup of FIMMR is also satisfactory.

Visual Analysis of Public Utility Service Problems in a Metropolis <=SEP=> Issues about city utility services reported by citizens can provide unprecedented insights into the various aspects of such services. Analysis of these issues can improve living quality through evidence-based decision making. However, these issues are complex, because of the involvement of spatial and temporal components, in addition to having multi-dimensional and multivariate natures. Consequently, exploring utility service problems and creating visual representations are difficult. To analyze these issues, we propose a visual analytics process based on the main tasks of utility service management. We also propose an aggregate method that transforms numerous issues into legible events and provide visualizations for events. In addition, we provide a set of tools and interaction techniques to explore such issues. Our approach enables administrators to make more informed decisions.