Query: Harnessing Automated Test Case Generators for GUI Testing in Industry

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Randoop: feedback-directed random testing for Java <=SEP=> R<scp>ANDOOP</scp> for Java generates unit tests for Java code using feedback-directed random test generation. Below we describe R<scp>ANDOOP</scp>'s input, output, and test generation algorithm. We also give an overview of RANDOOP's annotation-based interface for specifying configuration parameters that affect R<scp>ANDOOP</scp>'s behavior and output.

An orchestrated survey of methodologies for automated software test case generation <=SEP=> Test case generation is among the most labour-intensive tasks in software testing. It also has a strong impact on the effectiveness and efficiency of software testing. For these reasons, it has been one of the most active research topics in software testing for several decades, resulting in many different approaches and tools. This paper presents an orchestrated survey of the most prominent techniques for automatic generation of software test cases, reviewed in self-standing sections. The techniques presented include: (a) structural testing using symbolic execution, (b) model-based testing, (c) combinatorial testing, (d) random testing and its variant of adaptive random testing, and (e) search-based testing. Each section is odel-based testing contributed by world-renowned active researchers on the technique, and briefly covers the basic ideas underlying the method, the current state of the art, a discussion of the open research problems, and a rchestrated survey earch-based software testing

Pex-White Box Test Generation for .NET <=SEP=> Pex automatically produces a small test suite with high code coverage for a .NET program. To this end, Pex performs a systematic program analysis (using dynamic symbolic execution, similar to pathbounded model-checking) to determine test inputs for Parameterized Unit Tests. Pex learns the program behavior by monitoring execution traces. Pex uses a constraint solver to produce new test inputs which exercise different program behavior. The result is an automatically generated small test suite which often achieves high code coverage. In one case study, we applied Pex to a core component of the .NET runtime which had already been extensively tested over several years. Pex found errors, including a serious issue.

Compositional dynamic test generation <=SEP=> Dynamic test generation is a form of dynamic program analysis that attempts to compute test inputs to drive a program along a specific program path. Directed Automated Random Testing, or DART for short, blends dynamic test generation with model checking techniques with the goal of systematically executing all feasible program paths of a program while detecting various types of errors using run-time checking tools (like Purify, for instance). Unfortunately, systematically executing all feasible program paths does not scale to large, realistic programs.This paper addresses this major limitation and proposes to perform dynamic test generation compositionally, by adapting known techniques for interprocedural static analysis. Specifically, we introduce a new algorithm, dubbed SMART for Systematic Modular Automated Random Testing, that extends DART by testing functions in isolation, encoding test results as function summaries expressed using input preconditions and output postconditions, and then re-using those summaries when testing higher-level functions. We show that, for a fixed reasoning capability, our compositional approach to dynamic test generation (SMART) is both sound and complete compared to monolithic dynamic test generation (DART). In other words, SMART can perform dynamic test generation compositionally without any reduction in program path coverage. We also show that, given a bound on the maximum number of feasible paths in individual program functions, the number of program executions explored by SMART is linear in that bound, while the number of program executions explored by DART can be exponential in that bound. We present examples of C programs and preliminary experimental results that illustrate and validate empirically these properties.

Software Testing Research: Achievements, Challenges, Dreams <=SEP=> Software engineering comprehends several disciplines devoted to prevent and remedy malfunctions and to warrant adequate behaviour. Testing, the subject of this paper, is a widespread validation approach in industry, but it is still largely ad hoc, expensive, and unpredictably effective. Indeed, software testing is a broad term encompassing a variety of activities along the development cycle and beyond, aimed at different goals. Hence, software testing research faces a collection of challenges. A consistent roadmap of the most relevant challenges to be addressed is here proposed. In it, the starting point is constituted by some important past achievements, while the destination consists of four identified goals to which research ultimately tends, but which remain as unreachable as dreams. The routes from the achievements to the dreams are paved by the outstanding research challenges, which are discussed in the paper along with interesting ongoing work.

SEGAN: Speech Enhancement Generative Adversarial Network <=SEP=> Current speech enhancement techniques operate on the spectral domain and/or exploit some higher-level feature. The majority of them tackle a limited number of noise conditions and rely on first-order statistics. To circumvent these issues, deep networks are being increasingly used, thanks to their ability to learn complex functions from large example sets. In this work, we propose the use of generative adversarial networks for speech enhancement. In contrast to current techniques, we operate at the waveform level, training the model end-to-end, and incorporate 28 speakers and 40 different noise conditions into the same model, such that model parameters are shared across them. We evaluate the proposed model using an independent, unseen test set with two speakers and 20 alternative noise conditions. The enhanced samples confirm the viability of the proposed model, and both objective and subjective evaluations confirm the effectiveness of it. With that, we open the exploration of generative architectures for speech enhancement, which may progressively incorporate further speech-centric design choices to improve their performance.

The power of naive query segmentation <=SEP=> We address the problem of query segmentation: given a keyword query submitted to a search engine, the task is to group the keywords into phrases, if possible. Previous approaches to the problem achieve good segmentation performance on a gold standard but are fairly intricate. Our method is easy to implement and comes with a comparable accuracy.

Opposite Effects of Δ-9-Tetrahydrocannabinol and Cannabidiol on Human Brain Function and Psychopathology <=SEP=> Δ-9-tetrahydrocannabinol (Δ-9-THC) and Cannabidiol (CBD), the two main ingredients of the Cannabis sativa plant have distinct symptomatic and behavioral effects. We used functional magnetic resonance imaging (fMRI) in healthy volunteers to examine whether Δ-9-THC and CBD had opposite effects on regional brain function. We then assessed whether pretreatment with CBD can prevent the acute psychotic symptoms induced by Δ-9-THC. Fifteen healthy men with minimal earlier exposure to cannabis were scanned while performing a verbal memory task, a response inhibition task, a sensory processing task, and when viewing fearful faces. Subjects were scanned on three occasions, each preceded by oral administration of Δ-9-THC, CBD, or placebo. BOLD responses were measured using fMRI. In a second experiment, six healthy volunteers were administered Δ-9-THC intravenously on two occasions, after placebo or CBD pretreatment to examine whether CBD could block the psychotic symptoms induced by Δ-9-THC. Δ-9-THC and CBD had opposite effects on activation relative to placebo in the striatum during verbal recall, in the hippocampus during the response inhibition task, in the amygdala when subjects viewed fearful faces, in the superior temporal cortex when subjects listened to speech, and in the occipital cortex during visual processing. In the second experiment, pretreatment with CBD prevented the acute induction of psychotic symptoms by Δ-9-tetrahydrocannabinol. Δ-9-THC and CBD can have opposite effects on regional brain function, which may underlie their different symptomatic and behavioral effects, and CBD's ability to block the psychotogenic effects of Δ-9-THC.

Controlling privacy in recommender systems <=SEP=> Recommender systems involve an inherent trade-off between accuracy of recommendations and the extent to which users are willing to release information about their preferences. In this paper, we explore a two-tiered notion of privacy where there is a small set of “public” users who are willing to share their preferences openly, and a large set of “private” users who require privacy guarantees. We show theoretically and demonstrate empirically that a moderate number of public users with no access to private user information already suffices for reasonable accuracy. Moreover, we introduce a new privacy concept for gleaning relational information from private users while maintaining a first order deniability. We demonstrate gains from controlled access to private user preferences.

Graph Based Relational Features for Collective Classification <=SEP=> Statistical Relational Learning (SRL) methods have shown that classification accuracy can be improved by integrating relations between samples. Techniques such as iterative classification or relaxation labeling achieve this by propagating information between related samples during the inference process. When only a few samples are labeled and connections between samples are sparse, collective inference methods have shown large improvements over standard feature-based ML methods. However, in contrast to feature based ML, collective inference methods require complex inference procedures and often depend on the strong assumption of label consistency among related samples. In this paper, we introduce new relational features for standard ML methods by extracting information from direct and indirect relations. We show empirically on three standard benchmark datasets that our relational features yield results comparable to collective inference methods. Finally we show that our proposal outperforms these methods when additional information is available.

Key organizational factors in data warehouse architecture selection <=SEP=> a r t i c l e i n f o Keywords: Data warehousing Data warehouse architecture Archictecture selection Independent data marts Bus architecture Enterprise data warehouse Hub and spoke Federated Even though data warehousing has been in existence for over a decade, companies are still uncertain about a critical decision — which data warehouse architecture to implement? Based on the existing literature, theory, and interviews with experts, a research model was created that identifies the various contextual factors that affect the selection decision. The results from the field survey and multinomial logistic regression suggest that various combinations of organizational factors influence data warehouse architecture selection. The strategic view of the data warehouse prior to implementation emerged as a key determinant. The research suggests an overall model for predicting the data warehouse architecture selection decision. Over the past decade, many companies have made data warehouses the foundation of their decision support infrastructures. These data repositories provide a solution to a recurring problem that has plagued companies' decision support initiatives — the lack of clean, accurate, timely, and integrated data. Whether the data is used for queries and reporting, decision support systems (DSS), executive information systems (EIS), online analytical processing (OLAP), or data mining, data warehouses provide the data that " fuels " these applications [36]. Data warehouses are also critical enablers of current strategic initiatives such as customer relationship management (CRM), business performance management (BPM), and supply chain management [18,23,35,85]. Despite the importance and growing experience with data ware-housing, there is still a considerable discussion and disagreement over which architecture to use. Bill Inmon, commonly referred to as " the father of data warehousing, " advocates what is variously called the hub and spoke architecture (i.e., a centralized data warehouse with dependent data marts), Corporate Information Factory, DW 2.0 (i.e., Inmon's current term), or enterprise data warehouse architecture [23]. Another architecture alternative is the data mart bus architecture with linked dimensional data marts (i.e., bus architecture), advocated by Ralph Kimball, the other preeminent figure in data warehousing [52]. 1 These and other architectures (e.g., independent data marts and federated) have fundamental differences and strong advocates. The selection of an appropriate architecture is an important decision. A study by the Meta Group found that architecture selection is one of the key factors influencing data warehousing success [54]. A Gartner report identified the architecture selection decision as one of the five problem …

Experienced Grey Wolf Optimizer through Reinforcement Learning and Neural Networks <=SEP=> In this paper, a variant of Grey Wolf Optimizer (GWO) that uses reinforcement learning principles combined with neural networks to enhance the performance is proposed. The aim is to overcome, by reinforced learning, the common challenges of setting the right parameters for the algorithm. In GWO, a single parameter is used to control the exploration/exploitation rate which influences the performance of the algorithm. Rather than using a global way to change this parameter for all the agents, we use reinforcement learning to set it on an individual basis. The adaptation of the exploration rate for each agent depends on the agent’s own experience and the current terrain of the search space. In order to achieve this, an experience repository is built based on the neural network to map a set of agents’ states to a set of corresponding actions that specifically influence the exploration rate. The experience repository is updated by all the search agents to reflect experience and to enhance the future actions continuously. The resulted algorithm is called Experienced Grey Wolf Optimizer (EGWO) and its performance is assessed on solving feature selection problems and on finding optimal weights for neural networks algorithm. We use a set of performance indicators to evaluate the efficiency of the method. Results over various datasets demonstrate an advance of the EGWO over the original GWO and other meta-heuristics such as genetic algorithms and particle swarm optimization.

Stakeholder Identification in the Requirements Engineering Process <=SEP=> Adequate, timely and effective consultation of relevant stakeholders is of paramount importance in the requirements engineering process. However, the thorny issue of making sure that all relevant stakeholders are consulted has received less attention than other areas which depend on it, such as scenario-based requirements, involving users in development, negotiating between different viewpoints and so on. The literature suggests examples of stakeholders, and categories of stakeholder, but does not provide help in identifying stakeholders for a specific system. In this paper, we discuss current work in stakeholder identification, propose an approach to identifying relevant stakeholders for a specific system, and propose future directions for the work. 1. What is a ÔstakeholderÕ? There is a large body of literature in the strategic management area which discusses organisations in terms of a stakeholder model. Stakeholder analysis, it is claimed, can be used to analyse an organisationÕs performance and determine its future strategic direction. An oft-quoted definition of ÔstakeholderÕ, taken from a key reference in this literature is: ÔA stakeholder in an organisation is (by definition) any group or individual who can affect or is affected by the achievement of the organisationÕs objectives.Õ [10] A much broader definition, which has also been attributed to Freeman, is that a stakeholder is Ôanything influencing or influenced byÕ the firm, but it has been claimed that this definition is problematic because it leads to the identification of a very broad set of stakeholders. It is important to distinguish between influencers and stakeholders because while some potential stakeholders may indeed be both stakeholders and influencers, some who have a real stake in an enterprise may have no influence, e.g. a job applicant, while some influencers may have no stake, e.g. the media [8]. Information systems (IS) researchers have also taken up the idea of stakeholders: ÔWe define stakeholders as these participants <in the development process> together with any other individuals, groups or organisations whose actions can influence or be influenced by the development and use of the system whether directly or indirectly.Õ [22] In software engineering, stakeholders have been defined as: ÔThe people and organisations affected by the applicationÕ [3] ÔSystem stakeholders are people or organisations who will be affected by the system and who have a direct or indirect influence on the system requirementsÕ [16] ÔStakeholders are people who have a stake or interest in the projectÕ [4] A more explicit refinement of this definition is: ÔÉ anyone whose jobs will be altered, who supplies or gains information from it, or whose power or influence within the organisation will increase or decrease.Õ [7] They go on to say that ÔIt will frequently be the case that the formal ÔclientÕ who orders the system falls very low on the list of those affected. Be very wary of changes which take power, influence or control from some stakeholders without returning something tangible in its place.Õ [7] When faced with the practical problem of how to identify the set of stakeholders relevant to a specific project, these definitions are not particularly helpful. The main concern is that, although such definitions are usually accompanied by example groups of stakeholders, they are vague and may lead to consideration of inappropriate or incomplete groups of stakeholders. Categories of stakeholder include end-users, managers and others involved in the organisational processes influenced by the system, engineers responsible for system development and maintenance, customers of the Sharp, Finkelstein & Galal 11/12/99 2 organisation who will use the system to provide a service, external bodies such as regulators, domain experts, and so on. They will each have different goals, and will try to satisfy their own without recourse to others [16]. Cotterell and Hughes [4] suggest that stakeholders might be in one of three categories: internal to the project team; external to the project team, but internal to the organisation; and external to both the project team and the organisation. Newman and Lamming [20] suggest a different division: into those who will use the system directly or indirectly, and those who will be involved in developing the system. This distinction is also taken up with respect to the development of knowledge-based systems [22]. The set of stakeholders in the knowledge acquisition process and the set of stakeholders in the use of a knowledge-based system, are not necessarily identical; they are likely to vary in membership, and for those members in common, the type and level of stake they have is likely to vary. The IS literature suggests a different division again, into ÔhubsÕ or ÔsponsorsÕ and ÔspokesÕ or ÔadaptorsÕ, where the former are those initiating and sustaining the system, while the latter are those participating [21]. Macaulay [19] identifies four categories of stakeholder in any computer system: 1. Those responsible for design and development; 2. Those with a financial interest, responsible for its sale or purchase; 3. Those responsible for introduction and maintenance; 4. Those who have an interest in its use. But again, she offers no guidelines for identifying specific stakeholders for a given system. So far, we have not distinguished between individuals or groups and roles. As with other situations, the mapping between stakeholders and individuals or groups is not oneto-one. It is therefore more appropriate to think of stakeholders as roles rather than as specific people. In subsequent discussion, we shall use ÔstakeholderÕ to mean Ôstakeholder roleÕ. 2. Identifying stakeholders There has been little focus on the participants of the requirements engineering (RE) process, for example in terms of how to trace participants in the RE process and how to identify stakeholders [13]. All of the references cited above emphasise the importance of identifying stakeholders, and although they provide examples, or broad guidance for identifying them, none describes a model or a concrete approach for identifying stakeholders for a specific project. This deficiency has been noted in the management literature, and in the IS literature [21], where the approaches have been criticised for either assuming that stakeholders are ÔobviousÕ, or for providing broad categories which are too generic to be of practical use. Expert identification for knowledge-based systems development has similarities with stakeholder identification in RE, although here too there has been an assumption that experts are readily identifiable [22]. Pouloudi and Whitley [21] suggest four principles of stakeholder identification, and describe an approach which is based on these principles, and which they used to identify stakeholders in the drug use management domain. Lyytinen and Hirschheim [17] also provides some guidance for stakeholder identification for IS, while acknowledging that Ôthe identification of the set of all stakeholders is far from trivialÕ. Methods for requirements engineering, e.g. KAOS [5], do not directly support stakeholder identification; it seems to be assumed that it is straightforward. The task of identifying actors for use case development [15] has similarities with stakeholder identification, but is targeted only at a fraction of system stakeholders. Stakeholders are related to each other and interact with each other [22,11,17]. Interactions between them include: exchanging information, products, or instructions, or providing supporting tasks. Information about the stakeholders and the nature of their relationships and interactions needs to be identified and recorded. Dimensions of importance are: relationships between stakeholders, the relationship of each stakeholder to the system, and the priority to be given to each stakeholderÕs view. This information is needed to manage, interpret, balance and process stakeholder input. Reconciling different stakeholder views is beyond this paper, but is considered in [12]. 3. An approach to identifying stakeholders for requirements engineering Here, we propose an approach to discovering all relevant stakeholders of a specific system that we believe is domain-independent, effective and pragmatic. We draw on the literature cited above, and on aspects of ethical decision-making in software systems [18]. Our starting point is a set of stakeholders that we refer to as ÔbaselineÕ stakeholders. From these, we can recognise ÔsupplierÕ stakeholders and ÔclientÕ stakeholders: the former provides information or supporting tasks to the baseline, and the latter processes or inspects the products of the baseline. Other stakeholders that we call ÔsatelliteÕs interact with the baseline in a variety of ways. ÔInteractionÕ may involve communicating, reading a set of rules or guidelines, searching for information and so on. Our approach focuses on interactions between stakeholders rather than relationships between the system and the stakeholder, Sharp, Finkelstein & Galal 11/12/99 3 because they are easier to follow. Figure 1 illustrates the main elements of stakeholder identification. Figure 1. The main elements of stakeholder identification addressed by our approach 3.1 Baseline stakeholders We have identified four groups of baseline stakeholder: users, developers, legislators and decision-makers. We have dubbed these ÔbaselineÕ because the web of stakeholders and their relationships can be identified from them. The nature of each group is explored below. 3.1.1 Users. The term ÔuserÕ has many interpretations. For example, Holtzblatt and Jones [14] include in their definition of ÔusersÕ those who manage direct users, those who receive products from the system, those who test the system, those who make the purchasing decision, and those who use competitive products. Eason [9] identifies three categories of user: primary, secondary and te

Second-order complex random vectors and normal distributions <=SEP=> We formulate as a deconvolution problem the causalhoncausal non-Gaussian multichannel autoregressive (AR) parameter estimation problem. The super exponential aljporithm presented in a recent paper by Shalvi and Weinstein is generalized to the vector case. We present an adaptive implementation that is very attractive since it is higher order statistics (HOS) based b u t does not present the high comlputational complexity of methods proposed up to now.

One selector-one resistor (1S1R) crossbar array for high-density flexible memory applications <=SEP=> Lack of a suitable selection device to suppress sneak current has impeded the development of 4F2 crossbar memory array utilizing stable and scalable bipolar resistive-switching. We report a high-performance nonlinear bipolar selector realized by a simple Ni/TiO2/Ni MIM structure with a high current density of 105 A/cm2, and a Ni/TiO2/Ni/HfO2/Pt vertically stacked 1S1R cell capable of gigabit memory implementation. Furthermore, the demonstration of 1S1R array fabricated completely at room temperature on a plastic substrate highlights the promise of future extremely low-cost flexible nonvolatile memory.

A Low Cost Eeg Based Bci Prosthetic Using Motor Imagery <=SEP=> Brain Computer Interfaces (BCI) provide the opportunity to control external devices using the brain ElectroEncephaloGram (EEG) signals. In this paper we propose two software framework in order to control a 5 degree of freedom robotic and prosthetic hand. Results are presented where an Emotiv Cognitive Suite (i.e. the 1 st framework) combined with an embedded software system (i.e. an open source Arduino board) is able to control the hand through character input associated with the taught actions of the suite. This system provides evidence of the feasibility of brain signals being a viable approach to controlling the chosen prosthetic. Results are then presented in the second framework. This latter one allowed for the training and classification of EEG signals for motor imagery tasks. When analysing the system, clear visual representations of the performance and accuracy are presented in the results using a confusion matrix, accuracy measurement and a feedback bar signifying signal strength. Experiments with various acquisition datasets were carried out and with a critical evaluation of the results given. Finally depending on the classification of the brain signal a Python script outputs the driving command to the Arduino to control the prosthetic. The proposed architecture performs overall good results for the design and implementation of economically convenient BCI and prosthesis.

Methods for ordinal peer grading <=SEP=> Massive Online Open Courses have the potential to revolutionize higher education with their wide outreach and accessibility, but they require instructors to come up with scalable alternates to traditional student evaluation. Peer grading -- having students assess each other -- is a promising approach to tackling the problem of evaluation at scale, since the number of "graders" naturally scales with the number of students. However, students are not trained in grading, which means that one cannot expect the same level of grading skills as in traditional settings. Drawing on broad evidence that ordinal feedback is easier to provide and more reliable than cardinal feedback [5, 38, 29, 9], it is therefore desirable to allow peer graders to make ordinal statements (e.g. "project X is better than project Y") and not require them to make cardinal statements (e.g. "project X is a B-"). Thus, in this paper we study the problem of automatically inferring student grades from ordinal peer feedback, as opposed to existing methods that require cardinal peer feedback. We formulate the ordinal peer grading problem as a type of rank aggregation problem, and explore several probabilistic models under which to estimate student grades and grader reliability. We study the applicability of these methods using peer grading data collected from a real class --- with instructor and TA grades as a baseline --- and demonstrate the efficacy of ordinal feedback techniques in comparison to existing cardinal peer grading methods. Finally, we compare these peer-grading techniques to traditional evaluation techniques.

Watch Me Code: Programming Mentorship Communities on Twitch.tv <=SEP=> Live streaming-an emerging practice of broadcasting video of oneself in real time to an online audience-is often used by people to portray themselves engaged in a craft such as programming. Viewers of these 'creative streams' gather to watch the streamer at work and to interact with the streamer and other audience members. However, little is known about how creative streamers engage with their audience, how their viewership communities form and operate, and how creative streams may support learning. In this study, we used a participant-observer method to study game development streams on the live streaming site Twitch.tv. We found that live streams support the growth of learning-focused communities that mentor both the streamer and each other during and after streams. We show the influence of streamers in creating a space for learning and motivating learners. Finally, we discuss implications for online education and communities of practice.

A multi-dimensional meter-adaptive method for automatic segmentation of music <=SEP=> Music structure appears on a wide variety of temporal levels (notes, bars, phrases, etc). Its highest-level expression is therefore dependent on music's lower-level organization, especially beats and bars. We propose a method for automatic structure segmentation that uses musically meaningful information and is content-adaptive. It relies on a meter-adaptive signal representation that prevents from the use of empirical parameters. Moreover, our method is designed to combine multiple signal features to account for various musical dimensions. Finally, it also combines multiple structural principles that yield complementary results. The resulting algorithm proves to already outperform state-of-the-art methods, especially within small tolerance windows, and yet offers several encouraging improvement directions.

An Introduction to Locally Linear Embedding <=SEP=> Many problemsin informationprocessinginvolvesomeform of dimensionality reduction.Herewe describelocally linearembedding(LLE), anunsupervisedlearningalgorithmthat computeslow dimensional,neighborhood preservingembeddingsof high dimensionaldata.LLE attemptsto discover nonlinearstructurein high dimensionaldataby exploiting thelocal symmetries of linear reconstructions.Notably, LLE mapsits inputs into a single global coordinatesystemof lower dimensionality, and its optimizations— thoughcapableof generatinghighly nonlinearembeddings—donot involve localminima.We illustratethemethodon imagesof lips usedin audiovisual speechsynthesis.

X-shaped inferior extensor retinaculum and its doubtful use in the Bröstrom–Gould procedure <=SEP=> The inferior extensor retinaculum (IER) is an aponeurotic structure located in the anterior aspect of the ankle. According to the literature, it can be used to reinforce a repair of the anterior talofibular ligament in ankle instability. Despite its usual description as an Y-shaped structure, it is still unclear which part of the retinaculum is used for this purpose, or if it is instead the crural fascia that is being used. The purpose of this study is to define the anatomical characteristics of the IER to better understand its role in the Broström–Gould procedure. Twenty-one ankles were dissected. The morphology of the IER and its relationship with neighbouring structures were recorded. Seventeen (81%) of the IER in this study had an X-shaped morphology, with the presence of an additional oblique superolateral band. This band, by far the thinnest of the retinaculum, is supposed to be used to reinforce the repair of the anterior talofibular ligament. The intermediate dorsal cutaneous nerve (lateral branch of the superficial peroneal nerve) was found to cross the retinaculum in all cases. The IER is most commonly seen as an X-shaped structure, but the fact that the oblique superolateral band is a thin band of tissue probably indicates that it may not add significant strength to ankle stability. Furthermore, the close relationship of the retinaculum with the superficial peroneal nerve is another factor to consider before deciding to perform a Broström–Gould procedure. These anatomical findings advise against the use of the Gould augmentation.

Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies <=SEP=> Construction projects are initiated in dynamic environment which result in circumstances of high uncertainty and risks due to accumulation of many interrelated parameters. The purpose of this study is to use novel analytic tools to evaluate the construction projects and their overall risks under incomplete and uncertain situations. It was also aimed to place the risk in a proper category and predict the level of it in advance to develop strategies and counteract the high-risk factors. The study covers identifying the key risk criteria of construction projects at King Abdulaziz University (KAU), and assessing the criteria by the integrated hybrid methodologies. The proposed hybrid methodologies were initiated with a survey for data collection. The relative importance index (RII) method was applied to prioritize the project risks based on the data obtained. The construction projects were then categorized by fuzzy AHP and fuzzy TOPSIS methodologies. Fuzzy AHP (FAHP) was used to create favorable weights for fuzzy linguistic variable of construction projects overall risk. The fuzzy TOPSIS method is very suitable for solving group decision making problems under the fuzzy environment. It attempted to incorporate vital qualitative attributes in performance analysis of construction projects and transformed the qualitative data into equivalent quantitative measures. Thirty construction projects were studied with respect to five main criteria that are the time, cost, quality, safety and environment sustainability. The results showed that these novel methodologies are able to assess the overall risks of construction projects, select the project that has the lowest risk with the contribution of relative importance index. This approach will have potential applications in the future. Crown Copyright © 2014 Published by Elsevier B.V. All rights reserved.

Noise Modeling for RF CMOS Circuit Simulation <=SEP=> The RF noise in 0.18m CMOS technology has been measured and modeled. In contrast to some other groups, we find only a moderate enhancement of the drain current noise for shortchannel MOSFETs. The gate current noise on the other hand is more significantly enhanced, which is explained by the effects of the gate resistance. The experimental results are modeled with a nonquasi-static RF model, based on channel segmentation, which is capable of predicting both drain and gate current noise accurately. Experimental evidence is shown for two additional noise mechanisms: 1) avalanche noise associated with the avalanche current from drain to bulk and 2) shot noise in the direct-tunneling gate leakage current. Additionally, we show low-frequency noise measurements, which strongly point toward an explanation of the1 noise based on carrier trapping, not only in n-channel MOSFETs, but also in p-channel MOSFETs.

Topology Aware Fully Convolutional Networks for Histology Gland Segmentation <=SEP=> [1] Sirinukunwattana et al. Gland Segmentation in colon histology images: the GlaS contest, arXiv 2016 [2] Long et al. Fully convolutional networks for semantic segmentation, CVPR 2015 [3] Chend et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs, arXiv 2014 [4] Zheng et al. Conditional random fields as recurrent neural networks, ICCV 2015 Alexnet-FCN (6 layers) FCN-8s (16 layers) UNet (23 layers) DN (26 layers) 0.93 0.79 0.86 0.83

Impact of reducing STI-induced stress on layout dependence of MOSFET characteristics <=SEP=> Active-area layout dependence of MOSFET parametric characteristics and its reduction by reducing shallow trench isolation (STI)-induced mechanical stress were investigated. Threshold voltages (V/sub th/) and saturation drain currents (I/sub ds/) become sensitive to the active-area layout of MOSFET in scaled-down technology. This phenomenon is the effect of mechanical stress from STI edge, which reduces impurity diffusion in channel region and enhances carrier mobility. To reduce the STI-induced stress, we examined STI-wall-oxide nitridation and STI gap-fill-oxide densifying in pure N/sub 2/ ambient. These processes reduced the reoxidation of the STI wall oxide, therefore reduced the STI-induced stress. According to the new STI process, the active-area layout dependence of V/sub th/ and I/sub ds/ were reduced successfully.

Learning to predict eye fixations for semantic contents using multi-layer sparse network <=SEP=> In this paper, we present a novel model for saliency prediction under a unified framework of feature integration. The model distinguishes itself by directly learning from natural images and automatically incorporating higher-level semantic information in a scalable manner for gaze prediction. Unlike most existing saliency models that rely on specific features or object detectors, our model learns multiple stages of features that mimic the hierarchical organization of the ventral stream in the visual cortex and integrate them by adapting their weights based on the ground-truth fixation data. To accomplish this, we utilize a multi-layer sparse network to learn low-, midand high-level features from natural images and train a linear support vector machine (SVM) for weight adaption and feature integration. Experimental results show that our model could learn high-level semantic features like faces and texts and can perform competitively among existing approaches in predicting eye fixations. & 2014 Elsevier B.V. All rights reserved.

E2E NLG Challenge Submission: Towards Controllable Generation of Diverse Natural Language <=SEP=> In natural language generation (NLG), the task is to generate utterances from a more abstract input, such as structured data. An added challenge is to generate utterances that contain an accurate representation of the input, while reflecting the fluency and variety of human-generated text. In this paper, we report experiments with NLG models that can be used in task oriented dialogue systems. We explore the use of additional input to the model to encourage diversity and control of outputs. While our submission does not rank highly using automated metrics, qualitative investigation of generated utterances suggests the use of additional information in neural network NLG systems to be a promising research direction.

A Novel Earth Mover's Distance Methodology for Image Matching with Gaussian Mixture Models <=SEP=> The similarity or distance measure between Gaussian mixture models (GMMs) plays a crucial role in content-based image matching. Though the Earth Mover's Distance (EMD) has shown its advantages in matching histogram features, its potentials in matching GMMs remain unclear and are not fully explored. To address this problem, we propose a novel EMD methodology for GMM matching. We first present a sparse representation based EMD called SR-EMD by exploiting the sparse property of the underlying problem. SR-EMD is more efficient and robust than the conventional EMD. Second, we present two novel ground distances between component Gaussians based on the information geometry. The perspective from the Riemannian geometry distinguishes the proposed ground distances from the classical entropy-or divergence-based ones. Furthermore, motivated by the success of distance metric learning of vector data, we make the first attempt to learn the EMD distance metrics between GMMs by using a simple yet effective supervised pair-wise based method. It can adapt the distance metrics between GMMs to specific classification tasks. The proposed method is evaluated on both simulated data and benchmark real databases and achieves very promising performance.

Collaborative color calibration for multi-camera systems <=SEP=> This paper proposes a collaborative color calibration meth od for multi-camera systems. The multi-camera color calibr t on problem is formulated as an overdetermined linear system, i n which a dynamic range shaping is incorporated to ensure the high contrasts for captured images. The cameras are calibrated w ith the parameters obtained by solving the linear system. Fo r non-planar multi-camera systems, we design a novel omnidirectional co lor checker and present a method for establishing global cor respondences to facilitate automatic color calibration without m anual adjustment. According to experimental results on bot h synthetic and real-system datasets, the proposed method shows high perfo rmance in achieving inter-camera color consistency and hig h dynamic range. Thanks to the generality of the linear system formula tion and the flexibility of the designed color checker, the pr oposed method is applicable to various multi-camera systems.

Dynamic Reserve Prices for Repeated Auctions: Learning from Bids - Working Paper <=SEP=> A large fraction of online advertisements are sold via repeated second price auctions. In these auctions, the reserve price is the main tool for the auctioneer to boost revenues. In this work, we investigate the following question: Can changing the reserve prices based on the previous bids improve the revenue of the auction, taking into account the long-term incentives and strategic behavior of the bidders? We show that if the distribution of the valuations is known and satisfies the standard regularity assumptions, then the optimal mechanism has a constant reserve. However, when there is uncertainty in the distribution of the valuations and competition among the bidders, previous bids can be used to learn the distribution of the valuations and to update the reserve prices. We present a simple approximately incentive-compatible and optimal dynamic reserve mechanism that can significantly improve the revenue over the best static reserve in such settings.