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We propose a novel logic, called Frame Logic (FL), that extends first-order logic (with recursive definitions) using a construct Sp(.) that captures the implicit supports of formulas -- the precise subset of the universe upon which their meaning depends. Using such supports, we formulate proof rules that facilitate frame reasoning elegantly when the underlying model undergoes change. We show that the logic is expressive by capturing several data-structures and also exhibit a translation from a precise fragment of separation logic to frame logic. Finally, we design a program logic based on frame logic for reasoning with programs that dynamically update heaps that facilitates local specifications and frame reasoning. This program logic consists of both localized proof rules as well as rules that derive the weakest tightest preconditions in FL., This manuscript is an extended and revised version of the publication with the same title that appeared at ESOP 2022 (https://doi.org/10.1007/978-3-030-44914-8_19). It is currently under review

This paper proposes a comprehensive steady-state analysis for a front-end Modular Multi-Parallel Rectifiers (MMR) system with a shared DC-link. A generalised mathematical representation for n number of parallel rectifiers is proposed. The MMR is analysed for both Continuous Conduction Mode (CCM) and Discontinuous Conduction Mode (DCM). In addition, the CCM and DCM boundary of the MMR is identified mathematically. A 60kW Simulink model for the MMR is utilised to confirm the proposed steady-state mathematical modelling equations.

Projection techniques are often used to visualize high-dimensional data, allowing users to better understand the overall structure of multi-dimensional spaces on a 2D screen. Although many such methods exist, comparably little work has been done on generalizable methods of inverse-projection -- the process of mapping the projected points, or more generally, the projection space back to the original high-dimensional space. In this paper we present NNInv, a deep learning technique with the ability to approximate the inverse of any projection or mapping. NNInv learns to reconstruct high-dimensional data from any arbitrary point on a 2D projection space, giving users the ability to interact with the learned high-dimensional representation in a visual analytics system. We provide an analysis of the parameter space of NNInv, and offer guidance in selecting these parameters. We extend validation of the effectiveness of NNInv through a series of quantitative and qualitative analyses. We then demonstrate the method's utility by applying it to three visualization tasks: interactive instance interpolation, classifier agreement, and gradient visualization.

Methods to measure work stress generally rely on subjective measures from questionnaires or require dedicated sensors that are cumbersome to wear and interfere with the task. To address this problem, we propose a method to detect stress unobtrusively using commodity devices (keyboards, mice) instrumented with pressure sensors. We propose a minimalist design that can be easily replicated by other researchers using off-the-shelf and low-cost hardware. We validate the design in a laboratory experiment that simulates office tasks and mild stressors while avoiding methodological limitations of previous studies. We compare stress-detection performance when using conventional features reported in the literature (keystroke dynamics, mouse trajectories) augmented with information from pressure sensors. Our results indicate that pressure provides additional information for stress discrimination; adding pressure information to keystroke dynamics and mouse trajectories improves classification performance by 6% and 3%, respectively. These results show how devices that are already part of the modern workplace may be used and enhanced to automatically and unobtrusively detect stress.

Genomic research emerges from collaborative work within and across different scientific disciplines. A diverse range of visualisation techniques has been employed to aid this research, yet relatively little is known as to how these techniques facilitate collaboration. We conducted a case study of collaborative research within a biomedical institute to learn more about the role visualisation plays in genomic mapping. Interviews were conducted with molecular biologists (N = 5) and bioinformaticians (N = 6). We found that genomic research comprises a variety of distinct disciplines engaged in complex analytic tasks that each resist simplification, and their complexity influences how visualisations were used. Visualisation use was impacted by group-specific interactions and temporal work patterns. Visualisations were also crucial to the scientific workflow, used for both question formation and confirmation of hypotheses, and acted as an anchor for the communication of ideas and discussion. In the latter case, two approaches were taken: providing collaborators with either interactive or static imagery representing a viewpoint. The use of generic software for simplified visualisations, and quick production and curation was also noted. We discuss these findings with reference to group-specific interactions and present recommendations for improving collaborative practices through visual analytics.

Exploring large virtual environments, such as cities, is a central task in several domains, such as gaming and urban planning. VR systems can greatly help this task by providing an immersive experience; however, a common issue with viewing and navigating a city in the traditional sense is that users can either obtain a local or a global view, but not both at the same time, requiring them to continuously switch between perspectives, losing context and distracting them from their analysis. In this paper, our goal is to allow users to navigate to points of interest without changing perspectives. To accomplish this, we design an intuitive navigation interface that takes advantage of the strong sense of spatial presence provided by VR. We supplement this interface with a perspective that warps the environment, called UrbanRama, based on a cylindrical projection, providing a mix of local and global views. The design of this interface was performed as an iterative process in collaboration with architects and urban planners. We conducted a qualitative and a quantitative pilot user study to evaluate UrbanRama and the results indicate the effectiveness of our system in reducing perspective changes, while ensuring that the warping doesn't affect distance and orientation perception., Comment: Video: https://www.youtube.com/watch?v=M8BFZnxq-Qg

We study the notion of consistency between a 3D shape and a 2D observation and propose a differentiable formulation which allows computing gradients of the 3D shape given an observation from an arbitrary view. We do so by reformulating view consistency using a differentiable ray consistency (DRC) term. We show that this formulation can be incorporated in a learning framework to leverage different types of multi-view observations e.g. foreground masks, depth, color images, semantics etc. as supervision for learning single-view 3D prediction. We present empirical analysis of our technique in a controlled setting. We also show that this approach allows us to improve over existing techniques for single-view reconstruction of objects from the PASCAL VOC dataset., Comment: To appear at CVPR 2017. Project webpage : https://shubhtuls.github.io/drc/

Based on the connection between the categorical derivation of classical programs from specifications and the category-theoretic approach to quantum physics, this paper contributes to extending the laws of classical program algebra to quantum programming. This aims at building correct-by-construction quantum circuits to be deployed on quantum devices such as those available at the IBM Q Experience. Quantum circuit reversibility is ensured by minimal complements, extended recursively. Measurements are postponed to the end of such recursive computations, termed "quantamorphisms", thus maximising the quantum effect. Quantamorphisms are classical catamorphisms which, extended to ensure quantum reversibility, implement quantum cycles (vulg. for-loops) and quantum folds on lists. By Kleisli correspondence, quantamorphisms can be written as monadic functional programs with quantum parameters. This enables the use of Haskell, a monadic functional programming language, to perform the experimental work. Such calculated quantum programs prepared in Haskell are pushed through Quipper to the Qiskit interface to IBM Q quantum devices. The generated quantum circuits - often quite large - exhibit the predicted behaviour. However, running them on real quantum devices incurs into a significant amount of errors. As quantum devices are constantly evolving, an increase in reliability is likely in the near future, allowing for our programs to run more accurately., 18 pages

In this article, we introduce a framework for the geometric design and fabrication of a family of geometrically interlocking space-filling shapes, which we call woven tiles. Our framework is based on a unique combination of (1) Voronoi partitioning of space using curve segments as the Voronoi sites and (2) the design of these curve segments based on weave patterns closed under symmetry operations. The underlying weave geometry provides an interlocking property to the tiles and the closure property under symmetry operations ensure single tile can fill space. In order to demonstrate this general framework, we focus on specific symmetry operations induced by fabric weaving patterns. We specifically showcase the design and fabrication of woven tiles on flat and curved domains by using the most common 2-fold fabrics, namely, plain, twill, and satin weaves. We further evaluate and compare the mechanical behavior of the so created woven tiles through finite element analysis.

Much software, whether beneficent or malevolent, is distributed only as binaries, sans source code. Absent source code, understanding binaries' behavior can be quite challenging, especially when compiled under higher levels of compiler optimization. These optimizations can transform comprehensible, ``natural" source constructions into something entirely unrecognizable. Reverse engineering binaries, especially those suspected of being malevolent or guilty of intellectual property theft, are important and time-consuming tasks. There is a great deal of interest in tools to ``decompile" binaries back into more natural source code to aid reverse engineering. Decompilation involves several desirable steps, including recreating source-language constructions, variable names, and perhaps even comments. One central step in creating binaries is optimizing function calls, using steps such as inlining. Recovering these (possibly inlined) function calls from optimized binaries is an essential task that most state-of-the-art decompiler tools try to do but do not perform very well. In this paper, we evaluate a supervised learning approach to the problem of recovering optimized function calls. We leverage open-source software and develop an automated labeling scheme to generate a reasonably large dataset of binaries labeled with actual function usages. We augment this large but limited labeled dataset with a pre-training step, which learns the decompiled code statistics from a much larger unlabeled dataset. Thus augmented, our learned labeling model can be combined with an existing decompilation tool, Ghidra, to achieve substantially improved performance in function call recovery, especially at higher levels of optimization.

On-board embedded software developed for spaceflight systems (space software) must adhere to stringent software quality assurance procedures. For example, verification and validation activities are typically performed and assessed by third party organizations. To further minimize the risk of human mistakes, space agencies, such as the European Space Agency (ESA), are looking for automated solutions for the assessment of software testing activities, which play a crucial role in this context. Over the years, mutation analysis has shown to be a promising solution for the automated assessment of test suites; it consists of measuring the quality of a test suite in terms of the percentage of injected faults leading to a test failure. A number of optimization techniques, addressing scalability and accuracy problems, have been proposed to facilitate the industrial adoption of mutation analysis. However, to date, two major problems prevent space agencies from enforcing mutation analysis in space software development. In this paper, we enhance mutation analysis optimization techniques to enable their applicability to embedded software and propose a pipeline that successfully integrates them to address scalability and accuracy issues in this context, as described above. Further, we report on the largest study involving embedded software systems in the mutation analysis literature. Our research is part of a research project funded by ESA ESTEC involving private companies (GomSpace Luxembourg and LuxSpace) in the space sector. These industry partners provided the case studies reported in this paper; they include an on-board software system managing a microsatellite currently on-orbit, a set of libraries used in deployed cubesats, and a mathematical library certified by ESA., Accepted for publication on IEEE TRANSACTIONS ON SOFTWARE ENGINEERING

Rendering a translucent material involves integrating the product of the transmittance-weighted irradiance and the BSSRDF over the surface of it. In previous methods, this spatial integral was computed by creating a dense distribution of discrete points over the surface or by importance-sampling based on the BSSRDF. Both of these approaches necessitate specifying the number of samples, which affects both the quality and the computation time for rendering. An insufficient number of samples leads to noise and artifacts in the rendered image and an excessive number results in a prohibitively long rendering time. In this article, we propose an error estimation method for translucent materials in a many-light rendering framework. Our adaptive sampling can automatically determine the number of samples so that the estimated relative error of each pixel intensity is less than a user-specified threshold. We also propose an efficient method to generate the sampling points that make large contributions to the pixel intensity taking into account the BSSRDF. This enables us to use a simple uniform sampling, instead of costly importance sampling based on the BSSRDF. The experimental results show that our method can accurately estimate the error. In addition, in comparison with the previous methods, our sampling method achieves better estimation accuracy in equal-time.

We present a case study on a journey about a personal data collection of carnivorous plant species habitats, and the resulting scientific exploration of location data biases, data errors, location hiding, and data plausibility. While initially driven by personal interest, our work led to the analysis and development of various means for visualizing threats to insight from geo-tagged social media data. In the course of this endeavor we analyzed local and global geographic distributions and their inaccuracies. We also contribute Motion Plausibility Profiles-a new means for visualizing how believable a specific contributor's location data is or if it was likely manipulated. We then compared our own repurposed social media dataset with data from a dedicated citizen science project. Compared to biases and errors in the literature on traditional citizen science data, with our visualizations we could also identify some new types or show new aspects for known ones. Moreover, we demonstrate several types of errors and biases for repurposed social media data. Please note that people with color impairments may consider our alternative paper version.

Software projects capture information in various kinds of artifacts, including source code, tests, and documentation. Such artifacts routinely encode information that is redundant, i.e., when a specification encoded in the source code is also separately tested and documented. Without supporting technology, such redundancy easily leads to inconsistencies and a degradation of documentation quality. We designed a tool-supported technique, called DScribe, that leverages redundancy between tests and documentation to generate consistent and checkable documentation and unit tests based on a single source of information. DScribe generates unit tests and documentation fragments based on a novel template and artifact generation technology. By pairing tests and documentation generation, DScribe provides a mechanism to automatically detect and replace outdated documentation. Our evaluation of the Apache Commons IO library revealed that of 835 specifications about exception handling, 85% of them were not tested or correctly documented, and DScribe could be used to automatically generate 97% of the tests and documentation.

Context: The use of standards is considered a vital part of any engineering discipline. So one could expect that standards play an important role in Requirements Engineering (RE) as well. However, little is known about the actual knowledge and use of RE-related standards in industry. Objective: In this article, we investigate to which extent standards and related artifacts such as templates or guidelines are known and used by RE practitioners. Method: To this end, we have conducted a questionnaire-based online survey. We could analyze the replies from 90 RE practitioners using a combination of closed and open-text questions. Results: Our results indicate that the knowledge and use of standards and related artifacts in RE is less widespread than one might expect from an engineering perspective. For example, about 47% of the respondents working as requirements engineers or business analysts do not know the core standard in RE, ISO/IEC/IEEE 29148. Participants in our study mostly use standards by personal decision rather than being imposed by their respective company, customer, or regulator. Beyond insufficient knowledge, we also found cultural and organizational factors impeding the widespread adoption of standards in RE. Conclusions: Overall, our results provide empirically informed insights into the actual use of standards and related artifacts in RE practice and - indirectly - about the value that the current standards create for RE practitioners., Preprint accepted for publication at IEEE Transactions on Software Engineering and presented as Journal First at the International Requirements Engineering conference 2021. Latest update: Several smaller corrections along the creation of the final version of the manuscript

Realistic 3D facial modeling and animation have been increasingly used in many graphics, animation, and virtual reality applications. However, generating realistic fine-scale wrinkles on 3D faces, in particular, on animated 3D faces, is still a challenging problem that is far away from being resolved. In this article we propose an end-to-end system to automatically augment coarse-scale 3D faces with synthesized fine-scale geometric wrinkles. By formulating the wrinkle generation problem as a supervised generation task, we implicitly model the continuous space of face wrinkles via a compact generative model, such that plausible face wrinkles can be generated through effective sampling and interpolation in the space. We also introduce a complete pipeline to transfer the synthesized wrinkles between faces with different shapes and topologies. Through many experiments, we demonstrate our method can robustly synthesize plausible fine-scale wrinkles on a variety of coarse-scale 3D faces with different shapes and expressions.

In molecular analysis, Spatial Distribution Functions (SDF) are fundamental instruments in answering questions related to spatial occurrences and relations of atomic structures over time. Given a molecular trajectory, SDFs can, for example, reveal the occurrence of water in relation to particular structures and hence provide clues of hydrophobic and hydrophilic regions. For the computation of meaningful distribution functions, the definition of molecular reference structures is essential. Therefore we introduce the concept of an internal frame of reference (IFR) for labeled point sets that represent selected molecular structures, and we propose an algorithm for tracking the IFR over time and space using a variant of Kabschs algorithm. This approach lets us generate a consistent space for the aggregation of the SDF for molecular trajectories and molecular ensembles. We demonstrate the usefulness of the technique by applying it to temporal molecular trajectories as well as ensemble datasets. The examples include different docking scenarios with DNA, insulin, and aspirin. Funding agencies:This work was supported through grants from the ExcellenceCenter at Link¨oping and Lund in Information Technology(ELLIIT) and the Swedish e-Science Research Centre(SeRC). The authors thanks the Swedish National Infrastructurefor Computing (SNIC) for providing computingresources.

code available at the following address:https://gitlab.inria.fr/marmando/deep-mesh-denoizing; International audience; We examine the problem of mesh denoising, which consists of removing noise from corrupted 3D meshes while preserving existing geometric features. Most mesh denoising methods require a lot of mesh-specific parameter fine-tuning, to account for specific features and noise types. In recent years, data-driven methods have demonstrated their robustness and effectiveness with respect to noise and feature properties on a wide variety of geometry and image problems. Most existing mesh denoising methods still use hand-crafted features, and locally denoise facets rather than examine the mesh globally. In this work, we propose the use of a fully end-to-end learning strategy based on graph convolutions, where meaningful features are learned directly by our network. It operates on a graph of facets, directly on the existing topology of the mesh, without resampling, and follows a multi-scale design to extract geometric features at different resolution levels. Similar to most recent pipelines, given a noisy mesh, we first denoise face normals with our novel approach, then update vertex positions accordingly. Our method performs significantly better than the current state-of-the-art learning-based methods. Additionally, we show that it can be trained on noisy data, without explicit correspondence between noisy and ground-truth facets. We also propose a multi-scale denoising strategy, better suited to correct noise with a low spatial frequency.

We report on a large-scale empirical study investigating the relevance of socio-technical congruence over key basic software quality metrics, namely, bugs and churn. In particular, we explore whether alignment or misalignment of social communication structures and technical dependencies in large software projects influences software quality. To this end, we have defined a quantitative and operational notion of socio-technical congruence, which we call socio-technical motif congruence (STMC). STMC is a measure of the degree to which developers working on the same file or on two related files, need to communicate. As socio-technical congruence is a complex and multi-faceted phenomenon, the interpretability of the results is one of our main concerns, so we have employed a careful mixed-methods statistical analysis. In particular, we provide analyses with similar techniques as employed by seminal work in the field to ensure comparability of our results with the existing body of work. The major result of our study, based on an analysis of 25 large open-source projects, is that STMC is not related to project quality measures -- software bugs and churn -- in any temporal scenario. That is, we find no statistical relationship between the alignment of developer tasks and developer communications on the one hand, and project outcomes on the other hand. We conclude that, wherefore congruence does matter as literature shows, then its measurable effect lies elsewhere., 29 pages, 21 figures. To appear in IEEE Transactions on Software Engineering

Augmented reality (AR) offers new ways to visualize information on-the-go. As noted in related work, AR graphics presented via optical see-through AR displays are particularly prone to color blending, whereby intended graphic colors may be perceptually altered by real-world backgrounds, ultimately degrading usability. This work adds to this body of knowledge by presenting a methodology for assessing AR interface color robustness, as quantitatively measured via shifts in the CIE color space, and qualitatively assessed in terms of users' perceived color name. We conducted a human factors study where twelve participants examined eight AR colors atop three real-world backgrounds as viewed through an in-vehicle AR head-up display (HUD); a type of optical see-through display used to project driving-related information atop the forward-looking road scene. Participants completed visual search tasks, matched the perceived AR HUD color against the WCS color palette, and verbally named the perceived color. We present analysis that suggests blue, green, and yellow AR colors are relatively robust, while red and brown are not, and discuss the impact of chromaticity shift and dispersion on outdoor AR interface design. While this work presents a case study in transportation, the methodology is applicable to a wide range of AR displays in many application domains and settings.

One truism of deep learning is that the automatic feature engineering (seen in the first layers of those networks) excuses data scientists from performing tedious manual feature engineering prior to running DL. For the specific case of deep learning for defect prediction, we show that that truism is false. Specifically, when we preprocess data with a novel oversampling technique called fuzzy sampling, as part of a larger pipeline called GHOST (Goal-oriented Hyper-parameter Optimization for Scalable Training), then we can do significantly better than the prior DL state of the art in 14/20 defect data sets. Our approach yields state-of-the-art results significantly faster deep learners. These results present a cogent case for the use of oversampling prior to applying deep learning on software defect prediction datasets., Comment: v3, revision 2 (minor revision); submitted to TSE

In recent years, Python has experienced explosive growth in adoption, particularly among open source projects. While Python's dynamically-typed nature provides developers with powerful programming abstractions, that same dynamic type system allows for type-related defects to accumulate in code bases. To aid in the early detection of type-related defects, type annotations were introduced into the Python ecosystem (i.e., PEP-484) and static type checkers like mypy have appeared on the market. While applying a type checker like mypy can in theory help to catch type-related defects before they impact users, little is known about the real impact of adopting a type checker to reveal defects in Python projects. In this paper, we study the extent to which Python projects benefit from such type checking features. For this purpose, we mine the issue tracking and version control repositories of 210 Python projects on GitHub. Inspired by the work of Gao et al. on type-related defects in JavaScript, we add type annotations to test whether detects an error that would have helped developers to avoid real defects. We observe that 15% of the defects could have been prevented by mypy. Moreover, we find that there is no significant difference between the experience level of developers committing type-related defects and the experience of developers committing defects that are not type-related. In addition, a manual analysis of the anti-patterns that most commonly lead to type-checking faults reveals that the redefinition of Python references, dynamic attribute initialization and incorrectly handled Null objects are the most common causes of type-related faults. Since our study is conducted on fixed public defects that have gone through code reviews and multiple test cycles, these results represent a lower bound on the benefits of adopting a type checker. Therefore, we recommend incorporating a static type checker like mypy into the development workflow, as not only will it prevent type-related defects but also mitigate certain anti-patterns during development.

This article demonstrates that nonmaximum suppression (NMS), which is commonly used in object detection (OD) tasks to filter redundant detection results, is no longer secure. Considering that NMS has been an integral part of OD systems, thwarting the functionality of NMS can result in unexpected or even lethal consequences for such systems. In this article, an adversarial example attack that triggers malfunctioning of NMS in OD models is proposed. The attack, namely, Daedalus, compresses the dimensions of detection boxes to evade NMS. As a result, the final detection output contains extremely dense false positives. This can be fatal for many OD applications, such as autonomous vehicles and surveillance systems. The attack can be generalized to different OD models, such that the attack cripples various OD applications. Furthermore, a way of crafting robust adversarial examples is developed by using an ensemble of popular detection models as the substitutes. Considering the pervasive nature of model reuse in real-world OD scenarios, Daedalus examples crafted based on an ensemble of substitutes can launch attacks without knowing the parameters of the victim models. The experimental results demonstrate that the attack effectively stops NMS from filtering redundant bounding boxes. As the evaluation results suggest, Daedalus increases the false positive rate in detection results to 99.9% and reduces the mean average precision scores to 0, while maintaining a low cost of distortion on the original inputs. It also demonstrates that the attack can be practically launched against real-world OD systems via printed posters.

With the wide range application of machine learning in healthcare for helping humans drive crucial decisions, data privacy becomes an inevitable concern due to the utilization of sensitive data such as patients records and registers of a company. Thus, constructing a privacy preserving machine learning model while still maintaining high accuracy becomes a challenging problem. In this paper, we propose two differentially private algorithms, i.e., Output Perturbation with aGM (OPERA) and Gradient Perturbation with aGM (GRPUA) for empirical risk minimization, a useful method to obtain a globally optimal classifier, by leveraging the analytic Gaussian mechanism (aGM) to achieve privacy preservation of sensitive medical data in a healthcare system. We theoretically analyze and prove utility upper bounds of proposed algorithms and compare them with prior algorithms in the literature. The analyses show that in the high privacy regime, our proposed algorithms can achieve a tighter utility bound for both settings: strongly convex and non-strongly convex loss functions. Besides, we evaluate the proposed private algorithms on three benchmark datasets, i.e., Adult, BANK and IPUMS-BR. The simulation results demonstrate that our approaches can achieve higher accuracy and lower objective values compared with existing ones in all three datasets while providing differential privacy guarantees.

The article is concerned with a central contribution of designing to information visualization in the digital humanities. The activity is characterized as one of externalization, instantiation in visible or tangible form of ideas that might otherwise be internal to the minds of the designer and other participants. A spectrum of different interpretations of this process in the existing literature is discussed, focusing on early attempts to theorize designing as a means of creating new knowledge, and especially the ways in which that new knowledge contributes to defining or redefining the question or problem to be addressed. The arguments are illustrated with recent practical examples from the authors’ own work in designing with a range of cultural organizations, museums and archives. The article concludes with reflections on how projects may best benefit from this work of design, empowering the designer as a co-researcher, alongside the historian, curator or other humanities scholar.

The British Medical Journal has recently started making visual abstracts to summarise published research studies. These 1024 × 1024 px images give a quick overview of a trial’s participants, design, and key findings. These visual abstracts are designed to help busy health professionals and researchers get a quick overview of newly published research. The present article describes simple pragmatic evaluations of these visual abstracts: analysis of social media stats and an opportunistic reader survey. Our goals were to identify how useful our readers found this new visual format, and whether there were any improvements we could make. The social media stats were initially very promising. Longer term performance over several visual abstracts, however, was not as strong, suggesting a possible halo effect provided by the novelty of a new presentation format. The survey proved to be a quick and valuable way of getting feedback on the design of the initial template, and resulted in several design adjustments.

Immigrants are central to the identity of the United States, the population of which has grown in number and diversity as a function of new arrivals from around the globe. This article describes a visualization project that uses the visual metaphor of tree rings to explore the contribution of immigrants to the country’s population. Immigrants and native-born persons are represented and differentiated as cells in trees, with layered annual rings capturing patterns of population growth. These rings register, in their shape and color, certain environmental conditions. In order to mimic the natural process by which growth rings are formed, we devised a computational system that simulates the growth of trees as if cells were data-units. Dendrochronology involves dating certain events by analyzing patterns of growth in trees. Analogously, in our visualizations the rings can be counted and dated, showing the chronological evolution of the population. The dendrochronology theme is a poetic take on the data, yet it is also a functional and conceptual space that is used to construct language and rationales on that data. The tree-growth process not only inspires the appearance of the visualizations but also informs the rules of the computational system that creates them.

Data visualizations are often represented in public discourse as objective proof of facts. However, a visualization is only a single translation of reality, just like any other media, representation devices, or modes of representation. If we wish to encourage thoughtful, informed, and literate consumption of data visualizations, it is crucial that we consider why they are often presented and interpreted as objective. We reflect theoretically on data visualization as a system of representation historically anchored in science, rationalism, and notions of objectivity. It establishes itself within a lineage of conventions for visual representations which extends from the Renaissance to the present and includes perspective drawing, photography, cinema and television, as well as computer graphics. By examining our tendency to see credibility in data visualizations and grounding that predisposition in a historical context, we hope to encourage more critical and nuanced production and interpretation of data visualizations in the public discourse.

For means of communication, persuasion is a natural and critical part of conveying a message. Data visualizations, being means of communication themselves, are used as rhetorical instruments, but how they persuade has yet to be fully understood. Based on George Campbell’s rhetorical theory, this paper presents the results of an empirical study testing the effectiveness of appeals to emotion through proximity techniques – the contextual framing of a visualization. The findings indicate that people feel greater interest towards a topic when the visualized data are more relevant to them, and that data representing events closer in time are more affecting.

As cultural institutions are digitizing their artifacts and interlinking their collections, new opportunities emerge to engage with cultural heritage. However, it is the often comprehensive and complex nature of collections that can make it difficult to grasp their distribution and extent across a variety of dimensions. After a brief introduction to the research area of collection visualizations, this paper presents a design study visualizing an aggregated collection from diverse cultural institutions in Germany. We detail our iterative design process leading to prototypical implementations of four stylistically and functionally coordinated visualizations, each one focusing on different facets of the collection.

Affective computing is a field of great interest in many computer vision applications, including video surveillance, behaviour analysis, and human-robot interaction. Most of the existing literature has addressed this field by analysing different sets of face features. However, in the last decade, several studies have shown how body movements can play a key role even in emotion recognition. The majority of these experiments on the body are performed by trained actors whose aim is to simulate emotional reactions. These unnatural expressions differ from the more challenging genuine emotions, thus invalidating the obtained results. In this paper, a solution for basic non-acted emotion recognition based on 3D skeleton and Deep Neural Networks (DNNs) is provided. The proposed work introduces three majors contributions. First, unlike the current state-of-the-art in non-acted body affect recognition, where only static or global body features are considered, in this work also temporal local movements performed by subjects in each frame are examined. Second, an original set of global and time-dependent features for body movement description is provided. Third, to the best of out knowledge, this is the first attempt to use deep learning methods for non-acted body affect recognition. Due to the novelty of the topic, only the UCLIC dataset is currently considered the benchmark for comparative tests. On the latter, the proposed method outperforms all the competitors.

With the growing number of competing Web services that provide similar functionality, Quality-of-Service (QoS) prediction is becoming increasingly important for various QoS-aware approaches of Web services. Collaborative filtering (CF), which is among the most successful personalized prediction techniques for recommender systems, has been widely applied to Web service QoS prediction. In addition to using conventional CF techniques, a number of studies extend the CF approach by incorporating additional information about services and users, such as location, time, and other contextual information from the service invocations. There are also some studies that address other challenges in QoS prediction, such as adaptability, credibility, privacy preservation, and so on. In this survey, we summarize and analyze the state-of-the-art CF QoS prediction approaches of Web services and discuss their features and differences. We also present several Web service QoS datasets that have been used as benchmarks for evaluating the predition accuracy and outline some possible future research directions.

Cross-cloud data migration is one of the prevailing challenges faced by mobile users, which is an essential process when users change their mobile phones to a different provider. However, due to the insufficient local storage and computational capabilities of the smart phones, it is often very difficult for users to backup all data from the original cloud servers to their mobile phones in order to further upload the downloaded data to the new cloud provider. To solve this problem, we propose an efficient data migration model between cloud providers and construct a mutual authentication and key agreement scheme based on elliptic curve certificate-free cryptography for peer-to-peer cloud. The proposed scheme helps to develop trust between different cloud providers and lays a foundation for the realization of cross-cloud data migration. Mathematical verification and security correctness of our scheme is evaluated against notable existing schemes of data migration, which demonstrate that our proposed scheme exhibits a better performance than other state-of-the-art scheme in terms of the achieved reduction in both the computational and communication cost.

Automatic Dynamic Facial Expression Recognition (DFER) is challenging since how to effectively capture facial temporal dynamics is still an open problem. As the variations of facial expressions is a dynamic system that satisfies underlying rules, it is essential to explore the fundamental temporal properties for recognizing dynamic expressions. Inspired by the phase space reconstruction method for time series analysis, we propose a Phase Space Reconstruction Network (PSRNet) for learning spatio-temporal facial features. First, 3D convolutional neural networks are used to extract spatial and short-term features, which indicate each frame's state termed as observations in the phase space. All the observations compose the trajectory of the dynamical system. Then, a data-driven across-correlation matrix is inferred to reveal the relationship of the observations. With this matrix, the phase space reconstruction module reconstructs the trajectory by aggregating the observations adaptively. Reconstructed observations represent the gradual process of dynamic facial expressions, which is beneficial to recognize these expressions. The experiment results on Oulu, MMI, and CK+ demonstrate that PSRNet can extract more informative and representative spatio-temporal features for DFER. Moreover, the visualization reveals that the reconstructed features have global consistency in facial regions and find the underlying evolutionary pattern of dynamic facial expression.

As an increasing number of asteroids are being discovered, detecting them using limited propulsion resources and time has become an urgent problem in the aerospace field. However, there is no universal fast asteroid sequence selection method that finds the trajectories for multiple low-thrust spacecraft for detecting a large number of asteroids. Furthermore, the calculation efficiency of the traditional trajectory optimization method is low, and it requires a large number of iterations. Therefore, this study combines Monte Carlo tree search (MCTS) with spacecraft trajectory optimization. A fast MCTS pruning algorithm is proposed, which can quickly complete asteroid sequence selection and trajectory generation for multispacecraft exploration of multiple asteroids. By combining the Bezier shape-based (SB) method and MCTS, this study realizes the fast search of the exploration sequence and the efficient optimization of the continuous transfer trajectories. In the simulation example, compared with the traversal algorithm, the MCTS pruning algorithm obtained the global optimal detection sequence of the search tree in a very short time. Under the same conditions, the Bezier SB method obtained the transfer trajectory with a better performance index faster than the finite Fourier series SB method. Performances of the proposed method are illustrated through a complex asteroid multiflyby mission design.

Static visual attributes such as color and shape are used with great success in visual charts designed to be displayed in static, hard-copy form. However, nowadays digital displays become ubiquitous in the visualization of any form of data, lifting the confines of static presentations. In this article, we propose incorporating data-driven animations to bring static charts to life, with the purpose of encoding and emphasizing certain attributes of the data. We lay out a design space for data-driven animated effects and experiment with three versatile effects, marching ants, geometry deformation and gradual appearance. For each, we provide practical details regarding their mode of operation and extent of interaction with existing visual encodings. We examine the impact and effectiveness of our enhancements through an empirical user study to assess preference as well as gauge the influence of animated effects on human perception in terms of speed and accuracy of visual understanding.

The emerging 4D printing techniques open new horizons for fabricating self-actuated deformable objects by combing strength of 3D printing and stimuli-responsive shape memory materials. This work focuses on designing self-actuated deformable solids for 4D printing such that a solid can be programmed into a temporary shape and later recovers to its original shape after heating. To avoid a high material cost, we choose a dual-material strategy that mixes an expensive thermo-responsive shape memory polymer (SMP) material with a common elastic material, % for fabricating objects, which however leads to undesired deformation at the shape programming stage. We model this shape programming process as two elastic models with different parameters linked by a median shape based on customizing a constitutive model of thermo-responsive SMPs. Taking this material modeling as a foundation, we formulate our design problem as a nonconvex optimization to find the distribution of SMP materials over the whole object as well as the median shape, and develop an efficient and parallelizable method to solve it. We show that our proposed approach is able to design self-actuated deformable objects that cannot be achieved by state of the art approaches, and demonstrate their usefulness with three example applications.

Code review is a powerful technique to ensure high quality software and spread knowledge of best coding practices between engineers. Unfortunately, code reviewers may have biases about authors of the code they are reviewing, which can lead to inequitable experiences and outcomes. In principle, anonymous author code review can reduce the impact of such biases by withholding an author's identity from a reviewer. In this paper, to understand the engineering effects of using author anonymous code review in a practical setting, we applied the technique to 5217 code reviews performed by 300 software engineers at Google. Our results suggest that during anonymous author code review, reviewers can frequently guess authors identities; that focus is reduced on reviewer-author power dynamics; and that the practice poses a barrier to offline, high-bandwidth conversations. Based on our findings, we recommend that those who choose to implement anonymous author code review should reveal the time zone of the author by default, have a break-the-glass option for revealing author identity, and reveal author identity directly after the review.

Optimization sequences are often employed in compilers to improve the performance of programs, but may trigger critical compiler bugs, e.g., compiler crashes. Although many methods have been developed to automatically test compilers, no systematic work has been conducted to detect compiler bugs when applying arbitrary optimization sequences. To resolve this problem, two main challenges need to be addressed, namely the acquisition of representative optimization sequences and the selection of representative testing programs, due to the enormous number of optimization sequences and testing programs. In this study, we propose CTOS, a novel compiler testing method based on differential testing, for detecting compiler bugs caused by optimization sequences of LLVM. CTOS firstly leverages the technique Doc2Vec to transform optimization sequences into vectors to capture the information of optimizations and their orders simultaneously. Secondly, a method based on the region graph and call relationships is developed in CTOS to construct the vector representations of testing programssuch that the semantics and the structure information of programs can be captured simultaneously. Then, with the vector representations of optimization sequences and testing programs, a centroid based selection scheme is proposed to address the above two challenges. Finally, CTOS takes in the representative optimization sequences and testing programs as inputs, and tests each testing program with all the representative optimization sequences. If there is an output that is different from the majority of others of a given testing program, then the corresponding optimization sequence is deemed to trigger a compiler bug. Our evaluation demonstrates that CTOS significantly outperforms the baselines by up to 24.76% 45.56% in terms of the bug-finding capability on average. Within seven month evaluations on LLVM, we have reported 104 valid bugs within 5 types, of which 21 have been confirmed or fixed. Most of those bugs are crash bugs (57) and wrong code bugs (24). 47 unique optimizations are identified to be faulty and 15 of them are loop related optimizations.

Security issues have resulted in severe damage to the cloud computing environment, adversely affecting the healthy and sustainable development of cloud computing. Intrusion detection is one of the technologies for protecting the cloud computing environment from malicious attacks. However, network traffic in the cloud computing environment is characterized by large scale, high dimensionality, and high redundancy, these characteristics pose serious challenges to the development of cloud intrusion detection systems. Deep learning technology has shown considerable potential for intrusion detection. Therefore, this study aims to use deep learning to extract essential feature representations automatically and realize high detection performance efficiently. An effective stacked contractive autoencoder (SCAE) method is presented for unsupervised feature extraction. By using the SCAE method, better and robust low-dimensional features can be automatically learned from raw network traffic. A novel cloud intrusion detection system is designed on the basis of the SCAE and support vector machine (SVM) classification algorithm. The SCAE+SVM approach combines both deep and shallow learning techniques, and it fully exploits their advantages to significantly reduce the analytical overhead. Experiments show that the proposed SCAE+SVM method achieves higher detection performance compared to three other state-of-the-art methods on two well-known intrusion detection evaluation datasets, namely KDD Cup 99 and NSL-KDD.

We present a multi-level area balancing technique for laying out clustered graphs to facilitate a comprehensive understanding of the complex relationships that exist in various fields, such as life sciences and sociology. Clustered graphs are often used to model relationships that are accompanied by attribute-based grouping information. Such information is essential for robust data analysis, such as for the study of biological taxonomies or educational backgrounds. Hence, the ability to smartly arrange textual labels and packing graphs within a certain screen space is therefore desired to successfully convey the attribute data . Here we propose to hierarchically partition the input screen space using Voronoi tessellations in multiple levels of detail. In our method, the position of textual labels is guided by the blending of constrained forces and the forces derived from centroidal Voronoi cells. The proposed algorithm considers three main factors: (1) area balancing, (2) schematized space partitioning, and (3) hairball management. We primarily focus on area balancing, which aims to allocate a uniform area for each textual label in the diagram. We achieve this by first untangling a general graph to a clustered graph through textual label duplication, and then coupling with spanning-tree-like visual integration. We illustrate the feasibility of our approach with examples and then evaluate our method by comparing it with well-known conventional approaches and collecting feedback from domain experts.

While existing prediction models built on popular deep architectures have shown promising results in facial depression recognition, they still lack sufficient discriminative power due to the issues of 1) limited amount of labeled depression data for deep representation learning and, 2) large variation in facial expression across different persons of the same depression score and the subtle difference in facial expression across different depression levels. In this paper, we formulate the facial depression recognition as a label distribution learning (LDL) problem, and propose a deep joint label distribution and metric learning (DJ-LDML) method to address these issues. In DJ-LDML, LDL exploits label relevance inherent in depression data to implicitly increase the amount of training data associated with each depression level without actually enlarging the dataset, while deep metric learning (DML) aims at learning a deep ordinal embedding with a specifically designed label-aware histogram loss, allowing semantics similarity between video sequences (described by ordinal labels) to be preserved for discriminative feature learning. The two learning modules in our DJ-LDML work collaboratively to enhance the representation ability and discriminative power of the deeply learned spatiotemporal feature, leading to improved depression prediction. We empirically evaluate our method on two benchmark datasets and the results demonstrate the effectiveness of our formulation.

To facilitate the rapid release cadence of modern software (on the order of weeks, days, or even hours), software development organizations invest in practices like Continuous Integration (CI), where each change submitted by developers is built (e.g., compiled, tested, linted) to detect problematic changes early. A fast and efficient build process is crucial to provide timely CI feedback to developers. If CI feedback is too slow, developers may switch contexts to other tasks, which is known to be a costly operation for knowledge workers. Thus, minimizing the build execution time for CI services is an important task. While recent work has made several important advances in the acceleration of CI builds, optimizations often depend upon explicitly defined build dependency graphs (e.g., make, Gradle, CloudBuild, Bazel). These hand-maintained graphs may be (a) underspecified, leading to incorrect build behaviour; or (b) overspecified, leading to missed acceleration opportunities. In this paper, we propose Kotinos'a language-agnostic approach to infer data from which build acceleration decisions can be made without relying upon build specifications. After inferring this data, our approach accelerates CI builds by caching the build environment and skipping unaffected build steps. Kotinos is at the core of a commercial CI service with a growing customer base. To evaluate Kotinos, we mine 14,364 historical CI build records spanning three proprietary and seven open-source software projects. We find that: (1) at least 87.9% of the builds activate at least one Kotinos acceleration; and (2) 74% of accelerated builds achieve a speed-up of two-fold with respect to their non-accelerated counterparts. Moreover, (3) the benefits of Kotinos can also be replicated in open source software systems; and (4) Kotinos imposes minimal resource overhead (i.e., < 1% median CPU usage, 2 MB - 2.2 GB median memory usage, and 0.4 GB - 5.2 GB median storage overhead) and does not compromise build outcomes. Our results suggest that migration to Kotinos yields substantial benefits with minimal investment of effort (e.g., no migration of build systems is necessary).

An increasing number of companies aim to enable their development teams to work in an agile manner. When introducing agile teams, companies face several challenges. This paper explores the kind of leadership needed to support teams to work in an agile way. One theoretical agile leadership concept describes a Scrum Master who is supposed to empower the team to lead itself. Empirical findings on such a leadership role are controversial. We still have not understood how leadership unfolds in a team that is by definition self-organizing. Further exploration is needed to better understand leadership in agile teams. Our goal is to explore how leadership changes while the team matures using the example of the Scrum Master. Through a grounded theory study containing 75 practitioners from 11 divisions at the Robert Bosch GmbH we identified a set of nine leadership roles that are transferred from the Scrum Master to the Development Team while it matures. We uncovered that a leadership gap and a supportive internal team climate are enablers of the role transfer process, whereas role conflicts may diminish the role transfer. To make the Scrum Master change in a mature team, team members need to receive trust and freedom to take on a leadership role which was previously filled by the Scrum Master. We conclude with practical implications for managers, Product Owners, Development Teams and Scrum Masters which they can apply in real settings., Robert Bosch Automotive Steering GmbH, Projekt DEAL

Traditional distribution of programming problems seems to create dissatisfaction among students. To overcome this problem, complexity measurement of programming problems is necessary. Although software is the outcome of human ingenious activity, cognitive informatics plays a significant role in understanding its ultimate characteristics. In this article, we have proposed a software complexity measurement algorithm based on cognitive weight of basic control structure that shrink the limitations of existing measures. Cognitive weight concept of any basic control structure are morally based on the rational capacity of Human Brain. According to cognitive data, we have developed a new software tool using Java Standard Edition and MySQL to measure the cognitive complexity by following our developed algorithm. This software is structured and developed based on the outcome of our research data which is capable of determining the complexity value of several programming languages. This will assist the instructors allocating the problems among the learners by preserving the equivalent level of difficulty. Therefore, the developed complexity measurement tool will ensure the students to get problems with identical difficulty level for assessment.

International audience; In this paper we enhance the EasyCrypt proof assistant to reason about computational complexity of adversaries. The key technical tool is a Hoare logic for reasoning about computational complexity (execution time and oracle calls) of adversarial computations. Our Hoare logic is built on top of the module system used by EasyCrypt for modeling adversaries. We prove that our logic is sound w.r.t. the semantics of EasyCrypt programs-we also provide full semantics for the EasyCrypt module system, which was previously lacking. We showcase (for the first time in EasyCrypt and in other computer-aided cryptographic tools) how our approach can express precise relationships between the probability of adversarial success and their execution time. In particular, we can quantify existentially over adversaries in a complexity class, and express general composition statements in simulation-based frameworks. Moreover, such statements can be composed to derive standard concrete security bounds for cryptographic constructions whose security is proved in a modular way. As a main benefit of our approach, we revisit security proofs of some well-known cryptographic constructions and we present a new formalization of Universal Composability (UC).

This paper advocates programming high-performance code using partial evaluation. We present a clean-slate programming system with a simple, annotation-based, online partial evaluator that operates on a CPS-style intermediate representation. Our system exposes code generation for accelerators (vectorization/parallelization for CPUs and GPUs) via compiler-known higher-order functions that can be subjected to partial evaluation. This way, generic implementations can be instantiated with target-specific code at compile time. In our experimental evaluation we present three extensive case studies from image processing, ray tracing, and genome sequence alignment. We demonstrate that using partial evaluation, we obtain high-performance implementations for CPUs and GPUs from one language and one code base in a generic way. The performance of our codes is mostly within 10%, often closer to the performance of multi man-year, industry-grade, manually-optimized expert codes that are considered to be among the top contenders in their fields.

We describe1 two case studies of AvatarStaging theatrical mixed reality framework combining avatars and performers acting in an artistic context. We outline a qualitative approach toward the condition for stage presence for the avatars. We describe the motion control solutions we experimented with from the perspective of building a protocol of avatar direction in a mixed reality appropriate to live performance.