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“zones of flow (iii)’’ is a photo-sensitive installation that investigates the fluid and changing connections between people, sea and land. It addresses the instantaneous but sometimes asynchronous connectivity between things as they move, are placed or displaced in their environments. The artwork brings origami structures, electronics and audiovisual media together to create an immersive cogitative environment. A video projection depicting water surfaces is mapped onto a 2.5m paper-boat. Underneath, arrays of light sensors receive waves of information and send signals to small paper-boats scattered across the floor. As the flotilla of small paper-boats is activated it becomes a changing water surface where non-linear patterns emerge over time.

Despite the central importance of research papers to scientific progress, they can be difficult to read. Comprehension is often stymied when the information needed to understand a passage resides somewhere else: in another section, or in another paper. In this work, we envision how interfaces can bring definitions of technical terms and symbols to readers when and where they need them most. We introduce ScholarPhi, an augmented reading interface with four novel features: (1) tooltips that surface position-sensitive definitions from elsewhere in a paper, (2) a filter over the paper that "declutters" it to reveal how the term or symbol is used across the paper, (3) automatic equation diagrams that expose multiple definitions in parallel, and (4) an automatically generated glossary of important terms and symbols. A usability study showed that the tool helps researchers of all experience levels read papers. Furthermore, researchers were eager to have ScholarPhi's definitions available to support their everyday reading., 18 pages, 17 figures, 2 tables. To appear at the 2021 ACM CHI Conference on Human Factors in Computing Systems. For associated video, see https://youtu.be/yYcQf-Yq8B0. v2 changes: expanded discussion of design process and implementation; improved figure design. v3 changes: fixed typo in cell of Table 2; updated HEDDEx and Schwarz-Hearst accuracy in Section 5.3

Automatically detecting the positions of key-points (e.g., facial key-points or finger key-points) in an image is an essential problem in many applications, such as driver's gaze detection and drowsiness detection in automated driving systems. With the recent advances of Deep Neural Networks (DNNs), Key-Points detection DNNs (KP-DNNs) have been increasingly employed for that purpose. Nevertheless, KP-DNN testing and validation have remained a challenging problem because KP-DNNs predict many independent key-points at the same time -- where each individual key-point may be critical in the targeted application -- and images can vary a great deal according to many factors. In this paper, we present an approach to automatically generate test data for KP-DNNs using many-objective search. In our experiments, focused on facial key-points detection DNNs developed for an industrial automotive application, we show that our approach can generate test suites to severely mispredict, on average, more than 93% of all key-points. In comparison, random search-based test data generation can only severely mispredict 41% of them. Many of these mispredictions, however, are not avoidable and should not therefore be considered failures. We also empirically compare state-of-the-art, many-objective search algorithms and their variants, tailored for test suite generation. Furthermore, we investigate and demonstrate how to learn specific conditions, based on image characteristics (e.g., head posture and skin color), that lead to severe mispredictions. Such conditions serve as a basis for risk analysis or DNN retraining., to appear in ISSTA 2021

Using machine learning (ML) services, both service customers and providers need to monitor complex contractual constraints of ML service that are strongly related to ML models and data. Therefore, establishing and monitoring comprehensive ML contracts are crucial in ML serving. This paper demonstrates a set of features and utilities of the QoA4ML framework for ML contracts.

Children and young people make extensive and varied use of digital and online technologies, yet issues about how their personal data may be collected and used by online platforms are rarely discussed. Additionally, despite calls to increase awareness, schools often do not cover these topics, instead focusing on online safety issues, such as being approached by strangers, cyberbullying or access to inappropriate content. This paper presents the results of one of the activities run as part of eleven workshops with 13-18 year olds, using co-designed activities to encourage critical thinking. Sets of 'data cards' were used to stimulate discussion about sharing and selling of personal data by online technology companies. Results highlight the desire and need for increased awareness about the potential uses of personal data amongst this age group, and the paper makes recommendations for embedding this into school curriculums as well as incorporating it into interaction design, to allow young people to make informed decisions about their online lives.

Nowadays, recommendation systems play an indispensable role in many fields, including e-commerce, finance, economy, and gaming. There is emerging research on publication venue recommendation systems to support researchers when submitting their scientific work. Several publishers such as IEEE, Springer, and Elsevier have implemented their submission recommendation systems only to help researchers choose appropriate conferences or journals for submission. In this work, we present a demo framework to construct an effective recommendation system for paper submission. With the input data (the title, the abstract, and the list of possible keywords) of a given manuscript, the system recommends the list of top relevant journals or conferences to authors. By using state-of-the-art techniques in natural language understanding, we combine the features extracted with other useful handcrafted features. We utilize deep learning models to build an efficient recommendation engine for the proposed system. Finally, we present the User Interface (UI) and the architecture of our paper submission recommendation system for later usage by researchers.

Cybersecurity is now more important than ever, and so is education in this field. However, the cybersecurity domain encompasses an extensive set of concepts, which can be taught in different ways and contexts. To understand the state of the art of cybersecurity education and related research, we examine papers from the ACM SIGCSE and ACM ITiCSE conferences. From 2010 to 2019, a total of 1,748 papers were published at these conferences, and 71 of them focus on cybersecurity education. The papers discuss courses, tools, exercises, and teaching approaches. For each paper, we map the covered topics, teaching context, evaluation methods, impact, and the community of authors. We discovered that the technical topic areas are evenly covered (the most prominent being secure programming, network security, and offensive security), and human aspects, such as privacy and social engineering, are present as well. The interventions described in SIGCSE and ITiCSE papers predominantly focus on tertiary education in the USA. The subsequent evaluation mostly consists of collecting students' subjective perceptions via questionnaires. However, less than a third of the papers provide supplementary materials for other educators, and none of the authors published their dataset. Our results provide orientation in the area, a synthesis of trends, and implications for further research. Therefore, they are relevant for instructors, researchers, and anyone new in the field of cybersecurity education. The information we collected and synthesized from individual papers are organized in a publicly available dataset., Comment: ACM SIGCSE 2020 conference, 7 pages, 3 figures, 1 table

Message-based programming frameworks facilitate the development and execution of core distributed computing algorithms today. Their twofold aim is to expose a programming model that minimises logical errors incurred during translation from an algorithmic specification to executable program, and also to provide an efficient runtime for event pattern-matching and scheduling of distributed components. Kompics Scala is a framework that allows for a direct, streamlined translation from a formal algorithm specification to practical code by reducing the cognitive gap between the two representations. Furthermore, its runtime decouples event pattern-matching and component execution logic yielding clean, thoroughly expected behaviours. Our evaluation shows low and constant performance overhead of Kompics Scala compared to similar frameworks that otherwise fail to offer the same level of model clarity. QC 20180111

International audience; Chameleon is a software that combines computer vision feature-matching algorithms with an open database format to allow the incorporation of dynamic HTML5 interactive content over any type of document (e.g. PDF files, Power-Point documents, etc.) without modifying existing applications or the source document. It thus allows the provision and viewing of an enhanced version of a research paper with embedded interactive demonstrations or videos. It can also be used to perform live demonstrations of interaction techniques while giving a presentation without having to switch tools.

In this paper, we analyze security vulnerabilities in the binding layer of browser code, and propose a research agenda to prevent these weaknesses with (1) static bug checkers and (2) new embedded domain specific languages (EDSLs). Browser vulnerabilities may leak browsing data and sometimes allow attackers to completely compromise users' systems. Some of these security holes result from programmers' difficulties with managing multiple tightly-coupled runtime systems-typically JavaScript and C++. In this paper, we survey the vulnerabilities in code that connects C++ and JavaScript, and explain how they result from differences in the two languages' type systems, memory models, and control flow constructs. With this data, we design a research plan for using static checkers to catch bugs in existing binding code and for designing EDSLs that make writing new, bug-free binding code easier.

A number of measures for quantifying information leakage of a program have been proposed. Most of these measures evaluate a program as a whole by quantifying how much information can be leaked on average by different program outputs. While these measures perfectly fit for static program analyses, they cannot be used by dynamic analyses since they do not specify what information an attacker learns through observing one concrete program output. In this paper we study the existing definitions of quantitative information flow. Our goal is to find the definition of dynamic leakage -- it should evaluate how much information an attacker learns when she observes one program output.Surprisingly, we find out that none of the existing definitions provide a suitable measure for dynamic leakage. We hence open a new research question in quantitative information flow area: which definition of dynamic leakage is suitable?

Recent efforts have sought to design new smart contract programming languages that make writing blockchain programs safer. But programs on the blockchain are beholden only to the safety properties enforced by the blockchain itself: even the strictest language-only properties can be rendered moot on a language-oblivious blockchain due to inter-contract interactions. Consequently, while safer languages are a necessity, fully realizing their benefits necessitates a language-aware redesign of the blockchain itself.To this end, we propose that the blockchain be viewed as a typechain: a chain of typed programs --- not arbitrary blocks --- that are included iff they typecheck against the existing chain. Reaching consensus, or blockchecking, validates typechecking in a byzantine fault-tolerant manner. Safety properties traditionally enforced by a runtime are instead enforced by a type system with the aim of statically capturing smart contract correctness.To provide a robust level of safety, we contend that a typechain must minimally guarantee (1) asset linearity and liveness, (2) physical resource availability, including CPU and memory, (3) exceptionless execution, or no early termination, (4) protocol conformance, or adherence to some state machine, and (5) inter-contract safety, including reentrancy safety. Despite their exacting nature, typechains are extensible, allowing for rich libraries that extend the set of verified properties. We expand on typechain properties and present examples of real-world bugs they prevent.

Botnets continue to be a threat to organizations, thus various machine learning-based botnet detectors have been proposed. However, the capability of such systems in detecting new or unseen botnets is crucial to ensure its robustness against the rapid evolution of botnets. Moreover, it prolongs the effectiveness of the system in detecting bots, avoiding frequent and time-consuming classifier re-training. We present BOTection, a privacy-preserving bot detection system that models the bot network flow behavior as a Markov Chain. The Markov Chain state transitions capture the bots' network behavior using high-level flow features as states, producing content-agnostic and encryption resilient behavioral features. These features are used to train a classifier to first detect flows produced by bots, and then identify their bot families. We evaluate our system on a dataset of over 7M malicious flows from 12 botnet families, showing its capability of detecting bots' network traffic with 99.78% F-measure and classifying it to a malware family with a 99.09% F-measure. Notably, due to the modeling of general bot network behavior by the Markov Chains, BOTection can detect traffic belonging to unseen bot families with an F-measure of 93.03% making it robust against malware evolution.

With the trend of adopting FPGAs in data centers, various FPGA acceleration platforms have been developed in recent years. Each server could incorporate one or many of these FPGAs at different compute hierarchy levels to match its workload intensity. FPGAs could either be used as IO-attached accelerators or be closely integrated with CPU as on-chip co-processors. For a more data-centric approach, an FPGA could be moved closer to the data medium (RAM or disk) and serve as a near-memory or near-storage accelerator. In this work, we present a quantitative model and in-depth analysis of application characteristics to determine when an application is more suitable for each acceleration hierarchy. We analyze 18 benchmarks from six domains and create a preliminary guideline for both application and hardware developers.

Keyphrases associated with research papers provide an effective way to find useful information in the large and growing scholarly digital collections. However, keyphrases are not always provided with the papers, but they need to be extracted from their content. In this paper, we explore keyphrase extraction formulated as sequence labeling and utilize the power of Conditional Random Fields in capturing label dependencies through a transition parameter matrix consisting of the transition probabilities from one label to the neighboring label. We aim at identifying the features that, by themselves or in combination with others, perform well in extracting the descriptive keyphrases for a paper. Specifically, we explore word embeddings as features along with traditional, document-specific features for keyphrase extraction. Our results on five datasets of research papers show that the word embeddings combined with document specific features achieve high performance and outperform strong baselines for this task.

Automatically matching reviewers to papers is a crucial step of the peer review process for venues receiving thousands of submissions. Unfortunately, common paper matching algorithms often construct matchings suffering from two critical problems: (1) the group of reviewers assigned to a paper do not collectively possess sufficient expertise, and (2) reviewer workloads are highly skewed. In this paper, we propose a novel local fairness formulation of paper matching that directly addresses both of these issues. Since optimizing our formulation is not always tractable, we introduce two new algorithms, FairIR and FairFlow, for computing fair matchings that approximately optimize the new formulation. FairIR solves a relaxation of the local fairness formulation and then employs a rounding technique to construct a valid matching that provably maximizes the objective and only compromises on fairness with respect to reviewer loads and papers by a small constant. In contrast, FairFlow is not provably guaranteed to produce fair matchings, however it can be 2x as efficient as FairIR and an order of magnitude faster than matching algorithms that directly optimize for fairness. Empirically, we demonstrate that both FairIR and FairFlow improve fairness over standard matching algorithms on real conference data. Moreover, in comparison to state-of-the-art matching algorithms that optimize for fairness only, FairIR achieves higher objective scores, FairFlow achieves competitive fairness, and both are capable of more evenly allocating reviewers., Appears at KDD 2019 Research Track, 20 pages

As computer science enrollments continue to surge, exam grading requires significant instructional resources. Online grading platforms have been developed in recent years and have been adopted at a number of institutions; however, their effectiveness compared with traditional grading on paper is not fully known. This study is the first in CS to compare online and paper grading. Comparing overall time to grade, including all factors, online grading doesn't show a consistent advantage compared with paper grading. We observed that online grading is much faster during the actual grading phase, but some of this benefit is offset by the additional overhead prior to grading (e.g., scanning) for online grading. Examining student and grader preferences based on feedback, both groups show a strong preference for the online format, predominately due to the convenience of the online platform. Graders report being able to grade more accurately online with the ability to modify rubrics, but that grading on paper tends to result in more social interactions among graders.

This paper explores parallel nondeterministic programming as an extension to the C programming language; it provides constructs for specifying code containing ambiguous choice as introduced by McCarthy. A translator to plain C code was implemented as an extension to the ableC language specification. Translation involves a transformation to continuation passing style, providing lazy choice by storing continuation closures in a separate task buffer. This exploration considers various search evaluation approaches and their impact on correctness and performance. Multiple search drivers were implemented, including single-threaded depth-first search, a combined breadth- and depth-first approach, as well as two approaches to parallelism. Several benchmark applications were created using the extension, including n-Queens, SAT, and triangle peg solitaire. The simplest parallel search driver, using independent threads, showed the best performance in most cases, providing a significant speedup over the sequential versions. Adding task sharing between threads showed similar or slightly improved performance.

In this paper, we introduce PANDORA---a framework that complements existing parallelizing compilers by automatically discovering application- and architecture-specialized approximations. We demonstrate that PANDORA creates approximations that extract massive amounts of parallelism from inherently sequential code by eliminating loop-carried dependencies---a long-time goal of the compiler research community. Compared to exact parallel baselines, preliminary results show speedups ranging from 2.3x to 81x with acceptable error for many usage scenarios.

Real-time systems are receiving increasing attention with the emerging application scenarios that are safety-critical, complex in functionality, high on timing-related performance requirements, and cost-sensitive, such as autonomous vehicles. Development of real-time systems is error-prone and highly dependent on the sophisticated domain expertise, making it a costly process. There is a trend of the existing software without the real-time notion being re-developed to realise real-time features, e.g., in the big data technology. This paper utilises the principles of model-driven engineering (MDE) and proposes the first methodology that automatically converts standard time-sharing Java applications to real-time Java applications. It opens up a new research direction on development automation of real-time programming languages and inspires many research questions that can be jointly investigated by the embedded systems, programming languages as well as MDE communities.

This mixed-methods study examines the effects of a tablet-based checklist system on team performance during a dynamic and safety-critical process of trauma resuscitation. We compared team performance from 47 resuscitations that used a paper checklist to that from 47 cases with a digital checklist to determine if digitizing a checklist led to improvements in task completion rates and in how fast the tasks were initiated for 18 most critical assessment and treatment tasks. We also compared if the checklist compliance increased with the digital design. We found that using the digital checklist led to more frequent completions of the initial airway assessment task but fewer completions of ear and lower extremities exams. We did not observe any significant differences in time to task performance, but found increased compliance with the checklist. Although improvements in team performance with the digital checklist were minor, our findings are important because they showed no adverse effects as a result of the digital checklist introduction. We conclude by discussing the takeaways and implications of these results for effective digitization of medical work.

The Internet of Things paradigm lacks end-to-end privacy solutions to consider its full adoption in real life scenarios in the near future. The recent enactment of the EU General Data Protection Regulation (GDPR) indeed emphasises the need for stronger security and privacy measures for personal data processing and free movement, including consent management and accountability by the data controller and processor. In this paper, we suggest an architecture to enforce end-to-end data usage control in Distributed Event-Based Systems (DEBS), from data producers to consumer services, taking into account some of the GDPR requirements concerning consent management and data processing transparency. Our architecture proposal is based on UCONABC usage control models, which we overlap with a distributed hash table overlay for scalability and fault-tolerance concerns, and across and within systems data usage control. Our proposal highlights the benefits of combining both DEBS and end-user usage control architectures. To complete our approach, we quickly survey existing encryption models that ensure data confidentiality in topic-based Publish/Subscribe systems and highlight the remaining obstacles to transpose them to content-based DEBS with an overlay of brokers.

Imprecise composite location references formed using ad hoc spatial expressions in English text makes the geocoding task challenging for both inference and evaluation. Typically such spatial expressions fill in unestablished areas with new toponyms for finer spatial referents. For example, the spatial extent of the ad hoc spatial expression "north of" or "50 minutes away from" in relation to the toponym "Dayton, OH" refers to an ambiguous, imprecise area, requiring translation from this qualitative representation to a quantitative one with precise semantics using systems such as WGS84. Here we highlight the challenges of geocoding such referents and propose a general formal representation that employs background knowledge, semantic approximations and rules, and fuzzy linguistic variables. We also discuss an appropriate evaluation technique for the task that is based on human contextualized and subjective judgment.

We describe a system for paper-cutting with a knife with a blade attached to the tip of the stylus on a drawing display. The purpose of this research is to support controlling cutting pressure for novices. Novices tend to cut paper with an unstable pressure stronger than necessary. Therefore, some instructors teach practicing to control the pressure to novices We have developed a device to support cutting practice by a stylus and drawing display. We measured the difference between pressures of novices and experts. In addition, we developed a system that encourages appropriate pressure based on expert pressure. This system shows the pressure difference between the user and the experts in color and sound. We experimented to compare the effectiveness of the system. As a result, the novices practiced with the system, the range of pressure and variation improved than the existing practice method.

Creative iterative development over the past several years has generated an extensive set of computational tools, learning resources, and materials in the realm of paper mechatronics - an educational craft and design approach that weaves computational and mechanical elements into established traditions of children's construction with paper. Here, we both reflect upon our past and recent work of paper mechatronics, then look to the near- to medium-term future to speculate upon both the emerging trends in technology design and expanding learning potential of this medium for children along material, spatial, and temporal dimensions. We summarize lessons learned through various children's workshops with our materials; and we use these lessons as a foundation upon which to create a wide variety of novel tools and activities in educational papercrafting. We speculate upon the frontiers of this work based on current convergences and shifts in tangible creative computational media.

Computer networks are ubiquitous systems growing exponentially with a predicted 50 billion devices connected by 2050. This dramatically increases the potential attack surface of Internet networks. A key issue in cyber defense is to detect, categorize and identify these attacks, the way they are propagated and their potential impacts on the systems affected. The research presented in this paper models cyber attacks at large by considering the Internet as a complex system in which attacks are propagated over a network. We model an attack as a path from a source to a target, and where each attack is categorized according to its intention. We setup an experimental testbed with the concept of honeypot that evaluates the spatio-temporal distribution of these Internet attacks. The preliminary results show a series of patterns in space and time that illustrate the potential of the approach, and how cyber attacks can be categorized according to the concept and measure of entropy.

The integration of electronics and paper is a burgeoning topic for tangible interaction. Here we show how to use the Korean technique of hand-felting paper known as joomchi to embed electronics in paper after the sheet forming process. This method requires the use of specialized papers that are amenable to joomchi. We present embedded LEDs, multi-layer circuit "sheets", and speakers. While labor-intensive, joomchi both enables one to completely integrate paper and electronics, while also making tangible connections between ancient and modern craft.

Seventeen years after its initial publication at ICSE 2000, the Representational State Transfer (REST) architectural style continues to hold significance as both a guide for understanding how the World Wide Web is designed to work and an example of how principled design, through the application of architectural styles, can impact the development and understanding of large-scale software architecture. However, REST has also become an industry buzzword: frequently abused to suit a particular argument, confused with the general notion of using HTTP, and denigrated for not being more like a programming methodology or implementation framework. In this paper, we chart the history, evolution, and shortcomings of REST, as well as several related architectural styles that it inspired, from the perspective of a chain of doctoral dissertations produced by the University of California's Institute for Software Research at UC Irvine. These successive theses share a common theme: extending the insights of REST to new domains and, in their own way, exploring the boundary of software engineering as it applies to decentralized software architectures and architectural design. We conclude with discussion of the circumstances, environment, and organizational characteristics that gave rise to this body of work.

Symbolic parallelism is a fresh look at the decade-old problem of turning sequential, imperative, code into associative reduction kernels, based on the realization that map/reduce is at its core a staging problem: how can programs be separated so that part of the computation can be done before loop-carried dependencies become available? Previous work has investigated dynamic approaches that build symbolic summaries while the actual data is processed. In this short paper, we approach the problem from the static side, and show that with simple syntax- or type-driven transformations, we can readily transform large classes of imperative groupby-aggregate programs into map/reduce parallelism with deterministic overhead.

Red Shift is a new design pattern for implementing parsers. The pattern draws ideas from traditional shift-reduce parsing as well as procedural PEG parsers. Red Shift parsers behaves like shift-reduce parsers, but eliminate ambiguity by always prioritizing reductions over shifts. To compensate the resulting lack of expressivity, reducers are not simple reduction rules but full-blown procedures written in a general-purpose host language. I found many advantages to this style of parser. In particular, we can generate high-quality error messages more easily; and compose different style of parsers. I also speculate about how Red Shift parsers may improve partial compilation in the context of an IDE.

Combining social network information with collaborative filtering recommendation algorithms has successfully reduced some of the drawbacks of collaborative filtering and increased the accuracy of recommendations. However, all approaches in the domain of research paper recommendation have used explicit social relations that users have initiated which has the problem of low recommendation coverage. We argued that the available data in social bookmarking Web sites such as CiteULike or Mendeley could be exploited to connect similar users using implicit social connections based on their bookmarking behavior. In this paper, we proposed three different implicit social networks-readership, co-readership, and tag-based and we compared the recommendation accuracy of several recommendation algorithms using data from the proposed social networks as input to the recommendation algorithms. Then, we tested which implicit social network provides the best recommendation accuracy. We found that, for the most part, the social recommender is the best algorithm and that the readership network with reciprocal social relations provides the best information source for recommendations but with low coverage. However, the co-readership network provide good recommendation accuracy and better user coverage of recommendation.

Algebraic data types and pattern matching are key features of func- tional programming languages. Exhaustivity checking of pattern matching is a safety belt that defends against unmatched excep- tions at runtime and boosts type safety. However, the presence of language features like inheritance, typecase, traits, GADTs, path- dependent types and union types makes the checking difficult and the algorithm complex. In this paper we propose a generic algorithm that decouples the checking algorithm from specific type theories. The decoupling makes the algorithm simple and enables easy customization for specific type systems.

Quantitative information-flow analysis (QIF) is an important approach to assess confidentiality property of software systems. A crucial step towards its practical application is to develop automatic techniques for measuring the information leakage in a system. In this paper, we address this question in the context of deterministic imperative programs and under the min-entropy measure of information leakage. In this context, calculating the maximum leakage of a program reduces to counting the number of possible outputs that it can produce. Our approach is based on an new abstract domain over implication graphs. Unlike numeric abstract domains, implication graphs describe relationships among bits. By executing a program on the domain over implication graphs, we can determine some implication constraints among pairs of bits in the output. By counting the number of solutions to the implication constraints, we can deduce an upper-bound on the leakage. We present the mathematical definition of the abstract domain, and explore its effectiveness in measuring leakage on a few case studies.

The purpose of this research is to synthesize and transform real world physical environments (PE) into a CAVE automatic virtual reality system (CAVE) by using three-dimensional (3D) models of cultural and historical artifacts. 3D models are often used in many applications including visualizations and digital preservation. Virtual reality is used to improve perception and sensation and to better understand products and environments for studying human factors and behaviors. As a pilot study, we developed and prototyped a customizable 3D physical environment using historical data and archives into an interactive CAVE virtual reality (VR) system. We then conducted a study of user preferences using pretest and post-test questionnaires of the CAVE versus paper-based artifacts.

Internet of Things (IoT) applications, systems and services are subject to law. We argue that for the IoT to develop lawfully, there must be technical mechanisms that allow the enforcement of specified policy, such that systems align with legal realities. The audit of policy enforcement must assist the apportionment of liability, demonstrate compliance with regulation, and indicate whether policy correctly captures legal responsibilities. As both systems and obligations evolve dynamically, this cycle must be continuously maintained.This poses a huge challenge given the global scale of the IoT vision. The IoT entails dynamically creating new services through managed and flexible data exchange. Data management is complex in this dynamic environment, given the need to both control and share information, often across federated domains of administration.We see middleware playing a key role in managing the IoT. Our vision is for a middleware-enforced, unified policy model that applies end-to-end, throughout the IoT. This is because policy cannot be bound to things, applications, or administrative domains, since functionality is the result of composition, with dynamically formed chains of data flows.We have investigated the use of Information Flow Control (IFC) to manage and audit data flows in cloud computing; a domain where trust can be well-founded, regulations are more mature and associated responsibilities clearer. We feel that IFC has great potential in the broader IoT context. However, the sheer scale and the dynamic, federated nature of the IoT pose a number of significant research challenges.

Many of today's enterprise-scale wireless networks are protected by the WPA2-Enterprise Protected Extensible Authentication Protocol (PEAP). In this paper it is demonstrated how an attacker can steal a user's credentials and gain unauthorized access to such networks, by utilizing a class of vulnerable devices as MSCHAPv2 challenge response oracles. More specifically this paper explains how on these devices, Lightweight EAP (LEAP) MSCHAPv1 credentials can be captured and converted to PEAP MSCHAPv2 credentials by using a rogue Access Point. This man-in-the-middle vulnerability was found to be present in all current versions of Apple's iOS and OS X operating systems, and may impact other devices as well. A proof-of-concept implementation is available that shows how Authentication Server certificate validation and certificate pinning mechanisms may be bypassed. Mitigation strategies for the attack and protective actions which can be undertaken by end-users are also described in this paper.

Edge computing promises to bring computation close to the end-users to support emergent applications such as virtual reality. However, the computational capacity at the edge of the network is currently limited. To become a pervasive paradigm, edge computing needs highly dispersed decentralized deployments, that, contrary to cloud, cannot benefit from economies of scale. In this situation, crowdsourcing appears attractive - there are plenty of computing devices at the disposal of the general public, and these devices are located exactly where computing power is needed the most - at the edge of the network. Crowdsourcing has been a success maker for scientific computing projects, e.g., SETI@home, or distributed ledger systems empowering decentralized finance. However, as of now, there is no crowdsourced system that addresses the needs of edge computing. In this position paper, we aim to identify the causes of this shortcoming, analyze the potential ways to overcome it, and outline future directions.

The aim of the SPICE project is to build social cohesion, both between and within citizen communities, by developing tools and methods to support citizen curation. We define citizen curation as a process in which cultural objects are used as a resource by citizens to develop their own personal interpretations. Within communities, citizens can use their interpretations to build a representation of themselves and their shared perspective on culture. Interpretations can also be used to support social cohesion across groups. In this short position paper we outline the methodologies and technologies needed to be built in order to build a recommender system of cultural objects that will implement these goals of social cohesion and inclusion.

International audience; Domain-Specific Languages (DSLs) manifest themselves in remarkably diverse shapes, ranging from internal DSLs embedded as a mere fluent API within a programming language, to external DSLs with dedicated syntax and tool support. Although different shapes have different pros and cons, combining them for a single language is problematic: language designers usually commit to a particular shape early in the design process, and it is hard to reconsider this choice later. In this new ideas paper, we envision a language engineering approach enabling (i) language users to manipulate language constructs in the most appropriate shape according to the task at hand, and (ii) language designers to combine the strengths of different technologies for a single DSL. We report on early experiments and lessons learned building Prism, our prototype approach to this problem. We illustrate its applicability in the engineering of a simple shape-diverse DSL implemented conjointly in Rascal, EMF, and Java. We hope that our initial contribution will raise the awareness of the community and encourage future research.

Eye height manipulations have previously been found to affect judgments of object size and egocentric distance in both real and immersive virtual environments. In this short paper we report the results of an experiment that explores people’s sensitivity to various offsets of their eye height in VR using a forced-choice task in a wide variety of different architectural models. Our goal is to better understand the range of eye height manipulations that can be surreptitiously employed under different environmental conditions. We exposed each of 10 standing participants to a total of 121 randomly-ordered trials, spanning 11 different eye height offsets between –80cm to +80cm, in 11 different highly detailed virtual indoor environments, and asked them to report whether they felt that their (invisible) feet were floating above or sunken below the virtual floor. We fit psychometric functions to the pooled data and to the data from each virtual environment and each participant individually. In the pooled data, we found a point-of-subjective-equality (PSE) very close to zero (–3.8cm), and 25% and 75% detection thresholds of –16.1cm and +8.6cm respectively, for an uncertainty interval of 24.7cm. We also observed some interesting variations in the results between individual rooms, which we discuss in more detail in the paper. Our findings can help to inform VR developers about users’ sensitivity to incorrect eye height placement, to elucidate the potential impact of various features of interior spaces on people’s tolerance of eye height manipulations, and to inform future work seeking to employ eye height manipulations to mitigate distance underestimation in VR.

A slogan coined at the recent first Levi 6G Summit by Peter Wetter of Nokia Bell Labs was “the 6G is about the 6th sense”. This can be understood in at least two ways. One is that the network just knows what to do in all kinds of situations because of the use of AI and the second is that 6G radio will be widely used to sense the environment where the users are. In this view, 6G is seen as a continuation of the merge of the physical and the virtual words. An outcome of the Summit is a 6G White Paper documenting the ideas of some 60 invited people from the 300 participants about the future generation coming after 5G. This paper provides further discussion and justification of the trust and security aspects of the Networking Chapter in the White Paper. The paper focuses on principles and only refers to some verification in order not to clutter the discussion with technical detail. Opinions expressed here are of the author of this paper who was also the main editor of the Networking Chapter. The members of the White paper group on Networking or the editors of the White paper should not be held liable for the views expressed in the paper.

In this short paper, we revisit the celebrated Naor?Naor?Reingold (NNR) protocol for ?[convincing] people you know where Waldo is without revealing information about his location?. We observe that, despite oft-repeated claims to the contrary, the NNR protocol is neither zero-knowledge nor a proof of knowledge. We propose a slightly more elaborate version that is both of these things?but still eminently suitable for children?s playdates (and the classroom).

Consider a customer who needs to fulfill a shopping list, and also a personal shopper who is willing to buy and resell to customers the goods in their shopping lists. It is in the personal shopper's best interest to find (shopping) routes that (i) minimize the time serving a customer, in order to be able to serve more customers, and (ii) minimize the price paid for the goods, in order to maximize his/her potential profit when reselling them. Those are typically competing criteria leading to what we refer to as the Personal Shopper's Dilemma query, i.e., to determine where to buy each of the required goods while attempting to optimize both criteria at the same time. Given the query's NP-hardness we propose a heuristic approach to determine a subset of the sub-optimal routes under any linear combination of the aforementioned criteria, i.e., the query's approximate linear skyline set. In order to measure the effectiveness of our approach we also introduce two new metrics, optimality and coverage gaps w.r.t. an optimal, but computationally expensive, baseline solution. Our experiments, using realistic city-scale datasets, show that our proposed approach is two orders of magnitude faster than the baseline and yields low values for the optimality and coverage gaps., Comment: An abridged version of this paper will appear at The 28th ACM SIGSPATIAL Intl Conf. on Advances in Geographic Information Systems 2020 (ACM SIGSPATIAL 2020), Seattle, Washington, USA, November 3-6, 2020

Intermittent computing could be combined with low-powered visible light communication systems to facilitate novel sensor networks. This position paper provides a framework for future research by highlighting synergies between the technologies, and explores how techniques developed in intermittently-powered systems can be used to provide enhanced stability to ultra-low power visible light communication.

Mobile app designers aim to develop the best mobile software interfaces in the least amount of time, and rely on testing ideas with prototypes in lieu of building costly, fully functioning applications. Yet, designers cannot effectively prototype some complex app experiences, including augmented reality applications like Pokemon GO, because existing tools lack the needed features, or because prototyping in them is too time intensive to be feasible. To solve this problem, we introduce Lake, a mobile application prototyping tool that enables the creation of complex mobile applications with the same ease as paper prototyping. By leveraging the Wizard of Oz technique used in paper prototyping in our digital medium, we enable designers to prototype at the same low cost as paper, but at a much higher fidelity. Through a pilot study (N=6), we find that designers are able to gather organic in-context feedback from complex prototypes made with Lake.

Interest is growing in dialog systems which engage users in conversations that are not entirely focussed on the immediate performance of clearly defined practical tasks. Interest is also growing in data-driven methods for dialog system design, with increasing focus on sequence-to-sequence deep learning models, inspired by success in the machine translation sphere. In this position paper, we discuss research on casual conversation or social talk and current methods in design of systems, highlighting areas which need reconciliation.

Artificial Intelligence (AI) can now automate the algorithm selection, feature engineering, and hyperparameter tuning steps in a machine learning workflow. Commonly known as AutoML or AutoAI, these technologies aim to relieve data scientists from the tedious manual work. However, today's AutoAI systems often present only limited to no information about the process of how they select and generate model results. Thus, users often do not understand the process, neither do they trust the outputs. In this short paper, we provide a first user evaluation by 10 data scientists of an experimental system, AutoAIViz, that aims to visualize AutoAI's model generation process. We find that the proposed system helps users to complete the data science tasks, and increases their understanding, toward the goal of increasing trust in the AutoAI system., Comment: 5 pages, 1 figure, IUI2020

Logarithmic number systems (LNS) reduce hardware complexity for multiplication and division in embedded systems, at the cost of more complicated addition and subtraction. Existing LNS typically use base-2, meaning that representable numbers are some (often fractional) power of two. We argue that other bases should be considered. The base of the LNS determines the distribution of values and may reduce representation errors when converting inputs to LNS in domain-specific embedded hardware accelerators. Further, LNS addition and subtraction are normally implemented with lookup tables whose properties may be a function of the base. We show that other bases can lower both representation and addition and subtraction error. We consider the case of 8-bit LNS, when converting from 8-bit floating point (FP). We find that base-1.984 significantly reduces the conversion error. Where we can scale values, base-1.851 reduces the error to just 0.011 units of least precision (ULP). A suitable base can also reduce average arithmetic errors. For example, base-1.802 LNS has an average error of 0.242 ULP and 0.212 ULP as compared to 0.243 ULP and 0.226 ULP for addition and subtraction, respectively, for base-2.

Transaction processing has been an active area of research for several decades. A fundamental characteristic of classical transaction processing protocols is non-determinism, which causes them to suffer from performance issues on modern computing environments such as main-memory databases using many-core, and multi-socket CPUs and distributed environments. Recent proposals of deterministic transaction processing techniques have shown great potential in addressing these performance issues. In this position paper, I argue for a queue-oriented transaction processing paradigm that leads to better design and implementation of deterministic transaction processing protocols. I support my approach with extensive experimental evaluations and demonstrate significant performance gains.

Taking the 'A' out of 'AR' means implementing the augmented elements of an interface and contextual elements of reality in a more controlled context to allow for proof of concept evaluations. This paper proposes a prototyping technique that bridges the gap between traditional paper prototyping methods used for interface design and evaluation, and the challenges associated with the development of visual, context-aware augmented reality (AR) applications. An initial evaluation of this technique was conducted through the examination of a small-scale case study of user evaluation sessions of a mobile application.