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While there are several frameworks for CNN inference on mobile GPUs, they do not achieve real-time processing for the most of the CNNs that aim at reasonable accuracy since they all employ kernel-by-kernel execution model and do not effectively support INT8 quantization yet. In this paper, we reveal that mobile GPUs suffer from large kernel launch overhead unlike server GPUs, and then propose an on-device deep learning inference framework that can achieve real-time inference of CNNs on mobile GPUs by removing kernel launch overhead and by effectively exploiting INT8 quantization. We have evaluated the proposed framework with a state-of-the-art CNN based face detector (RetinaFace), and observed up to 2.01X of speedup compared to ARM Compute Library (ACL) on a commodity smartphone.

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?

With the spread of electronic devices such as smartphones and tablets, the transfer of electronic information such as images and videos has become commonplace. To exchange electronic information using e-mail or social network services a sender needs to know the recipient's contact information. However, if the recipient is a newly-encountered person or an ad hoc partner, a significant number of them are reluctant to exchange contact details. To address this issue, we propose an interaction method for transferring electronic information by tearing a sheet of paper. This is an approach that utilizes the fact that when a piece of paper is torn into two pieces, the features of the torn edges of the two pieces match.

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

Test paper generation is a multi-objective parameter optimization problem under multi-constraints. Aiming at the defects of test paper quality and speed in intelligent test paper generation, the genetic algorithm is improved on the basis of the original genetic algorithm. From the coding method of the test questions, the establishment of weighted objective function to optimize the fitness function, the selection of crossover operator, the setting of mutation, and the combination of optimization strategy and roulette to select the operation, the optimization design is carried out. It achieves good adaptability and can generate test paper quickly and successfully, which improves the efficiency of generating test paper. The simulation results show that, under the same constraints, the algorithm can generate test papers successfully, and improve the success rate and efficiency of generating test papers, and effectively optimize the problems in automatic generating test papers.

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.

We describe gtutr, an interactive feedback system designed to assist students in developing context-free grammars and corresponding ANTLR parsers. It intelligently controls students' access to a large test suite for the target language. After each submission, gtutr analyzes any failing tests and uses the Needleman-Wunsch sequence alignment algorithm over the tests' rule traces to identify and eliminate similar failing tests. This reduces the redundancy in the feedback given to the students and prevents them from being overloaded. gtutr uses simple gamification to encourage independent problem solving by students: it gives as little information as possible, and students need to prompt the system for further details such as failing tests similar to or different from already seen tests, or even for hints about rules that are the most likely to contain faults. It tracks the students' information requests and uses this to attenuate marks following an instructor-set penalty schema. The system also visualizes test outcomes over multiple submissions, helping students to keep track of the effects of their changes as their grammar development progresses.

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.

Types indexed with extra type-level information are a powerful tool for statically enforcing domain-specific security properties. In many cases, this extra information is runtime-irrelevant, and so it can be completely erased at compile-time without degrading the performance of the compiled code. In practice, however, the added bureaucracy often disrupts the development process, as programmers must completely adhere to new complex constraints in order to even compile their code.In this work we present WRIT, a plugin for the GHC Haskell compiler that relaxes the type checking process in the presence of runtime-irrelevant constraints. In particular, WRIT can automatically coerce between runtime equivalent types, allowing users to run programs even in the presence of some classes of type errors. This allows us to gradually secure our code while still being able to compile at each step, separating security concerns from functional correctness.Moreover, we present a novel way to specify which types should be considered equivalent for the purpose of allowing the program to run, how ambiguity at the type level should be resolved and which constraints can be safely ignored and turned into warnings.

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.

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.

Current research in HCI with immigrants predominantly focuses on their practical needs and little attention is given to their cultural identities. As such, we aim to understand how newcomers reflect their cultural values within domestic settings. We explore this by provoking memories immigrants associate with physical spaces inside their homes. Hence, we built "Our Home Sketcher": a paper-based home drafting tool that allows novice users to design their homes by sketching and implicitly expressing their space, light, and privacy preferences. The collected drawings are then fed into a computer algorithm that produces 3D models of the sketched houses. This process of design acts as an artifact-driven storytelling for heritage sharing and rapport building within migrant communities. We engage 13 Middle Eastern newcomers in Canada with the tool and use Halbwachs' [44] theory of collective memory to frame how home sketching provokes former experiences. Our findings show a strong longing for reclaiming the past, narrating space-related oral history, and designing beyond current limitations.

Game map interfaces provide an alternative perspective on the worlds players inhabit.compared to navigation applications popular in day-to-day life, game maps have different affordances to match players' situated goals. To contextualize and understand these differences and how they developed, we present a historical chronicle of game map interfaces. Starting from how games came to involve maps, we trace how maps are first separate from the game, becoming more and more integrated into play until converging in smartphone-style interfaces. We synthesize several game history texts with critical engagement with 123 key games to develop this map-focused chronicle, from which we highlight trends and opportunities for future map designs. Our work contributes a record of trends in game map interfaces that can serve as a source of reference and inspiration to game designers, digital physical-world map designers, and game scholars.

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.

Digital signal processors (DSPs) offer cutting-edge energy efficiency for embedded multimedia computations, but writing high-performance DSP code requires expert tuning. Programmers need to work at a low level of abstraction, manually tailoring vendor-specific instructions to enable vector and VLIW parallelism. Diospyros is a synthesizing compiler that searches for optimal data layouts to enable efficient vectorized code on DSPs. Preliminary results show that for small fixed-size matrix multiply and 2D convolution, Diospyros achieves a 6.4--7.6x speedup compared to vendor-provided optimized kernels, and a 6.5--31.3x speedup over loop-based kernels optimized with the vendor's included compiler.

One of the greatest challenges in automated driving is the ability to acquire, access and query the data pertaining to high resolution 3D objects from multiple heterogeneous sources. Specifically, the information extraction needs to be done by fusing data from both sensors and databases, and with real-time constraints. Existing structures and algorithmic approaches designed for regular maps - or even regular features in High Definition maps - are not optimal to handle the various challenges. In this paper, we review the importance and roles of high resolution 3D objects in High Definition maps being used in autonomous driving applications and summarize the characteristics of 3D objects compared to other regular map features. We also describe an end-to-end pipeline of a system targeting such problems and emphasize the challenges and feasible solutions to each part of the pipeline. Last but not least, we define the quantified evaluation metrics for each task and introduce the dataset that we built for this objective.

In this paper, we describe Multi-Itinerary Optimization (MIO) - a novel Bing maps service that automates the process of building itineraries for multiple agents while optimizing their routes to save travel time or distance. MIO can be used by organizations with a fleet of vehicles and drivers, mobile salesforce, or a team of personnel in the field in order to maximize workforce efficiency. MIO accounts for service time windows, duration, and priority, as well as traffic conditions between locations, resulting in challenging algorithmic problems at multiple levels (e.g., calculating travel-time distance matrices at scale, scheduling services for multiple agents). To support an end-to-end cloud service with turnaround times of a few seconds, our algorithm design targets a sweet spot between accuracy and performance. Towards that end, we build a scalable solution based on the ALNS meta-heuristic. Our experiments show that accounting for traffic significantly improves solution quality: MIO not only avoids violating time-window constraints, but also completes up to 17% more services compared to traffic-agnostic mechanisms. Further, our solution generates itineraries with better accuracy than both a cutting-edge heuristic (LKH3) and an Integer-Programming based algorithm, with twice and orders-of-magnitude faster running times, respectively.

Points of interest (POI) are central in many applications such as tourism, itinerary search, crisis management. Cartographic providers usually represent these POI with a spatial entity. However, the description of these entities may significantly vary from one provider to another (e.g., missing properties, outdated information, conflicting values). Spatial entity matching (or record linkage) aims at detecting correspondences between entities referring to the same POI. Most existing approaches have a fixed function for combining similarity measures, thus limiting customization. Besides, evaluating the matching quality is a difficult task since a ground truth dataset cannot be built for all entities and providers. In this paper, we describe GeoAlign, an application that allows fine-grained tuning for spatial entity matching. A merging step is also provided using different strategies. Finally, we propose to estimate the quality of correspondences based on the differences between combination functions and to visualize this estimation in GeoAlign.

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.

Scratchpad memories (SPM) are often used to boost the performance of application-specific embedded systems. In embedded systems, main memories are vulnerable to external attacks such as bus snooping or memory extraction. Therefore it is desirable to guarantee the security of data in a main memory. In software-managed SPM, it is possible to provide security in main memory by performing software-assistant encryption. In this paper, we present an efficient compiler for security aware scratch pad Memory (SA-SPM), which ensures the security of main memories in SPM-based embedded systems. Our compiler is the first approach to support full encryption of memory regions (i.e. stack, heap, code, and static variables) in a SPM-based system. Furthermore, to reduce the energy consumption and improve the lifetime of a non-volatile main memory by decreasing the number of bit flips, we propose a new dual encryption scheme for a SPM-based system. Our experimental results show that the proposed dual encryption scheme reduces the number of bit flips by 31.8% compared with the whole encryption.

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.

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.

In the coming era of connected autonomous vehicles, data-driven traffic optimization will reach its full potential. By collecting highly detailed real-time traffic data from sensors and vehicles, a traffic management system will have the full view of the entire road network, allowing it to plan traffic in a virtual world that replicates the real road network. This will bring significant innovations to transport-domain applications. We prototype a traffic management system that can perform traffic optimization with connected autonomous vehicles. We propose two route assignment algorithms that aim to reduce traffic delays by reducing intersecting routes. The proposed algorithms and two state-of-the-art route assignment algorithms are implemented in the prototype system. We evaluate the algorithms with both synthetic and real road networks. The experimental results show that the proposed algorithms outperform competitors in terms of the travel times of the routes.

We base everything that we do as researchers on what we write. Primarily for graduate students and young researchers, it is hard to turn a research project into a successful CHI publication. This struggle continues for postdocs and young professors trying to author excellent reviews for the CHI community that pinpoint flaws and improvements in research papers. This third edition of the successful CHI paper writing course offers hands-on advice and more in-depth tutorials on how to write papers with clarity, substance, and style. It is structured into three 80-minute units with a focus on writing CHI papers.

We present Paper Tuner, a user-controlled interface for recommending papers and presentations at a research conference. The availability of multiple sources of information about user interests makes hybrid recommendation approach attractive in a conference context, but traditional static parallel hybridization makes is hard to generate a single ranking that can address different needs. We introduce a novel slider-based user interface that allows users to control the importance of different relevance source and even reverse the impact of specific sources. The log analysis of system usage during in a real conference context revealed an extensive use of sliders. Moreover, nearly half of the users applied the reverse functionality while using the sliders.

The human visual system uses cast shadows to judge the three-dimensional layout of an object. The purpose of this installation is to demonstrate novel visual illusions of depth and transparency for paper materials, which is induced by the conventional light projection of cast shadow patterns. Thus, this installation focuses on perceptual rather than technical aspects. Illuminating a target object, the spatial vicinity of the object is darkened to produce the visual impression of a shadow of the object. By controlling the blurriness of the cast shadow and/or spatial distance between the object and its shadow, a perceptual change in the layout of the object is induced. The audience can interactively enjoy visual experiences wherein objects and letters on a paper perceptually float in the air. We also demonstrate that it is possible to edit the material appearance of a real object by manipulating the shape of the shadow; an opaque colored paper appear to be a transparent color film floating in the air.

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.

In recent years, tourism informatization has become the key point and the frontier of theoretical research in the construction of smart tourism in various regions of China. With the development of computer technology and the emergence of augmented reality technology, enhanced paper tourism map has become a novel way of map expression and interaction. At first, this paper describes the principle of enhancing tourism map and proposes a scheme of paper tourism map enhanced expression based on AR technology. Then the combination of AR technology and paper tourism map has been discussed as the key content. Finally, the tourist attractions of Jiashan County of Zhejiang Province is taken as a case study to implement a smart tourism AR application system, which presents various approaches of map expression and interaction, expanding the information as well as cognitive function of paper tourism maps. Augmented tourism maps enable meaningful integration between smart mobile devices and paper tourism maps, providing a hyper-real, immersive and virtual travelling experience, offering quick and effective solutions for future smart emergency response.

We present a tangible memory notebook--reMi--that records the ambient sounds and translates them into a tangible and shareable memory using animated paper. The paper replays the recorded sounds and deforms its shape to generate synchronized motions with the sounds. Computer-mediated communication interfaces have allowed us to share, record and recall memories easily through visual records. However, those digital visual-cues that are trapped behind the device's 2D screen are not the only means to recall a memory we experienced with more than the sense of vision. To develop a new way to store, recall and share a memory, we investigate how tangible motion of a paper that represents sound can enhance the "reminiscence".

For papers reporting on experiments, the types of experiment performed are important, as are the results they generate. Tables are frequently used to show experimental results and are suited to understanding accurate numerical values. However, they are not suited to visually comparing or observing changes in many values at once. In this paper, we propose an automatic graph-generation method for data presented in tabular form. We first convert PDF files of scholarly papers into XML files to analyze the structure of their tables. We then generate graphs based on this table-structure analysis. We also examine the types of graphs that are easy to understand visually.

Sentiment analysis has been widely explored in many text domains, including product reviews, movie reviews, tweets, and so on. However, there are very few studies trying to perform sentiment analysis in the domain of peer reviews for scholarly papers, which are usually long and introducing both pros and cons of a paper submission. In this paper, we for the first time investigate the task of automatically predicting the overall recommendation/decision (accept, reject, or sometimes borderline) and further identifying the sentences with positive and negative sentiment polarities from a peer review text written by a reviewer for a paper submission. We propose a multiple instance learning network with a novel abstract-based memory mechanism (MILAM) to address this challenging task. Two evaluation datasets are constructed from the ICLR open reviews and evaluation results verified the efficacy of our proposed model. Our model much outperforms a few existing models in different experimental settings. We also find the generally good consistency between the review texts and the recommended decisions, except for the borderline reviews.

In this paper we describe a semantic approach to visualization of 3D cyberspace of scientific papers and their research front using web-based 3D graphic. The most cited and significant documents in this cyberspace are represented by spheres of a large size and the distance between documents is proportional to their semantic similarity. A new measure of semantic similarity of documents is proposed that is determined by the maximum correlation between explicit and implicit connectivity of the documents. A new science contextual citation index (SCCI) that is defined by a correlation maximum with a science citation index (SCI) is proposed and implemented. SCCI can more accurately measure scientific impact, find significant documents and evaluate new articles with zero SCI. Significant similar articles confirm each other and form clusters in the cyberspace. Research front exists as a set of such clusters. The proposed cyberspace implemented by WebVR and interactive 3D graphics can be considered as a dynamic learning environment that is convenient for discovering new significant articles, ideas and trends. It can be a powerful tool of information integration, because it allows you to visualize documents of different languages in a single space.

The Paper Piano is a beginner-friendly hands-on activity where participants build a working piano using paper and conductive paint and tune it to the notes of the pentatonic scale. The piano is powered by an Arduino microcontroller. This is part of a series of activities to teach science and engineering concepts using building, crafts, and art.In this activity, participants learn about electrical resistance, how electricity flows through circuits, conductive materials, and the pentatonic scale. We have successfully demonstrated this activity to learners from elementary school age to college students, and participants frequently express genuine astonishment at their ability to construct a working electronic musical instrument out of everyday craft materials.https://www.youtube.com/watch?v=q6lTidhGcgE

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.

This panel will highlight papers that have received the Best Paper Award at the SIGMIS CPR conference going back to 2000 (with those since 2004 named the "Magid Igbaria Best Paper Award"). We will start the panel with a high-level over-view of papers that received the Best Paper Award - a sum-mary of the topics, research methods, types of organizations or populations studied, and publication outcomes after receiving the Best Paper Award. The main portion of the panel will feature authors describing how they regard conference papers as part of their research portfolio. A key goal of the panel is to elicit authors' perspectives regarding whether conference papers are end goals themselves, an intermediate outcome that has value to their career only if later published in a journal, or a stepping stone to multiple later options.

In Building Automation Systems contextual information about sensors is frequently missing or hard-coded in the control code. Retrieving this data is time consuming and error-prone, but necessary to write any type of control application. Automating metadata acquisition is a new and active area of research. Methods to infer metadata from sensor labels or from recorded data have been previously proposed. However, these methods are ineffective in uncovering the association between HVAC components. In fact, measured variables (pressures, temperatures, flows, valve positions) have slow and attenuated responses to changes in input variables, thus impairing the efficacy of correlation methods. In addition, sensor readings are frequently constrained between physical limits and kept around setpoints by nested control loops. For this reason, pure statistical methods fail to capture the differences between sensor streams and are unable to classify them. In this article, we propose a new method for discovering functional relationships between Air Handling Units and Variable-Air-Volume Boxes from sensor data. The method utilizes perturbations of subsystem variables, while guaranteeing that the building zones remain within comfort boundaries. When applied to an existing building, our proposed method reveals correct associations in ~80% of the cases, and outperforms other methods.

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.

This paper describes data structures and algorithms for efficient implementation of GIS operations for large datasets on multicore Intel CPUs and on NVIDA GPUs. Typical operations are boolean combinations of polygons and map overlay. Efficient parallelization prefers simple regular data structures, such as structures of arrays of plain old datatypes. Warps of 32 threads are required to execute the same instruction (or be idle). Ideally, the data used by adjacent threads is adjacent in memory. Minimizing storage is important, as is accessing it in a regular pattern. That disparages pointers, linked lists, and trees. That implies that explicitly representing global topology is bad. If using only local topological formulae is sufficient, then it will be much faster. E.g., for many operations on a 2-D map (aka planar graph), the set of oriented edges suffices. Each edge knows the locations of its endpoints and the ids of its adjacent polygons. Any mass operation, such as area computation or point location, can be implemented as a map-reduce. All these techniques also apply in 3D to CAD/CAM and additive manufacturing. Indeed they are more important there.

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.

In this paper, we introduce languages as first-class citizens as a sub-paradigm of language-oriented programming. In this approach, language definitions are in the context of a general purpose programming language with the same status as any other expression. In particular, language definitions are elevated to be run-time values, that can be assigned to variables, passed to functions, returned by functions, and inserted into lists, to name a few possibilities. This approach offers flexible features in the run-time creation and modification of languages, and may promote new idioms in language-oriented programming. As a proof of concept, we have designed and implemented lang-n-play, a functional language with languages as first-class citizens. We present the features of lang-n-play with an example, and show that they naturally enable dynamic programming scenarios.

JavaScript has seen meteoric growth in popularity as it has in- creasingly become the language of choice for developers, both for front-end web development and server code development through various JavaScript frameworks and Node.js. Part of the reason for its wide use is that it is a prototype based language with dynamic types, making it easy to learn and program in. This flexibility and ease of programming comes at the cost of performance. There are two sources of significant slowdown. First, since the number and type of properties of prototypes is dynamic, accessing a property involves a slow dictionary lookup, as opposed to it being present at a fixed offset from the base address. Second, the dynamism in type of values necessitates wrapping and unwrapping of values into objects with a variety of checks including for type of the value. To mitigate these performance problems, this paper proposes JSCore, a core specialized for JavaScript execution, that vastly reduces the performance degradation due to the above two causes. It uses a hardware lookup table to accelerate property access, and extends the data path to store data types with the data, nearly eliminating the second source of slowdown. Combining the two, JSCore accelerates real world JavaScript applications by 23%.

With the increasing popularity of mobile devices, drivers and passengers will naturally want to connect their devices to their cars. Malicious entities can and likely will try to attack such systems in order to compromise other vehicular components or eavesdrop on privacy-sensitive information. It is imperative, therefore, to address security concerns from the onset of these technologies. While guaranteeing secure wireless vehicle-to-mobile communication is crucial to the successful integration of mobile devices in vehicular environments, usability is of equally critical importance. With MVSec, we propose novel approaches to secure vehicle-to-mobile communication tailored specifically for vehicular environments. We present novel security protocols and provide complete implementation and user study results.

Smart cities offer more and more real-time information provided by sensor networks and traffic cameras. This information can be very valuable for transportation planing. For instance, knowing which parking spots are currently available close to my destination is very valuable in order to reduce the travel time and thus, maximize the resource usage and minimize the traffic load. The future development of this information is usually uncertain. However, algorithms for routing applications should consider that new information will become available during travelling along the computed path. In order to exploit the provided information to a full extend, it is not sufficient to compute a static route or travel plan because the optimality of the plan might degrade as the state of the environment might consistently change. We argue that in order to plan transportation and understand observed trajectories in smart environments, it is necessary to compute action policies (i.e. the most promising action for all situations and in particular, the encountered situations) instead of static routes. Analogously, it makes sense to understand human behaviour based on the sequence of decisions in the encountered situations. To compute and analyse policies, the field of reinforcement learning already provides a rich set of tools. We describe existing approaches of reinforcement learning in spatial tasks and highlight which new challenges arise from developing reinforcement learning techniques for smart environments.

Microscopic traffic simulators play a major role to carry research on transportation problems. Microscopic traffic simulation is powerful because it enables efficient analysis of complex traffic problems to the highest level of detail. We developed Scalable Microscopic Adaptive Road Traffic Simulator (SMARTS) [14] that can perform large-scale simulations at a high speed by utilizing distributed computing resources. Previous results show that SMARTS can run 1.14 times faster than real time when simulating one million vehicles for the city of Melbourne on 30 distributed processors, while producing highly accurate simulation results. SMARTS' pluggable architecture allows it to be easily extended to simulate specific scenarios of interest to users. In this demonstration we show how SMARTS can be used to simulate an intersection design, the P-turn, in a major intersection of Melbourne. Our simulation shows the impact of the design on the traffic flow, confirming the justification for introduction of the particular intersection. The demo can be used as a template for future use of the simulator for other traffic problems.

Regular expressions are extended by splitting the terminals into left brackets, right brackets, and neutral terminals. These extended regular expressions define a superset of regular languages. Their languages are parsed in linear time in the size of the input. The addition of annotations to these regular expressions results in more detailed parse trees.

Any text can be presented as an infinite number of topic models.Any of these topics would not have any attributes that would make it possible to break them up into classes. The Author has suggested additive regularization when creating models to single out topic clusters from the Probabilistic Latent Semantic Analysis, PLSA.The method proposed by the Author allows singling out topic classes based on their density in the document-topic space (Matrix Θ) for a selected collection of documents.Such segmentation is similar to hierarchical variant LDA (HLDA) but has no such drawback as that the LDA models have.

Systems neuroscience studies involving in-vivo models often require realtime data processing. In these studies, many events must be monitored and processed quickly, including behavior of the subject (e.g., movement of a limb) or features of neural data (e.g., a neuron transmitting an action potential). Unfortunately, most realtime platforms are proprietary, require specific architectures, or are limited to low-level programming languages. Here we present a hardware-independent, open-source realtime computation platform that supports high-level programming. The resulting platform, LiCoRICE, can process on order 10e10 bits/sec of network data at 1 ms ticks with 18.2 µs jitter. It connects to various inputs and outputs (e.g., DIO, Ethernet, database logging, and analog line in/out) and minimizes reliance on custom device drivers by leveraging peripheral support via the Linux kernel. Its modular architecture supports model-based design for rapid prototyping with C and Python/Cython and can perform numerical operations via BLAS/LAPACK-optimized NumPy that is statically compiled via Numba’s pycc. LiCoRICE is not only suitable for systems neuroscience research, but also for applications requiring closed-loop realtime data processing from robotics and control systems to interactive applications and quantitative financial trading.