Search

With this position paper, we want to draw the attention of the community toward theoretical work and practical opportunities that have been overlooked so far regarding concepts, principles, and design knowledge for the user experience of Augmented and Mixed Reality content, I/O devices, interactions, applications, and systems. Despite considerable innovations in commercial products and research prototypes enabling Augmented and Mixed Reality worlds, how to design great user experiences in such worlds has been overall neglected at core, while the information and knowledge currently available to practitioners cover usability aspects mostly. Therefore, we advocate for theoretical foundations of the user experience in Augmented and Mixed Reality and propose several directions for more scientific research in this regard.

Deploying context management systems at a global scale comes with a number of challenges and requirements. We argue that the hypermedia model and the agent-oriented paradigm help achieve the vision of Context-as-a-Service. We categorize challenges according to context processing concerns and use a scenario to exemplify how the proposed architectural principles help overcome the challenges.

Based on the concept of explanation sheets, we present an approach to make spreadsheets easier to understand and thus easier to use and maintain. We identify the notion of explanation soundness and show that explanation sheets which conform to simple rules of formula coverage provide sound explanations. We also present a practical evaluation of explanation sheets based on samples drawn from widely used spreadsheet corpora and based on a small user study.In addition to supporting spreadsheet understanding and maintenance, our work on explanation sheets has also uncovered several general principles of explanation languages that can help guide the design of explanations for other programming and domain-specific languages., This work is partially supported by the National Science Foundation under the grant CCF-1717300. Work financed by European Regional Development Fund through the Operational Programme for Competitiveness and Internationalization COMPETE 2020 Programme and by National Funds through the Portuguese funding agency FCT project POCI-01-0145-FEDER-016718 and NOVA LINCS UID/CEC/04516/2013, and by FLAD project 233/2014.

In this paper, we demonstrate IllumiPaper: a system that provides new forms of paper-integrated visual feedback and enables multiple input channels to enhance digital paper applications. We aim to take advantage of traditional form sheets, including their haptic qualities, simplicity, and archivability, and simultaneously integrate rich digital functionalities such as dynamic status queries, real-time notifications, and visual feedback for widget controls. Our approach builds on emerging, novel paper-based technologies. We describe a fabrication process that allow us to directly integrate segment-based displays, touch and flex sensors, as well as digital pen input on the paper itself. With our fully functional research platform we demonstrate an interactive prototype for an industrial form-filling maintenance application to service computer networks that covers a wide range of typical paper-related tasks.

We explored the paper augmentation technique where a tablet computer is placed under the paper to modify its content appearance and create the illusion of a static object appearing perceptually dynamic. Our approach is based on the Deformation Lamps (DL) technique commonly used with front projection. However, we applied it to rear projection, which removes the need for a fixed hardware setup or handling and wearing dedicated hardware. The key challenge was to ensure the appropriate mixing of light reflecting from the paper (to see what is printed on the paper) and the light passing though the paper (to see what the tablet screen is projecting) since (i) a tablet screen is a relatively weak light source when being used in rear projection and (ii) commonly, paper has high opacity so the content from one side is not visible on the other side. In order to achieve the appropriate mixing of light, we reduced the opacity of the paper by perforating it (thus allowing more light to pass through), and modified the algorithm for generating dynamic luminance animation by adding gamma and brightness correction. The outcomes provide a guide on how to modify the paper and the algorithm in order to achieve the DL effect with rear projection using a tablet computer.

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

Function-as-a-Service (FaaS) has raised a growing interest in how to "tame" serverless to enable domain-specific use cases such as data-intensive applications and machine learning (ML), to name a few. Recently, several systems have been implemented for training ML models. Certainly, these research articles are significant steps in the correct direction. However, they do not completely answer the nagging question of when serverless ML training can be more cost-effective compared to traditional "serverful" computing. To help in this task, we propose MLLess, a FaaS-based ML training prototype built atop IBM Cloud Functions. To boost cost-efficiency, MLLess implements two key optimizations: a significance filter and a scale-in auto-tuner, and leverages them to specialize model training to the FaaS model. Our results certify that MLLess can be 15X faster than serverful ML systems [24] at a lower cost for ML models (such as sparse logistic regression and matrix factorization) that exhibit fast convergence.

Developers of applications relying on cryptographic libraries can easily make mistakes in their use. Popular dynamic languages such as JavaScript make testing or verifying such applications particularly challenging. In this paper, we present our ongoing work toward a methodology for automatically checking security properties in JavaScript code. Our main idea is to attach security annotations to values that encode properties of interest. We illustrate our idea using examples and, as an initial step in our line of work, we present a formalization of security annotations in a statically typed lambda calculus. As next steps, we will translate our annotations to a dynamically typed formalization of JavaScript such as λJS and implement a runtime checked type extension using code instrumentation for full JavaScript.

Paper-based digital microfluidic biochips (P-DMFBs) have recently attracted great attention for its low-cost, in-place, and fast fabrication. This technology is essential for agile bio-assay development and deployment. P-DMFBs print electrodes and associate control lines on paper to control droplets and complete bio-assays. However, P-DMFBs have following issues: 1) control line interference may cause unwanted droplet movements, 2) avoiding control interference degrades assay performance and routability, 3) single layer fabrication limits routability, and 4) expensive ink cost limits low-cost benefits of P-DMFBs. To solve above issues, this work proposes an interference-free design methodology to design P-DMFBs with fast assay speed, better routability, and compact printing area. The contributions are as follows: First, we categorize control interference into soft and hard. Second, we identify only soft interference happens and propose to remove soft control interference constraints. Third, we propose an interference-free design methodology. Finally, we propose a cost-efficient ILP-based fluidic design module. Experimental results show proposed method outperforms prior work [14] across all bio-assay benchmarks. Compared to previous work, our cost-optimized designs use only 47%~78% area, gain 3.6%~16.2% more routing resources, and achieve 0.97x~1.5x shorter assay completion time. Our performance-optimized designs can accelerate assay speed by 1.05x~1.65x using 81%~96% printed area.

Paper-based digital microfluidic biochips (P-DMFBs) have emerged as a safe, low-cost, and fast-responsive platform for biochemical assays. In P-DMFB, droplet manipulations are executed by the electrowetting technology. In order to enable the electrowetting technology, pattern arrays of electrodes and control lines are coated on paper with a hydrophobic Teflon film and dielectric parylene-C film. Different from traditional DMFBs, the manufacturing of P-DMFBs is efficient and inexpensive since the electrodes and control lines are printed on photo paper with an inkjet printer. Active paper-based hybridized chip (APHC) is a type of P-DMFBs that has open and closed part. APHC enjoys more convenience than common P-DMFBs since it has no need to fabricate and maintain the micro gap between glass and paper chip, which requires highly delicate treatments. However, the pattern rails of electrodes in APHCs are denser than traditional P-DMFBs, which makes existing electrode routing algorithm fail in APHCs. To deal with the challenge in electrode routing of APHCs, this paper proposes a multi-commodity network flow-based routing algorithm, which simultaneously maximizes the routability and minimizes the total wire length of control lines. The multi-commodity flow model can utilize the pin-sharing between electrodes, which can improve routability and reduce the detour of routing lines. Moreover, the activation sequences of electrodes are considered, which guarantees that the bioassay will not be interfered with after pin-sharing. The proposed method achieves a 100% successful routing rate on real-life APHCs while other electrode routing method cannot solve the electrode routing of APHCs successfully.

FT-Matrix is a typical vector-SIMD architecture that refines the cooperation between scalar and vector units. This approach is widely used in digital signal processing, high-performance computing, and artificial intelligence, among other fields. FT-Matrix currently adopts C vector extension as the main programming model, improving the utilization efficiency of SIMD by providing explicit vector extension API. Moreover, it is difficult to efficiently transplant parallel programs (OpenCL, CUDA) adopted by users. This paper proposes an automatic mapping and code optimization method for OpenCL kernels on FT-Matrix architecture. The proposed approach solves these challenges by means of work item coalescing, slicing and rotation, and instruction-level code optimization. Preliminary results show that our method can achieve high performance and good hardware utilization for OpenCL kernels, as well as decreasing the programming difficulty on FT-Matrix.

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.

We propose a typesafe abstraction to tensors (i.e. multidimensional arrays) exploiting the type-level programming capabilities of Scala through heterogeneous lists (HList), and showcase typesafe abstractions of common tensor operations and various neural layers such as convolution or recurrent neural networks. This abstraction could lay the foundation of future typesafe deep learning frameworks that runs on Scala/JVM.

The current demand for apps has greatly moved the software development market. Due their massive distribution and use by thousands of different users, special attention needs to be paid to the interfaces design and the ways of interaction with these apps. An efficient way to evaluate these aspects in advance is prototyping. However, prototyping is often overlooked because of the additional cost it has put into the app development process and the time that may require it. The aim of this paper is to present a prototyping process for apps based on the paper construction of interface drafts and their interactions. Later these drafts are captured by the camera of a smartphone, generating a version of the app that can be used by the client to evaluate the interactions. This process also generates a digital version of the project based on a Domain Specific Language (DSL), whereby this prototype can be refined, generating, if necessary, a new executable version for evaluation or a new paper model to be presented and modified by the client. The validation of the proposal is done through a case study, in which an application is modeled on paper through a stencil for Android interface. Thus, generating the digital version of the project as well as the codes that implement the screens and the navigation structures between them.

To support the replication of "Instance of Interest Detection", which was presented at MM'19, this companion paper provides the details of the artifacts. Instance of Interest Detection (IOID) aims to provide instance-level user interest modeling for image semantic description. In this paper, we explain the file structure of the source code and publish the details of our IOID dataset, which can be used to retrain the model with custom parameters. We also provide a program for component analysis to help other researchers to do experiments with alternative models that are not included in our experiments. Moreover, we provide a demo program for using our model easily.

This companion paper supports the experimental replication of paper "Outfit Compatibility Prediction and Diagnosis with Multi-Layered Comparison Network", which is presented at ACM Multimedia 2019. We provide the software package for replicating the implementation of Multi-Layered Comparison Network (MCN), as well as the Polyvore-T dataset and baseline methods compared in the original paper. This paper contains the guides to reproduce the experiment results including outfit compatibility prediction, outfit diagnosis and automatic outfit revision.

A Trusted Execution Environment (TEE) provides an isolated environment for sensitive workloads. However, the code running in the TEE may contain vulnerabilities that could be exploited by attackers and further leveraged to corrupt the TEE. The increasing buggy code inside the TEE concerns the security of the entire TEE. In this position paper, we present the challenges towards securing trusted execution environments and potential mitigation.

In this study, we evaluate a new electronic mobile application, PGA (Programming Grading Assistant). It was designed to make grading paper-based exams easier for graders and professors. Not only does it facilitate grading, but PGA also provides students with more meaningful (semantic) feedback than non-PGA grading. We outline the PGA grading process, the execution of a user study, and the results from that study that prove the effectiveness of PGA technology. Other results indicate that PGA awarded grades are equivalent to red-pen awarded grades and that PGA grades are more consistent across the board.

Biological systems exhibit extraordinary levels of diversity. Embodied behaviours are the emergent property of many loosely-coupled parallel processes, from the microscopic level, e.g. materials, to more abstracted levels, e.g. organs and limbs. We summarise our investigations into using a synthetic methodology, i.e. an understanding-by-building approach, for designing the complex interactions of falling paper shapes. By studying how simple systems such as a falling paper shape behave, we can analyse the specific characteristics of the interaction between morphology and the environment, and how this leads to programmable non-trivial behaviours. We present current results and discuss the implications on the future of design in robotics.

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

We present Self-powered Paper INterfaces (SPIN) combining folding paper creases with triboelectric nanogenerator (TENG). Embedding TENG into paper creases, we developed a design editor and set of fabrication techniques to create paper-based interfaces that power sensors and actuators. Our SPIN design editor enables users to design their own crease pattern by changing parameters, embed power generating modules into the design, estimate total power generation, and export the files. Then following the fabrication instructions, users can cut and crease materials, and assemble them to build their own interfaces. We employ repetitive push-and-pull based embodied interactions with the mechanism of paper creases and demonstrate four application examples that show new expressive possibilities applying different scales of embodied interactions.

Verification techniques have been applied to the design of secure protocols for decades. However, relatively few efforts have been made to ensure that verified designs are also implemented securely. Static code verification techniques offer one way to bridge the verification gap between design and implementation, but require substantial expertise and manual labor to realize in practice. In this short paper, we propose black-box runtime verification as an alternative approach to extend the security guarantees of protocol designs to their implementations. Instead of instrumenting the complete protocol implementation, our approach only requires instrumenting common cryptographic libraries and network interfaces with a runtime monitor that is automatically synthesized from the protocol specification. This lightweight technique allows the effort for instrumentation to be shared among different protocols and ensures security with presumably minimal performance overhead.

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.

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.

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.

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.

The development and evaluation of prototypes is an important part of game development. This study aimed to establish whether the form factor, either paper or an iPad, would affect children's ratings of a prototype game. The 42 participants were aged between 7 and 11 and used the Fun Toolkit to measure user experience. The results showed that the form factor had little influence over their overall ratings of the game concept. However, the children rated the aesthetics higher on the paper version compared to the iPad, despite the graphics being identical, suggesting a potential effect of the form factor on this aspect of the prototype. Participants were also asked if they would pay to download the game as a measure of engagement, but their comments indicated that parental controls might influence the results when evaluating monetary transactions with children.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

We present a printed paper actuator as a low cost, reversible and electrical actuation and sensing method. This is a novel but easily accessible enabling technology that expands upon the library of actuation-sensing materials in HCI. By integrating three physical phenomena, including the bilayer bending actuation, the shape memory effect of the thermoplastic and the current-driven joule heating via conductive printing filament, we developed the actuator by simply printing a single layer conductive Polylactide (PLA) on a piece of copy paper via a desktop fused deposition modeling (FDM) 3D printer. This paper describes the fabrication process, the material mechanism, and the transformation primitives, followed by the electronic sensing and control methods. A software tool that assists the design, simulation and printing toolpath generation is introduced. Finally, we explored applications under four contexts: robotics, interactive art, entertainment and home environment.

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.

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%.

Editorial pre-screening is the first step in academic peer review. The deluge of research papers and the huge amount of submissions being made to journals these days makes editorial decision a very challenging task. The current work attempts to investigate certain impact factors that may have a role in the editorial decision making process. The proposed work exhibits potential for the development of an AI-assisted peer review system which could aid the editors as well as the authors in making appropriate decisions in reasonable time and thus accelerate the overall process of scholarly publishing.

How to monitor students' learning in daily learning behavior and guide teachers to teach more rationally are very important problems. While the digital teaching resources are popular, we can not ignore the information of traditional paper media. We design a homework processing system to solve the problems. First of all, we begin the operation from the acquisition side, by photo taking and image preprocessing, we make the homework picture with semantic meanings. Then we construct an answer reference system, which shows the answers of different kinds of incorrectbess as samples for teachers to refer to when correcting. Semantic pictures are dispatched to teachers handheld devices for intelligent and precise correct activities. We divide correct process into determining the degree of error and giving comments to evaluate and inspire students. Finally, we design a web page for homework data management and correction statistics to help teachers understand the homework completion status of students. The design of homework correction scheme is easy to deploy and can be used in most teaching environments.