Search

We propose new solutions that can efficiently address the problem of malicious memory access from pluggable computer peripherals and microcontrollers embedded within a system-on-chip. This problem represents a serious emerging threat to total-system computer security. Previous work has shown that existing defenses are insufficient and poorly deployed, in part due to performance concerns. In this paper we explore the threat and its implications for system architecture. We propose a range of protection techniques, from lightweight to heavyweight, across different classes of systems. We consider how emerging capability architectures (and specifically the CHERI protection model) can enhance protection and provide a convenient bridge to describe interactions among software and hardware components. Finally, we describe how new schemes may be more efficient than existing defenses.

This companion paper is to support the replication of paper "Campus3D: A Photogrammetry Point Cloud Benchmark for Outdoor Scene Hierarchical Understanding", which was presented at ACM Multimedia 2020. The supported paper's main purpose was to provide a photogrammetry point cloud-based dataset with hierarchical multilabels to facilitate the area of 3D deep learning. Based on this provided dataset and source code, in this work, we build a complete package to reimplement the proposed methods and experiments (i.e., the hierarchical learning framework and the benchmarks of the hierarchical semantic segmentation task). Specifically, this paper contains the technical details of the package, including file structure, dataset preparation, installation package, and the conduction of the experiment. We also present the replicated experiment results and indicate our contributions to the original implementation.

In this companion paper, we provide the details of the reproducibility artifacts of the paper "Visual Relation of Interest Detection" presented at MM'20. Visual Relation of Interest Detection (VROID) aims to detect visual relations that are important for conveying the main content of an image. In this paper, we explain the file structure of the source code and publish the details of our ViROI dataset, which can be used to retrain the model with custom parameters. We also detail the scripts for component analysis and comparison with other methods and list the parameters that can be modified for custom training and inference.

In this paper we reproduce experimental results presented in our earlier work titled "Describing Subjective Experiment Consistency by $p$-Value P-P Plot" that was presented in the course of the 28th ACM International Conference on Multimedia. The paper aims at verifying the soundness of our prior results and helping others understand our software framework. We present artifacts that help reproduce tables, figures and all the data derived from raw subjective responses that were included in our earlier work. Using the artifacts we show that our results are reproducible. We invite everyone to use our software framework for subjective responses analyses going beyond reproducibility efforts., Comment: Please refer to the original publication: https://dl.acm.org/doi/10.1145/3474085.3477935 Related paper: https://dl.acm.org/doi/abs/10.1145/3394171.3413749 or arXiv:2009.13372

This chapter focuses on cost control by analyzing the intelligent control system of waste paper deinking production line, and finally designs a solution. In the aspect of control index, machine learning has designed a solution that pays less attention to the benefit of a single control loop and focuses on maximizing the overall benefit of the production line. In the aspect of control demand, according to the complexity of waste paper deinking production line, a distributed control solution is designed Finally, according to the dynamic fuzziness in machine learning system, the basic concept of dynamic fuzzy machine learning is constructed by using dynamic fuzzy set The dynamic fuzzy machine learning model is described and the related learning algorithm is given. Finally, a simulation example is given, and satisfactory results are obtained by comparing with the elastic BP algorithm RPROP of neural network.

Containers are a mainstream virtualization technique commonly used to run stateful workloads over persistent storage. In multi-tenant hosts with high utilization, resource contention at the system kernel often leads to inefficient handling of the container I/O. Assuming a distributed storage architecture for scalability, resource sharing is particularly problematic at the client hosts serving the applications of competing tenants. Although increasing the scalability of a system kernel can improve resource efficiency, it is highly challenging to refactor the kernel for fair access to system services. As a realistic alternative, we isolate the storage I/O paths of different tenants by serving them with distinct clients running at user level. We introduce the Danaus client architecture to let each tenant access the container root and application filesystems over a private host path. We developed a Danaus prototype that integrates a union filesystem with a Ceph distributed filesystem client and a configurable shared cache. Across different host configurations, workloads and systems, Danaus achieves improved performance stability because it handles I/O with reserved per-tenant resources and avoids intensive kernel locking. Danaus offers up to 14.4x higher throughput than a popular kernel-based client under conditions of I/O contention. In comparison to a FUSE-based user-level client, Danaus also reduces by 14.2x the time to start 256 high-performance webservers. Based on our extensive experience from building and evaluating Danaus, we share several valuable lessons that we learned about resource contention, file management, service separation and performance stability.

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.

This companion paper supports the replication of the fashion trend forecasting experiments with the KERN (Knowledge Enhanced Recurrent Network) method that we presented in the ICMR 2020. We provide an artifact that allows the replication of the experiments using a Python implementation. The artifact is easy to deploy with simple installation, training and evaluation. We reproduce the experiments conducted in the original paper and obtain similar performance as previously reported. The replication results of the experiments support the main claims in the original paper.

Our eventual goal regarding this study is to support students with developing paper-writing skill. In order to achieve this goal, we have been trying to find characteristic features of good papers through analyzing educational and other kinds of data. We take conference papers as target data and suppose full/regular papers are good because they are chosen as reviewers evaluate them more valuable to be presented in the conference than other papers. In our series of study, we have been surveying the differences of full and short papers. In this paper, we aim to investigate further differences of them by taking different analysis method. We have been using the numbers of occurrences of each word in full/short papers as the data for analysis. In this paper we use the numbers of full/short papers that contain the word instead of the total numbers of occurrences of words. We define a new index of a word which shows how likely it is used in full or short papers. We discuss its effectiveness by applying it to an experiment of distinguishing full and short papers.

Video cameras are becoming ubiquitous in our daily lives. With the recent advancement of Artificial Intelligence (AI), live video analytics are enabling various useful services, including traffic monitoring and campus surveillance. However, current video analytics systems are highly limited in leveraging the enormous opportunities of the deployed cameras due to (i) centralized processing architecture (i.e., cameras are treated as dumb streaming-only sensors), (ii) hard-coded analytics capabilities from tightly coupled hardware and software, (iii) isolated and fragmented camera deployment from different service providers, and (iv) independent processing of camera streams without any collaboration. In this paper, we envision a full-fledged system for software-defined video analytics with cross-camera collaboration that overcomes the aforementioned limitations. We illustrate its detailed system architecture, carefully analyze the key system requirements with representative app scenarios, and derive potential research issues along with a summary of the status quo of existing works.

Storm and flood disasters such as typhoons and torrential rains are becoming more intense and frequent. The national government and municipalities must respond to such natural disasters as soon as possible. When the scale of damage is large; however, it takes much time to investigate the severity of damage, and the initial response can be delayed. If we could precisely and rapidly estimate the severity of damage for each city at an early stage, the national government would be able to better support the municipalities, and consequently respond quickly to help citizens. In this paper, we propose a novel approach to estimate the severity of disaster damage within a short time period after a disaster occurs by exploiting real-time population data generated from cellular networks. First, we investigate the relationship between real-time population data and the severity of damage. Then, we design a Graph Convolutional Networks for Disaster Damage Estimation, called D2E-GCN, which fully exploits the directed and weighted characteristics of human mobility graph. We conduct an offline evaluation on real-world datasets including two typhoons that hit Japan. The evaluation results show that the proposed method outperforms baseline methods which do not consider the graph structure of cities, and the proposed method can estimate the severity of damage approximately 48 hours after typhoons passed. Moreover, we find the experimental insight that the estimation performance can be significantly affected by the graph construction method for GCN models.

Blind natural video quality assessment (BVQA), also known as no-reference video quality assessment, is a highly active research topic. In our recent contribution titled "Blind Natural Video Quality Prediction via Statistical Temporal Features and Deep Spatial Features" published in ACM Multimedia 2020, we proposed a two-level video quality model employing statistical temporal features and spatial features extracted by a deep convolutional neural network (CNN) for this purpose. At the time of publishing, the proposed model (CNN-TLVQM) achieved state-of-the-art results in BVQA. In this paper, we describe the process of reproducing the published results by using CNN-TLVQM on two publicly available natural video quality datasets.

The author claimed for the artifact associated with his paper the following ACM Reproducibility badges:(1) Artifact Available,(2) Artifact Evaluated-Functional,(3) Results Reproduced. After an in-depth review process, we agree to assign all the requested badges as we found it to meet the following requirements:i) it is uploaded on a persistent repository, accessible via a DOI; ii) it is well documented, consistent with the presented data, complete of all the necessary software sources and packages, and exercisable; iii) it is exhaustive in the reproduction of all the relevant data of the paper. Some curves in some reproduced plots are truncated, due to the computational limits imposed by the short-term deadline of the review process. Nevertheless, the overall trends are respected, and the curves are supporting the paper's claims.

Measuring the quality of research work is an essential component of the scientific process. With the ever-growing rates of articles being submitted to top-tier conferences, and the potential consistency and bias issues in the peer review process identified by scientific community, it is thus of great necessary and challenge to automatically evaluate submissions. Existing works mainly focus on exploring relevant factors and applying machine learning models to simply be accurate at predicting the acceptance of a given academic paper, while ignoring the interpretability power which is required by a wide range of applications. In this paper, we propose a framework to construct decision sets that consist of unordered if-then rules for predicting paper acceptance. We formalize decision set learning problem via a joint objective function that simultaneously optimize accuracy and interpretability of the rules, rather than organizing them in a hierarchy. We evaluate the effectiveness of the proposed framework by applying it on a public scientific peer reviews dataset. Experimental results demonstrate that the learned interpretable decision sets by our framework performs on par with state-of-the-art classification algorithms which optimize exclusively for predictive accuracy and much more interpretable than rule-based methods.

New discoveries in science are often built upon previous knowledge. Ideally, such dependency information should be made explicit in a scientific knowledge graph. The Keystone Framework was proposed for tracking the validity dependency among papers. A keystone citation indicates that the validity of a given paper depends on a previously published paper it cites. In this paper, we propose and evaluate a strategy that repurposes rhetorical category classifiers for the novel application of extracting keystone citations that relate to research methods. Five binary rhetorical category classifiers were constructed to identify Background, Objective, Methods, Results, and Conclusions sentences in biomedical papers. The resulting classifiers were used to test the strategy against two datasets. The initial strategy assumed that only citations contained in Methods sentences were methods keystone citations, but our analysis revealed that citations contained in sentences classified as either Methods or Results had a high likelihood to be methods keystone citations. Future work will focus on fine tuning the rhetorical category classifiers, experimenting with multiclass classifiers, evaluating the revised strategy with more data, and constructing a larger gold standard citation context sentence dataset for model training.

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.

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

Over 100 fatalities and more than 8000 injuries are reported on average every day in the US caused by motor vehicle accidents. In order to provide drivers a safer travel plan, we present a machine learning powered risk profiler for road segments using geo-spatial data. We built an end-to-end pipeline to extract static road features from map data and combined them with other data such as weather and traffic patterns. Our approach proposes novel methods for data pre-processing and feature engineering using statistical and clustering methods. Our model achieves significant performance improvement for risk prediction using hyper-parameter optimization (HPO) and the open source AutoGluon library to optimize the ML model. Finally, an enduser visualization interface is developed in the form of interactive maps. The results indicate 31% improvement in model performance compared to baseline when model is applied to a new geo location. We tested this approach on six major cities in the US. The findings of this research will provide users a tool to quantitatively assess accident risk at road segment level.

Trusted execution as offered by Intel's Software Guard Extensions (SGX) is considered as an enabler to protect the integrity and confidentiality of stateful workloads such as key-value stores and databases in untrusted environments. These systems are typically long running and require extension mechanisms built on top of dynamic loading as well as hot-patching to avoid downtimes and apply security updates faster. However, such essential mechanisms are currently neglected or even missing in combination with trusted execution. We present sgx-dl, a lean framework that enables dynamic loading of enclave code at the function level and hot-patching of dynamically loaded code. Additionally, sgx-dl is the first framework to utilize the new SGX version 2 features and also provides a versioning mechanism for dynamically loaded code. Our evaluation shows that sgx-dl introduces a performance overhead of less than 5% and shrinks application downtime by an order of magnitude in the case of a database system.

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.

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.

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.

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.

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.

Keeping up with the rapid growth of Deep Learning (DL) research is a daunting task. While existing scientific literature search systems provide text search capabilities and can identify similar papers, gaining an in-depth understanding of a new approach or an application is much more complicated. Many publications leverage multiple modalities to convey their findings and spread their ideas - they include pseudocode, tables, images and diagrams in addition to text, and often make publicly accessible their implementations. It is important to be able to represent and query them as well. We utilize RDF Knowledge graphs (KGs) to represent multimodal information and enable expressive querying over modalities. In our demo we present an approach for extracting KGs from different modalities, namely text, architecture images and source code. We show how graph queries can be used to get insights into different facets (modalities) of a paper, and its associated code implementation. Our innovation lies in the multimodal nature of the KG we create. While our work is of direct interest to DL researchers and practitioners, our approaches can also be leveraged in other scientific domains.

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.

In graduate education, the quality of academic papers can reflect individual scientific research achievements. This study compared the differences in linguistic features between the papers of experts and graduate students with natural language processing. More specifically, for revealing the problems existing in the writing of graduate students, this study analyzed academic papers in a journal as experts and those of graduate students by using Chinese word segmentation. The study found that the graduate students and experts have differences in the use of various parts of speech (i.e., nouns, verbs, adjectives, pronouns, and conjunctions) and connective words (i.e., organization, comparison, summarization, etc.). The results may be helpful for further designing scaffolding to improve the writing quality of graduate students’ papers.

Named entity recognition has been extensively studied in the past decade. The state-of-the-art models, trained on general text such as Wikipedia articles and newsletters, have achieved F_1>0.90. Entity types are focused on people, location, organization, etc. However, entity recognition from domain-specific text, in particular research papers, is still challenging. In this paper, we perform a comparative study of sequence tagging (ST) methods on this task using a manually curated corpus from biomedical papers on Lyme disease. Each model we compare consists of a ST and a non-ST classification component. In this pilot study, we freeze the non-ST classifier to study how the ST component performs with variants of the conditional random field (CRF) and bidirectional long short-term memory (BiLSTM). The results shed light on the importance of pre-trained word embeddings such as ELMo and the residual unit. The attention mechanism and enriched features do not seem to boost the performance in recognizing entity mentions and their positions, which is likely to be caused by the relatively small training sample. We plan to improve the model by increasing the training corpus size and trying different combinations of features.

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

This paper proposes a method that analyzes the tendency of the paper for the originality detection of papers. This method is aiming at the behavior of idea plagiarism. The plagiarism detection task can be divided into three steps which are retrieval of candidate source document set, selection of source document set and post processing. The texts are modeled by the topic model and then are divided into several topic blocks by combining the topic modeling with the classical text segmentation method TextTiling. Then, the topic blocks which may be the source block of plagiarism are filtered out by calculating topic similarities among these topic fragments. On this basis, topic words of the topic block to be measured are extracted and its idea tendency aiming at each topic word are analyzed by the sentiment analysis method based on the dictionary. And further the source topic block set is filtered out based on the idea tendency Finally, a method of evaluating the original degree of the text is given for the intelligent plagiarism detection by the fusion of topic similarity and the result of the analysis of idea tendencies. Through experimental verification, this method can effectively evaluate the originality of the paper and has a good accuracy.

Nowadays, there are more and more papers submitted to various periodicals and conferences. Typically, reviewers need to read through the paper and give a review comment and score to it based on somehow certain criterion. This review process is labor intensive and time-consuming. Recently, AI technology is widely used to alleviate human labor burden. Can machine learn from human to review papers automatically? In this paper, we propose a collaborative grammar and innovation model - DeepReviewer to achieve automatic paper review. This model learning the semantic, grammar and innovative features of an article by three main well-designed components simultaneously. Moreover, these three factors are integrated by an attention layer to get the final review score of the paper. We crawled paper review data from Openreview and built a real data set. Experimental results demonstrate that our model exceeds many baselines.

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.

The need to analyze and execute binaries from legacy ISAs on new or different ISAs has been addressed in a variety of ways over the past few decades. Solutions using complementary static and dynamic binary translation techniques have been deployed in most real-world situations. As new ISAs are designed and legacy ISAs re-examined, the need for binary translation infrastructure re-emerges, and needs to be re- engineered all over again. Work is in progress with a goal to make such re-engineering efforts easier by using some of the software tools that would irrespectively be developed or available for a new or existing ISA. To that end, this paper presents a static binary raiser that translates binaries to LLVM IR. Native binaries for a new ISA are generated from the raised LLVM IR using the LLVM compiler backend. This technique enables development of a single raiser per legacy ISA, irrespective of the new target ISA. The result of such a raiser can then leverage compiler back-ends of new ISAs, thus simplifying the development of binary translator for the new ISA . This work leverages the existing LLVM infrastructure to implement a static raiser that currently supports raising x64 and Arm32 binaries to LLVM IR. The raiser is built as an LLVM tool – similar to llvm-objdump or clang and does not have any dependencies outside of those needed to build LLVM. This paper describes the phases of the raiser and gives the current status and limitations.

Rootkits are malware that attempt to compromise the system’s functionalities while hiding their existence. Various rootkits have been proposed as well as different software defenses, but only very few hardware defenses. We position hardware-enhanced rootkit defenses as an interesting research opportunity for computer architects, especially as many new hardware defenses for speculative execution attacks are being actively considered. We first describe different techniques used by rootkits and their prime targets in the operating system. We then try to shed insights on what the main challenges are in providing a rootkit defense, and how these may be overcome. We show how a hypervisor-based defense can be implemented, and provide a full prototype implementation in an open-source cloud computing platform, OpenStack. We evaluate the performance overhead of different defense mechanisms. Finally, we point to some research opportunities for enhancing resilience to rootkit-like attacks in the hardware architecture.

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.

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.

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.

Recent research work has explored solutions to address the problem of increased energy consumption by providing feedback to individuals about their consumption patterns, thus motivating them for conservative actions. In particular, university dormitory setting provides an excellent venue to explore effectiveness of energy feedback mechanisms. Most of such dormitory studies used a web-based portal for enabling competition among students as motivation for energy saving. Moreover, the majority of them have been conducted in the developed world. In this paper, we report a 6-week study conducted in an Indian university with 432 students (18 groups), comparing five different eco-feedback techniques. The results show that Daily Individual Paper Feedback encourages more conservation, both among males and females, with 19.4% and 7.6% reduction, respectively. We conclude with a discussion on the importance of easy and regular availability of information, effectiveness of paper-based feedback, and role of gender in eco-feedback.

In this paper, we introduce Sketch-Play-Learn, an augmented paper-based tabletop system to support students learning of the principles of light behaviour. This project emphasises the use of the most ubiquitous and inexpensive learning medium, which is paper. Students engage themselves in completing a goal-based task sketched by the teacher on paper and observing the visual feedback on the same paper. We argue that augmenting the paper with digital information while providing a tabletop setup provides a novel and appealing approach to learning for classroom use. In this paper, we discuss the motivations for our project and describe the design and implementation our system. We also describe an initial evaluation with children and outline future research goals.

Devices in the Internet of Things (IoT) introduce unique security challenges due to their operating conditions and device limitations. Existing security solutions based on classical cryptography have significant drawbacks in IoT devices, primarily due to the possibility of physical and side channel attacks. As an alternative approach, this position paper advocates the use of physical unclonable functions (PUFs) as a security primitive for developing security solutions for IoT devices. Preliminary work on developing a PUF based mutual authentication protocol is also presented.

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 recent years, attribute-based access control has been recognized as a convenient way to specify access mediation policies that leverage attributes originating from different security domains, e.g., independently-run organizations or supporting platforms. However, this new paradigm, while allowing for enhanced flexibility and convenience, may also open the door to new kinds of attacks based on forging or impersonating attributes, thus potentially allowing for attackers to gain unintended access to protected resources. In order to alleviate this problem, we present an ongoing effort based on moving target defense, an emerging technique for proactively providing security measurements.In our approach, we aim to analyze attribute-based data obtained at runtime in order to dynamically change policy configurations over time. We present our approach by leveraging a case study based in electronic health records, another trending methodology widely used in practice for mediating access to sensitive healthcare information in mission-critical applications.

Most academic authors refer to the literature when introducing their proposed methods and the data used in their experiments. These references can be very helpful when trying to understand a paper; however, some authors do not always state clearly the specific part of the referenced work they are referring the reader to and it can be quite laborintensive to have to read the whole document to identify the relevant information. In this paper, we propose a method for estimating the appropriate parts of a referenced work as the "cited parts," with the aim of reducing this burden. We first extract sentences in an academic paper that cites references to the literature as "citing sentences." We then vectorize the citing sentences and all the sentences in the cited papers using doc2vec and estimate the most appropriate cited part as the sentence that has the most similar feature vector to that of the citing sentence. To evaluate the proposed method, we conducted experiments using English-language papers and a questionnaire survey that asked subjects to evaluate the appropriateness of the cited parts estimated by the method. The experiments showed that this approach's success in estimating the appropriate parts of a cited paper as the cited parts depended on the citation intention of the citing sentences.

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.

Our work presents a method to use paper as an input device while reading on a mobile device, where the user turns a physical page in the real world in order to turn a page in the digital world. Our goal in this work is to replicate the feedback and affordances one would receive from a printed book on a mobile device, where to fully replicate the reading experience the user would need to turn pages as they would naturally with a printed book. Through a small study we discovered a number of ways that pages are often turned and these techniques became vital to the project. We describe a prototype device which uses paper as an input device with transparent electrodes and bend sensors embedded to pages, so that the turning and bending of pages can be digitally detected and addressed. The prototype is able to detect the page turns and bends made by the user and the state of each page.

Fireworks are used around the world to salute popular events such as festivals, weddings, and public or private celebrations. Besides their entertaining effects fireworks are essentially colored explosives which are sometimes directly used as weapons. Modern fireworks systems heavily rely on `wireless pyrotechnic firing systems'. Those `embedded cyber-physical systems' (ECPS) are able to remotely control pyrotechnic composition ignition. The failure to properly secure these computer sub-systems may have disastrous, if not deadly, consequences. They rely on standardized wireless communications, off the shelf embedded hardware and custom firmware.In this short paper, we describe our experience in discovering and exploiting a wireless firing system in a short amount of time without any prior knowledge of such systems. In summary, we demonstrate our methodology starting from analysis of firmware, the discovery of vulnerabilities and finally by demonstrating a real world attack. Finally, we stress that the security of pyrotechnic firing systems should be considered seriously, which could be achieved through improved safety compliance requirements and control.

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.