Query: AutoLock: Why Cache Attacks on ARM Are Harder Than You Think

Relevant passages:
ARMageddon: Cache Attacks on Mobile Devices <=SEP=> In the last 10 years, cache attacks on Intel x86 CPUs have gained increasing attention among the scientific community and powerful techniques to exploit cache side channels have been developed. However, modern smartphones use one or more multi-core ARM CPUs that have a different cache organization and instruction set than Intel x86 CPUs. So far, no cross-core cache attacks have been demonstrated on non-rooted Android smartphones. In this work, we demonstrate how to solve key challenges to perform the most powerful cross-core cache attacks Prime+Probe, Flush+Reload, Evict+Reload, and Flush+Flush on non-rooted ARM-based devices without any privileges. Based on our techniques, we demonstrate covert channels that outperform state-of-the-art covert channels on Android by several orders of magnitude. Moreover, we present attacks to monitor tap and swipe events as well as keystrokes, and even derive the lengths of words entered on the touchscreen. Eventually, we are the first to attack cryptographic primitives implemented in Java. Our attacks work across CPUs and can even monitor cache activity in the ARM TrustZone from the normal world. The techniques we present can be used to attack hundreds of millions of Android devices.

Cache Template Attacks: Automating Attacks on Inclusive Last-Level Caches <=SEP=> Recent work on cache attacks has shown that CPU caches represent a powerful source of information leakage. However, existing attacks require manual identification of vulnerabilities, i.e., data accesses or instruction execution depending on secret information. In this paper, we present Cache Template Attacks. This generic attack technique allows us to profile and exploit cachebased information leakage of any program automatically, without prior knowledge of specific software versions or even specific system information. Cache Template Attacks can be executed online on a remote system without any prior offline computations or measurements. Cache Template Attacks consist of two phases. In the profiling phase, we determine dependencies between the processing of secret information, e.g., specific key inputs or private keys of cryptographic primitives, and specific cache accesses. In the exploitation phase, we derive the secret values based on observed cache accesses. We illustrate the power of the presented approach in several attacks, but also in a useful application for developers. Among the presented attacks is the application of Cache Template Attacks to infer keystrokes and—even more severe—the identification of specific keys on Linux and Windows user interfaces. More specifically, for lowercase only passwords, we can reduce the entropy per character from log2(26) = 4.7 to 1.4 bits on Linux systems. Furthermore, we perform an automated attack on the Ttable-based AES implementation of OpenSSL that is as efficient as state-of-the-art manual cache attacks.

TruSpy: Cache Side-Channel Information Leakage from the Secure World on ARM Devices <=SEP=> As smart, embedded devices are increasingly integrated into our daily life, the security of these devices has become a major concern. The ARM processor family, which powers more than 60% of embedded devices, introduced TrustZone technology to offer security protection via an isolated execution environment called secure world. Caches in TrustZone-enabled processors are extended with a nonsecure (NS) bit to indicate whether a cache line is used by the secure world or the normal world. This cache design improves system performance by eliminating the need to perform cache flush during world switches; however, it also enables cache contention between the two worlds. In this work, we present TruSpy, the first study of timingbased cache side-channel information leakage of TrustZone. Our proposed attack exploits the cache contention between normal world and secure world to recover secret information from secure world. Two attacks are proposed in TruSpy, namely, the normal world OS attack and the normal world Android app attack. In the OS-based attack, the attacker is able to access virtual-to-physical address translation and high precision timers. In the Android app-based attack, these tools are unavailable to the attacker, so we devise a novel method that uses the expected channel statistics to allocate memory for cache probing. We also show how an attacker might use the less accurate performance event interface as a timer. Using the T-table based AES implementation in OpenSSL 1.0.1f as an example, we demonstrate that it is possible for a normal world attacker to steal a fine-grained secret from the secure world using a timing-based cache side-channel. We can recover the full AES encryption key via either the OS-based attack or the Android app-based attack. Since our zero permission TruSpy attack is based on the cache design in TrustZone enabled ARM processors, it poses a significant threat to a wide array of devices. To mitigate the newly discovered threat, we also propose both application-based and system-oriented countermeasures.

Return-Oriented Flush-Reload Side Channels on ARM and Their Implications for Android Devices <=SEP=> Cache side-channel attacks have been extensively studied on x86 architectures, but much less so on ARM processors. The technical challenges to conduct side-channel attacks on ARM, presumably, stem from the poorly documented ARM cache implementations, such as cache coherence protocols and cache flush operations, and also the lack of understanding of how different cache implementations will affect side-channel attacks. This paper presents a systematic exploration of vectors for flush-reload attacks on ARM processors. flush-reload attacks are among the most well-known cache side-channel attacks on x86. It has been shown in previous work that they are capable of exfiltrating sensitive information with high fidelity. We demonstrate in this work a novel construction of flush-reload side channels on last-level caches of ARM processors, which, particularly, exploits return-oriented programming techniques to reload instructions. We also demonstrate several attacks on Android OS (e.g., detecting hardware events and tracing software execution paths) to highlight the implications of such attacks for Android devices.

Cache Games -- Bringing Access-Based Cache Attacks on AES to Practice <=SEP=> Side channel attacks on cryptographic systems exploit information gained from physical implementations rather than theoretical weaknesses of a scheme. In recent years, major achievements were made for the class of so called access-driven cache attacks. Such attacks exploit the leakage of the memory locations accessed by a victim process. In this paper we consider the AES block cipher and present an attack which is capable of recovering the full secret key in almost real time for AES-128, requiring only a very limited number of observed encryptions. Unlike previous attacks, we do not require any information about the plaintext (such as its distribution, etc.). Moreover, for the first time, we also show how the plaintext can be recovered without having access to the cipher text at all. It is the first working attack on AES implementations using compressed tables. There, no efficient techniques to identify the beginning of AES rounds is known, which is the fundamental assumption underlying previous attacks. We have a fully working implementation of our attack which is able to recover AES keys after observing as little as 100 encryptions. It works against the OpenS SL 0.9.8n implementation of AES on Linux systems. Our spy process does not require any special privileges beyond those of a standard Linux user. A contribution of probably independent interest is a denial of service attack on the task scheduler of current Linux systems (CFS), which allows one to observe (on average) every single memory access of a victim process.

Using GLCM and Gabor filters for classification of PAN images <=SEP=> In the present research we have used GLCM and Gabor filters to extract texture features in order to classify PAN images. The main drawback of GLCM algorithm is its time-consuming nature. In this work, we proposed a fast GLCM algorithm to overcome the mentioned weakness of traditional GLCM. The fast GLCM is capable of extracting approximately the same features as the traditional GLCM does, but in a really much less time (in the best case, 180 times faster, and in the worst case, 30 times faster). The other weakness of the traditional GLCM is its lower accuracies in the region near the class borders. As Gabor filters are more powerful in border regions, we have tried to combine Gabor features with GLCM features. In this way we would compensate the latter mentioned weakness of GLCM. Experimental results show good capabilities of the proposed fast GLCM and the feature fusion method in classification of PAN images.

Exploring knowledge sharing in ERP implementation: an organizational culture framework <=SEP=> This is a multi-site case study of firms that have implemented enterprise resource planning (ERP) systems. It examines eight dimensions of culture and their impact on how ERP implementation teams are able to effectively share knowledge across diverse functions and perspectives during ERP implementation. Through synthesizing the data, we develop a cultural configuration that shows the dimensions of culture that best facilitate knowledge sharing in ERP implementation. The results also indicate ways that firms may overcome cultural barriers to knowledge sharing. A model is developed that demonstrates the link between the dimensions of culture and knowledge sharing during ERP implementation. Possible research questions on which future research can be based are also identified. D 2004 Elsevier B.V. All rights reserved.

External Data Access And Indexing In AsterixDB <=SEP=> Traditional database systems offer rich query interfaces (SQL) and efficient query execution for data that they store. Recent years have seen the rise of Big Data analytics platforms offering query-based access to "raw" external data, e.g., file-resident data (often in HDFS). In this paper, we describe techniques to achieve the qualities offered by DBMSs when accessing external data. This work has been built into Apache AsterixDB, an open source Big Data Management System. We describe how we build distributed indexes over external data, partition external indexes, provide query consistency across access paths, and manage external indexes amidst concurrent activities. We compare the performance of this new AsterixDB capability to an external-only solution (Hive) and to its internally managed data and indexes.

Realtime FPGA-based processing unit for a high-resolution automotive MIMO radar platform <=SEP=> Next generations of high-resolution automotive radar sensors rely on powerful, real-time capable processing units. High sampling rates, large antenna arrays as well as hard real-time constraints require the use of both parallel architectures and high-bandwidth memory interfaces. This paper presents a novel architecture of a FPGA-based multiple-input multiple-output (MIMO) radar sensor processing unit. Sampling rates up to 250 MSPS and a maximum of 16 parallel receiving channels can be used, resulting in a maximum data rate of 56GBit/s. The processing chain consists of a flexible FIR filter, a range-Doppler processing unit using windowed FFTs and an ordered statistics constant-false-alarm-rate (OS-CFAR) unit for optimal target detection and data reduction. The realized target system is composed of a Virtex7-FPGA and a 1GByte SDRAM memory. The resource usage of the FPGA implementation is analyzed in order to provide estimations for future system designs. Finally, the resulting performance of the system is verified in connection with a prototype MIMO radar front-end. High-resolution measurements of moving scenes have been carried out to validate the correct operation of the system.

Relation between language experiences in preschool classrooms and children's kindergarten and fourth-grade language and reading abilities. <=SEP=> Indirect effects of preschool classroom indexes of teacher talk were tested on fourth-grade outcomes for 57 students from low-income families in a longitudinal study of classroom and home influences on reading. Detailed observations and audiotaped teacher and child language data were coded to measure content and quantity of verbal interactions in preschool classrooms. Preschool teachers' use of sophisticated vocabulary during free play predicted fourth-grade reading comprehension and word recognition (mean age=9; 7), with effects mediated by kindergarten child language measures (mean age=5; 6). In large group preschool settings, teachers' attention-getting utterances were directly related to later comprehension. Preschool teachers' correcting utterances and analytic talk about books, and early support in the home for literacy predicted fourth-grade vocabulary, as mediated by kindergarten receptive vocabulary.

A tensor-based deep learning framework <=SEP=> This paper presents an unsupervised deep learning framework that derives spatio-temporal features for human-robot interaction. The respective models extract high-level features from low-level ones through a hierarchical network, viz. the Hierarchical Temporal Memory (HTM), providing at the same time a solution to the curse of dimensionality in shallow techniques. The presented work incorporates the tensor-based framework within the operation of the nodes and, thus, enhances the feature derivation procedure. This is due to the fact that tensors allow the preservation of the initial data format and their respective correlation and, moreover, attain more compact representations. The computational nodes form spatial and temporal groups by exploiting the multilinear algebra, subsequently express the samples according to those groups in terms of proximity. This generic framework may be applied in a diverse of visual data, whilst it has been examined on sequences of color and depth images, exhibiting remarkable performance.

On Resampling Algorithms for Particle Filters <=SEP=> In this paper a comparison is made between four frequently encountered resampling algorithms for particle filters. A theoretical framework is introduced to be able to understand and explain the differences between the resampling algorithms. This facilitates a comparison of the algorithms with respect to their resampling quality and computational complexity. Using extensive Monte Carlo simulations the theoretical results are verified. It is found that systematic resampling is favourable, both in terms of resampling quality and computational complexity.

Contributions from research on anger and cognitive dissonance to understanding the motivational functions of asymmetrical frontal brain activity <=SEP=> Research has suggested that approach-related positive emotions are associated with greater left frontal brain activity and that withdrawal-related negative emotions are associated with greater right frontal brain activity. Different explanations have been proposed. One posits that frontal asymmetry is due to emotional valence (positivity/negativity), one posits that frontal asymmetry is due to motivational direction (approach/withdrawal), and one posits that frontal asymmetry is due to a combination of emotional valence and motivational direction (positive-approach/negative-withdrawal). Because research had confounded emotional valence and motivational direction, the theoretical explanation was muddled. Solely supporting the motivational direction model, recent research has revealed that anger and cognitive dissonance, emotions with negative valence and approach motivational tendencies, are related to relatively greater left frontal activity.

A Deep Learning Approach to Human Activity Recognition Based on Single Accelerometer <=SEP=> In this paper, we propose an acceleration-based human activity recognition method using popular deep architecture, Convolution Neural Network (CNN). In particular, we construct a CNN model and modify the convolution kernel to adapt the characteristics of tri-axial acceleration signals. Also, for comparison, we use some widely used methods to accomplish the recognition task on the same dataset. The large dataset we constructed consists of 31688 samples from eight typical activities. The experiment results show that the CNN works well, which can reach an average accuracy of 93.8% without any feature extraction methods.

Motives for using Facebook, patterns of Facebook activities, and late adolescents' social adjustment to college. <=SEP=> Previous studies have confirmed that Facebook, the leading social networking site among young people, facilitates social connections among college students, but the specific activities and motives that foster social adjustment remain unclear. This study examined associations between patterns of Facebook activity, motives for using Facebook, and late adolescents' social adjustment to the college environment. Anonymous self-report survey data from 193 mostly European American students (M age = 20.32; 54 % female) attending a major Midwestern university indicated that motives and activity patterns were associated directly with social adjustment, but the association between one activity, status updating, and social adjustment also was moderated by the motive of relationship maintenance. Findings provide a more comprehensive portrait of how Facebook use may foster or inhibit social adjustment in college.

The eyeBook – Using Eye Tracking to Enhance the Reading Experience <=SEP=> Introduction A rapid development of eye tracking technology has been observed in recent years. Today’s eye trackers can determine the current focus point of the eye precisely while being relatively unobtrusive in their application. Also, a variety of research and commercial groups has been working on this technology, and there is a growing interest for such devices on the market. Eye tracking has great potential and it can be assumed that it will advance further and might become a widespread technology used at a large number of personal or office computer workplaces. Approaches using simple webcams for eye tracking already exist, for example webcams integrated into laptop computers by default. Thus, they allow for new kinds of applications using eye gaze data. However, not only eye tracking technology is advancing rapidly to an easily usable state. Additionally, during the past 100 years researchers gathered a considerable amount of knowledge on eye movements, why and how they occur, and what they might mean. So, today, we have the technology and knowledge for tracking and analyzing eye movements, making an excellent starting point for sophisticated interactive gaze-based applications. Naive approaches where gaze data is directly employed for interacting with the system, e. g., pressing buttons on the screen with the “blink of an eye” generally have serious problems. Because the eyes are organs used for perceiving the world and not for manipulating the world, it is hard and against human nature to control eye movements deliberately. However, a highly promising approach is just to observe eye movements of the user during his or her daily work in front of the computer, to infer user intentions based on eye movement behavior, and to provide assistance where helpful. Gaze can be seen as a proxy for the user’s attention, and eye movements are known to be usually tightly coupled with cognitive processes in the brain, so that a great deal about those processes can be observed by eye tracking. For example, by interpreting eye movements, reading behavior of the user can be detected, which most likely entails cognitive processes of understanding with regard to the currently read text. In this paper we are focusing particularly on reading behavior since reading is probably the most common activity of knowledge workers sitting in front of a computer screen. We present an algorithm for online reading detection based on eye tracking data and introduce an application for assisted and augmented reading called the eyeBook. The idea behind the eyeBook is to create an interactive and entertaining reading experience. The system observes, which text parts are currently being read by the user on the screen and generates appropriate effects such as playing sounds, presenting

CRM Strategies for A Small-Sized Online Shopping Mall Based on Association Rules and Sequential Patterns <=SEP=> Data mining has a tremendous contribution to the extraction of knowledge and information which have been hidden in a large volume of data. This study has proposed customer relationship management (CRM) strategies for a small-sized online shopping mall based on association rules and sequential patterns obtained by analyzing the transaction data of the shop. We first defined the VIP customer in terms of recency, frequency and monetary value. Then, we developed a model which classifies customers into VIP or non-VIP, using various techniques such as decision tree, artificial neural network and bagging with each of these as a base classifier. Last, we identified association rules and sequential patterns from the transactions of VIPs, and then these rules and patterns were utilized to propose CRM strategies for the online shopping mall.

Research of Workflow Access Control Strategy based on Trust <=SEP=> The traditional workflow access control strategy has often found to be inadequate to detect and restrain malicious behavior effectively. With the aim to solve this problem, this paper presents a new workflow access control model based on trust, and a new access control strategy with an authorization process. This strategy introduces user behavior evaluation, trust computation and role hierarchy into role access control strategy. Through the trust computation of user behavior, it can dynamically adjust user's role and permissions, realizing the dynamic authorization process. Theory analysis and simulation experiments show that this access control strategy is more sensitive in dynamic authorization, and it has fine-grained trust computation. Also this strategy can detect malicious behaviors in time, effectively restraining malicious behavior harming the system so enhancing the security of the system.

Design of Metal Interconnects for Stretchable Electronic Circuits using Finite Element Analysis <=SEP=> In this work, the design of flexible and stretchable interconnections is presented. These interconnections are done by embedding sinuous electroplated metallic wires in a stretchable substrate material. A silicone material was chosen as substrate because of its low stiffness and high elongation before break. Common metal conductors used in the electronic industry have very limited elastic ranges; therefore a metallization design is crucial to allow stretchability of the conductors going up to 100%. Different configurations were simulated and compared among them and based on these results, a horseshoe like shape was suggested. This design allows a large deformation with the minimum stress concentration. Moreover, the damage in the metal is significantly reduced by applying narrow metallization schemes. In this way, each conductor track has been split in four parallel lines of 15 mum and 15 mum space in order to improve the mechanical performance without limiting the electrical characteristics. Compared with the single copper or gold trace, the calculated stress was reduced up to 10 times.

Using a Graph-based Coherence Model in Document-Level Machine Translation <=SEP=> Although coherence is an important aspect of any text generation system, it has received little attention in the context of machine translation (MT) so far. We hypothesize that the quality of document-level translation can be improved if MT models take into account the semantic relations among sentences during translation. We integrate the graph-based coherence model proposed by Mesgar and Strube (2016) with Docent1 (Hardmeier et al., 2012; Hardmeier, 2014) a document-level machine translation system. The application of this graph-based coherence modeling approach is novel in the context of machine translation. We evaluate the coherence model and its effects on the quality of the machine translation. The result of our experiments shows that our coherence model slightly improves the quality of translation in terms of the average Meteor score.

Post-Processing OCR Text using Web-Scale Corpora <=SEP=> We introduce a (semi-)automatic OCR post-processing system that utilizes web-scale linguistic corpora in providing high-quality correction. This paper is a comprehensive system overview with the focus on the computational procedures, applied linguistic analysis, and processing optimization.

Characteristics of personal space during obstacle circumvention in physical and virtual environments. <=SEP=> It is not known how the flexible protective zone maintained around oneself during locomotion (personal space or PS; see [Gérin-Lajoie M, Richards CL, McFadyen BJ. The negotiation of stationary and moving obstructions during walking: anticipatory locomotor adaptations and preservation of personal space. Motor Control 2005;9:242-69]) is modulated with walking speed, whether both sides of the PS are symmetrical, and whether the circumvention of physical and virtual obstructions elicit the same use of such PS. Personal space was measured in ten adults as they circumvented a cylindrical obstacle that was stationary within their path. Both left and right passes were performed at natural self-selected, slow and fast walking speeds. The same circumvention task was also performed at natural speeds in an immersive virtual environment (VE) replicating the same obstruction scenario. The shape and size of PS were maintained across walking speeds, and a smaller PS was generally observed on the dominant side. The general shape and lateral bias of the PS were preserved in the VE while its size was slightly increased. The systematic behavior across walking speeds and types of environment and the lateral bias suggest that PS is used to control navigation. This study deepens our understanding of normal adaptive walking behavior and has implications for the development of better tools for the assessment and retraining of locomotor capacity in different populations, from people with walking deficits to elite athletes. Since the PS behavior was shown to be robust in the VE used for this study, the virtual reality technology is proposed as a promising platform for the development of such assessment and retraining applications.

Multi-scale retinex for color image enhancement <=SEP=> The retinex is a human perception-based image processing algorithm which provides color constancy and dynamic range compression. We have previously reported on a single-scale retinex (SSR) and shown that it can either achieve color/lightness rendition or dynamic range compression, but not both simultaneously. We now present a multi-scale retinex (MSR) which overcomes this limitation for most scenes. Both color rendition and dynamic range compression are successfully accomplished except for some \patho-logical" scenes that have very strong spectral characteristics in a single band.

Vertical ridge augmentation in the esthetic zone. <=SEP=> The reconstruction of deficient alveolar ridges using vertical and/or horizontal guided bone regeneration techniques allows for ideal implant placement, which is crucial for function and also for esthetically successful outcomes. Unlike in the past, when meeting a patient's functional demands was sufficient, many patients now have greater expectations from their implant restoration. Hence, it is no longer enough simply to restore the edentulous space with a functioning tooth or teeth. It has been suggested that patients now measure their final restoration using the contralateral natural tooth as the gold standard. Both subjective and objective levels of patient information on dental implants have increased significantly in the last decade. As a result of this demand, implant literature has inherited and developed specific esthetic parameters and patient-centered outcomes from studies in the restorative field. Unfortunately, studies reporting on guided bone regeneration in the esthetic zone entirely lack such parameters and outcomes. Currently, there is a strong need for a consensus on objective and well-defined parameters to assess the esthetics in bone regeneration and subsequently on implant dentistry.

A Lossy Image Codec Based on Index Coding <=SEP=> In this paper we propose a new lossy image codec based on index coding. Both J. Shapiro’s embedded zerotree wavelet algorithm, and A. Said and W. A. Pearlman’s codetree algorithm (which is the state-of-the-art method today) use spatial orientation tree structures to implicitly locate the significant wavelet transform coefficients. Here a direct approach to find the positions of these significant coefficients is presented. The new algorithm combines the discrete wavelet transform, differential coding, variable-length coding of integers, ordered bit plane transmission, and adaptive arithmetic coding. The encoding can be stopped at any point, which allows a target rate or distortion metric to be met exactly. The bits in the bit stream are generated in the order of importance, yielding a fully embedded code to successively approximate the original image source; thus it’s well suited for progressive image transmission. The decoder can also terminate the decoding at any point, and produce a lower bit rate reconstruction image. Our algorithm is very simple in its form (which will make the encoding and decoding very fast), requires no training of any kind or prior knowledge of image sources, and has a clear geometric structure. The image coding results of it are quite competitive with almost all previous reported image compression algorithms on standard test images. 456 1068.0314/96$5.00

Extracting lines of curvature from noisy point clouds <=SEP=> We present a robust framework for extracting lines of curvature from point clouds. First, we show a novel approach to denoising the input point cloud using robust statistical estimates of surface normal and curvature which automatically rejects outliers and corrects points by energy minimization. Then the lines of curvature are constructed on the point cloud with controllable density. Our approach is applicable to surfaces of arbitrary genus, with or without boundaries, and is statistically robust to noise and outliers while preserving sharp surface features. We show our approach to be effective over a range of synthetic and real-world input datasets with varying amounts of noise and outliers. The extraction of curvature information can benefit many applications in CAD, computer vision and graphics for point cloud shape analysis, recognition and segmentation. Here, we show the possibility of using the lines of curvature for feature-preserving mesh construction directly from noisy point clouds.

Qualitative clinical evaluation of scapular dysfunction: a reliability study. <=SEP=> The purpose of this study was to determine the intrarater and interrater reliability of a clinical evaluation system for scapular dysfunction. No commonly accepted terminology presently exists for describing the abnormal dynamic scapular movement patterns that are commonly associated with shoulder injury. A method of observation was devised for clinical evaluation of scapular dysfunction. Blinded evaluators (2 physicians and 2 physical therapists) were familiarized with the evaluation method of scapular movement patterns before viewing a videotape of 26 subjects with and without scapular dysfunction. Each evaluator was asked to categorize the predominant scapular movement pattern observed during bilateral humeral scaption and abduction motions. Reliability was assessed by a kappa coefficient. Intertester reliability (kappa = 0.4) was found to be slightly lower than intratester reliability (kappa = 0.5). These results indicate that, with refinement, this qualitative evaluation method may allow clinicians to standardize the categorization of dynamic scapular dysfunction patterns.

Visual Interactive Creation, Customization, and Analysis of Data Quality Metrics <=SEP=> During data preprocessing, analysts spend a significant part of their time and effort profiling the quality of the data along with cleansing and transforming the data for further analysis. While quality metrics—ranging from general to domain-specific measures—support assessment of the quality of a dataset, there are hardly any approaches to visually support the analyst in customizing and applying such metrics. Yet, visual approaches could facilitate users’ involvement in data quality assessment. We present MetricDoc, an interactive environment for assessing data quality that provides customizable, reusable quality metrics in combination with immediate visual feedback. Moreover, we provide an overview visualization of these quality metrics along with error visualizations that facilitate interactive navigation of the data to determine the causes of quality issues present in the data. In this article, we describe the architecture, design, and evaluation of MetricDoc, which underwent several design cycles, including heuristic evaluation and expert reviews as well as a focus group with data quality, human-computer interaction, and visual analytics experts.

Using Features From Pre-trained TimeNET For Clinical Predictions <=SEP=> Predictive models based on Recurrent Neural Networks (RNNs) for clinical time series have been successfully used for various tasks such as phenotyping, in-hospital mortality prediction, and diagnostics. However, RNNs require large labeled data for training and are computationally expensive to train. Pre-training a network for some supervised or unsupervised tasks on a dataset, and then fine-tuning via transfer learning for a related end-task can be an efficient way to leverage deep models for scenarios that lack in either computational resources or labeled data, or both. In this work, we consider an approach to leverage a deep RNN – namely TimeNet [Malhotra et al., 2017] – that is pre-trained on a large number of diverse publicly available time-series from UCR Repository [Chen et al., 2015]. TimeNet maps varyinglength time series to fixed-dimensional feature vectors and acts as an off-the-shelf feature extractor. TimeNet-based approach overcome the need for hand-crafted features, and allows for use of traditional easy-to-train and interpretable linear models for the end-task, while still leveraging the features from a deep neural network. Empirical evaluation of the proposed approach on MIMIC-III1 data suggests promising direction for future exploration: our results are comparable to existing benchmarks while our models require lesser training and hyperparameter tuning effort.

"You've Got E-Mail!" ... Shall I Deal With It Now? Electronic Mail From the Recipient's Perspective <=SEP=> This article considers the nature of e-mail from the recipient’s perspective—what the seeminglyfreeandeasycommunicationreallycosts therecipient. Informationgathered by electronic monitoring software is shown to be at odds with the results of an online survey of e-mail users’ perceptions of their e-mail experience—users drastically underestimate the disruptive effects of e-mail. The conclusion is that the constant monitoring of e-mail actually reduces productivity and that there is a need for increased power, control,andawarenessonthepartof thee-mail recipient toensure thate-mail remainsatool rather than a tyrant. It is necesssary to alert the user of the true cost of e-mail alerts.