Multiple Pages Research Articles

Centaur

Due to the high density storage demand coming from applications from different domains, 3D NAND flash is becoming a promising candidate to replace 2D NAND flash as the dominant non-volatile memory. However, denser 3D NAND presents various performance and reliability issues, which can be addressed by the 3D NAND specific full-sequence program (FSP) operation. The FSP programs multiple pages simultaneously to mitigate the performance degradation caused by the long latency 3D NAND baseline program operations. However, the FSP-enabled 3D NAND-based SSDs introduce lifetime degradation due to the larger write granularities accessed by the FSP. To address the lifetime issue, in this paper, we propose and experimentally evaluate Centaur, a heterogeneous 2D/3D NAND heterogeneous SSD, as a solution. Centaur has three main components: a lifetime-aware inter-NAND request dispatcher, a lifetime-aware inter-NAND work stealer, and a data migration strategy from 2D NAND to 3D NAND. We used twelve SSD workloads to compare Centaur against a state-of-the-art 3D NAND-based SSD with the same capacity. Our experimental results indicate that the SSD lifetime and performance are improved by 3.7x and 1.11x, respectively, when using our 2D/3D heterogeneous SSD.

Stress related to electronic health record (EHR) use among oncology trainees: A quality improvement study to optimize information gathering.

11015 Background: Poor EHR usability is associated with physician burnout [1]. Oncology providers are tasked with coordinating data from multiple sources/tabs within an EHR (biomarkers, genetic data, imaging, etc.) to make treatment plan decisions. It is hypothesized that oncology trainees will have decreased stress using an EHR tool to optimize data gathering. Methods: This single-institution quality improvement study aims to decrease the time and stress associated with navigating the EHR to review/document laboratory data among first-year clinical oncology fellows rotating on consult services at The Mount Sinai Hospital as measured by self-reported survey data. An EHR tool was built that pulls into the note a drop-down list of user-selectable laboratory panels grouped according to hematologic/oncologic disease presentations. A survey was sent to all fellows in the program to assess attitudes toward the EHR. All 1st-year fellows were given access to the EHR tool as they are the ones who primarily rotate on consult services. After 3 months, a follow-up survey was sent to 1st-year clinical fellows to assess satisfaction with the EHR intervention. Results: For the baseline survey, there are a total of 17 respondents (response rate 77%). 70.6% of fellows believe using the EHR increases their level of stress. The most prevalent reasons cited for frustrations with the EHR are “Hard to read other providers’ notes” (82.4%), “Gathering information from multiple different pages” (76.5%), “Increased documentation requirements” (76.5%), “Inability to quickly navigate the system” (64.7%). For the follow-up survey targeting 1st-year fellows, there are a total of 6 respondents (response rate 100%). 5 fellows tried the EHR tool at least once. Of those 5 fellows, 100% use it daily in clinical practice while on consult services, believe it makes notes more legible, believe it saves time spent in chart review, and believe it decreases their stress associated with navigating the EHR. Conclusions: A majority of clinical oncology fellows believe that the EHR contributes to their stress. The reasons cited suggest that interventions that optimize note-writing and chart-reviewing may decrease stress. The majority of fellows who used this EHR tool believe the tool improved their efficiency and stress levels. This suggests more widespread use of this tool among oncology clinicians may contribute to wellness although further studies to increase utilization must be taken.

Involving patients as key stakeholders in the design of cardiovascular implantable electronic device data dashboards: Implications for patient care.

BackgroundData from remote monitoring (RM) of cardiovascular implantable electronic devices (CIEDs) currently are not accessible to patients despite demand. The typical RM report contains multiple pages of data for trained technicians to read and interpret and requires a patient-centered approach to be curated to meet individual user needs.ObjectiveThe purpose of this study was to understand which RM data elements are important to patients and to gain design insights for displaying meaningful data in a digital dashboard.MethodsAdults with implantable cardioverter–defibrillators (ICDs) and pacemakers (PMs) participated in this 2-phase, user-centered design study. Phase 1 included a card-sorting activity to prioritize device data elements. Phase 2 included one-on-one design sessions to gather insights and feedback about a visual display (labels and icons).ResultsTwenty-nine adults (mean age 71.8 ± 11.6 years; 51.7% female; 89.7% white) participated. Priority data elements for both ICD and PM groups in phase 1 (n = 19) were related to cardiac episodes, device activity, and impedance values. Recommended replacement time for battery was high priority for the PM group but not the ICD group. Phase 2 (n = 10) revealed that patients would like descriptive, nontechnical terms to depict the data and icons that are intuitive and informative.ConclusionThis user-centered design study demonstrated that patients with ICDs and PMs were able to prioritize specific data from a comprehensive list of data elements that they had never seen before. This work contributes to the goal of sharing RM data with patients in a way that optimizes the RM feature of CIEDs for improving patient outcomes and clinical care.

REACT: Scalable and High-Performance Regular Expression Pattern Matching Accelerator for In-Storage Processing

This article proposes REACT, a regular expression matching accelerator, which can be embedded in a modern Solid-State Drive (SSD) and a novel data access scheduling algorithm for high matching throughput. Specifically, REACT, including our data access scheduling algorithm, increases the utilization of SSD and the degree of internal memory parallelism for pattern matching processes. While the low-level flash exhibits long latency, modern SSDs in practice achieve high I/O performance by utilizing the massive internal parallelism at the system-level. However, exploiting the parallelism is limited for pattern matching since the sub-blocks, which constitute an input data and can be placed in multiple flash pages, should be tested in a sequence to process the input correctly. This limitation can induce low utilization of the accelerator. To address this challenge, the proposed REACT simultaneously processes multiple input streams with a parallel processing architecture to maximize matching throughput by hiding the long and irregular latency. The scheduling algorithm finds a data stream which requires a sub-block in closest time and prioritizes the access request to reduce the data stall of REACT. REACT achieves maximum 22.6 percent of matching throughput improvement on a 16-channel high-performance SSD compared to the accelerator without the proposed scheduling algorithm.

Azimuthal multiplexing 3D diffractive optics

Diffractive optics have increasingly caught the attention of the scientific community. Classical diffractive optics are 2D diffractive optical elements (DOEs) and computer-generated holograms (CGHs), which modulate optical waves on a solitary transverse plane. However, potential capabilities are missed by the inherent two-dimensional nature of these devices. Previous work has demonstrated that extending the modulation from planar (2D) to volumetric (3D) enables new functionalities, such as generating space-variant functions, multiplexing in the spatial or spectral domain, or enhancing information capacity. Unfortunately, despite significant progress fueled by recent interest in metasurface diffraction, 3D diffractive optics still remains relatively unexplored. Here, we introduce the concept of azimuthal multiplexing. We propose, design, and demonstrate 3D diffractive optics showing this multiplexing effect. According to this new phenomenon, multiple pages of information are encoded and can be read out across independent channels by rotating one or more diffractive layers with respect to the others. We implement the concept with multilayer diffractive optical elements. An iterative projection optimization algorithm helps solve the inverse design problem. The experimental realization using photolithographically fabricated multilevel phase layers demonstrates the predicted performance. We discuss the limitations and potential of azimuthal multiplexing 3D diffractive optics.

Requirements-driven data warehouse design based on enhanced pivot tables

The design of data warehouses (DWs) is based on both their data sources and users’ requirements. The more closely the DW multidimensional schema reflects the stakeholders’ needs, the more effectively they will make use of the DW content for their OLAP analyses. Thus, considerable attention has been given in the literature to DW requirements analysis, including requirements elicitation, specification and validation. Unfortunately, traditional approaches are based on complex formalisms that cannot be used with decision makers who have no previous experience with DWs and OLAP. This forces a sharp separation between elicitation and specification. To cope with this problem, we propose a new requirements analysis process where pivot tables, a well-known representation for multidimensional data often used by decision makers, are enhanced to be used both for elicitation and as a specification formalism. A pivot table is a two-dimensional spreadsheet that supports the analyses of multidimensional data by nesting several dimensions on the x- or y-axis and displaying data on multiple pages. The requirements analysis process we propose is iterative and relies on both unstructured and structured interviews; particular attention is given to enable the design of irregular multidimensional schemata, which are often present in real-world DWs but can hardly be understood by unskilled users. Finally, we validate our proposal using a real case study in the biodiversity domain.

Paediatric Free Open Access Meducation (FOAM): behaviours, trends and implications

IntroductionFree Open Access Meducation (FOAM) describes online resources assisting learning in medicine. Little is known about users or their behaviours.MethodsUsing Google Analytics for a popular FOAM site (www.paedatricfoam.com), we explored...

TLB Coalescing for Multi-Grained Page Migration in Hybrid Memory Systems

Superpages have long been proposed to enlarge the coverage of translation lookaside buffer (TLB). They are extremely beneficial for reducing address translation overhead in big memory systems, such as hybrid memory systems that composed of DRAM and non-volatile memories (NVMs). However, superpages conflict with fine-grained memory migration, one of the key techniques in hybrid memory systems to improve performance and energy efficiency. Fine-grained page migrations usually require to splinter superpages, mitigating the benefit of TLB hardware for superpages. In this paper, we present Tamp, an efficient memory management mechanism to support multiple page sizes in hybrid memory systems. We manage large-capacity NVM using superpages, and use a relatively small size of DRAM to cache hot base pages within the superpages. We find that there are remarkable contiguity exist for hot base pages in superpages. In response, we bind those contiguous hot pages together and migrate them to DRAM. We also propose multi-grained TLBs to coalesce multiple page address translations into a single TLB entry. Our experimental results show that Tamp can significantly reduce TLB misses by 62.4% on average, and improve application performance (IPC) by 16.2%, compared to a page migration policy without TLB coalescing support.

Automatically Discovering Relevant Images From Web Pages

Web pages contain irrelevant images along with relevant images. The classification of these images is an error-prone process due to the number of design variations of web pages. Using multiple web pages provides additional features that improve the performance of relevant image extraction. Traditional studies use the features extracted from a single web page. However, in this study, we enhance the performance of relevant image extraction by employing the features extracted from different web pages consisting of standard news, galleries, video pages, and link pages. The dataset obtained from these web pages contains 100 different web pages for each 200 online news websites from 58 different countries. For discovering relevant images, the most straightforward approach extracts the largest image on the web page. This approach achieves a 0.451 F-Measure score as a baseline. Then, we apply several machine learning methods using features in this dataset to find the most suitable machine learning method. The best f-Measure score is 0.822 using the AdaBoost classifier. Some of these features have been utilized in previous web data extraction studies. To the best of our knowledge, 15 new features are proposed for the first time in this study for discovering the relevant images. We compare the performance of the AdaBoost classifier on different feature sets. The proposed features improve the f-Measure by 35 percent. Besides, using only the cache feature, which is the most prominent feature, corresponds to 7 percent of this improvement.

Sullivan, Who Is Always Too Loud by Micol Ostow

Reviewed by: Sullivan, Who Is Always Too Loud by Micol Ostow Natalie Berglind Ostow, Micol Sullivan, Who Is Always Too Loud; illus. by Brian Biggs. Roaring Brook, 2020 [40p] Trade ed. ISBN 9781250307729 $18.99 Reviewed from digital galleys Ad 3-6 yrs Sullivan is a very loud boy who can’t seem to quiet down, even when he wakes the family baby, makes the dog start barking, and disturbs Mr. Jenkins downstairs. He is sent to the Peace Place at school and tries to explain how his loudness works: “I have loudness. In my body. Bubbling up. Always. When Sullivan channels his loudness to fetch a classmate across the playground who can’t hear the teacher, he is praised by his teacher for using his volume for the forces of good. Biggs’ art emulates children’s doodles, with a robust naïveté to the draftsmanship: the characters have huge heads and wide mouths, and the grainy colored-pencil textures contrast with the white backgrounds. Sullivan’s face literally bubbles with his loudness until it explodes into a carbonated word that spans multiple pages, and negative space is well utilized to emphasize the sheer volume of Sullivan’s outbursts. Sullivan’s internal impulses are effectively conveyed, but there’s a focus on conformity rather than understanding that’s somewhat troubling in light of Sullivan’s apparent neurodiversity. However, viewers may find some comfort in the lesson that the behavior that elicits discomfort may in other situations save the day. Copyright © 2020 The Board of Trustees of the University of Illinois

Using Internet videos to learn about controversies: Evaluation and integration of multiple and multimodal documents by primary school students

In many Internet videos authors appear in front of the camera to present their particular view on a topic. Given the high consumption rate of Internet videos by teenagers, we explored the pros and cons of using these videos to learn about complex topics, compared to learning from textual web pages. Specifically, we studied how 207 primary school students (grades 4–6) evaluated and integrated multiple and multimodal web pages (text or video) while learning about the pros and cons of bottled water. Results showed no major role of modality in students' source memory, as measured by citations in their responses to an integration question and their memory for sources. Nevertheless, modality exerted a strong influence on students' beliefs about the topic because, after the study period, they defended the views described in the videos more than those presented in texts. Finally, modality tended to influence students’ integration, with participants who learned from two textual webpages including almost twice as many inferences in their responses as those who learned from two videos. We discuss the results in light of current theories of evaluation and integration of multimodal information and (shallow) digital reading, and we elaborate on the pros and cons of using Internet videos in Primary School.

Multi-page Labeling of Small-screen Maps with a Graph-coloring Approach

Abstract. Annotating small-screen maps with additional content such as labels for points of interest is a highly challenging problem that requires new algorithmic solutions. A common labeling approach is to select a maximum-size subset of all labels such that no two labels constitute a graphical conflict and to display only the selected labels in the map. A disadvantage of this approach is that a user often has to zoom in and out repeatedly to access all points of interest in a certain region. Since this can be very cumbersome, we suggest an alternative approach that allows the scale of the map to be kept fixed. Our approach is to distribute all labels on multiple pages through which the user can navigate, for example, by swiping the pages from right to left. We in particular optimize the assignment of the labels to pages such that no page contains two conflicting labels, more important labels appear on the first pages, and sparsely labeled pages are avoided. Algorithmically, we reduce this problem to a weighted and constrained graph coloring problem based on a graph representing conflicts between labels such that an optimal coloring of the graph corresponds to a multi-page labeling. We propose a simple greedy heuristic that is fast enough to be deployed in web-applications. We evaluate the quality of the obtained labelings by comparing them with optimal solutions, which we obtain by means of integer linear programming formulations. In our evaluation on real-world data we particularly show that the proposed heuristic achieves near-optimal solutions with respect to the chosen objective function and that it substantially improves the legibility of the labels in comparison to the simple strategy of assigning the labels to pages solely based on the labels’ weights.

Modular verification of web page layout

Automated verification can ensure that a web page satisfies accessibility, usability, and design properties regardless of the end user's device, preferences, and assistive technologies. However, state-of-the-art verification tools for layout properties do not scale to large pages because they rely on whole-page analyses and must reason about the entire page using the complex semantics of the browser layout algorithm. This paper introduces and formalizes modular layout proofs. A modular layout proof splits a monolithic verification problem into smaller verification problems, one for each component of a web page. Each component specification can use rely/guarantee-style preconditions to make it verifiable independently of the rest of the page and enabling reuse across multiple pages. Modular layout proofs scale verification to pages an order of magnitude larger than those supported by previous approaches. We prototyped these techniques in a new proof assistant, Troika. In Troika, a proof author partitions a page into components and writes specifications for them. Troika then verifies the specifications, and uses those specifications to verify whole-page properties. Troika also enables the proof author to verify different component specifications with different verification tools, leveraging the strengths of each. In a case study, we use Troika to verify a large web page and demonstrate a speed-up of 13--1469x over existing tools, taking verification time from hours to seconds. We develop a systematic approach to writing Troika proofs and demonstrate it on 8 proofs of properties from prior work to show that modular layout proofs are short, easy to write, and provide benefits over existing tools.

A Study of Current State of Privacy Policies in Social Networks

Objective: The study aims is to bring forward the hardship in understanding the current scenario of privacy policies in social networks from a user/customer’s perspective. Methods: To understand how privacy policies have evolved through time, two of the biggest and most influential social networks, namely ‘google.com’ and ‘twitter.com’ have been studied. Their privacy policies have been analyzed from the time of inception, carefully taking each change into account. Other metrics like word and technical jargon usage has also been considered. Findings: Policies are getting harder to read with time. This includes increasing amount of ambiguous statements like, ‘we may’, more technical language, decreasing number of examples, and exponentially increasing length of the policies. There’s also the practice of splitting the privacy policy over multiple pages that social networks are using in order to make their policies small and readable, which has a negative effect. Applications/improvements: The study restarts an open discussion on how policies should evolve with evolving inclusion of technology in people’s lives. As people rely more on technology than ever, our study shows that rethinking the way we share privacy policies is required. Keywords: Open Social Networks, Privacy, Privacy Policy, Security, Terms of Service, User Awareness.

Web Scraping Using R

The ubiquitous use of the Internet in daily life means that there are now large reservoirs of data that can provide fresh insights into human behavior. One of the key barriers preventing more researchers from utilizing online data is that they do not have the skills to access the data. This Tutorial addresses this gap by providing a practical guide to scraping online data using the popular statistical language R. Web scraping is the process of automatically collecting information from websites. Such information can take the form of numbers, text, images, or videos. This Tutorial shows readers how to download web pages, extract information from those pages, store the extracted information, and do so across multiple pages of a website. A website has been created to assist readers in learning how to web-scrape. This website contains a series of examples that illustrate how to scrape a single web page and how to scrape multiple web pages. The examples are accompanied by videos describing the processes involved and by exercises to help readers increase their knowledge and practice their skills. Example R scripts have been made available at the Open Science Framework.

DCADE: divide and conquer alignment with dynamic encoding for full page data extraction

In this paper, we consider the problem of full schema induction from either multiple list pages or singleton pages with the same template. Existing approaches do not work well for this problem because they use fixed abstraction schemes that are suitable for data-rich detection, but they are not appropriate for small records and complex data found in other sections. We propose an unsupervised full schema web data extraction via Divide-and-Conquer Alignment with Dynamic Encoding (DCADE for short). We define the Content Equivalence Class (CEC) and Typeset Equivalence Class (TEC) based on leaf node content. We then combine HTML attributes (i.e., id and class) in the paths for various levels of encoding, so that the proposed algorithm can align leaf nodes by exploring patterns at various levels from specific to general. We conducted experiments on 49 real-world websites used in TEX and ExAlg. The proposed DCADE achieved a 0.962 F1 measure for non-recordset data extraction (denoted by FD), and a 0.936 F1 measure for recordset data extraction (denoted by FS), which outperformed other page-level web data extraction methods, i.e., DCA (FD= 0.660), TEX (FD= 0.454 and FS= 0.549), RoadRunner (FD= 0.396 and FS= 0.330), and UWIDE (FD= 0.260 and FS= 0.081).

Cricket Scrapper: A Tool Developed to Extract Cricket Players Data

Objective: To develop a generic web scraping tool that can extract cricket player’s data for research purposes. Methods: Cricket as a sport has greatly plagued the world with its importance and entertaining nature. For a team the performance of each individual player has great importance for their victory. In this paper, an automated data scraping mechanism is devised to extract and store data of cricket players from a cricket website. This website contain web-enabled database of cricket players where the data is contained on multiple web pages and proposed mechanism is designed to extract this variation as per requirement of researcher. Findings: This tool will be helpful for data scientists/researchers to extract dataset as per their requirements. Applications: This generic tool will be applicable in analyzing the formation of cricket team as per rating of individual player. Moreover, this tool will contribute as an essential ingredient for research related in the domain of cricket analytics. Keywords: Cricket Analytics, Information Extraction, Scrapper, Web Content Extraction

Using an Exponential Random Graph Model to Recommend Academic Collaborators

Academic collaboration networks can be formed by grouping different faculty members into a single group. Grouping these faculty members together is a complex process that involves searching multiple web pages in order to collect and analyze information, and establishing new connections among prospective collaborators. A recommender system (RS) for academic collaborations can help reduce the time and effort required to establish a new collaboration. Content-based recommendation system make recommendations based on similarity without taking social context into consideration. Hybrid recommender systems can be used to combine similarity and social context. In this paper, we propose a weighting method that can be used to combine two or more social context factors in a recommendation engine that leverages an exponential random graph model (ERGM) based on historical network data. We demonstrate our approach using real data from collaborations with faculty members at the College of Computer and Information Sciences (CCIS) in Saudi Arabia. Our results demonstrate that weighting social context factors helps increase recommendation accuracy for new users.

An Editor's Top Ten

An Editor's Top Ten

Interactive web-based visualization and sharing of phylogenetic trees using phylogeny.IO.

Traditional static publication formats make visualization, exploration, and sharing of massive phylogenetic trees difficult. A phylogenetic study often involves hundreds of taxa, and the resulting tree has to be split across multiple journal pages, or be shrunk onto one, which jeopardizes legibility. Furthermore, additional data layers, such as species-specific information or time calibrations are often displayed in separate figures, making the entire picture difficult for readers to grasp. Web-based technologies, such as the Data Driven Document (D3) JavaScript library, were created to overcome such challenges by allowing interactive displays of complex data sets. The new phylogeny.IO web server (https://phylogeny.io) overcomes this issue by allowing users to easily import, annotate, and share interactive phylogenetic trees. It allows a range of static (e.g. such as shapes and colors) and dynamic (e.g. pop-up text and images) annotations. Annotated trees can be saved on the server for subsequent modification or they may be shared as IFrame HTML objects, easily embeddable in any web page. The principal goal of phylogeny.IO is not to produce publication-ready figures, but rather to provide a simple and intuitive annotation interface that allows easy and rapid sharing of figures in blogs, lecture notes, press releases, etc.