nils-herrmann/clean_scirepeval huggingface dataset:
Identifying Meaningful Citations

We introduce the novel task of identifying important citations in scholarly literature, i.e., citations that indicate that the cited work is used or extended in the new effort. We believe this task is a crucial component in algorithms that detect and follow research topics and in methods that measure the quality of publications. We model this task as a supervised classification problem at two levels of detail: a coarse one with classes (important vs. non-important), and a more detailed one with four importance classes. We annotate a dataset of approximately 450 citations with this information, and release it publicly. We propose a supervised classification approach that addresses this task with a battery of features that range from citation counts to where the citation appears in the body of the paper, and show that, our approach achieves a precision of 65% for a recall of 90%.
Locally adaptive discounting in multi sensor occupancy grid fusion

Obstacle fusion algorithms usually perform obstacle association and gating in order to improve the obstacle position if it was detected by multiple sensors. However, this strategy is not common in multi sensor occupancy grid fusion. Thus, the quality of the fused grid, in terms of obstacle position accuracy, largely depends on the sensor with the lowest accuracy. In this paper an efficient method to associate obstacles across sensor grids is proposed. Imprecise sensors are discounted locally in cells where a more accurate sensor, that detected the same obstacle, derived free space. Furthermore, fixed discount factors to optimize false negative and false positive rates are used. Because of its generic formulation with the covariance of each sensor grid, the method is scalable to any sensor setup. The quantitative evaluation with a highly precise navigation map shows an increased obstacle position accuracy compared to standard evidential occupancy grid fusion.
Efficacy of a near-infrared light device in pediatric intravenous cannulation: a randomized controlled trial.

To determine whether the use of a near-infrared light venipuncture aid (VeinViewer; Luminetx Corporation, Memphis, Tenn) would improve the rate of successful first-attempt placement of intravenous (IV) catheters in a high-volume pediatric emergency department (ED).



Patients younger than 20 years with standard clinical indications for IV access were randomized to have IV placement by ED nurses (in 3 groups stratified by 5-year blocks of nursing experience) using traditional methods (standard group) or with the aid of the near-infrared light source (device group). If a vein could not be cannulated after 3 attempts, patients crossed over from one study arm to the other, and study nurses attempted placement with the alternative technique. The primary end point was first-attempt success rate for IV catheter placement. After completion of patient enrollment, a questionnaire was completed by study nurses as a qualitative assessment of the device.



A total of 123 patients (median age, 3 years) were included in the study: 62 in the standard group and 61 in the device group. There was no significant difference in first-attempt success rate between the standard (79.0%, 95% confidence interval [CI], 66.8%-88.3%) and device (72.1%, 95% CI, 59.2%-82.9%) groups. Of the 19 study nurses, 14 completed the questionnaire of whom 70% expressed neutral or unfavorable assessments of the device in nondehydrated patients without chronic underlying medical conditions and 90% found the device a helpful tool for patients in whom IV access was difficult.



First-attempt success rate for IV placement was nonsignificantly higher without than with the assistance of a near-infrared light device in a high-volume pediatric ED. Nurses placing IVs did report several benefits to use of the device with specific patient groups, and future research should be conducted to demonstrate the role of the device in these patients.
Socket augmentation: rationale and technique.

The consequences of exodontia include alveolar bone resorption and ultimately atrophy to basal bone of the edentulous site/ridges. Ridge resorption proceeds quickly after tooth extraction and significantly reduces the possibility of placing implants without grafting procedures. The aims of this article are to describe the rationale behind alveolar ridge augmentation procedures aimed at preserving or minimizing the edentulous ridge volume loss. Because the goal of these approaches is to preserve bone, exodontia should be performed to preserve as much of the alveolar process as possible. After severance of the supra- and subcrestal fibrous attachment using scalpels and periotomes, elevation of the tooth frequently allows extraction with minimal socket wall damage. Extraction sockets should not be acutely infected and be completely free of any soft tissue fragments before any grafting or augmentation is attempted. Socket bleeding that mixes with the grafting material seems essential for success of this procedure. Various types of bone grafting materials have been suggested for this purpose, and some have shown promising results. Coverage of the grafted extraction site with wound dressing materials, coronal flap advancement, or even barrier membranes may enhance wound stability and an undisturbed healing process. Future controlled clinical trials are necessary to determine the ideal regimen for socket augmentation.
What's Different about Social Media Networks? A Framework and Research Agenda

Recent years have witnessed the rapid proliferation and widespread adoption of a new class of information technologies, commonly known as social media. Researchers often rely on social network analysis (SNA) in attempting to understand these technologies, often without considering how the novel capabilities of social media platforms might affect the underlying theories of SNA, which were developed primarily through studies of offline social networks. This article outlines several key differences between traditional offline social networks and online social media networks by juxtaposing an established typology of social network research with a well-regarded definition of social media platforms that articulates four key features. The results show that at four major points of intersection, social media has considerable theoretical implications for SNA. In exploring these points of intersection, this study outlines a series of theoretically distinctive research questions for SNA in social media contexts. These points of intersection offer considerable opportunities for researchers to investigate the theoretical implications introduced by social media and lay the groundwork for a robust social media agenda potentially spanning multiple disciplines. FORTHCOMING AT MIS QUARTERLY, THEORY AND REVIEW***
Data Mining; A Conceptual Overview

This tutorial provides an overview of the data mining process. The tutorial also provides a basic understanding of how to plan, evaluate and successfully refine a data mining project, particularly in terms of model building and model evaluation. Methodological considerations are discussed and illustrated. After explaining the nature of data mining and its importance in business, the tutorial describes the underlying machine learning and statistical techniques involved. It describes the CRISP-DM standard now being used in industry as the standard for a technology-neutral data mining process model. The paper concludes with a major illustration of the data mining process methodology and the unsolved problems that offer opportunities for research. The approach is both practical and conceptually sound in order to be useful to both academics and practitioners.
Synchronous data flow

Data flow is a natural paradigm for describing DSP applications for concurrent implementation on parallel hardware. Data flow programs for signal processing are directed graphs where each node represents a function and each arc represents a signal path. Synchronous data flow (SDF) is a special case of data flow (either atomic or large grain) in which the number of data samples produced or consumed by each node on each invocation is specified a priori. Nodes can be scheduled statically (at compile time) onto single or parallel programmable processors so the run-time overhead usually associated with data flow evaporates. Multiple sample rates within the same system are easily and naturally handled. Conditions for correctness of SDF graph are explained and scheduling algorithms are described for homogeneous parallel processors sharing memory. A preliminary SDF software system for automatically generating assembly language code for DSP microcomputers is described. Two new efficiency techniques are introduced, static buffering and an extension to SDF to efficiently implement conditionals.
A Self-Organizing Strategy for Power Flow Control of Photovoltaic Generators in a Distribution Network

The focus of this paper is to develop a distributed control algorithm that will regulate the power output of multiple photovoltaic generators (PVs) in a distribution network. To this end, the cooperative control methodology from network control theory is used to make a group of PV generators converge and operate at certain (or the same) ratio of available power, which is determined by the status of the distribution network and the PV generators. The proposed control only requires asynchronous information intermittently from neighboring PV generators, making a communication network among the PV units both simple and necessary. The minimum requirement on communication topologies is also prescribed for the proposed control. It is shown that the proposed analysis and design methodology has the advantages that the corresponding communication networks are local, their topology can be time varying, and their bandwidth may be limited. These features enable PV generators to have both self-organizing and adaptive coordination properties even under adverse conditions. The proposed method is simulated using the IEEE standard 34-bus distribution network.
ChipWhisperer: An Open-Source Platform for Hardware Embedded Security Research

This paper introduces a complete side channel analysis toolbox, inclusive of the analog capture hardware, target device, capture software, and analysis software. The highly modular design allows use of the hardware and software with a variety of existing systems. The hardware uses a synchronous capture method which greatly reduces the required sample rate, while also reducing the data storage requirement, and improving synchronization of traces. The synchronous nature of the hardware lends itself to fault injection, and a module to generate glitches of programmable width is also provided. The entire design (hardware and software) is open-source, and maintained in a publicly available repository. Several long example capture traces are provided for researchers looking to evaluate standard cryptographic implementations.
A multi-camera vision system for fall detection and alarm generation

In-house video surveillance can represent an excellent support for people with some difficulties (e.g., elderly or disabled people) living alone and with a limited autonomy. New hardware technologies and in particular digital cameras are now affordable and they have recently gained credit as tools for (semi-)automatically assuring the people’s safety. In this paper a multi camera vision system for detecting and tracking people and recognizing dangerous behaviors and events such as a fall is presented. In such a situation a suitable alarm can be sent, for example by means of a SMS. A novel technique of warping people’s silhouette is proposed to exchange visual information between partially overlapped cameras whenever a camera handover occurs. Finally, a multi-client and multi-threaded transcoding video server delivers live video streams to operators/remote users in order to check the validity of a received alarm. Semantic and event based transcoding algorithms are used to optimize the bandwidth usage. A tworoom setup has been created in our lab to test the performance of the overall system and some of the obtained results are reported.
360° Video Viewing Dataset in Head-Mounted Virtual Reality

360° videos and Head-Mounted Displays (HMDs) are geing increasingly popular. However, streaming 360° videos to HMDs is challenging. is is because only video content in viewers’ Fieldof-Views (FoVs) is rendered, and thus sending complete 360° videos wastes resources, including network bandwidth, storage space, and processing power. Optimizing the 360° video streaming to HMDs is, however, highly data and viewer dependent, and thus dictates real datasets. However, to our best knowledge, such datasets are not available in the literature. In this paper, we present our datasets of both content data (such as image saliency maps and motion maps derived from 360° videos) and sensor data (such as viewer head positions and orientations derived from HMD sensors). We put extra eorts to align the content and sensor data using the timestamps in the raw log les. e resulting datasets can be used by researchers, engineers, and hobbyists to either optimize existing 360° video streaming applications (like rate-distortion optimization) and novel applications (like crowd-driven cameramovements). We believe that our dataset will stimulate more research activities along this exciting new research direction. Ta Chen, and Cheng-Hsin Hsu. 2017. 360° Video Viewing Dataset in Head-Mounted Virtual Reality. In Proceedings ofMMSys’17, Taipei, Taiwan, June 20-23, 2017, 6 pages.  hp://dx.doi.org/10.1145/3083187.3083219 CCS Concept • Information systems→Multimedia streaming
Intelligent malware detection based on file relation graphs

Due to its damage to Internet security, malware and its detection has caught the attention of both anti-malware industry and researchers for decades. Many research efforts have been conducted on developing intelligent malware detection systems. In these systems, resting on the analysis of file contents extracted from the file samples, like Application Programming Interface (API) calls, instruction sequences, and binary strings, data mining methods such as Naive Bayes and Support Vector Machines have been used for malware detection. However, driven by the economic benefits, both diversity and sophistication of malware have significantly increased in recent years. Therefore, anti-malware industry calls for much more novel methods which are capable to protect the users against new threats, and more difficult to evade. In this paper, other than based on file contents extracted from the file samples, we study how file relation graphs can be used for malware detection and propose a novel Belief Propagation algorithm based on the constructed graphs to detect newly unknown malware. A comprehensive experimental study on a real and large data collection from Comodo Cloud Security Center is performed to compare various malware detection approaches. Promising experimental results demonstrate that the accuracy and efficiency of our proposed method outperform other alternate data mining based detection techniques.
Pattern Extraction for Time Series Classification

In this paper, we propose some new tools to allow machine learning classiiers to cope with time series data. We rst argue that many time-series classiication problems can be solved by detecting and combining local properties or patterns in time series. Then, a technique is proposed to nd patterns which are useful for classiication. These patterns are combined to build interpretable classiication rules. Experiments , carried out on several artiicial and real problems, highlight the interest of the approach both in terms of interpretability and accuracy of the induced classiiers.
Modality of input and vocabulary acquisition

Tetyana Sydorenko Michigan State University This study examines the effect of input modality (video, audio, and captions, i.e., onscreen text in the same language as audio) on (a) the learning of written and aural word forms, (b) overall vocabulary gains, (c) attention to input, and (d) vocabulary learning strategies of beginning L2 learners. Twenty-six second-semester learners of Russian participated in this study. Group one (N = 8) saw video with audio and captions (VAC); group two (N = 9) saw video with audio (VA); group three (N = 9) saw video with captions (VC). All participants completed written and aural vocabulary tests and a final questionnaire.
PyCUDA and PyOpenCL: A scripting-based approach to GPU run-time code generation

High-performance computing has recently seen a surge of interest in heterogeneous systems, with an emphasis on modern Graphics Processing Units (GPUs). These devices offer tremendous potential for performance and efficiency in important large-scale applications of computational science. However, exploiting this potential can be challenging, as one must adapt to the specialized and rapidly evolving computing environment currently exhibited by GPUs. One way of addressing this challenge is to embrace better techniques and develop tools tailored to their needs. This article presents one simple technique, GPU run-time code generation (RTCG), along with PyCUDA and PyOpenCL, two open-source toolkits that support this technique. In introducing PyCUDA and PyOpenCL, this article proposes the combination of a dynamic, high-level scripting language with the massive performance of a GPU as a compelling two-tiered computing platform, potentially offering significant performance and productivity advantages over conventional single-tier, static systems. The concept of RTCG is simple and easily implemented using existing, robust infrastructure. Nonetheless it is powerful enough to support (and encourage) the creation of custom application-specific tools by its users. The premise of the paper is illustrated by a wide range of examples where the technique has been applied with considerable success.
Context Dependent Movie Recommendations Using a Hierarchical Bayesian Model

We use a hierarchical Bayesian approach to model user preferences in different contexts or settings. Unlike many previous recommenders, our approach is content-based. We assume that for each context, a user has a different set of preference weights which are linked by a common, “generic context” set of weights. The approach uses Expectation Maximization (EM) to estimate both the generic context weights and the context specific weights. This improves upon many current recommender systems that do not incorporate context into the recommendations they provide. In this paper, we show that by considering contextual information, we can improve our recommendations, demonstrating that it is useful to consider context in giving ratings. Because the approach does not rely on connecting users via collaborative filtering, users are able to interpret contexts in different ways and invent their own
Electric Load Forecasting Using An Artificial Neural Network

This paper presents an artificial neural network(ANN) approach to electric load forecasting. The ANN is used to learn the relationship among past, current and future temperatures and loads. In order to provide the forecasted load, the ANN interpolates among the load and temperature data in a training data set. The average absolute errors of the one-hour and 24-hour ahead forecasts in our test on actual utility data are shown to be 1.40% and 2.06%, respectively. This compares with an average error of 4.22% for 24hour ahead forecasts with a currently used forecasting technique applied to the same data.
On the Temporal Dynamics of Opinion Spamming: Case Studies on Yelp

Recently, the problem of opinion spam has been widespread and has attracted a lot of research attention. While the problem has been approached on a variety of dimensions, the temporal dynamics in which opinion spamming operates is unclear. Are there specific spamming policies that spammers employ? What kind of changes happen with respect to the dynamics to the truthful ratings on entities. How do buffered spamming operate for entities that need spamming to retain threshold popularity and reduced spamming for entities making better success? We analyze these questions in the light of time-series analysis on Yelp. Our analyses discover various temporal patterns and their relationships with the rate at which fake reviews are posted. Building on our analyses, we employ vector autoregression to predict the rate of deception across different spamming policies. Next, we explore the effect of filtered reviews on (long-term and imminent) future rating and popularity prediction of entities. Our results discover novel temporal dynamics of spamming which are intuitive, arguable and also render confidence on Yelp’s filtering. Lastly, we leverage our discovered temporal patterns in deception detection. Experimental results on large-scale reviews show the effectiveness of our approach that significantly improves the existing approaches.
Interactive visual supports for children with autism

Interventions to support children with autism often include the use of visual supports, which are cognitive tools to enable learning and the production of language. Although visual supports are effective in helping to diminish many of the challenges of autism, they are difficult and time-consuming to create, distribute, and use. In this paper, we present the results of a qualitative study focused on uncovering design guidelines for interactive visual supports that would address the many challenges inherent to current tools and practices. We present three prototype systems that address these design challenges with the use of large group displays, mobile personal devices, and personal recording technologies. We also describe the interventions associated with these prototypes along with the results from two focus group discussions around the interventions. We present further design guidance for visual supports and discuss tensions inherent to their design.
The role of site features, user attributes, and information verification behaviors on the perceived credibility of web-based information

Data from 574 participants were used to assess perceptions of message, site, and sponsor credibility across four genres of websites; to explore the extent and effects of verifying web-based information; and to measure the relative influence of sponsor familiarity and site attributes on perceived credibility.The results show that perceptions of credibility differed, such that news organization websites were rated highest and personal websites lowest, in terms of message, sponsor, and overall site credibility, with e-commerce and special interest sites rated between these, for the most part.The results also indicated that credibility assessments appear to be primarily due to website attributes (e.g. design features, depth of content, site complexity) rather than to familiarity with website sponsors. Finally, there was a negative relationship between self-reported and observed information verification behavior and a positive relationship between self-reported verification and internet/web experience. The findings are used to inform the theoretical development of perceived web credibility. 319 new media & society  NMS-075015.qxd 9/3/07 11:54 AM Page 319  Not for commercial use or unauthorized distribution. at Universiteit van Amsterdam SAGE on April 25, 2007 http://nms.sagepub.com Downloaded from
Temporal Discourse Models For Narrative Structure

Getting a machine to understand human narratives has been a classic challenge for NLP and AI. This paper proposes a new representation for the temporal structure of narratives. The representation is parsimonious, using temporal relations as surrogates for discourse relations. The narrative models, called Temporal Discourse Models, are treestructured, where nodes include abstract events interpreted as pairs of time points and where the dominance relation is expressed by temporal inclusion. Annotation examples and challenges are discussed, along with a report on progress to date in creating annotated corpora.