nils-herrmann/clean_scirepeval huggingface dataset:
How do People Make Sense of Unfamiliar Visualizations?: A Grounded Model of Novice's Information Visualization Sensemaking

In this paper, we would like to investigate how people make sense of unfamiliar information visualizations. In order to achieve the research goal, we conducted a qualitative study by observing 13 participants when they endeavored to make sense of three unfamiliar visualizations (i.e., a parallel-coordinates plot, a chord diagram, and a treemap) that they encountered for the first time. We collected data including audio/video record of think-aloud sessions and semi-structured interview; and analyzed the data using the grounded theory method. The primary result of this study is a grounded model of NOvice's information VIsualization Sensemaking (NOVIS model), which consists of the five major cognitive activities: 1 encountering visualization, 2 constructing a frame, 3 exploring visualization, 4 questioning the frame, and 5 floundering on visualization. We introduce the NOVIS model by explaining the five activities with representative quotes from our participants. We also explore the dynamics in the model. Lastly, we compare with other existing models and share further research directions that arose from our observations.
MapMarker: Extraction of Postal Addresses and Associated Information for General Web Pages

Address information is essential for people’s daily life. People often need to query addresses of unfamiliar location through Web and then use map services to mark down the location for direction purpose. Although both address information and map services are available online, they are not well combined. Users usually need to copy individual address from a Web site and paste it to another Web site with map services to locate its direction. Such copy and paste operations have to be repeated if multiple addresses are listed on a single page such as public school list or apartment list. Furthermore, associated information with individual address has to be copied and included on each marker for better comprehension. Our research is devoted to automate the above process and make the combination an easier task for users. The main techniques applied here include postal address extraction and associated information extraction. We apply sequence labeling algorithm based on Conditional Random Fields (CRFs) to train models for address extraction. Meanwhile, using the extracted addresses as landmarks, we apply pattern mining to identify the boundaries of address blocks and extract associated information with each individual address. The experimental result shows high F-score at 91% for postal address extraction and 87% accuracy for associated information extraction.
UNIK: unsupervised social network spam detection

Social network spam increases explosively with the rapid development and wide usage of various social networks on the Internet. To timely detect spam in large social network sites, it is desirable to discover unsupervised schemes that can save the training cost of supervised schemes. In this work, we first show several limitations of existing unsupervised detection schemes. The main reason behind the limitations is that existing schemes heavily rely on spamming patterns that are constantly changing to avoid detection. Motivated by our observations, we first propose a sybil defense based spam detection scheme SD2 that remarkably outperforms existing schemes by taking the social network relationship into consideration. In order to make it highly robust in facing an increased level of spam attacks, we further design an unsupervised spam detection scheme, called UNIK. Instead of detecting spammers directly, UNIK works by deliberately removing non-spammers from the network, leveraging both the social graph and the user-link graph. The underpinning of UNIK is that while spammers constantly change their patterns to evade detection, non-spammers do not have to do so and thus have a relatively non-volatile pattern. UNIK has comparable performance to SD2 when it is applied to a large social network site, and outperforms SD2 significantly when the level of spam attacks increases. Based on detection results of UNIK, we further analyze several identified spam campaigns in this social network site. The result shows that different spammer clusters demonstrate distinct characteristics, implying the volatility of spamming patterns and the ability of UNIK to automatically extract spam signatures.
Efficient, Feature-based, Conditional Random Field Parsing

Discriminative feature-based methods are widely used in natural language processing, but sentence parsing is still dominated by generative methods. While prior feature-based dynamic programming parsers have restricted training and evaluation to artificially short sentences, we present the first general, featurerich discriminative parser, based on a conditional random field model, which has been successfully scaled to the full WSJ parsing data. Our efficiency is primarily due to the use of stochastic optimization techniques, as well as parallelization and chart prefiltering. On WSJ15, we attain a state-of-the-art F-score of 90.9%, a 14% relative reduction in error over previous models, while being two orders of magnitude faster. On sentences of length 40, our system achieves an F-score of 89 .0%, a 36% relative reduction in error over a generative baseline.
Real-time ink simulation using a grid-particle method

This paper presents an effective method to simulate the ink diffusion process in real time that yields realistic visual effects. Our algorithm updates the dynamic ink volume using a hybrid grid-particle method: the fluid velocity field is calculated with a low-resolution grid structure, whereas the highly detailed ink effects are controlled and visualized with the particles. To facilitate user interaction and extend this method, we propose a particle-guided method that allows artists to design the overall states using the coarse-resolution particles and to preview the motion quickly. To treat coupling with solids and other fluids, we update the grid-particle representation with no-penetration boundary conditions and implicit interaction conditions. To treat moving ‘‘ink-emitting’’ objects, we introduce an extra drag-force model to enhance the particle motion effects; this force might not be physically accurate, but it proves effective for producing animations. We also propose an improved ink rendering method that uses particle sprites and motion blurring techniques. The simulation and the rendering processes are efficiently implemented on graphics hardware at interactive frame rates. Compared to traditional fluid simulation methods that treat water and ink as two mixable fluids, our method is simple but effective: it captures various ink effects, such as pinned boundaries (Nelson, 2005 ) and filament patterns (Shiny et al., 2010 ), while still running in real time, it allows easy control of the animation, it includes basic solid–fluid interactions, and it can address multiple ink sources without complex interface tracking. Our method is attractive for animation production and art design. & 2012 Elsevier Ltd. All rights reserved.
Primed for Violence : The Role of Gender Inequality in Predicting Internal Conflict

We know, most notably through Ted Gurr’s research, that ethnic discrimination can lead to ethnopolitical rebellion–intrastate conflict. I seek to discover what impact, if any, gender inequality has on intrastate conflict. Although democratic peace scholars and others highlight the role of peaceful domestic behavior in predicting state behavior, many scholars have argued that a domestic environment of inequality and violenceFstructural and cultural violenceFresults in a greater likelihood of violence at the state and the international level. This project contributes to this line of inquiry and further tests the grievance theory of intrastate conflict by examining the norms of violence that facilitate a call to arms. And in many ways, I provide an alternative explanation for the significance of some of the typical economic measuresFthe greed theoryFbased on the link between discrimination, inequality, and violence. I test whether states characterized by higher levels of gender inequality are more likely to experience intrastate conflict. Ultimately, the basic link between gender inequality and intrastate conflict is confirmedFstates characterized by gender inequality are more likely to experience intrastate conflict, 1960–2001.
Design and comparison of CMOS Current Mode Logic latches

A comprehensive study of the MOS Current Mode Logic (MCML) is presented. Operation of a conventional MCML latch is analyzed and some modified structures are described. A novel structure is proposed for increased stability with reduced delay parameters. General problems with single-ended to differential conversion are addressed. Comparative performance measures of Master-Slave (MS) latches are presented in a 0.18-/spl mu/m CMOS technology.
A Review of Facebook Research in the Social Sciences.

With over 800 million active users, Facebook is changing the way hundreds of millions of people relate to one another and share information. A rapidly growing body of research has accompanied the meteoric rise of Facebook as social scientists assess the impact of Facebook on social life. In addition, researchers have recognized the utility of Facebook as a novel tool to observe behavior in a naturalistic setting, test hypotheses, and recruit participants. However, research on Facebook emanates from a wide variety of disciplines, with results being published in a broad range of journals and conference proceedings, making it difficult to keep track of various findings. And because Facebook is a relatively recent phenomenon, uncertainty still exists about the most effective ways to do Facebook research. To address these issues, the authors conducted a comprehensive literature search, identifying 412 relevant articles, which were sorted into 5 categories: descriptive analysis of users, motivations for using Facebook, identity presentation, the role of Facebook in social interactions, and privacy and information disclosure. The literature review serves as the foundation from which to assess current findings and offer recommendations to the field for future research on Facebook and online social networks more broadly.
Total variation regularization of local-global optical flow

More data fidelity terms in variational optical flow methods improve the estimation's robustness. A robust and anisotropic smoother enhances the specific fill-in process. This work presents a combined local-global (CLG) approach with total variation regularization. The combination of bilateral filtering and anisotropic (image driven) regularization is used to control the propagation phenomena. The resulted method, CLG-TV, is able to compute larger displacements in a reasonable time. The numerical scheme is highly parallelizable and runs in real-time on current generation graphic processing units.
DudeTM: Building Durable Transactions with Decoupling for Persistent Memory

Emerging non-volatile memory (NVM) offers non-volatility, byte-addressability and fast access at the same time. To make the best use of these properties, it has been shown by empirical evidence that programs should access NVM directly through CPU load and store instructions, so that the overhead of a traditional file system or database can be avoided. Thus, durable transactions become a common choice of applications for accessing persistent memory data in a crash consistent manner. However, existing durable transaction systems employ either undo logging, which requires a fence for every memory write, or redo logging, which requires intercepting all memory reads within transactions.
 This paper presents DUDETM, a crash-consistent durable transaction system that avoids the drawbacks of both undo logging and redo logging. DUDETM uses shadow DRAM to decouple the execution of a durable transaction into three fully asynchronous steps. The advantage is that only minimal fences and no memory read instrumentation are required. This design also enables an out-of-the-box transactional memory (TM) to be used as an independent component in our system. The evaluation results show that DUDETM adds durability to a TM system with only 7.4 ~ 24.6% throughput degradation. Compared to the existing durable transaction systems, DUDETM provides 1.7times to 4.4times higher throughput. Moreover, DUDETM can be implemented with existing hardware TMs with minor hardware modifications, leading to a further 1.7times speedup.
A Comparative Evaluation of Log-Based Process Performance Analysis Techniques

Process mining has gained traction over the past decade and an impressive body of research has resulted in the introduction of a variety of process mining approaches measuring process performance. Having this set of techniques available, organizations might find it difficult to identify which approach is best suited considering context, performance indicator, and data availability. In light of this challenge, this paper aims at introducing a framework for categorizing and selecting performance analysis approaches based on existing research. We start from a systematic literature review for identifying the existing works discussing how to measure process performance based on information retrieved from event logs. Then, the proposed framework is built starting from the information retrieved from these studies taking into consideration different aspects of performance analysis.
Cross-Gender and Cross-Dialect Tone Recognition for Vietnamese

We investigate tone recognition in Vietnamese across gender and dialects. In addition to well-known parameters such as single fundamental frequency (F0) values and energy features, we explore the impact of harmonicity on recognition accuracy, as well as that of the PaIntE parameters, which quantify the shape of the F0 contour over complete syllables instead of providing more local single values. Using these new features for tone recognition in the GlobalPhone database, we observe significant improvements of approx. 1% in recognition accuracy when adding harmonicity, and of another approx. 4% when adding the PaIntE parameters. Furthermore, we analyze the influence of gender and dialect on recognition accuracy. The results show that it is easier to recognize tones for female than for male speakers, and easier for the Northern dialect than for the Southern dialect. Moreover, we achieve reasonable results testing models across gender, while the performance drops strongly when testing across dialects.
Data mining methods and models

This inspiring book becomes one that is very booming. After published, this book can steal the market and book lovers to always run out of this book. And now, we will not let you run out any more to get this book. Why should be data mining methods and models? As a book lover, you must know that enjoying the book to read should be relevant to how you exactly need now. If they are not too much relevance, you can take the way of the inspirations to create for new inspirations.
The assessment of body image investment: an extensive revision of the appearance schemas inventory.

The Appearance Schemas Inventory (ASI) is a 14-item instrument that assesses body image investment in relation to certain beliefs or assumptions about the importance, meaning, and influence of appearance in one's life. Despite empirical support of the ASI, critical examination evinces several limitations of this assessment. These problems entail the inclusion of explicitly self-evaluative items and social stereotypes, few behavioral items, and a repeated failure to find expected gender differences on the ASI.



We initially constructed a 45-item measure (40 new items plus 5 original items) and administered it, along with the original ASI and other validational assessments, to 603 college students (468 women and 135 men).



The end result was a 20-item revision of the inventory (ASI-R), which included two factors: Self-Evaluative Salience and Motivational Salience. For both genders, the composite ASI-R and its two factors had high internal consistency and were significantly convergent with other pertinent measures of body image and psychosocial functioning. The ASI-R and its two subscales showed significant gender differences, whereas the original ASI did not. We also examined racial differences on the ASI-R, its correlations with body mass, and its unique contribution to the prediction of disturbed eating attitudes.



We offer this measure as an improved, psychometrically sound replacement for the ASI.
Taming the beast: Free and open-source massive point cloud web visualization

Powered by WebGL, some renderers have recently become available for the visualization of point cloud data over the web, for example Plasio or Potree. We have extended Potree to be able to visualize massive point clouds and we have successfully used it with the second national Lidar survey of the Netherlands, AHN2, with 640 billion points. In addition to the visualization, the publicly available service at http://ahn2.pointclouds.nl/ also features a multi-resolution download tool, a geographic name search bar, a measurement toolkit, a 2D orientation map with field of view depiction, a demo mode and the tuning of the visualization parameters. Potree relies on reorganizing the point cloud data into an multi-resolution octree data structure. However, this reorganization is very time consuming for massive data sets. Hence, we have used a divide and conquer approach to decrease the octree creation time. To achieve such performance improvement we divided the entire space into smaller cells, generated an octree for each of them in a distributed manner and then we merged them into a single massive octree. The merging is possible because the extent of all the nodes of the octrees is known and fixed. All the developed tools are free and open-source (FOSS) and they can be used to visualize over the web other massive point clouds.
Learning Kernels for Unsupervised Domain Adaptation with Applications to Visual Object Recognition

Domain adaptation aims to correct the mismatch in statistical properties between the source domain on which a classifier is trained and the target domain to which the classifier is to be applied. In this paper, we address the challenging scenario of unsupervised domain adaptation, where the target domain does not provide any annotated data to assist in adapting the classifier. Our strategy is to learn robust features which are resilient to the mismatch across domains and then use them to construct classifiers that will perform well on the target domain. To this end, we propose novel kernel learning approaches to infer such features for adaptation. Concretely, we explore two closely related directions. In the first direction, we propose unsupervised learning of a geodesic flow kernel (GFK). The GFK summarizes the inner products in an infinite sequence of feature subspaces that smoothly interpolates between the source and target domains. In the second direction, we propose supervised learning of a kernel that discriminatively combines multiple base GFKs. Those base kernels model the source and the target domains at fine-grained granularities. In particular, each base kernel pivots on a different set of landmarks—the most useful data instances that reveal the similarity between the source and the target domains, thus bridging them to achieve adaptation. Our approaches are computationally convenient, automatically infer important hyper-parameters, and are capable of learning features and classifiers discriminatively without demanding labeled data from the target domain. In extensive empirical studies on standard benchmark recognition datasets, our appraches yield state-of-the-art results compared to a variety of competing methods.
Recent system applications of short-pulse ultra-wideband (UWB) technology

Developed in the early 1960s, time-domain electromagnetics, the study of electromagnetic-wave propagation from a time-domain perspective, has given birth to a fascinating new technology, which today is commonly referred to as ultra-wideband (UWB). It has now been slightly more than 25 years since the 1978 seminal paper of Bennett and Ross, which summarized UWB's early applications. It thus seems appropriate, given the tremendous increase in interest in the technology since the Federal Communications Commission modified its Part 15 rules to accommodate UWB transmissions, to take a look at more recent system applications of this unique technology. This paper provides a brief historical perspective of UWB, discusses recent techniques for the generation and reception of short-pulse electromagnetic waveforms, and examines a number of recently developed UWB systems in the communications, radar, and precision-positioning fields. Finally, a brief assessment of future trends for the technology is provided.
A survey of multicore processors

General-purpose multicore processors are being accepted in all segments of the industry, including signal processing and embedded space, as the need for more performance and general-purpose programmability has grown. Parallel processing increases performance by adding more parallel resources while maintaining manageable power characteristics. The implementations of multicore processors are numerous and diverse. Designs range from conventional multiprocessor machines to designs that consist of a "sea" of programmable arithmetic logic units (ALUs). In this article, we cover some of the attributes common to all multicore processor implementations and illustrate these attributes with current and future commercial multicore designs. The characteristics we focus on are application domain, power/performance, processing elements, memory system, and accelerators/integrated peripherals.
A comparative evaluation of procedural level generators in the Mario AI framework

The field is now in a state where there is a glut of content generators, each serving different purposes and using a variety of techniques. It is difficult to understand, quantitatively or qualitatively, what makes one generator different from another in terms of its output. To remedy this, we have conducted a large-scale comparative evaluation of level generators for the Mario AI Benchmark, a research-friendly clone of the classic platform game Super Mario Bros. In all, we compare the output of seven different level generators from the literature, based on different algorithmic methods, plus the levels from the original Super Mario Bros game. To compare them, we have defined six expressivity metrics, of which two are novel contributions in this paper. These metrics are shown to provide interestingly different characterizations of the level generators. The results presented in this paper, and the accompanying source code, is meant to become a benchmark against which to test new level generators and expressivity metrics.
Accurate tracking in NLOS environments using integrated IMU and fixed lag smoother

Multi-sensor data fusion using Inertial Measurement Units (IMUs) is a promising technique for improving the performance of positioning systems. However, the performance of conventional sensor fusion algorithms based on the Kalman Filter (KF) is compromised in indoor environments due to non-line-of-sight (NLOS) propagation. In this paper, we propose a semi-real time tracking algorithm which uses a fixed lag smoother for sensor fusion and achieves high accuracy in NLOS environments. The computational complexity of the algorithm is taken into consideration and is reduced by decreasing the operating rate of the smoother. The performance of the proposed algorithm is validated experimentally using a real indoor positioning platform. It is shown that the 90th percentile positioning error for a pedestrian is reduced by 42% using the proposed semi-real time tracking algorithm with 10 s lag, compared with using a KF-based real time tracking algorithm.