nils-herrmann/clean_scirepeval huggingface dataset:
Personal information management: From information consumption to curation

An implicit, but pervasive view in the information science community is that people are perpetual seekers after new public information, incessantly identifying and consuming new information by browsing the Web and accessing public collections. One aim of this review is to move beyond this consumer characterization, which regards information as a public resource containing novel data that we seek out, consume, and then discard. Instead, I want to focus on a very different view: where familiar information is used as a personal resource that we keep, manage, and (sometimes repeatedly) exploit. I call this information curation. I first summarize limitations of the consumer perspective. I then review research on three different information curation processes: keeping, management, and exploitation. I describe existing work detailing how each of these processes is applied to different types of personal data: documents, e-mail messages, photos, and Web pages. The research indicates people tend to keep too much information, with the exception of contacts and Web pages. When managing information, strategies that rely on piles as opposed to files provide surprising benefits. And in spite of the emergence of desktop search, exploitation currently remains reliant on manual methods such as navigation. Several new technologies have the potential to address important
The Socialbot Network : When Bots Socialize for Fame & Money

A new breed of computer programs called socialbots are now online, and they can be used to breach users’ privacy, spread misinformation to bias the public opinion, and compromise the graph of a targeted social network [1, 2]. A socialbot controls a fictitious user profile and has the ability to execute basic online activities (e.g., posting a message, sending a connection request). What makes a socialbot different from other self-declared bots (e.g., bots posting weather forecasts in Twitter) is that it is designed to infiltrate online communities by passing itself off as a human being. Socialbots can be used to manipulate the graph of a targeted social network in order to establish a centralized or influential social position in it. This position can be then exploited to mount DDoS attacks, promote products, or to spread propaganda in a viral way. For example, Ratkiewicz et al.  described the use of Twitter bots to spread misinformation in the run-up to the US political elections. As the socialbots infiltrate a targeted social network, they also harvest valuable users’ data which is useful for online profiling and large-scale spam campaigns. In fact, a new report showed that spammers are turning to online social networking platforms for distributing their messages [7], which explains the dramatic drop in the world-wide email spam during the recent months . This gave rise to black-market businesses that offer multi-featured socialbots for as high as $29 per bot . Many techniques have been proposed that try to identify socialbots based on their likely unordinary behavior (see  for an example on Twitter). In this research, we take a proactive step and investigate the feasibility of operating an organized army of socialbots which we call a Socialbot Network (SbN). A SbN is a group of socialbots that collaborate in infiltrating social networks under the orchestration of one or many “master” bots. We study the security and privacy implications of operating such a SbN on a large scale. In particular, we answer questions about the types of collaboration these socialbots can utilize, the required infrastructure, the network-wide observations that can be exploited to improve the potential infiltration, and the economics of operating a SbN on a large scale. Finally, we present a set of challenges that future social network security systems have to overcome in order to mitigate the potential threat of a SbN.
A Comparative Analysis of Models for Predicting Delays in Air Traffic Networks

In this paper, we compare the performance of different approaches to predicting delays in air traffic networks. We consider three classes of models: A recently-developed aggregate model of the delay network dynamics, which we will refer to as the Markov Jump Linear System (MJLS), classical machine learning techniques like Classification and Regression Trees (CART), and three candidate Artificial Neural Network (ANN) architectures. We show that prediction performance can vary significantly depending on the choice of model/algorithm, and the type of prediction (for example, classification vs. regression). We also discuss the importance of selecting the right predictor variables, or features, in order to improve the performance of these algorithms. The models are evaluated using operational data from the National Airspace System (NAS) of the United States. The ANN is shown to be a good algorithm for the classification problem, where it attains an average accuracy of nearly 94% in predicting whether or not delays on the 100 most-delayed links will exceed 60 min, looking two hours into the future. The MJLS model, however, is better at predicting the actual delay levels on different links, and has a mean prediction error of 4.7 min for the regression problem, for a 2 hr horizon. MJLS is also better at predicting outbound delays at the 30 major airports, with a mean error of 6.8 min, for a 2 hr prediction horizon. The effect of temporal factors, and the spatial distribution of current delays, in predicting future delays are also compared. The MJLS model, which is specifically designed to capture aggregate air traffic dynamics, leverages on these factors and outperforms the ANN in predicting the future spatial distribution of delays. In this manner, a tradeoff between model simplicity and prediction accuracy is revealed. Keywordsdelay prediction; network delays; machine learning; artificial neural networks; data mining
Blog effects on brand attitude and purchase intention

This research attempts to investigate the effects of blog marketing on brand attitude and purchase intention. The elements of blog marketing are identified as community identification, interpersonal trust, message exchange, and two-way communication. The relationships among variables are pictured on the fundamental research framework provided by this study. Data were collected via an online questionnaire and 727 useable samples were collected and analyzed utilizing AMOS 5.0. The empirical findings show that the blog marketing elements can impact on brand attitude positively except for the element of community identification. Further, the analysis result also verifies the moderating effects on the relationship between blog marketing elements and brand attitude.
Creating Individual Agents through Personality Traits

In the era of globalization, concepts such as individualization and personalization become more and more important in virtual systems. With the goal of creating a more familiar interaction between human and machines, it makes sense to create a consistent and believable model of personality. This paper presents an explicit model of personality, based in the Five Factor Model, which aims at the creation of distinguishable personalities by using the personality traits to automatically influence cognitive processes: appraisal, planning,coping, and bodily expression.
Combining Intra- and Multi-sentential Rhetorical Parsing for Document-level Discourse Analysis

We propose a novel approach for developing a two-stage document-level discourse parser. Our parser builds a discourse tree by applying an optimal parsing algorithm to probabilities inferred from two Conditional Random Fields: one for intrasentential parsing and the other for multisentential parsing. We present two approaches to combine these two stages of discourse parsing effectively. A set of empirical evaluations over two different datasets demonstrates that our discourse parser significantly outperforms the stateof-the-art, often by a wide margin.
A Novel Compact Torsional Spring for Series Elastic Actuators for Assistive Wearable Robots

The introduction of intrinsic compliance in the actuation system of assistive robots improves safety and dynamical adaptability. Furthermore, in the case of wearable robots for gait assistance, the exploitation of conservative compliant elements as energy buffers can mimic the intrinsic dynamical properties of legs during locomotion. However, commercially available compliant components do not generally allow to meet the desired requirements in terms of admissible peak load, as typically required by gait assistance, while guaranteeing low stiffness and a compact and lightweight design. This paper presents a novel compact monolithic torsional spring to be used as the basic component of a modular compliant system for Series Elastic Actuators. The spring, whose design was re ned through an iterative FEA-based optimization process, has an external diameter of 85 mm, a thickness of 3 mm and a weight of 61.5 g. The spring, characterized using a custom dynamometric test bed, shows a linear torque vs. angle characteristic. The compliant element has a stiffness of 98 N m/rad and it is capable of withstanding a maximum torque of 7.68 N m. A good agreement between simulated and experimental data was observed, with a maximum resultant error of 6%. By arranging a number of identical springs in series or in parallel, it is possible to render different torque vs. angle characteristics, in order to match the speci c applications requirements.
Identifying References to Datasets in Publications

Research data and publications are usually stored in separate and structurally distinct information systems. Often, links between these resources are not explicitly available which complicates the search for previous research. In this paper, we propose a pattern induction method for the detection of study references in full texts. Since these references are not specified in a standardized way and may occur inside a variety of different contexts – i.e., captions, footnotes, or continuous text – our algorithm is required to induce very flexible patterns. To overcome the sparse distribution of training instances, we induce patterns iteratively using a bootstrapping approach. We show that our method achieves promising results for the automatic identification of data references and is a first step towards building an integrated information system.
Direct and indirect aggression during childhood and adolescence: a meta-analytic review of gender differences, intercorrelations, and relations to maladjustment.

This meta-analytic review of 148 studies on child and adolescent direct and indirect aggression examined the magnitude of gender differences, intercorrelations between forms, and associations with maladjustment. Results confirmed prior findings of gender differences (favoring boys) in direct aggression and trivial gender differences in indirect aggression. Results also indicated a substantial intercorrelation (r = .76) between these forms. Despite this high intercorrelation, the 2 forms showed unique associations with maladjustment: Direct aggression is more strongly related to externalizing problems, poor peer relations, and low prosocial behavior, and indirect aggression is related to internalizing problems and higher prosocial behavior. Moderation of these effect sizes by method of assessment, age, gender, and several additional variables were systematically investigated.
Position Sensorless Control Based on Coordinate Transformation for Brushless DC Motor Drives

This paper presents a novel method for sensorless brushless dc (BLDC) motor drives. Based on the characteristics of the back electromotive force (EMF), the rotor position signals would be constructed. It is intended to construct these signals, which make the phase difference between the constructed signals and the back EMFs controllable. Then, the rotor-position error can be compensated by controlling the phase-difference angle in real time. In this paper, the rotor-position-detection error is analyzed. Using the TMS320F2812 chip as a control core, some experiments have been carried out on the prototype, which is the surface-mounted permanent magnet BLDC motor, and the experimental results verify the analysis and demonstrate advantages of the proposed sensorless-control method.
Marker-less Deformable Mesh Tracking for Human Shape and Motion Capture

We present a novel algorithm to jointly capture the motion and the dynamic shape of humans from multiple video streams without using optical markers. Instead of relying on kinematic skeletons, as traditional motion capture methods, our approach uses a deformable high-quality mesh of a human as scene representation. It jointly uses an image-based 3D correspondence estimation algorithm and a fast Laplacian mesh deformation scheme to capture both motion and surface deformation of the actor from the input video footage. As opposed to many related methods, our algorithm can track people wearing wide apparel, it can straightforwardly be applied to any type of subject, e.g. animals, and it preserves the connectivity of the mesh over time. We demonstrate the performance of our approach using synthetic and captured real-world video sequences and validate its accuracy by comparison to the ground truth.
MonoFusion: Real-time 3D reconstruction of small scenes with a single web camera

MonoFusion allows a user to build dense 3D reconstructions of their environment in real-time, utilizing only a single, off-the-shelf web camera as the input sensor. The camera could be one already available in a tablet, phone, or a standalone device. No additional input hardware is required. This removes the need for power intensive active sensors that do not work robustly in natural outdoor lighting. Using the input stream of the camera we first estimate the 6DoF camera pose using a sparse tracking method. These poses are then used for efficient dense stereo matching between the input frame and a key frame (extracted previously). The resulting dense depth maps are directly fused into a voxel-based implicit model (using a computationally inexpensive method) and surfaces are extracted per frame. The system is able to recover from tracking failures as well as filter out geometrically inconsistent noise from the 3D reconstruction. Our method is both simple to implement and efficient, making such systems even more accessible. This paper details the algorithmic components that make up our system and a GPU implementation of our approach. Qualitative results demonstrate high quality reconstructions even visually comparable to active depth sensor-based systems such as KinectFusion.
Cancer statistics, 2012.

Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths expected in the United States in the current year and compiles the most recent data on cancer incidence, mortality, and survival based on incidence data from the National Cancer Institute, the Centers for Disease Control and Prevention, and the North American Association of Central Cancer Registries and mortality data from the National Center for Health Statistics. A total of 1,638,910 new cancer cases and 577,190 deaths from cancer are projected to occur in the United States in 2012. During the most recent 5 years for which there are data (2004-2008), overall cancer incidence rates declined slightly in men (by 0.6% per year) and were stable in women, while cancer death rates decreased by 1.8% per year in men and by 1.6% per year in women. Over the past 10 years of available data (1999-2008), cancer death rates have declined by more than 1% per year in men and women of every racial/ethnic group with the exception of American Indians/Alaska Natives, among whom rates have remained stable. The most rapid declines in death rates occurred among African American and Hispanic men (2.4% and 2.3% per year, respectively). Death rates continue to decline for all 4 major cancer sites (lung, colorectum, breast, and prostate), with lung cancer accounting for almost 40% of the total decline in men and breast cancer accounting for 34% of the total decline in women. The reduction in overall cancer death rates since 1990 in men and 1991 in women translates to the avoidance of about 1,024,400 deaths from cancer. Further progress can be accelerated by applying existing cancer control knowledge across all segments of the population, with an emphasis on those groups in the lowest socioeconomic bracket.
Wildest Faces: Face Detection and Recognition in Violent Settings

With the introduction of large-scale datasets and deep learning models capable of learning complex representations, impressive advances have emerged in face detection and recognition tasks. Despite such advances, existing datasets do not capture the difficulty of face recognition in the wildest scenarios, such as hostile disputes or fights. Furthermore, existing datasets do not represent completely unconstrained cases of low resolution, high blur and large pose/occlusion variances. To this end, we introduce the Wildest Faces dataset, which focuses on such adverse effects through violent scenes. The dataset consists of an extensive set of violent scenes of celebrities from movies. Our experimental results demonstrate that state-of-the-art techniques are not well-suited for violent scenes, and therefore, Wildest Faces is likely to stir further interest in face detection and recognition research.
Ambulance location and relocation models

This article traces the evolution of ambulance location and relocation models proposed over the past 30 years. The models are classified in two main categories. Deterministic models are used at the planning stage and ignore stochastic considerations regarding the availability of ambulances. Probabilistic models reflect the fact that ambulances operate as servers in a queueing system and cannot always answer a call. In addition, dynamic models have been developed to repeatedly relocate ambulances throughout the day.
Neuroplasticity: Changes in grey matter induced by training

Does the structure of an adult human brain alter in response to environmental demands? Here we use whole-brain magnetic-resonance imaging to visualize learning-induced plasticity in the brains of volunteers who have learned to juggle. We find that these individuals show a transient and selective structural change in brain areas that are associated with the processing and storage of complex visual motion. This discovery of a stimulus-dependent alteration in the brain's macroscopic structure contradicts the traditionally held view that cortical plasticity is associated with functional rather than anatomical changes.
Color laser printer identification using photographed halftone images

Due to the spread of color laser printers to the general public, numerous forgeries are made by color laser printers. Printer identification is essential to preventing damage caused by color laser printed forgeries. This paper presents a new method to identify a color laser printer using photographed halftone images. First, we preprocess the photographed images to extract the halftone pattern regardless of the variation of the illumination conditions. Then, 15 halftone texture features are extracted from the preprocessed images. A support vector machine is used to be trained and classify the extracted features. Experiments are performed on seven color laser printers. The experimental results show that the proposed method is suitable for identifying the source color laser printer using photographed images.
Substucture Discovery in the SUBDUE System

Because many databases contain or can be embellished with structural information, a method for identifying interesting and repetitive substructures is an essential component to discovering knowledge in such databases. This paper describes the SUBDUE system, which uses the minimum description length (MDL) principle to discover substructures that compress the database and represent structural concepts in the data. By replacing previously-discovered substructures in the data, multiple passes of SUBDUE produce a hierarchical description of the structural regularities in the data. Inclusion of background knowledgeguides SUBDUE toward appropriate substructures for a particular domain or discovery goal, and the use of an inexact graph match allows a controlled amount of deviations in the instance of a substructure concept. We describe the application of SUBDUE to a variety of domains. We also discuss approaches to combining SUBDUE with non-structural discovery systems.
DataXFormer: An Interactive Data Transformation Tool

While syntactic transformations require the application of a formula on the input values, such as unit conversion or date format conversions, semantic transformations, such as "zip code to city", require a look-up in some reference data. We recently presented DataXFormer, a system that leverages Web tables, Web forms, and expert sourcing to cover a wide range of transformations. In this demonstration, we present the user-interaction with DataXFormer and show scenarios on how it can be used to transform data and explore the effectiveness and efficiency of several approaches for transformation discovery, leveraging about 112 million tables and online sources.
Revisiting Android reuse studies in the context of code obfuscation and library usages

In the recent years, studies of design and programming practices in mobile development are gaining more attention from researchers. Several such empirical studies used Android applications (paid, free, and open source) to analyze factors such as size, quality, dependencies, reuse, and cloning. Most of the studies use executable files of the apps (APK files), instead of source code because of availability issues (most of free apps available at the Android official market are not open-source, but still can be downloaded and analyzed in APK format). However, using only APK files in empirical studies comes with some threats to the validity of the results. In this paper, we analyze some of these pertinent threats. In particular, we analyzed the impact of third-party libraries and code obfuscation practices on estimating the amount of reuse by class cloning in Android apps. When including and excluding third-party libraries from the analysis, we found statistically significant differences in the amount of class cloning 24,379 free Android apps. Also, we found some evidence that obfuscation is responsible for increasing a number of false positives when detecting class clones. Finally, based on our findings, we provide a list of actionable guidelines for mining and analyzing large repositories of Android applications and minimizing these threats to validity