nils-herrmann/clean_scirepeval huggingface dataset:
A User-Centered Segmentation Method for Complex Historical Manuscripts Based on Document Graphs

In historical manuscripts, humans can detect handwritten words, lines, and decorations with lightness even if they do not know the language or the script. Yet for automatic processing this task has proven elusive, especially in the case of handwritten documents with complex layouts, which is why semiautomatic methods that integrate the human user into the process are needed. In this paper, we introduce a user-centered segmentation method based on document graphs and scribbling interaction. The graphs capture a sparse representation of the document's structure that can then be edited by the user with a stylus on a touch-sensitive screen. We evaluate the proposed method on a newly introduced database of historical manuscripts with complex layout and demonstrate, first, that the document graphs are already close to the desired segmentation and, second, that scribbling allows a natural and efficient interaction.
History of click-speaking populations of Africa inferred from mtDNA and Y chromosome genetic variation.

Little is known about the history of click-speaking populations in Africa. Prior genetic studies revealed that the click-speaking Hadza of eastern Africa are as distantly related to click speakers of southern Africa as are most other African populations. The Sandawe, who currently live within 150 km of the Hadza, are the only other population in eastern Africa whose language has been classified as part of the Khoisan language family. Linguists disagree on whether there is any detectable relationship between the Hadza and Sandawe click languages. We characterized both mtDNA and Y chromosome variation of the Sandawe, Hadza, and neighboring Tanzanian populations. New genetic data show that the Sandawe and southern African click speakers share rare mtDNA and Y chromosome haplogroups; however, common ancestry of the 2 populations dates back >35,000 years. These data also indicate that common ancestry of the Hadza and Sandawe populations dates back >15,000 years. These findings suggest that at the time of the spread of agriculture and pastoralism, the click-speaking populations were already isolated from one another and are consistent with relatively deep linguistic divergence among the respective click languages.
Effects of four computer-mediated communications channels on trust development

When virtual teams need to establish trust at a distance, it is advantageous for them to use rich media to communicate. We studied the emergence of trust in a social dilemma game in four different communication situations: face-to-face, video, audio, and text chat. All three of the richer conditions were significant improvements over text chat. Video and audio conferencing groups were nearly as good as face-to-face, but both did show some evidence of what we term delayed trust (slower progress toward full cooperation) and fragile trust (vulnerability to opportunistic behavior)
The lamellipodium: where motility begins.

Lamellipodia, filopodia and membrane ruffles are essential for cell motility, the organization of membrane domains, phagocytosis and the development of substrate adhesions. Their formation relies on the regulated recruitment of molecular scaffolds to their tips (to harness and localize actin polymerization), coupled to the coordinated organization of actin filaments into lamella networks and bundled arrays. Their turnover requires further molecular complexes for the disassembly and recycling of lamellipodium components. Here, we give a spatial inventory of the many molecular players in this dynamic domain of the actin cytoskeleton in order to highlight the open questions and the challenges ahead.
Building Chinese Discourse Corpus with Connective-driven Dependency Tree Structure

In this paper, we propose a Connectivedriven Dependency Tree (CDT) scheme to represent the discourse rhetorical structure in Chinese language, with elementary discourse units as leaf nodes and connectives as non-leaf nodes, largely motivated by the Penn Discourse Treebank and the Rhetorical Structure Theory. In particular, connectives are employed to directly represent the hierarchy of the tree structure and the rhetorical relation of a discourse, while the nuclei of discourse units are globally determined with reference to the dependency theory. Guided by the CDT scheme, we manually annotate a Chinese Discourse Treebank (CDTB) of 500 documents. Preliminary evaluation justifies the appropriateness of the CDT scheme to Chinese discourse analysis and the usefulness of our manually annotated CDTB corpus.
Effects of weighted vests on attention, impulse control, and on-task behavior in children with attention deficit hyperactivity disorder.

In this study, we examined the effectiveness of using weighted vests for improving attention, impulse control, and on-task behavior in children with attention deficit hyperactivity disorder (ADHD).  In a randomized, two-period crossover design, 110 children with ADHD were measured using the Conners' Continuous Performance Test-II (CPT-II) task.  In the weighted vest condition, the participants did show significant improvement in all three attentional variables of the CPT-II task, including inattention; speed of processing and responding; consistency of executive management; and three of four on-task behaviors, including off task, out of seat, and fidgets. No significant improvements in impulse control and automatic vocalizations were found.  Although wearing a weighted vest is not a cure-all strategy, our findings support the use of the weighted vest to remedy attentional and on-task behavioral problems of children with ADHD.
High predation is of key importance for dominance of small-bodied zooplankton in warm shallow lakes: evidence from lakes, fish exclosures and surface sediments

The mean body size of limnetic cladocerans decreases from cold temperate to tropical regions, in both the northern and the southern hemisphere. This size shift has been attributed to both direct (e.g. physiological) or indirect (especially increased predation) impacts. To provide further information on the role of predation, we compiled results from several studies of subtropical Uruguayan lakes using three different approaches: (i) field observations from two lakes with contrasting fish abundance, Lakes Rivera and Rodó, (ii) fish exclusion experiments conducted in in-lake mesocosms in three lakes, and (iii) analyses of the Daphnia egg bank in the surface sediment of eighteen lakes. When fish predation pressure was low due to fish kills in Lake Rivera, large-bodied Daphnia appeared. In contrast, small-sized cladocerans were abundant in Lake Rodó, which exhibited a typical high abundance of fish. Likewise, relatively large cladocerans (e.g. Daphnia and Simocephalus) appeared in fishless mesocosms after only 2 weeks, most likely hatched from resting egg banks stored in the surface sediment, but their abundance declined again after fish stocking. Moreover, field studies showed that 9 out of 18 Uruguayan shallow lakes had resting eggs of Daphnia in their surface sediment despite that this genus was only recorded in three of the lakes in summer water samples, indicating that Daphnia might be able to build up populations at low risk of predation. Our results show that medium and large-sized zooplankton can occur in subtropical lakes when fish predation is removed. The evidence provided here collectively confirms the hypothesis that predation, rather than high-temperature induced physiological constraints, is the key factor determining the dominance of small-sized zooplankton in warm lakes.
Vehicle Classification and Speed Estimation Using Combined Passive Infrared/Ultrasonic Sensors

In this paper, a new sensing device that can simultaneously monitor traffic congestion and urban flash floods is presented. This sensing device is based on the combination of passive infrared sensors (PIRs) and ultrasonic rangefinder, and is used for real-time vehicle detection, classification, and speed estimation in the context of wireless sensor networks. This framework relies on dynamic Bayesian Networks to fuse heterogeneous data both spatially and temporally for vehicle detection. To estimate the speed of the incoming vehicles, we first use cross correlation and wavelet transform-based methods to estimate the time delay between the signals of different sensors. We then propose a calibration and self-correction model based on Bayesian Networks to make a joint inference by all sensors about the speed and the length of the detected vehicle. Furthermore, we use the measurements of the ultrasonic and the PIR sensors to perform vehicle classification. Validation data (using an experimental dual infrared and ultrasonic traffic sensor) show a 99% accuracy in vehicle detection, a mean error of 5 kph in vehicle speed estimation, a mean error of 0.7m in vehicle length estimation, and a high accuracy in vehicle classification. Finally, we discuss the computational performance of the algorithm, and show that this framework can be implemented on low-power computational devices within a wireless sensor network setting. Such decentralized processing greatly improves the energy consumption of the system and minimizes bandwidth usage.
Update on the Family Strain Questionnaire: A Tool for the General Screening of Caregiving-Related Problems

The growing prevalence of chronic diseases and home-based treatments has led to the introduction of a large number of instruments for assessing the caregiving-related problems associated with specific diseases, but our Family Strain Questionnaire (FSQ) was designed to provide a basis for general screening and comparison regardless of the disease. We here describe the final validation of its psychometric characteristics.  The FSQ consists of a brief semi-structured interview and 44 dichotomic items, and has now been administered to 811 caregivers (285 were simultaneously administered other questionnaires assessing anxiety and depressive symptoms). After a factorial analysis confirmed the 5-factor structure identified in previous studies (emotional burden, problems in social involvement, need for knowledge about the disease, satisfaction with family relationships, and thoughts about death), we undertook correlation and reliability analyses, and a receiver operating characteristics curve analysis designed to determine the cut-off point for the emotional problems identified by the first factor. Finally, univariate ANOVA with Bonferroni's post-hoc test was used to compare the disease-specific scores.  The validity and reliability of the FSQ is good, and its factorial structure refers to areas that are internationally considered as being of general importance. The semi-structured interview collects information concerning the socio-economic status of caregivers and their convictions/interpretations concerning the diseases of their patients.  The FSQ can be used as a single instrument for the general assessment of caregiving-related problems regardless of the reference disease. This makes it possible to reduce administration and analysis times, and compare the problems experienced by the caregivers of patients with different diseases.
FUSION OF LIDAR DATA AND AERIAL IMAGERY FOR A MORE COMPLETE SURFACE DESCRIPTION

Photogrammetry is the traditional method of surface reconstruction such as the generation of DTMs. Recently, LIDAR emerged as a new technology for rapidly capturing data on physical surfaces. The high accuracy and automation potential results in a quick delivery of DEMs/DTMs derived from the raw laser data. The two methods deliver complementary surface information. Thus it makes sense to combine data from the two sensors to arrive at a more robust and complete surface reconstruction. This paper describes two aspects of merging aerial imagery and LIDAR data. The establishment of a common reference frame is an absolute prerequisite. We solve this alignment problem by utilizing sensor-invariant features. Such features correspond to the same object space phenomena, for example to breaklines and surface patches. Matched sensor invariant features lend themselves to establishing a common reference frame. Feature-level fusion is performed with sensor specific features that are related to surface characteristics. We show the synergism between these features resulting in a richer and more abstract surface description.
Augmented Reality in Architectural Construction , Inspection , and Renovation

We present our preliminary work in developing augmented reali ty systems to improve methods for the construction, inspection, and renovatio n of architectural structures. Augmented reality systems add virtual computer-generated mate rial to the surrounding physical world. Our augmented reality systems use see-through headworn displays to overlay graphics and sounds on a person’s naturally occurring vision and hearing. As the person moves about, the position and orientation of his or her head is tracked, allowing the overlaid material to remai n tied to the physical world. We describe an experimental augmented reality system tha t shows the location of columns behind a finished wall, the location of re-bar s inside one of the columns, and a structural analysis of the column. We also discuss our pre liminary work in developing an augmented reality system for improving the constructio n of spaceframes. Potential uses of more advanced augmented reality systems are presented.
Machine Learning Techniques—Reductions Between Prediction Quality Metrics

Machine learning involves optimizing a loss function on unlabeled data points given examples of labeled data points, where the loss function measures the performance of a learning algorithm. We give an overview of techniques, called reductions, for converting a problem of minimizing one loss function into a problem of minimizing another, simpler loss function. This tutorial discusses how to create robust reductions that perform well in practice. The reductions discussed here can be used to solve any supervised learning problem with a standard binary classification or regression algorithm available in any machine learning toolkit. We also discuss common design flaws in folklore reductions.
Two Decades of MIMO Design Tradeoffs and Reduced-Complexity MIMO Detection in Near-Capacity Systems

A pair of salient tradeoffs have driven the multiple-input multiple-output (MIMO) systems developments. More explicitly, the early era of MIMO developments was predominantly motivated by the multiplexing-diversity tradeoff between the Bell Laboratories layered space-time and space-time block coding. Later, the linear dispersion code concept was introduced to strike a flexible tradeoff. The more recent MIMO system designs were motivated by the performance-complexity tradeoff, where the spatial modulation and space-time shift keying concepts eliminate the problem of inter-antenna interference and perform well with the aid of low-complexity linear receivers without imposing a substantial performance loss on generic maximum-likelihood/max a posteriori -aided MIMO detection. Against the background of the MIMO design tradeoffs in both uncoded and coded MIMO systems, in this treatise, we offer a comprehensive survey of MIMO detectors ranging from hard decision to soft decision. The soft-decision MIMO detectors play a pivotal role in approaching to the full-performance potential promised by the MIMO capacity theorem. In the near-capacity system design, the soft-decision MIMO detection dominates the total complexity, because all the MIMO signal combinations have to be examined, when both the channel’s output signal and the a priori log-likelihood ratios gleaned from the channel decoder are taken into account. Against this background, we provide reduced-complexity design guidelines, which are conceived for a wide-range of soft-decision MIMO detectors.
Comparison of the subject-oriented and the Petri net based approach for business process automation

The subject-oriented modelling approach  significally differs from the classic Petri net based approach of many business process modeling languages like EPC [9], Business Process Model and Notation (BPMN) [11], and also Yet Another Workflow Language (YAWL) . In this work, we compare the two approaches by modeling a case study called "Procure to Pay"[3], a typical business process where some equipment for a construction site is rented and finally paid. The case study is not only modelled but also automated using the Metasonic Suite for the subject-oriented and YAWL for the Petri net based approach.
Stochastic neighbor embedding (SNE) for dimension reduction and visualization using arbitrary divergences

We present a systematic approach to the mathematical treatment of the t-distributed stochastic neighbor embedding (t-SNE) and the stochastic neighbor embedding (SNE) method. This allows an easy adaptation of the methods or exchange of their respective modules. In particular, the divergence which measures the difference between probability distributions in the original and the embedding space can be treated independently from other components like, e.g. the similarity of data points or the data distribution. We focus on the extension for different divergences and propose a general framework based on the consideration of Fréchet-derivatives. This way the general approach can be adapted to the user specific needs. & 2012 Elsevier B.V. All rights reserved.
Beyond Security and Privacy Perception: An Approach to Biometric Authentication Perception Change

The aim of the paper is to shed light on the factors affecting perception shifts in biometrics authentication. This study explores trust relationships in the adoption of biometrics using the valence framework to understand and explain the individual’s evaluation of risk concerning biometrics. Hypotheses are developed to suggest that individuals’ intention to use biometrics is influenced by trust in the vendor. An experiment to test the hypotheses is described. Expected contributions, limitations, and possibilities for future research are noted.
Simultaneous Real-Time Segmentation of Diversified Barcode Symbols in Complex Background

Traditional barcode localization methods based on image analysis are sensitive to the types of the target symbols and the environment they are applied to. To develop intelligent barcode reading system used in industry, a real-time region based barcode segmentation approach which is available for various types of linear and two-dimensional symbols is proposed. The two-stage approach consists of the target region connection part by orientation detection and morphological operation, and the target region detection part by efficient contour based connected component labeling. The result of target location, which is represented by the coordinates of the upper left corner and the lower right corner of its bounding box, is robust to the barcode orientation, noise, and uneven environment illumination. The segmentation method is proved to work well in the real-time barcode reading system. In the experiments, 100% of the DATAMATRIX codes, 99.3% of the Code39 symbols and 98.7% PDF417 codes are corrected segmented.
Sentence boundary detection in arabic speech

This paper presents an automatic system to detect sentence boundaries in speech recognition transcripts. Two systems were developed that use independent sources of information. One is a linguistic system that uses linguistic features in a statistical language model while the other is an acoustic system that uses prosodic features in a feed-forward neural network model. A third system was developed that combines the scores from the acoustic and the linguistic systems in a Maximum-Likelihood framework. All systems outlined in this paper are essentially language-independent but all our experiments were conducted on the Arabic Broadcast News speech recognition transcripts. Our experiments show that while the acoustic system outperforms the linguistic system, the combined system achieves the best performance at detecting sentence boundaries.
A Real-time Underwater Object Detection Algorithm for Multibeam Forward Looking Sonar ?

A novel algorithm for the detection of underwater man-made objects in forwardlooking sonar imagery is proposed. The algorithm takes advantage of the integral-image representation to quickly compute features, and progressively reduces the computational load by working on smaller portions of the image along the detection process phases. By adhering to the proposed scheme, real-time detection on sonar data onboard an autonomous vehicle is made possible. The proposed method does not require training data, as it dynamically takes into account environmental characteristics of the sensed sonar data. The proposed approach has been implemented and integrated into the software system of the Gemellina autonomous surface vehicle, and is able to run in real time. The validity of the proposed approach is demonstrated on real experiments carried out at sea with the Gemellina autonomous surface vehicle.
Salt stress upregulates periplasmic arabinogalactan proteins: using salt stress to analyse AGP function.

Arabinogalactan proteins (AGPs) are implicated in cell expansion by unknown mechanisms, thus AGP content and cell-expansion rate might be correlated. We used Yariv reagent to quantify release rates and distribution of AGP at the cell surface of tobacco BY-2 cells: plasma membrane (M); soluble periplasmic AGPs released by cell rupture (S); cell wall (W); and growth medium (Gsink). In contrast to earlier reports, we observed massive upregulation of AGPs in salt-stressed cells, and hence the absence of a simple, direct cause-and-effect relationship between growth rate and AGP release. There was a more subtle connection. A dynamic flux model, M-->S-->W-->Gsink, indicated that turnover was nondegradative, with little free diffusion of AGPs trapped in the pectic matrix of nonadapted cells where transmural migration of high molecular-weight AGPs occurred mainly by plug flow (apposition and extrusion). In contrast, however, an up to sixfold increased AGP release rate in the slower-growing salt-adapted cells indicated a greatly increased rate of AGP diffusion through a much more highly porous pectic network. We hypothesize that classical AGPs act as pectin plasticizers. This explains how beta-D-glycosyl Yariv reagents might inhibit expansion growth by crosslinking monomeric AGPs, and thus mimic an AGP loss-of-function mutation.
Assimilation of Remotely-Sensed Leaf Area Index into a Dynamic Vegetation Model for Gross Primary Productivity Estimation

Quantitative estimation of the magnitude and variability of gross primary productivity (GPP) is required to study the carbon cycle of the terrestrial ecosystem. Using ecosystem models and remotely-sensed data is a practical method for accurately estimating GPP. This study presents a method for assimilating high-quality leaf area index (LAI) products retrieved from satellite data into a process-oriented Lund-Potsdam-Jena dynamic global vegetation model (LPJ-DGVM) to acquire accurate GPP. The assimilation methods, including the Ensemble Kalman Filter (EnKF) and a proper orthogonal decomposition (POD)-based ensemble four-dimensional (4D) variational assimilation method (PODEn4DVar), incorporate information provided by observations into the model to achieve a better agreement between the model-estimated and observed GPP. The LPJ-POD scheme performs better with a correlation coefficient of r = 0.923 and RMSD of 32.676 gC/m2/month compared with the LPJ-EnKF scheme (r = 0.887, RMSD = 38.531 gC/m2/month) and with no data assimilation (r = 0.840, RMSD = 45.410 gC/m2/month). Applying the PODEn4DVar method into LPJ-DGVM for simulating GPP in China shows that the annual amount of GPP in China varied between 5.92 PgC and 6.67 PgC during 2003–2012 with an annual mean of 6.35 PgC/yr. This study demonstrates that integrating remotely-sensed data with dynamic global vegetation models through data assimilation methods has potential in optimizing the simulation and that the LPJ-POD scheme shows better performance in improving GPP estimates, which can provide a favorable way for accurately estimating dynamics of ecosystems.