nils-herrmann/clean_scirepeval huggingface dataset:
An Improved Automatic Algorithm for Global Eddy Tracking Using Satellite Altimeter Data

In this paper, we propose a new hybrid mesoscale eddy tracking method to enhance the eddy tracking accuracy from global satellite altimeter data. This method integrates both physical and geometric eddy properties (including the distance between eddies, the area and amplitude of eddy, and the shape of the eddy edge) via the output of detection and the calculation of Hausdorff distance, which could describe the similarity between eddy boundaries. We applied the proposed hybrid method to several previously reported eddies and compared the results with those from two traditional tracking methods. A quantitative comparison indicates that the hybrid algorithm can better reveal eddy signals in terms of their spatial scale, amplitude, lifespan, and splitting. The hybrid method was used for global mesoscale eddies tracking from 1993 to 2015. Global distributions of net eddy numbers revealed that the sources of eddies are located along the eastern boundaries of the world oceans, while the sinks of eddies are located along the western boundaries. The lifespan distribution of eddies exhibited steep growth from high and low latitudes to middle latitudes. A clear divergent pathway demonstrates that cyclonic/anticyclonic eddies tend to travel poleward/equatorward in the world oceans.
Robust single particle tracking in live cell time-lapse sequences

Single-particle tracking (SPT) is often the rate-limiting step in live-cell imaging studies of subcellular dynamics. Here we present a tracking algorithm that addresses the principal challenges of SPT, namely high particle density, particle motion heterogeneity, temporary particle disappearance, and particle merging and splitting. The algorithm first links particles between consecutive frames and then links the resulting track segments into complete trajectories. Both steps are formulated as global combinatorial optimization problems whose solution identifies the overall most likely set of particle trajectories throughout a movie. Using this approach, we show that the GTPase dynamin differentially affects the kinetics of long- and short-lived endocytic structures and that the motion of CD36 receptors along cytoskeleton-mediated linear tracks increases their aggregation probability. Both applications indicate the requirement for robust and complete tracking of dense particle fields to dissect the mechanisms of receptor organization at the level of the plasma membrane.
Acoustic source location in a three-dimensional space using crosspower spectrum phase

A microphone array can be used to locate a dominant acoustic source in a given environment. This capability is successfully employed to locate an active talker in teleconferencing or other multi-speaker applications. In this work the source location is obtained in two steps: 1) a Time Difference Of Arrival (TDOA) computation between the signals of the array; 2) an \optimal" source location based on the interchannel delay estimates and on a geometrical description of the sensor arrangement. The Crosspower Spectrum Phase technique was used for TDOA estimation, while a Maximum Likelihood approach was followed to derive the source coordinates. Source location experiments in a three-dimensional space were performed by means of an array of 8 microphones. For this purpose both a loudspeaker and a real talker were used to collect data in a large noisy and reverberant room.
The Theory of Planned Behavior and Parental Involvement : A Theoretical Framework for Narrowing the Achievement Gaps

This theoretical paper presents a theoretical model that shows the effect of cultural aspects mentioned in the theory of planned behavior, namely, subject norms on parental involvement behaviors of parents. The paper reports on the findings of several studies that demonstrate the relationship between parental involvement and student achievement in school in several cultural contexts. The findings and conclusions of this theoretical paper suggest that emphasis needs to be placed on increasing parental involvement in children’s education as a tool to decrease achievement gaps between students. More specifically, parental involvement can be seen as a tool to overcome achievement gaps caused by socioeconomic differences between students’ families.
Identifying Users With Alternate Behaviors of Lurking and Active Participation in Multilayer Social Networks

With the growing complexity of scenarios relating to online social networks (OSNs), there is an emergence of effective models and methods for understanding the characteristics and dynamics of multiple interconnected types of user relations. Profiles on different OSNs belonging to the same user can be linked using the multilayer structure, opening to unprecedented opportunities for user behavior analysis in a complex system. In this paper, we leverage the importance of studying the dichotomy between information-producers (contributors) and information-consumers (lurkers), and their interplay over a multilayer network, in order to effectively analyze such different roles a user may take on different OSNs. In this respect, we address the novel problem of identification and characterization of opposite behaviors that users may alternately exhibit over multiple layers of a complex network. We propose the first ranking method for alternate lurker-contributor behaviors on a multilayer OSN, dubbed mlALCR. Performance of mlALCR has been assessed quantitatively as well as qualitatively, and comparatively against methods designed for ranking either contributors or lurkers, on four real-world multilayer networks. Empirical evidence shows the significance and uniqueness of mlALCR in being able to mine alternate lurker-contributor behaviors over different layer networks.
Telecommunication Fraud Detection Using Data Mining techniques

This document presents the final report of the thesis “Telecommunication Fraud Detection Using Data Mining Techniques”, were a study is made over the effect of the unbalanced data, generated by the Telecommunications Industry, in the construction and performance of classifiers that allows the detection and prevention of frauds. In this subject, an unbalanced data set is characterized by an uneven class distribution where the amount of fraudulent instances (positive) is substantially smaller than the amount normal instances (negative). This will result in a classifier which is most likely to classify data has belonging to the normal class then to the fraud class. At first, an overall inspection is made over the data characteristics and the Naive Bayes model, which is the classifier selected to do the anomaly detection on these experiments. After the characteristics are presented, a feature engineering stage is done with the intent to extend the information contained in the data creating a depper relation with the data itself and model characteristics. A previously proposed solution that consists on undersampling the most abundant class (normal) before building the model, is presented and tested. In the end, the new proposals are presented. The first proposal is to study the effects of changing the intrinsic class distribution parameter in the Naive Bayes model and evaluate its performance. The second proposal consists in estimating margin values that when applied to the model output, attempt to bring more positive instances from previous negative classification. All of these suggested models are validated over a monte-carlo experiment, using data with and without the engineered features.
Teleoperation control of Baxter robot using body motion tracking

In this paper, we use Kinect Xbox 360 sensor to implement the motion control of Baxter robot, a semi humanoid robot with limbs of 7 DOF joints with collision avoidance capabilities. Two different methods using vector approach and inverse kinematics approach have been designed to achieve a given task. Primitive experiments have been carried out to verify the effectiveness of the developed methods. Human motions is captured by Kinect sensor and calculated with Processing Software using SimpleOpenNI wrapper for OpenNI and NITE. UDP protocol is adopted to send reference motion to Baxter robot. Python and RosPy script programming kit is used to calculate joint angles of Baxter robot based on vector approach and inverse kinematics approach. The experimental results demonstrate that both of our proposed approaches have achieved satisfactory performance.
Mechanisms of action in youth psychotherapy.

In this review, we address a basic, but unanswered, question about psychosocial interventions for youth: How does psychotherapy work?



We propose a framework for using mediation analysis to answer this question, and we review the youth therapy outcome literature for evidence on mediating mechanisms. We focus our review on clinical trials of empirically supported treatments for youth anxiety, depression, and disruptive behavior (N = 67).



Contrary to previous reports indicating that potential mediators are rarely assessed, 63% of the studies included measures of potential mediating mechanisms in their designs. Across treatment domains, percentages ranged from 22% of the studies of learning-based interventions for anxiety (i.e., systematic desensitization, modeling, and reinforced practice) to 91% of parent training investigations. Despite the rather extensive assessment of potential mediators, only six studies included any attempt to use the measures in a formal mediation test. Thus, despite the positive effects of treatments and surprisingly ample assessment of mediators, we still know remarkably little about how youth psychotherapies work.



We note common problems that hampered mediation testing (e.g., the design of many trials made it difficult to determine the temporal order of change in the mechanism and outcome), and we offer recommendations for improving study design to better assess mechanisms of therapeutic action. We also note the need to test mediation among referred youth treated in representative practice settings to complement the laboratory-based evidence on therapy mechanisms that prevails to date.
LSCitter: building social machines by augmenting existing social networks with interaction models

We present LSCitter, an implemented framework for supporting human interaction on social networks with formal models of interaction, designed as a generic tool for creating social machines on existing infrastructure. Interaction models can be used to choreograph distributed systems, providing points of coordination and communication between multiple interacting actors. While existing social networks specify how interactions happen---who messages go to and when, the effects of carrying out actions---these are typically implicit, opaque and non user-editable. Treating interaction models as first class objects allows the creation of electronic institutions, on which users can then choose the kinds of interaction they wish to engage in, with protocols which are explicit, visible and modifiable. However, there is typically a cost to users to engage with these institutions. In this paper we introduce the notion of "shadow institutions", where actions on existing social networks are mapped onto formal interaction protocols, allowing participants access to computational intelligence in a seamless, zero-cost manner to carry out computation and store information.
Broadband RF energy harvesting system covering CDMA, GSM900, GSM1800, 3G bands with inherent impedance matching

A broadband RF energy harvesting system for harvesting energy from 800 MHz to 2.5 GHz is presented. A passive voltage amplification scheme and inherent matching technique using higher antenna impedance is employed to enhance the output voltage of the rectifier. A planar broadband folded dipole antenna having an input impedance of 300Ω is designed and fabricated. The measured VSWR is less than 2 from 825 MHz to 1.27 GHz and 1.67 GHz to 2.17 GHz. A RF energy harvesting system consisting of the high impedance antenna, Schottky diode based rectifier and a self oscillating boost converter is used to charge a 12 μAh battery to 3.8 V. At 844 MHz for a received power of -10.7 dBm the battery charges to 3.8 V. At 1.73 GHz the battery charges to 3.8 V for a received power of -5.72 dBm. Also using the designed RF energy harvesting system, a voltage of 4.4 V is measured for a load of 3 kΩ at 10 cm from a cell phone making a call.
Comparative Evaluation of Binary Features

Performance evaluation of salient features has a long-standing tradition in computer vision. In this paper, we fill the gap of evaluation for the recent wave of binary feature descriptors, which aim to provide robustness while achieving high computational efficiency. We use established metrics to embed our assessment into the body of existing evaluations, allowing us to provide a novel taxonomy unifying both traditional and novel binary features. Moreover, we analyze the performance of different detector and descriptor pairings, which are often used in practice but have been infrequently analyzed. Additionally, we complement existing datasets with novel data testing for illumination change, pure camera rotation, pure scale change, and the variety present in photo-collections. Our performance analysis clearly demonstrates the power of the new class of features. To benefit the community, we also provide a website for the automatic testing of new description methods using our provided metrics and datasets (www.cs.unc.edu/feature-evaluation).
Research on DALI and Development of Master-Slave module

The DALI-protocol is a new standard for communication with electronic control gears in the lighting industry. Besides the background information and advantage, its electronic specification and communication specification are expounded. Then a design of master-slave module of DALI is presented in the paper, which realizes the DALI communication based on MC68HC908. The receiving and transmitting circuits in the module and the software frame are described with emphasis, which are designed according to the specification of DALI system and improved in some aspects. The module can also be applied in lighting control for conserving energy and more diversity, and it is more applicable with some special modifications for the popularity of DALI in the field of illumination
Social Networks Applied

Traditionally, social-network models have been descriptive rather than predictive. They’re built at a very coarse level, typically with only a few global parameters, and aren’t useful for predicting the network’s behavior. In the past, this was due largely to lack of data; the networks available for study were small and few and contained minimal information about each node. Fortunately, the Internet’s rise has changed this dramatically. Massive quantities of data on large social networks are available from blogs, knowledge-sharing sites, collaborative-filtering systems, online gaming, social-networking sites, newsgroups, chat rooms, and so on. These networks typically number in the tens of thousands to millions of nodes. They often contain sufficient information to build models of individual nodes, which we can then assemble into models of the networks they’re part of. This gives us an unprecedented level of detail in social-network analysis, along with the potential for new understanding, useful predictions, and their productive use in decision making. My colleagues and I have begun to build social-network models at this scale using data from the Epinions knowledge-sharing site, the EachMovie collaborative-filtering system, and others.1,2 These models let us design viralmarketing plans that maximize positive word of mouth among customers. In our experiments, this has made it possible to achieve much higher profits than if we ignored interactions among customers and the corresponding network effects, as traditional marketing does.
A Single-center Analysis on the Learning Curve of Male-to-Female Penoscrotal Vaginoplasty by Multiple Surgical Measures.

To asses and quantify the learning curve (LC) of the penoscrotal inversion flap vaginoplasty (PSV).



We retrospectively reviewed clinical records of 69 patients who underwent PSV from January 2005 to January 2015. Two validated methods were used: a scatterplot representation and a splitting group. We selected as primary outcomes the operative time and vaginal depth. Surgical outcomes including blood losses, hospital stay, and postoperative complications such as vaginal stenosis or atresia or urethral meatus stenosis were also evaluated.



The overall median operative time was 245 minutes. Severe intraoperative complications were not reported. The overall incidence of postoperative major complications was 21.7 %, most of them being urethral issues. The splitting group analysis revealed a statistically remarkable difference between groups for the operative time (P < .01), the vaginal depth (P = .01), the hospital stay (P < .01), and the intraoperative complication rate (P = .01). On the contrary, no differences were evidenced between the cohorts for the amount of blood loss (P = .08). The scatterplot logarithmic analysis demonstrated a clear visible LC for most parameters. The operative time showed a sharp decrease within the first 20-30 cases, reaching a plateau after 40 cases. Considering the analysis of the vaginal depth, the logarithmic scatterplot curve evidenced a slight increase within the first 10 cases, reaching a clear stabilization after nearly 30-40 cases.



An evident LC for PSV is detectable, consisting of at least 40 cases needed to the surgical team to develop adequate skills to guarantee a safe and high-quality procedure.
Epinephrine enhancement of human memory consolidation: Interaction with arousal at encoding

Abundant evidence indicates that endogenous stress hormones like epinephrine and cortisol modulate memory consolidation in animals. Despite this evidence, there has been no demonstration that endogenous stress hormones modulate memory consolidation in humans. In the present study, healthy subjects viewed a series of 21 slides, and immediately after received an intravenous infusion of either saline or epinephrine (40 or 80 ng/kg/min). Memory for the first three (primacy) and last three (recency) slides viewed was assessed with an incidental free recall test one week later. Epinephrine dose-dependently increased memory for the primacy slides, but did not affect memory of the recency slides. A subsequent experiment involving new subjects revealed significantly higher electrodermal responses to the primacy compared with recency slides. These findings support the view (Gold & McGaugh, 1975) that endogenous stress hormones modulate memory consolidation for experiences that induce their release. Additionally, they suggest that in humans these hormones may interact with the degree of arousal at initial encoding of information to modulate memory consolidation processes for that information.
Self-compassion increases self-improvement motivation.

Can treating oneself with compassion after making a mistake increase self-improvement motivation? In four experiments, the authors examined the hypothesis that self-compassion motivates people to improve personal weaknesses, moral transgressions, and test performance. Participants in a self-compassion condition, compared to a self-esteem control condition and either no intervention or a positive distraction control condition, expressed greater incremental beliefs about a personal weakness (Experiment 1); reported greater motivation to make amends and avoid repeating a recent moral transgression (Experiment 2); spent more time studying for a difficult test following an initial failure (Experiment 3); exhibited a preference for upward social comparison after reflecting on a personal weakness (Experiment 4); and reported greater motivation to change the weakness (Experiment 4). These findings suggest that, somewhat paradoxically, taking an accepting approach to personal failure may make people more motivated to improve themselves.
Identifying extremism in social media with multi-view context-aware subset optimization

Cyber threat intelligence and security informatics play critical roles in the identification of the principal influencers and threats associated with criminal activity and extremism in web-based communities. This paper presents an effective, dynamic learning framework utilizing domain specific context information to design a novel classification model that can robustly identify malicious social media posts (e.g. enrollment propaganda for extremist groups) with expressions of extremism or criminal intent. Research towards automated identification of extreme online posts and their associated key suspects and threats faces numerous challenges, 1) Online data, particularly social media data, originated from numerous independent and heterogeneous sources are largely unstructured. 2) The tactics, techniques, and procedures (TTPs) of criminal activity and extremism are constantly evolving. 3) There are limited ground truth data to support the development of effective classification technologies. In this paper, we present a human-machine collaborative, semi-supervised learning system that can efficiently and effectively identify malicious social media posts in presence of these challenges. Our system and framework develops an initial classifier from limited annotated data and in an interactive manner evolves dynamically into a sophisticated model using shortlisted relevant samples, identified via a graph-based optimization method, solvable by maximum flow algorithm. This same method also may be used to refine the classifier as TTPs evolve. Under this framework, the classifier performance converges faster using roughly 1–2 orders of magnitude fewer annotated samples, as compared to fully supervised solutions, resulting in a reasonably acceptable accuracy of nearly 80%. We validate our framework using a large collection of English and non-English flagged words extracted from three web-based forums and manually verified by multiple independent annotators.
Compressive coded aperture imaging

Nonlinear image reconstruction based upon sparse representations of images has recently received widespread attention with the emerging framework of compressed sensing (CS). This theory indicates that, when feasible, judicious selection of the type of distortion induced by measurement systems may dramatically improve our ability to perform image reconstruction. However, applying compressed sensing theory to practical imaging systems poses a key challenge: physical constraints typically make it infeasible to actually measure many of the random projections described in the literature, and therefore, innovative and sophisticated imaging systems must be carefully designed to effectively exploit CS theory. In video settings, the performance of an imaging system is characterized by both pixel resolution and field of view. In this work, we propose compressive imaging techniques for improving the performance of video imaging systems in the presence of constraints on the focal plane array size. In particular, we describe a novel yet practical approach that combines coded aperture imaging to enhance pixel resolution with superimposing subframes of a scene onto a single focal plane array to increase field of view. Specifically, the proposed method superimposes coded observations and uses wavelet-based sparsity recovery algorithms to reconstruct the original subframes. We demonstrate the effectiveness of this approach by reconstructing with high resolution the constituent images of a video sequence.