nils-herrmann/clean_scirepeval huggingface dataset:
Discovering influencers for marketing in the blogosphere

Discovering influential bloggers will not only allow us to understand better the social activities taking place in the blogosphere, but will also provide unique opportunities for sales and advertising. In this paper, we develop an MIV (marketing influential value) model to evaluate the influential strength and identify the influential bloggers in the blogosphere. We analyze three dimensions of blog characteristics (network-based, content-based, and activeness-based factors) and utilize an artificial neural network (ANN) to discover potential bloggers. Based on peer and official evaluations, the experimental results show that the proposed framework outperforms two social-network-based methods (out-degree and betweenness centrality algorithms) and two content-based mechanisms (review rating and popular author approaches). The proposed framework can be effectively applied to support marketers or advertisers in promoting their products or services.
Time and cognitive load in working memory.

According to the time-based resource-sharing model (P. Barrouillet, S. Bernardin, & V. Camos, 2004), the cognitive load a given task involves is a function of the proportion of time during which it captures attention, thus impeding other attention-demanding processes. Accordingly, the present study demonstrates that the disruptive effect on concurrent maintenance of memory retrievals and response selections increases with their duration. Moreover, the effect on recall performance of concurrent activities does not go beyond their duration insofar as the processes are attention demanding. Finally, these effects are not modality specific, as spatial processing was found to disrupt verbal maintenance. These results suggest a sequential and time-based function of working memory in which processing and storage rely on a single and general purpose attentional resource needed to run executive processes devoted to constructing, maintaining, and modifying ephemeral representations.
Feature Cross-Substitution in Adversarial Classification

The success of machine learning, particularly in supervised settings, has led to numerous attempts to apply it in adversarial settings such as spam and malware detection. The core challenge in this class of applications is that adversaries are not static data generators, but make a deliberate effort to evade the classifiers deployed to detect them. We investigate both the problem of modeling the objectives of such adversaries, as well as the algorithmic problem of accounting for rational, objective-driven adversaries. In particular, we demonstrate severe shortcomings of feature reduction in adversarial settings using several natural adversarial objective functions, an observation that is particularly pronounced when the adversary is able to substitute across similar features (for example, replace words with synonyms or replace letters in words). We offer a simple heuristic method for making learning more robust to feature cross-substitution attacks. We then present a more general approach based on mixed-integer linear programming with constraint generation, which implicitly trades off overfitting and feature selection in an adversarial setting using a sparse regularizer along with an evasion model. Our approach is the first method for combining an adversarial classification algorithm with a very general class of models of adversarial classifier evasion. We show that our algorithmic approach significantly outperforms state-of-the-art alternatives.
Association between thyroid autoantibodies and miscarriage and preterm birth: meta-analysis of evidence

To evaluate the association between thyroid autoantibodies and miscarriage and preterm birth in women with normal thyroid function. To assess the effect of treatment with levothyroxine on pregnancy outcomes in this group of women.



Systematic review and meta-analysis.



Medline, Embase, Cochrane Library, and SCISEARCH (inception-2011) without any language restrictions. We used a combination of key words to generate two subsets of citations, one indexing thyroid autoantibodies and the other indexing the outcomes of miscarriage and preterm birth.



Studies that evaluated the association between thyroid autoantibodies and pregnancy outcomes were selected in a two stage process. Two reviewers selected studies that met the predefined and explicit criteria regarding population, tests, and outcomes.



Odds ratios from individual studies were pooled separately for cohort and case-control studies with the random effects model.



30 articles with 31 studies (19 cohort and 12 case-control) involving 12,126 women assessed the association between thyroid autoantibodies and miscarriage. Five studies with 12,566 women evaluated the association with preterm birth. Of the 31 studies evaluating miscarriage, 28 showed a positive association between thyroid autoantibodies and miscarriage. Meta-analysis of the cohort studies showed more than tripling in the odds of miscarriage with the presence of thyroid autoantibodies (odds ratio 3.90, 95% confidence interval 2.48 to 6.12; P < 0.001). For case-control studies the odds ratio for miscarriage was 1.80, 1.25 to 2.60; P = 0.002). There was a significant doubling in the odds of preterm birth with the presence of thyroid autoantibodies (2.07, 1.17 to 3.68; P = 0.01). Two randomised studies evaluated the effect of treatment with levothyroxine on miscarriage. Both showed a fall in miscarriage rates, and meta-analysis showed a significant 52% relative risk reduction in miscarriages with levothyroxine (relative risk 0.48, 0.25 to 0.92; P=0.03). One study reported on the effect of levothyroxine on the rate of preterm birth, and noted a 69% relative risk reduction (0.31, 0.11 to 0.90).



The presence of maternal thyroid autoantibodies is strongly associated with miscarriage and preterm delivery. There is evidence that treatment with levothyroxine can attenuate the risks.
Detection of Human Breathing and Heartbeat by Remote Radar

The radar capable to detect and perform diagnostics of a state of the human being behind obstacles and in conditions of bad visibility could be used for the manifold applications. The potential consumers of the radar are: rescue services, antiterrorist detachments and law-enforcement bodies. It is possible also to use this device in medicine. In this paper, the experiments with using of continuouswave subsurface radar are described. The recorded oscillograms and their frequency spectrums for heartbeat, respiration and articulation of a man, which is taking place behind an obstacle (wall), are presented.
Efficient Processing of Uncertain Events in Rule-Based Systems

There is a growing need for systems that react automatically to events. While some events are generated externally and deliver data across distributed systems, others need to be derived by the system itself based on available information. Event derivation is hampered by uncertainty attributed to causes such as unreliable data sources or the inability to determine with certainty whether an event has actually occurred, given available information. Two main challenges exist when designing a solution for event derivation under uncertainty. First, event derivation should scale under heavy loads of incoming events. Second, the associated probabilities must be correctly captured and represented. We present a solution to both problems by introducing a novel generic and formal mechanism and framework for managing event derivation under uncertainty. We also provide empirical evidence demonstrating the scalability and accuracy of our approach.
Customer segmentation and strategy development based on customer lifetime value: A case study

The more a marketing paradigm evolves, the more long-term relationship with customers gains its importance. CRM, a recent marketing paradigm, pursues long-term relationship with profitable customers. It can be a starting point of relationship management to understand and measure the true value of customers since marketing management as a whole is to be deployed toward the targeted customers and profitable customers, to foster customers’ full profit potential. Corporate success depends on an organization’s ability to build and maintain loyal and valued customer relationships. Therefore, it is essential to build refined strategies for customers based on their value. In this paper, we propose a framework for analyzing customer value and segmenting customers based on their value. After segmenting customers based on their value, strategies building according to customer segment will be illustrated through a case study on a wireless telecommunication company. q 2005
Using parallel stiffness to achieve improved locomotive efficiency with the Sandia STEPPR robot

In this paper we introduce STEPPR (Sandia Transmission-Efficient Prototype Promoting Research), a bipedal robot designed to explore efficient bipedal walking. The initial iteration of this robot achieves efficient motions through powerful electromagnetic actuators and highly back-drivable synthetic rope transmissions. We show how the addition of parallel elastic elements at select joints is predicted to provide substantial energetic benefits: reducing cost of transport by 30 to 50 percent. Two joints in particular, hip roll and ankle pitch, reduce dissipated power over three very different gait types: human walking, human-like robot walking, and crouched robot walking. Joint springs based on this analysis are tested and validated experimentally. Finally, this paper concludes with the design of two unique parallel spring mechanisms to be added to the current STEPPR robot in order to provide improved locomotive efficiency.
Brain-Computer Interfaces Based on Visual Evoked Potentials

Recently, electroencephalogram (EEG)-based brain- computer interfaces (BCIs) have become a hot spot in the study of neural engineering, rehabilitation, and brain science. In this article, we review BCI systems based on visual evoked potentials (VEPs). Although the performance of this type of BCI has already been evaluated by many research groups through a variety of laboratory demonstrations, researchers are still facing many difficulties in changing the demonstrations to practically applicable systems. On the basis of the literature, we describe the challenges in developing practical BCI systems. Also, our recent work in the designs and implementations of the BCI systems based on steady-state VEPs (SSVEPs) is described in detail. The results show that by adequately considering the problems encountered in system design, signal processing, and parameter optimization, SSVEPs can provide the most useful information about brain activities using the least number of electrodes. At the same time, system cost could be greatly decreased and usability could be readily improved, thus benefiting the implementation of a practical BCI.
Mindfulness practice leads to increases in regional brain gray matter density

Therapeutic interventions that incorporate training in mindfulness meditation have become increasingly popular, but to date little is known about neural mechanisms associated with these interventions. Mindfulness-Based Stress Reduction (MBSR), one of the most widely used mindfulness training programs, has been reported to produce positive effects on psychological well-being and to ameliorate symptoms of a number of disorders. Here, we report a controlled longitudinal study to investigate pre-post changes in brain gray matter concentration attributable to participation in an MBSR program. Anatomical magnetic resonance (MR) images from 16 healthy, meditation-naïve participants were obtained before and after they underwent the 8-week program. Changes in gray matter concentration were investigated using voxel-based morphometry, and compared with a waiting list control group of 17 individuals. Analyses in a priori regions of interest confirmed increases in gray matter concentration within the left hippocampus. Whole brain analyses identified increases in the posterior cingulate cortex, the temporo-parietal junction, and the cerebellum in the MBSR group compared with the controls. The results suggest that participation in MBSR is associated with changes in gray matter concentration in brain regions involved in learning and memory processes, emotion regulation, self-referential processing, and perspective taking.
Cutting the last wires for mobile communications by microwave power transfer

The advancements in microwave power transfer (MPT) over recent decades have enabled wireless power transfer over long distances. The latest breakthroughs in wireless communication - massive MIMO, small cells, and millimeterwave communication - make wireless networks suitable platforms for implementing MPT. This can lead to the elimination of the “last wires” connecting mobile devices to the grid for recharging, thereby tackling a huge long-standing ICT challenge. Furthermore, the seamless integration between MPT and wireless communication opens up a new area called wirelessly powered communications (WPC) where many new research directions arise, such as simultaneous information and power transfer, WPC network architectures, and techniques for safe and efficient WPC. This article provides an introduction to WPC by describing the key features of WPC, shedding light on a set of frequently asked questions, and identifying the key design issues and discussing possible solutions.
Inverse dynamics control of floating base systems using orthogonal decomposition

Model-based control methods can be used to enable fast, dexterous, and compliant motion of robots without sacrificing control accuracy. However, implementing such techniques on floating base robots, e.g., humanoids and legged systems, is non-trivial due to under-actuation, dynamically changing constraints from the environment, and potentially closed loop kinematics. In this paper, we show how to compute the analytically correct inverse dynamics torques for model-based control of sufficiently constrained floating base rigid-body systems, such as humanoid robots with one or two feet in contact with the environment. While our previous inverse dynamics approach relied on an estimation of contact forces to compute an approximate inverse dynamics solution, here we present an analytically correct solution by using an orthogonal decomposition to project the robot dynamics onto a reduced dimensional space, independent of contact forces. We demonstrate the feasibility and robustness of our approach on a simulated floating base bipedal humanoid robot and an actual robot dog locomoting over rough terrain.
SimpleScience: Lexical Simplification of Scientific Terminology

Lexical simplification of scientific terms represents a unique challenge due to the lack of a standard parallel corpora and fast rate at which vocabulary shift along with research. We introduce SimpleScience, a lexical simplification approach for scientific terminology. We use word embeddings to extract simplification rules from a parallel corpora containing scientific publications and Wikipedia. To evaluate our system we construct SimpleSciGold, a novel gold standard set for science-related simplifications. We find that our approach outperforms prior context-aware approaches at generating simplifications for scientific terms.
Exactly-once Delivery in a Content-based Publish-Subscribe System

This paper presents a general knowledge model for propagating information in a content-based publish-subscribe system. The model is used to derive an efficient and scalable protocol for exactly-once delivery to large numbers (tens of thousands per broker) of content-based subscribers in either publisher order or uniform total order. Our protocol allows intermediate content filtering at each hop, but requires persistent storage only at the publishing site. It is tolerant of message drops, message reorderings, node failures, and link failures, and maintains only “soft” state at intermediate nodes. We evaluate the performance of our implementation both under failure-free conditions and with
Microwave Breast Imaging With a Monostatic Radar-Based System: A Study of Application to Patients

A prototype microwave breast imaging system is used to scan a small group of patients. The prototype implements a monostatic radar-based approach to microwave imaging and utilizes ultra-wideband signals. Eight patients were successfully scanned, and several of the resulting images show responses consistent with the clinical patient histories. These encouraging results motivate further studies of microwave imaging for breast health assessment.
Cyclostationary Feature Detection in Cognitive Radio using Different Modulation Schemes

The various spectrum sensing schemes involved in cognitive radio have always been researched and discussed. An ideal detection scheme should be fast, accurate and efficient. Cyclostationary feature detection is a detection scheme that satisfies all these criteria. The method also possesses the ability to distinguish between noise and the primary user signal. One major advantage of cyclostationary feature detection method is that in addition to identifying the primary user signal, it also identifies the modulation scheme used by the primary user. This paper investigates the cyclostationary feature detection method under different modulation schemes. In this paper, cooperative spectrum sensing is performed which involves the cooperation among various cognitive relay nodes. Cooperative spectrum sensing is thus found to be an effective technique to improve the detection performance by exploring the spatial diversity of various relay nodes.
A Fire-Alarming Method Based on Video Processing

This paper presents a fire-alarming method based on video processing. We propose a system that uses color and motion information extracted from video sequences to detect fire. Flame can be recognized according to its color which is a primary element of fire images. Thus choosing a suitable color model is the key to detect flames from fire images. An effective fire detection criterion based on color model is proposed in this paper by intensive experiments and trainings. The detection criterion is used to make a raw localization of fire regions first. However, color alone is not enough for fire detection. To identify a real burning fire, in addition to chromatic features, dynamic features are usually adopted to distinguish other fire aliases. In this paper, both the growth of fire region and the invariability of flame are utilized to further detect the fire regions as a complement of the detection criterion. The effectiveness of the proposed fire-alarming method is demonstrated by the experiments implemented on a large number of scenes.
Counterfactual reasoning about intent for interactive navigation in dynamic environments

Many modern robotics applications require robots to function autonomously in dynamic environments including other decision making agents, such as people or other robots. This calls for fast and scalable interactive motion planning. This requires models that take into consideration the other agent's intended actions in one's own planning. We present a real-time motion planning framework that brings together a few key components including intention inference by reasoning counterfactually about potential motion of the other agents as they work towards different goals. By using a light-weight motion model, we achieve efficient iterative planning for fluid motion when avoiding pedestrians, in parallel with goal inference for longer range movement prediction. This inference framework is coupled with a novel distributed visual tracking method that provides reliable and robust models for the current belief-state of the monitored environment. This combined approach represents a computationally efficient alternative to previously studied policy learning methods that often require significant offline training or calibration and do not yet scale to densely populated environments. We validate this framework with experiments involving multi-robot and human-robot navigation. We further validate the tracker component separately on much larger scale unconstrained pedestrian data sets.
Kinematic-free position control of a 2-DOF planar robot arm

This paper challenges the well-established assumption in robotics that in order to control a robot it is necessary to know its kinematic information, that is, the arrangement of links and joints, the link dimensions and the joint positions. We propose a kinematic-free robot control concept that does not require any prior kinematic knowledge. The concept is based on our hypothesis that it is possible to control a robot without explicitly measuring its joint angles, by measuring instead the effects of the actuation on its end-effector. We implement a proof-of-concept encoderless robot controller and apply it for the position control of a physical 2-DOF planar robot arm. The prototype controller is able to successfully control the robot to reach a reference position, as well as to track a continuous reference trajectory. Notably, we demonstrate how this novel controller can cope with something that traditional control approaches fail to do: adapt to drastic kinematic changes such as 100% elongation of a link, 35-degree angular offset of a joint, and even a complete overhaul of the kinematics involving the addition of new joints and links.
Modeling group structures in pedestrian crowd simulation

Grouping is a common phenomenon in pedestrian crowds and plays important roles in affecting crowd behavior. Group modeling is still an open challenging problem and has not been incorporated by existing crowd simulation models. Motivated by the need of group modeling for crowd behavior simulation, this paper presents a unified and well-defined framework for modeling the structure aspect of different groups in a pedestrian crowd. Both intra-group structure and inter-group relationships are considered and their effects on the crowd behavior are modeled. Based on this framework, an agent-based crowd simulation system is developed and crowd behavior simulations using two different group structures are presented. The simulation results show that the developed framework allows different group structures to be easily modeled. Besides, different group sizes, intra-group structures and inter-group relationships can have significant impacts on crowd behavior.
Dynamic modeling and inverse dynamic control of mobile robot

This paper presents a dynamic modeling and control studies of mobile robot. The robot is mainly built all around three wheels and platform. A mathematical model of unicycle mobile robot is determined by kinematic and dynamic model. The dynamic modelling equations are based on Lagrangian formulation. The motion control strategy is based on the inverse of dynamic control. This leads to accurate tracking of trajectory. The goal to reach in this paper is to improve the performances of inverse dynamic control of mobile robot. The validity of the proposed controller is demonstrated by the simulation of two wheels mobile robots case.