nils-herrmann/clean_scirepeval huggingface dataset:
Human mobility modeling at metropolitan scales

Models of human mobility have broad applicability in fields such as mobile computing, urban planning, and ecology. This paper proposes and evaluates WHERE, a novel approach to modeling how large populations move within different metropolitan areas. WHERE takes as input spatial and temporal probability distributions drawn from empirical data, such as Call Detail Records (CDRs) from a cellular telephone network, and produces synthetic CDRs for a synthetic population. We have validated WHERE against billions of anonymous location samples for hundreds of thousands of phones in the New York and Los Angeles metropolitan areas. We found that WHERE offers significantly higher fidelity than other modeling approaches. For example, daily range of travel statistics fall within one mile of their true values, an improvement of more than 14 times over a Weighted Random Waypoint model. Our modeling techniques and synthetic CDRs can be applied to a wide range of problems while avoiding many of the privacy concerns surrounding real CDRs.
A Scalable Rule Engine Architecture for Service Execution Frameworks

Rule engines form an essential component of most service execution frameworks in a Service Oriented Architecture (SOA) ecosystem. The efficiency of a service execution framework critically depends on the performance of the rule engine it uses to manage it's operations. Most common rule engines suffer from the fundamental performance issues of the Rete algorithm that they internally use for faster matching of rules against incoming facts. In this paper, we present the design of a scalable architecture of a service rule engine, where a rule clustering and hashing based mechanism is employed for lazy loading of relevant service rules and a prediction based technique for rule evaluation is used for faster actuation of the rules. We present experimental results to demonstrate the efficacy of the proposed rule engine framework over contemporary ones.
Machine learning based handover management for improved QoE in LTE

This paper presents a machine learning based handover management scheme for LTE to improve the Quality of Experience (QoE) of the user in the presence of obstacles. We show that, in this scenario, a state-of-the-art handover algorithm is unable to select the appropriate target cell for handover, since it always selects the target cell with the strongest signal without taking into account the perceived QoE of the user after the handover. In contrast, our scheme learns from past experience how the QoE of the user is affected when the handover was done to a certain eNB. Our performance evaluation shows that the proposed scheme substantially improves the number of completed downloads and the average download time compared to state-of-the-art. Furthermore, its performance is close to an optimal approach in the coverage region affected by an obstacle.
Spatial and temporal crowding in amblyopia

Spatial crowding is a well-known deficit in amblyopia. We have previously reported evidence suggesting that the inability to isolate stimuli in space in crowded displays (spatial crowding) is a largely independent component of the amblyopic deficit in visual acuity, which is typically found in strabismic amblyopia [Bonneh, Y., Sagi, D., & Polat, U. (2004a). Local and non-local deficits in amblyopia: Acuity and spatial interactions.  Research, 44, 3009-3110]. Here, we extend this result to the temporal domain by measuring visual acuity (VA) for a single pattern in a rapid serial visual presentation (RSVP-VA, N=15) for fast ("crowded") and slow ("uncrowded") presentations. We found that strabismic amblyopes but not anisometropic amblyopes or normal controls exhibited a significant difference between VA under the fast and slow conditions. We further compared the "temporal crowding" measure to two measures of spatial crowding: (1) static Tumbling-E acuity in multi-pattern crowded displays (N=26) and (2) Gabor alignment with lateral flankers (N=20). We found that all three measures of crowding (one temporal and two spatial) were highly correlated across subjects while being largely independent of the visual acuity for a single isolated pattern, with both spatial and temporal crowding being high and correlated in strabismus and low in anisometropia. This suggests that time and space are related in crowding, at least in amblyopia.
A Controlled Crowdsourcing Approach for Practical Ontology Extensions and Metadata Annotations

Traditional approaches to ontology development have a large lapse between the time when a user using the ontology has found a need to extend it and the time when it does get extended. For scientists, this delay can be weeks or months and can be a significant barrier for adoption. We present a new approach to ontology development and data annotation enabling users to add new metadata properties on the fly as they describe their datasets, creating terms that can be immediately adopted by others and eventually become standardized. This approach combines a traditional, consensus-based approach to ontology development, and a crowdsourced approach where expert users (the crowd) can dynamically add terms as needed to support their work. We have implemented this approach as a socio-technical system that includes: 1) a crowdsourcing platform to support metadata annotation and addition of new terms, 2) a range of social editorial processes to make standardization decisions for those new terms, and 3) a framework for ontology revision and updates to the metadata created with the previous version of the ontology. We present a prototype implementation for the paleoclimate community, the Linked Earth Framework, currently containing 700 datasets and engaging over 50 active contributors. Users exploit the platform to do science while extending the metadata vocabulary, thereby producing useful and practical metadata.
Self-Serving Justifications : Doing Wrong and Feeling Moral

Unethical behavior by “ordinary” people poses significant societal and personal challenges. We present a novel framework centered on the role of self-serving justification to build upon and advance the rapidly expanding research on intentional unethical behavior of people who value their morality highly. We propose that self-serving justifications emerging before and after people engage in intentional ethical violations mitigate the threat to the moral self, enabling them to do wrong while feeling moral. Pre-violation justifications lessen the anticipated threat to the moral self by redefining questionable behaviors as excusable. Post-violation justifications alleviate the experienced threat to the moral self through compensations that balance or lessen violations. We highlight the psychological mechanisms that prompt people to do wrong and feel moral, and suggest future research directions regarding the temporal dimension of self-serving justifications of ethical misconduct.
Optical-Flow based Self-Supervised Learning of Obstacle Appearance applied to MAV Landing

Monocular optical flow has been widely used to detect obstacles in Micro Air Vehicles (MAVs) during visual navigation. However, this approach requires significant movement, which reduces the efficiency of navigation and may even introduce risks in narrow spaces. In this paper, we introduce a novel setup of self-supervised learning (SSL), in which optical flow cues serve as a scaffold to learn the visual appearance of obstacles in the environment. We apply it to a landing task, in which initially ‘surface roughness’ is estimated from the optical flow field in order to detect obstacles. Subsequently, a linear regression function is learned that maps appearance features represented by texton distributions to the roughness estimate. After learning, the MAV can detect obstacles by just analyzing a still image. This allows the MAV to search for a landing spot without moving. We first demonstrate this principle to work with offline tests involving images captured from an on-board camera, and then demonstrate the principle in flight. Although surface roughness is a property of the entire flow field in the global image, the appearance learning even allows for the pixel-wise segmentation of obstacles.
Penile injection with silicone: case report and review of the literature.

Liquid injectable silicone (LIS) has been used for soft tissue augmentation in excess of 50 years. Until recently, all literature on penile augmentation with LIS consisted of case reports or small cases series, most involving surgical intervention to correct the complications of LIS. New formulations of LIS and new methodologies for injection have renewed interest in this procedure.



We reported a case of penile augmentation with LIS and reviewed the pertinent literature.



Comprehensive literature review was performed using PubMed. We performed additional searches based on references from relevant review articles.



Injection of medical grade silicone for soft tissue augmentation has a role in carefully controlled study settings. Historically, the use of LIS for penile augmentation has had poor outcomes and required surgical intervention to correct complications resulting from LIS.



We currently discourage the use of LIS for penile augmentation until carefully designed and evaluated trials have been completed.
An Empirical Evaluation of Similarity Measures for Time Series Classification

Time series are ubiquitous, and a measure to assess their similarity is a core part of many computational systems. In particular, the similarity measure is the most essential ingredient of time series clustering and classification systems. Because of this importance, countless approaches to estimate time series similarity have been proposed. However, there is a lack of comparative studies using empirical, rigorous, quantitative, and large-scale assessment strategies. In this article, we provide an extensive evaluation of similarity measures for time series classification following the aforementioned principles. We consider 7 different measures coming from alternative measure ‘families’, and 45 publicly-available time series data sets coming from a wide variety of scientific domains. We focus on out-of-sample classification accuracy, but in-sample accuracies and parameter choices are also discussed. Our work is based on rigorous evaluation methodologies and includes the use of powerful statistical significance tests to derive meaningful conclusions. The obtained results show the equivalence, in terms of accuracy, of a number of measures, but with one single candidate outperforming the rest. Such findings, together with the followed methodology, invite researchers on the field to adopt a more consistent evaluation criteria and a more informed decision regarding the baseline measures to which new developments should be compared.
Smoke detection algorithm for intelligent video surveillance system

An efficient smoke detection algorithm on color video sequences obtained from a stationary camera is proposed. Our algorithm considers dynamic and static features of smoke and is composed of basic steps: preprocessing; slowly moving areas and pixels segmentation in a current input frame based on adaptive background subtraction; merge slowly moving areas with pixels into blobs; classification of the blobs obtained before. We use adaptive background subtraction at a stage of moving detection. Moving blobs classification is based on optical flow calculation, Weber contrast analysis and takes into account primary direction of smoke propagation. Real video surveillance sequences were used for smoke detection with utilization our algorithm. A set of experimental results is presented in the paper.
Stereo-vision for autonomous industrial inspection robots

Camera-based stereo-vision provides cost-efficient vision capabilities for robotic systems. The objective of this paper is to examine the performance of stereo-vision as means to enable a robotic inspection cell for haptic quality testing with the ability to detect relevant information related to the inspection task. This information comprises the location and 3D representation of a complex object under inspection as well as the location and type of quality features which are subject to the inspection task. Among the challenges is the low-distinctiveness of features in neighboring area, inconsistent lighting, similar colors as well as low intra-class variances impeding the retrieval of quality characteristics. The paper presents the general outline of the vision chain as well as performance analysis of various algorithms for relevant steps in the machine vision chain thus indicating the capabilities and drawbacks of a camera-based stereo-vision for flexible use in complex machine vision tasks.
A slotted post-wall waveguide array with interdigital structure for 45/spl deg/ linear and dual polarization

This work proposes a dual-polarized planar antenna; two post-wall slotted waveguide arrays with orthogonal 45/spl deg/ linearly-polarized waves interdigitally share the aperture on a single layer substrate. Uniform excitation of the two-dimensional slot array is confirmed by experiment in the 25 GHz band. The isolation between two slot arrays is also investigated in terms of the relative displacement along the radiation waveguide axis in the interdigital structure. The isolation is 33.0 dB when the relative shift of slot position between the two arrays is -0.5/spl lambda//sub g/, while it is only 12.8 dB when there is no shift. The cross-polarization level in the far field is -25.2 dB for a -0.5/spl lambda//sub g/ shift, which is almost equal to that of the isolated single polarization array. It is degraded down to -9.6 dB when there is no shift.
Forest Density Estimation

We consider the problem of finding the best arm in a stochastic multi-armed bandit game. The regret of a forecaster is here defined by the gap between the mean reward of the optimal arm and the mean reward of the ultimately chosen arm. We propose a highly exploring UCB policy and a new algorithm based on successive rejects. We show that these algorithms are essentially optimal since their regret decreases exponentially at a rate which is, up to a logarithmic factor, the best possible. However, while the UCB policy needs the tuning of a parameter depending on the unobservable hardness of the task, the successive rejects policy benefits from being parameter-free, and also independent of the scaling of the rewards. As a by-product of our analysis, we show that identifying the best arm (when it is unique) requires a number of samples of order (up to a log(K) factor) ∑ i 1/∆ 2 i , where the sum is on the suboptimal arms and ∆i represents the difference between the mean reward of the best arm and the one of arm i. This generalizes the well-known fact that one needs of order of 1/∆ samples to differentiate the means of two distributions with gap ∆.
Prognostic and predictive value of long non-coding RNA GAS5 and mircoRNA-221 in colorectal cancer and their effects on colorectal cancer cell proliferation, migration and invasion.

LncRNA and microRNA play an important role in the development of human cancers; they can act as a tumor suppressor gene or an oncogene. LncRNA GAS5, originating from the separation from tumor suppressor gene cDNA subtractive library, is considered as an oncogene in several kinds of cancers. The expression of miR-221 affects tumorigenesis, invasion and metastasis in multiple types of human cancers. However, there's very little information on the role LncRNA GAS5 and miR-221 play in CRC. Therefore, we conducted this study in order to analyze the association of GAS5 and miR-221 with the prognosis of CRC and preliminary study was done on proliferation, metastasis and invasion of CRC cells. In the present study, we demonstrate the predictive value of long non-coding RNA GAS5 (lncRNA GAS5) and mircoRNA-221 (miR-221) in the prognosis of colorectal cancer (CRC) and their effects on CRC cell proliferation, migration and invasion.



One hundred and fifty-eight cases with CRC patients and 173 cases of healthy subjects that with no abnormalities, who've been diagnosed through colonoscopy between January 2012 and January 2014 were selected for the study. After the clinicopathological data of the subjects, tissue, plasma and exosomes were collected, lncRNA GAS5 and miR-221 expressions in tissues, plasma and exosomes were measured by reverse transcription quantitative polymerase chain reaction (RT-qPCR). The diagnostic values of lncRNA GAS5 and miR-221 expression in tissues, plasma and exosomes in patients with CRC were analyzed using receiver operating characteristic curve (ROC). Lentiviral vector was constructed for the overexpression of lncRNA GAS5, and SW480 cell line was used for the transfection of the experiment and assigned into an empty vector and GAS5 groups. The cell proliferation, migration and invasion were tested using a cell counting kit-8 assay and Transwell assay respectively.



The results revealed that LncRNA GAS5 was upregulated while the miR-221 was downregulated in the tissues, plasma and exosomes of patients with CRC. The results of ROC showed that the expressions of both lncRNA GAS5 and miR-221 in the tissues, plasma and exosomes had diagnostic value in CRC. While the LncRNA GAS5 expression in tissues, plasma and exosomes were associated with the tumor node metastasis (TNM) stage, Dukes stage, lymph node metastasis (LNM), local recurrence rate and distant metastasis rate, the MiR-221 expression in tissues, plasma and exosomes were associated with tumor size, TNM stage, Dukes stage, LNM, local recurrence rate and distant metastasis rate. LncRNA GAS5 and miR-221 expression in tissues, plasma and exosomes were found to be independent prognostic factors for CRC. Following the overexpression of GAS5, the GAS5 expressions was up-regulated and miR-221 expression was down-regulated; the rate of cell proliferation, migration and invasion were decreased.
Dynamic trot-walking with the hydraulic quadruped robot — HyQ: Analytical trajectory generation and active compliance control

This paper presents a trajectory generator and an active compliance control scheme, unified in a framework to synthesize dynamic, feasible and compliant trot-walking locomotion cycles for a stiff-by-nature hydraulically actuated quadruped robot. At the outset, a CoP-based trajectory generator that is constructed using an analytical solution is implemented to obtain feasible and dynamically balanced motion references in a systematic manner. Initial conditions are uniquely determined for symmetrical motion patterns, enforcing that trajectories are seamlessly connected both in position, velocity and acceleration levels, regardless of the given support phase. The active compliance controller, used simultaneously, is responsible for sufficient joint position/force regulation. An admittance block is utilized to compute joint displacements that correspond to joint force errors. In addition to position feedback, these joint displacements are inserted to the position control loop as a secondary feedback term. In doing so, active compliance control is achieved, while the position/force trade-off is modulated via the virtual admittance parameters. Various trot-walking experiments are conducted with the proposed framework using HyQ, a ~ 75kg hydraulically actuated quadruped robot. We present results of repetitive, continuous, and dynamically equilibrated trot-walking locomotion cycles, both on level surface and uneven surface walking experiments.
Online cost-sensitive neural network classifiers for non-stationary and imbalanced data streams

Classifying non-stationary and imbalanced data streams encompasses two important challenges, namely concept drift and class imbalance. Concept drift is changes in the underlying function being learnt, and class imbalance is vast difference between the numbers of instances in different classes of data. Class imbalance is an obstacle for the efficiency of most classifiers. Previous methods for classifying non-stationary and imbalanced data streams mainly focus on batch solutions, in which the classification model is trained using a chunk of data. Here, we propose two online classifiers. The classifiers are one-layer NNs. In the proposed classifiers, class imbalance is handled with two separate cost-sensitive strategies. The first one incorporates a fixed and the second one an adaptive misclassification cost matrix. The proposed classifiers are evaluated on 3 synthetic and 8 real-world datasets. The results show statistically significant improvements in imbalanced data metrics.
An Integrated Model for Effective Saliency Prediction

In this paper, we proposed an integrated model of both semantic-aware and contrast-aware saliency (SCA) combining both bottom-up and top-down cues for effective eye fixation prediction. The proposed SCA model contains two pathways. The first pathway is a deep neural network customized for semantic-aware saliency, which aims to capture the semantic information in images, especially for the presence of meaningful objects and object parts. The second pathway is based on on-line feature learning and information maximization, which learns an adaptive representation for the input and discovers the high contrast salient patterns within the image context. The two pathways characterize both long-term and short-term attention cues and are integrated using maxima normalization. Experimental results on artificial images and several benchmark dataset demonstrate the superior performance and better plausibility of the proposed model over both classic approaches and recent deep models.
Hand Gesture Recognition using Multi-Scale Colour Features, Hierarchical Models and Particle Filtering

This paper presents algorithms and a prototype system for hand tracking and hand posture recognition. Hand postures are represented in terms of hierarchies of multi-scale colour image features at different scales, with qualitative inter-relations in terms of scale, position and orientation. In each image, detection of multi-scale colour features is performed. Hand states are then simultaneously detected and tracked using particle filtering, with an extension of layered sampling referred to as hierarchical layered sampling. Experiments are presented showing that the performance of the system is substantially improved by performing feature detection in colour space and including a prior with respect to skin colour. These components have been integrated into a real-time prototype system, applied to a test problem of controlling consumer electronics using hand gestures. In a simplified demo scenario, this system has been successfully tested by participants at two fairs during 2001.
Power transformer for a single-stage bidirectional and isolated ac-dc matrix converter for energy storage systems

This paper presents an approach to design the transformer and the link inductor for the high-frequency link matrix converter. The proposed method aims to systematize the design process of the HF-link using analytic and software tools. The models for the characterization of the core and winding losses have been reviewed. Considerations about the practical implementation and construction of the magnetic devices are also provided. The software receives the inputs from the mathematical analysis and runs the optimization to find the best design. A 10 kW / 20 kHz transformer plus a link inductor are designed using this strategy achieving a combined efficiency of 99.32%.