nils-herrmann/clean_scirepeval huggingface dataset:
The Rainbow Scale for Assessing Breast Ptosis: Validation of Three Different Views.

Photographic scales have become an increasingly used tool in objectively assessing outcomes in aesthetic surgery. However, no online photographic scale for assessing breast ptosis has been developed yet that is readily available.



This study was designed to validate the online Rainbow Scale for the assessment of breast ptosis for the anterior-posterior (AP), lateral, and oblique views.



For the five grades of the Rainbow Scale format, standardized reference photographs were selected. Six plastic surgeons rated 15 photographs for each view three times. Intra- and inter-observer agreements were calculated by using the weighted kappa coefficient and differences in intra- and inter-observer agreements between the three views were assessed for statistical significance using the Kruskal-Wallis test.



The mean intra-observer agreements were 0.91 (range, 0.82-0.98) for the AP view, 0.88 (range, 0.77-1.00) for the oblique view, and 0.86 (range, 0.74-0.97) for the lateral view and did not vary significantly between all three views. The mean inter-observer agreements were 0.88 (range, 0.77-0.95) for the AP view, 0.84 (range, 0.72-0.94) for the oblique view, and 0.82 (range, 0.58-0.95) for the lateral view. The mean inter-observer agreements of the AP view varied significantly from the oblique view (P = .012) and the lateral view (P = .001).



The Rainbow Scale for breast ptosis has been validated for the AP view, the lateral view, and the oblique view and is reproducible and reliable for the assessment of breast ptosis in three different views in an online setup.
Pictorial Structures for Object Recognition

In this paper we present a computationally efficient framework for part-based modeling and recognition of objects. Our work is motivated by the pictorial structure models introduced by Fischler and Elschlager. The basic idea is to represent an object by a collection of parts arranged in a deformable configuration. The appearance of each part is modeled separately, and the deformable configuration is represented by spring-like connections between pairs of parts. These models allow for qualitative descriptions of visual appearance, and are suitable for generic recognition problems. We address the problem of using pictorial structure models to find instances of an object in an image as well as the problem of learning an object model from training examples, presenting efficient algorithms in both cases. We demonstrate the techniques by learning models that represent faces and human bodies and using the resulting models to locate the corresponding objects in novel images.
A Multi-Dialect, Multi-Genre Corpus of Informal Written Arabic

This paper presents a multi-dialect, multi-genre, human annotated corpus of dialectal Arabic with data obtained from both online newspaper commentary and Twitter. Most Arabic corpora are small and focus on Modern Standard Arabic (MSA). There has been recent interest, however, in the construction of dialectal Arabic corpora (Zaidan and Callison-Burch, 2011a; Al-Sabbagh and Girju, 2012). This work differs from previously constructed corpora in two ways. First, we include coverage of five dialects of Arabic: Egyptian, Gulf, Levantine, Maghrebi and Iraqi. This is the most complete coverage of any dialectal corpus known to the authors. In addition to data, we provide results for the Arabic dialect identification task that outperform those reported in Zaidan and Callison-Burch (2011a).
Sampling-Based Motion and Symbolic Action Planning with geometric and differential constraints

To compute collision-free and dynamically-feasibile trajectories that satisfy high-level specifications given in a planning-domain definition language, this paper proposes to combine sampling-based motion planning with symbolic action planning. The proposed approach, Sampling-based Motion and Symbolic Action Planner (SMAP), leverages from sampling-based motion planning the underlying idea of searching for a solution trajectory by selectively sampling and exploring the continuous space of collision-free and dynamically-feasible motions. Drawing from AI, SMAP uses symbolic action planning to identify actions and regions of the continuous space that sampling-based motion planning can further explore to significantly advance the search. The planning layers interact with each-other through estimates on the utility of each action, which are computed based on information gathered during the search. Simulation experiments with dynamical models of vehicles carrying out tasks given by high-level STRIPS specifications provide promising initial validation, showing that SMAP efficiently solves challenging problems.
Classification of Opinion Mining Techniques

The important part to gather the information is always seems as what the people think. The growing availability of opinion rich resources like online review sites and blogs arises as people can easily seek out and understand the opinions of others. Users express their views and opinions regarding products and services. These opinions are subjective information which represents user&apos;s sentiments, feelings or appraisal related to the same. The concept of opinion is very broad. In this paper we focus on the Classification of opinion mining techniques that conveys user&apos;s opinion i. e. positive or negative at various levels. The precise method for predicting opinions enable us, to extract sentiments from the web and foretell online customer&apos;s preferences, which could prove valuable for marketing research. Much of the research work had been done on the processing of opinions or sentiments recently because opinions are so important that whenever we need to make a decision we want to know others&apos; opinions. This opinion is not only important for a user but is also useful for an organization.
Design Space Exploration of Network Processor Architectures

We describe an approach to explore the design space for architectures of packet processing devices on the system level. Our method is specific to the application domain of packet processors and is based on (1) models for packet processing tasks, a specification of the workload generated by traffic streams, and a description of the feasible space of architectures including computation and communication resources, (2) a measure to characterize the performance of network processors under different usage scenarios, (3) a new method to estimate end-to-end packet delays and queuing memory, taking task scheduling policies and bus arbitration schemes into account, and (4) a evolutionary algorithm for multi-objective design space exploration. Our method is analytical and based on a high level of abstraction, where the goal is to quickly identify interesting architectures, which may be subjected to a more detailed evaluation, e.g. using simulation. The feasibility of our approach is shown by a detailed case study, where the final output is three candidate architectures, representing different cost versus performance tradeoffs.
Phase Coherent Digital Communications for Wireless Optical Links in Turbid Underwater Environments

Previous studies by the authors have included a theoretical and experimental investigation of the spatial distribution of an optical signal used for communications in underwater scattering environments. Presented here is an experimental study of how scattering affects the temporally encoded information bearing component of the optical signal. Short range underwater optical links employing BPSK, QPSK, 8- PSK, 16-QAM, and 32-QAM modulation are implemented in a laboratory setting, yielding data rates up to 5 Mb/s. The effect of link quality is examined versus water turbidity.
Polynomial method for Procedural Terrain Generation

A systematic fractal brownian motion approach is proposed for generating coherent noise, aiming at procedurally generating realistic terrain and textures. Two models are tested and compared to Perlin noise method for two-dimensional height map generation. A fractal analysis is performed in order to compare fractal behaviour of generated data to real terrain coastlines from the point of view of fractal dimension. Performance analysis show that one of the described schemes requires half as many primitive operations than Perlin noise while producing data of equivalent quality.
Support Vector Machines For Synthetic Aperture Radar Automatic Target Recognition

Algorithms that produce classifiers with large margins, such as support vector machines (SVMs), AdaBoost, etc. are receiving more and more attention in the literature. This paper presents a real application of SVMs for synthetic aperture radar automatic target recognition (SAR/ATR) and compares the result with conventional classifiers. The SVMs are tested for classification both in closed and open sets (recognition). Experimental results showed that SVMs outperform conventional classifiers in target classification. Moreover, SVMs with the Gaussian kernels are able to form a local " bounded " decision region around each class that presents better rejection to confusers.
Contingency planning over probabilistic hybrid obstacle predictions for autonomous road vehicles

This paper presents a novel optimization based path planner that can simultaneously plan multiple contingency paths to account for the uncertain actions of dynamic obstacles. This planner addresses the particular problem of collision avoidance for autonomous road vehicles which are required to safely interact with other vehicles with unknown intentions. The presented path planner utilizes an efficient spline based trajectory representation and fast but accurate collision probability approximations to enable the simultaneous optimization of multiple contingency paths.
Estimating conversion rate in display advertising from past erformance data

In targeted display advertising, the goal is to identify the best opportunities to display a banner ad to an online user who is most likely to take a desired action such as purchasing a product or signing up for a newsletter. Finding the best ad impression, i.e., the opportunity to show an ad to a user, requires the ability to estimate the probability that the user who sees the ad on his or her browser will take an action, i.e., the user will convert. However, conversion probability estimation is a challenging task since there is extreme data sparsity across different data dimensions and the conversion event occurs rarely. In this paper, we present our approach to conversion rate estimation which relies on utilizing past performance observations along user, publisher and advertiser data hierarchies. More specifically, we model the conversion event at different select hierarchical levels with separate binomial distributions and estimate the distribution parameters individually. Then we demonstrate how we can combine these individual estimators using logistic regression to accurately identify conversion events. In our presentation, we also discuss main practical considerations such as data imbalance, missing data, and output probability calibration, which render this estimation problem more difficult but yet need solving for a real-world implementation of the approach. We provide results from real advertising campaigns to demonstrate the effectiveness of our proposed approach.
A Stochastic Approximation Method

Let M(x) denote the expected value at level x of the response to a certain experiment. M(x) is assumed to be a monotone function of x but is unknown tot he experiment, and it is desire to find the solution x=0 of the equation M(x) = a, where x is a given constant. we give a method for making successive experiments at levels x1, x2,... in such a way that x, will tend to 0 in probability.
Coordinating Hundreds of Cooperative, Autonomous Vehicles in Warehouses

The Kiva warehouse management system creates a new paradigm for pick-pack-and-ship warehouses that significantly improves worker productivity. The Kiva system uses movable storage shelves that can be lifted by small, autonomous robots. By bringing the product to the worker, productivity is increased by a factor of two or more, while simultaneously improving accountability and flexibility. A Kiva installation for a large distribution center may require 500 or more vehicles. As such, the Kiva system represents the first commercially available, large-scale autonomous robot system. The first permanent installation of a Kiva system was deployed in the summer of 2006.
Noise cancellation in cognitive radio systems: A performance comparison of evolutionary algorithms

Noise cancellation is one of the important signal processing functions of any communication system, as noise affects data integrity. In existing systems, traditional filters are used to cancel the noise from the received signals. These filters use fixed hardware which is capable of filtering specific frequency or a range of frequencies. However, next generation communication technologies, such as cognitive radio, will require the use of adaptive filters that can dynamically reconfigure their filtering parameters for any frequency. To this end, a few noise cancellation techniques have been proposed, including least mean squares (LMS) and its variants. However, these algorithms are susceptible to non-linear noise and fail to locate the global optimum solution for de-noising. In this paper, we investigate the efficiency of two global search optimization based algorithms, genetic algorithm and particle swarm optimization in performing noise cancellation in cognitive radio systems. These algorithms are implemented and their performances are compared to that of LMS using bit error rate and mean square error as performance evaluation metrics. Simulations are performed with additive white Gaussian noise and random nonlinear noise. Results indicate that GA and PSO perform better than LMS for the case of AWGN corrupted signal but for non-linear random noise PSO outperforms the other two algorithms.
Multiple asynchronous stimulus- and task-dependent hierarchies (STDH) within the visual brain's parallel processing systems.

Results from a variety of sources, some many years old, lead ineluctably to a re-appraisal of the twin strategies of hierarchical and parallel processing used by the brain to construct an image of the visual world. Contrary to common supposition, there are at least three 'feed-forward' anatomical hierarchies that reach the primary visual cortex (V1) and the specialized visual areas outside it, in parallel. These anatomical hierarchies do not conform to the temporal order with which visual signals reach the specialized visual areas through V1. Furthermore, neither the anatomical hierarchies nor the temporal order of activation through V1 predict the perceptual hierarchies. The latter shows that we see (and become aware of) different visual attributes at different times, with colour leading form (orientation) and directional visual motion, even though signals from fast-moving, high-contrast stimuli are among the earliest to reach the visual cortex (of area V5). Parallel processing, on the other hand, is much more ubiquitous than commonly supposed but is subject to a barely noticed but fundamental aspect of brain operations, namely that different parallel systems operate asynchronously with respect to each other and reach perceptual endpoints at different times. This re-assessment leads to the conclusion that the visual brain is constituted of multiple, parallel and asynchronously operating task- and stimulus-dependent hierarchies (STDH); which of these parallel anatomical hierarchies have temporal and perceptual precedence at any given moment is stimulus and task related, and dependent on the visual brain's ability to undertake multiple operations asynchronously.
It's only a computer: Virtual humans increase willingness to disclose

Research has begun to explore the use of virtual humans (VHs) in clinical interviews (Bickmore, Gruber, & Picard, 2005). When designed as supportive and ‘‘safe’’ interaction partners, VHs may improve such screenings by increasing willingness to disclose information (Gratch, Wang, Gerten, & Fast, 2007). In health and mental health contexts, patients are often reluctant to respond honestly. In the context of health-screening interviews, we report a study in which participants interacted with a VH interviewer and were led to believe that the VH was controlled by either humans or automation. As predicted, compared to those who believed they were interacting with a human operator, participants who believed they were interacting with a computer reported lower fear of self-disclosure, lower impression management, displayed their sadness more intensely, and were rated by observers as more willing to disclose. These results suggest that automated VHs can help overcome a significant barrier to obtaining truthful patient information.
A study of prefix hijacking and interception in the internet

There have been many incidents of prefix hijacking in the Internet. The hijacking AS can blackhole the hijacked traffic. Alternatively, it can transparently intercept the hijacked traffic by forwarding it onto the owner. This paper presents a study of such prefix hijacking and interception with the following contributions: (1). We present a methodology for prefix interception, (2). We estimate the fraction of traffic to any prefix that can be hijacked and intercepted in the Internet today, (3). The interception methodology is implemented and used to intercept real traffic to our prefix, (4). We conduct a detailed study to detect ongoing prefix interception.
 We find that: Our hijacking estimates are in line with the impact of past hijacking incidents and show that ASes higher up in the routing hierarchy can hijack a significant amount of traffic to any prefix, including popular prefixes. A less apparent result is that the same holds for prefix interception too. Further, our implementation shows that intercepting traffic to a prefix in the Internet is almost as simple as hijacking it. Finally, while we fail to detect ongoing prefix interception, the detection exercise highlights some of the challenges posed by the prefix interception problem.
Combining Harris interest points and the SIFT descriptor for fast scale-invariant object recognition

In the recent past, the recognition and localization of objects based on local point features has become a widely accepted and utilized method. Among the most popular features are currently the SIFT features, the more recent SURF features, and region-based features such as the MSER. For time-critical application of object recognition and localization systems operating on such features, the SIFT features are too slow (500-600 ms for images of size 640×480 on a 3 GHz CPU). The faster SURF achieve a computation time of 150-240 ms, which is still too slow for active tracking of objects or visual servoing applications. In this paper, we present a combination of the Harris corner detector and the SIFT descriptor, which computes features with a high repeatability and very good matching properties within approx. 20 ms. While just computing the SIFT descriptors for computed Harris interest points would lead to an approach that is not scale-invariant, we will show how scale-invariance can be achieved without a time-consuming scale space analysis. Furthermore, we will present results of successful application of the proposed features within our system for recognition and localization of textured objects. An extensive experimental evaluation proves the practical applicability of our approach.
Optimal Reconstruction with a Small Number of Views

Estimating positions of world points from features observed in images is a key problem in 3D reconstruction, image mosaicking, simultaneous localization and mapping and structure from motion. We consider a special instance in which there is a dominant ground plane G viewed from a parallel viewing plane S above it. Such instances commonly arise, for example, in aerial photography. Consider a world point g ∈ G and its worst case reconstruction uncertainty ε(g,S) obtained by merging all possible views of g chosen from S. We first show that one can pick two views sp and sq such that the uncertainty ε(g, {sp, sq}) obtained using only these two views is almost as good as (i.e. within a small constant factor of) ε(g,S). Next, we extend the result to the entire ground plane G and show that one can pick a small subset of S ′ ⊆ S (which grows only linearly with the area of G) and still obtain a constant factor approximation, for every point g ∈ G, to the minimum worst case estimate obtained by merging all views in S. Our results provide a view selection mechanism with provable performance guarantees which can drastically increase the speed of scene reconstruction algorithms. In addition to theoretical results, we demonstrate their effectiveness in an application where aerial imagery is used for monitoring farms and orchards.
Neural network model for time series prediction by reinforcement learning

Two important issues when constructing a neural network (NN) for time series prediction: proper selection of (1) the input dimension and (2) the time delay between the inputs. These two parameters determine the structure, computing complexity and accuracy of the NN. This paper is to formulate an autonomous data-driven approach to identify a parsimonious structure for the NN so as to reduce the prediction error and enhance the modeling accuracy. The reinforcement learning based dimension and delay estimator (RLDDE) is proposed. It involves a trial-error learning process to formulate a selection policy for designating the above-mentioned two parameters. The proposed method is evaluated by the prediction of the benchmark sunspot time series.
Dual band, dual polarized, rail mount MIMO stadium antenna

This paper presents a dual band, dual polarized rail mount MIMO antenna designed to provide mobile wireless coverage in the stadium environment. The high band (1695–2690 MHz) antenna is a dual polarized vertical printed dipole array with dual slant linear polarization. The low band (698–960 MHz) elements are vertically and horizontally polarized omnidirectional sleeve monopole antennas.
Analysis of Various Sentiment Classification Techniques

Sentiment analysis is an ongoing research area in the field of text mining. People post their review in form of unstructured data so opinion extraction provides overall opinion of reviews so it does best job for customer, people, organization etc. The main aim of this paper is to find out approaches that generate output with good accuracy. This paper presents recent updates on papers related to classification of sentiment analysis of implemented various approaches and algorithms. The main contribution of this paper is to give idea about that careful feature selection and existing classification approaches can give better accuracy.
Accelerometer-based tilt estimation of a rigid body with only rotational degrees of freedom

An estimation algorithm is developed for determining pitch and roll angles (tilt) of a rigid body fixed at a pivot point using multiple accelerometers. The estimate is independent of the rigid body dynamics; the method is applicable both in static conditions and for any dynamic motion of the body. No dynamic model is required for the estimator; only the mounting positions of the sensors need to be known. The proposed estimator is the optimal linear estimate in a least-squares sense if knowledge of the system dynamics is not used. The estimate may be used as a basis for further filtering and fusion techniques, such as sensor fusion with rate gyro data. The estimation algorithm is applied to the problem of state estimation for the Balancing Cube, a rigid structure that can actively balance on its corners. Experimental results are provided.