nils-herrmann/clean_scirepeval huggingface dataset:
Baseball4D: A tool for baseball game reconstruction & visualization

While many sports use statistics and video to analyze and improve game play, baseball has led the charge throughout its history. With the advent of new technologies that allow all players and the ball to be tracked across the entire field, it is now possible to bring this understanding to another level. From discrete positions across time, we present techniques to reconstruct entire baseball games and visually explore each play. This provides opportunities to not only derive new metrics for the game, but also allow us to investigate existing measures with targeted visualizations. In addition, our techniques allow users to filter on demand so specific situations can be analyzed both in general and according to those situations. We show that gameplay can be accurately reconstructed from the raw position data and discuss how visualization and statistical methods can combine to better inform baseball analyses.
Data Mining in Sports: A Systematic Review

Data mining technique has attracted attention in the information industry and society as a whole, because of the big amount of data and the imminent need to transform that data into useful information and knowledge. Recently conducted studies with successfully demarcated results using this technique, to estimate several parameters in a variety of domains. However, the effective use of data in some areas is still developing, as is the case of sports, which has shown moderate growth. In this context, the objective of this article is to present a systematic review of the literature about research involving sports data mining. As systematic searches were made out in five databases, resulting in 21 articles that answered a question that grounded this article.
Automatic road sign detecion and classification based on support vector machines and HOG descriptos

This paper examines the detection and classification of road signs in color-images acquired by a low cost camera mounted on a moving vehicle. A new method for the detection and classification of road signs is proposed based on color based detection, in order to locate regions of interest. Then, a circular Hough transform is applied to complete detection taking advantage of the shape properties of the road signs. The regions of interest are finally represented using HOG descriptors and are fed into trained Support Vector Machines (SVMs) in order to be recognized. For the training procedure, a database with several training examples depicting Greek road sings has been developed. Many experiments have been conducted and are presented, to measure the efficiency of the proposed methodology especially under adverse weather conditions and poor illumination. For the experiments training datasets consisting of different number of examples were used and the results are presented, along with some possible extensions of this work.
UWB antennas for ground penetrating radar application

Presented paper describes results of comparison study on antennas for ground penetrating radar (GPR, undersurface radar) operating in frequency band from 500 MHz to 3 GHz. Typical representatives of elementary antennas, planar monopoles, dipole antennas, tapered slot antennas, TEM horns and frequency independent antennas are firstly optimized for the operation band in CST design environment. Then structures are described and compared from the impedance, radiation and time characteristic point of view to enable a simple choice of suitable structures for manufacture. Selected antennas are prepared for the manufacture and comparison with their models.
On the application of IoT: Monitoring of troughs water level using WSN

The need of monitoring the water level of troughs is increasing. This is parallel with the growing of Wireless Sensor Network and Internet of Thing. By combining both approach, cattlemen can monitoring their troughs ubiquitous using their own personal device. This paper develop such system by using LoRa™ as the media between sensor hub and nodes, while Raspberry Pi is used as the gateway to push data into server. We have successfully tested the functionality of node to hub join mechanism, where a node should join to the closest hub, which marked by the highest RSSI of beacon signal. The designed system is able to wirelessly sense trough water level up to 50 simulated nodes.
BeWell: Sensing Sleep, Physical Activities and Social Interactions to Promote Wellbeing

Smartphone sensing and persuasive feedback design is enabling a new generation of wellbeing apps capable of automatically monitoring multiple aspects of physical and mental health. In this article, we present BeWell+ the next generation of the BeWell smartphone wellbeing app, which monitors user behavior along three health dimensions, namely sleep, physical activity, and social interaction. BeWell promotes improved behavioral patterns via feedback rendered as an ambient display on the smartphone’s wallpaper. With BeWell+, we introduce new mechanisms to address key limitations of the original BeWell app; specifically, (1) community adaptive wellbeing feedback, which generalizes to diverse user communities (e.g., elderly, children) by promoting better behavior yet remains realistic to the user’s lifestyle; and, (2) wellbeing adaptive energy allocation, which prioritizes monitoring fidelity and feedback responsiveness on specific health dimensions (e.g., sleep) where the user needs additional help. N. D. Lane ( ) Microsoft Research Asia, Beijing, China e-mail: niclane@microsoft.com M. Lin · X. Yang · S. Ali · E. Berke · A. T. Campbell Dartmouth College, Hanover, NH 03755, USA H. Lu Intel Lab, Santa Clara, CA 95054, USA G. Cardone University of Bologna, Bologna, Italy A. Doryab Carnegie Mellon University, Pittsburgh, PA 15213, USA M. Mohammod · T. Choudhury Cornell University, Ithaca, NY 14850, USA We evaluate BeWell+ with a 27 person, 19 day field trial. Our findings show that not only can BeWell+ operate successfully on consumer smartphones; but also users understand feedback and respond by taking steps towards leading healthier lifestyles.
Global Correspondence Optimization for Non-Rigid Registration of Depth Scans

We present a registration algorithm for pairs of deforming and partial range scans that addresses the challenges of non-rigid registration within a single non-linear optimization. Our algorithm simultaneously solves for correspondences between points on source and target scans, confidence weights that measure the reliability of each correspondence and identify non-overlapping areas, and a warping field that brings the source scan into alignment with the target geometry. The optimization maximizes the region of overlap and the spatial coherence of the deformation while minimizing registration error. All optimization parameters are chosen automatically; hand-tuning is not necessary. Our method is not restricted to part-in-whole matching, but addresses the general problem of partial matching, and requires no explicit prior correspondences or feature points. We evaluate the performance and robustness of our method using scan data acquired by a structured light scanner and compare our method with existing non-rigid registration algorithms.
The effect of construal level on subjective probability estimates.

In a series of studies, we examined novel predictions drawn from a conceptualization of probability as psychological distance. Manipulating construal level in a number of different ways and examining a variety of probability judgments, we found that participants led to adopt a high-level-construal mind-set made lower probability assessments than did those led to adopt a low-level-construal mind-set. Moreover, this occurred even when construal level was manipulated in a context separate from the judgment task and the manipulation was unrelated in content to the events being judged. These findings suggest that broad processing variables can exert a widespread influence on probability judgment.
Probabilistic Analysis of Plug-In Electric Vehicles Impact on Electrical Grid Through Homes and Parking Lots

Plug-in electric vehicles in the future will possibly emerge widely in city areas. Fleets of such vehicles in large numbers could be regarded as considerable stochastic loads in view of the electrical grid. Moreover, they are not stabled in unique positions to define their impact on the grid. Municipal parking lots could be considered as important aggregators letting these vehicles interact with the utility grid in certain positions. A bidirectional power interface in a parking lot could link electric vehicles with the utility grid or any storage and dispersed generation. Such vehicles, depending on their need, could transact power with parking lots. Considering parking lots equipped with power interfaces, in more general terms, parking-to-vehicle and vehicle-to-parking are propose here instead of conventional grid-to-vehicle and vehicle-to-grid concepts. Based on statistical data and adopting general regulations on vehicles (dis)charging, a novel stochastic methodology is presented to estimate total daily impact of vehicles aggregated in parking lots on the grid. Different scenarios of plug-in vehicles' penetration are suggested in this paper and finally, the scenarios are simulated on standard grids that include several parking lots. The results show acceptable penetration level margins in terms of bus voltages and grid power loss.
Generating ER Diagrams from Requirement Specifications Based On Natural Language Processing

An Entity Relationship (ER) data model is a high level conceptual model that describes information as entities, attributes, and relationships. Entity relationship modeling designed to facilitate database design. The abstract nature of Entity Relationship diagrams can be discouraging task to both designers and student alike. This paper deals with the problem of extracting ER elements from natural language specifications using Natural Language Processing (NLP). The approach provides the opportunity of using natural language documents as a source of knowledge for generating ER data model. The structural approach is used to parse specification syntactically based a predefined set of on heuristics rules. Extracted words with its Part Of Speech (POS) mapped into entities, attributes and relationships, which are the basic elements of ER diagrams.
Emotion Detection and Sentiment Analysis in Text Corpus: A Differential Study with Informal and Formal Writing Styles

Text, either online or offline can be presented in two different writing styles: formal and informal writing style. A Piece of text may contain a lot of emotion, ideas or feelings. Various techniques and methods are present in the field of Opinion Mining and Sentiment Analysis to extract the emotions from text. This paper presents a differential analysis of Formal and Informal text pieces in the field of Sentiment Classification. This paper presents a study and analysis of differences of approaches used for Emotion Detection and Sentiment Analysis for both cases. In this study, 10 formal text pieces in form of poetry, proverbs, essay and document are analyzed. These text pieces are present in 7 different International languages (i. e. Persian, Spanish, Chinese, Arabic, Malaysian, English, Ottoman). Informal text, in form of chats, emails, review sites and micro blogs, written in different International languages (Korean, Persian and English) are considered in this study. Various machine learning based methods -Support Vector Machine(SVM), Naive Bayes (NB), Decision Tree are more often used in classification of literary arts especially poetry. Statistical Machine learning approach Support Vector Machine outperforms all other methods in case of poetry and NB performed well in case of Informal Writing Style.
Semantics, conceptual spaces, and the meeting of minds

We present an account of semantics that is not construed as a mapping of language to the world but rather as a mapping between individual meaning spaces. The meanings of linguistic entities are established via a “meeting of minds.” The concepts in the minds of communicating individuals are modeled as convex regions in conceptual spaces. We outline a mathematical framework, based on fixpoints in continuous mappings between conceptual spaces, that can be used to model such a semantics. If concepts are convex, it will in general be possible for interactors to agree on joint meaning even if they start out from different representational spaces. Language is discrete, while mental representations tend to be continuous—posing a seeming paradox. We show that the convexity assumption allows us to address this problem. Using examples, we further show that our approach helps explain the semantic processes involved in the composition of expressions.
A tamper-proof software watermark using code encryption

Utilizing a modified PPCT structure, a tamper-proof software watermark solution with code-based encryption is proposed. The General Chinese Remainder Theorem is exploited to split the watermark which is represented as a big number into pieces to enhance stealth. Changes to the source and object code are made to embed the watermark, and according to certain policies some parts of object code are encrypted with an en/decryption key which is highly coupled with object code to increase robustness and tamper-proof capability.
Floor Layout Planning Using Artificial Intelligence Technique

In the era of e-commerce while buying furniture online the customers obviously feel the need for visual representation of the arrangement of their furniture. Even when doing interiors of the house it's difficult to just rely on assumptions about best layouts possible and professional help may become quite expensive. In this project, we make use of Genetic Algorithm (GA) which is an Artificial Intelligence technique to display various optimal arrangements of furniture. The basic idea behind using GA is developing an evolutionary design model. This is done by generating chromosomes for each possible solution and then performing a crossover between them in each generation until an optimum fitness function is reached. Modification in chromosome representation may also be done for better results. The proposed system will generate different layout designs for the furniture keeping in consideration the structure of a master bedroom.
Can Microblogs Predict Music Charts? An Analysis of the Relationship Between #Nowplaying Tweets and Music Charts

Twitter is one of the leading social media platforms, where hundreds of millions of tweets cover a wide range of topics, including the music a user is listening to. Such #nowplaying tweets may serve as an indicator for future charts, however, this has not been thoroughly studied yet. Therefore, we investigate to which extent such tweets correlate with the Billboard Hot 100 charts and whether they allow for music charts prediction. The analysis is based on #nowplaying tweets and the Billboard charts of the years 2014 and 2015. We analyze three different aspects in regards to the time series representing #nowplaying tweets and the Billboard charts: (i) the correlation of Twitter and the Billboard charts, (ii) the temporal relation between those two and (iii) the prediction performance in regards to charts positions of tracks. We find that while there is a mild correlation between tweets and the charts, there is a temporal lag between these two time series for 90% of all tracks. As for the predictive power of Twitter, we find that incorporating Twitter information in a multivariate model results in a significant decrease of both the mean RMSE as well as the variance of rank predictions.
Diagnoses and timing of 30-day readmissions after hospitalization for heart failure, acute myocardial infarction, or pneumonia.

To better guide strategies intended to reduce high rates of 30-day readmission after hospitalization for heart failure (HF), acute myocardial infarction (MI), or pneumonia, further information is needed about readmission diagnoses, readmission timing, and the relationship of both to patient age, sex, and race.



To examine readmission diagnoses and timing among Medicare beneficiaries readmitted within 30 days after hospitalization for HF, acute MI, or pneumonia.



We analyzed 2007-2009 Medicare fee-for-service claims data to identify patterns of 30-day readmission by patient demographic characteristics and time after hospitalization for HF, acute MI, or pneumonia. Readmission diagnoses were categorized using an aggregated version of the Centers for Medicare & Medicaid Services' Condition Categories. Readmission timing was determined by day after discharge.



We examined the percentage of 30-day readmissions occurring on each day (0-30) after discharge; the most common readmission diagnoses occurring during cumulative periods (days 0-3, 0-7, 0-15, and 0-30) and consecutive periods (days 0-3, 4-7, 8-15, and 16-30) after hospitalization; median time to readmission for common readmission diagnoses; and the relationship between patient demographic characteristics and readmission diagnoses and timing.



From 2007 through 2009, we identified 329,308 30-day readmissions after 1,330,157 HF hospitalizations (24.8% readmitted), 108,992 30-day readmissions after 548,834 acute MI hospitalizations (19.9% readmitted), and 214,239 30-day readmissions after 1,168,624 pneumonia hospitalizations (18.3% readmitted). The proportion of patients readmitted for the same condition was 35.2% after the index HF hospitalization, 10.0% after the index acute MI hospitalization, and 22.4% after the index pneumonia hospitalization. Of all readmissions within 30 days of hospitalization, the majority occurred within 15 days of hospitalization: 61.0%, HF cohort; 67.6%, acute MI cohort; and 62.6%, pneumonia cohort. The diverse spectrum of readmission diagnoses was largely similar in both cumulative and consecutive periods after discharge. Median time to 30-day readmission was 12 days for patients initially hospitalized for HF, 10 days for patients initially hospitalized for acute MI, and 12 days for patients initially hospitalized for pneumonia and was comparable across common readmission diagnoses. Neither readmission diagnoses nor timing substantively varied by age, sex, or race.



Among Medicare fee-for-service beneficiaries hospitalized for HF, acute MI, or pneumonia, 30-day readmissions were frequent throughout the month after hospitalization and resulted from a similar spectrum of readmission diagnoses regardless of age, sex, race, or time after discharge.
An Adaptation of the Fast Fourier Transform for Parallel Processing

A modified version of the Fast Fourier Transform is developed and described. This version is well adapted for use in a special-purpose computer designed for the purpose. It is shown that only three operators are needed. One operator replaces successive pairs of data points by their sums and differences. The second operator performs a fixed permutation which is an ideal shuffle of the data. The third operator permits the multiplication of a selected subset of the data by a common complex multiplier.
If, as seems reasonable, the slowest operation is the complex multiplications required, then, for reasonably sized date sets—e.g. 512 complex numbers—parallelization by the method developed should allow an increase of speed over the serial use of the Fast Fourier Transform by about two orders of magnitude.
It is suggested that a machine to realize the speed improvement indicated is quite feasible.
The analysis is based on the use of the Kronecker product of matrices. It is suggested that this form is of general use in the development and classification of various modifications and extensions of the algorithm.
A survey of fuzzy web mining

The Internet has become an unlimited resource of knowledge, and is thus widely used in many applications. Web mining plays an important role in discovering such knowledge. This mining can be roughly divided into three categories, including Web usage mining, Web content mining, and Web structure mining. Data and knowledge on the Web may, however, consist of imprecise, incomplete, and uncertain data. Because fuzzy-set theory is often used to handle such data, several fuzzy Web-mining techniques have been proposed to reveal fuzzy and linguistic knowledge. This paper reviews these techniques according to the three Web-mining categories above—fuzzy Web usage mining, fuzzy Web content mining, and fuzzy Web structure mining. Some representative approaches in each category are introduced and compared.
Effective Estimation of Driver Drowsiness Based on Eye Status Detection and Analysis

One of the primary causes of traffic accidents is the loss of alertness of the drivers involved. In several cases it has been found to be due to a driver falling asleep on the wheel or becoming too drowsy. Accidents caused due to driver drowsiness are difficult to prevent by traffic laws. Often, the drivers are not mindful of their lowered state of senses and the dangerous repercussions it may have and they continue driving. In some expensive vehicles, high tech cameras have been installed that seek to monitor the alertness levels of the drivers but they are not cost effective and cannot reach out to the masses. In this paper, we have outlined the design of a very simple and economical system which deals with this issue. In this system, the eye status of the driver is monitored, recorded and analyzed. The difference in the blinking pattern of an alert-driver and a drowsy-driver helps us to determine the level of alertness of the driver. If the alertness level falls below a certain threshold, an alarm is raised to warn the driver and a red beacon, attached to the rear of the vehicle, is flashed to warn other drivers. The system has been successfully tested and found to be extremely effective.
Four-directional pixel-wise polarization CMOS image sensor using air-gap wire grid on 2.5-μm back-illuminated pixels

Polarization information is useful in highly functional imaging. This paper presents a four-directional pixel-wise polarization CMOS image sensor using an air-gap wire grid on 2.5-μm back-illuminated pixels. The fabricated air-gap wire grid polarizer achieved a transmittance of 63.3 % and an extinction ratio of 85 at 550 nm, outperforming conventional polarization sensors. The pixel-wise polarizers fabricated with the wafer process on back-illuminated image sensors exhibit good oblique-incidence characteristics, even with small polarization pixels of 2.5 μm. The proposed image sensor realizes mega-pixel various fusion-imaging applications, such as surface reflection reduction, highly accurate depth mapping, and condition-robust surveillance.
Applying Clickstream Data Mining to Real-Time Web Crawler Detection and Containment Using ClickTips Platform

Web crawler uncontrolled widespread has led to undesired situations of server overload and contents misuse. Most programs still have legitimate and useful goals, but standard detection heuristics have not evolved along with Web crawling technology and are now unable to identify most of today’s programs. In this paper, we propose an integrated approach to the problem that ensures the generation of upto-date decision models, targeting both monitoring and clickstream differentiation. The ClickTips platform sustains Web crawler detection and containment mechanisms and its data webhousing system is responsible for clickstream processing and further data mining. Web crawler detection and monitoring helps preserving Web server performance and Web site privacy and clickstream differentiated analysis provides focused report and interpretation of navigational patterns. The generation of up-to-date detection models is based on clickstream data mining and targets not only well-known Web crawlers, but also camouflaging and previously unknown programs. Experiments with different real-world Web sites are optimistic, proving that the approach is not only feasible but also adequate.