nils-herrmann/clean_scirepeval huggingface dataset:
Anomaly detection model of user behavior based on principal component analysis
Venue: J. Ambient Intell. Humaniz. Comput.

A new anomaly detection model which is based on principal component analysis (PCA) is proposed in this paper. Our schema proposes a method to extract the user's behavior and analyzes the features selected as representative of the user's access. The PCA method is introduced to the anomaly detection model which adopts its improvements to make it more consistent with anomaly detection system design to describe the user's behavior more completely and to improve the efficiency and stability of the algorithm. This paper also uses our scheme to the anomaly detection of the database system. Finally, the data sets from the internet are used to test the feasibility of this model. The experimental results show that our model can detect normal and abnormal user behavior precisely and effectively.
Research on Healthy Anomaly Detection Model Based on Deep Learning from Multiple Time Series Physiological Signals
Venue: Sci. Program.

Health is vital to every human being. To further improve its already respectable medical technology, the medical community is transitioning towards a proactive approach which anticipates and mitigates risks before getting ill. This approach requires measuring the physiological signals of human and analyzes these data at regular intervals. In this paper, we present a novel approach to apply deep learning in physiological signals analysis that allows doctor to identify latent risks. However, extracting high level information from physiological time series data is a hard problem faced by the machine learning communities. Therefore, in this approach, we apply model based on convolutional neural network that can automatically learn features from raw physiological signals in an unsupervised manner and then based on the learned features use multivariate Gauss distribution anomaly detection method to detect anomaly data. Our experiment is shown to have a significant performance in physiological signals anomaly detection. So it is a promising tool for doctor to identify early signs of illness even if the criteria are unknown a priori.
Union inclusiveness and temporary agency workers: The role of power resources and union ideology

This article investigates the determinants of union inclusiveness towards agency workers in Western Europe, using an index which combines unionization rates with dimensions of collective agreements covering agency workers. Using fuzzy set Qualitative Comparative Analysis, we identify two combinations of conditions leading to inclusiveness: the 'Northern path' includes high union density, high bargaining coverage and high union authority, and is consistent with the power resources approach. The 'Southern path' combines high union authority, high bargaining coverage, statutory regulations of agency work and working class orientation, showing that ideology rather than institutional incentives shapes union strategies towards the marginal workforce.
TOWARDS AN INTEGRATED APPROACH TO DISASTER MANAGEMENT AND FOOD SAFETY GOVERNANCE

The paper begins with a description of Canadian food safety institutions and follows with an overview of basic models of governance. A leading food safety governance model, emphasizing risk communication and public participation, and a normative version of the disaster management cycle are then highlighted. In recent years, thinking in disaster management and food safety governance has placed increased emphasis on deliberation and dialogue, denoting greater inclusiveness and influence of stakeholders in risk communication and government decision making. And while disaster management and food safety governance bear important similarities, they are mostly viewed as stand alone systems. The 2008 Canadian listeriosis crisis spurred our interest in developing a simplified, and yet more broadly encapsulating, food safety model. Exploring the intersection between the essential components of the disaster management cycle with those of a leading food safety governance framework permitted us to develop a more comprehensive and integrative conceptual framework. Keeping the essential components, we offer a revised, integrative framework to bridge the gap between disaster management and food safety governance. This framework has positive implications for public participation and risk communication. It could serve to strengthen food safety decision making by consolidating the spectra within which public participation and risk communication are undertaken. In particular, the framework streamlines the management phase of food safety governance with the mitigation phase of the disaster management cycle, allowing deliberative risk communication actions to mitigate potential food safety disasters and enabling governance actors to consider a broader range of food safety issues.
Attention Based Convolutional Neural Network for Semantic Relation Extraction
Venue: COLING

Nowadays, neural networks play an important role in the task of relation classification. In this paper, we propose a novel attention based convolutional neural network architecture for this task. Our model makes full use of word embedding, part of speech tag embedding and position embedding information. Word level attention mechanism is able to better determine which parts of the sentence are most influential with respect to the two entities of interest. This architecture enables learning some important features from task specific labeled data, forgoing the need for external knowledge such as explicit dependency structures. Experiments on the SemEval 2010 Task 8 benchmark dataset show that our model achieves better performances than several state of the art neural network models and can achieve a competitive performance just with minimal feature engineering.
The Demographics of galactic bulges in the SDSS database

We present a new database of our two dimensional bulge disk decompositions for 14,233 galaxies drawn from SDSS DR12 in order to examine the properties of bulges residing in the local universe $0.005 z 0.05$ We performed decompositions in $g$ and $r$ bands by utilizing the \sc{galfit} software. The bulge colors and bulge to total ratios are found to be sensitive to the details in the decomposition technique, and hence we hereby provide full details of our method. The $g r$ colors of bulges derived are almost constantly red regardless of bulge size except for the bulges in the low bulge to total ratio galaxies $B/T_{\rm r} \lesssim 0.3$ Bulges exhibit similar scaling relations to those followed by elliptical galaxies, but the bulges in galaxies with lower bulge to total ratios clearly show a gradually larger departure in slope from the elliptical galaxy sequence. The scatters around the scaling relations are also larger for the bulges in galaxies with lower bulge to total ratios. Both the departure in slopes and larger scatters are likely originated from the presence of young stars. The bulges in galaxies with low bulge to total ratios show signs of a frosting of young stars so substantial that their luminosity weighted Balmer line ages are as small as 1\,Gyr in some cases. While bulges seem largely similar in optical properties to elliptical galaxies, they do show clear and systematic departures as a function of bulge to total ratio. The stellar properties and perhaps associated formation processes of bulges seem much more diverse than those of elliptical galaxies.
Novel calibrations of virial black hole mass estimators in active galaxies based on X ray luminosity and optical/near infrared emission lines

It is currently only possible to accurately weigh, through reverberation mapping (RM) the masses of super massive black holes (BHs) in active galactic nuclei (AGN) for a small group of local and bright broad line AGN. Statistical demographic studies can be carried out considering the empirical scaling relation between the size of the broad line region (BLR) and the AGN optical continuum luminosity. There are still biases, however, against low luminosity or reddened AGN, in which the rest frame optical radiation can be severely absorbed or diluted by the host galaxy and the BLR emission lines can be hard to detect.  Our purpose is to widen the applicability of virial based single epoch (SE) relations to measure reliably the BH masses for low luminosity or intermediate and type 2 AGN, which the current methodology misses. We achieve this goal by calibrating virial relations based on unbiased quantities: the hard X ray luminosities in the 2 10 keV and 14 195 keV bands that are less sensitive to galaxy contamination, and the full width at half maximum (FWHM) of the most important rest frame near infrared (NIR) and optical BLR emission lines.  We built a sample of RM AGN with both X ray luminosity, broad optical and NIR FWHM measurements available to calibrate new virial BH mass estimators.  We found that the FWHM of the H a H b and NIR lines (i.e. Pa a Pa b and He i l 10830) all correlate with each other with negligible or small offsets. This result allowed us to derive virial BH mass estimators based on either the 2 10 keV or 14 195 keV luminosity. We also took into account the recent determination of the different virial coefficients, f for pseudo and classical bulges. By splitting the sample according to the bulge type and adopting separate f factors, we found that our virial relations predict BH masses of AGN hosted in pseudo bulges ~0.5 dex smaller than in classical bulges. Assuming the same average f factor for both populations, a difference of ~0.2 dex is still found.
Capacidad predictiva de los parametros antropometricos y de maduracion sobre el rendimiento de adolescentes noveles en remo ergometro

espanolResum Nuestro objetivo fue establecer que parametros antropometricos y de madura cion se correlacionan con el rendimiento en remo ergometro en una muestra de 114 adolescentes de ambos sexos sin experiencia previa en remo. Los resultados muestran una gran correlacion entre masa corporal y rendimiento, aunque esta correlacion disminuyo cuando la masa corporal se ajusto para compensar la mayor resistencia del lastre. La estatura, la longitud de piernas y la envergadura de brazos se correlacionaron moderadamente tras el ajuste de la masa corporal en los varones, pero no en las ninas. La compensacion de la maduracion antropometrica mostro una gran correlacion con el rendimiento, pero disminuyo tras aplicar el ajuste por talla. La prediccion de la estatura adulta revelo que pocos de estos adolescentes alcanzarian la estatura de los deportistas de elite de remo de peso libre. EnglishWe aimed to establish which anthropometric and maturity offset parameters correlate with rowing ergometer performance in a sample of 114 adolescent, rowing inexperienced boys and girls. Results showed high correlations between body mass and performance, but these reduced when body mass was scaled to account for increased on water drag resistance. Height, leg length and arm span remained moderately correlated after size adjustment in boys, but not in girls. Anthropometric maturity offset showed a high correlation with performance, but decreased with size adjustment. Final height estimation revealed that few of these adolescents would reach the height of elite open weight competitors
Nutricion y Rendimiento Deportivo

Sports, Cardiovascular and Wellness Nutrition dietetic practice group (Jackie Buell, PhD, RD, CSSD, ATC Ohio State University, Columbus, OH) Amanda Carlson Phillips, MS, RD, CSSD (EXOS Phoenix, AZ) Sharon Denny, MS, RD (Academy Knowledge Center, Chicago, IL) D. Enette Larson Meyer, PhD, RD, FACSM (University of Wyoming, Laramie, WY) Mary Pat Raimondi, MS, RD (Academy Policy Initiatives Advocacy, Washington DC)
Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science
Venue: SpringerPlus

Until recently tactical analysis in elite soccer were based on observational data using variables which discard most contextual information. Analyses of team tactics require however detailed data from various sources including technical skill, individual physiological performance, and team formations among others to represent the complex processes underlying team tactical behavior. Accordingly, little is known about how these different factors influence team tactical behavior in elite soccer. In parts, this has also been due to the lack of available data. Increasingly however, detailed game logs obtained through next generation tracking technologies in addition to physiological training data collected through novel miniature sensor technologies have become available for research. This leads however to the opposite problem where the shear amount of data becomes an obstacle in itself as methodological guidelines as well as theoretical modelling of tactical decision making in team sports is lacking. The present paper discusses how big data and modern machine learning technologies may help to address these issues and aid in developing a theoretical model for tactical decision making in team sports. As experience from medical applications show, significant organizational obstacles regarding data governance and access to technologies must be overcome first. The present work discusses these issues with respect to tactical analyses in elite soccer and propose a technological stack which aims to introduce big data technologies into elite soccer research. The proposed approach could also serve as a guideline for other sports science domains as increasing data size is becoming a wide spread phenomenon.
Electronic medical record phenotyping using the anchor and learn framework
Venue: J. Am. Medical Informatics Assoc.

Electronic medical records (EMRs) hold a tremendous amount of information about patients that is relevant to determining the optimal approach to patient care. As medicine becomes increasingly precise, a patient's electronic medical record phenotype will play an important role in triggering clinical decision support systems that can deliver personalized recommendations in real time. Learning with anchors presents a method of efficiently learning statistically driven phenotypes with minimal manual intervention.  We developed a phenotype library that uses both structured and unstructured data from the EMR to represent patients for real time clinical decision support. Eight of the phenotypes were evaluated using retrospective EMR data on emergency department patients using a set of prospectively gathered gold standard labels.  We built a phenotype library with 42 publicly available phenotype definitions. Using information from triage time, the phenotype classifiers have an area under the ROC curve (AUC) of infection 0.89, cancer 0.88, immunosuppressed 0.85, septic shock 0.93, nursing home 0.87, anticoagulated 0.83, cardiac etiology 0.89, and pneumonia 0.90. Using information available at the time of disposition from the emergency department, the AUC values are infection 0.91, cancer 0.95, immunosuppressed 0.90, septic shock 0.97, nursing home 0.91, anticoagulated 0.94, cardiac etiology 0.92, and pneumonia 0.97.  The resulting phenotypes are interpretable and fast to build, and perform comparably to statistically learned phenotypes developed with 5000 manually labeled patients.  Learning with anchors is an attractive option for building a large public repository of phenotype definitions that can be used for a range of health IT applications, including real time decision support.
Medical Record System Using Blockchain, Big Data and Tokenization
Venue: ICICS

This paper will discuss the major aspects of medical records, blockchain and big data. In turn, it will discuss the advantage and disadvantage of using blockchain on medical records storage and retrieval. It will also discuss the alternatives of using blockchain and big data techniques. Different aspects of medical records will be investigated briefly: (1) integrity, (2) viewing control, (3) viewing approval, (4) western medicine and chinese medicine practice, (5) storage size and duration, (6) deletion and purge, (7) file format conversion, (8) data migration, (9) report interpretation, etc. Characteristics of blockchain and big data analytics will be explored briefly with description. A conclusion will summarize the approaches. References will be provided for further research and investigation.
Auto Target Tracking Robot Design Based on Smartphone

This paper describes a robot tracking control design based on a smartphone using a commercial microprocessor. The system hardware consists of four major parts: an Android smartphone with an embedded camera, a Microchip microprocessor, a motor driver circuit and an Attacknid robot. First, an image of the surrounding environment is captured by the high definition camera embedded in the smartphone. The target is then recognized from the image using an algorithm developed in Android OS and OpenCV library. Third, motion control and laser activation strategies are achieved using the proposed algorithm implemented in Java. Fourth, the motion commands are delivered to the microchip processor through a USB interface. Finally, the processor produces a pulse width modulation (PWM) voltage to control the robot's motion and activate the laser diode according commands sent from the smart phone. Experimental results demonstrate the feasibility of this proposed architecture.
Interaction Networks for Learning about Objects, Relations and Physics
Venue: NIPS

Reasoning about objects, relations, and physics is central to human intelligence, and a key goal of artificial intelligence. Here we introduce the interaction network, a model which can reason about how objects in complex systems interact, supporting dynamical predictions, as well as inferences about the abstract properties of the system. Our model takes graphs as input, performs object and relation centric reasoning in a way that is analogous to a simulation, and is implemented using deep neural networks. We evaluate its ability to reason about several challenging physical domains: n body problems, rigid body collision, and non rigid dynamics. Our results show it can be trained to accurately simulate the physical trajectories of dozens of objects over thousands of time steps, estimate abstract quantities such as energy, and generalize automatically to systems with different numbers and configurations of objects and relations. Our interaction network implementation is the first general purpose, learnable physics engine, and a powerful general framework for reasoning about object and relations in a wide variety of complex real world domains.
Genomics and Proteomics Studies of Zolpidem, Necopidem, Alpidem, Saripidem, Miroprofen, Zolimidine, Olprinone and Abafungin as Antitumor, Peptide Antibiotics, Antiviral and Central Nervous System (CNS) Drugs

Zolpidem, Necopidem, Alpidem, Saripidem, Miroprofen, Zolimidine, Olprinone and Abafungin  have severed as rich sources of variety of medicinal, pharmaceutical, anti tumor, antibiotics and antiviral drugs  and biological properties [1 18] These compounds represent an important class of Nitrogen, Oxygen, Phosphorus and Sulfur heterocyclic and they constitute useful intermediates in organic synthesis [19 39] Zolpidem, Necopidem, Alpidem, Saripidem, Miroprofen, Zolimidine, Olprinone and Abafungin have proven to be very versatile reagents for heterocyclization and many diverse products can be prepared from the addition of these compounds to Nitrogen, Oxygen, Phosphorus and Sulfur containing compounds.
Effect of Mobile Marketing on Customer oriented Brand Equity in Insurance Industry

According to privatization of insurance companies and regarding the competitive climate over insurance industry in next years, undoubtedly there are some effective factors to obtain a proper place in customers' mind such that they remain loyal to the insurance company. One of these is 'brand equity' The purpose of this paper is to study the effect of mobile marketing on creating and enhancing brand equity dimensions. Brand equity is a multidimensional notion that may be increased by enhanced dimensions. In this regard, mobile marketing and customer oriented brand equity are defined according to Aaker model. This is a descriptive field study as it states research title and deals with it through descriptive data; in addition, the relationship between mobile marketing and brand equity dimensions are investigated using a researcher made 24 item questionnaire. The questionnaire's reliability (74% was determined by Cronbach alpha coefficient. Research statistical population was clients of insurance industry. Since the population was infinite, research sample included 384 individuals measured by Cochran formula. Collected data were analyzed using analytical statistics. The significance relationship between research variables was tested by Pearson correlation coefficient and regression analysis through SPSS software. Results indicate that mobile marketing is one of effective factors influencing awareness, association and perceived quality. Moreover, mobile customer relationship management (CRM) causes increased customer loyalty and provides the opportunity for the return of. As a result, insurers, in order to increase the value of brand equity, must invest on mobile marketing.
Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches
Venue: J. Mach. Learn. Res.

We present a method for extracting depth information from a rectified image pair. Our approach focuses on the first stage of many stereo algorithms: the matching cost computation. We approach the problem by learning a similarity measure on small image patches using a convolutional neural network. Training is carried out in a supervised manner by constructing a binary classification data set with examples of similar and dissimilar pairs of patches. We examine two network architectures for this task: one tuned for speed, the other for accuracy. The output of the convolutional neural network is used to initialize the stereo matching cost. A series of post processing steps follow: cross based cost aggregation, semiglobal matching, a left right consistency check, subpixel enhancement, a median filter, and a bilateral filter. We evaluate our method on the KITTI 2012, KITTI 2015, and Middlebury stereo data sets and show that it outperforms other approaches on all three data sets.