nils-herrmann/clean_scirepeval huggingface dataset:
Another Look at Causality: Discovering Scenario-Specific Contingency Relationships with No Supervision

Contingency discourse relations play an important role in natural language understanding. In this paper we propose an unsupervised learning model to automatically identify contingency relationships between scenario-specific events in web news articles (on the Iraq war and on hurricane Katrina). The model generates ranked contingency relationships by identifying appropriate candidate event pairs for each scenario of a particular domain. Scenario-specific events, contributing towards the same objectives in a domain, are likely to be dependent on each other, and thus form good candidates for contingency relationships. In order to evaluate the ranked contingency relationships, we rely on the manipulation theory of causation and a comparison of precision-recall performance curves. We also perform various tests which bring insights into how people perceive causality. For example, our findings show that the larger the distance between two events, the more likely it becomes for the annotators to identify them as non-causal.
Phishing Attack Detection , Classification and Proactive Prevention using Fuzzy Logic and Data Mining Algorithm

This paper presents a design for removing phishing sites or phishing pages that are hosted probably without the knowledge of the website owner or host server. Initially the system assesses and classifies phishing emails using Fuzzy Logic and the RIPPER Data Mining algorithm. In assessing the Phishing email, Fuzzy Logic linguistic descriptors are assigned to a range of values for each key phishing characteristic indicators. The membership function is designed for each Phishing email characteristic descriptor. The Data Mining RIPPER algorithm is used to characterize the Phishing emails and classify them based on both content-based and non-content based characteristics of Phishing emails. Furthermore, after the email has been assessed and classified as a Phishing email, the system proactively gets rid of the Phishing site or Phishing page by sending a notification to the System Administrator of the host server that it is hosting a Phishing site which may result in the removal of the site. The results showed that the RIPPER algorithm achieved 85.4% for correctly classified Phishing emails and 14.6% for wrongly classified Phishing emails based on publicly available datasets from Phistank. Furthermore, after classifying the Phishing email, the system retrieves the location, IP address and contact information of the host server by performing a Whois query. The system then sends a notification to the System Administrator of the host server to indicate that it is hosting a Phishing site. The removal success rate of the identified phishing sites is 81.81% based on the notifications sent to the host of the different phishing pages.
Rectified Linear Units Improve Restricted Boltzmann Machines

Restricted Boltzmann machines were developed using binary stochastic hidden units. These can be generalized by replacing each binary unit by an infinite number of copies that all have the same weights but have progressively more negative biases. The learning and inference rules for these “Stepped Sigmoid Units” are unchanged. They can be approximated efficiently by noisy, rectified linear units. Compared with binary units, these units learn features that are better for object recognition on the NORB dataset and face verification on the Labeled Faces in the Wild dataset. Unlike binary units, rectified linear units preserve information about relative intensities as information travels through multiple layers of feature detectors.
8 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

and a Ph.D in statistics from Stanford University in 1984. His Ph.D thesis is titled Principal Curves and Surfaces, which is now an active area of research, and bears a close relationship to self organizing maps. He spent 9 years in the Statistics and Data Analysis research department at AT&T Bell laboratories, Murray Hill, New Jersey. His research has focused on applied modelling problems, especially non-parametric regression and classiication. Robert Tibshirani is a professor in the Statistics and Biostatistics departments at University of Toronto. He received a B.Sc. degree from the University of Waterloo, and a Ph.D in statistics from Stanford University in 1984. He has many research articles on nonparametric regression and classiication. has been an active researcher on bootstrap technology for the past 11 years. His 1984 Ph.D thesis spawned the currently lively research area known as Local Likelihood. He is a recent recipient of a Guggen-heim Foundation fellowship. He is a fellow of the American Statistical association and the Institute of Mathematical Statistics. HASTIE AND TIBSHIRANI: DISCRIMINANT ADAPTIVE NEAREST NEIGHBOR CLASSIFICATION 9 reduction. 10] proposed a technique close to ours for the two class problem. In our terminology they used our metric with W = I and = 0, with B determined locally in a neighborhood of size K M. In eeect this extends the neighborhood innnitely in the null space of the local between class directions, but they restrict this neighborhood to the original K M observations. This amounts to projecting the local data onto the line joining the two local centroids. In our experiments this approach tended to perform on average 10% worse than our metric, and we did not pursue it further. 11] extended this to J > 2 classes, but here their approach diiers even more from ours. They computed a weighted average of the J local centroids from the overall average, and project the data onto it, a one-dimensional projection. Even with = 0 we project the data onto the subspace containing the local centroids, and deform the metric appropriately in that subspace. 12] recognized a shortfall of the Short and Fukanaga approach, since the averaging can cause cancellation, and proposed other metrics to avoid this. Although their metrics diier from ours, the Chi-squared motivation for our metric (3) was inspired by the metrics developed in their paper. We have not tested out their proposals, but they report results of experiments with …
A 14-Item Mediterranean Diet Assessment Tool and Obesity Indexes among High-Risk Subjects: The PREDIMED Trial

Independently of total caloric intake, a better quality of the diet (for example, conformity to the Mediterranean diet) is associated with lower obesity risk. It is unclear whether a brief dietary assessment tool, instead of full-length comprehensive methods, can also capture this association. In addition to reduced costs, a brief tool has the interesting advantage of allowing immediate feedback to participants in interventional studies. Another relevant question is which individual items of such a brief tool are responsible for this association. We examined these associations using a 14-item tool of adherence to the Mediterranean diet as exposure and body mass index, waist circumference and waist-to-height ratio (WHtR) as outcomes.



Cross-sectional assessment of all participants in the "PREvención con DIeta MEDiterránea" (PREDIMED) trial.



7,447 participants (55-80 years, 57% women) free of cardiovascular disease, but with either type 2 diabetes or ≥ 3 cardiovascular risk factors. Trained dietitians used both a validated 14-item questionnaire and a full-length validated 137-item food frequency questionnaire to assess dietary habits. Trained nurses measured weight, height and waist circumference.



Strong inverse linear associations between the 14-item tool and all adiposity indexes were found. For a two-point increment in the 14-item score, the multivariable-adjusted differences in WHtR were -0.0066 (95% confidence interval, -0.0088 to -0.0049) for women and -0.0059 (-0.0079 to -0.0038) for men. The multivariable-adjusted odds ratio for a WHtR>0.6 in participants scoring ≥ 10 points versus ≤ 7 points was 0.68 (0.57 to 0.80) for women and 0.66 (0.54 to 0.80) for men. High consumption of nuts and low consumption of sweetened/carbonated beverages presented the strongest inverse associations with abdominal obesity.



A brief 14-item tool was able to capture a strong monotonic inverse association between adherence to a good quality dietary pattern (Mediterranean diet) and obesity indexes in a population of adults at high cardiovascular risk.
Challenges of Big Data Analysis.

Big Data bring new opportunities to modern society and challenges to data scientists. On one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity, and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This article gives overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasize on the viability of the sparsest solution in high-confidence set and point out that exogeneous assumptions in most statistical methods for Big Data can not be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions.
THE LOCAL LANGLANDS CORRESPONDENCE FOR GLn OVER p-ADIC FIELDS

We extend our methods from [24] to reprove the Local Langlands Correspondence for GLn over p-adic fields as well as the existence of `-adic Galois representations attached to (most) regular algebraic conjugate self-dual cuspidal automorphic representations, for which we prove a local-global compatibility statement as in the book of Harris-Taylor, . In contrast to the proofs of the Local Langlands Correspondence given by Henniart, [13], and Harris-Taylor, , our proof completely by-passes the numerical Local Langlands Correspondence of Henniart, . Instead, we make use of a previous result from [24] describing the inertia-invariant nearby cycles in certain regular situations.
Plyometric training improves power and agility in Jamaica's national netball team.

Plyometric training is widely used in conditioning, power training and in prevention and rehabilitation of injuries in some sports. This study sought to investigate the effect of a three-week plyometric training programme on jump performance and agility in Jamaican national netball players.



Eighteen national netballers participated in a Plyometric training programme. Subjects were evaluated using the Vertical Jump Test, the Broad Jump Test and the Illinois Agility Test prior to the start of the programme and at week 3.



The data were analysed using SPSS version 12 for Windows. A One-Sample Kolmogorov-Smirnov Test showed normal distribution of data and a paired samples t-test was used to determine whether the mean change in jump performance and agility was significant.



All eighteen subjects completed the three weeks of training. Three subjects were shown to be outliers at week 3 and this resulted in data from 15 subjects being analysed. At the end of the three weeks, there were significant improvements in Vertical Jump Scores (p = 0.023), Broad Jump Scores (p = 0.002) and Agility scores (p = 0.045).



Three weeks of Plyometric training can lead to significant improvements in jump performance and agility and should be integrated into the national training programme at intervals yet to be determined.
System Dynamics and Uncertainty

System Dynamics is often used for dealing with dynamically complex issues that are also uncertain. This paper reviews how uncertainty is dealt with in System Dynamics modeling, where uncertainties are located in models, which types of uncertainties are dealt with, and which levels of uncertainty could be handled. Shortcomings of System Dynamics and its practice in dealing with uncertainty are distilled from this review and reframed as opportunities. Potential opportunities for dealing with uncertainty in System Dynamics that are discussed here include (i) dealing explicitly with difficult sorts of uncertainties, (ii) using multi-model approaches for dealing with alternative assumptions and multiple perspectives, (iii) clearly distinguishing sensitivity analysis from uncertainty analysis and using them for different purposes, (iv) moving beyond invariant model boundaries, (v) using multi-method approaches, advanced techniques and new tools, and (vi) further developing and using System Dynamics strands for dealing with deep uncertainty.
Ontology-based news recommendation

Recommending news items is traditionally done by term-based algorithms like TF-IDF. This paper concentrates on the benefits of recommending news items using a domain ontology instead of using a term-based approach. For this purpose, we propose Athena, which is an extension to the existing Hermes framework. Athena employs a user profile to store terms or concepts found in news items browsed by the user. Based on this information, the framework uses a traditional method based on TF-IDF, and several ontology-based methods to recommend new articles to the user. The paper concludes with the evaluation of the different methods, which shows that the new ontology-based method that we propose in this paper performs better (w.r.t. accuracy, precision, and recall) than the traditional method and, with the exception of one measure (recall), also better than the other considered ontology-based approaches.
NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey

Todayâ€TMs wireless networks are characterized by a fixed spectrum assignment policy. However, a large portion of the assigned spectrum is used sporadically and geographical variations in the utilization of assigned spectrum ranges from 15% to 85% with a high variance in time. The limited available spectrum and the inefficiency in the spectrum usage necessitate a new communication paradigm to exploit the existing wireless spectrum opportunistically. This new networking paradigm is referred to as NeXt Generation (xG) Networks as well as Dynamic Spectrum Access (DSA) and cognitive radio networks. The term xG networks is used throughout the paper. The novel functionalities and current research challenges of the xG networks are explained in detail. More specifically, a brief overview of the cognitive radio technology is provided and the xG network architecture is introduced. Moreover, the xG network functions such as Purchase Export Previous article Next article Check if you have access through your login credentials or your institution.
CCL: Cross-modal Correlation Learning With Multigrained Fusion by Hierarchical Network

Cross-modal retrieval has become a highlighted research topic for retrieval across multimedia data such as image and text. A two-stage learning framework is widely adopted by most existing methods based on deep neural network (DNN): The first learning stage is to generate separate representation for each modality and the second learning stage is to get the cross-modal common representation. However the existing methods have three limitations: 1) In the first learning stage they only model intramodality correlation but ignore intermodality correlation with rich complementary context. 2) In the second learning stage they only adopt shallow networks with single-loss regularization but ignore the intrinsic relevance of intramodality and intermodality correlation. 3) Only original instances are considered while the complementary fine-grained clues provided by their patches are ignored. For addressing the above problems this paper proposes a cross-modal correlation learning (CCL) approach with multigrained fusion by hierarchical network and the contributions are as follows: 1) In the first learning stage CCL exploits multilevel association with joint optimization to preserve the complementary context from intramodality and intermodality correlation simultaneously. 2) In the second learning stage a multitask learning strategy is designed to adaptively balance the intramodality semantic category constraints and intermodality pairwise similarity constraints. 3) CCL adopts multigrained modeling which fuses the coarse-grained instances and fine-grained patches to make cross-modal correlation more precise. Comparing with 13 state-of-the-art methods on 6 widely-used cross-modal datasets the experimental results show our CCL approach achieves the best performance.
A survey of indoor positioning systems for wireless personal networks

Recently, indoor positioning systems (IPSs) have been designed to provide location information of persons and devices. The position information enables location-based protocols for user applications. Personal networks (PNs) are designed to meet the users' needs and interconnect users' devices equipped with different communications technologies in various places to form one network. Location-aware services need to be developed in PNs to offer flexible and adaptive personal services and improve the quality of lives. This paper gives a comprehensive survey of numerous IPSs, which include both commercial products and research-oriented solutions. Evaluation criteria are proposed for assessing these systems, namely security and privacy, cost, performance, robustness, complexity, user preferences, commercial availability, and limitations.We compare the existing IPSs and outline the trade-offs among these systems from the viewpoint of a user in a PN.
FATAUVA-Net: An Integrated Deep Learning Framework for Facial Attribute Recognition, Action Unit Detection, and Valence-Arousal Estimation

Facial expression recognition has been investigated for many years, and there are two popular models: Action Units (AUs) and the Valence-Arousal space (V-A space) that have been widely used. However, most of the databases for estimating V-A intensity are captured in laboratory settings, and the benchmarks "in-the-wild" do not exist. Thus, the First Affect-In-The-Wild Challenge released a database for V-A estimation while the videos were captured in wild condition. In this paper, we propose an integrated deep learning framework for facial attribute recognition, AU detection, and V-A estimation. The key idea is to apply AUs to estimate the V-A intensity since both AUs and V-A space could be utilized to recognize some emotion categories. Besides, the AU detector is trained based on the convolutional neural network (CNN) for facial attribute recognition. In experiments, we will show the results of the above three tasks to verify the performances of our proposed network framework.
The Graph SLAM Algorithm with Applications to Large-Scale Mapping of Urban Structures

This article presents GraphSLAM, a unifying algorithm for the offline SLAM problem. GraphSLAM is closely related to a recent sequence of research papers on applying optimization techniques to SLAM problems. It transforms the SLAM posterior into a graphical network, representing the log-likelihood of the data. It then reduces this graph using variable elimination techniques, arriving at a lowerdimensional problems that is then solved using conventional optimization techniques. As a result, GraphSLAM can generate maps with 108 or more features. The paper discusses a greedy algorithm for data association, and presents results for SLAM in urban environments with occasional GPS measurements. SLAM, robot navigation, localization, mapping
A Deep Semantic Natural Language Processing Platform

This paper describes AllenNLP, a platform for research on deep learning methods in natural language understanding. AllenNLP is designed to support researchers who want to build novel language understanding models quickly and easily. It is built on top of PyTorch, allowing for dynamic computation graphs, and provides (1) a flexible data API that handles intelligent batching and padding, (2) highlevel abstractions for common operations in working with text, and (3) a modular and extensible experiment framework that makes doing good science easy. It also includes reference implementations of high quality approaches for both core semantic problems (e.g. semantic role labeling (Palmer et al., 2005)) and language understanding applications (e.g. machine comprehension (Rajpurkar et al., 2016)). AllenNLP is an ongoing open-source effort maintained by engineers and researchers at the Allen Institute for Artificial Intelligence.
Home Location Identification of Twitter Users

We present a new algorithm for inferring the home location of Twitter users at different granularities, including city, state, time zone, or geographic region, using the content of users’ tweets and their tweeting behavior. Unlike existing approaches, our algorithm uses an ensemble of statistical and heuristic classifiers to predict locations and makes use of a geographic gazetteer dictionary to identify place-name entities. We find that a hierarchical classification approach, where time zone, state, or geographic region is predicted first and city is predicted next, can improve prediction accuracy. We have also analyzed movement variations of Twitter users, built a classifier to predict whether a user was travelling in a certain period of time, and use that to further improve the location detection accuracy. Experimental evidence suggests that our algorithm works well in practice and outperforms the best existing algorithms for predicting the home location of Twitter users.
Why men and women continue to use social networking sites: The role of gender differences

Organizations increasingly use social media and especially social networking sites (SNS) to support their marketing agenda, enhance collaboration, and develop new capabilities. However, the success of SNS initiatives is largely dependent on sustainable user participation. In this study, we argue that the continuance intentions of users may be gendersensitive. To theorize and investigate gender differences in the determinants of continuance intentions, this study draws on the expectation-confirmation model, the uses and gratification theory, as well as the self-construal theory and its extensions. Our survey of 488 users shows that while both men and women are motivated by the ability to selfenhance, there are some gender differences. Specifically, while women are mainly driven by relational uses, such as maintaining close ties and getting access to social information on close and distant networks, men base their continuance intentions on their ability to gain information of a general nature. Our research makes several contributions to the discourse in strategic information systems literature concerning the use of social media by individuals and organizations. Theoretically, it expands the understanding of the phenomenon of continuance intentions and specifically the role of the gender differences in its determinants. On a practical level, it delivers insights for SNS providers and marketers into how satisfaction and continuance intentions of male and female SNS users can be differentially promoted. Furthermore, as organizations increasingly rely on corporate social networks to foster collaboration and innovation, our insights deliver initial recommendations on how organizational social media initiatives can be supported with regard to gender-based differences.