nils-herrmann/clean_scirepeval huggingface dataset:
Discovery of Spatial Association Rules in Geographic Information Databases

Spatial data mining i e discovery of interesting implicit knowledge in spatial databases is an important task for understanding and use of spatial data and knowledge bases In this paper an e cient method for mining strong spatial association rules in geographic infor mation databases is proposed and studied A spatial association rule is a rule indicating certain association relationship among a set of spatial and possibly some nonspatial predicates A strong rule indicates that the pat terns in the rule have relatively frequent occurrences in the database and strong implication relationships Several optimization techniques are ex plored including a two step spatial computation technique approximate computation on large sets and re ned computations on small promising patterns shared processing in the derivation of large predicates at mul tiple concept levels etc Our analysis shows that interesting association rules can be discovered e ciently in large spatial databases
Classification in Data Mining

Text classification is the process of classifying documents into predefined categories based on their content. Text classification is the primary requirement of text retrieval systems, which retrieve texts in response to a user query, and text understanding systems, which transform text in some way such as producing summaries, answering questions or extracting data. We have proposed a Text Classification system for classifying abstract of different research papers. In this System we have extracted keywords using Porter Stemmer and Tokenizer. The word set is formed from the derived keywords using Association Rule and Apriori algorithm. The Probability of the word set is calculated using naive bayes classifier and then the new abstract inserted by the user is classified as belonging to one of the various classes. The accuracy of the system is found satisfactory. It requires less training data as compared to other classification system.
The Internet of nano-things

Nanotechnology promises new solutions for many applications in the biomedical, industrial and military fields as well as in consumer and industrial goods. The interconnection of nanoscale devices with existing communication networks and ultimately the Internet defines a new networking paradigm that is further referred to as the Internet of Nano-Things. Within this context, this paper discusses the state of the art in electromagnetic communication among nanoscale devices. An in-depth view is provided from the communication and information theoretic perspective, by highlighting the major research challenges in terms of channel modeling, information encoding and protocols for nanonetworks and the Internet of Nano-Things.
FIXED POINT THEOREM FOR INTEGRAL TYPE CONTRACTION QUASI b-METRIC SPACE

In this paper, we introduce contractive conditions of integral type in the setting of dislocated quasi b-metric spaces. Using contractive conditions of integral type, we have presented a fixed point theorem in the framework of dislocated quasi b-metric spaces. Our established result generalize and extend various fixed point theorems of the literature in the context of dislocated quasi b-metric spaces. An example is given in the support of our main results.
Cloud security issues and challenges: A survey

The cloud computing provides on demand services over the Internet with the help of a large amount of virtual storage. The main features of cloud computing is that the user does not have any setup of expensive computing infrastructure and the cost of its services is less. In the recent years, cloud computing integrates with the industry and many other areas, which has been encouraging the researcher to research on new related technologies. Due to the availability of its services & scalability for computing processes individual users and organizations transfer their application, data and services to the cloud storage server. Regardless of its advantages, the transformation of local computing to remote computing has brought many security issues and challenges for both consumer and provider. Many cloud services are provided by the trusted third party which arises new security threats. The cloud provider provides its services through the Internet and uses many web technologies that arise new security issues. This paper discussed about the basic features of the cloud computing, security issues, threats and their solutions. Additionally, the paper describes several key topics related to the cloud, namely cloud architecture framework, service and deployment model, cloud technologies, cloud security concepts, threats, and attacks. The paper also discusses a lot of open research issues related to the cloud security. Cloud Computing, Cloud Framework, Cloud Security, Cloud Security Challenges, Cloud Security Issues
Estimation of quantile mixtures via L-moments and trimmed L-moments

Moments or cumulants have been traditionally used to characterize a probability distribution or an observed data set. Recently, L-moments and trimmed L-moments have been noticed as appealing alternatives to the conventional moments. This paper promotes the use of L-moments proposing new parametric families of distributions that can be estimated by the method of L-moments. The theoretical L-moments are defined by the quantile function i.e. the inverse of cumulative distribution function. An approach for constructing parametric families from quantile functions is presented. Because of the analogy to mixtures of densities, this class of parametric families is called quantile mixtures. The method of L-moments is a natural way to estimate the parameters of quantile mixtures. As an example, two parametric families are introduced: the normal-polynomial quantile mixture and the Cauchypolynomial quantile mixture. The proposed quantile mixtures are applied to model monthly, weekly and daily returns of some major stock indexes.
Extracting contract elements

We study how contract element extraction can be automated. We provide a labeled dataset with gold contract element annotations, along with an unlabeled dataset of contracts that can be used to pre-train word embeddings. Both datasets are provided in an encoded form to bypass privacy issues. We describe and experimentally compare several contract element extraction methods that use manually written rules and linear classifiers (logistic regression, SVMs) with hand-crafted features, word embeddings, and part-of-speech tag embeddings. The best results are obtained by a hybrid method that combines machine learning (with hand-crafted features and embeddings) and manually written post-processing rules.
Outcomes With Edoxaban Versus Warfarin in Patients With Previous Cerebrovascular Events: Findings From ENGAGE AF-TIMI 48 (Effective Anticoagulation With Factor Xa Next Generation in Atrial Fibrillation-Thrombolysis in Myocardial Infarction 48).

Patients with atrial fibrillation and previous ischemic stroke (IS)/transient ischemic attack (TIA) are at high risk of recurrent cerebrovascular events despite anticoagulation. In this prespecified subgroup analysis, we compared warfarin with edoxaban in patients with versus without previous IS/TIA.



ENGAGE AF-TIMI 48 (Effective Anticoagulation With Factor Xa Next Generation in Atrial Fibrillation-Thrombolysis in Myocardial Infarction 48) was a double-blind trial of 21 105 patients with atrial fibrillation randomized to warfarin (international normalized ratio, 2.0-3.0; median time-in-therapeutic range, 68.4%) versus once-daily edoxaban (higher-dose edoxaban regimen [HDER], 60/30 mg; lower-dose edoxaban regimen, 30/15 mg) with 2.8-year median follow-up. Primary end points included all stroke/systemic embolic events (efficacy) and major bleeding (safety). Because only HDER is approved, we focused on the comparison of HDER versus warfarin.



Of 5973 (28.3%) patients with previous IS/TIA, 67% had CHADS2 (congestive heart failure, hypertension, age, diabetes, prior stroke/transient ischemic attack) >3 and 36% were ≥75 years. Compared with 15 132 without previous IS/TIA, patients with previous IS/TIA were at higher risk of both thromboembolism and bleeding (stroke/systemic embolic events 2.83% versus 1.42% per year; P<0.001; major bleeding 3.03% versus 2.64% per year; P<0.001; intracranial hemorrhage, 0.70% versus 0.40% per year; P<0.001). Among patients with previous IS/TIA, annualized intracranial hemorrhage rates were lower with HDER than with warfarin (0.62% versus 1.09%; absolute risk difference, 47 [8-85] per 10 000 patient-years; hazard ratio, 0.57; 95% confidence interval, 0.36-0.92; P=0.02). No treatment subgroup interactions were found for primary efficacy (P=0.86) or for intracranial hemorrhage (P=0.28).



Patients with atrial fibrillation with previous IS/TIA are at high risk of recurrent thromboembolism and bleeding. HDER is at least as effective and is safer than warfarin, regardless of the presence or the absence of previous IS or TIA.
POMDP-Based Statistical Spoken Dialog Systems: A Review

Statistical dialog systems (SDSs) are motivated by the need for a data-driven framework that reduces the cost of laboriously handcrafting complex dialog managers and that provides robustness against the errors created by speech recognizers operating in noisy environments. By including an explicit Bayesian model of uncertainty and by optimizing the policy via a reward-driven process, partially observable Markov decision processes (POMDPs) provide such a framework. However, exact model representation and optimization is computationally intractable. Hence, the practical application of POMDP-based systems requires efficient algorithms and carefully constructed approximations. This review article provides an overview of the current state of the art in the development of POMDP-based spoken dialog systems.
Gradient boosting factorization machines

Recommendation techniques have been well developed in the past decades. Most of them build models only based on user item rating matrix. However, in real world, there is plenty of auxiliary information available in recommendation systems. We can utilize these information as additional features to improve recommendation performance. We refer to recommendation with auxiliary information as context-aware recommendation. Context-aware Factorization Machines (FM) is one of the most successful context-aware recommendation models. FM models pairwise interactions between all features, in such way, a certain feature latent vector is shared to compute the factorized parameters it involved. In practice, there are tens of context features and not all the pairwise feature interactions are useful. Thus, one important challenge for context-aware recommendation is how to effectively select "good" interaction features. In this paper, we focus on solving this problem and propose a greedy interaction feature selection algorithm based on gradient boosting. Then we propose a novel Gradient Boosting Factorization Machine (GBFM) model to incorporate feature selection algorithm with Factorization Machines into a unified framework. The experimental results on both synthetic and real datasets demonstrate the efficiency and effectiveness of our algorithm compared to other state-of-the-art methods.
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

We present a tutorial on Bayesian optimization, a method of finding the maximum of expensive cost functions. Bayesian optimization employs the Bayesian technique of setting a prior over the objective function and combining it with evidence to get a posterior function. This permits a utility-based selection of the next observation to make on the objective function, which must take into account both exploration (sampling from areas of high uncertainty) and exploitation (sampling areas likely to offer improvement over the current best observation). We also present two detailed extensions of Bayesian optimization, with experiments—active user modelling with preferences, and hierarchical reinforcement learning— and a discussion of the pros and cons of Bayesian optimization based on our experiences.
Corporate Social Responsibility , Ethical Leadership , and Trust Propensity : A Multi-Experience Model of Perceived Ethical Climate

Existing research on the formation of employee ethical climate perceptions focuses mainly on organization characteristics as antecedents, and although other constructs have been considered, these constructs have typically been studied in isolation. Thus, our understanding of the context in which ethical climate perceptions develop is incomplete. To address this limitation, we build upon the work of Rupp ( 2011) to develop and test a multi-experience model of ethical climate which links aspects of the corporate social responsibility (CSR), ethics, justice, and trust literatures and helps to explain how employees’ ethical climate perceptions form. We argue that in forming ethical climate perceptions, employees consider the actions or characteristics of a complex web of actors. Specifically, we propose that employees look (1) outward at how communities are impacted by their organization’s actions (e.g., CSR), (2) upward to make inferences about the ethicality of leaders in their organizations (e.g., ethical leadership), and (3) inward at their own propensity to trust others as they form their perceptions. Using a multiple-wave field study (N = 201) conducted at a privately held US corporation, we find substantial evidence in support of our model.
2 Time domain Frequency domain Activity signal Feature extraction Model training Mean Variance Amplitude

Sensor-based activity recognition seeks the profound high-level knowledge about human activity from multitudes of low-level sensor readings. Conventional pattern recognition approaches have made tremendous progress in the past years. However, most of those approaches heavily rely on heuristic hand-crafted feature extraction methods, which dramatically hinder their generalization performance. Additionally, those methods often produce unsatisfactory results for unsupervised and incremental learning tasks. Meanwhile, the recent advancement of deep learning makes it possible to perform automatic high-level feature extraction thus achieves promising performance in many areas. Since then, deep learning based methods have been widely adopted for the sensor-based activity recognition tasks. In this paper, we survey and highlight the recent advancement of deep learning approaches for sensor-based activity recognition. Specifically, we summarize existing literatures from three aspects: sensor modality, deep model and application. We also present a detailed discussion and propose grand challenges for future direction.
LATENT DOMAIN TRANSFER: CROSSING MODALITIES WITH BRIDGING AUTOENCODERS

Domain transfer is an exciting and challenging branch of machine learning because models must learn to smoothly transfer between domains, preserving local variations and capturing many aspects of variation without labels. However, most successful applications to date require the two domains to be closely related (e.g., image-to-image, video-video), utilizing similar or shared networks to transform domain-specific properties like texture, coloring, and line shapes. Here, we demonstrate that it is possible to transfer across modalities (e.g., image-to-audio) by first abstracting the data with latent generative models and then learning transformations between latent spaces. We find that a simple variational autoencoder is able to learn a shared latent space to bridge between two generative models in an unsupervised fashion, and even between different types of models (e.g., variational autoencoder and a generative adversarial network). We can further impose desired semantic alignment of attributes with a linear classifier in the shared latent space. The proposed variation autoencoder enables preserving both locality and semantic alignment through the transfer process, as shown in the qualitative and quantitative evaluations. Finally, the hierarchical structure decouples the cost of training the base generative models and semantic alignments, enabling computationally efficient and data efficient retraining of personalized mapping functions.
Spectral properties of chaotic signals generated by the skew tent map

Chaotic signals have been considered potentially attractive in many signal processing applications ranging from wideband communication systems to cryptography and watermarking. Besides, some devices as nonlinear adaptive filters and phase-locked loops can present chaotic behavior. In this paper, we derive analytical expressions for the autocorrelation sequence, power spectral density and essential bandwidth of chaotic signals generated by the skew tent map. From these results, we suggest possible applications in communication systems. & 2009 Elsevier B.V. All rights reserved.
The effect of type and timing of feedback on learning from multiple-choice tests.

Two experiments investigated how the type and timing of feedback influence learning from a multiple-choice test. First, participants read 12 prose passages, which covered various general knowledge topics (e.g., The Sun) and ranged between 280 and 300 words in length. Next, they took an initial six-alternative, multiple-choice test on information contained in the passages. Feedback was given immediately for some of the multiple-choice items or after delay for other items. Participants were either shown the correct answer as feedback (standard feedback) or were allowed to keep answering until the correct answer was discovered (answer-until-correct feedback). Learning from the test was assessed on a delayed cued-recall test. The results indicated that delayed feedback led to superior final test performance relative to immediate feedback. However, type of feedback did not matter: discovering the correct answer through answer-until-correct feedback produced equivalent performance relative to standard feedback. This research suggests that delaying the presentation of feedback after a test is beneficial to learning because of the spaced presentation of information.
Adolescents' moral evaluations and ratings of cyberbullying: The effect of veracity and intentionality behind the event

The constant exposure to electronic media has increased the likelihood of adolescents experiencing hurtful events such as cyberbullying. The current study examined how adolescents’ moral evaluations of cyberbullying are affected by different aspects of the event including falsity of posts, power imbalance and intention to harm. Adolescents between 12–13 years of age (n = 77) and 15–16 years of age (n = 77) read moral vignettes and were asked to evaluate the actions of the protagonist. They were also asked if the behaviour in the vignette was an incident of cyberbullying. Participants also filled out a questionnaire about their own experiences with cyberbullying. It was found that adolescents evaluated as more negative those situations depicting false stories, imbalance of power, and intention to harm; Younger children had difficulties recognizing the intentions behind on-line posts. Adolescents who have cyber-intimidated others were less severe in their evaluations and less likely to rate vignettes as cyberbullying compared to other youth.
Gender differences in self-concept and academic achievement among visually impaired pupils in Kenya

Variation in self concept and academic achievement particularly among the visually impaired pupils has not been conclusively studied. The purpose of this study was therefore to determine if there were gender differences in self-concept and academic achievement among visually impaired pupils in Kenya .The population of the study was 291 visually impaired pupils. A sample of 262 respondents was drawn from the population by stratified random sampling technique based on their sex (152 males and 110 females). Two instruments were used in this study: Pupils’ self-concept and academic achievement test. Data analysis was done at p≤0.05 level of significance. The t test was used to test the relationship between self-concept and achievement. The data was analyzed using Analysis of Variance (ANOVA) structure. The study established that there were indeed gender differences in self-concept among visually impaired pupils in Kenya. The study therefore recommend that the lower self-concept observed among boys should be enhanced by giving counseling and early intervention to this group of pupils with a view to helping them accept their disability.
Real Time Implementation of RTOS based Vehicle Tracking System

A vehicle or fleet management system is implemented for tracking the movement of the vehicle at any time from any location. This proposed system helps in real time tracking of the vehicle using a smart phone application. This method is easy and efficient when compared to other implementations. In emerging technology of developing IOT (Internet of Things) the generic 8 bit/16 bit micro controllers are replaced by 32bit micro controllers in the embedded systems. This has many advantages like use of 32bit micro controller’s scalability, reusability and faster execution speed. Implementation of RTOS is very much necessary for having a real time system. RTOS features are application portability, reusability, more efficient use of system resources. The proposed system uses a 32bit ARM7 based microcontroller with an embedded Real Time Operating System (RTOS).The vehicle unit application is written on FreeRTOS. The peripheral drivers like UART, External interrupt are developed for RTOS aware environment. The vehicle unit consists of a GPS/GPRS module where the position of the vehicle is got from the Global Positioning System (GPS) and the General Packet Radio Service (GPRS) is used to update the timely information of the vehicle position. The vehicle unit updates the location to the Fleet management application on the web server. The vehicle management is a java based web application integrated with MySQL Database. The web application in the proposed system is based on OpenGTS open source vehicle tracking application. A GoTrack Android application is configured to work with web application. The smart phone application also provides a separate login for administrator to add, edit and remove the vehicles on the fleet management system. The users and administrators can continuously monitor the vehicle using a smart phone application.
WHY AND WHEN DOES THE GENDER GAP REVERSE ? DIVERSITY GOALS AND THE PAY PREMIUM FOR HIGH POTENTIAL WOMEN

Abundant research has documented a gender pay gap; women earn less than men, all else being equal. Against the backdrop of an overall female penalty, we propose that the widespread adoption of diversity goals in organizations creates a female premium for certain women. We integrate the economic principle of supply and demand with theory from the field of strategic human resource management and theorize that individuals perceive high-potential women—who have the abilities needed to reach the upper echelons of organizations, where women remain underrepresented—as more valuable for achieving organizational diversity goals than high-potential men and, in turn, reward them with higher pay. Two field studies (Studies 1 and 3) and two laboratory experiments (Studies 2 and 4) reveal a female premium that is unique to high-potential women (Studies 1 and 2), driven by perceptions that high-potential women have more diversity value than high-potential men (Studies 2 and 4), and larger in contexts where diversity goals are stronger (Studies 3 and 4). Our theory and findings challenge the assumption that the gender pay gap uniformly disadvantages women and offer new insight into why and when the female penalty reverses and becomes a female premium.