nils-herrmann/clean_scirepeval huggingface dataset:
Performance of Double-Output Induction Generator for Wind Energy Conversion Systems

With growing concerns about environmental pollution and a possible energy shortage, great efforts have been taken by the governments around the world to implement renewable energy programs, based mainly on wind power, solar energy, small hydro-electric power, etc. With improving techniques, reducing costs and low environmental impact, wind energy seems certain to play a major part in the world's energy future. Due to its many advantages such as the improved power quality, high energy efficiency and controllability, etc., the variable speed wind turbine using a double-output induction generator (DOIG) is becoming a popular concept and thus the modeling of the DOIG based wind turbine becomes an interesting research topic. As the wind power penetration continually increases, power utilities concerns are shifting focus from the power quality issue to the stability problem caused by the wind power connection. In such cases, it becomes important to consider the wind power impact properly in the power system planning and operation. Unfortunately, few power system analysis tools have included wind turbine models such as have been developed for traditional Power generators. The paper develops analytical steady-state and dynamic models to provide this insight and correlates the operating performance of DOIG for wind energy conversion systems using MATLAB-Simulink environment. This paper considers a grid-connected system; a further paper will describe a stand-alone system.
Unsupervised Feature Learning using Self-organizing Maps

In recent years a great amount of research has focused on algorithms that learn features from unlabeled data. In this work we propose a model based on the Self-Organizing Map (SOM) neural network to learn features useful for the problem of automatic natural images classification. In particular we use the SOM model to learn single-layer features from the extremely challenging CIFAR-10 dataset, containing 60.000 tiny labeled natural images, and subsequently use these features with a pyramidal histogram encoding to train a linear SVM classifier. Despite the large number of images, the proposed feature learning method requires only few minutes on an entry-level system, however we show that a supervised classifier trained with learned features provides significantly better results than using raw pixels values or other handcrafted features designed specifically for image classification. Moreover, exploiting the topological property of the SOM neural network, it is possible to reduce the number of features and speed up the supervised training process combining topologically close neurons, without repeating the feature learning process.
Extraction of Pharmacokinetic Evidence of Drug–Drug Interactions from the Literature

Drug-drug interaction (DDI) is a major cause of morbidity and mortality and a subject of intense scientific interest. Biomedical literature mining can aid DDI research by extracting evidence for large numbers of potential interactions from published literature and clinical databases. Though DDI is investigated in domains ranging in scale from intracellular biochemistry to human populations, literature mining has not been used to extract specific types of experimental evidence, which are reported differently for distinct experimental goals. We focus on pharmacokinetic evidence for DDI, essential for identifying causal mechanisms of putative interactions and as input for further pharmacological and pharmacoepidemiology investigations. We used manually curated corpora of PubMed abstracts and annotated sentences to evaluate the efficacy of literature mining on two tasks: first, identifying PubMed abstracts containing pharmacokinetic evidence of DDIs; second, extracting sentences containing such evidence from abstracts. We implemented a text mining pipeline and evaluated it using several linear classifiers and a variety of feature transforms. The most important textual features in the abstract and sentence classification tasks were analyzed. We also investigated the performance benefits of using features derived from PubMed metadata fields, various publicly available named entity recognizers, and pharmacokinetic dictionaries. Several classifiers performed very well in distinguishing relevant and irrelevant abstracts (reaching F1≈0.93, MCC≈0.74, iAUC≈0.99) and sentences (F1≈0.76, MCC≈0.65, iAUC≈0.83). We found that word bigram features were important for achieving optimal classifier performance and that features derived from Medical Subject Headings (MeSH) terms significantly improved abstract classification. We also found that some drug-related named entity recognition tools and dictionaries led to slight but significant improvements, especially in classification of evidence sentences. Based on our thorough analysis of classifiers and feature transforms and the high classification performance achieved, we demonstrate that literature mining can aid DDI discovery by supporting automatic extraction of specific types of experimental evidence.
Online Dictionary Learning for Kernel LMS

Adaptive filtering algorithms operating in reproducing kernel Hilbert spaces have demonstrated superiority over their linear counterpart for nonlinear system identification. Unfortunately, an undesirable characteristic of these methods is that the order of the filters grows linearly with the number of input data. This dramatically increases the computational burden and memory requirement. A variety of strategies based on dictionary learning have been proposed to overcome this severe drawback. In the literature, there is no theoretical work that strictly analyzes the problem of updating the dictionary in a time-varying environment. In this paper, we present an analytical study of the convergence behavior of the Gaussian least-mean-square algorithm in the case where the statistics of the dictionary elements only partially match the statistics of the input data. This theoretical analysis highlights the need for updating the dictionary in an online way, by discarding the obsolete elements and adding appropriate ones. We introduce a kernel least-mean-square algorithm with ℓ1-norm regularization to automatically perform this task. The stability in the mean of this method is analyzed, and the improvement of performance due to this dictionary adaptation is confirmed by simulations.
Investigation of user behavior on social networking sites

Social networking sites (SNS) are used for social and professional interaction with people. SNS popularity has encouraged researchers to analyze the relationship of activities performed on SNS with user behavior. In doing so, the term "user behavior" is rather used ambiguously with different interpretations, which makes it difficult to identify studies on user behavior in relation to SNS. This phenomenon has encouraged this thorough research on the characteristics of user behavior being discussed in the literature. Therefore, in this study, we aim to identify, analyze, and classify the characteristics associated with user behavior to answer the research questions designed to conduct this research. A mapping study (also called scoping study), which is a type of systematic literature review, is employed to identify potential studies from digital databases through a developed protocol. Thematic analysis is carried out for the classification of user behavior. We identified 116 primary studies for full analysis. This study found seven characteristics associated with behavior that have direct influence on SNS use and nine factors that have an indirect effect. All studies were conducted largely under seven areas that set the context of these studies. Findings show that the research on SNS is still in its early stage. The range of topics covered in the analyzed studies is quite expansive, although the depth in terms of number of studies under each topic is quite limited. This study reports that activities performed on SNS are either associated with user behavior or reflect personality characteristics. The findings of this study could be used by practitioners to evaluate their SNS platforms and develop more user-centered applications. These studies can also help organizations to understand better the needs of their employees.
A cost-minimization algorithm for fast location tracking in mobile wireless networks

Location tracking is one of the most important issues in providing real-time applications over wireless networks due to its effect to quality of service (QoS), such as end-to-end delay, bandwidth utilization, and connection dropping probability. In this paper, we study cost minimization for locating mobile users under delay constraints in mobile wireless networks. Specifically, a new location tracking algorithm is developed to determine the position of mobile terminals under delay constraints, while minimizing the average locating cost based on a unimodal property. We demonstrate that the new algorithm not only results in minimum locating cost, but also has a lower computational complexity compared to existing algorithms. Furthermore, detailed searching procedures are discussed under both deterministic and statistic delay bounds. Numerical results for a variety of location probability distributions show that our algorithm compares favorably with existing algorithms.
End-to-End Airplane Detection Using Transfer Learning in Remote Sensing Images

Airplane detection in remote sensing images remains a challenging problem due to the complexity of backgrounds. In recent years, with the development of deep learning, object detection has also obtained great breakthroughs. For object detection tasks in natural images, such as the PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning) VOC (Visual Object Classes) Challenge, the major trend of current development is to use a large amount of labeled classification data to pre-train the deep neural network as a base network, and then use a small amount of annotated detection data to fine-tune the network for detection. In this paper, we use object detection technology based on deep learning for airplane detection in remote sensing images. In addition to using some characteristics of remote sensing images, some new data augmentation techniques have been proposed. We also use transfer learning and adopt a single deep convolutional neural network and limited training samples to implement end-to-end trainable airplane detection. Classification and positioning are no longer divided into multistage tasks; end-to-end detection attempts to combine them for optimization, which ensures an optimal solution for the final stage. In our experiment, we use remote sensing images of airports collected from Google Earth. The experimental results show that the proposed algorithm is highly accurate and meaningful for remote sensing object detection.
Bridge or continue Coumadin for device surgery: a randomized controlled trial rationale and design.

Many patients requiring cardiac arrhythmia device surgery are on chronic oral anticoagulation therapy. The periprocedural management of their anticoagulation presents a dilemma to physicians, particularly in the subset of patients with moderate-to-high risk of arterial thromboembolic events. Physicians have responded by treating patients with bridging anticoagulation while oral anticoagulation is temporarily discontinued. However, there are a number of downsides to bridging anticoagulation around device surgery; there is a substantial risk of significant device pocket hematoma with important clinical sequelae; bridging anticoagulation may lead to more arterial thromboembolic events and bridging anticoagulation is expensive.



In response to these issues, a number of centers have explored the option of performing device surgery without cessation of oral anticoagulation. The observational data suggest a greatly reduced hematoma rate with this strategy. Despite these encouraging results, most physicians are reluctant to move to operating on continued Coumadin in the absence of confirmatory data from a randomized trial.



We have designed a prospective, single-blind, randomized, controlled trial to address this clinical question. In the conventional arm, patients will be bridged. In the experimental arm, patients will continue on oral anticoagulation and the primary outcome is clinically significant hematoma. Our study has clinical relevance to at least 70 000 patients per year in North America.
Data Hiding Using Fibonacci EDGE Based Steganography for Cloud Data

Cloud Computing is a powerful, flexible, cost efficient platform for providing consumer IT services over the Internet. However Cloud Computing has various levels of risk factors because most important information is outsourced by third party vendors, which means harder to maintain the level of security for data. Steganography is art of hiding information in an image. In this most of the techniques are based on the Least Significant Bit(LSB) bit ,but the hackers easily detect as it embed data sequentially in all pixels .Instead of embedding data sequentially some of the techniques choose randomly. A better approach for this chooses edge pixels for embedding data. So we propose novel technique to hide the data in the Fibonacci edge pixels of an image by extending previous edge based algorithms. This algorithm hides the data in the Fibonacci edge pixels of an image and thus ensures better security against attackers.
Interactive assembly algorithms for molecular cloning

Molecular biologists routinely clone genetic constructs from DNA segments and formulate plans to assemble them. However, manual assembly planning is complex, error prone and not scalable. We address this problem with an algorithm-driven DNA assembly planning software tool suite called Raven (http://www.ravencad.org/) that produces optimized assembly plans and allows users to apply experimental outcomes to redesign assembly plans interactively. We used Raven to calculate assembly plans for thousands of variants of five types of genetic constructs, as well as hundreds of constructs of variable size and complexity from the literature. Finally, we experimentally validated a subset of these assembly plans by reconstructing four recombinase-based 'genetic counter' constructs and two 'repressilator' constructs. We demonstrate that Raven's solutions are significantly better than unoptimized solutions at small and large scales and that Raven's assembly instructions are experimentally valid.
Prevalence and determinants of essential newborn care practices in the Lawra District of Ghana

There was less than satisfactory progress, especially in sub-Saharan Africa, towards child and maternal mortality targets of Millennium Development Goals (MDGs) 4 and 5. The main aim of this study was to describe the prevalence and determinants of essential new newborn care practices in the Lawra District of Ghana.



A cross-sectional study was carried out in June 2014 on a sample of 422 lactating mothers and their children aged between 1 and 12 months. A systematic random sampling technique was used to select the study participants who attended post-natal clinic in the Lawra district hospital.



Of the 418 newborns, only 36.8% (154) was judged to have had safe cord care, 34.9% (146) optimal thermal care, and 73.7% (308) were considered to have had adequate neonatal feeding. The overall prevalence of adequate new born care comprising good cord care, optimal thermal care and good neonatal feeding practices was only 15.8%. Mothers who attained at least Senior High Secondary School were 20.5 times more likely to provide optimal thermal care [AOR 22.54; 95% CI (2.60-162.12)], compared to women had no formal education at all. Women who received adequate ANC services were 4.0 times (AOR  =  4.04 [CI: 1.53, 10.66]) and 1.9 times (AOR  =  1.90 [CI: 1.01, 3.61]) more likely to provide safe cord care and good neonatal feeding as compared to their counterparts who did not get adequate ANC. However, adequate ANC services was unrelated to optimal thermal care. Compared to women who delivered at home, women who delivered their index baby in a health facility were 5.6 times more likely of having safe cord care for their babies (AOR = 5.60, Cl: 1.19-23.30), p = 0.03.



The coverage of essential newborn care practices was generally low. Essential newborn care practices were positively associated with high maternal educational attainment, adequate utilization of antenatal care services and high maternal knowledge of newborn danger signs. Therefore, greater improvement in essential newborn care practices could be attained through proven low-cost interventions such as effective ANC services, health and nutrition education that should span from community to health facility levels.
False data injection attacks against state estimation in wireless sensor networks

In this paper we study the effect of false data injection attacks on state estimation carried over a sensor network monitoring a discrete-time linear time-invariant Gaussian system. The steady state Kalman filter is used to perform state estimation while a failure detector is employed to detect anomalies in the system. An attacker wishes to compromise the integrity of the state estimator by hijacking a subset of sensors and sending altered readings. In order to inject fake sensor measurements without being detected the attacker will need to carefully design his actions to fool the estimator as abnormal sensor measurements would result in an alarm. It is important for a designer to determine the set of all the estimation biases that an attacker can inject into the system without being detected, providing a quantitative measure of the resilience of the system to such attacks. To this end, we will provide an ellipsoidal algorithm to compute its inner and outer approximations of such set. A numerical example is presented to further illustrate the effect of false data injection attack on state estimation.
Detecting partnership in location-based and online social networks

Existing approaches to identify the tie strength between users involve typically only one type of network. To date, no studies exist that investigate the intensity of social relations and in particular partnership between users across social networks. To fill this gap in the literature, we studied over 50 social proximity features to detect the tie strength of users defined as partnership in two different types of networks: location-based and online social networks. We compared user pairs in terms of partners and non-partners and found significant differences between those users. Following these observations, we evaluated the social proximity of users via supervised and unsupervised learning approaches and establish that location-based social networks have a great potential for the identification of a partner relationship. In particular, we established that location-based social networks and correspondingly induced features based on events attended by users could identify partnership with 0.922 AUC, while online social network data had a classification power of 0.892 AUC. When utilizing data from both types of networks, a partnership could be identified to a great extent with 0.946 AUC. This article is relevant for engineers, researchers and teachers who are interested in social network analysis and mining.
Quantifying the reflective DDoS attack capability of household IoT devices

Distributed Denial-of-Service (DDoS) attacks are increasing in frequency and volume on the Internet, and there is evidence that cyber-criminals are turning to Internet-of-Things (IoT) devices such as cameras and vending machines as easy launchpads for large-scale attacks. This paper quantifies the capability of consumer IoT devices to participate in reflective DDoS attacks. We first show that household devices can be exposed to Internet reflection even if they are secured behind home gateways. We then evaluate eight household devices available on the market today, including lightbulbs, webcams, and printers, and experimentally profile their reflective capability, amplification factor, duration, and intensity rate for TCP, SNMP, and SSDP based attacks. Lastly, we demonstrate reflection attacks in a real-world setting involving three IoT-equipped smart-homes, emphasising the imminent need to address this problem before it becomes widespread.
AppLP: A Dialogue on Applications of Logic Programming

Focus and scope The focus of the workshop was applications of logic programming, i.e., application problems, in whole or in part, that are solved by using logic programming languages and systems. A particular theme of interest was to explore the ease of development and maintenance, clarity, performance, and tradeoffs among these features, brought about by programming using a logic paradigm. The goal was to help provide directions for future research advances and application development. Real-world problems increasingly involve complex data and logic, making the use of logic programming more and more beneficial for such complex applications. Despite the diverse areas of application, their common underlying requirements are centered around ease of development and maintenance, clarity, performance, integration with other tools, and tradeoffs among these properties. Better understanding of these important principles will help advance logic programming research and lead to benefits for logic programming applications. The workshop was organized around four main areas of application: Enterprise Software, Control Systems, Intelligent Agents, and Deep Analysis. These general areas included topics such as business intelligence, ontology management, text processing, program analysis, model checking, access control, network programming, resource allocation, system optimization, decision making, and policy administration. The issues proposed for discussion included language features, implementation efficiency, tool support and integration, evaluation methods, as well as teaching and training.
Category independent object discovery via background modeling

Traditional approaches for object discovery assume that there are common characteristics among objects, and then attempt to extract features specific to objects in order to discriminate objects from background. However, the assumption “common features” may not hold, considering different variations between and within objects. Instead, we look at this problem from a different angle: if we can identify background regions, then the rest should belong to foreground. In this paper, we propose to model background to localize possible object regions. Our method is based on the observations: (1) background has limited categories, such as sky, tree, water, ground, etc., and can be easier to recognize, while there are millions of objects in our world with different shapes, colors and textures; (2) background is occluded because of foreground objects. Thus, we can localize objects based on voting from fore/background occlusion boundary. Our contribution lies: (1) we use graph-based image segmentation to yield high quality segments, which effectively leverages both flat segmentation and hierarchical segmentation approaches; (2) we model background to infer and rank object hypotheses. More specifically, we use background appearance and discriminative patches around fore/background boundary to build the background model. The experimental results show that our method can generate good quality object proposals and rank them where objects are covered highly within a small pool of proposed regions.