nils-herrmann/clean_scirepeval huggingface dataset:
2,4 D Past, Present, and Future: A Review
Venue: Weed Technology

Since its discovery and initial commercialization in the 1940s, 2,4 D has been an important tool for weed control in a wide variety of crop and noncrop uses. Work studying its chemistry, physiology, mode of action, toxicology, environmental behavior, and efficacy has not only helped elucidate the characteristics of 2,4 D but also provided basic methods that have been used to investigate the properties of hundreds of herbicides that followed it. Much of the information published by researchers over 60 yr ago is still pertinent to understanding the performance of 2,4 D today. Further, new studies continue to be published, especially regarding the mechanisms of 2,4 D action at the molecular level. New uses for 2,4 D, sometimes enabled by biotechnology, continue to be developed. This review strives to provide an overall understanding of 2,4 D activity in plants, plant sensitivity to 2,4 D, toxicological impacts, and current and future uses. Desde su descubrimiento y su comercializacion inicial en los anos 1940s, el 2,4 D ha sido una herramienta importante para el control de malezas en una amplia variedad de cultivos y para usos no agricolas. Trabajos estudiando su quimica, fisiologia, modo de accion, toxicologia, comportamiento en el ambiente, y eficacia han ayudado no solamente a elucidar las caracteristicas del 2,4 D, pero tambien han brindado metodos basicos que han sido usados para investigar las propiedades de cientos de herbicidas que lo han seguido. La mayoria de la informacion publicada por investigadores durante los ultimos 60 anos es todavia pertinente para el entendimiento del desempeno de 2,4 D, hoy en dia. Ademas, nuevos estudios continuan siendo publicados, especialmente en relacion a los mecanismos de accion del 2,4 D a nivel molecular. Nuevos usos para el 2,4 D, algunas veces producto de la biotecnologia, continuan siendo desarrollados. Esta revision busca brindar un entendimiento general de la actividad de 2,4 D en las plantas, la sensibilidad de las plantas al 2,4 D, los impactos toxicologicos, y sus usos presentes y futuros.
Booter blacklist: Unveiling DDoS for hire websites
Venue: 2016 12th International Conference on Network and Service Management (CNSM)

The expansion of Distributed Denial of Service (DDoS) for hire websites, known as Booters, has radically modified both the scope and stakes of DDoS attacks. Until recently, however, Booters have only received little attention from the research community. Given their impact, addressing the challenges associated with this phenomenon is crucial. In this paper, we present a rigorous methodology to identify a comprehensive set of existing Booters in the Internet. The methodology relies on well defined mechanisms to generate a Booter blacklist, from crawling suspect URLs to characterizing and classifying the collected URLs. The list obtained using the methodology presented in this paper has a classification accuracy of 95.5% which is 10.5% better compared to previous work. We also demonstrate the usage of our methodology applied by the Dutch NREN, SURFNet, which started using our blacklist to extend their Booters' activities monitoring.
Stress Testing the Booters: Understanding and Undermining the Business of DDoS Services
Venue: WWW

DDoS for hire services, also known as booters, have commoditized DDoS attacks and enabled abusive subscribers of these services to cheaply extort, harass and intimidate businesses and people by taking them offline. However, due to the underground nature of these booters, little is known about their underlying technical and business structure. In this paper, we empirically measure many facets of their technical and payment infrastructure. We also perform an analysis of leaked and scraped data from three major booters Asylum Stresser, Lizard Stresser and VDO which provides us with an in depth view of their customers and victims. Finally, we conduct a large scale payment intervention in collaboration with PayPal and evaluate its effectiveness as a deterrent to their operations. Based on our analysis, we show that these booters are responsible for hundreds of thousands of DDoS attacks and identify potentially promising methods to undermine these services by increasing their costs of operation.
Classification of DDoS attack tools and its handling techniques and strategy at application layer
Venue: 2016 2nd International Conference on Advances in Computing, Communication, Automation (ICACCA) (Fall)

Computer networks basically consist of seven layers in all at different levels. The seventh layer i.e. application layer is responsible to fulfill the user's requests. Distributed Denial of Service Attack (DDoS) is a condition in which upper three layers of any computer network generally stop their jobs to fulfill the request of clients. DDoS attacks at seventh layer have become highly complex to solve. Various companies hire attack developers or purchase attacking tools to pull down the business of their competitors. DDoS attack's developers are continuously adding new features in this weapon which makes application detector unable to identify. This paper deals with the classification of DDoS threats based on abnormal behavior at application layer and provides summarized information about various DDoS Tools. Moreover, it categorizes DDoS attack handling techniques based on monitoring, preventing, detecting, and mitigating concepts. Hence this paper aims to handle the DDoS attack issues at application layer and therefore providing information about loopholes of handling techniques, and hence better understanding for future advancements in this area.
Generalized Discriminant Analysis (GDA) for Improved i Vector Based Speaker Recognition
Venue: INTERSPEECH

In general, the majority of recent speaker recognition systems employ an i Vector configuration as their front end. Postprocessing of i Vectors usually requires a Linear Discriminant Analysis (LDA) phase to reduce the dimensions of the i Vectors as well as improve discrimination of speaker classes based on the Fisher criterion. Given that channel, noise, and other types of mismatch are generally present in the data, it is better to discriminate the speaker's data non linearly. Generalized Discriminant Analysis (GDA) uses kernel functions to map the data into a high dimensional feature space which leads to nonlinear discriminant analysis. In this study, we replace LDA with GDA in an i Vector based speaker recognition system and study the effectiveness of various kernel functions. It is shown, based on equal error rate (EER) and minimum of detection cost function, that GDA not only improves performance for regular test utterances, but is also useful for short duration test segments. NIST2010 Speaker Recognition Evaluation (SRE) core and extended core (coreext) conditions are employed for experiments; in addition, we evaluate the system for short duration segments on the 10 sec test condition and truncated coreext test data. The relative improvement in EER is 20% for the cosine kernel employed here with GDA processing.
Experimental results on implicit and explicit time discretization of equivalent control based sliding mode control

This chapter presents a set of experimental results concerning the sliding mode control of an electro pneumatic system. The controller is implemented \em via} a micro processor as a discrete time input. Three discrete time control strategies are considered for the implementation of the discontinuous part of the sliding mode controller: explicit discretizations with and without saturation, and an implicit discretization (that is very easy to implement as a projection on the interval 1,1] While the explicit implementation is known to generate numerical chattering, the implicit one is expected to significantly reduce chattering while keeping the accuracy. The experimental results reported in this work remarkably confirm that the implicit discrete time sliding mode supersedes the explicit ones, with several important features: chattering in the control input is almost eliminated (while the explicit and saturated controllers behave like high frequency bang bang inputs) the input magnitude depends only on the perturbation size and is `independent' of the controller gain and sampling time. On the contrary the explicit controller shows obvious chattering for all sampling times, its magnitude increases as the controller gain increases, and it does not reduce when the sampling period augments. The tracking errors are comparable for both methods, though the implicit method keeps the precision when the control gain increases, which is not the case for the explicit one. Introducing a saturation in the explicit controller does not allow to significantly improve the explicit controller behaviour if one does not take care of the saturation width.
Accounting for failure: risk based regulation and the problems of ensuring healthcare quality in the NHS
Venue: Health, risk society

In this paper, we examine why risk based policy instruments have failed to improve the proportionality, effectiveness, and legitimacy of healthcare quality regulation in the National Health Service (NHS) in England. Rather than trying to prevent all possible harms, risk based approaches promise to rationalise and manage the inevitable limits of what regulation can hope to achieve by focusing regulatory standard setting and enforcement activity on the highest priority risks, as determined through formal assessments of their probability and consequences. As such, risk based approaches have been enthusiastically adopted by healthcare quality regulators over the last decade. However, by drawing on historical policy analysis and in depth interviews with 15 high level UK informants in 2013 2015, we identify a series of practical problems in using risk based policy instruments for defining, assessing, and ensuring compliance with healthcare quality standards. Based on our analysis, we go on to consider why, despite a succession of failures, healthcare regulators remain committed to developing and using risk based approaches. We conclude by identifying several preconditions for successful risk based regulation: goals must be clear and trade offs between them amenable to agreement; regulators must be able to reliably assess the probability and consequences of adverse outcomes; regulators must have a range of enforcement tools that can be deployed in proportion to risk; and there must be political tolerance for adverse outcomes.
DNN based prediction model for spatio temporal data
Venue: SIGSPATIAL/GIS

Advances in location acquisition and wireless communication technologies have led to wider availability of spatio temporal (ST) data, which has unique spatial properties (i.e. geographical hierarchy and distance) and temporal properties (i.e. closeness, period and trend) In this paper, we propose a Deep learning based prediction model for Spatio Temporal data (DeepST) We leverage ST domain knowledge to design the architecture of DeepST, which is comprised of two components: spatio temporal and global. The spatio temporal component employs the framework of convolutional neural networks to simultaneously model spatial near and distant dependencies, and temporal closeness, period and trend. The global component is used to capture global factors, such as day of the week, weekday or weekend. Using DeepST, we build a real time crowd flow forecasting system called UrbanFlow1. Experiment results on diverse ST datasets verify DeepST's ability to capture ST data's spatio temporal properties, showing the advantages of DeepST beyond four baseline methods.
Towards Machine Comprehension of Spoken Content: Initial TOEFL Listening Comprehension Test by Machine
Venue: INTERSPEECH

Multimedia or spoken content presents more attractive information than plain text content, but it's more difficult to display on a screen and be selected by a user. As a result, accessing large collections of the former is much more difficult and time consuming than the latter for humans. It's highly attractive to develop a machine which can automatically understand spoken content and summarize the key information for humans to browse over. In this endeavor, we propose a new task of machine comprehension of spoken content. We define the initial goal as the listening comprehension test of TOEFL, a challenging academic English examination for English learners whose native language is not English. We further propose an Attention based Multi hop Recurrent Neural Network (AMRNN) architecture for this task, achieving encouraging results in the initial tests. Initial results also have shown that word level attention is probably more robust than sentence level attention for this task with ASR errors.
Modeling Local Item Dependence in Cloze and Reading Comprehension Test Items Using Testlet Response Theory

In this study the magnitudes of local dependence generated by cloze test items and reading comprehension items were compared and their impact on parameter estimates and test precision was investigated. An advanced English as a foreign language reading comprehension test containing three reading passages and a cloze test was analyzed with a two parameter logistic testlet response model and a two parameter logistic item response model. Results showed that the cloze test produced substantially higher magnitudes of local dependence than reading items, albeit the levels of local dependency produced by reading items was not ignorable. Further analyses demonstrated that while even substantial magnitudes of testlet effect does not impact parameter estimates it does influence test reliability and information. Implications of the research for foreign language proficiency testing, where testlets are regularly used, are discussed.
Application of a Cognitive Diagnostic Model to a High Stakes Reading Comprehension Test

General cognitive diagnostic models (CDM) such as the generalized deterministic input, noisy, "and" gate (G DINA) model are flexible in that they allow for both compensatory and noncompensatory relationships among the subskills within the same test. Most of the previous CDM applications in the literature have been add ons to simulation studies. Although there are some applications of CDMs such as the Fusion Model and the Rule Space Model to educational assessment data in general and second language data in particular, there are few studies applying general models such as the G DINA. The purpose of the present study was to demonstrate the application of the G DINA to the reading comprehension data of a high stakes test. To this end, an initial Q matrix was developed, validated, and cross validated. The skill profiles of the test takers were estimated using the "CDM" package in R. Throughout, the process of constructing and validating a Q matrix was elaborated on, the benefits of general models were emphasized, and implications for research investigating inter skill relationships were discussed. Finally, suggestions for further research, to better take advantage of the flexibilities of general diagnostic models, were presented.
The selection of cognitive diagnostic models for a reading comprehension test

Cognitive diagnostic models (CDMs) have great promise for providing diagnostic information to aid learning and instruction, and a large number of CDMs have been proposed. However, the assumptions and performances of different CDMs and their applications in regard to reading comprehension tests are not fully understood. In the present study, we compared the performance of a saturated model (G DINA) two compensatory models (DINO, ACDM) and two non compensatory models (DINA, RRUM) with the Michigan English Language Assessment Battery (MELAB) reading test. Compared to the saturated G DINA model, the ACDM showed comparable model fit and similar skill classification results. The RRUM was slightly worse than the ACDM and G DINA in terms of model fit and classification results, whereas the more restrictive DINA and DINO performed much worse than the other three models. The findings of this study highlighted the process and considerations pertinent to model selection in applications of CDMs with reading tests.
Effects of Strength of Accent on an L2 Interactive Lecture Listening Comprehension Test

This article reports on a study which aimed to determine the effect of strength of accent on listening comprehension of interactive lectures. Test takers (N 21,726) listened to an interactive lecture given by one of nine speakers and responded to six comprehension items. The test taker responses were analyzed with the Rasch computer program WINSTEPS to investigate the relative difficulty of the items associated with the nine versions of the interactive lectures. Results indicated that comprehension of interactive lectures was diminished with quite light accents, as has been found with monologic lectures.
Test Anxiety and a High Stakes Standardized Reading Comprehension Test: A Behavioral Genetics Perspective
Venue: Merrill Palmer quarterly

Past research suggests that reading comprehension test performance does not rely solely on targeted cognitive processes such as word reading, but also on other nontarget aspects such as test anxiety. Using a genetically sensitive design, we sought to understand the genetic and environmental etiology of the association between test anxiety and reading comprehension as measured by a high stakes test. Mirroring the behavioral literature of test anxiety, three different dimensions of test anxiety were examined in relation to reading comprehension namely, intrusive thoughts, autonomic reactions, and off task behaviors. Participants included 425 sets of twins from the Florida Twin Project on Reading. The results indicated test anxiety was negatively associated with reading comprehension test performance, specifically through common shared environmental influences. The significant contribution of test anxiety to reading comprehension on a high stakes test supports the notion that nontargeted factors might interfere with accurately assessing students' reading abilities.
Determination of Nutrient Concentrations in Simulated Rainfall Runoff from Poultry House Dust Deposited Adjacent to Exhaust Fans

Until recently, little attention has been given to the loss of nutrients that may occur in stormwater runoff from poultry houses. Dust emitted from poultry house fans and deposited near the fans has been shown to contain similar amounts of phosphorus (P) and nitrogen (N) as in poultry litter, thus, there is need for information on the potential of runoff to transport deposited dust off site. The objectives of this study are to quantify P and N in simulated rainfall runoff from sites immediately adjacent to fans (sidewall and tunnel) from a commercial poultry house in northwest Arkansas. Runoff from fan plots range in total P (TP) and total N (TN) concentration from 1.0 to 26.0 mgL 1 and from 5.1 to 189 mgL 1. The concentration of P and N in runoff from plots adjacent to sidewall fans is significantly lower (P 0.05) during warmer (June to August; 1 and 2 mgL 1 for TP and TN, respectively) than cooler months (November through March; 3 and 7 mgL 1 for TP and TN, respectively) In contrast, TP and TN concentrations of runoff from tunnel fan plots are significantly greater (P 0.05) during warm (14 and 170 mgL 1, respectively) than cool months (5 and 60 mgL 1, respectively) The results of this research indicate that conservation practices are needed around poultry production houses to minimize the potential for runoff of nutrients in emitted dust entering nearby surface waters.
Highly sensitive derivatization reagents possessing positively charged structures for the determination of oligosaccharides in glycoproteins by high performance liquid chromatography electrospray ionization tandem mass spectrometry.
Venue: Journal of chromatography. A

We have developed three kinds of novel derivatization reagents (4 CEBTPP, 4 CBBTPP, 5 COTPP) with triphenylphosphine (TPP) as a basic structure carrying a permanent positive charge for resolution of the oligosaccharides in glycoprotein using high performance liquid chromatography electrospray ionization tandem mass spectrometry (LC ESI MS/MS) The synthesized reagents reacted with the sialylglycosylamine of the sialylglycopeptide after treatment by PNGase F. The final derivatives were analyzed by ESI MS and sensitively detected in the selected reaction monitoring (SRM) mode. Furthermore, the limits of detection (S/N=3) on the SRM chromatograms were at the fmol level (30fmol) Therefore, we used the limit of detection of the reagent products detected by the SRM and evaluated the utility of each reagent. Among the reagents, the positively charged 4 CEBTPP derivative's peak area was the highest; 4 CEBTPP with a positively charged structure showed about a 20 times greater sensitivity for the glycosylamine of the SGP product compared to the conventional fluorescence reagent, Fmoc Cl. In addition, various fragment ions based on the carbohydrate units also appeared in the MS/MS spectra. Among the fragment ions, m/z 627.37 (CE=40eV) corresponding to 4 CEBTPP GlcNAc and m/z 120.09 (CE=100eV) corresponding to 4 CEBTPP are the most important ones for identifying the oligosaccharide. 4 CEBTPP SGA was easily identified by the selected ion chromatogram in the product ion scan (m/z 120.09) and in the precursor ion scan (m/z 627.37) by MS/MS detection. The derivatized analytes have a high ionization efficiency and they are detected with a high sensitivity in the electrospray ionization. The novel derivatization reagent with a multi function provided a higher sensitivity for the oligosaccharide analysis, as well as a better specificity and feasibility. Furthermore, several oligosaccharides in fetuin and ribonuclease B were successfully identified by the proposed procedure.
A Two Factor Model Better Explains Heterogeneity in Negative Symptoms: Evidence from the Positive and Negative Syndrome Scale
Venue: Front. Psychol.

Acknowledging separable factors underlying negative symptoms may lead to better understanding and treatment of negative symptoms in individuals with schizophrenia. The current study aimed to test whether the negative symptoms factor (NSF) of the Positive and Negative Syndrome Scale (PANSS) would be better represented by expressive and experiential deficit factors, rather than by a single factor model, using confirmatory factor analysis (CFA) Two hundred and twenty individuals with schizophrenia spectrum disorders completed the PANSS; subsamples additionally completed the Brief Negative Symptom Scale (BNSS) and the Motivation and Pleasure Scale Self Report (MAP SR) CFA results indicated that the two factor model fit the data better than the one factor model; however, latent variables were closely correlated. The two factor model's fit was significantly improved by accounting for correlated residuals between N2 (emotional withdrawal) and N6 (lack of spontaneity and flow of conversation) and between N4 (passive social withdrawal) and G16 (active social avoidance) possibly reflecting common method variance. The two NSF factors exhibited differential patterns of correlation with subdomains of the BNSS and MAP SR. These results suggest that the PANSS NSF would be better represented by a two factor model than by a single factor one, and support the two factor model's adequate criterion related validity. Common method variance among several items may be a potential source of measurement error under a two factor model of the PANSS NSF.