nils-herrmann/clean_scirepeval huggingface dataset:
HTTP State Management Mechanisms with Multiple Addresses User Agents
Year: 2015

HTTP servers usually save session states in their persistent storage indexed by session cookies generated by the HTTP servers. It is up to the HTTP user agent to send this session cookie on each HTTP request. Some HTTP servers check whether the cookie is associated with the HTTP user agent by the means of the user agent IP address. Everything linking a state to an IP address (such as OAuth access code) to an IP address has the same issue. If the Happy Eyeball mechanism is used to select between IPv6 and IPv4, it may happen that while using the same HTTP server, some HTTP requests are done over IPv6 and the others over IPv4, which leads to two different sets of session states in the HTTP server. This has the consequence of inconsistencies at the HTTP server. The only purpose of this document is to document this issue in more details than in section 8.2 of RFC 6883 including security considerations and mitigations. A similar problem arises with the use of non RFC 6888 compliant Carrier Grade NAT (CGN) devices used to access an IPv4 only HTTP server or HTTP user agent using multi homing.
Investigating Critical Frequency Bands and Channels for EEG Based Emotion Recognition with Deep Neural Networks
Venue: IEEE Transactions on Autonomous Mental Development
Year: 2015

To investigate critical frequency bands and channels, this paper introduces deep belief networks (DBNs) to constructing EEG based emotion recognition models for three emotions: positive, neutral and negative. We develop an EEG dataset acquired from 15 subjects. Each subject performs the experiments twice at the interval of a few days. DBNs are trained with differential entropy features extracted from multichannel EEG data. We examine the weights of the trained DBNs and investigate the critical frequency bands and channels. Four different profiles of 4, 6, 9, and 12 channels are selected. The recognition accuracies of these four profiles are relatively stable with the best accuracy of 86.65% which is even better than that of the original 62 channels. The critical frequency bands and channels determined by using the weights of trained DBNs are consistent with the existing observations. In addition, our experiment results show that neural signatures associated with different emotions do exist and they share commonality across sessions and individuals. We compare the performance of deep models with shallow models. The average accuracies of DBN, SVM, LR, and KNN are 86.08% 83.99% 82.70% and 72.60% respectively.
An empirical exploration of CTC acoustic models
Venue: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2016

The connectionist temporal classification (CTC) loss function has several interesting properties relevant for automatic speech recognition (ASR) applied on top of deep recurrent neural networks (RNNs) CTC learns the alignments between speech frames and label sequences automatically, which removes the need for pre generated frame level labels. CTC systems also do not require context decision trees for good performance, using context independent (CI) phonemes or characters as targets. This paper presents an extensive exploration of CTC based acoustic models applied to a variety of ASR tasks, including an empirical study of the optimal configuration and architectural variants for CTC. We observe that on large amounts of training data, CTC models tend to outperform state of the art hybrid approach. Further experiments reveal that CTC can be readily ported to syllable based languages, and can be enhanced by employing improved feature front ends.
'Putting knowledge in power' learning and innovation in the British Army of the First World War
Year: 2015

Learning is critical to battlefield success. (Ceteris\ (paribus\ victory becomes more likely when militaries adapt faster and more effectively than their opponents. This thesis examines the effectiveness of the British army's process for learning and adaptation across six different operational theatres during the First World War. Using a series of case studies, it considers how the army shared knowledge, responded to change, and integrated newcomers. It finds that the army's attitudes towards learning were more thoroughgoing than hitherto thought. With its pre war ethos and increased fluidity in wartime, the army displayed organisational and cultural flexibility across all theatres, encouraging a culture of innovation through the promotion of informal learning and tactical diversity. In a broader sense, the thesis does three things. First, it moves beyond the standard Western Front narrative of learning in the First World War, offering a more rounded examination of the army's experience. Secondly, it highlights the complexity of military learning, considering that which occurs institutionally, between formations, and between theatres. Finally, it reflects on the importance of an organisation's ethos when faced with uncertainty. This thesis, therefore, offers a point of departure for future studies of traditionally bureaucratic institutions and their ability to learn and innovate.
Time Narrative Discourse in the Novel: The Blind Owl (Bofe Kor)
Year: 2017

Narratology is relatively a new science that took on the scientific aspects by the structuralist studies. There are different, similar and even contradictory, ideas rose in this regard. Gerard Genette's view of time is considered as one of the most significant elements of the narrative discourse in the review on the narrative in literature. The modern novel in failing to comply with chronological time is dramatically receptive to this type of criticism. This analytical research paper examines the novel The Blind Owl (Bofe Kor) by Sadeq Hedayat with the aim of helping to read and understand the novel approach according to narrative discourse. The results show that the time in this novel does not follow the chronographic rules; in other words, the time fluctuates under the influence of the retrospective and prospective time disorder and thus, the time disorders and other factors such as repetition, redundancy and etc. cause the slow acceleration in narrative that led to more prolong the narrative text than the story.
Early home learning environment predicts children's 5th grade academic skills
Year: 2019

We examined whether the early learning environment predicts children's 5th grade skills in 2,204 families from ethnically diverse, low income backgrounds; tested the mediating roles of children's pre kindergarten school related skills and later learning environment; and asked whether lagged associations generalize across White, Black, Hispanic English speaking, and Hispanic Spanish speaking samples. Children's early learning environment comprised measures of literacy activities, the quality of mothers' engagements with children, and learning materials assessed at 14 months, 2 and 3 years, and at pre kindergarten; learning environments were again assessed in 5th grade. At pre kindergarten and in 5th grade, children were assessed on pre academic and academic skills respectively. Early learning environments predicted children's 5th grade academic skills, and children's pre kindergarten skills and 5th grade learning environment mediated longitudinal associations. The early learning environment supports the emergence of pre academic skills that are stable into early adolescence, and pathways generalize across ethnic/racial groups.
Long term monitoring of land cover changes based on Landsat imagery to improve hydrological modelling in West Africa
Year: 2008

The spatial and temporal variability of land cover changes is a fundamental parameter to integrate when modelling water resources in order to reproduce the relations between rainfall and surface flow more precisely. This is particularly important in West Africa, where the land cover has been changing for more than 40 years under the combined impact of climatic effects and human activities. In this study, we evaluated the potential of Landsat imagery to monitor the vegetation cover in the upper Niger watershed (120 000 km2) using archive images from MSS, TM and ETM+ sensors covering three periods of time around 1975, 1985, and 2000. Because of the heterogeneity of the acquisition dates, the spatial and spectral resolution of the images, and the scale of analysis, we chose a simple system of classification. Pretreatments were applied to reduce variations between the images. Vegetation indices (NDVI) were then calculated and subsequently thresholded using the same land cover classification system. The thresholds were then optimized by automated recursive calculations of confusion matrices and control parcels. Our results revealed that although the accuracy was not perfect, it was nevertheless possible to estimate changes using an unconventional spatio temporal scale. The resulting changes were characterized by a moderate trend to deforestation with a corresponding increase in bare soils, soils with sparse vegetation, and shrublands. The spatial layers produced were then combined with a soil map to incorporate changes in surface conditions in the hydrological modelling of the Niger River.
Non commutative Calculus, Optimal Transport and Functional Inequalities in Dissipative Quantum Systems
Venue: Journal of statistical physics
Year: 2020

We study dynamical optimal transport metrics between density matrices associated to symmetric Dirichlet forms on finite dimensional $C^ C algebras. Our setting covers arbitrary skew derivations and it provides a unified framework that simultaneously generalizes recently constructed transport metrics for Markov chains, Lindblad equations, and the Fermi Ornstein Uhlenbeck semigroup. We develop a non nommutative differential calculus that allows us to obtain non commutative Ricci curvature bounds, logarithmic Sobolev inequalities, transport entropy inequalities, and spectral gap estimates.
Predictive modeling to identify potential participants of a disease management program hypertension
Venue: Expert review of pharmacoeconomics outcomes research
Year: 2020

Based on the premise of limited health care resources, decision makers pursue to allocate disease management programs (DMP) more targeted.  Based on routine data from a private health insurance company, a prediction model was developed to estimate the individual risk for future in patient stays of patients eligible for a DMP Hypertension. The database included anonymous claims data of 38,284 policyholders with a diagnosis in the year 2013. A cutoff point of =70% was used for selecting candidates with a risk for future hospitalization. Using a logistic regression model, we estimated the model's prognostic power, the occurrence of clinical events, and the resource use.  Overall, the final model shows acceptable prognostic power (detection rate 64.3% sensitivity 68.7% positive predictive value (PPV) 64.1% area under the curve (AUC) 0.72) The comparison between the selected hypothetical DMP group with a predicted (LOH) =70% showed additional costs of about 69% for the DMP group compared to insure with a LOH <70%  The predictive analytical approach may identify potential DMP participants with a high risk of increased health services utilization and in patient stays.
Protecting against Website Fingerprinting with Multihoming
Venue: Proc. Priv. Enhancing Technol.
Year: 2020

Anonymous communication tools, such as Tor, are extensively employed by users who want to keep their web activity private. But recent works have shown that when a local, passive adversary observes nothing more than the timestamp, size and direction (incoming or outgoing) of the packets, it can still identify with high accuracy the website accessed by a user. Several defenses against these website fingerprinting attacks have been proposed but they come at the cost of a significant overhead in traffic and/or website loading time. We propose a defense against website fingerprinting which exploits multihoming, where a user can access the Internet by sending the traffic through multiple networks. With multihoming, it is possible to protect against website fingerprinting by splitting traffic among the networks, i.e. by removing packets from one network and sending them through another, whereas current defenses can only add packets. This enables us to design a defense with no traffic overhead that, as we show through extensive experimentation against state of the art attacks, reaches the same level of privacy as the best existing practical defenses. We describe and evaluate a proof ofconcept implementation of our defense and show that is does not add significant loading time overhead. Our solution is compatible with other state of the art defenses, and we show that combining it with another defense further improves privacy.
EFEKTIFITAS PELVIC ROCKING TERHADAP LAMA PERSALINAN, DILATASI SERVIK DAN PENURUNAN KEPALA JANIN PADA IBU PRIMIGRAVIDA
Year: 2017

Proses kemajuan persalinan kala I fase aktif pada primigravida dipengaruhi oleh dilatasi servik dan penurunan kepala janin kedasar panggul. Pelvic rocking merupakan cara untuk mempercepat proses persalinan yaitu dengan menggerakan panggul searah putaran selama kontraksi berlangsung. Mengayunkan dan menggoyangkan panggul kearah depan dan ke belakang, sisi kanan kiri dan melingkar akan terasa lebih relaks dan mempermudah membukanya jalan lahir pada waktu persalinan. Tujuan dari penelitian ini adalah mengetahui efektifitas pelvic rocking terhadap lamanya persalinan, dilatasi servik dan penurunan kepala janin. Penelitian ini merupakan penelitian kuantitatif dengan rancangan quasi experiment, yaitu post test only desing. Sampel pada penelitian ini yaitu 23 ibu bersalin primigravida kala I fase aktif. Sampel dikelompokkan menjadi dua, kelompok yang diberikan pelvic rocking dan kelompok yang diberikan asuhan standar. Hasil analisis data memiliki perbedaan antara 2 kelompok intervensi dengan nilai P 0.05 tidak memiliki pengaruh antara pelvic rocking terhadap penurunan kepala janin kala I fase aktif pada 2 kelompok perlakuan.
Regulating environmental responsibility for the multinational oil industry: continuing challenges for international law
Venue: International Journal of Law in Context
Year: 2015

States utilise international law to create opportunities within global markets for private transnational economic actors, such as multinational oil companies, to invest and/or operate within foreign jurisdictions. However, there is a lack of directly enforceable international mechanisms against these private actors when they cause environmental damage abroad. International law responses to this problem range from the establishment of international compulsory compensation schemes, the proposed expansion of the doctrine of state responsibility to include liability for private actors, and more recently through litigation in the home states of multinational oil companies. However, both international jurisprudence and US, Dutch and British domestic case law reveal an ambivalence towards holding such private transnational economic actors legally accountable in their home state jurisdictions for violations committed abroad. Certain states (the US and France) that have suffered environmental damage from the activities of multinational oil companies have responded by reasserting their domestic regulatory powers to require immediate clean up and compensation, prior to domestic judicial litigation. Other states (Nigeria) are unable to achieve the same level of effective enforcement due to their weaker political and economic bargaining positions.
Use of the Montreal Cognitive Assessment in Evaluations of Adjudicative Competency
Year: 2014

This archival study explored the impact of performance on the Montreal Cognitive Assessment (MoCA) a brief cognitive screen performed as part of a mental status examination (MSE) on adjudicative competency opinions provided for 194 criminal defendants, in examinations that did not include a competency specific instrument. The MoCA's suggested cutoff of 26 as indicative of "mild cognitive impairment" was not determinative of examiners' conclusions regarding competency related capacities. However, when scores were considered dimensionally, MoCA performance during the MSE accounted for additional significant variance in examiners' competency opinions, when added to a logistic regression model containing age and presence of psychotic symptoms.
The Efficiency of the Anderson Darling Test With a Limited Sample Size: An Application to Backtesting Counterparty Credit Risk Internal Models
Year: 2019

This work presents a theoretical and empirical evaluation of Anderson Darling test when the sample size is limited. The test can be applied in order to backtest the risk factors dynamics in the context of Counterparty Credit Risk modelling. We show the limits of such test when backtesting the distributions of an interest rate model over long time horizons and we propose a modified version of the test that is able to detect more efficiently an underestimation of the model's volatility. Finally we provide an empirical application.
Nonlinear antecedents of visitors' satisfaction with the oktoberfest in Brazil
Year: 2020

Cultural festivals help to promote, improve and preserve tourist and cultural heritages, generating business with great economic value for the destination. It is important that festivals organisers verify the visitors' perception about the quality of the festival, and use this information to develop improvement activities. For better understand the overall assessment of participants on festivals, this study compares the results of using linear regression with the results using a non linear method, PRCA Penalty reward Contrast Analysis, to explain participants' general evaluation of the festival. Interviewing 270 participants in the 32nd edition of the Oktoberfest in Blumenau, Brazil, we found that PRCA best explains the overall assessment of the festival by the participants, than using linear regression analysis. The results demonstrate that knowing possible nonlinearities, we can better understand the overall perception of the festival's quality, which allows us better deciding about what to offer or improve on it.
Hyperspectral Image Classification Using Random Occlusion Data Augmentation
Venue: IEEE Geoscience and Remote Sensing Letters
Year: 2019

Convolutional neural networks (CNNs) have become a powerful tool for remotely sensed hyperspectral image (HSI) classification due to their great generalization ability and high accuracy. However, owing to the huge amount of parameters that need to be learned and to the complex nature of HSI data itself, these approaches must deal with the important problem of overfitting, which can lead to inadequate generalization and loss of accuracy. In order to mitigate this problem, in this letter, we adopt random occlusion, a recently developed data augmentation (DA) method for training CNNs, in which the pixels of different rectangular spatial regions in the HSI are randomly occluded, generating training images with various levels of occlusion and reducing the risk of overfitting. Our results with two well known HSIs reveal that the proposed method helps to achieve better classification accuracy with low computational cost.
Editorial: The meaning of preference
Year: 2017

Anyone who has seen the item of correspondence in the last issue of Lighting Research and Technology will be aware that an argument has broken out in the field of light source spectrum optimization. The argument is about the status of studies of preferred light spectra. The authors of the correspondence argue that colour preference is a misleading concept with no scientific validity. Unlike colour fidelity, it is not objective, nor is it based on colour science. Rather, colour preference is subjective and unstable. It will change in different situations and over time depending on the presence or absence of different surface colours, the culture of the observers, the context in which the lighting is seen and the vagaries of fashion. While argument may rage over how 'scientific' measurements of colour preference are there can be little doubt that peoples' preferences do matter commercially. Anyone who has tried to sell a product knows that it is much easy to do so if the product matches peoples' preferences than if it does not. I remember attending a lecture in which the speaker expounded the virtues of a teapot designed on scientific principles so that turbulent flow was eliminated as were dribbles. Unfortunately, he also confessed that it had proved impossible to get the design accepted by the public because the teapot looked like a bedpan. Given that preference for a light source spectrum matters, is it a legitimate subject of study, and how should such studies be evaluated? Given the ability of solid state lighting to produce almost any spectrum, attempting to answer questions such as what light spectrum is most preferred, when and where and by whom is obviously legitimate although the results may not add much to scientific understanding. As for how such studies should be evaluated, in addition to the usual matters of experimental design and statistical analysis, this comes down to identifying the relevance of the conditions examined for any proposed application. For assessing relevance, it is always necessary to know the assumed context and the number and culture of the observers. Three other aspects that should be considered are the nature and number of surface colours included in the scene, the time for which the observer was exposed to the light spectrum and the size of the visual field over which the light spectrum appears. The hues and saturations of the surface colours present matter because preference is likely to be affected by how well the surface colours present are rendered by the light spectrum. Time of exposure is important because colour adaptation may alter the strength of the preference. Visual field size may be significant because preference may be different when the observer is immersed in the light spectrum rather than looking at a display on a screen. It is only by considering such questions that the relevance of a specific study to a proposed application can be determined.
An Ensemble of Condition Based Classifiers for Device Independent Detailed Human Activity Recognition Using Smartphones
Venue: Inf.
Year: 2018

Human activity recognition is increasingly used for medical, surveillance and entertainment applications. For better monitoring, these applications require identification of detailed activity like sitting on chair/floor, brisk/slow walking, running, etc. This paper proposes a ubiquitous solution to detailed activity recognition through the use of smartphone sensors. Use of smartphones for activity recognition poses challenges such as device independence and various usage behavior in terms of where the smartphone is kept. Only a few works address one or more of these challenges. Consequently, in this paper, we present a detailed activity recognition framework for identifying both static and dynamic activities addressing the above mentioned challenges. The framework supports cases where (i) dataset contains data from accelerometer; and the (ii) dataset contains data from both accelerometer and gyroscope sensor of smartphones. The framework forms an ensemble of the condition based classifiers to address the variance due to different hardware configuration and usage behavior in terms of where the smartphone is kept (right pants pocket, shirt pockets or right hand) The framework is implemented and tested on real data set collected from 10 users with five different device configurations. It is observed that, with our proposed approach, 94% recognition accuracy can be achieved.