nils-herrmann/clean_scirepeval huggingface dataset:
Wireless control using reinforcement learning for practical web QoE
Venue: Comput. Commun.
Year: 2020

Wireless networks show several challenges not found in wired networks, due to the dynamics of data transmission. Besides, home wireless networks are managed by non technical people, and providers do not implement full management services because of the difficulties of manually managing thousands of devices. Thus, automatic management mechanisms are desirable. However, such control mechanisms are hard to achieve in practice because we do not always have a model of the process to be controlled, or the behavior of the environment is dynamic. Thus, the control must adapt to changing conditions, and it is necessary to identify the quality of the control executed from the perspective of the user of the network service. This article proposes a control loop for transmission power and channel selection, based on Software Defined Networking and Reinforcement Learning (RL) and capable of improving Web Quality of Experience metrics, thus benefiting the user. We evaluate a prototype in which some Access Points are controlled by a single controller or by independent controllers. The control loop uses the predicted Mean Opinion Score (MOS) as a reward, thus the system needs to classify the web traffic. We proposed a semi supervised learning method to classify the web sites into three classes (light, average and heavy) that groups pages by their complexity, i.e. number and size of page elements. These classes define the MOS predictor used by the control loop. The proposed web site classifier achieves an average score of 87 1 classifying 500 unlabeled examples with only fifteen known examples, with a sub second runtime. Further, the RL control loop achieves higher Mean Opinion Score (up to 167% in our best result) than the baselines. The page load time of clients browsing heavy web sites is improved by up to 6.6x.
Brain inspired spiking neural networks for decoding and understanding muscle activity and kinematics from electroencephalography signals during hand movements
Venue: Scientific reports
Year: 2021

Compared to the abilities of the animal brain, many Artificial Intelligence systems have limitations which emphasise the need for a Brain Inspired Artificial Intelligence paradigm. This paper proposes a novel Brain Inspired Spiking Neural Network (BI SNN) model for incremental learning of spike sequences. BI SNN maps spiking activity from input channels into a high dimensional source space which enhances the evolution of polychronising spiking neural populations. We applied the BI SNN to predict muscle activity and kinematics from electroencephalography signals during upper limb functional movements. The BI SNN extends our previously proposed eSPANNet computational model by integrating it with the 'NeuCube' brain inspired SNN architecture. We show that BI SNN can successfully predict continuous muscle activity and kinematics of upper limb. The experimental results confirmed that the BI SNN resulted in strongly correlated population activity and demonstrated the feasibility for real time prediction. In contrast to the majority of Brain Computer Interfaces (BCIs) that constitute a 'black box' BI SNN provide quantitative and visual feedback about the related brain activity. This study is one of the first attempts to examine the feasibility of finding neural correlates of muscle activity and kinematics from electroencephalography using a brain inspired computational paradigm. The findings suggest that BI SNN is a better neural decoder for non invasive BCI.
Sociodemographic and clinical features of postpartum depression among Turkish women: a prospective study
Venue: BMC Pregnancy Childbirth
Year: 2015

BackgroundPostpartum depression (PPD) is moderate to severe depression in a woman after she has given birth. Findings from several well designed studies reflect great variability in rates, from 10 to 22% and also in risk factors for PPD. This variability may reflect geographical location. The incidence and risk factors for PPD among Turkish women are not well documented. It is, however, important to understand the risk factors to develop preventive intervention strategies. This study aims to examine the prevalence of PPD and associated risk factors among a sample of women receiving services at a tertiary obstetrics hospital in Ankara, Turkey.MethodsA sample of 671 women, between 36 and 40 gestational weeks, were enrolled and screened for depressive symptomatology using the Hospital Depression Inventory. Sociodemographic and clinical data were also collected. At a subsequent postpartum evaluation, 6 8 weeks post delivery, 540 of the 671 were screened using the Edinburgh Postnatal Depression Scale (EPDS) for PPD.ResultsEighty three (15.4% of the 540 women had scores above the cutoff point >13) on the EPDS. Statistically significant correlations were found between antenatal, prenatal and postpartum depression scores (r 0.24) Women reporting suicidal thoughts during pregnancy (OR: 6.99) history of past PPD (OR: 6.64) physical violence during pregnancy (OR: 6.20) or during the postpartum period (OR: 5.87) previous psychiatric history (OR: 4.16) depressive symptoms during pregnancy (OR: 1.70) subjectively lower level of satisfaction with the pregnancy (OR:0. 69) a history of premenstrual syndrome (PMS) (OR: 2.05) and unplanned pregnancy (OR: 1.69) had higher odds for developing PPD.ConclusionOne in six mothers screened as positive for PPD. Women who had previously been diagnosed with PPD, reported suicidal thoughts during pregnancy, or had been exposed to physical violence were at especially high risk for postpartum depression. To prevent and treat postpartum depression, special attention should be paid to women reporting these characteristics.
Ancient Egyptian Civilization
Year: 2009

Assignments 7 and 8 focus on two early civilizations, those of Ancient Egypt and the Maya, each of which we examine in some detail. Assignment 7 discusses the origins of state organized societies and the theoretical controversies surrounding their origins. This theoretical background is essential for understanding some of the factors which contributed to the rise and fall of pre industrial civilizations. Then we analyze the rise, and workings of, one of the world's earliest civilizations, along the banks of the Nile River.
Wind power forecasting: A case study in terrain using artificial intelligence
Year: 2017

The rapidly increasing world population and developing technology bring about the need for energy. Limited reserves of fossil fuels and the damages caused on the environment by these fuels have necessitated the move towards renewable energy sources such as wind. Wind energy is at the top among renewable energy resources as a type of economical and rapidly improving form of energy production. Wind, which as an intermittent and stochastic structure may change momentary changes and this situation makes it difficult to estimate its power accurately. It is crucial to accurately estimate wind power to calculate energy unit costs beforehand and ensure safe connection with the power grid. The purpose of this study is to accurately estimate wind power at 61 m at the wind measurement station by using 25,926 units of real time data including data on wind power, wind direction, temperature, humidity and pressure at 31 m. For this purpose, 100 artificial neural networks were trained and tested using the method of Multilayer Perceptron (MLP) The system's error rates were analyzed, and the lowest rate of error was at the MLP 6 24 1 network with 6 inputs, 24 hidden layers, exponential hidden activation, and output activation as identity.
An Introduction to Probabilistic Spiking Neural Networks: Probabilistic Models, Learning Rules, and Applications
Venue: IEEE Signal Processing Magazine
Year: 2019

Spiking neural networks (SNNs) are distributed trainable systems whose computing elements, or neurons, are characterized by internal analog dynamics and by digital and sparse synaptic communications. The sparsity of the synaptic spiking inputs and the corresponding event driven nature of neural processing can be leveraged by energy efficient hardware implementations, which can offer significant energy reductions as compared to conventional artificial neural networks (ANNs) The design of training algorithms for SNNs, however, lags behind hardware implementations: most existing training algorithms for SNNs have been designed either for biological plausibility or through conversion from pretrained ANNs via rate encoding.
Australia's engagement with its 'near abroad' a change of direction under the Labor government, 2007 10?
Year: 2011

Under the Labor government of 2007 10, Australian policies towards the Pacific island states, East Timor and Indonesia showed a large measure of continuity with the policies pursued by the previous conservative Coalition government. This continuity is to be explained by the fact that the problems in the region did not change significantly; there was a large degree of consensus in Australian politics about Australian objectives in the region, but there were some differences as to means, particularly in relation to the South Pacific. This study highlights the impact of proximity and regional variations in international politics on Australian policies in relation to neighbouring countries.
Extended SMART Algorithms for Non negative Matrix Factorization
Venue: ICAISC
Year: 2006

In this paper we derive a family of new extended SMART (Simultaneous Multiplicative Algebraic Reconstruction Technique) algorithms for Non negative Matrix Factorization (NMF) The proposed algorithms are characterized by improved efficiency and convergence rate and can be applied for various distributions of data and additive noise. Information theory and information geometry play key roles in the derivation of new algorithms. We discuss several loss functions used in information theory which allow us to obtain generalized forms of multiplicative NMF learning adaptive algorithms. We also provide flexible and relaxed forms of the NMF algorithms to increase convergence speed and impose an additional constraint of sparsity. The scope of these results is vast since discussed generalized divergence functions include a large number of useful loss functions such as the Amari a divergence, Relative entropy, Bose Einstein divergence, Jensen Shannon divergence, J divergence, Arithmetic Geometric (AG) Taneja divergence, etc. We applied the developed algorithms successfully to Blind (or semi blind) Source Separation (BSS) where sources may be generally statistically dependent, however are subject to additional constraints such as nonnegativity and sparsity. Moreover, we applied a novel multilayer NMF strategy which improves performance of the most proposed algorithms.
Magnetotherapy, alternative medicines, Hippocratic oath
Venue: Biomedical engineering online
Year: 2008

Caring for patients is the central objective of health care. The Hippocratic Oath, which is sworn by all physicians and forgotten by many, does not promise glory but stresses the obligation to care for patients. But "caring for patients" in these days of high technology medicine, is more than a matter of laying of hands by physicians who, by and large, are ignorant of basic science and engineering, and employing methods that are not well supported by good scientific studies. Close collaboration of physicians, biomedical engineers, and basic medical scientists is needed to help clarify the confusion and misuse surrounding the application of magnetic fields for medicine. It is time to move past the stage of amateur research and claims published on the Internet, and on to well controlled double blinded studies with adequate engineering support and papers published in conventional scientific journals. While the prospects do not look good for a breakthrough in therapies using weak magnetic fields, perhaps something useful will be uncovered.
Impaired awareness of hypoglycemia in children and adolescents with type 1 diabetes mellitus in north of Jordan
Venue: BMC Endocrine Disorders
Year: 2019

BackgroundHypoglycemia is a common complication of insulin therapy in patients with Type 1 Diabetes Mellitus (DM) Awareness of hypoglycemic symptoms helps patients to recognize hypoglycemia and initiate self treatment. Impaired Awareness of Hypoglycemia (IAH) exposes patients to severe hypoglycemia, which could be associated with seizures and unconsciousness. This study aimed to assess IAH, frequency of hypoglycemia, severe hypoglycemia and intensity of hypoglycemic symptoms among children and adolescents with Type 1 DM in North of Jordan.MethodsData were collected from 94 children and adolescents with Type 1 DM. Clarke's and Edinburgh surveys were used to assess IAH and individual symptoms of hypoglycemia, respectively. Frequency of hypoglycemia and other related information were obtained by self reporting or from medical records.Results16.0% of participants were having IAH, 66.0% of participants reported recurrent hypoglycemia >once/month) and 18.0% of participants developed =1 severe hypoglycemia during the previous year. IAH was not associated with age, gender, duration of DM, HbA1c, insulin regimen, adherence to insulin or development of severe hypoglycemia (p values> 0.05) Instead, IAH was associated with frequency of hypoglycemia during the previous 6 months (p value< 0.01) Hunger, tiredness, dizziness, drowsiness, inability to concentrate, trembling and weakness were the most common symptoms felt by participants when they develop hypoglycemia. Hunger was the only common symptom that was significantly higher in children compared to adolescent (p value 0.01).ConclusionsThis study has reported low prevalence of IAH in children and adolescents with Type 1 DM in North of Jordan. IAH was more common in subjects with more frequent hypoglycemia.
Online Education: Worldwide Status, Challenges, Trends, and Implications
Year: 2018

Online education in its various modes has been growing steadily worldwide due to the confluence of new technologies, global adoption of the Internet, and intensifying demand for a workforce trained periodically for the ever evolving digital economy. Online education is on track to become mainstream by 2025. This editorial documents country level factors that impact quantity and quality of online education. Such factors include industry (business) governments at local, state, and federal levels; country laws; ICT capacity; Internet/mobile technology diffusion; and income and digital divide. We provide implications for country and world organizations concerning online education.
Rabota s SUBD MySQL v IntelliJ IDEA v redaktsii Community
Year: 2018

Annotatsiia. Osnovoiu stat'i iavliaetsia lektsiia, kotoruiu ia chitaiu v universitete v kurse <Platformy korporativnykh informatsionnykh sistem> V nei rassmotreny ochen' slabo osveshchennye v pechati voprosy raboty s populiarnoi SUBD MySQL v integrirovannoi srede razrabotki programmnogo obespecheniia na Java ot kompanii JetBrains. Kliuchevye slova: baza dannykh, SUBD MySQL, MySQL, IntelliJ IDEA, sreda razrabotki programmnogo obespecheniia na Java, Java, JDBC draiver, Java Data Base Connectivity, Community Edition, DataBase Navigator.
FPGA based low complexity high throughput real time hardware accelerator for robust watermarking
Venue: Journal of Real Time Image Processing
Year: 2019

This paper presents an FPGA based hardware accelerator for robust watermarking. By applying some novel transformation techniques, the DCT/IDCT algorithm is simplified in a hardware friendly way by replacing the original complex multiplication/division and nonlinear function (cosine function) with addition and shift operations. Moreover, the architecture of this real time accelerator is proposed with pipeline technique to improve throughput and hardware efficiency. The entire design is implemented on FPGA, which achieves up to 3.2 GBps (Giga Bytes per second) throughput. Finally, the experimental results show that the proposed accelerator has good performance.
The psychosocial sources of sexual interest in older couples
Year: 2018

Sexual activity with one's partner is an important component of well being and is linked to physical, emotional, and cognitive health. However, it is unclear why some older adults are more interested in sex and some less so. Their own characteristics, those of their partner, and characteristics of the relationship may all be important. We define sexual interest as consisting both of the motivation to seek sex with a partner and willingness to have sex when asked. We measure this construct using data from both members of 953 couples in the National Social Life, Health, and Aging Project. We focus in this article on the impact of psychological and social factors on older adults' interest in sex, as well as the impact of their partners' characteristics on their own interest in sex. We find that individuals with high perceived positive marital quality, a more positive and open personality, a large network of family, and better physical health showed greater interest in sex. Characteristics of their partner generally had little association with sexual interest.
Compressive Summarization with Plausibility and Salience Modeling
Venue: EMNLP
Year: 2020

Compressive summarization systems typically rely on a crafted set of syntactic rules to determine what spans of possible summary sentences can be deleted, then learn a model of what to actually delete by optimizing for content selection (ROUGE) In this work, we propose to relax the rigid syntactic constraints on candidate spans and instead leave compression decisions to two data driven criteria: plausibility and salience. Deleting a span is plausible if removing it maintains the grammaticality and factuality of a sentence, and spans are salient if they contain important information from the summary. Each of these is judged by a pre trained Transformer model, and only deletions that are both plausible and not salient can be applied. When integrated into a simple extraction compression pipeline, our method achieves strong in domain results on benchmark summarization datasets, and human evaluation shows that the plausibility model generally selects for grammatical and factual deletions. Furthermore, the flexibility of our approach allows it to generalize cross domain: our system fine tuned on only 500 samples from a new domain can match or exceed an in domain extractive model trained on much more data.
23rd International Meshing Roundtable (IMR23) An inside out method for arbitrary polyhedra
Year: 2014

Meshes consisting of arbitrary polyhedra have gained popularity in the CFD community over the last decade, as a consequence of the advances in the solver technology. Furthermore, polyhedral meshes can be generated by retrieving the dual of a tetrahedral mesh, and there exist many methods which can generate tetrahedral meshes in complex geometries. In addition, polyhedral meshes have lower number of cells and better accuracy than their tetrahedral counterpart  This note presents an eort to develop an inside out method for generation of polyhedral meshes, which does not require high quality input geometry, and is tolerable to small gaps, cracks and protrusions. Unlike methods which use hex dominated templates, it is not so sensitive to the alignment of the template with the geometry because most of mesh faces have either five or six vertices. The method is tested on examples of both academic and industrial interest.  Peer review under responsibility of organizing committee of the 23rd International Meshing Roundtable (IMR23)
Comparative phylogeography of three skink species (Oligosoma moco, O. smithi, O. suteri; Reptilia: Scincidae) in northeastern New Zealand.
Venue: Molecular phylogenetics and evolution
Year: 2008

Sea level fluctuations during the Pliocene and Pleistocene have shaped the landscape of the Northland region of New Zealand. We examined the comparative phylogeography of three skink species (Oligosoma moco, O. smithi, O. suteri) in northeastern New Zealand in order to investigate the impact of the historical processes that have prevailed since the Pliocene on the Northland fauna. O. moco, O. smithi and O. suteri have similar distributions across northeastern New Zealand, frequently occurring in sympatry. We obtained sequence data from across the entire range of each species, targeting the ND2 mitochondrial gene. Using Neighbor Joining, Maximum likelihood and Bayesian methods, our analysis revealed contrasting phylogeographic patterns in each species. We found substantial phylogeographic structure within O. moco, with three distinct clades identified. Similarly, deep phylogeographic divergence was evident within O. smithi, with three distinct clades present. Clade 1 included O. smithi populations from the Three Kings Islands and the western coastline of Northland, while Clade 2 encompassed the remainder of the range. However, since Clade 3 corresponded to a described species (O. microlepis) O. smithi might represent a species complex. In both O. moco and O. smithi, divergences among clades are estimated to have occurred in the Pliocene, with divergences within clades occurring during the Pleistocene. In contrast, genetic divergence among O. suteri populations was extremely limited and indicative of more recent divergences during the Pleistocene. The lack of phylogeographic structure in O. suteri might be a consequence of its oviparous reproductive mode, which restricted its distribution to warm northern refugia during glacial maxima. Differences in the ecology and biology of each species might have produced contrasting responses to the same historical processes, and ultimately diverse phylogeographic patterns. Our study reveals an absence of consistent and concordant phylogeographic patterns in the Northland biota, even within the same taxonomic group.
3D tracking of water hazards with polarized stereo cameras
Venue: 2017 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2017

Current self driving car systems operate well in sunny weather but struggle in adverse conditions. One of the most commonly encountered adverse conditions involves water on the road caused by rain, sleet, melting snow or flooding. While some advances have been made in using conventional RGB camera and LIDAR technology for detecting water hazards, other sources of information such as polarization offer a promising and potentially superior approach to this problem in terms of performance and cost. In this paper, we present a novel stereo polarization system for detecting and tracking water hazards based on polarization and color variation of reflected light, with consideration of the effect of polarized light from sky as function of reflection and azimuth angles. To evaluate this system, we present a new large 'water on road' datasets spanning approximately 2 km of driving in various on road and off road conditions and demonstrate for the first time reliable water detection and tracking over a wide range of realistic car driving water conditions using polarized vision as the primary sensing modality. Our system successfully detects water hazards up to more than 100m. Finally, we discuss several interesting challenges and propose future research directions for further improving robust autonomous car perception in hazardous wet conditions using polarization sensors.