nils-herrmann/clean_scirepeval huggingface dataset:
Chapter 7 Computers and Aviation

Although animal flight has a history of 300 million years, serious thought about human flight has a history of a few hundred years, dating from Leonardo da Vinci, 1 and successful human flight has only been achieved during the last 110 years. This is summarized in the attached figures 7.1-7.4. To some extent, this parallels the history of computing. Serious thought about computing dates back to Pascal and Leibnitz. While there was a notable attempt by Babbage to build a working computer in the 19 th century, successful electronic computers were finally achieved in the 40s, almost exactly contemporaneously with the development of the first successful jet aircraft. The early history of computers is summarized in figures 7.5-7.8. Tables 7.1 and 7.2 summarize the more recent progress in the development of supercomputers and microprocessors. Although airplane design had reached quite an advanced level by the 30s, exemplified by aircraft such as the DC-3 (Douglas Commercial-3) and the Spitfire (figure 7.2), the design of high speed aircraft requires an entirely new level of sophistication. This has led to a fusion of engineering, mathematics and computing, as indicated in figure 7.9.
A media computation course for non-majors

Computing may well become considered an essential part of a liberal education, but introductory programming courses will not look like the way that they do today. Current CSI course are failing dramatically. We are developing a new course, to be taught starting in Spring 2003, which uses computation for communication as a guiding principle. Students learn to program by writing Python programs for manipulating sound, images, and movies. This paper describes the course development and the tools developed for the course. The talk will include the first round of assessment results.
Materials for fuel-cell technologies

Fuel cells convert chemical energy directly into electrical energy with high efficiency and low emission of pollutants. However, before fuel-cell technology can gain a significant share of the electrical power market, important issues have to be addressed. These issues include optimal choice of fuel, and the development of alternative materials in the fuel-cell stack. Present fuel-cell prototypes often use materials selected more than 25 years ago. Commercialization aspects, including cost and durability, have revealed inadequacies in some of these materials. Here we summarize recent progress in the search and development of innovative alternative materials.
The Slow Oscillation in Cortical and Thalamic Networks: Mechanisms and Functions

During even the most quiescent behavioral periods, the cortex and thalamus express rich spontaneous activity in the form of slow (<1 Hz), synchronous network state transitions. Throughout this so-called slow oscillation, cortical and thalamic neurons fluctuate between periods of intense synaptic activity (Up states) and almost complete silence (Down states). The two decades since the original characterization of the slow oscillation in the cortex and thalamus have seen considerable advances in deciphering the cellular and network mechanisms associated with this pervasive phenomenon. There are, nevertheless, many questions regarding the slow oscillation that await more thorough illumination, particularly the mechanisms by which Up states initiate and terminate, the functional role of the rhythmic activity cycles in unconscious or minimally conscious states, and the precise relation between Up states and the activated states associated with waking behavior. Given the substantial advances in multineuronal recording and imaging methods in both in vivo and in vitro preparations, the time is ripe to take stock of our current understanding of the slow oscillation and pave the way for future investigations of its mechanisms and functions. My aim in this Review is to provide a comprehensive account of the mechanisms and functions of the slow oscillation, and to suggest avenues for further exploration.
NoSta-D Named Entity Annotation for German: Guidelines and Dataset

We describe the annotation of a new dataset for German Named Entity Recognition (NER). The need for this dataset is motivated by licensing issues and consistency issues of existing datasets. We describe our approach to creating annotation guidelines based on linguistic and semantic considerations, and how we iteratively refined and tested them in the early stages of annotation in order to arrive at the largest publicly available dataset for German NER, consisting of over 31,000 manually annotated sentences (over 591,000 tokens) from German Wikipedia and German online news. We provide a number of statistics on the dataset, which indicate its high quality, and discuss legal aspects of distributing the data as a compilation of citations. The data is released under the permissive CC-BY license, and will be fully available for download in September 2014 after it has been used for the GermEval 2014 shared task on NER. We further provide the full annotation guidelines and links to the annotation tool used for the creation of this resource.
Developing Targeted Health Service Interventions Using the PRECEDE-PROCEED Model: Two Australian Case Studies

This paper provides an overview of the applicability of the PRECEDE-PROCEED Model to the development of targeted nursing led chronic illness interventions.  Changing health care practice is a complex and dynamic process that requires consideration of social, political, economic, and organisational factors. An understanding of the characteristics of the target population, health professionals, and organizations plus identification of the determinants for change are also required. Synthesizing this data to guide the development of an effective intervention is a challenging process. The PRECEDE-PROCEED Model has been used in global health care settings to guide the identification, planning, implementation, and evaluation of various health improvement initiatives.  Using a reflective case study approach, this paper examines the applicability of the PRECEDE-PROCEED Model to the development of targeted chronic care improvement interventions for two distinct Australian populations: a rapidly expanding and aging rural population with unmet palliative care needs and a disadvantaged urban community at higher risk of cardiovascular disease.  The PRECEDE-PROCEED Model approach demonstrated utility across diverse health settings in a systematic planning process. In environments characterized by increasing health care needs, limited resources, and growing community expectations, adopting planning tools such as PRECEDE-PROCEED Model at a local level can facilitate the development of the most effective interventions. Relevance to Clinical Practice. The PRECEDE-PROCEED Model is a strong theoretical model that guides the development of realistic nursing led interventions with the best chance of being successful in existing health care environments.
Malaria knowledge and long-lasting insecticidal net use in rural communities of central Côte d'Ivoire

To improve effectiveness of malaria control interventions, it is essential to deepen the knowledge of contextual factors that govern people's practice for preventive and curative measures. The aim of this study was to determine factors that influence the use of long-lasting insecticidal nets (LLINs) in three rural communities of Côte d'Ivoire, two of which benefited from recent interventions. The study was carried out in 957 households in three villages (Bozi, N'Dakonankro and Yoho) located in central Côte d'Ivoire. Indicators of socioeconomic position (SEP), malaria knowledge and practice, placing special emphasis on LLINs, were investigated during a cross-sectional questionnaire survey. Principal component analysis was used to calculate the SEP of households by means of a list of household assets ownership. The concentration index was used to assess the direction of the association between SEP and a given variable. To compare groups or means, Fisher's exact test, χ2 and Kruskal-Wallis test were used, as appropriate. Significant differences were found between SEP and reported malaria symptoms, such as fever or hot body, convulsion, anaemia and jaundice (yellow eyes). Individuals from the least poor group cited more often the use of bed nets and insecticide-treated nets (ITNs) compared to poorer groups. The mean number of individuals reporting the use of bed nets and LLINs was different between groups with different educational level. Moreover, the mean number of LLINs in a household was influenced by the presence of children below five years of age. The study not only confirmed that education and SEP play important roles in the prevention and control of malaria and promotion of health in general, but pointed at the basic essential knowledge and the key behavioural elements that should guide education and learning processes among the poorer segments of the population. In turn, such knowledge may change behaviour and lead to an increased utilization of LLINs.
The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models

Defect prediction models that are trained on class imbalanced datasets (i.e., the proportion of defective and clean modules is not equally represented) are highly susceptible to produce inaccurate prediction models. Prior research compares the impact of class rebalancing techniques on the performance of defect prediction models. Prior research efforts arrive at contradictory conclusions due to the use of different choice of datasets, classification techniques, and performance measures. Such contradictory conclusions make it hard to derive practical guidelines for whether class rebalancing techniques should be applied in the context of defect prediction models. In this paper, we investigate the impact of 4 popularly-used class rebalancing techniques on 10 commonly-used performance measures and the interpretation of defect prediction models. We also construct statistical models to better understand in which experimental design settings that class rebalancing techniques are beneficial for defect prediction models. Through a case study of 101 datasets that span across proprietary and open-source systems, we recommend that class rebalancing techniques are necessary when quality assurance teams wish to increase the completeness of identifying software defects (i.e., Recall). However, class rebalancing techniques should be avoided when interpreting defect prediction models. We also find that class rebalancing techniques do not impact the AUC measure. Hence, AUC should be used as a standard measure when comparing defect prediction models.
An 8.9mW 25Gb/s inductorless 1:4 DEMUX in 90nm CMOS

A low-power inductorless 1:4 DEMUX in 90 nm CMOS is presented. It is capable of operating at 25 Gb/s with a power supply voltage of 1.05V, and the power consumption is 8.9mW. Its area is 29 × 40μm2. The DEMUX consists primarily of latches in differential pseudo-NMOS logic style. This logic style has a near-rail-to-rail logic swing and is much more scalable under low Vdd than the current-mode logic, commonly used for high-performance SerDes. It is also compatible with the conventional CMOS logic, and direct connection with CMOS logic gates is possible without logic level conversion. The low power, small footprint, and reasonable speed could be beneficial for chip-to-chip communication within a multi-chip module.
Using Machine Learning to Detect Cyberbullying

Cyber bullying is the use of technology as a medium to bully someone. Although it has been an issue for many years, the recognition of its impact on young people has recently increased. Social networking sites provide a fertile medium for bullies, and teens and young adults who use these sites are vulnerable to attacks. Through machine learning, we can detect language patterns used by bullies and their victims, and develop rules to automatically detect cyber bullying content. The data we used for our project was collected from the website Formspring.me, a question-and-answer formatted website that contains a high percentage of bullying content. The data was labeled using a web service, Amazon's Mechanical Turk. We used the labeled data, in conjunction with machine learning techniques provided by the Weka tool kit, to train a computer to recognize bullying content. Both a C4.5 decision tree learner and an instance-based learner were able to identify the true positives with 78.5% accuracy.
A Light-Weight Solution to Preservation of Access Pattern Privacy in Un-trusted Clouds

Cloud computing is a new computing paradigm that is gaining increased popularity. More and more sensitive user data are stored in the cloud. The privacy of users’ access pattern to the data should be protected to prevent un-trusted cloud servers from inferring users’ private information or launching stealthy attacks. Meanwhile, the privacy protection schemes should be efficient as cloud users often use thin client devices to access the cloud. In this paper, we propose a lightweight scheme to protect the privacy of data access pattern. Comparing with existing state-of-the-art solutions, our scheme incurs less communication and computational overhead, requires significantly less storage space at the cloud user, while consuming similar storage space at the cloud server. Rigorous proofs and extensive evaluations have been conducted to demonstrate that the proposed scheme can hide the data access pattern effectively in the long run after a reasonable number of accesses have been made.
Holistically Constrained Local Model: Going Beyond Frontal Poses for Facial Landmark Detection

Facial landmark detection has received much attention in recent years, with two detection paradigms emerging: local approaches, where each facial landmark is modeled individually and with the help of a shape model; and holistic approaches, where the face appearance and shape are modeled jointly. In recent years both of these approaches have shown great performance gains for facial landmark detection even under "in-the-wild" conditions of varying illumination, occlusion and image quality. However, their accuracy and robustness are very often reduced for profile faces where face alignment is more challenging (e.g., no more facial symmetry, less defined features and more variable background). In this paper, we present a new model, named Holistically Constrained Local Model (HCLM), which unifies local and holistic facial landmark detection by integrating head pose estimation, sparse-holistic landmark detection and dense-local landmark detection. We evaluate our new model on two publicly available datasets, 300-W and AFLW, as well as a newly introduced dataset, IJB-FL which includes a larger proportion of profile face poses. Our HCLM model shows state-of-the-art performance, especially with extreme head poses.
Contextual Collaborative Filtering via Hierarchical Matrix Factorization

Matrix factorization (MF) has been demonstrated to be one of the most competitive techniques for collaborative filtering. However, state-of-the-art MFs do not consider contextual information, where ratings can be generated under different environments. For example, users select items under various situations, such as happy mood vs. sad, mobile vs. stationary, movies vs. book, etc. Under different contexts, the preference of users are inherently different. The problem is that MF methods uniformly decompose the rating matrix, and thus they are unable to factorize for different contexts. To amend this problem and improve recommendation accuracy, we introduce a “hierarchical” factorization model by considering the local context when performing matrix factorization. The intuition is that: as ratings are being generated from heterogeneous environments, certain user and item pairs tend to be more similar to each other than others, and hence they ought to receive more collaborative information from each other. To take the contextual information into consideration, the proposed “contextual collaborative filtering” approach splits the rating matrix hierarchically by grouping similar users and items together, and factorizes each sub-matrix locally under different contexts. By building an ensemble model, the approach further avoids over-fitting with less parameter tuning. We analyze and demonstrate that the proposed method is a model-averaging gradient boosting model, and its error rate can be bounded. Experimental results show that it outperforms three state-of-the-art algorithms on a number of real-world datasets (MovieLens, Netflix, etc). The source code and datasets are available for download.
DeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level Sign Language Translation

There is an undeniable communication barrier between deaf people and people with normal hearing ability. Although innovations in sign language translation technology aim to tear down this communication barrier, the majority of existing sign language translation systems are either intrusive or constrained by resolution or ambient lighting conditions. Moreover, these existing systems can only perform single-sign ASL translation rather than sentence-level translation, making them much less useful in daily-life communication scenarios. In this work, we fill this critical gap by presenting DeepASL, a transformative deep learning-based sign language translation technology that enables ubiquitous and non-intrusive American Sign Language (ASL) translation at both word and sentence levels. DeepASL uses infrared light as its sensing mechanism to non-intrusively capture the ASL signs. It incorporates a novel hierarchical bidirectional deep recurrent neural network (HB-RNN) and a probabilistic framework based on Connectionist Temporal Classification (CTC) for word-level and sentence-level ASL translation respectively. To evaluate its performance, we have collected 7, 306 samples from 11 participants, covering 56 commonly used ASL words and 100 ASL sentences. DeepASL achieves an average 94.5% word-level translation accuracy and an average 8.2% word error rate on translating unseen ASL sentences. Given its promising performance, we believe DeepASL represents a significant step towards breaking the communication barrier between deaf people and hearing majority, and thus has the significant potential to fundamentally change deaf people's lives.
The influence of maternal smoking on transferrin sialylation and fetal biometric parameters.

Transferrin is a glycosylated protein responsible for transporting iron, an essential metal responsible for proper fetal development. Tobacco is a heavily used xenobiotic having a negative impact on the human body and pregnancy outcomes. Aims of this study was to examine the influence of tobacco smoking on transferrin sialic acid residues and their connection with fetal biometric parameters in women with iron-deficiency.



The study involved 173 samples from pregnant women, smokers and non-smokers, iron deficient and not. Transferrin sialylation was determined by capillary electrophoresis. The cadmium (Cd) level was measured by atomic absorption and the sialic acid concentration by the resorcinol method.



Women with iron deficiencies who smoked gave birth earlier than non-smoking, non-iron-deficient women. The Cd level, but not the cotinine level, was positively correlated with transferrin sialylation in the blood of iron-deficient women who smoked; 3-, 4-, 5- and 6-sialoTf correlated negatively with fetal biometric parameters in the same group.



It has been shown the relationship between Cd from tobacco smoking and fetal biometric parameters observed only in the iron deficient group suggests an additive effect of these two factors, and indicate that mothers with anemia may be more susceptible to Cd toxicity and disturbed fetal development.
Prenatal diagnosis and postmortem findings of Neu-laxova syndrome Neu-laxova sendromunun prenatal tan ı s ı ve postmortem bulgular ı

Address for Correspondence / Yazışma Adresi: Doç. Dr. Ebru Tarim, Baskent Üniversitesi Adana Uygulama ve Arastırma Merkezi, Kadın Hastalıkları ve Doğum Anabilim Dalı, Baraj Yolu 1. Durak 01110, Adana, Turkey Phone: +90 322 458 68 68-2203 e.mail: eatarim@hotmail.com doi:10.5152/jtgga.2010.44 Neu-laxova syndrome is a lethal, autosomal recessive condition associated with ectodermal abnormalities and other characteristic features, including microcephaly, marked intrauterine growth restriction, limb deformities, central nervous system malformations and abnormal facial features, consisting of severe proptosis with ectropion, hypertelorism, micrognathia, flattened nose, malformed ears, and gaping mouth. Here we present a fetus having a dysmorphic face with proptotic eyes, retracted eye lids, depressed nasal bridge and micrognathia at 25 weeks of gestation. The extremities were contracted and no fetal movements were observed during the ultrasonographic examination. The fetus also had microcephaly and the amniotic fluid was increased. The pregnancy was terminated and the abnormalities demonstrated on prenatal ultrasound were confirmed at autopsy.
moocRP: An Open-source Analytics Platform

In this paper, we address issues of transparency, modularity, and privacy with the introduction of an open source, web-based data repository and analysis tool tailored to the Massive Open Online Course community. The tool integrates data request/authorization and distribution workflows as well as a simple analytics module upload format to enable reuse and replication of analytics results among instructors and researchers. We survey the evolving landscape of competing data models, all of which can be accommodated in the platform. Data model descriptions are provided to analytics authors who choose, much like with smartphone app stores, to write for any number of data models depending on their needs and the proliferation of the particular data model. Two case study examples of analytics and interactive visualizations are described in the paper. The result is a simple but effective approach to learning analytics immediately applicable to X consortium institutions and beyond.
Computer Simulation of a Laboratory Hydraulic System with Matlab-Simulink

The article presents some selected problems related to modeling and simulation of hydraulic servo-systems (valve + cylinder), using MATLAB-Simulink package. For this purpose, is taken into account the basic mathematical model of certain selected elements and phenomena that occur in a hydraulic servo system. Models are represented as block diagrams adapted to the software package requirements. Afterward, the simulation results are compared with laboratory measurements. Laboratory measurements have been performed in Laboratory for hydraulics and pneumatics at Faculty of Engineering, University of Rijeka.