nils-herrmann/clean_scirepeval huggingface dataset:
Symbolic road marking recognition using convolutional neural networks

This paper investigates the use of Convolutional Neural Networks for classification of painted symbolic road markings. Previous work on road marking recognition is mostly based on either template matching or on classical feature extraction followed by classifier training which is not always effective and based on feature engineering. However, with the rise of deep neural networks and their success in ADAS systems, it is natural to investigate the suitability of CNN for road marking recognition. Unlike others, our focus is solely on road marking recognition and not detection; which has been extensively explored and conventionally based on MSER feature extraction of the IPM images. We train five different CNN architectures with variable number of convolution/max-pooling and fully connected layers, and different resolution of road mark patches. We use a publicly available road marking data set and incorporate data augmentation to enhance the size of this data set which is required for training deep nets. The augmented data set is randomly partitioned in 70% and 30% for training and testing. The best CNN network results in an average recognition rate of 99.05% for 10 classes of road markings on the test set.
Redundancy effect in multimedia learning: A closer look

The generally accepted assumption by most multimedia researchers is that learning is inhibited when on-screen text and narration containing the same information is presented simultaneously, rather than on-screen text or narration alone. This is known as the verbal redundancy effect. Are there situations where the reverse is true? This research was designed to investigate the reverse redundancy effect for non-native English speakers learning English reading comprehension, where two instructional modes were used the redundant mode and the modality mode. In the redundant mode, static pictures and audio narration were presented with synchronized redundant on-screen text. In the modality mode, only static pictures and audio were presented. In both modes, learners were allowed to control the pacing of the lessons. Participants were 209 Yemeni learners in their first year of tertiary education. Examination of text comprehension scores indicated that those learners who were exposed to the redundancy mode performed significantly better than learners in the modality mode. They were also significantly more motivated than their counterparts in the modality mode. This finding has added an important modification to the redundancy effect. That is the reverse redundancy effect is true for multimedia learning of English as a foreign language for students where textual information was foreign to them. In such situations, the redundant synchronized on-screen text did not impede learning; rather it reduced the cognitive load and thereby enhanced learning.
Fifty years of research in artificial intelligence

Artificial Intelligence (AI) has come of age. Two thousand and six marked the fiftieth anniversary of the Dartmouth Conference, where the term Artificial Intelligence was accepted as the official label for a new discipline that seemed to hold great promise in the pursuit of understanding the human mind. AI, as the nascent discipline came to be known in public and academic discourse, has accomplished a lot during this period, breaking new ground and providing deep insights into our minds, our technologies, and the relationship between them. But AI has also failed significantly, making false promises and often manifesting a kind of unbridled enthusiasm that is emblematic of Hollywood-style projects. This chapter seeks to capture both of these aspects: AI’s successes, accomplishments, and contributions to science, technology, and intellectual inquiry, on one hand, and its failures, fallacies, and shortcomings, on the other. The history of AI can, furthermore, be reviewed from different perspectives— humanistic, cognitive, sociological, and philosophical, among others. This review examines AI from two key perspectives—scientific and engineering. The former represents AI claims about the human mind and the nature of intelligence; the latter embodies the wide array of computer systems that are built by AI practitioners or by others who have, or claim to have, taken inspiration from ideas in AI in order to solve a practical problem in an area of application. Ideally, the scientific face should guide the engineering one and the engineering face would provide support and substance to its scientific counterpart. In reality, however, that relationship is not as straightforward as it should be, turning AI into a schizophrenic Janus. The way AI practitioners “talk” about these two faces complicates the situation even further, as we shall see. This review seeks to provide a balanced portrait of the two faces. Currently, we are witnessing a resurgence of interest in, and application of, AI in areas such as education, video gaming, financial forecasting, medical diagnosis, health and elderly care, data mining, self-aware computing, and the Semantic Web. The list is illustrative, but it clearly indicates the broad range of application domains that draw on AI techniques for ideas and solutions. As a general heuristic, whenever one encounters something qualified as “smart” or “intelligent”—as in CHAPTER 5
Discriminative Learning of Bayesian Networks via Factorized Conditional Log-Likelihood

We propose an efficient and parameter-free scoring criterio n, the factorized conditional log-likelihood (̂fCLL), for learning Bayesian network classifiers. The propo sed score is an approximation of the conditional log-likelihood criterion. The approximation is devised in order to guarantee decomposability over the network structure, as w ell as efficient estimation of the optimal parameters, achieving the same time and space complexity as the traditional log-likelihood scoring criterion. The resulting criterion has an information-the oretic interpretation based on interaction information, which exhibits its discriminative nature. To evaluate the performance of the proposed criterion, we present an empirical comparison with state-o f-the-art classifiers. Results on a large suite of benchmark data sets from the UCI repository show tha t f̂CLL-trained classifiers achieve at least as good accuracy as the best compared classifiers, us ing significantly less computational resources.
A Fluctuation Smoothing Approach for Unsupervised Automatic Short Answer Grading

We offer a fluctuation smoothing computational approach for unsupervised automatic short answer grading (ASAG) techniques in the educational ecosystem. A major drawback of the existing techniques is the significant effect that variations in model answers could have on their performances. The proposed fluctuation smoothing approach, based on classical sequential pattern mining, exploits lexical overlap in students’ answers to any typical question. We empirically demonstrate using multiple datasets that the proposed approach improves the overall performance and significantly reduces (up to 63%) variation in performance (standard deviation) of unsupervised ASAG techniques. We bring in additional benchmarks such as (a) paraphrasing of model answers and (b) using answers by k top performing students as model answers, to amplify the benefits of the proposed approach.
Teamwork and patient safety in dynamic domains of healthcare: a review of the literature.

This review examines current research on teamwork in highly dynamic domains of healthcare such as operating rooms, intensive care, emergency medicine, or trauma and resuscitation teams with a focus on aspects relevant to the quality and safety of patient care.



Evidence from three main areas of research supports the relationship between teamwork and patient safety: (1) Studies investigating the factors contributing to critical incidents and adverse events have shown that teamwork plays an important role in the causation and prevention of adverse events. (2) Research focusing on healthcare providers' perceptions of teamwork demonstrated that (a) staff's perceptions of teamwork and attitudes toward safety-relevant team behavior were related to the quality and safety of patient care and (b) perceptions of teamwork and leadership style are associated with staff well-being, which may impact clinician' ability to provide safe patient care. (3) Observational studies on teamwork behaviors related to high clinical performance have identified patterns of communication, coordination, and leadership that support effective teamwork.



In recent years, research using diverse methodological approaches has led to significant progress in team research in healthcare. The challenge for future research is to further develop and validate instruments for team performance assessment and to develop sound theoretical models of team performance in dynamic medical domains integrating evidence from all three areas of team research identified in this review. This will help to improve team training efforts and aid the design of clinical work systems supporting effective teamwork and safe patient care.
A nonlinear adaptive observer approach for state of charge estimation of lithium-ion batteries

A state of charge estimation method for lithium ion batteries is presented. First, the problem is formulated, and existing literature is reviewed. Then an equivalent circuit is used to model the battery, and an indirect nonlinear adaptive observer approach is developed to estimate the state of charge. Parameter identification and state estimation scheme are discussed. Simulation result based on real-world battery test data is shown to support the validity of the proposed method. Issues related to production code implementation are also addressed.
Extended Bandwidth Piezoelectric Lorentz Force Magnetometer Based on a Mechanically Coupled Beam Resonator Array

We present a piezoelectric-on-silicon Lorentz force magnetometer (LFM) based on a mechanically coupled array of clamped–clamped beam resonators for the detection of lateral ( $xy$  plane) magnetic fields with an extended operating bandwidth of 1.36 kHz. The proposed device exploits piezoelectric transduction to greatly enhance the electromechanical coupling efficiency, which benefits the device sensitivity. Coupling multiple clamped–clamped beams increases the area for piezoelectric transduction, which further increases the sensitivity. The reported device has the widest operating bandwidth among LFMs reported to date with comparable normalized sensitivity despite the quality factor being limited to 30 when operating at ambient pressure instead of vacuum as in most cases of existing LFMs.
Circular Polarization of Periodic Leaky-Wave Antennas With Axial Asymmetry: Theoretical Proof and Experimental Demonstration

This paper includes two contributions. First, it proves that the series and shunt radiation components, corresponding to longitudinal and transversal electric fields, respectively, are always in phase quadrature in axially asymmetric periodic leaky-wave antennas (LWAs), so that these antennas are inherently elliptically polarized. This fact is theoretically proven and experimentally illustrated by two case-study examples, a composite right/left-handed (CRLH) LWA and a series-fed patch (SFP) LWA. Second, it shows (for the case of the SFP LWA) that the axial ratio is controlled and minimized by the degree of axial asymmetry.
Tightly integrated deep learning and symbolic programming on a single neuromorphic chip

This work integrates deep learning and symbolic programming paradigms into a unified method for deploying applications to a neuromorphic system. The approach removes the need for coordination among disjoint co-processors by embedding both types entirely on a neuromorphic processor. This integration provides a flexible approach for using each technique where it performs best. A single neuromorphic solution can seamlessly deploy neural networks for classifying sensor-driven noisy data obtained from the environment alongside programmed symbolic logic to processes the input from the networks. We present a concrete implementation of the proposed framework using the TrueNorth neuromorphic processor to play blackjack using a pre-programmed optimal strategy algorithm combined with a neural network trained to classify card images as input. Future extensions of this approach will develop a symbolic neuromorphic compiler for automatically creating networks from a symbolic programming language.
Predicting flight delays with artificial neural networks: Case study of an airport

Air transportation has an important place among transportation systems and it is indispensable for the flights to perform their voyages in scheduled time in order to ensure the comfort of passengers and controllability of operational costs. There are several reasons for flight delays like weather conditions, excessive intensity in air traffic, accidents or closed airfields, conditions that will lead to an increase in distances between planes and operational delays in ground services. In this study, using the data collected from the sensors located in the airport and the information about the flight, the goal is develop a machine learning model to estimate departure delays of flights using artificial neural networks.
Cancer incidence and mortality patterns in Europe: estimates for 40 countries in 2012.

Cancer incidence and mortality estimates for 25 cancers are presented for the 40 countries in the four United Nations-defined areas of Europe and for the European Union (EU-27) for 2012.



We used statistical models to estimate national incidence and mortality rates in 2012 from recently-published data, predicting incidence and mortality rates for the year 2012 from recent trends, wherever possible. The estimated rates in 2012 were applied to the corresponding population estimates to obtain the estimated numbers of new cancer cases and deaths in Europe in 2012.



There were an estimated 3.45 million new cases of cancer (excluding non-melanoma skin cancer) and 1.75 million deaths from cancer in Europe in 2012. The most common cancer sites were cancers of the female breast (464,000 cases), followed by colorectal (447,000), prostate (417,000) and lung (410,000). These four cancers represent half of the overall burden of cancer in Europe. The most common causes of death from cancer were cancers of the lung (353,000 deaths), colorectal (215,000), breast (131,000) and stomach (107,000). In the European Union, the estimated numbers of new cases of cancer were approximately 1.4 million in males and 1.2 million in females, and around 707,000 men and 555,000 women died from cancer in the same year.



These up-to-date estimates of the cancer burden in Europe alongside the description of the varying distribution of common cancers at both the regional and country level provide a basis for establishing priorities to cancer control actions in Europe. The important role of cancer registries in disease surveillance and in planning and evaluating national cancer plans is becoming increasingly recognised, but needs to be further advocated. The estimates and software tools for further analysis (EUCAN 2012) are available online as part of the European Cancer Observatory (ECO) (http://eco.iarc.fr).
cplint on SWISH: Probabilistic Logical Inference with a Web Browser

cplint on SWISH is a web application that allows users to perform reasoning tasks on probabilistic logic programs. Both inference and learning systems can be performed: conditional probabilities with exact, rejection sampling and Metropolis-Hasting methods. Moreover, the system now allows hybrid programs, i.e., programs where some of the random variables are continuous. To perform inference on such programs likelihood weighting and particle filtering are used. cplint on SWISH is also able to sample goals’ arguments and to graph the results. This paper reports on advances and new features of cplint on SWISH, including the capability of drawing the binary decision diagrams created during the inference processes.
Overcoming bias to learn about controversial topics

Deciding whether a claim is true or false often requires a deeper understanding of the evidence supporting and contradicting the claim. However, when presented with many evidence documents, users do not necessarily read and trust them uniformly. Psychologists and other researchers have shown that users tend to follow and agree with articles and sources that hold viewpoints similar to their own, a phenomenon known as confirmation bias. This suggests that when learning about a controversial topic, human biases and viewpoints about the topic may affect what is considered “trustworthy” or credible. It is an interesting challenge to build systems that can help users overcome this bias and help them decide the truthfulness of claims. In this article, we study various factors that enable humans to acquire additional information about controversial claims in an unbiased fashion. Specifically, we designed a user study to understand how presenting evidence with contrasting viewpoints and source expertise ratings affect how users learn from the evidence documents. We find that users do not seek contrasting viewpoints by themselves, but explicitly presenting contrasting evidence helps them get a well-rounded understanding of the topic. Furthermore, explicit knowledge of the credibility of the sources and the context in which the source provides the evidence document not only affects what users read but also whether they perceive the document to be credible.
Physiological effects of tourism and associated food provisioning in an endangered iguana

Deliberately feeding wildlife is an increasingly popular tourism-related activity despite a limited understanding of long-term impacts on the species being fed. As a result, tourist behaviours that may have adverse impacts on imperiled species have often been encouraged without the necessary evaluation or oversight. Here, we report the responses of Northern Bahamian Rock Iguanas (Cyclura cychlura) to human-visitation pressure and associated food provisioning. We compared a variety of blood chemistry parameters of iguanas subjected to supplemental feeding at popular tourist destinations with iguanas occurring on islands where supplemental feeding does not take place. We demonstrate that male and female iguanas inhabiting tourist-visited islands where supplemental feeding occurs do not differ in body condition or baseline stress and stress response (determined by corticosterone levels) compared with iguanas from non-visited islands. Both males and females from tourist-visited sites experienced a greater incidence of endoparasitic infection and atypical loose faeces. Indicators of dietary nutrition, including glucose, potassium, and uric acid values, also differed for both sexes from tourist-visited and unvisited islands. Male iguanas from visited islands differed significantly from those on non-visited islands in calcium, cholesterol, cobalt, copper, magnesium, packed cell volume, selenium, and triglyceride concentrations, whereas female iguanas from visited islands differed significantly in ionized calcium. Although the interpretation of these differences is challenging, chronic biochemical stressors could compromise individual health over time or decrease survivorship during periods of environmental stress. We suggest protocols that can be adopted throughout the region to ensure that supplemental feeding has fewer impacts on these long-lived iguanas.
Modeling Topics and Behavior of Microbloggers: An Integrated Approach

Microblogging encompasses both user-generated content and behavior. When modeling microblogging data, one has to consider personal and background topics, as well as how these topics generate the observed content and behavior. In this article, we propose the Generalized Behavior-Topic (GBT) model for simultaneously modeling background topics and users’ topical interest in microblogging data. GBT considers multiple topical communities (or realms) with different background topical interests while learning the personal topics of each user and the user’s dependence on realms to generate both content and behavior. This differentiates GBT from other previous works that consider either one realm only or content data only. By associating user behavior with the latent background and personal topics, GBT helps to model user behavior by the two types of topics. GBT also distinguishes itself from other earlier works by modeling multiple types of behavior together. Our experiments on two Twitter datasets show that GBT can effectively mine the representative topics for each realm. We also demonstrate that GBT significantly outperforms other state-of-the-art models in modeling content topics and user profiling.
ObSteiner: An Exact Algorithm for the Construction of Rectilinear Steiner Minimum Trees in the Presence of Complex Rectilinear Obstacles

In this paper, we present ObSteiner, an exact algorithm for the construction of obstacle-avoiding rectilinear Steiner minimum trees (OARSMTs) among complex rectilinear obstacles. This is the first paper to propose a geometric approach to optimally solve the OARSMT problem among complex obstacles. The optimal solution is constructed by the concatenation of full Steiner trees among complex obstacles, which are proven to be of simple structures in this paper. ObSteiner is able to handle complex obstacles, including both convex and concave ones. Benchmarks with hundreds of terminals among a large number of obstacles are solved optimally in a reasonable amount of time.
Normative values of olfactory function testing using the 'sniffin' sticks'.

Quantitative olfactory assessment is often neglected in clinical practice, although olfactory loss can assist to diagnosis and may lead to significant morbidity. "Sniffin' Sticks" is a modern test of nasal chemosensory performance that is based on penlike odor-dispensing devices. It consists of three tests of olfactory function: odor threshold, odor discrimination, and odor identification. The results of this test may be presented as a composite threshold-discrimination-identification (TDI) score. The aim of this study was first to develop normative data of olfactory function for the Greek population using this test and second to relate olfactory performance to age, sex, and side examined.



The authors conducted a prospective clinical trial.



A total of 93 healthy subjects were included in the study, 48 males and 45 females, mean age of 44.5 years (range, 6-84 years).



A database of normal values for olfactory testing was established for the Greek population. Females performed better than males and older subjects performed less efficiently in all tests. We also found a right nostril advantage compared with the left. Additionally, scores obtained from bilateral presentation were similar with scores obtained from the nostril with the better performance.



The "Sniffin' Sticks" can be used effectively in the Greek population to evaluate olfactory performance. Mean values of olfactory tests obtained were better in comparison with data from settings located in central and northern Europe.