nils-herrmann/clean_scirepeval huggingface dataset:
‘ ‘ Why Do We Celebrate ... ? ’ ’ Filling Traditions with Meaning in an Ethnically Diverse Swedish Preschool

The Swedish preschool is an important socializing agent because the great majority of children aged, from 1 to 5 years, are enrolled in an early childhood education program. This paper explores how preschool teachers and children, in an ethnically diverse preschool, negotiate the meaning of cultural traditions celebrated in Swedish preschools. Particular focus is given to narrative representations of cultural traditions as they are co-constructed and negotiated in preschool practice between teachers and children. Cultural traditions are seen as shared events in the children’s preschool life, as well as symbolic resources which enable children and preschool teachers to conceive themselves as part of a larger whole. The data analyzed are three videotaped circle time events focused on why a particular tradition is celebrated. Methodologically the analysis builds on a narrative approach inspired by Bakhtin’s notion of addressivity and on Alexander’s ideas about dialogic teaching. The results of the analysis show that the teachers attempt to achieve a balance between transferring traditional cultural and religious values and realizing a child-centered pedagogy, emphasizing the child’s initiative. The analyses also show that narratives with a religious tonality generate some uncertainty on how to communicate with the children about the traditions that are being discussed. These research findings are important because, in everyday practice, preschool teachers enact whether religion is regarded as an essential part of cultural socialization, while acting both as keepers of traditions and agents of change.
CanTree: a canonical-order tree for incremental frequent-pattern mining

Since its introduction, frequent-pattern mining has been the subject of numerous studies, including incremental updating. Many existing incremental mining algorithms are Apriori-based, which are not easily adoptable to FP-tree-based frequent-pattern mining. In this paper, we propose a novel tree structure, called CanTree (canonical-order tree), that captures the content of the transaction database and orders tree nodes according to some canonical order. By exploiting its nice properties, the CanTree can be easily maintained when database transactions are inserted, deleted, and/or modified. For example, the CanTree does not require adjustment, merging, and/or splitting of tree nodes during maintenance. No rescan of the entire updated database or reconstruction of a new tree is needed for incremental updating. Experimental results show the effectiveness of our CanTree in the incremental mining of frequent patterns. Moreover, the applicability of CanTrees is not confined to incremental mining; CanTrees can also be applicable to other frequent-pattern mining tasks including constrained mining and interactive mining.
Antimicrobial activities of commercial essential oils and their components against food-borne pathogens and food spoilage bacteria

This study was undertaken to determine the in vitro antimicrobial activities of 15 commercial essential oils and their main components in order to pre-select candidates for potential application in highly perishable food preservation. The antibacterial effects against food-borne pathogenic bacteria (Listeria monocytogenes, Salmonella Typhimurium, and enterohemorrhagic Escherichia coli O157:H7) and food spoilage bacteria (Brochothrix thermosphacta and Pseudomonas fluorescens) were tested using paper disk diffusion method, followed by determination of minimum inhibitory (MIC) and bactericidal (MBC) concentrations. Most of the tested essential oils exhibited antimicrobial activity against all tested bacteria, except galangal oil. The essential oils of cinnamon, oregano, and thyme showed strong antimicrobial activities with MIC ≥ 0.125 μL/mL and MBC ≥ 0.25 μL/mL. Among tested bacteria, P. fluorescens was the most resistant to selected essential oils with MICs and MBCs of 1 μL/mL. The results suggest that the activity of the essential oils of cinnamon, oregano, thyme, and clove can be attributed to the existence mostly of cinnamaldehyde, carvacrol, thymol, and eugenol, which appear to possess similar activities against all the tested bacteria. These materials could be served as an important natural alternative to prevent bacterial growth in food products.
Perceptual issues in augmented reality

Between the extremes of real life and Virtual Reality lies the spectrum of Mixed Reality, in which views of the real world are combined in some proportion with views of a virtual environment. Combining direct view, stereoscopic video, and stereoscopic graphics, Augmented Reality describes that class of displays that consists primarily of a real environment, with graphic enhancements or augmentations. Augmented Virtuality describes that class of displays that enhance the virtual experience by adding elements of the real environment. All Mixed Reality systems are limited in their capability of accurately displaying and controlled all relevant depth cues, and as a result, perceptual biases can interfere with task performance. In this paper we identify and discuss eighteen issues that pertain to Mixed Reality in general, and Augmented Reality in particular.
Work in progress — Developmental engineering: Using observation for understanding child behaviors as precursors to engineering thinking

Children's experiences in early childhood have significant lasting effects in their overall development and in the United States today the majority of young children spend considerable amounts of time in early childhood education settings. At the national level, there is an expressed concern about the low levels of student interest and success in science, technology, engineering, and mathematics (STEM). Bringing these two conversations together our research focuses on how young children of preschool age exhibit behaviors that we consider relevant in engineering. There is much to be explored in STEM education at such an early age, and in order to proceed we created an experimental observation protocol in which we identified various pre-engineering behaviors based on pilot observations, related literature and expert knowledge. This protocol is intended for use by preschool teachers and other professionals interested in studying engineering in the preschool classroom.
Potential Cyberattacks on Automated Vehicles

Vehicle automation has been one of the fundamental applications within the field of intelligent transportation systems (ITS) since the start of ITS research in the mid-1980s. For most of this time, it has been generally viewed as a futuristic concept that is not close to being ready for deployment. However, recent development of “self-driving” cars and the announcement by car manufacturers of their deployment by 2020 show that this is becoming a reality. The ITS industry has already been focusing much of its attention on the concepts of “connected vehicles” (United States) or “cooperative ITS” (Europe). These concepts are based on communication of data among vehicles (V2V) and/or between vehicles and the infrastructure (V2I/I2V) to provide the information needed to implement ITS applications. The separate threads of automated vehicles and cooperative ITS have not yet been thoroughly woven together, but this will be a necessary step in the near future because the cooperative exchange of data will provide vital inputs to improve the performance and safety of the automation systems. Thus, it is important to start thinking about the cybersecurity implications of cooperative automated vehicle systems. In this paper, we investigate the potential cyberattacks specific to automated vehicles, with their special needs and vulnerabilities. We analyze the threats on autonomous automated vehicles and cooperative automated vehicles. This analysis shows the need for considerably more redundancy than many have been expecting. We also raise awareness to generate discussion about these threats at this early stage in the development of vehicle automation systems.
Brown adipose tissue: function and physiological significance.

The function of brown adipose tissue is to transfer energy from food into heat; physiologically, both the heat produced and the resulting decrease in metabolic efficiency can be of significance. Both the acute activity of the tissue, i.e., the heat production, and the recruitment process in the tissue (that results in a higher thermogenic capacity) are under the control of norepinephrine released from sympathetic nerves. In thermoregulatory thermogenesis, brown adipose tissue is essential for classical nonshivering thermogenesis (this phenomenon does not exist in the absence of functional brown adipose tissue), as well as for the cold acclimation-recruited norepinephrine-induced thermogenesis. Heat production from brown adipose tissue is activated whenever the organism is in need of extra heat, e.g., postnatally, during entry into a febrile state, and during arousal from hibernation, and the rate of thermogenesis is centrally controlled via a pathway initiated in the hypothalamus. Feeding as such also results in activation of brown adipose tissue; a series of diets, apparently all characterized by being low in protein, result in a leptin-dependent recruitment of the tissue; this metaboloregulatory thermogenesis is also under hypothalamic control. When the tissue is active, high amounts of lipids and glucose are combusted in the tissue. The development of brown adipose tissue with its characteristic protein, uncoupling protein-1 (UCP1), was probably determinative for the evolutionary success of mammals, as its thermogenesis enhances neonatal survival and allows for active life even in cold surroundings.
Big Data Clustering: A Review

Clustering is an essential data mining and tool for analyzing big data. There are difficulties for applying clustering techniques to big data duo to new challenges that are raised with big data. As Big Data is referring to terabytes and petabytes of data and clustering algorithms are come with high computational costs, the question is how to cope with this problem and how to deploy clustering techniques to big data and get the results in a reasonable time. This study is aimed to review the trend and progress of clustering algorithms to cope with big data challenges from very first proposed algorithms until today’s novel solutions. The algorithms and the targeted challenges for producing improved clustering algorithms are introduced and analyzed, and afterward the possible future path for more advanced algorithms is illuminated based on today’s available technologies and frameworks.
Real-time lane detection and tracking on high performance computing devices

Road lane detection and tracking methods are the state of the art in present driver assistance systems. However, lane detection methods that exploit the parallel processing capabilities of heterogeneous high performance computing devices such as FPGAs (or GPUs), a technology that potentially will replace ECUs in a coming generation of cars, are a rare subject of interest. In this thesis a road lane detection and tracking algorithm is developed and implemented, especially designed to incorporate one or many, and even heterogeneous, hardware accelerators. Road lane markings are detected and tracked with a Sequential Monte Carlo (SQR) method. Lane detection is done by populating a pre-processed gradient image with randomly sampled, straight lines. Each line is assigned a weight according to its position and the best positioned lines are used to represent the lane markings. Subsequently, lane tracking is performed with the help of a particle filter. The code was tested on three devices, one GPU the NVIDIA GeForce GTX 660 TI and two FPGAs the ALTERA Stratix V and the ALTERA Cyclone V SOC. The tests revealed a processing frame rate of up to 627 Hz on the GPU, 478 Hz on the Stratix V FPGA and 38 Hz on the Cyclone V SOC. They also showed a significant improvement in accuracy and robustness, a 2.4-4.6 times faster execution on the GPU, a 8.4-29.7 times faster execution on the Stratix V and a reduction of memory consumption by 71.94 % compared to a similar lane detection method. The algorithm was tested on different recorded videos, on independent benchmark datasets and in multiple test drives, confronting it with a wide range of scenarios, such as varying lighting conditions, presence of disturbing shadows or light beams and varying traffic densities. In all these scenarios the algorithm proved to be very robust to detect and track one or multiple lane markings.
On brewing fresh espresso: LinkedIn's distributed data serving platform

Espresso is a document-oriented distributed data serving platform that has been built to address LinkedIn's requirements for a scalable, performant, source-of-truth primary store. It provides a hierarchical document model, transactional support for modifications to related documents, real-time secondary indexing, on-the-fly schema evolution and provides a timeline consistent change capture stream. This paper describes the motivation and design principles involved in building Espresso, the data model and capabilities exposed to clients, details of the replication and secondary indexing implementation and presents a set of experimental results that characterize the performance of the system along various dimensions.
 When we set out to build Espresso, we chose to apply best practices in industry, already published works in research and our own internal experience with different consistency models. Along the way, we built a novel generic distributed cluster management framework, a partition-aware change- capture pipeline and a high-performance inverted index implementation.
Hierarchical reinforcement learning for situated natural language generation

Natural Language Generation systems in interactive settings often face a multitude of choices, given that the communicative effect of each utterance they generate depends crucially on the interplay between its physical circumstances, addressee and interaction history. This is particularly true in interactive and situated settings. In this paper we present a novel approach for situated Natural Language Generation in dialogue that is based on hierarchical reinforcement learning and learns the best utterance for a context by optimisation through trial and error. The model is trained from human–human corpus data and learns particularly to balance the trade-off between efficiency and detail in giving instructions: the user needs to be given sufficient information to execute their task, but without exceeding their cognitive load. We present results from simulation and a task-based human evaluation study comparing two different versions of hierarchical reinforcement learning: One operates using a hierarchy of policies with a large state space and local knowledge, and the other additionally shares knowledge across generation subtasks to enhance performance. Results show that sharing knowledge across subtasks achieves better performance than learning in isolation, leading to smoother and more successful interactions that are better perceived by human users.
Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search

While existing work on neural architecture search (NAS) tunes hyperparameters in a separate post-processing step, we demonstrate that architectural choices and other hyperparameter settings interact in a way that can render this separation suboptimal. Likewise, we demonstrate that the common practice of using very few epochs during the main NAS and much larger numbers of epochs during a post-processing step is inefficient due to little correlation in the relative rankings for these two training regimes. To combat both of these problems, we propose to use a recent combination of Bayesian optimization and Hyperband for efficient joint neural architecture and hyperparameter search.
A tutorial on human activity recognition using body-worn inertial sensors

The last 20 years have seen ever-increasing research activity in the field of human activity recognition. With activity recognition having considerably matured, so has the number of challenges in designing, implementing, and evaluating activity recognition systems. This tutorial aims to provide a comprehensive hands-on introduction for newcomers to the field of human activity recognition. It specifically focuses on activity recognition using on-body inertial sensors. We first discuss the key research challenges that human activity recognition shares with general pattern recognition and identify those challenges that are specific to human activity recognition. We then describe the concept of an Activity Recognition Chain (ARC) as a general-purpose framework for designing and evaluating activity recognition systems. We detail each component of the framework, provide references to related research, and introduce the best practice methods developed by the activity recognition research community. We conclude with the educational example problem of recognizing different hand gestures from inertial sensors attached to the upper and lower arm. We illustrate how each component of this framework can be implemented for this specific activity recognition problem and demonstrate how different implementations compare and how they impact overall recognition performance.
Improving Image-Sentence Embeddings Using Large Weakly Annotated Photo Collections

This paper studies the problem of associating images with descriptive sentences by embedding them in a common latent space. We are interested in learning such embeddings from hundreds of thousands or millions of examples. Unfortunately, it is prohibitively expensive to fully annotate this many training images with ground-truth sentences. Instead, we ask whether we can learn better image-sentence embeddings by augmenting small fully annotated training sets with millions of images that have weak and noisy annotations (titles, tags, or descriptions). After investigating several state-of-the-art scalable embedding methods, we introduce a new algorithm called Stacked Auxiliary Embedding that can successfully transfer knowledge from millions of weakly annotated images to improve the accuracy of retrieval-based image description.
Artificial intelligence in radiology

Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced. In this Opinion article, Hosny et al. discuss the application of artificial intelligence to image-based tasks in the field of radiology and consider the advantages and challenges of its clinical implementation.
Independent comparative study of PCA, ICA, and LDA on the FERET data set

Face recognition is one of the most successful applications of image analysis and understanding and has gained much attention in recent years. Various algorithms were proposed and research groups across the world reported different and often contradictory results when comparing them. The aim of this paper is to present an independent, comparative study of three most popular appearance-based face recognition projection methods (PCA, ICA, and LDA) in completely equal working conditions regarding preprocessing and algorithm implementation. We are motivated by the lack of direct and detailed independent comparisons of all possible algorithm implementations (e.g., all projection–metric combinations) in available literature. For consistency with other studies, FERET data set is used with its standard tests (gallery and probe sets). Our results show that no particular projection–metric combination is the best across all standard FERET tests and the choice of appropriate projection–metric combination can only be made for a specific task. Our results are compared to other available studies and some discrepancies are pointed out. As an additional contribution, we also introduce our new idea of hypothesis testing across all ranks when comparing performance results. VC 2006 Wiley Periodicals, Inc.  2005; . DOI 10.1002/ima.20059
Teaching Tactics and Dialog in AutoTutor

The Tutoring Research Group at the University of Memphis has developed a computer tutor (called AutoTutor) that simulates the discourse patterns and pedagogical strategies of a typical human tutor. The dialog tactics were based on a previous project that dissected 100 hours of naturalistic tutoring sessions. AutoTutor is currently targeted for college students in introductory computer literacy courses, who learn the fundamentals of hardware, operating systems, and the Internet. Instead of merely being an information delivery system, AutoTutor serves as a discourse prosthesis (or collaborative scaffold) that assists the student in actively constructing knowledge. A dialog manager coordinates the conversation that occurs between a learner and a pedagogical agent, whereas lesson content and world knowledge are represented in a curriculum script and latent semantic analysis. The agent is a talking head with discoursesensitive facial expressions and synthesized speech. Evaluations of AutoTutor have shown that the tutoring system improves learning and memory of the lessons by .5 to .6 standard deviation units. This article describes the components of AutoTutor and contrasts two versions that follow somewhat different teaching tactics.
Deep Reinforcement Learning with Skill Library : Exploring with Temporal Abstractions and coarse approximate Dynamics Models

Reinforcement learning is a computational approach to learn from interaction. However, learning from scratch using reinforcement learning requires exorbitant number of interactions with the environment even for simple tasks. One way to alleviate the problem is to reuse previously learned skills as done by humans. This thesis provides frameworks and algorithms to build and reuse Skill Library. Firstly, we extend the Parameterized Action Space formulation using our Skill Library to multi-goal setting and show improvements in learning using hindsight at coarse level. Secondly, we use our Skill Library for exploring at a coarser level to learn the optimal policy for continuous control. We demonstrate the benefits, in terms of speed and accuracy, of the proposed approaches for a set of real world complex robotic manipulation tasks in which some state-of-the-art methods completely fail.