nils-herrmann/clean_scirepeval huggingface dataset:
Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene

Vision-based object detection is one of the fundamental functions in numerous traffic scene applications such as self-driving vehicle systems and advance driver assistance systems (ADAS). However, it is also a challenging task due to the diversity of traffic scene and the storage, power and computing source limitations of the platforms for traffic scene applications. This paper presents a generalized Haar filter based deep network which is suitable for the object detection tasks in traffic scene. In this approach, we first decompose a object detection task into several easier local regression tasks. Then, we handle the local regression tasks by using several tiny deep networks which simultaneously output the bounding boxes, categories and confidence scores of detected objects. To reduce the consumption of storage and computing resources, the weights of the deep networks are constrained to the form of generalized Haar filter in training phase. Additionally, we introduce the strategy of sparse windows generation to improve the efficiency of the algorithm. Finally, we perform several experiments to validate the performance of our proposed approach. Experimental results demonstrate that the proposed approach is both efficient and effective in traffic scene compared with the state-of-the-art.
Learning Information Extraction Rules for Semi-Structured and Free Text

A wealth of on-line text information can be made available to automatic processing by information extraction (IE) systems. Each IE application needs a separate set of rules tuned to the domain and writing style. WHISK helps to overcome this knowledge-engineering bottleneck by learning text extraction rules automatically. WHISK is designed to handle text styles ranging from highly structured to free text, including text that is neither rigidly formatted nor composed of grammatical sentences. Such semi-structured text has largely been beyond the scope of previous systems. When used in conjunction with a syntactic analyzer and semantic tagging, WHISK can also handle extraction from free text such as news stories.
Qualitative Methods in Research on Teaching

This chapter reviews basic issues of theory and method in approaches to research on teaching that are alternatively called ethnographic, qualitative, participant observational, case study, symbolic interactionist, phenomenological, constructivist, or interpretive. These approaches are all slightly different, but each bears strong family resemblance to the others. The set of related approaches is relatively new in the field of research on teaching. The approaches have emerged as significant in the decade of the 1960s in England and in the 1970s in the United States, Australia, New Zealand, and Germany. Because interest in these approaches is so recent, the previous editions of the Handbook of Research on Teaching do not contain a chapter devoted to participant observational research. Accordingly, this chapter attempts to describe r~s and their theoretical _nresup~s in considerable detail and does not attempt an exhaustive review of the rapidly growing literature in the field. Such a review will be appropriate for the next edition of this handbook. From this point on I will use the term interpretive to refer to the whole family of approaches to participant observational research. I adopt this term for three reasons: (a) It is more inclusive than many of the others (e.g., ethnography, case study); (b) W Blake
On Drug Dosage Control Using Description Defeasible Logic

Drug-drug and drug-disease interactions are generally harmful. Some of these interactions could result from improper drug doses prescribed for a particular disease or from the unawareness the patient suffers from another disease. We aim in this paper to make a first step towards developing a knowledge based system that supports the decision making process undertaken by the physicians and pharmacists when prescribing drugs and the proper dosages. We need to formally represent knowledge about the drugs (e.g., family to which it belongs, proper doses), diseases and the conditions of the patient (e.g., patient with high cholesterol (LDL) and muscle pain disease). The drug which will employ in our case study is Lipitor. Lipitor is a cholesterol lowering drug from the statin family. The diseases which we will consider are liver and kidney diseases.
A Dynamic Target Visual Positioning Method Based on ROI

The method of visual positioning can be mainly divided into fixed camera system and mobile camera system. In this paper, we propose a dynamic target positioning method based on ROI (regions of interest), which utilizes the deep learning method to detect targets and employs the fixed camera system to locate the targets. The ROI method proposed only process the region of target, which can reduce the time-consuming, and it can solve the problem that none or less feature points of the target is detected in 3D reconstruction. We make a dataset of the experimental car and use YOLOv2 to train the dataset, by which the training model of the experimental car is obtained; then the trained model is used to detect the experimental car in the video data which acquired by two USB cameras and get the ROI of the moving target. According to the triangulation method, only the ROI of the image data at the same time is reconstructed, and the average of the obtained coordinates as the position of the car at that moment. In the experiment, we use the positions obtained by optitrack system as the true values, and compare the positions got by the method of this paper (ROI method) with the true value. The experimental results show that the ROI method proposed can be used to locate the dynamic target with the positioning accuracy at the cm level.
Collaborative Learning for Deep Neural Networks

We introduce collaborative learning in which multiple classifier heads of the same network are simultaneously trained on the same training data to improve generalization and robustness to label noise with no extra inference cost. It acquires the strengths from auxiliary training, multi-task learning and knowledge distillation. There are two important mechanisms involved in collaborative learning. First, the consensus of multiple views from different classifier heads on the same example provides supplementary information as well as regularization to each classifier, thereby improving generalization. Second, intermediate-level representation (ILR) sharing with backpropagation rescaling aggregates the gradient flows from all heads, which not only reduces training computational complexity, but also facilitates supervision to the shared layers. The empirical results on CIFAR and ImageNet datasets demonstrate that deep neural networks learned as a group in a collaborative way significantly reduce the generalization error and increase the robustness to label noise.
GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild

In this work, we introduce a large high-diversity database for generic object tracking, called GOT-10k. GOT-10k is backboned by the semantic hierarchy of WordNet . It populates a majority of 563 object classes and 87 motion patterns in real-world, resulting in a scale of over 10 thousand video segments and 1.5 million bounding boxes. To our knowledge, GOT-10k is by far the richest motion trajectory dataset, and its coverage of object classes is more than a magnitude wider than similar scale counterparts [20], [23]. By publishing GOT-10k, we hope to encourage the development of generic purposed trackers that work for a wide range of moving objects and under diverse real-world scenarios. To promote generalization and avoid the evaluation results biased to seen classes, we follow the one-shot principle [35] in dataset splitting where training and testing classes are zero-overlapped. We also carry out a series of analytical experiments to select a compact while highly representative testing subset – it embodies 84 object classes and 32 motion patterns with only 180 video segments, allowing for efficient evaluation. Finally, we train and evaluate a number of representative trackers on GOT-10k and analyze their performance. The evaluation results suggest that tracking in real-world unconstrained videos is far from being solved, and only 40% of frames are successfully tracked using top ranking trackers. The database and toolkits are publicly available at https://got-10k.github.io.
Behavioral Cloning for Lateral Motion Control of Autonomous Vehicles Using Deep Learning

Current trend of the automotive industry combined with research by the major tech companies has proved that self-driving vehicles are the future. With successful demonstration of neural network based autonomous driving, NVIDIA has introduced a new paradigm for autonomous driving software. The biggest challenge for self-driving cars is autonomous lateral control. An end-to-end model seems very promising in providing a complete software stack for autonomous driving. Although this system is not ready to be provided as a feature in the market today, it is one of the many steps in the right direction to make self-driving cars a reality. The work described in this paper focusses on how an end-to-end model is implemented. The subtleties of training a successful end-to-end model are highlighted with the aim of providing an insight on deep learning and software required for neural network training. Detailed analyses of data acquisition and training systems are provided and installation procedures for all required tools and software discussed. TORCS is used for developing and testing the end-to-end model. Approximately ten hours of driving data was collected from two different tracks. Using four hours of data from a track, we trained a deep neural network to steer a car inside simulation. Even with such a small training set, the end-to-end model developed demonstrated capabilities to maintain lanes and complete laps in different tracks. For a multilane track, like the one used for training, the model demonstrated an autonomy of 96.62%. For single lane unknown tracks, the model steered the vehicle successfully for 89.02% of the time. The results indicate that end-to-end learning and behavioral cloning can be used to drive autonomously in new and unknown scenarios.
Effect of Video Game Practice on Spatial Skills in Girls and Boys

A study of the effect of video game practice on spatial abilities in girls ond boys wos carried out. Spatial performance, measured using two subtests of a computerized spatial skills battery, was significantly better in boys than in girls during pretest assessment. Subjects then practiced on an action video game, Marble Madness, or a computerized word game, Conjecture. Video game practice wos significantly more effective than the word game in improving spatiol performance on the posttest assessment; there was no significant interaction of gender with experimental treotment. However, video game practice was more effective for children who started out with relatively poor spatial skills. The pattern of results suggests that video games may be useful in equalizing individual differences in spatial skill performance, including those associated with gender.
Content-based recommendation techniques for requirements engineering

Assuring quality in software development processes is often a complex task. In many cases there are numerous needs which cannot be fulfilled with the limited resources given. Consequently it is crucial to identify the set of necessary requirements for a software project which needs to be complete and conflict-free. Additionally, the evolution of single requirements (artifacts) plays an important role because the quality of these artifacts has an impact on the overall quality of the project. To support stakeholders in mastering these tasks there is an increasing interest in AI techniques. In this paper we presents two content-based recommendation approaches that support the Requirements Engineering (RE) process. First, we propose a Keyword Recommender to increase requirements reuse. Second, we define a thesaurus enhanced Dependency Recommender to help stakeholders finding complete and conflict-free requirements. Finally, we present studies conducted at the Graz University of Technology to evaluate the applicability of the proposed recommendation technologies.
Scalable Gaussian Process Regression Using Deep Neural Networks

We propose a scalable Gaussian process model for regression by applying a deep neural network as the feature-mapping function. We first pre-train the deep neural network with a stacked denoising auto-encoder in an unsupervised way. Then, we perform a Bayesian linear regression on the top layer of the pre-trained deep network. The resulting model, Deep-Neural-Network-based Gaussian Process (DNN-GP), can learn much more meaningful representation of the data by the finite-dimensional but deep-layered feature-mapping function. Unlike standard Gaussian processes, our model scales well with the size of the training set due to the avoidance of kernel matrix inversion. Moreover, we present a mixture of DNN-GPs to further improve the regression performance. For the experiments on three representative large datasets, our proposed models significantly outperform the state-of-the-art algorithms of Gaussian process regression.
Fully Integrated Startup at 70 mV of Boost Converters for Thermoelectric Energy Harvesting

This paper presents an inductive DC-DC boost converter for energy harvesting using a thermoelectric generator with a minimum startup voltage of 70 mV and a regulated output voltage of 1.25 V. With a typical generator resistance of 40 Ω, an output power of 17 μW can be provided, which translates to an end-to-end efficiency of 58%. The converter employs Schmitt-trigger logic startup control circuitry and an ultra-low voltage charge pump using modified Schmitt-trigger driving circuits optimized for driving capacitive loads. Together with a novel ultra-low leakage power switch and the required control scheme, to the best of the authors' knowledge, this enables the lowest minimum voltage with fully integrated startup.
A Framework for Selecting Deep Learning Hyper-parameters

Recent research has found that deep learning architectures show significant improvements over traditional shallow algorithms when mining high dimensional datasets. When the choice of algorithm employed, hyper-parameter setting, number of hidden layers and nodes within a layer are combined, the identification of an optimal configuration can be a lengthy process. Our work provides a framework for building deep learning architectures via a stepwise approach, together with an evaluation methodology to quickly identify poorly performing architectural configurations. Using a dataset with high dimensionality, we illustrate how different architectures perform and how one algorithm configuration can provide input for fine-tuning more complex models.
Greenhouse Temperature and Humidity Intelligent Control System

The article based on embedded database of greenhouse temperature and humidity control system intelligent. Put forward by embedded database system set up in an ideal environment for data greenhouse temperature and humidity control, greenhouse crops in the process of growth under control. To address the growth of greenhouse crops in the process of temperature and humidity controlled environment is not ideal, at the same time improve the efficiency of control and cost-effective system. The article focuses on the structure of the control system, hardware, software design and system control strategy. The control system has a simple hardware structure, cost-effective, easy to use and maintenance, temperature and humidity data, and other advantages of good stability.  Embedded database, Temperature and Humidity, Control ; data filtering, Greenhouse,
Spatial PixelCNN: Generating Images from Patches

In this paper we propose Spatial PixelCNN, a conditional autoregressive model that generates images from small patches. By conditioning on a grid of pixel coordinates and global features extracted from a Variational Autoencoder (VAE), we are able to train on patches of images, and reproduce the full-sized image. We show that it not only allows for generating high quality samples at the same resolution as the underlying dataset, but is also capable of upscaling images to arbitrary resolutions (tested at resolutions up to 50×) on the MNIST dataset. Compared to a PixelCNN++ baseline, Spatial PixelCNN quantitatively and qualitatively achieves similar performance on the MNIST dataset.
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We report on attempts to use currently available automated text analysis tools to identify possible biased treatment by Politifact of Democratic vs. Republican speakers, through language. We begin by noting that there is no established method for detecting such differences, and indeed that “bias” is complicated and difficult to operationalize into a measurable quantity. This report includes several analyses that are representative of the tools available from natural language processing at this writing. In each case, we offer (i) what we would expect to see in the results if the method picked up on differential treatment between Democrats vs. Republicans, (ii) what we actually observe, and (iii) potential problems with the analysis; in some cases we also suggest (iv) future analyses that might be more revelatory.
Grounded Theory Research : Procedures , Canons , and Evaluative Criteria

Using grounded theory as an example, this paper examines three methodological questions that are generally applicable to all qualitative methods. How should the usual scientific canons be reinterpreted for qualitative research? How should researchers report the procedures and canons used in their research? What evaluative criteria should be used in judging the research products? We propose that the criteria should be adapted to fit the procedures of the method. We demonstrate how this can be done for grounded theory and suggest criteria for evaluating studies following this approach. We argue that other qualitative researchers might be similarly specific about their procedures and evaluative criteria.
ERP Event Log Preprocessing: Timestamps vs. Accounting Logic

Process mining has been gaining significant attention in academia and practice. A promising first step to apply process mining in the audit domain was taken with the mining of process instances from accounting data. However, the resulting process instances constitute graphs. Commonly, timestamp oriented event log formats require a sequential list of activities and do not support graph structures. Thus, event log based process mining techniques cannot readily be applied to accounting data. To close this gap, we present an algorithm that determines an activity sequence from accounting data. With this algorithm, mined process instance graphs can be decomposed in a way they fit into sequential event log formats. Event log based process mining techniques can then be used to construct process models. A case study demonstrates the effectiveness of the presented approach. Results reveal that the preprocessing of the event logs considerably improves the derived process models.
The consume - create spectrum: balancing convenience and computational thinking in stem learning

Future school science standards, such as the Next Generation Science Standards (NGSS), emphasize the integration of simulation and modeling activities in the classroom environment. The extremes of these activities have two vastly different implementations. On one hand, a teacher can have students experiment on a pre-made simulation associated with the material. On the other hand, students can use, for example, an end-user programming tool to create the simulation from scratch. This allows students to not only experiment on, but also, to model the real world phenomenon being studied- a key component of computational thinking. However, the greater amount of time necessary for student authoring of simulations can make such an approach infeasible in the classroom environment. This paper presents a spectrum of strategies for integrating simulations into class- rooms emphasizing our research at the Scalable Game Design Lab, University of Colorado Boulder as well as research from other entities. Starting at consuming simulations and adding more user interaction and authoring elements begins to provide a gentle slope from consumption towards simulation creation. Results indicate that many of these strategies are quite effective.
Children learning computer science concepts via Alice game-programming

Programming environments that incorporate drag-and-drop methods and many pre-defined objects and operations are being widely used in K-12 settings. But can middle school students learn complex computer science concepts by using these programming environments when computer science is not the focus of the course? In this paper, we describe a semester-long game-programming course where 325 middle school students used Alice. We report on our analysis of 231 final games where we measured the frequency of successful execution of programming constructs. Our results show that many games exhibit successful uses of high level computer science concepts such as student-created abstractions, concurrent execution, and event handlers.We discuss the implications of these results for designing effective game programming courses for young students.
Snippets: Taking the High Road to a Low Level

When building a compiler for a high-level language, certain intrinsic features of the language must be expressed in terms of the resulting low-level operations. Complex features are often expressed by explicitly weaving together bits of low-level IR, a process that is tedious, error prone, difficult to read, difficult to reason about, and machine dependent. In the Graal compiler for Java, we take a different approach: we use snippets of Java code to express semantics in a high-level, architecture-independent way. Two important restrictions make snippets feasible in practice: they are compiler specific, and they are explicitly prepared and specialized. Snippets make Graal simpler and more portable while still capable of generating machine code that can compete with other compilers of the Java HotSpot VM.
Combination of Hidden Markov Random Field and Conjugate Gradient for Brain Image Segmentation

Image segmentation is the process of partitioning the image into significant regions easier to analyze. Nowadays, segmentation has become a necessity in many practical medical imaging methods as locating tumors and diseases. Hidden Markov Random Field model is one of several techniques used in image segmentation. It provides an elegant way to model the segmentation process. This modeling leads to the minimization of an objective function. Conjugate Gradient algorithm (CG) is one of the best known optimization techniques. This paper proposes the use of the Conjugate Gradient algorithm (CG) for image segmentation, based on the Hidden Markov Random Field. Since derivatives are not available for this expression, finite differences are used in the CG algorithm to approximate the first derivative. The approach is evaluated using a number of publicly available images, where ground truth is known. The Dice Coefficient is used as an objective criterion to measure the quality of segmentation. The results show that the proposed CG approach compares favorably with other variants of Hidden Markov Random Field segmentation algorithms.