nils-herrmann/clean_scirepeval huggingface dataset:
Purposely Teaching for the Promotion of Higher-order Thinking Skills : A Case of Critical Thinking

This longitudinal case-study aimed at examining whether purposely teaching for the promotion of higher order thinking skills enhances students’ critical thinking (CT), within the framework of science education. Within a pre-, post-, and post–post experimental design, high school students, were divided into three research groups. The experimental group (n=57) consisted of science students who were exposed to teaching strategies designed for enhancing higher order thinking skills. Two other groups: science (n=41) and non-science majors (n=79), were taught traditionally, and acted as control. By using critical thinking assessment instruments, we have found that the experimental group showed a statistically significant improvement on critical thinking skills components and disposition towards critical thinking subscales, such as truth-seeking, open-mindedness, self-confidence, and maturity, compared with the control groups. Our findings suggest that if teachers purposely and persistently practice higher order thinking strategies for example, dealing in class with real-world problems, encouraging open-ended class discussions, and fostering inquiry-oriented experiments, there is a good chance for a consequent development of critical thinking capabilities.
Intelligent control scheme for twin rotor MIMO system

This paper presents a control problem involving an experimental propeller setup that is called the twin rotor multi-input multi-output system (TRMS). The control objective is to make the beam of the TRMS move quickly and accurately to the desired attitudes, both the pitch angle and the azimuth angle in the condition of decoupling between two axes. It is difficult to design a suitable controller because of the influence between the two axes and nonlinear movement. For easy demonstration in the vertical and horizontal separately, the TRMS is decoupled by the main rotor and tail rotor. An intelligent control scheme which utilizes a hybrid PID controller is implemented to this problem. Simulation results show that the new approach to the TRMS control problem can improve the tracking performance and reduce control energy.
Learning to rank search results for time-sensitive queries

Retrieval effectiveness of temporal queries can be improved by taking into account the time dimension. Existing temporal ranking models follow one of two main approaches: 1) a mixture model linearly combining textual similarity and temporal similarity, and 2) a probabilistic model generating a query from the textual and temporal part of document independently. In this paper, we propose a novel time-aware ranking model based on learning-to-rank techniques. We employ two classes of features for learning a ranking model, entity-based and temporal features, which are derived from annotation data. Entity-based features are aimed at capturing the semantic similarity between a query and a document, whereas temporal features measure the temporal similarity. Through extensive experiments we show that our ranking model significantly improves the retrieval effectiveness over existing time-aware ranking models.
A fully end-to-end deep learning approach for real-time simultaneous 3D reconstruction and material recognition

This paper addresses the problem of simultaneous 3D reconstruction and material recognition and segmentation. Enabling robots to recognise different materials (concrete, metal etc.) in a scene is important for many tasks, e.g. robotic interventions in nuclear decommissioning. Previous work on 3D semantic reconstruction has predominantly focused on recognition of everyday domestic objects (tables, chairs etc.), whereas previous work on material recognition has largely been confined to single 2D images without any 3D reconstruction. Meanwhile, most 3D semantic reconstruction methods rely on computationally expensive post-processing, using Fully-Connected Conditional Random Fields (CRFs), to achieve consistent segmentations. In contrast, we propose a deep learning method which performs 3D reconstruction while simultaneously recognising different types of materials and labeling them at the pixel level. Unlike previous methods, we propose a fully end-to-end approach, which does not require hand-crafted features or CRF post-processing. Instead, we use only learned features, and the CRF segmentation constraints are incorporated inside the fully end-to-end learned system. We present the results of experiments, in which we trained our system to perform real-time 3D semantic reconstruction for 23 different materials in a real-world application. The run-time performance of the system can be boosted to around 10Hz, using a conventional GPU, which is enough to achieve realtime semantic reconstruction using a 30fps RGB-D camera. To the best of our knowledge, this work is the first real-time end-to-end system for simultaneous 3D reconstruction and material recognition.
Robotic surgery: a current perspective.

To review the history, development, and current applications of robotics in surgery.



Surgical robotics is a new technology that holds significant promise. Robotic surgery is often heralded as the new revolution, and it is one of the most talked about subjects in surgery today. Up to this point in time, however, the drive to develop and obtain robotic devices has been largely driven by the market. There is no doubt that they will become an important tool in the surgical armamentarium, but the extent of their use is still evolving.



A review of the literature was undertaken using Medline. Articles describing the history and development of surgical robots were identified as were articles reporting data on applications.



Several centers are currently using surgical robots and publishing data. Most of these early studies report that robotic surgery is feasible. There is, however, a paucity of data regarding costs and benefits of robotics versus conventional techniques.



Robotic surgery is still in its infancy and its niche has not yet been well defined. Its current practical uses are mostly confined to smaller surgical procedures.
Outer Product-based Neural Collaborative Filtering

In this work, we contribute a new multi-layer neural network architecture named ONCF to perform collaborative filtering. The idea is to use an outer product to explicitly model the pairwise correlations between the dimensions of the embedding space. In contrast to existing neural recommender models that combine user embedding and item embedding via a simple concatenation or element-wise product, our proposal of using outer product above the embedding layer results in a two-dimensional interaction map that is more expressive and semantically plausible. Above the interaction map obtained by outer product, we propose to employ a convolutional neural network to learn high-order correlations among embedding dimensions. Extensive experiments on two public implicit feedback data demonstrate the effectiveness of our proposed ONCF framework, in particular, the positive effect of using outer product to model the correlations between embedding dimensions in the low level of multi-layer neural recommender model.
The Factor Structure of the System Usability Scale

Since its introduction in 1986, the 10-item System Usability Scale (SUS) has been assumed to be unidimensional. Factor analysis of two independent SUS data sets reveals that the SUS actually has two factors – Usability (8 items) and Learnability (2 items). These new scales have reasonable reliability (coefficient alpha of .91 and .70, respectively). They correlate highly with the overall SUS (r = .985 and .784, respectively) and correlate significantly with one another (r = .664), but at a low enough level to use as separate scales. A sensitivity analysis using data from 19 tests had a significant Test by Scale interaction, providing additional evidence of the differential utility of the new scales. Practitioners can continue to use the current SUS as is, but, at no extra cost, can also take advantage of these new scales to extract additional information from their SUS data.
Towards Editorial Transparency in Computational Journalism

The increasing frequency of use of data and code in journalistic projects drives the need to develop guidelines or frameworks for how to responsibly and accountably employ algorithms and data in acts of journalism. One route to the accountable use of algorithms in journalistic work is to develop standards and expectations for transparency. In this paper we describe steps toward transparency with respect to computational journalism drawing from two case studies. The first case study concerns algorithmic accountability reporting where data collected via the Uber API and government sources were analyzed to understand quality of Uber service across Washington D.C. The second case centers on editorial transparency in the creation of a tool – in this case, a Twitter bot – built as an exploration of automated surfacing of anecdotal comments from news articles. Based on our experiences in these two cases we describe approaches to sharing data and code. The benefits of transparency as well as considerations such as licensing and documentation are discussed.
Background Subtraction Based on Low-Rank and Structured Sparse Decomposition

Low rank and sparse representation based methods, which make few specific assumptions about the background, have recently attracted wide attention in background modeling. With these methods, moving objects in the scene are modeled as pixel-wised sparse outliers. However, in many practical scenarios, the distributions of these moving parts are not truly pixel-wised sparse but structurally sparse. Meanwhile a robust analysis mechanism is required to handle background regions or foreground movements with varying scales. Based on these two observations, we first introduce a class of structured sparsity-inducing norms to model moving objects in videos. In our approach, we regard the observed sequence as being constituted of two terms, a low-rank matrix (background) and a structured sparse outlier matrix (foreground). Next, in virtue of adaptive parameters for dynamic videos, we propose a saliency measurement to dynamically estimate the support of the foreground. Experiments on challenging well known data sets demonstrate that the proposed approach outperforms the state-of-the-art methods and works effectively on a wide range of complex videos.
Introduction to Cryptography - Principles and Applications

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FOREST FIRE RISK ZONE MAPPING FROM SATELLITE IMAGERY AND GIS A CASE STUDY

Forest fires are one of the major natural risk in the Mediterranean countries. In such areas, fires occur frequently and there is a need for supranational approaches that analyse wide scenarios of factors involved and global fire effects. It is impossible to control nature, but is possible to map forest fire risk zone and thereby minimise the frequency of fire. Fire risk must involve both ignition and spreading risk. It is necessary to be able to estimate the spread of fire starting in any stand of a forest, giving the burning conditions. Spreading of forest fire can impose a threat to the natural coverage of land and safety of population. Early detection of forest fires is essential in reduction of fire damage. Satellite data plays a vital role in identifying and mapping forest fire and in recording the frequency at which different vegetation types/zones are effected. A geographic information system (GIS) can be used effectively to combine different forest-fire causing factors for demanding the forest fire risk zone map. A risk model for fire spreading is set up for Gallipoli Peninsula as a pilot zone, because it continually faces a forest fire problem. It is based upon a combination of remote sensing and GIS data. In this study, the LANDSAT satellite images were used. In order to search the effects of the fire occurred in the region on the 25 July 1994, the satellite images both before the fire and after the fire were used. Parameters that effect the fire such as topography and vegetation with the other land use information including population, settlements, forest fire towers, fire stations, intervention places, the characteristics of the staff that will intervene and transportation were integrated within GIS. The shortest way of intervention during the disaster and the areas to be emptied were questioned. For these analyses ARCGIS software was used. Land use information were obtained from the satellite images in this study. In this phase the distinction of spices in the forest was determined using supervised classification. The lands that have priorities in case of fire were decided by combining the moisture of the land and slope classes that were determined by conventional approaches with the satellite images. The results of the analysis were shown by reports and graphics. The test region results should be applied all over Turkey.
Simulating wireless and mobile networks in OMNeT++ the MiXiM vision

Wireless communication has attracted considerable interest in the research community, and many wireless networks are evaluated using discrete event simulators like OMNeT++. Although OMNeT++ provides a powerful and clear simulation framework, it lacks of direct support and a concise modeling chain for wireless communication. Both is provided by MiXiM. MiXiM joins and extends several existing simulation frameworks developed for wireless and mobile simulations in OMNeT++. It provides detailed models of the wireless channel (fading, etc.), wireless connectivity, mobility models, models for obstacles and many communication protocols especially at the Medium Access Control (MAC) level. Further, it provides a user-friendly graphical representation of wireless and mobile networks in OMNeT++, supporting debugging and defining even complex wireless scenarios. Though still in development, MiXiM already is a powerful tool for performance analysis of wireless networks. Its extensive functionality and clear concept may motivate researches to contribute to this open-source project .
PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors

We present PPF-FoldNet for unsupervised learning of 3D local descriptors on pure point cloud geometry. Based on the folding-based auto-encoding of well known point pair features, PPF-FoldNet offers many desirable properties: it necessitates neither supervision, nor a sensitive local reference frame, benefits from point-set sparsity, is end-to-end, fast, and can extract powerful rotation invariant descriptors. Thanks to a novel feature visualization, its evolution can be monitored to provide interpretable insights. Our extensive experiments demonstrate that despite having six degree-of-freedom invariance and lack of training labels, our network achieves state of the art results in standard benchmark datasets and outperforms its competitors when rotations and varying point densities are present. PPF-FoldNet achieves 9% higher recall on standard benchmarks, 23% higher recall when rotations are introduced into the same datasets and finally, a margin of > 35% is attained when point density is significantly decreased.
Bag of contour fragments for robust shape classification

Shape representation is a fundamental problem in computer vision. Current approaches to shape representation mainly focus on designing low-level shape descriptors which are robust to rotation, scaling and deformation of shapes. In this paper, we focus on mid-level modeling of shape representation. We develop a new shape representation called Bag of Contour Fragments (BCF) inspired by classical Bag of Words (BoW) model. In BCF, a shape is decomposed into contour fragments each of which is then individually described using a shape descriptor, e.g., the Shape Context descriptor, and encoded into a shape code. Finally, a compact shape representation is built by pooling shape codes in the shape. Shape classification with BCF only requires an efficient linear SVM classifier. In our experiments, we fully study the characteristics of BCF, show that BCF achieves the state-of-the-art performance on several well-known shape benchmarks, and can be applied to real image classification problem.
A hybrid approach for content extraction with text density and visual importance of DOM nodes

Additional contents in web pages, such as navigation panels, advertisements, copyrights and disclaimer notices, are typically not related to the main subject and may hamper the performance of Web data mining. They are traditionally taken as noises and need to be removed properly. To achieve this, two intuitive and crucial kinds of information—the textual information and the visual information of web pages—is considered in this paper. Accordingly, Text Density and Visual Importance are defined for the Document Object Model (DOM) nodes of a web page. Furthermore, a content extraction method with these measured values is proposed. It is a fast, accurate and general method for extracting content from diverse web pages. And with the employment of DOM nodes, the original structure of the web page can be preserved. Evaluated with the CleanEval benchmark and with randomly selected pages from well-known Web sites, where various web domains and styles are tested, the effect of the method is demonstrated. The average F1-scores with our method were 8.7 % higher than the best scores among several alternative methods.
A printed dipole antenna with tapered slot feed for ultrawide-band applications

In this paper a planar antenna is studied for ultrawide-band (UWB) applications. This antenna consists of a wide-band tapered-slot feeding structure, curved radiators and a parasitic element. It is a modification of the conventional dual exponential tapered slot antenna and can be viewed as a printed dipole antenna with tapered slot feed. The design guideline is introduced, and the antenna parameters including return loss, radiation patterns and gain are investigated. To demonstrate the applicability of the proposed antenna to UWB applications, the transfer functions of a transmitting-receiving system with a pair of identical antennas are measured. Transient waveforms as the transmitting-receiving system being excited by a simulated pulse are discussed at the end of this paper.
A BIO-INSPIRED CONDYLAR KNEE JOINT FOR KNEE PROSTHETICS

This paper presents a novel bio-inspired condylar prosthetic knee joint developed at the University of Bristol. The bio-inspired condylar joint mimics the structure and biomechanics of the human knee joint. The joint contains an inverted parallelogram four-bar mechanism combined with a cam mechanism. The joint has a favourable mechanical advantage compared with a hinge joint. The joint is also compact and robust. An adultsized prototype joint has been designed and tested. The prototype joint contains a long cable for the ligaments with a mechanism for adjusting preload. Compared with other prosthetic joints, the condylar joint has the advantages that it is simple and closely mimics human biomechanics. This paper presents the design of the new artifi cial knee joint and some of the test results. The joint can be used in artifi cial legs and also for knee implants. A rapid prototyping procedure is also presented that enables a custom-sized prosthetic knee joint to be made very quickly and from just a few key dimensions. This process has the potential to improve the quality of surgical implants.
Learning Mutual Visibility Relationship for Pedestrian Detection with a Deep Model

Detecting pedestrians in cluttered scenes is a challenging problem in computer vision. The difficulty is added when several pedestrians overlap in images and occlude each other. We observe, however, that the occlusion/visibility statuses of overlapping pedestrians provide useful mutual relationship for visibility estimation—the visibility estimation of one pedestrian facilitates the visibility estimation of another. In this paper, we propose a mutual visibility deep model that jointly estimates the visibility statuses of overlapping pedestrians. The visibility relationship among pedestrians is learned from the deep model for recognizing co-existing pedestrians. Then the evidence of co-existing pedestrians is used for improving the single pedestrian detection results. Compared with existing image-based pedestrian detection approaches, our approach has the lowest average miss rate on the Caltech-Train dataset and the ETH dataset. Experimental results show that the mutual visibility deep model effectively improves the pedestrian detection results. The mutual visibility deep model leads to 6–15 % improvements on multiple benchmark datasets.
Information Processing and Dynamics in Minimally Cognitive Agents

There has been considerable debate in the literature about the relative merits of information processing versus dynamical approaches to understanding cognitive processes. In this article, we explore the relationship between these two styles of explanation using a model agent evolved to solve a relational categorization task. Specifically, we separately analyze the operation of this agent using the mathematical tools of information theory and dynamical systems theory. Information-theoretic analysis reveals how task-relevant information flows through the system to be combined into a categorization decision. Dynamical analysis reveals the key geometrical and temporal interrelationships underlying the categorization decision. Finally, we propose a framework for directly relating these two different styles of explanation and discuss the possible implications of our analysis for some of the ongoing debates in cognitive science.
Octave Bandwidth Compact Turnstile-Based Orthomode Transducer

A 64% instantaneous bandwidth scalable turnstile-based orthomode transducer to be used in the so-called extended C-band satellite link is presented. The proposed structure overcomes the current practical bandwidth limitations by adding a single-step widening at the junction of the four output rectangular waveguides. This judicious modification, together with the use of reduced-height waveguides and E-plane bends and power combiners, enables to approach the theoretical structure bandwidth limit with a simple, scalable and compact design. The presented orthomode transducer architecture exhibits a return loss better than 25 dB, an isolation between rectangular ports better than 50 dB and a transmission loss less than 0.04 dB in the 3.6-7 GHz range, which represents state-of-the-art achievement in terms of bandwidth.