nils-herrmann/clean_scirepeval huggingface dataset:
Self-Regulation in Academic Writing Tasks

This study investigated writing beliefs, self-regulatory behaviors, and epistemology beliefs of preservice teachers in academic writing tasks. Students completed self-report measures of selfregulation, epistemology, and beliefs about writing. Both knowledge and regulation of cognition were positively related to writing enjoyment, and knowledge of cognition was negatively related to beliefs of ability as a fixed entity. Enjoyment of writing was related to learnability and selfassessment. It may be that students who are more self-regulated during writing also believe they can learn to improve their writing skills. It may be, however, that students who believe writing is learnable will exert the effort to self-regulate during writing. Student beliefs and feelings about learning and writing play an important and complex role in their self-regulation behaviors. Suggestions for instruction are included, and continued research of students’ beliefs and selfregulation in naturalistic contexts is recommended.
Smart Initialization Yields Better Convergence Properties in Deep Abstractive Summarization

Abstractive text summarization has been proposed as an alternative to the inherently limited extractive methods, but extant work is plagued with high training times. In this work, we introduce a set of extensions, including novel initialization techniques, that allow contemporary models to achieve 10x faster training time and comparable results. Our work also provides substantial evidence against the accepted evaluation metric for abstractive summarization, and establishes a speed benchmark for further research.ive text summarization has been proposed as an alternative to the inherently limited extractive methods, but extant work is plagued with high training times. In this work, we introduce a set of extensions, including novel initialization techniques, that allow contemporary models to achieve 10x faster training time and comparable results. Our work also provides substantial evidence against the accepted evaluation metric for abstractive summarization, and establishes a speed benchmark for further research.
Self-Integrating Rogowski Coil for High-Impulse Current Measurement

This paper presents the operating principle, the performance under impulse condition, and the design of a self-integrating Rogowski coil for measuring high-impulse currents. Oscillatory and overdamped unidirectional impulse currents are generated up to 10 kA and measured by different methods, namely, two commercial impulse current transformers, a resistive shunt, and the newly designed self-integrating Rogowski coil. For the resistive method, the voltage drop across the shunt affects the total voltage measured across the test object and causes distortion in the case of simultaneous measurement by an impulse current transformer having a small aperture. The Rogowski coil theory under impulse current conditions is presented. The concept of transmission lines is used, and the line parameters are optimized to achieve a self-integrating mode and avoid spurious effects of reflections and stray capacitances. The output voltage linearity of the designed self-integrating Rogowski coil is checked using different linear and nonlinear loads and coil termination resistances. It is found that at a termination resistance of 1 ¿, satisfactory impulse current waveforms are measured by taking the commercial impulse current transformer as a reference signal. Results reveal that the measurement errors for the current peak and front and tail times are ±2%, ¿6%, and ¿ 12%, respectively. Overdamped impulse currents are generated by different generator capacitances, where comparisons between the measured and calculated current waveform parameters have shown good agreement.
Training recurrent networks using the extended Kalman filter

The extended Kalman lter (EKF) can be used as an on-line algorithm to determine the weights in a recurrent network given target outputs as it runs. This paper notes some relationships between the EKF as applied to recurrent net learning and some simpler techniques that are more widely used. In particular, making certain simpliications to the EKF gives rise to an algorithm essentially identical to the real-time recurrent learning (RTRL) algorithm. Since the EKF involves adjusting unit activity in the network, it also provides a principled generalization of the teacher forcing technique. Prelinary simulation experiments on simple nite-state Boolean tasks indicate that the EKF can provide substantial speed-up in number of time steps required for training on such problems when compared with simpler on-line gradient algorithms. The computational requirements of the EKF are steep, but turn out to scale with network size at the same rate as RTRL. These observations are intended to provide insights that may lead to recurrent net training techniques that allow better control over the tradeoo between computational cost and convergence time.
IRRIGATION WITH MAGNETIZED WATER , A NOVEL TOOL FOR IMPROVING CROP PRODUCTION IN EGYPT

Agricultural sciences take an interest not only in the common and valued crop-forming factors, but also in those less expensive, safe environmentally and generally underestimated. The technology of magnetic water has been developed and subsequently used widely in the field of agriculture in many countries such as Australia, USA, China and Japan. Desperate its importance, it is not yet explored in Egypt. Therefore, the present work was carried out to study the response of some food crops using magnetized water for irrigation purpose under green house condition. Monocotyledonous such as wheat and flax and dicotyledonous such as chickpea and lentil plants were selected for the present study. Based on the results of our experiments revealed all the plants which were irrigated with magnetic water exhibited a remarkable increase in vegetative growth and biochemical constitutions. Further the results indicated that the number of protein bands got increased in plants treated with magnetized water when compared to untreated control plants. Moreover, the magnetized water treatment increased yield and yield component traits of all crops. Over two seasons, the increases in seeds yield/plant in monocotyledonous crops reached to 9.10% and 31.3% for flax and wheat, respectively and in dicotyledonous crops reached to 24.9% and 38.5%, for lentil and check pea, respectively compared with crops irrigated with tap water. It appears that the preliminary study on utilization of magnetized water can led to improve quantity and quality of crop production under Egyptian condition.
Sistema de monitoreo remoto de sensores de temperatura y corriente usando una red hibrida bus CAN-Zigbee

Resumen. Este trabajo trata sobre un sistema de monitoreo remoto a través de una pantalla inteligente para sensores de temperatura y corriente utilizando una red híbrida CAN−ZIGBEE. El CAN bus es usado como medio de transmisión de datos a corta distancia mientras que Zigbee es empleado para que cada nodo de la red pueda interactuar de manera inalámbrica con el nodo principal. De esta manera la red híbrida combina las ventajas de cada protocolo de comunicación para intercambiar datos. El sistema cuenta con cuatro nodos, dos son CAN y reciben la información de los sensores y el resto son Zigbee. Estos nodos están a cargo de transmitir la información de un nodo CAN de manera inalámbrica y desplegarla en una pantalla inteligente.
Broadband and planar microstrip-to-waveguide transitions in millimeter-wave band

Broadband and planar microstrip-to-waveguide transitions are developed in the millimeter-wave band. Novel printed pattern is applied to the microstrip substrate in the ordinary back-short-type transition to operate over extremely broad frequency bandwidth. Furthermore, in order to realize flat and planar transition which does not need back-short waveguide, the transition is designed in multi-layer substrate. Both transitions are fabricated and their performances are measured and simulated in the millimeter-wave band.
High performance concrete with steel slag aggregate

The effect of the steel slag aggregate aging on mechanical properties of the high performance concrete is analysed in the paper. The effect of different aging periods of steel slag aggregate on mechanical properties of high performance concrete is studied. It was observed that properties of this concrete are affected by the steel slag aggregate aging process. The compressive strength increases with an increase in the aging period of steel slag aggregate. The flexural strength, Young’s modulus, and impact strength of concrete, increase at the rate similar to that of the compressive strength. The workability and the abrasion loss of concrete decrease with an increase of the steel slag aggregate aging period.
Go-Explore: a New Approach for Hard-Exploration Problems

A grand challenge in reinforcement learning is intelligent exploration, especially when rewards are sparse or deceptive. Two Atari games serve as benchmarks for such hard-exploration domains: Montezuma’s Revenge and Pitfall. On both games, current RL algorithms perform poorly, even those with intrinsic motivation, which is the dominant method to encourage exploration and improve performance on hardexploration domains. To address this shortfall, we introduce a new algorithm called Go-Explore. It exploits the following principles: (1) remember states that have previously been visited, (2) first return to a promising state (without exploration), then explore from it, and (3) solve simulated environments through exploiting any available means (including by introducing determinism), then robustify (create a policy that can reliably perform the solution) via imitation learning. The combined effect of these principles generates dramatic performance improvements on hardexploration problems. On Montezuma’s Revenge, without being provided any domain knowledge, Go-Explore scores over 43,000 points, almost 4 times the previous state of the art. Go-Explore can also easily harness human-provided domain knowledge, and when augmented with it Go-Explore scores a mean of over 650,000 points on Montezuma’s Revenge. Its max performance of nearly 18 million surpasses the human world record by an order of magnitude, thus meeting even the strictest definition of “superhuman” performance. On Pitfall, Go-Explore with domain knowledge is the first algorithm to score above zero. Its mean performance of almost 60,000 points also exceeds expert human performance. Because GoExplore can produce many high-performing demonstrations automatically and cheaply, it also outperforms previous imitation learning work in which the solution was provided in the form of a human demonstration. Go-Explore opens up many new research directions into improving it and weaving its insights into current RL algorithms. It may also enable progress on previously unsolvable hard-exploration problems in a variety of domains, especially the many that often harness a simulator during training (e.g. robotics).
A new 16-bit high speed and variable stage carry skip adder

Adders are basic integral part of arithmetic circuits. The adders have been realized with two styles: fixed stage size and variable stage size. In this paper, fixed stage and variable stage carry skip adder configurations have been analyzed and then a new 16-bit high speed variable stage carry skip adder is proposed by modifying the existing structure. The proposed adder has seven stages where first and last stage are of 1 bit each, it keeps increasing steadily till the middle stage which is the bulkiest and hence is the nucleus stage. The delay and power consumption in the proposed adder is reduced by 61.75% and 8% respectively. The proposed adder is implemented and simulated using 90 nm CMOS technology in Cadence Virtuoso. It is pertinent to mention that the delay improvement in the proposed adder has been achieved without increase in any power consumption and circuit complexity. The adder proposed in this work is suitable for high speed and low power VLSI based arithmetic circuits.
Globalization and English in Chinese higher education

This paper sets China’s education of English majors within the changing global and national context. It examines the impact of accelerating globalisation and the rise of global English, the adjustment of China’s English language policy, the growth of the education of English majors and the challenges faced by this sector of education. To adapt to the changes, efforts have been made to change the training models, revise the national curriculum and update textbooks. The introduction of six new training models is significant: “English major plus courses in other specialisms”, “English major plus an orientation towards other disciplines”, “English major plus a minor”, “A major plus English language”, “English language plus another foreign language”, and “Dual degree: BA degree of English language and literature plus another BA degree”. Turning out ‘composite-type’ graduates has become a training objective of the curriculum for English majors, with consequent implications for the future development of this sector of education in China.
Has Marijuana Legalization Increased Marijuana Use Among US Youth?

Since 2012, citizens in Alaska, Colorado, Oregon, and Washington have voted to legalize the recreational use of marijuana by adults. Advocates of legalization have argued that prohibition wastes scarce law enforcement resources by selectively arresting minority users of a drug that has fewer adverse health effects than alcohol.1,2 It would be better, they argue, to legalize, regulate, and tax marijuana, like  Opponents of legalization argue that it will increase marijuana use among youth because it will make marijuana more available at a cheaper price and reduce the perceived risks of its use.4 Cerdá et al5 have assessed these concerns by examining the effects of marijuana legalization in Colorado and Washington on attitudes toward marijuana and reported marijuana use among young people. They used surveys from Monitoring the Future between 2010 and 2015 to examine changes in the perceived risks of occasional marijuana use and self-reported marijuana use in the last 30 days among students in eighth, 10th, and 12th grades in Colorado and Washington before and after legalization. They compared these changes with changes among students in states in the contiguous United States that had not legalized marijuana (excluding Oregon, which legalized in 2014). The perceived risks of using marijuana declined in all states, but there was a larger decline in perceived risks and a larger increase in marijuana use in the past 30 days among eighth and 10th graders from Washington than among students from other states. They did not find any such differences between students in Colorado and students in other US states that had not legalized, nor did they find any of these changes in 12th graders in Colorado or Washington. If the changes observed in Washington are attributable to legalization, why were there no changes found in Colorado? The authors suggest that this may have been because Colorado’s medical marijuana laws were much more liberal before legalization than those in Washington. After 2009, Colorado permitted medical marijuana to be supplied through for-profit dispensaries and allowed advertising of medical marijuana products. This hypothesisissupportedbyotherevidencethattheperceivedrisks of marijuana use decreased and marijuana use increased among young people in Colorado after these changes in 2009.6
Read + Verify: Machine Reading Comprehension with Unanswerable Questions

Machine reading comprehension with unanswerable questions aims to abstain from answering when no answer can be inferred. In addition to extract answers, previous works usually predict an additional “no-answer” probability to detect unanswerable cases. However, they fail to validate the answerability of the question by verifying the legitimacy of the predicted answer. To address this problem, we propose a novel read-then-verify system, which not only utilizes a neural reader to extract candidate answers and produce noanswer probabilities, but also leverages an answer verifier to decide whether the predicted answer is entailed by the input snippets. Moreover, we introduce two auxiliary losses to help the reader better handle answer extraction as well as noanswer detection, and investigate three different architectures for the answer verifier. Our experiments on the SQuAD 2.0 dataset show that our system obtains a score of 74.2 F1 on test set, achieving state-of-the-art results at the time of submission (Aug. 28th, 2018).
Deep Projective 3D Semantic Segmentation

Semantic segmentation of 3D point clouds is a challenging problem with numerous real-world applications. While deep learning has revolutionized the field of image semantic segmentation, its impact on point cloud data has been limited so far. Recent attempts, based on 3D deep learning approaches (3DCNNs), have achieved below-expected results. Such methods require voxelizations of the underlying point cloud data, leading to decreased spatial resolution and increased memory consumption. Additionally, 3D-CNNs greatly suffer from the limited availability of annotated datasets. In this paper, we propose an alternative framework that avoids the limitations of 3D-CNNs. Instead of directly solving the problem in 3D, we first project the point cloud onto a set of synthetic 2D-images. These images are then used as input to a 2D-CNN, designed for semantic segmentation. Finally, the obtained prediction scores are re-projected to the point cloud to obtain the segmentation results. We further investigate the impact of multiple modalities, such as color, depth and surface normals, in a multi-stream network architecture. Experiments are performed on the recent Semantic3D dataset. Our approach sets a new stateof-the-art by achieving a relative gain of 7.9%, compared to the previous best approach.
Information centric services in Smart Cities

A “Smart City” is intended as an urban environment which, supported by pervasive ICT systems, is able to offer advanced and innovative services to citizens in order to improve the overall quality of their life. In this context, the present contribution formulates a pioneering proposal, by drawing an advanced information centric platform for supporting the typical ICT services of a Smart City. It can easily embrace all available and upcoming wireless technologies, while enforcing, at the same time, ubiquitous and secure applications in many domains, such as,e-government and public administration, intelligent transportation systems, public safety, social, health-care, educational, building and urban planning, environmental, and energy and water management applications. All the details of the proposed approach have been carefully described by means of pragmatical use-cases, such as the management of administrative procedures, the starting of a new business in a given country, the navigation assistance, the signaling of an urban accident aimed at improving the public safety, the reservation of a medical examination, the remote assistance of patients, and the management of waste in a city. This description makes evident the real effectiveness of the present proposal in future urban environments.
Learning to Reweight Examples for Robust Deep Learning

Deep neural networks have been shown to be very powerful modeling tools for many supervised learning tasks involving complex input patterns. However, they can also easily overfit to training set biases and label noises. In addition to various regularizers, example reweighting algorithms are popular solutions to these problems, but they require careful tuning of additional hyperparameters, such as example mining schedules and regularization hyperparameters. In contrast to past reweighting methods, which typically consist of functions of the cost value of each example, in this work we propose a novel meta-learning algorithm that learns to assign weights to training examples based on their gradient directions. To determine the example weights, our method performs a meta gradient descent step on the current mini-batch example weights (which are initialized from zero) to minimize the loss on a clean unbiased validation set. Our proposed method can be easily implemented on any type of deep network, does not require any additional hyperparameter tuning, and achieves impressive performance on class imbalance and corrupted label problems where only a small amount of clean validation data is available.
Motion signal processing

Techniques from the image and signal processing domain can be successfully applied to designing, modifying, and adapting animated motion. For this purpose, we introduce multiresolution motion filtering, multitarget motion interpolation with dynamic timewarping, waveshaping and motion displacement mapping. The techniques are well-suited for reuse and adaptation of existing motion data such as joint angles, joint coordinates or higher level motion parameters of articulated figures with many degrees of freedom. Existing motions can be modified and combined interactively and at a higher level of abstraction than conventional systems support. This general approach is thus complementary to keyframing, motion capture, and procedural animation.
Multi-Modal Knowledge Representation Learning via Webly-Supervised Relationships Mining

Knowledge representation learning (KRL) encodes enormous structured information with entities and relations into a continuous low-dimensional semantic space. Most conventional methods solely focus on learning knowledge representation from single modality, yet neglect the complementary information from others. The more and more rich available multi-modal data on Internet also drive us to explore a novel approach for KRL in multi-modal way, and overcome the limitations of previous single-modal based methods. This paper proposes a novel multi-modal knowledge representation learning (MM-KRL) framework which attempts to handle knowledge from both textual and visual modal web data. It consists of two stages, i.e., webly-supervised multi-modal relationship mining, and bi-enhanced cross-modal knowledge representation learning. Compared with existing knowledge representation methods, our framework has several advantages: (1) It can effectively mine multi-modal knowledge with structured textual and visual relationships from web automatically. (2) It is able to learn a common knowledge space which is independent to both task and modality by the proposed Bi-enhanced Cross-modal Deep Neural Network (BC-DNN). (3) It has the ability to represent unseen multi-modal relationships by transferring the learned knowledge with isolated seen entities and relations into unseen relationships. We build a large-scale multi-modal relationship dataset (MMR-D) and the experimental results show that our framework achieves excellent performance in zero-shot multi-modal retrieval and visual relationship recognition.
The Creative Benefits Scale: Connecting Generativity to Life Satisfaction.

Long-term participation in creative activities has benefits for middle-aged and older people that may improve their adaptation to later life. We first investigated the factor structure of the Creative Benefits Scale and then used it to construct a model to help explain the connection between generativity and life satisfaction in adults who participated in creative hobbies. Participants included 546 adults between the ages of 40 and 88 (Mean = 58.30 years) who completed measures of life satisfaction, generativity, and the Creative Benefits Scale with its factors of Identity, Calming, Spirituality, and Recognition. Structural equation modeling was used to examine the connection of age with life satisfaction in older adults and to explore the effects of creativity on this relation. The proposed model of life satisfaction, incorporating age, creativity, and generativity, fit the data well, indicating that creativity may help explain the link between the generativity and life satisfaction.