nils-herrmann/clean_scirepeval huggingface dataset:
Hierarchical Multi-scale Attention Networks for Action Recognition

Recurrent Neural Networks (RNNs) have been widely used in natural language processing and computer vision. Among them, the Hierarchical Multi-scale RNN (HM-RNN), a kind of multi-scale hierarchical RNN proposed recently, can learn the hierarchical temporal structure from data automatically. In this paper, we extend the work to solve the computer vision task of action recognition. However, in sequence-to-sequence models like RNN, it is normally very hard to discover the relationships between inputs and outputs given static inputs. As a solution, attention mechanism could be applied to extract the relevant information from input thus facilitating the modeling of inputoutput relationships. Based on these considerations, we propose a novel attention network, namely Hierarchical Multi-scale Attention Network (HM-AN), by combining the HM-RNN and the attention mechanism and apply it to action recognition. A newly proposed gradient estimation method for stochastic neurons, namely Gumbel-softmax, is exploited to implement the temporal boundary detectors and the stochastic hard attention mechanism. To amealiate the negative effect of sensitive temperature of the Gumbel-softmax, an adaptive temperature training method is applied to better the system performance. The experimental results demonstrate the improved effect of HM-AN over LSTM with attention on the vision task. Through visualization of what have been learnt by the networks, it can be observed that both the attention regions of images and the hierarchical temporal structure can be captured by HM-AN.
K nearest neighbor for text summarization using feature similarity

In this research, we propose a particular version of KNN (K Nearest Neighbor) where the similarity between feature vectors is computed considering the similarity among attributes or features as well as one among values. The task of text summarization is viewed into the binary classification task where each paragraph or sentence is classified into the essence or non-essence, and in previous works, improved results are obtained by the proposed version in the text classification and clustering. In this research, we define the similarity which considers both attributes and attribute values, modify the KNN into the version based on the similarity, and use the modified version as the approach to the text summarization task. As the benefits from this research, we may expect the more compact representation of data items and the better performance. Therefore, the goal of this research is to implement the text summarization algorithm which represents data items more compactly and provides the more reliability.
Quick reference guide to rotary endodontic instruments-A comprehensive review

In the field of endodontics revolution transpired over the years. The modern endodontic specialty practice has little resemblance to the traditional means. There is a lot of transformation, in the materials used and the type of instrumentation. Initially, stainless steel was the material of choice and manual instrumentation was the only means of cleaning the root canals. However, there is a tremendous change in the modern endodontics, due to introduction of NiTi (Nickel Titanium) files and rotary instrumentation. NiTi was developed 40 years ago in the Naval Ordinance Laboratory (NOL) in Silver Springs, Maryland. Therefore, the acronym Nitinol is used worldwide for this unusual type of alloy with better flexibility and fracture resistance. Rotary systems that evolved over years in to different generations differ in the series of instruments included. Hence, it is essential for the beginners to understand the differences among all the available systems and their usage which are highlighted in this review article.
Reproducing and Regularizing the SCRN Model

We reproduce the Structurally Constrained Recurrent Network (SCRN) model, and then regularize it using the existing widespread techniques, such as naïve dropout, variational dropout, and weight tying. We show that when regularized and optimized appropriately the SCRN model can achieve performance comparable with the ubiquitous LSTMmodel in language modeling task on English data, while outperforming it on non-English data. Title and Abstract in Russian Воспроизведение и регуляризация SCRN модели Мы воспроизводим структурно ограниченную рекуррентную сеть (SCRN), а затем добавляем регуляризацию, используя существующие широко распространенные методы, такие как исключение (дропаут), вариационное исключение и связка параметров. Мы показываем, что при правильной регуляризации и оптимизации показатели SCRN сопоставимы с показателями вездесущей LSTM в задаче языкового моделирования на английских текстах, а также превосходят их на неанглийских данных.
Play as You Like: Timbre-enhanced Multi-modal Music Style Transfer

Style transfer of polyphonic music recordings is a challenging task when considering the modeling of diverse, imaginative, and reasonable music pieces in the style different from their original one. To achieve this, learning stable multi-modal representations for both domain-variant (i.e., style) and domaininvariant (i.e., content) information of music in an unsupervised manner is critical. In this paper, we propose an unsupervised music style transfer method without the need for parallel data. Besides, to characterize the multi-modal distribution of music pieces, we employ the Multi-modal Unsupervised Image-to-Image Translation (MUNIT) framework in the proposed system. This allows one to generate diverse outputs from the learned latent distributions representing contents and styles. Moreover, to better capture the granularity of sound, such as the perceptual dimensions of timbre and the nuance in instrument-specific performance, cognitively plausible features including mel-frequency cepstral coefficients (MFCC), spectral difference, and spectral envelope, are combined with the widely-used mel-spectrogram into a timberenhanced multi-channel input representation. The Relativistic average Generative Adversarial Networks (RaGAN) is also utilized to achieve fast convergence and high stability. We conduct experiments on bilateral style transfer tasks among three different genres, namely piano solo, guitar solo, and string quartet. Results demonstrate the advantages of the proposed method in music style transfer with improved sound quality and in allowing users to manipulate the output.
COGNITIVE APPRENTICESHIP IN EDUCATIONAL PRACTICE : RESEARCH ON SCAFFOLDING , MODELING , MENTORING , AND COACHING AS INSTRUCTIONAL STRATEGIES

Apprenticeship is an inherently social learning method with a long history of helping novices become experts in fields as diverse as midwifery, construction, and law. At the center of apprenticeship is the concept of more experienced people assisting less experienced ones, providing structure and examples to support the attainment of goals. Traditionally apprenticeship has been associated with learning in the context of becoming skilled in a trade or craft—a task that typically requires both the acquisition of knowledge, concepts, and perhaps psychomotor skills and the development of the ability to apply the knowledge and skills in a context-appropriate manner—and far predates formal schooling as it is known today. In many nonindustrialized nations apprenticeship remains the predominant method of teaching and learning. However, the overall concept of learning from experts through social interactions is not one that should be relegated to vocational and trade-based training while K–12 and higher educational institutions seek to prepare students for operating in an information-based society. Apprenticeship as a method of teaching and learning is just as relevant within the cognitive and metacognitive domain as it is in the psychomotor domain. In the last 20 years, the recognition and popularity of facilitating learning of all types through social methods have grown tremendously. Educators and educational researchers have looked to informal learning settings, where such methods have been in continuous use, as a basis for creating more formal instructional methods and activities that take advantage of these social constructivist methods. Cognitive apprenticeship— essentially, the use of an apprentice model to support learning in the cognitive domain—is one such method that has gained respect and popularity throughout the 1990s and into the 2000s. Scaffolding, modeling, mentoring, and coaching are all methods of teaching and learning that draw on social constructivist learning theory. As such, they promote learning that occurs through social interactions involving negotiation of content, understanding, and learner needs, and all three generally are considered forms of cognitive apprenticeship (although certainly they are not the only methods). This chapter first explores prevailing definitions and underlying theories of these teaching and learning strategies and then reviews the state of research in these area.
Build Your Own Visual-Inertial Drone: A Cost-Effective and Open-Source Autonomous Drone

This article describes an approach to building a cost-effective and research-grade visual-inertial (VI) odometry-aided vertical takeoff and landing (VTOL) platform. We utilize an off-the-shelf VI sensor, an onboard computer, and a quadrotor platform, all of which are factory calibrated and mass produced, thereby sharing similar hardware and sensor specifications [e.g., mass, dimensions, intrinsic and extrinsic of camera-inertial measurement unit (IMU) systems, and signal-to-noise ratio]. We then perform system calibration and identification, enabling the use of our VI odometry, multisensor fusion (MSF), and model predictive control (MPC) frameworks with off-the-shelf products. This approach partially circumvents the tedious parameter-tuning procedures required to build a full system. The complete system is extensively evaluated both indoors using a motioncapture system and outdoors using a laser tracker while performing hover and step responses and trajectory-following tasks in the presence of external wind disturbances. We achieve root-mean-square (rms) pose errors of 0.036 m with respect to reference hover trajectories. We also conduct relatively long distance (.180 m) experiments on a farm site, demonstrating a 0.82% drift error of the total flight distance. This article conveys the insights we acquired about the platform and sensor module and offers open-source code with tutorial documentation to the community.
A Sizing Methodology of the Synchronous Reluctance Motor for Traction Applications

Synchronous reluctance machines (SynRM) have been used widely in industries for instance, in ABB's new VSD product package based on SynRM technology. It is due to their unique merits such as high efficiency, fast dynamic response, and low cost. However, considering the major requirements for traction applications such as high torque and power density, low torque ripple, wide speed range, proper size, and capability of meeting a specific torque envelope, this machine is still under investigation to be developed for traction applications. Since the choice of motor for traction is generally determined by manufacturers with respect to three dominant factors: cost, weight, and size, the SynRM can be considered a strong alternative due to its high efficiency and lower cost. Hence, the machine's proper size estimation is a major step of the design process before attempting the rotor geometry design. This is crucial in passenger vehicles in which compactness is a requirement and the size and weight are indeed the design limitations. This paper presents a methodology for sizing a SynRM. The electric and magnetic parameters of the proposed machine in conjunction with the core dimensions are calculated. Then, the proposed method's validity and evaluation are done using FE analysis.
Multilingual Named-Entity Recognition from Parallel Corpora

We present a named-entity recognition (NER) system for parallel multilingual text. Our system handles three languages (i.e., English, French, and Spanish) and is tailored to the biomedical domain. For each language, we design a supervised knowledge-based CRF model with rich biomedical and general domain information. We use the sentence alignment of the parallel corpora, the word alignment generated by the GIZA++[8] tool, and Wikipedia-based word alignment in order to transfer system predictions made by individual language models to the remaining parallel languages. We re-train each individual language system using the transferred predictions and generate a final enriched NER model for each language. The enriched system performs better than the initial system based on the predictions transferred from the other language systems. Each language model benefits from the external knowledge extracted from biomedical and general domain resources.
Is anyone looking at me? Direct gaze detection in children with and without autism

Atypical processing of eye contact is one of the significant characteristics of individuals with autism, but the mechanism underlying atypical direct gaze processing is still unclear. This study used a visual search paradigm to examine whether the facial context would affect direct gaze detection in children with autism. Participants were asked to detect target gazes presented among distracters with different gaze directions. The target gazes were either direct gaze or averted gaze, which were either presented alone (Experiment 1) or within facial context (Experiment 2). As with the typically developing children, the children with autism, were faster and more efficient to detect direct gaze than averted gaze, whether or not the eyes were presented alone or within faces. In addition, face inversion distorted efficient direct gaze detection in typically developing children, but not in children with autism. These results suggest that children with autism use featural information to detect direct gaze, whereas typically developing children use configural information to detect direct gaze.
Contextual and Feature-based Models by PolyU Team at the NTCIR-13 STC-2 Task †

The PolyU team participated in the Chinese Short Text Conversation (STC) subtask of the NTCIR-13, the core task of NTCIR-13. At NTCIR-13, generation-based approaches and their evaluations are firstly introduced into the task. This minority report describes our methods to solving the STC problem including four retrieval-based and two generationbased typical approaches. We compare and discuss the official results.
A Survey on Routing in Anonymous Communication Protocols

The Internet has undergone dramatic changes in the past 2 decades and now forms a global communication platform that billions of users rely on for their daily activities. While this transformation has brought tremendous benefits to society, it has also created new threats to online privacy, such as omnipotent governmental surveillance. As a result, public interest in systems for anonymous communication has drastically increased. In this work, we survey previous research on designing, developing, and deploying systems for anonymous communication. Our taxonomy and comparative assessment provide important insights about the differences between the existing classes of anonymous communication protocols.
Application of Tunnel Boring Machines in Underground Mine Development

Because of their demonstrated capabilities in attaining high rates of advance in civil tunnel construction, the hard rock mining industry has always shown a major interest in the use of TBMs for mine development, primarily for development of entries, as well as ventilation, haulage and production drifts. The successful application of TBM technology to mining depends on the selection of the most suitable equipment and cutting tools for the rock and ground conditions to be encountered. In addition to geotechnical investigations and required rock testing, cutterhead design optimization is an integral part of the machine selection to ensure a successful application of the machines in a specific underground mine environment. This paper presents and discusses selected case histories of TBM applications in mining, the lessons learned, the process of laboratory testing together with machine selection and performance estimation methods.
Dual Learning for Machine Translation

While neural machine translation (NMT) is making good progress in the past two years, tens of millions of bilingual sentence pairs are needed for its training. However, human labeling is very costly. To tackle this training data bottleneck, we develop a dual-learning mechanism, which can enable an NMT system to automatically learn from unlabeled data through a dual-learning game. This mechanism is inspired by the following observation: any machine translation task has a dual task, e.g., English-to-French translation (primal) versus French-to-English translation (dual); the primal and dual tasks can form a closed loop, and generate informative feedback signals to train the translation models, even if without the involvement of a human labeler. In the dual-learning mechanism, we use one agent to represent the model for the primal task and the other agent to represent the model for the dual task, then ask them to teach each other through a reinforcement learning process. Based on the feedback signals generated during this process (e.g., the languagemodel likelihood of the output of a model, and the reconstruction error of the original sentence after the primal and dual translations), we can iteratively update the two models until convergence (e.g., using the policy gradient methods). We call the corresponding approach to neural machine translation dual-NMT. Experiments show that dual-NMT works very well on English↔French translation; especially, by learning from monolingual data (with 10% bilingual data for warm start), it achieves a comparable accuracy to NMT trained from the full bilingual data for the French-to-English translation task.
Abstractive Document Summarization with a Graph-Based Attentional Neural Model

Abstractive summarization is the ultimate goal of document summarization research, but previously it is less investigated due to the immaturity of text generation techniques. Recently impressive progress has been made to abstractive sentence summarization using neural models. Unfortunately, attempts on abstractive document summarization are still in a primitive stage, and the evaluation results are worse than extractive methods on benchmark datasets. In this paper, we review the difficulties of neural abstractive document summarization, and propose a novel graph-based attention mechanism in the sequence-to-sequence framework. The intuition is to address the saliency factor of summarization, which has been overlooked by prior works. Experimental results demonstrate our model is able to achieve considerable improvement over previous neural abstractive models. The data-driven neural abstractive method is also competitive with state-of-the-art extractive methods.ive summarization is the ultimate goal of document summarization research, but previously it is less investigated due to the immaturity of text generation techniques. Recently impressive progress has been made to abstractive sentence summarization using neural models. Unfortunately, attempts on abstractive document summarization are still in a primitive stage, and the evaluation results are worse than extractive methods on benchmark datasets. In this paper, we review the difficulties of neural abstractive document summarization, and propose a novel graph-based attention mechanism in the sequence-to-sequence framework. The intuition is to address the saliency factor of summarization, which has been overlooked by prior works. Experimental results demonstrate our model is able to achieve considerable improvement over previous neural abstractive models. The data-driven neural abstractive method is also competitive with state-of-the-art extractive methods.
Swing-up of the double pendulum on a cart by feedforward and feedback control with experimental validation

The swing-up maneuver of the double pendulum on a cart serves to demonstrate a new approach of inversion-based feedforward control design introduced recently. The concept treats the transition task as a nonlinear two-point boundary value problem of the internal dynamics by providing free parameters in the desired output trajectory for the cart position. A feedback control is designed with linear methods to stabilize the swing-up maneuver. The emphasis of the paper is on the experimental realization of the double pendulum swing-up, which reveals the accuracy of the feedforward/feedback control scheme.
Recent Progress of Face Image Synthesis

Face synthesis has been a fascinating yet challenging problem in computer vision and machine learning. Its main research effort is to design algorithms to generate photo-realistic face images via given semantic domain. It has been a crucial prepossessing step of main-stream face recognition approaches and an excellent test of AI ability to use complicated probability distributions. In this paper, we provide a comprehensive review of typical face synthesis works that involve traditional methods as well as advanced deep learning approaches. Particularly, Generative Adversarial Net (GAN) is highlighted to generate photo-realistic and identity preserving results. Furthermore, the public available databases and evaluation metrics are introduced in details. We end the review with discussing unsolved difficulties and promising directions for future research.
Deep Distributed Fusion Network for Air Quality Prediction

Accompanying the rapid urbanization, many developing countries are suffering from serious air pollution problem. The demand for predicting future air quality is becoming increasingly more important to government's policy-making and people's decision making. In this paper, we predict the air quality of next 48 hours for each monitoring station, considering air quality data, meteorology data, and weather forecast data. Based on the domain knowledge about air pollution, we propose a deep neural network (DNN)-based approach (entitled DeepAir), which consists of a spatial transformation component and a deep distributed fusion network. Considering air pollutants' spatial correlations, the former component converts the spatial sparse air quality data into a consistent input to simulate the pollutant sources. The latter network adopts a neural distributed architecture to fuse heterogeneous urban data for simultaneously capturing the factors affecting air quality, e.g. meteorological conditions. We deployed DeepAir in our AirPollutionPrediction system, providing fine-grained air quality forecasts for 300+ Chinese cities every hour. The experimental results on the data from three-year nine Chinese-city demonstrate the advantages of DeepAir beyond 10 baseline methods. Comparing with the previous online approach in AirPollutionPrediction system, we have 2.4%, 12.2%, 63.2% relative accuracy improvements on short-term, long-term and sudden changes prediction, respectively.
Job Satisfaction in Britain : Individual and Job Related Factors

Recently there is a resurgence of interest in the analysis of job satisfaction variables. Job satisfaction is correlated with labor market behavior such as productivity, quits and absenteeism. Recent work examined job satisfaction in relation to various factors. In this paper four different measures of job satisfaction are related to a variety of personal and job characteristics. We use a unique data of 28 240 British employees Workplace Employee Relations Survey (WERS97). Our data set is larger and more recent than in the previous studies. The four measures of job satisfaction considered are satisfaction with influence over job, satisfaction with amount of pay, satisfaction with sense of achievement and satisfaction with respect from supervisors. Although the job satisfaction measures we use are somewhat different than those that are previously used in the literature, a number of results that are commonly obtained with international data are found to hold in our data set as well.