nils-herrmann/clean_scirepeval huggingface dataset:
GeoWeb Crawler: An Extensible and Scalable Web Crawling Framework for Discovering Geospatial Web Resources

With the advance of the World-Wide Web (WWW) technology, people can easily share content on the Web, including geospatial data and web services. Thus, the “big geospatial data management” issues start attracting attention. Among the big geospatial data issues, this research focuses on discovering distributed geospatial resources. As resources are scattered on the WWW, users cannot find resources of their interests efficiently. While the WWW has Web search engines addressing web resource discovery issues, we envision that the geospatial Web (i.e., GeoWeb) also requires GeoWeb search engines. To realize a GeoWeb search engine, one of the first steps is to proactively discover GeoWeb resources on the WWW. Hence, in this study, we propose the GeoWeb Crawler, an extensible Web crawling framework that can find various types of GeoWeb resources, such as Open Geospatial Consortium (OGC) web services, Keyhole Markup Language (KML) and Environmental Systems Research Institute, Inc (ESRI) Shapefiles. In addition, we apply the distributed computing concept to promote the performance of the GeoWeb Crawler. The result shows that for 10 targeted resources types, the GeoWeb Crawler discovered 7351 geospatial services and 194,003 datasets. As a result, the proposed GeoWeb Crawler framework is proven to be extensible and scalable to provide a comprehensive index of GeoWeb.
Investigating Backtranslation in Neural Machine Translation

A prerequisite for training corpus-based machine translation (MT) systems – either Statistical MT (SMT) or Neural MT (NMT) – is the availability of high-quality parallel data. This is arguably more important today than ever before, as NMT has been shown in many studies to outperform SMT, but mostly when large parallel corpora are available; in cases where data is limited, SMT can still outperform NMT. Recently researchers have shown that back-translating monolingual data can be used to create synthetic parallel corpora, which in turn can be used in combination with authentic parallel data to train a highquality NMT system. Given that large collections of new parallel text become available only quite rarely, backtranslation has become the norm when building state-of-the-art NMT systems, especially in resource-poor scenarios. However, we assert that there are many unknown factors regarding the actual effects of back-translated data on the translation capabilities of an NMT model. Accordingly, in this work we investigate how using back-translated data as a training corpus – both as a separate standalone dataset as well as combined with human-generated parallel data – affects the performance of an NMT model. We use incrementally larger amounts of back-translated data to train a range of NMT systems for Germanc
Experimental Evaluation of Autonomous Driving Based on Visual Memory and Image-Based Visual Servoing

In this paper, the performance of a topological-metric visual-path-following framework is investigated in different environments. The framework relies on a monocular camera as the only sensing modality. The path is represented as a series of reference images such that each neighboring pair contains a number of common landmarks. Local 3-D geometries are reconstructed between the neighboring reference images to achieve fast feature prediction. This condition allows recovery from tracking failures. During navigation, the robot is controlled using image-based visual servoing. The focus of this paper is on the results from a number of experiments that were conducted in different environments, lighting conditions, and seasons. The experiments with a robot car show that the framework is robust to moving objects and moderate illumination changes. It is also shown that the system is capable of online path learning.
Deformable Stacked Structure for Named Entity Recognition

Neural architecture for named entity recognition has achieved great success in the field of natural language processing. Currently, the dominating architecture consists of a bidirectional recurrent neural network (RNN) as the encoder and a conditional random field (CRF) as the decoder. In this paper, we propose a deformable stacked structure for named entity recognition, in which the connections between two adjacent layers are dynamically established. We evaluate the deformable stacked structure by adapting it to different layers. Our model achieves the state-of-the-art performances on the OntoNotes dataset.
The NLM Medical Text Indexer System for Indexing Biomedical Literature

In the face of a growing workload and dwindling resources, the US National Library of Medicine (NLM) created the Indexing Initiative project in the mid-1990s. This cross-library team’s mission is to explore indexing methodologies that can help ensure that MEDLINE and other NLM document collections maintain their quality and currency and thereby contribute to NLM’s mission of maintaining quality access to the biomedical literature. The NLM Medical Text Indexer (MTI) is the main product of this project and has been providing indexing recommendations based on the Medical Subject Headings (MeSH) vocabulary since 2002. In 2011, NLM expanded MTI’s role by designating it as the first-line indexer (MTIFL) for a few journals; today the MTIFL workflow includes about 100 journals and continues to increase. Due to a close collaboration with the Index Section at NLM, MTI continues to grow and expand its ability to provide assistance to the indexers. This paper provides an overview of MTI’s functionality, performance, and its evolution over the years.
Generate Believable Causal Plots with User Preferences Using Constrained Monte Carlo Tree Search

We construct a large scale of causal knowledge in term of Fabula elements by extracting causal links from existing common sense ontology ConceptNet5. We design a Constrained Monte Carlo Tree Search (cMCTS) algorithm that allows users to specify positive and negative concepts to appear in the generated stories. cMCTS can find a believable causal story plot. We show the merits by experiments and discuss the remedy strategies in cMCTS that may generate incoherent causal plots.  Fabula elements, causal story plots, constrained Monte Carlo Tree Search, user preference, believable story generation
GraphRNN: A Deep Generative Model for Graphs

Modeling and generating graphs is fundamental for studying networks in biology, engineering, and social sciences. However, modeling complex distributions over graphs and then efficiently sampling from these distributions is challenging due to the non-unique, high-dimensional nature of graphs and the complex, non-local dependencies that exist between edges in a given graph. Here we propose GraphRNN, a deep autoregressive model that addresses the above challenges and approximates any distribution of graphs with minimal assumptions about their structure. GraphRNN learns to generate graphs by training on a representative set of graphs and decomposes the graph generation process into a sequence of node and edge formations, conditioned on the graph structure generated so far. In order to quantitatively evaluate the performance of GraphRNN, we introduce a benchmark suite of datasets, baselines and novel evaluation metrics based on Maximum Mean Discrepancy, which measure distances between sets of graphs. Our experiments show that GraphRNN significantly outperforms all baselines, learning to generate diverse graphs that match the structural characteristics of a target set, while also scaling to graphs 50× larger than previous deep models.
Anomaly detection of spectrum in wireless communication via deep auto-encoders

Anomaly detection is a typical task in many fields, as well as spectrum monitoring in wireless communication. Anomaly detection task of spectrum in wireless communication is quite different from other anomaly detection tasks, mainly reflected in two aspects: (a) the variety of anomaly types makes it impossible to get the label of abnormal data. (b) the complexity and the quantity of the electromagnetic environment data increase the difficulty of manual feature extraction. Therefore, a novelty learning model is expected to deal with the task of anomaly detection of spectrum in wireless communication. In this paper, we apply the deep-structure auto-encoder neural networks to detect the anomalies of spectrum, and the time–frequency diagram is acted as the feature of the learning model. Meanwhile, a threshold is used to distinguish the anomalies from the normal data. Finally, we evaluate the performance of our models with different number of hidden layers by our experiments. The results of numerical experiments demonstrate that a model with a deeper architecture achieves relatively better performance in our spectrum anomaly detection task.
Modeling Local Geometric Structure of 3D Point Clouds using Geo-CNN

Recent advances in deep convolutional neural networks (CNNs) have motivated researchers to adapt CNNs to directly model points in 3D point clouds. Modeling local structure has been proven to be important for the success of convolutional architectures, and researchers exploited the modeling of local point sets in the feature extraction hierarchy. However, limited attention has been paid to explicitly model the geometric structure amongst points in a local region. To address this problem, we propose GeoCNN, which applies a generic convolution-like operation dubbed as GeoConv to each point and its local neighborhood. Local geometric relationships among points are captured when extracting edge features between the center and its neighboring points. We first decompose the edge feature extraction process onto three orthogonal bases, and then aggregate the extracted features based on the angles between the edge vector and the bases. This encourages the network to preserve the geometric structure in Euclidean space throughout the feature extraction hierarchy. GeoConv is a generic and efficient operation that can be easily integrated into 3D point cloud analysis pipelines for multiple applications. We evaluate Geo-CNN on ModelNet40 and KITTI and achieve state-of-the-art performance.
Comparison of ECC and RSA Algorithm in Resource Constrained Devices

Nowadays, the usage of resource constrained devices is increasing and these devices are primarily working with sensitive data. Consequently, data security has become crucial for both producers and users. Limitation of resources is deemed as the major issue that makes these devices vulnerable. Attackers might exploit these limitations to get access to the valuable data. Therefore, carefully chosen and practically tested encryption algorithm must be applied to increase the device efficiency and mitigate the risk of sensitive data loss. This study will compare elliptic curve cryptography (ECC) algorithm with Key size of 160-bit and Rivest-Shamir-Adleman (RSA) algorithm with Key size of 1024-bit. As a result of this study utilizing ECC in resource constrained devices has advantages over RSA but ECC needs continues enhancement to satisfy the limitations of newly designed chips.
Understanding success and failure in customer relationship management

Customer Relationship Management (CRM) systems can help organizations manage customer interactions more effectively. Like many new technologies, CRM has been accompanied by vendor hype and stories of implementation failure. Work on critical success factors (CSFs) should encourage more appropriate implementation practice; however many CSF studies conclude with a list of factors but provide little further guidance. In particular, there is a need for stronger theoretical models of the entire CRM innovation process which can be used by managers to understand better the underlying causes of success and failure. This paper adopts a novel approach to this problem by firstly developing a conceptual model of CRM innovation and then converting this model into a dynamic simulation model. Some early simulation results illustrating changes in CRM benefits and organizational support over time are presented together with a discussion of the underlying causes and suggestions for how managers can counteract potential innovation failure.
Practical Tooling for Serverless Computing

Cloud applications are increasingly built from a mixture of runtime technologies. Hosted functions and service-oriented web hooks are among the most recent ones which are natively supported by cloud platforms. They are collectively referred to as serverless computing by application engineers due to the transparent on-demand instance activation and microbilling without the need to provision infrastructure explicitly. This half-day tutorial explains the use cases for serverless computing and the drivers and existing software solutions behind the programming and deployment model also known as Function-as-a-Service in the overall cloud computing stack. Furthermore, it presents practical open source tools for deriving functions from legacy code and for the management and execution of functions in private and public clouds.
Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits

We present a new algorithm for the contextual bandit learning problem, where the learner repeatedly takes an action in response to the observed context, observing the reward only for that action. Our method assumes access to an oracle for solving cost-sensitive classification problems and achieves the statistically optimal regret guarantee with only Õ( √ T ) oracle calls across all T rounds. By doing so, we obtain the most practical contextual bandit learning algorithm amongst approaches that work for general policy classes. We further conduct a proof-of-concept experiment which demonstrates the excellent computational and prediction performance of (an online variant of) our algorithm relative to several baselines.
Pedagogies of Engagement : Classroom-Based Practices

Educators, researchers, and policy makers have advocated student involvement for some time as an essential aspect of meaningful learning. In the past twenty years engineering educators have implemented several means of better engaging their undergraduate students, including active and cooperative learning, learning communities, service learning, cooperative education, inquiry and problem-based learning, and team projects. This paper focuses on classroom-based pedagogies of engagement, particularly cooperative and problem-based learning. It includes a brief history, theoretical roots, research support, summary of practices, and suggestions for redesigning engineering classes and programs to include more student engagement. The paper also lays out the research ahead for advancing pedagogies aimed at more fully enhancing students’ involvement in their learning.
Neuroblastoma: developmental biology, cancer genomics and immunotherapy

Neuroblastoma is a solid tumour that arises from the developing sympathetic nervous system. Over the past decade, our understanding of this disease has advanced tremendously. The future challenge is to apply the knowledge gained to developing risk-based therapies and, ultimately, improving outcome. In this Review we discuss the key discoveries in the developmental biology, molecular genetics and immunology of neuroblastoma, as well as new translational tools for bringing these promising scientific advances into the clinic.
Adversarial Example Generation with Syntactically Controlled Paraphrase Networks

We propose syntactically controlled paraphrase networks (SCPNs) and use them to generate adversarial examples. Given a sentence and a target syntactic form (e.g., a constituency parse), SCPNs are trained to produce a paraphrase of the sentence with the desired syntax. We show it is possible to create training data for this task by first doing backtranslation at a very large scale, and then using a parser to label the syntactic transformations that naturally occur during this process. Such data allows us to train a neural encoderdecoder model with extra inputs to specify the target syntax. A combination of automated and human evaluations show that SCPNs generate paraphrases that follow their target specifications without decreasing paraphrase quality when compared to baseline (uncontrolled) paraphrase systems. Furthermore, they are more capable of generating syntactically adversarial examples that both (1) “fool” pretrained models and (2) improve the robustness of these models to syntactic variation when used to augment their training data.
MDA : A Formal Approach to Game Design and Game Research

In this paper we present the MDA framework (standing for Mechanics, Dynamics, and Aesthetics), developed and taught as part of the Game Design and Tuning Workshop at the Game Developers Conference, San Jose 2001-2004. MDA is a formal approach to understanding games  one which attempts to bridge the gap between game design and development, game criticism, and technical game research. We believe this methodology will clarify and strengthen the iterative processes of developers, scholars and researchers alike, making it easier for all parties to decompose, study and design a broad class of game designs and game artifacts.
Machine Learning the Cryptocurrency Market

Machine learning and AI-assisted trading have attracted growing interest for the past few years. Here, we use this approach to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. We analyse daily data for 1, 681 cryptocurrencies for the period between Nov. 2015 and Apr. 2018. We show that simple trading strategies assisted by state-of-the-art machine learning algorithms outperform standard benchmarks. Our results show that non-trivial, but ultimately simple, algorithmic mechanisms can help anticipate the short-term evolution of the cryptocurrency market.
A linear time algorithm for 7-[3]coloring triangle-free hexagonal graphs

Given a graph G, a proper n − [p]-coloring is a mapping f : V (G) → 2{1,...,n} such that |f(v)| = p for any vertex v ∈ V (G) and f(v) ∩ f(u) = ∅ for any pair of adjacent vertices u and v. n − [p]-coloring is closely related to multicoloring. Finding multicoloring of induced subgraphs of the triangular lattice (called hexagonal graphs) has important applications in cellular networks. In this article we provide an algorithm to find a 7-[3]-coloring of triangle-free hexagonal graphs in linear time, which solves the open problem stated in  and improves the result of Sudeep and Vishwanathan [11], who proved the existence of a 14-[6]coloring. ∗This work was supported by grant N206 017 32/2452 for years 2007-2010
Study strategies of college students: are self-testing and scheduling related to achievement?

Previous studies, such as those by Kornell and Bjork (Psychonomic Bulletin & Review, 14:219-224, 2007) and Karpicke, Butler, and Roediger (Memory, 17:471-479, 2009), have surveyed college students' use of various study strategies, including self-testing and rereading. These studies have documented that some students do use self-testing (but largely for monitoring memory) and rereading, but the researchers did not assess whether individual differences in strategy use were related to student achievement. Thus, we surveyed 324 undergraduates about their study habits as well as their college grade point average (GPA). Importantly, the survey included questions about self-testing, scheduling one's study, and a checklist of strategies commonly used by students or recommended by cognitive research. Use of self-testing and rereading were both positively associated with GPA. Scheduling of study time was also an important factor: Low performers were more likely to engage in late-night studying than were high performers; massing (vs. spacing) of study was associated with the use of fewer study strategies overall; and all students-but especially low performers-were driven by impending deadlines. Thus, self-testing, rereading, and scheduling of study play important roles in real-world student achievement.
Multiway Attention Networks for Modeling Sentence Pairs

Modeling sentence pairs plays the vital role for judging the relationship between two sentences, such as paraphrase identification, natural language inference, and answer sentence selection. Previous work achieves very promising results using neural networks with attention mechanism. In this paper, we propose the multiway attention networks which employ multiple attention functions to match sentence pairs under the matching-aggregation framework. Specifically, we design four attention functions to match words in corresponding sentences. Then, we aggregate the matching information from each function, and combine the information from all functions to obtain the final representation. Experimental results demonstrate that the proposed multiway attention networks improve the result on the Quora Question Pairs, SNLI, MultiNLI, and answer sentence selection task on the SQuAD dataset.
Wireless charging system for electric vehicle

The paper overviews novel technique for wireless charging system of electric vehicle in which verifies the developed theory using battery charger application of electric vehicle. In electric vehicle charging of battery through charger and wire is inconvenient, hazardous and expensive. The existing gasoline and petrol engine technology vehicles are responsible for air, noise pollution as well as for greenhouse gases. The implemented wireless charging system of battery for Electric vehicle by inductive coupling method has been presented in this paper. The driving circuit is used between the transmitter coil & receiver coil where MOSFET is used for switching operation. The transmitter coil circuit is turn ON and OFF whenever the vehicle is present and absent respectively. The system is achieves 67% efficiency level while providing safety, reliability, low maintenance and long product life.
Real-Time Object Pose Estimation with Pose Interpreter Networks

In this work, we introduce pose interpreter networks for 6-DoF object pose estimation. In contrast to other CNN-based approaches to pose estimation that require expensively annotated object pose data, our pose interpreter network is trained entirely on synthetic pose data. We use object masks as an intermediate representation to bridge real and synthetic. We show that when combined with a segmentation model trained on RGB images, our synthetically trained pose interpreter network is able to generalize to real data. Our end-to-end system for object pose estimation runs in real-time (20 Hz) on live RGB data, without using depth information or ICP refinement.
Transforming Time Series into Complex Networks

We introduce transformations from time series data to the domain of complex networks which allow us to characterise the dynamics underlying the time series in terms of topological features of the complex network. We show that specific types of dynamics can be characterised by a specific prevalence in the complex network motifs. For example, lowdimensional chaotic flows with one positive Lyapunov exponent form a single family while noisy non-chaotic dynamics and hyper-chaos are both distinct. We find that the same phenomena is also true for discrete maplike data. These algorithms provide a new way of studying chaotic time series and equip us with a wide range of statistical measures previously not available in the field of nonlinear time series analysis.