nils-herrmann/clean_scirepeval huggingface dataset:
Exploring Neural Methods for Parsing Discourse Representation Structures

Neural methods have had several recent successes in semantic parsing, though they have yet to face the challenge of producing meaning representations based on formal semantics. We present a sequence-to-sequence neural semantic parser that is able to produce Discourse Representation Structures (DRSs) for English sentences with high accuracy, outperforming traditional DRS parsers. To facilitate the learning of the output, we represent DRSs as a sequence of flat clauses and introduce a method to verify that produced DRSs are well-formed and interpretable. We compare models using characters and words as input and see (somewhat surprisingly) that the former performs better than the latter. We show that eliminating variable names from the output using De Bruijn indices increases parser performance. Adding silver training data boosts performance even further.
A Fully Attention-Based Information Retriever

Recurrent neural networks are now the state-of-the-art in natural language processing because they can build rich contextual representations and process texts of arbitrary length. However, recent developments on attention mechanisms have equipped feedforward networks with similar capabilities, hence enabling faster computations due to the increase in the number of operations that can be parallelized. We explore this new type of architecture in the domain of question-answering and propose a novel approach that we call Fully Attention Based Information Retriever (FABIR). We show that FABIR achieves competitive results in the Stanford Question Answering Dataset (SQuAD) while having fewer parameters and being faster at both learning and inference than rival methods.
Vision-Based Real-Time Aerial Object Localization and Tracking for UAV Sensing System

This paper focuses on the problem of vision-based obstacle detection and tracking for unmanned aerial vehicle navigation. A real-time object localization and tracking strategy from monocular image sequences is developed by effectively integrating the object detection and tracking into a dynamic Kalman model. At the detection stage, the object of interest is automatically detected and localized from a saliency map computed via the image background connectivity cue at each frame; at the tracking stage, a Kalman filter is employed to provide a coarse prediction of the object state, which is further refined via a local detector incorporating the saliency map and the temporal information between two consecutive frames. Compared with existing methods, the proposed approach does not require any manual initialization for tracking, runs much faster than the state-of-the-art trackers of its kind, and achieves competitive tracking performance on a large number of image sequences. Extensive experiments demonstrate the effectiveness and superior performance of the proposed approach.
A Fall-back Strategy for Sentiment Analysis in Hindi : a Case Study

Sentiment Analysis (SA) research has gained tremendous momentum in recent times. However, there has been little work in this area for an Indian language. We propose in this paper a fall-back strategy to do sentiment analysis for Hindi documents, a problem on which, to the best of our knowledge, no work has been done until now. (A) First of all, we study three approaches to perform SA in Hindi. We have developed a sentiment annotated corpora in the Hindi movie review domain. The first of our approaches involves training a classifier on this annotated Hindi corpus and using it to classify a new Hindi document. (B) In the second approach, we translate the given document into English and use a classifier trained on standard English movie reviews to classify the document.
Towards unsupervised object Classification and Prediction in 3 D through Semantic Sampling

In this thesis I will investigate the potential use of pre-existing Volumetric Variational Auto-Encoder architectures for object in-filling and de-noising. From the experiments presetned here it can be seen that even with relatively simple architectures, complex and varied noises can be repaired by learning generative latent spaces from training with data augmentation. For further improving the VAE's predictive abilities, I propose two novel redefinition of the Variational Bayes Auto-Encoder architecture for management of partial, semantically scaled input samples. The Located-VAE (LVAE) and Prior-VAE (PVAE) are extensions of variational reconstruction networks that attempt to connect real-world sliding window object segments to a latent space of known 3D objects for classification and prediction. Their predictive abilities are shown visually through use of the Classification and Prediction through Auto-Encoder Network (CaPtAEN) application for basic reconstruction tasks, as well as reconstruction with varying noise qualities at input. The classification abilities are demonstrated empirically through comparison of latent space representations of segments taken from the same object. Finally, we argue that although voxel models are visually interesting to work with, the computational complexity and massive sparsity are prohibitive for working with high-resolution models and prevent learning of structured high-level 3D filters. The lack of filter descriptiveness is visually explained using the application presented in this work.
Quantification of Model Uncertainty in RANS Simulations : A Review

In computational fluid dynamics simulations of industrial flows, models based on the Reynoldsaveraged Navier–Stokes (RANS) equations are expected to play an important role in decades to come. However, model uncertainties are still a major obstacle for the predictive capability of RANS simulations. This review examines both the parametric and structural uncertainties in turbulence models. We review recent literature on data-free (uncertainty propagation) and data-driven (statistical inference) approaches for quantifying and reducing model uncertainties in RANS simulations. Moreover, the fundamentals of uncertainty propagation and Bayesian inference are introduced in the context of RANS model uncertainty quantification. Finally, the literature on uncertainties in scale-resolving simulations is briefly reviewed with particular emphasis on large eddy simulations.
Towards Exploring Literals to Enrich Data Linking in Knowledge Graphs

Knowledge graph completion is still a challenging solution that uses techniques from distinct areas to solve many different tasks. Most recent works, which are based on embedding models, were conceived to improve an existing knowledge graph using the link prediction task. However, even considering the ability of these solutions to solve other tasks, they did not present results for data linking, for example. Furthermore, most of these works focuses only on structural information, i.e., the relations between entities. In this paper, we present an approach for data linking that enrich entity embeddings in a model with their literal information and that do not rely on external information of these entities. The key aspect of this proposal is that we use a blocking scheme to improve the effectiveness of the solution in relation to the use of literals. Thus, in addition to the literals from object elements in a triple, we use other literals from subjects and predicates. By merging entity embeddings with their literal information it is possible to extend many popular embedding models. Preliminary experiments were performed on real-world datasets and our solution showed competitive results to the performance of the task of data linking.
A Deep Non-Negative Matrix Factorization Neural Network

Recently, deep neural network algorithms have emerged as one of the most successful machine learning strategies, obtaining state of the art results for speech recognition, computer vision, and classification of large data sets. Their success is due to advancement in computing power, availability of massive amounts of data and the development of new computational techniques. Some of the drawbacks to these deep neural networks are that they often require massive amounts of observed data, their feature representations are hard to interpret and they are not well mathematically understood when they will work, and why. Other strategies for data representation and feature extraction, such as topic modeling based strategies, have also recently progressed. Topic models, such as NMF, combine data modeling with optimization to learn interpretable and consistent feature structures in data. Previously criticized for their computational complexity, it is now possible to quickly perform topic modeling on massive streaming data sets. We introduce a deep non-negative matrix factorization framework capable of producing interpretable hierarchical classification of many types of data. Our proposed framework shows that it is possible to combine the interpretability and predictability of topic modeling learned representations with some of the power and accuracy of deep neural networks. Furthermore, we uncover a new connection between sparse matrix representations and deep learning models by combining multiple layers of NMF with a non-linear activation function and pooling, optimized by backpropagation.
The perceptron: a probabilistic model for information storage and organization in the brain.

The first of these questions is in the province of sensory physiology, and is the only one for which appreciable understanding has been achieved. This article will be concerned primarily with the second and third questions, which are still subject to a vast amount of speculation, and where the few relevant facts currently supplied by neurophysiology have not yet been integrated into an acceptable theory. With regard to the second question, two alternative positions have been maintained. The first suggests that storage of sensory information is in the form of coded representations or images, with some sort of one-to-one mapping between the sensory stimulus
Technology adoption and use theory review for studying scientists' continued use of cyber-infrastructure

In this paper, we seek to identify factors that might increase the likelihood of adoption and continued use of cyberinfrastructure by scientists. To do so, we review the main research on Information and Communications Technology (ICT) adoption and use by addressing research problems, theories and models used, findings, and limitations. We focus particularly on the individual user perspective. We categorize previous studies into two groups: Adoption research and post-adoption (continued use) research. In addition, we review studies specifically regarding cyberinfrastructure adoption and use by scientists and other special user groups. We identify the limitations of previous theories, models and research findings appearing in the literature related to our current interest in scientists’ adoption and continued use of cyber-infrastructure. We synthesize the previous theories and models used for ICT adoption and use, and then we develop a theoretical framework for studying scientists’ adoption and use of cyber-infrastructure. We also proposed a research design based on the research model developed. Implications for researchers and practitioners are provided.
Unsupervised Multi-modal Neural Machine Translation

Unsupervised neural machine translation (UNMT) has recently achieved remarkable results  with only large monolingual corpora in each language. However, the uncertainty of associating target with source sentences makes UNMT theoretically an ill-posed problem. This work investigates the possibility of utilizing images for disambiguation to improve the performance of UNMT. Our assumption is intuitively based on the invariant property of image, i.e., the description of the same visual content by different languages should be approximately similar. We propose an unsupervised multi-modal machine translation (UMNMT) framework based on the language translation cycle consistency loss conditional on the image, targeting to learn the bidirectional multi-modal translation simultaneously. Through an alternate training between multi-modal and uni-modal, our inference model can translate with or without the image. On the widely used Multi30K dataset, the experimental results of our approach are significantly better than those of the text-only UNMT on the 2016 test dataset.
Task-Oriented Dialog Agents Using Memory-Networks and Ensemble Learning

Task-oriented dialog agents are ever more relevant systems to engage with an user through voice or text natural language input to fulfill domain-specific tasks. Recently, neural based approaches are gaining popularity over traditional rule based systems thanks to promising results (Williams et al., 2017) in standard tasks (Bordes et al., 2016). While most modern approaches follow an architecture based on a pipeline of components (Young et al., 2013; Bocklisch et al., 2017; Williams et al., 2017), there is still much room for design in these architectures, and many promising models that can be incorporated in the pipeline. One such pipeline architecture is the Hybrid Code Networks (HCN) (Williams et al., 2017), which uses example human-bot conversations to train an LSTM (Hochreiter and Schmidhuber, 1997) to track conversation state and predict the next action. Inspired by the promising results from Bordes et al. (2016), this thesis focuses on building an architecture similar to HCN but improving the action policy by using a Memory Network instead of an LSTM and then using an ensemble that combines both the Memory Network and the LSTM from HCN into a single action policy. By combining the benefits of the HCN architecture with the Memory Network and the ensemble policy, the model achieves perfect scores in bAbI task 5, almost 4% accuracy improvement over Bordes et al. (2016) and almost 8% higher than the LSTM action policy from Williams et al. (2017), while the ensemble policy by itself proved responsible for more than 1% improvement over the HCN architecture on bAbI task 6. The final results prove not only the advantages of a Memory Network over an LSTM for some scenarios, but more importantly, the fact that both policies complement each other and benefit of working together, even when trained with the same data.
Detección del Fracaso Académico y Evaluación de la Práctica Docente mediante la Comunicación Automatizada con un Chatbot

Resumen—Este estudio contempla el uso de un modelo simple de comunicación automatizada a través de un chatbot de Telegram, denominado EDUtrack, que se conecta a Moodle, un Sistema de Gestión del Aprendizaje. Está pensado para docentes interesados en evaluar sus clases de alguna manera, ası́ como mantener informados a sus estudiantes sobre su rendimiento académico. EDUtrack define dos perfiles de usuario, estudiante y docente, que a través del uso de comandos interactúan con el bot para obtener información especı́fica. El estudiante será informado de su Factor de Riesgo Académico si se encuentra en riesgo de suspender la asignatura. Al mismo tiempo permitirá recoger la percepción de los estudiantes sobre la satisfacción de la práctica docente a través del uso de información lingüı́stica. El perfil de docente por su parte, podrá solicitar en cualquier momento el listado de los estudiantes con un Factor de Riesgo Académico crı́tico, ası́ como revisar el resultado de la evaluación proporcionada por los estudiantes de su práctica docente.
Automatic text recognition in digital videos

We have developed algorithms for automatic character segmentation in motion pictures which extract automatically and reliably the text in pre-title sequences, credit titles, and closing sequences with title and credits. The algorithms we propose make use of typical characteristics of text in videos in order to enhance segmentation and, consequently, recognition performance. As a result, we get segmented characters from video pictures. These can be parsed by any OCR software. The recognition results of multiple instances of the same character throughout subsequent frames are combined to enhance recognition result and to compute the final output. We have tested our segmentation algorithms in a series of experiments with video clips recorded from television and achieved good segmentation results.
Blockchain Consensus Protocols in the Wild

A blockchain is a distributed ledger for recording transactions, maintained by many nodes without central authority through a distributed cryptographic protocol. All nodes validate the information to be appended to the blockchain, and a consensus protocol ensures that the nodes agree on a unique order in which entries are appended. Consensus protocols for tolerating Byzantine faults have received renewed attention because they also address blockchain systems. This work discusses the process of assessing and gaining confidence in the resilience of a consensus protocols exposed to faults and adversarial nodes. We advocate to follow the established practice in cryptography and computer security, relying on public reviews, detailed models, and formal proofs; the designers of several practical systems appear to be unaware of this. Moreover, we review the consensus protocols in some prominent permissioned blockchain platforms with respect to their fault models and resilience against attacks.
Human Factors in Agile Software Development

Through our four years experiments on students' Scrum based agile software development (ASD) process, we have gained deep understanding into the human factors of agile methodology. We designed an agile project management tool - the HASE collaboration development platform to support more than 400 students self-organized into 80 teams to practice ASD. In this thesis, Based on our experiments, simulations and analysis, we contributed a series of solutions and insights in this researches, including 1) a Goal Net based method to enhance goal and requirement management for ASD process, 2) a novel Simple Multi-Agent Real-Time (SMART) approach to enhance intelligent task allocation for ASD process, 3) a Fuzzy Cognitive Maps (FCMs) based method to enhance emotion and morale management for ASD process, 4) the first large scale in-depth empirical insights on human factors in ASD process which have not yet been well studied by existing research, and 5) the first to identify ASD process as a human-computation system that exploit human efforts to perform tasks that computers are not good at solving. On the other hand, computers can assist human decision making in the ASD process.
ConceptNet 5.5: An Open Multilingual Graph of General Knowledge

Machine learning about language can be improved by supplying it with specific knowledge and sources of external information. We present here a new version of the linked open data resource ConceptNet that is particularly well suited to be used with modern NLP techniques such as word embeddings. ConceptNet is a knowledge graph that connects words and phrases of natural language with labeled edges. Its knowledge is collected from many sources that include expertcreated resources, crowd-sourcing, and games with a purpose. It is designed to represent the general knowledge involved in understanding language, improving natural language applications by allowing the application to better understand the meanings behind the words people use. When ConceptNet is combined with word embeddings acquired from distributional semantics (such as word2vec), it provides applications with understanding that they would not acquire from distributional semantics alone, nor from narrower resources such as WordNet or DBPedia. We demonstrate this with state-of-the-art results on intrinsic evaluations of word relatedness that translate into improvements on applications of word vectors, including solving SAT-style analogies.
Multi-granularity hierarchical attention fusion networks for reading comprehension and question answering

This paper describes a novel hierarchical attention network for reading comprehension style question answering, which aims to answer questions for a given narrative paragraph. In the proposed method, attention and fusion are conducted horizontally and vertically across layers at different levels of granularity between question and paragraph. Specifically, it first encode the question and paragraph with fine-grained language embeddings, to better capture the respective representations at semantic level. Then it proposes a multi-granularity fusion approach to fully fuse information from both global and attended representations. Finally, it introduces a hierarchical attention network to focuses on the answer span progressively with multi-level softalignment. Extensive experiments on the large-scale SQuAD and TriviaQA datasets validate the effectiveness of the proposed method. At the time of writing the paper (Jan. 12th 2018), our model achieves the first position on the SQuAD leaderboard for both single and ensemble models. We also achieves state-of-the-art results on TriviaQA, AddSent and AddOneSent datasets.
TriAD: a distributed shared-nothing RDF engine based on asynchronous message passing

We investigate a new approach to the design of distributed, shared-nothing RDF engines. Our engine, coined "TriAD", combines join-ahead pruning via a novel form of RDF graph summarization with a locality-based, horizontal partitioning of RDF triples into a grid-like, distributed index structure. The multi-threaded and distributed execution of joins in TriAD is facilitated by an asynchronous Message Passing protocol which allows us to run multiple join operators along a query plan in a fully parallel, asynchronous fashion. We believe that our architecture provides a so far unique approach to join-ahead pruning in a distributed environment, as the more classical form of sideways information passing would not permit for executing distributed joins in an asynchronous way. Our experiments over the LUBM, BTC and WSDTS benchmarks demonstrate that TriAD consistently outperforms centralized RDF engines by up to two orders of magnitude, while gaining a factor of more than three compared to the currently fastest, distributed engines. To our knowledge, we are thus able to report the so far fastest query response times for the above benchmarks using a mid-range server and regular Ethernet setup.
The Mediterranean diet: science and practice.

To provide an overview of research relevant to the Mediterranean diet.



Personal perspectives.



International.



Populations in Europe, North America, Asia.



Approximately 50 years ago, Keys and colleagues described strikingly low rates of coronary heart disease in the Mediterranean region, where fat intake was relatively high but largely from olive oil. Subsequent controlled feeding studies have shown that compared to carbohydrate, both monounsaturated and polyunsaturated fats reduce LDL and triglycerides and increase HDL cholesterol. Importantly, these beneficial metabolic effects are greater in the presence of underlying insulin resistance. In a detailed analysis within the Nurses' Health Study, trans fat from partially hydrogenated vegetable oils (absent in traditional Mediterranean diets) was most strongly related to risk of heart disease, and both polyunsaturated and monounsaturated fat were inversely associated with risk. Epidemiologic evidence has also supported beneficial effects of higher intakes of fruits and vegetables, whole grains, fish, and daily consumption of moderate amounts of alcohol. Together with regular physical activity and not smoking, our analyses suggest that over 80% of coronary heart disease, 70% of stroke, and 90% of type 2 diabetes can be avoided by healthy food choices that are consistent with the traditional Mediterranean diet.



Both epidemiologic and metabolic studies suggest that individuals can benefit greatly by adopting elements of Mediterranean diets.