nils-herrmann/clean_scirepeval huggingface dataset:
Quantifying the relation between performance and success in soccer

The availability of massive data about sports activities offers nowadays the opportunity to quantify the relation between performance and success. In this work, we analyze more than 6,000 games and 10 million events in the six major European leagues and investigate the relation between team performance and success in soccer competitions. We discover that a team’s success in the national tournament is significantly related to its typical performance. Moreover, we observe that while victory and defeats can be explained by the team’s performance during a game, draws are difficult to describe with a machine learning approach. We then perform a simulation of an entire season of the six leagues where the outcome of every game is replaced by a synthetic outcome (victory, defeat, or draw) based on a machine learning model trained on the previous seasons. We find that the final rankings in the simulated tournaments are close to the actual rankings in the real tournaments, suggesting that a complex systems’ view on soccer has the potential of revealing hidden patterns regarding the relation between performance and success.
Visual span in expert chess players: evidence from eye movements.

The reported research extends classic findings that after briefly viewing structured, but not random, chess positions, chess masters reproduce these positions much more accurately than less-skilled players. Using a combination of the gaze-contingent window paradigm and the change blindness flicker paradigm, we documented dramatically larger visual spans for experts while processing structured, but not random, chess positions. In addition, in a check-detection task, a minimized 3 x 3 chessboard containing a King and potentially checking pieces was displayed. In this task, experts made fewer fixations per trial than less-skilled players, and had a greater proportion of fixations between individual pieces, rather than on pieces. Our results provide strong evidence for a perceptual encoding advantage for experts attributable to chess experience, rather than to a general perceptual or memory superiority.
Shared Embedding Based Neural Networks for Knowledge Graph Completion

Knowledge Graphs (KGs) have facilitated many real-world applications (e.g., vertical search and intelligent question answering). However, they are usually incomplete, which affects the performance of such KG based applications. To alleviate this problem, a number of Knowledge Graph Completion (KGC) methods have been developed to predict those implicit triples. Tensor/matrix based methods and translation based methods have attracted great attention for a long time. Recently, neural network has been introduced into KGC due to its extensive superiority in many fields (e.g., natural language processing and computer vision), and achieves promising results. In this paper, we propose a Shared Embedding based Neural Network (SENN) model for KGC. It integrates the prediction tasks of head entities, relations and tail entities into a neural network based framework with shared embeddings of entities and relations, while explicitly considering the differences among these prediction tasks. Moreover, we propose an adaptively weighted loss mechanism, which dynamically adjusts the weights of losses according to the mapping properties of relations, and the prediction tasks. Since relation prediction usually performs better than head and tail entity predictions, we further extend SENN to SENN+ by employing it to assist head and tail entity predictions. Experiments on benchmark datasets validate the effectiveness and merits of the proposed SENN and SENN+ methods. The shared embeddings and the adaptively weighted loss mechanism are also testified to be effective.
Semi-supervised Learning for SVM-KNN

Compared with labeled data, unlabeled data are significantly easier to obtain. Currently, classification of unlabeled data is an open issue. In this paper a novel SVMKNN classification methodology based on Semi-supervised learning is proposed, we consider the problem of using a large number of unlabeled data to boost performance of the classifier when only a small set of labeled examples is available. We use the few labeled data to train a weaker SVM classifier and make use of the boundary vectors to improve the weaker SVM iteratively by introducing KNN. Using KNN classifier doesn’t enlarge the number of training examples only, but also improves the quality of the new training examples which are transformed from the boundary vectors. Experiments on UCI data sets show that the proposed methodology can evidently improve the accuracy of the final SVM classifier by tuning the parameters and can reduce the cost of labeling unlabeled examples.
The multi-component model of working memory: Explorations in experimental cognitive psychology

There are a number of ways one can hope to describe and explain cognitive abilities, each of them contributing a unique and valuable perspective. Cognitive psychology tries to develop and test functional accounts of cognitive systems that explain the capacities and properties of cognitive abilities as revealed by empirical data gathered by a range of behavioral experimental paradigms. Much of the research in the cognitive psychology of working memory has been strongly influenced by the multi-component model of working memory [Baddeley AD, Hitch GJ (1974) Working memory.  Recent advances in learning and motivation, Vol. 8 (Bower GA, ed), pp 47-90.  (1986) Working memory. Oxford, UK: Clarendon Press; Baddeley A. Working memory: Thought and action. ]. By expanding the notion of a passive short-term memory to an active system that provides the basis for complex cognitive abilities, the model has opened up numerous questions and new lines of research. In this paper we present the current revision of the multi-component model that encompasses a central executive, two unimodal storage systems: a phonological loop and a visuospatial sketchpad, and a further component, a multimodal store capable of integrating information into unitary episodic representations, termed episodic buffer. We review recent empirical data within experimental cognitive psychology that has shaped the development of the multicomponent model and the understanding of the capacities and properties of working memory. Research based largely on dual-task experimental designs and on neuropsychological evidence has yielded valuable information about the fractionation of working memory into independent stores and processes, the nature of representations in individual stores, the mechanisms of their maintenance and manipulation, the way the components of working memory relate to each other, and the role they play in other cognitive abilities. With many questions still open and new issues emerging, we believe that the multicomponent model will continue to stimulate research while providing a comprehensive functional description of working memory.
Program Synthesis for Character Level Language Modelling

We propose a statistical model applicable to character level language modeling and show that it is a good fit for both, program source code and English text. The model is parameterized by a program from a domain-specific language (DSL) that allows expressing non-trivial data dependencies. Learning is done in two phases: (i) we synthesize a program from the DSL, essentially learning a good representation for the data, and (ii) we learn parameters from the training data – the process is done via counting, as in simple language models such as n-gram. Our experiments show that the precision of our model is comparable to that of neural networks while sharing a number of advantages with n-gram models such as fast query time and the capability to quickly add and remove training data samples. Further, the model is parameterized by a program that can be manually inspected, understood and updated, addressing a major problem of neural networks.
Neural Discrete Representation Learning

Learning useful representations without supervision remains a key challenge in machine learning. In this paper, we propose a simple yet powerful generative model that learns such discrete representations. Our model, the Vector QuantisedVariational AutoEncoder (VQ-VAE), differs from VAEs in two key ways: the encoder network outputs discrete, rather than continuous, codes; and the prior is learnt rather than static. In order to learn a discrete latent representation, we incorporate ideas from vector quantisation (VQ). Using the VQ method allows the model to circumvent issues of “posterior collapse” -— where the latents are ignored when they are paired with a powerful autoregressive decoder -— typically observed in the VAE framework. Pairing these representations with an autoregressive prior, the model can generate high quality images, videos, and speech as well as doing high quality speaker conversion and unsupervised learning of phonemes, providing further evidence of the utility of the learnt representations.
Effective search space reduction for spell correction using character neural embeddings

We present a novel, unsupervised, and distance measure agnostic method for search space reduction in spell correction using neural character embeddings. The embeddings are learned by skip-gram word2vec training on sequences generated from dictionary words in a phonetic informationretentive manner. We report a very high performance in terms of both success rates and reduction of search space on the Birkbeck spelling error corpus. To the best of our knowledge, this is the first application of word2vec to spell correction.
NoSQL databases: a step to database scalability in web environment

The paper aims to focus on so-called NoSQL databases in the context of cloud computing.  Architectures and basic features of these databases are studied, particularly their horizontal scalability and concurrency model, that is mostly weaker than ACID transactions in relational SQL-like database systems.  Some characteristics like a data model and querying capabilities of NoSQL databases are discussed in more detail.  The paper shows vary different data models and query possibilities in a common terminology enabling comparison and categorization of NoSQL databases.
Neural Wikipedian: Generating Textual Summaries from Knowledge Base Triples

Most people do not interact with Semantic Web data directly. Unless they have the expertise to understand the underlying technology, they need textual or visual interfaces to help them make sense of it. We explore the problem of generating natural language summaries for Semantic Web data. This is non-trivial, especially in an open-domain context. To address this problem, we explore the use of neural networks. Our system encodes the information from a set of triples into a vector of fixed dimensionality and generates a textual summary by conditioning the output on the encoded vector. We train and evaluate our models on two corpora of loosely aligned Wikipedia snippets and DBpedia and Wikidata triples with promising results.
Relating kindergarten attention to subsequent developmental pathways of classroom engagement in elementary school.

We examine the relationship between children's kindergarten attention skills and developmental patterns of classroom engagement throughout elementary school in disadvantaged urban neighbourhoods. Kindergarten measures include teacher ratings of classroom behavior, direct assessments of number knowledge and receptive vocabulary, and parent-reported family characteristics. From grades 1 through 6, teachers also rated children's classroom engagement. Semi-parametric mixture modeling generated three distinct trajectories of classroom engagement (n = 1369, 50% boys). Higher levels of kindergarten attention were proportionately associated with greater chances of belonging to better classroom engagement trajectories compared to the lowest classroom engagement trajectory. In fact, improvements in kindergarten attention reliably increased the likelihood of belonging to more productive classroom engagement trajectories throughout elementary school, above and beyond confounding child and family factors. Measuring the development of classroom productivity is pertinent because such dispositions represent precursors to mental health, task-orientation, and persistence in high school and workplace behavior in adulthood.
Semantics-based Summarization of Entities in Knowledge Graphs

Gunaratna, Kalpa. PhD, Department of Computer Science and Engineering, Wright State University, 2017. Semantics-based Summarization of Entities in Knowledge Graphs. The processing of structured and semi-structured content on the Web has been gaining attention with the rapid progress in the Linking Open Data project and the development of commercial knowledge graphs. Knowledge graphs capture domain-specific or encyclopedic knowledge in the form of a data layer and add rich and explicit semantics on top of the data layer to infer additional knowledge. The data layer of a knowledge graph represents entities and their descriptions. The semantic layer on top of the data layer is called the schema (ontology), where relationships of the entity descriptions, their classes, and the hierarchy of the relationships and classes are defined. Today, there exist large knowledge graphs in the research community (e.g., encyclopedic datasets like DBpedia and Yago) and corporate world (e.g., Google knowledge graph) that encapsulate a large amount of knowledge for human and machine consumption. Typically, they consist of millions of entities and billions of facts describing these entities. While it is good to have this much knowledge available on the Web for consumption, it leads to information overload, and hence proper summarization (and presentation) techniques need to be explored. In this dissertation, we focus on creating both comprehensive and concise entity summaries at: (i) the single entity level and (ii) the multiple entity level. To summarize a single entity, we propose a novel approach called FACeted Entity Summarization (FACES) that considers importance, which is computed by combining popularity and uniqueness, and diversity of facts getting selected for the summary. We first conceptually group facts using semantic expansion and hierarchical incremental clustering techniques and form facets (i.e., groupings) that go beyond syntactic similarity. Then we rank both the facts and facets using Information Retrieval (IR) ranking techniques to pick the
NEUROCIRCUITRY OF ADDICTION

Addiction can be defined as drug-induced changes in the central nervous system (CNS) that produce maladaptive alterations in spontaneous behavior and in the behavioral response to readministration of that drug. Maladaptive behaviors include those identified as criteria for addiction in the DSM-IV. In general what most psychiatric metrics describe as addiction associated behaviors is the emergence of behaviors to obtain drug reward at the expense of engaging in behaviors to seek natural rewards, ranging from biological rewards such as sex to cultural rewards such as stable personal relationships. The substitution of drug reward for natural reward suggests that the neuropathology of addiction may reside in the same neural systems that mediate the detection and acquisition of natural rewards. This postulate forms a primary premise in the search for the neurobiological basis of addiction, and has revealed a circuit consisting of interconnections among limbic cortex, basal ganglia, and brainstem nuclei that is pathologically modified by repeated drug administration. The drug-induced changes in the structure and function of this circuit are progressive, and to some extent parallel the development of the behavioral characteristics of addiction. Over the last decade neurobiologists have come to describe the behavioral transition to addiction as a druginduced neuroplastic process (1–3). In parallel with the development and expression of addictive behaviors, the neurobiology of these two components of the transition to addiction can be described as: (a) the sequence of molecular events that establish the neuroplastic changes leading to addiction, and (b) the neuroplastic changes themselves. Accordingly, a number of molecular neuroplastic alterations have been identified in the brain after repeated drug administration, and some of these appear to be important in the development and/or expression of addictive behaviors. However, the process of identifying drug-induced changes is accelerating and producing a deluge of information that is proving increasingly difficult to integrate into a coherent sequence of neuroplastic changes that mediate addiction.
Nurse moral distress and cancer pain management: an ethnography of oncology nurses in India.

The majority of cancer patients in low- and middle-income countries (LMICs) present with late-stage, incurable disease, and basic tools to alleviate patient suffering-such as morphine-are often absent. Oncology nurses must cope with many challenges and may experience moral distress (MD), yet little research has examined this experience in LMICs.



This ethnographic study explored the experience of MD with oncology nurses (n = 37) and other providers (n = 22) in India and its potential relationship to opioid availability.



Data (semistructured interviews and field observations) were collected at a 300-bed government cancer hospital in urban South India over 9 months. Dedoose v.4.5.91 supported analysis of transcripts using a coding schema that mapped to an Integrated Model of Nurse Moral Distress and concepts that emerged from field notes.



Primary themes included "We feel bad," "We are alone and afraid," "We are helpless," and "We leave it." A weak link between MD and opioid availability was found.



Participants described significant work-related distress, but the moral dimension to this distress was less clear as some key aspects of the Integrated Model of Nurse Moral Distress were not supported. The concept of MD may have limited applicability in settings where alternative courses of action are unknown, or not feasible, and where differing social and cultural norms influence moral sensitivity.



Improving job-related conditions is prerequisite to creating an environment where MD can manifest. Educational initiatives in LMICs must account for the role of the oncology nurse and their contextual moral and professional obligations.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT representations can be fine-tuned with just one additional output layer to create state-of-theart models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE benchmark to 80.4% (7.6% absolute improvement), MultiNLI accuracy to 86.7% (5.6% absolute improvement) and the SQuAD v1.1 question answering Test F1 to 93.2 (1.5 absolute improvement), outperforming human performance by 2.0.
SenticNet 5: Discovering Conceptual Primitives for Sentiment Analysis by Means of Context Embeddings

With the recent development of deep learning, research in AI has gained new vigor and prominence. While machine learning has succeeded in revitalizing many research fields, such as computer vision, speech recognition, and medical diagnosis, we are yet to witness impressive progress in natural language understanding. One of the reasons behind this unmatched expectation is that, while a bottom-up approach is feasible for pattern recognition, reasoning and understanding often require a top-down approach. In this work, we couple sub-symbolic and symbolic AI to automatically discover conceptual primitives from text and link them to commonsense concepts and named entities in a new three-level knowledge representation for sentiment analysis. In particular, we employ recurrent neural networks to infer primitives by lexical substitution and use them for grounding common and commonsense knowledge by means of multi-dimensional scaling.
Big Data Meet Green Challenges: Greening Big Data

Nowadays, there are two significant tendencies, how to process the enormous amount of data, big data, and how to deal with the green issues related to sustainability and environmental concerns. An interesting question is whether there are inherent correlations between the two tendencies in general. To answer this question, this paper firstly makes a comprehensive literature survey on how to green big data systems in terms of the whole life cycle of big data processing, and then this paper studies the relevance between big data and green metrics and proposes two new metrics, effective energy efficiency and effective resource efficiency in order to bring new views and potentials of green metrics for the future times of big data.
Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation

Classic pipeline models for task-oriented dialogue system require explicit modeling the dialogue states and hand-crafted action spaces to query a domain-specific knowledge base. Conversely, sequence-to-sequence models learn to map dialogue history to the response in current turn without explicit knowledge base querying. In this work, we propose a novel framework that leverages the advantages of classic pipeline and sequence-to-sequence models. Our framework models a dialogue state as a fixed-size distributed representation and use this representation to query a knowledge base via an attention mechanism. Experiment on Stanford Multi-turn Multi-domain Taskoriented Dialogue Dataset shows that our framework significantly outperforms other sequenceto-sequence based baseline models on both automatic and human evaluation. Title and Abstract in Chinese 面向任务型对话中基于对话状态表示的序列到序列学习 面向任务型对话中,传统流水线模型要求对对话状态进行显式建模。这需要人工定义对 领域相关的知识库进行检索的动作空间。相反地,序列到序列模型可以直接学习从对话 历史到当前轮回复的一个映射,但其没有显式地进行知识库的检索。在本文中,我们提 出了一个结合传统流水线与序列到序列二者优点的模型。我们的模型将对话历史建模为 一组固定大小的分布式表示。基于这组表示,我们利用注意力机制对知识库进行检索。 在斯坦福多轮多领域对话数据集上的实验证明,我们的模型在自动评价与人工评价上优 于其他基于序列到序列的模型。
Business Intelligence System for Banking and Finance

Many firms develop an information technology strategy that includes the use of business intelligence software in the decision making process. In order to really achieve a solid return on investment on this type of software, the firm should have at least 10 years of detailed data on sales, purchases, staff costs, and other items that impact the overall cost of providing a service or good. Data cubes and reports can then be built to show trends, identify product success and failures, and provide a more holistic view of company activity. This paper describes such software “Business Intelligence System for Banking and Finance”.