nils-herrmann/clean_scirepeval huggingface dataset:
Multi-Dimensional Sequential Pattern Mining

Sequential pattern mining, which finds the set of frequent subsequences in sequence databases, is an important data-mining task and has broad applications. Usually, sequence patterns are associated with different circumstances, and such circumstances form a multiple dimensional space. For example, customer purchase sequences are associated with region, time, customer group, and others. It is interesting and useful to mine sequential patterns associated with multi-dimensional information.In this paper, we propose the theme of multi-dimensional sequential pattern mining, which integrates the multidimensional analysis and sequential data mining. We also thoroughly explore efficient methods for multi-dimensional sequential pattern mining. We examine feasible combinations of efficient sequential pattern mining and multi-dimensional analysis methods, as well as develop uniform methods for high-performance mining. Extensive experiments show the advantages as well as limitations of these methods. Some recommendations on selecting proper method with respect to data set properties are drawn.
A Survey on PageRank Computing

This survey reviews the research related to PageRank computing. Components of a PageRank vector serve as authority weights for Web pages independent of their textual content, solely based on the hyperlink structure of the Web. PageRank is typically used as a Web Search ranking component. This defines the importance of the model and the data structures that underly PageRank processing. Computing even a single PageRank is a difficult computational task. Computing many PageRanks is a much more complex challenge. Recently, significant effort has been invested in building sets of personalized PageRank vectors. PageRank is also used in many diverse applications other than ranking. Below we are interested in the theoretical foundations of the PageRank formulation, in accelerating of PageRank computing, in the effects of particular aspects of Web graph structure on optimal organization of computations, and in PageRank stability. We also review alternative models that lead to authority indices similar to PageRank and the role of such indices in applications other than Web Search. We also discuss link-based search personalization and outline some aspects of PageRank infrastructure from associated measures of convergence to link preprocessing.
The Generalized Reparameterization Gradient

The reparameterization gradient has become a widely used method to obtain Monte Carlo gradients to optimize the variational objective. However, this technique does not easily apply to commonly used distributions such as beta or gamma without further approximations, and most practical applications of the reparameterization gradient fit Gaussian distributions. In this paper, we introduce the generalized reparameterization gradient, a method that extends the reparameterization gradient to a wider class of variational distributions. Generalized reparameterizations use invertible transformations of the latent variables which lead to transformed distributions that weakly depend on the variational parameters. This results in new Monte Carlo gradients that combine reparameterization gradients and score function gradients. We demonstrate our approach on variational inference for two complex probabilistic models. The generalized reparameterization is e ective: even a single sample from the variational distribution is enough to obtain a low-variance gradient.
Inference in Probabilistic Graphical Models by Graph Neural Networks

A fundamental computation for statistical inference and accurate decision-making is to compute the marginal probabilities or most probable states of task-relevant variables. Probabilistic graphical models can efficiently represent the structure of such complex data, but performing these inferences is generally difficult. Messagepassing algorithms, such as belief propagation, are a natural way to disseminate evidence amongst correlated variables while exploiting the graph structure, but these algorithms can struggle when the conditional dependency graphs contain loops. Here we use Graph Neural Networks (GNNs) to learn a message-passing algorithm that solves these inference tasks. We first show that the architecture of GNNs is well-matched to inference tasks. We then demonstrate the efficacy of this inference approach by training GNNs on a collection of graphical models and showing that they substantially outperform belief propagation on loopy graphs. Our message-passing algorithms generalize out of the training set to larger graphs and graphs with different structure.
Portfolio Optimization with Mental Accounts

We integrate appealing features of Markowitz’s mean-variance portfolio theory (MVT) and Shefrin and Statman’s behavioral portfolio theory (BPT) into a new mental accounting (MA) framework. Features of the MA framework include an MA structure of portfolios, a definition of risk as the probability of failing to reach the threshold level in each mental account, and attitudes toward risk that vary by account. We demonstrate a mathematical equivalence between MVT, MA, and risk management using value at risk (VaR). The aggregate allocation across MA subportfolios is mean-variance efficient with short selling. Short-selling constraints on mental accounts impose very minor reductions in certainty equivalents, only if binding for the aggregate portfolio, offsetting utility losses from errors in specifying risk-aversion coefficients in MVT applications. These generalizations of MVT and BPT via a unified MA framework result in a fruitful connection between investor consumption goals and portfolio production.
Topic modeling: beyond bag-of-words

Some models of textual corpora employ text generation methods involving n-gram statistics, while others use latent topic variables inferred using the "bag-of-words" assumption, in which word order is ignored. Previously, these methods have not been combined. In this work, I explore a hierarchical generative probabilistic model that incorporates both n-gram statistics and latent topic variables by extending a unigram topic model to include properties of a hierarchical Dirichlet bigram language model. The model hyperparameters are inferred using a Gibbs EM algorithm. On two data sets, each of 150 documents, the new model exhibits better predictive accuracy than either a hierarchical Dirichlet bigram language model or a unigram topic model. Additionally, the inferred topics are less dominated by function words than are topics discovered using unigram statistics, potentially making them more meaningful.
Short Answer Grading Using String Similarity And Corpus-Based Similarity

Most automatic scoring systems use pattern based that requires a lot of hard and tedious work. These systems work in a supervised manner where predefined patterns and scoring rules are generated. This paper presents a different unsupervised approach which deals with students’ answers holistically using text to text similarity. Different String-based and Corpus-based similarity measures were tested separately and then combined to achieve a maximum correlation value of 0.504. The achieved correlation is the best value achieved for unsupervised approach Bag of Words (BOW) when compared to previous work. Automatic Scoring; Short Answer Grading; Semantic Similarity; String Similarity; Corpus-Based Similarity.
Warpage Simulation of a Multilayer Printed Circuit Board and Microelectronic Package Using the Anisotropic Viscoelastic Shell Modeling Technique That Considers the Initial Warpage

In this paper, the warpage simulation of a high-density multilayer printed circuit board (PCB) for solid-state disk drive (SSD) and microelectronic package was performed using the anisotropic viscoelastic shell modeling technique. The thermomechanical properties of various copper patterns were homogenized with the anisotropic shell model, which considered their viscoelastic properties. Then, warpage simulations of an SSD PCB unit/array and a full microelectronic package were performed; these simulations accounted for the initial warpage that occurred during fabrication using ABAQUS combined with a user-defined subroutine. Finally, it was demonstrated that both the maximum warpage and the remaining residual warpage of the full microelectronic package can be accurately predicted.
Efficient Exact Inference for 3D Indoor Scene Understanding

In this paper we propose the first exact solution to the problem of estimating the 3D room layout from a single image. This problem is typically formulated as inference in a Markov random field, where potentials count image features (e.g ., geometric context, orientation maps, lines in accordance with vanishing points) in each face of the layout. We present a novel branch and bound approach which splits the label space in terms of candidate sets of 3D layouts, and efficiently bounds the potentials in these sets by restricting the contribution of each individual face. We employ integral geometry in order to evaluate these bounds in constant time, and as a consequence, we not only obtain the exact solution, but also in less time than approximate inference tools such as message-passing. We demonstrate the effectiveness of our approach in two benchmarks and show that our bounds are tight, and only a few evaluations are necessary.
End-to-end Relation Extraction using Neural Networks and Markov Logic Networks

End-to-end relation extraction refers to identifying boundaries of entity mentions, entity types of these mentions and appropriate semantic relation for each pair of mentions. Traditionally, separate predictive models were trained for each of these tasks and were used in a “pipeline” fashion where output of one model is fed as input to another. But it was observed that addressing some of these tasks jointly results in better performance. We propose a single, joint neural network based model to carry out all the three tasks of boundary identification, entity type classification and relation type classification. This model is referred to as “All Word Pairs” model (AWP-NN) as it assigns an appropriate label to each word pair in a given sentence for performing end-to-end relation extraction. We also propose to refine output of the AWP-NN model by using inference in Markov Logic Networks (MLN) so that additional domain knowledge can be effectively incorporated. We demonstrate effectiveness of our approach by achieving better end-to-end relation extraction performance than all 4 previous joint modelling approaches, on the standard dataset of ACE 2004.
Video Game Addiction : Past , Present and Future

Gaming addiction has become a topic of increasing research interest. The last decade has witnessed a significant increase in the number of empirical studies examining various aspects of problematic video game play and video game addiction. This paper begins with a brief past history of how research into video game addiction has changed over the last three decades (i.e., the 1980s, 1990s and 2000s). It then examines more thoroughly the contemporary research literature by analyzing the (i) prevalence of problematic video game use and video game addiction, (ii) negative consequences of excessive video game use, (iii) factors associated with problematic video game use and video game addiction, and (iv) the treatment of problematic video game use and video game addiction. The paper concludes by looking at the trends in the field and a somewhat theoretical examination of what the future of video game
CN-DBpedia: A Never-Ending Chinese Knowledge Extraction System

Great efforts have been dedicated to harvesting knowledge bases from online encyclopedias. These knowledge bases play important roles in enabling machines to understand texts. However, most current knowledge bases are in English and non-English knowledge bases, especially Chinese ones, are still very rare. Many previous systems that extract knowledge from online encyclopedias, although are applicable for building a Chinese knowledge base, still suffer from two challenges. The first is that it requires great human efforts to construct an ontology and build a supervised knowledge extraction model. The second is that the update frequency of knowledge bases is very slow. To solve these challenges, we propose a never-ending Chinese Knowledge extraction system, CN-DBpedia, which can automatically generate a knowledge base that is of ever-increasing in size and constantly updated. Specially, we reduce the human costs by reusing the ontology of existing knowledge bases and building an end-to-end facts extraction model. We further propose a smart active update strategy to keep the freshness of our knowledge base with little human costs. The 164 million API calls of the published services justify the success of our system.
DAAL: Deep activation-based attribute learning for action recognition in depth videos

In this paper, we propose a joint semantic preserving action attribute learning framework for action recognition from depth videos, which is built on multistream deep neural networks. More specifically, this paper describes the idea to explore action attributes learned from deep activations. Multiple stream deep neural networks rather than conventional hand-crafted low-level features are employed to learn the deep activations. An undirected graph is utilized to model the complex semantics among action attributes and is integrated into our proposed joint action attribute learning algorithm. Experiments on several public datasets for action recognition demonstrate that 1) the deep activations achieve the state-ofthe-art discriminative performance as feature vectors and 2) the attribute learner can produce generic attributes, and thus obtains decent performance on zero-shot action recognition.
Life and death in the trash heap: The ubiquitin proteasome pathway and UCHL1 in brain aging, neurodegenerative disease and cerebral Ischemia

The ubiquitin proteasome pathway (UPP) is essential for removing abnormal proteins and preventing accumulation of potentially toxic proteins within the neuron. UPP dysfunction occurs with normal aging and is associated with abnormal accumulation of protein aggregates within neurons in neurodegenerative diseases. Ischemia disrupts UPP function and thus may contribute to UPP dysfunction seen in the aging brain and in neurodegenerative diseases. Ubiquitin carboxy-terminal hydrolase L1 (UCHL1), an important component of the UPP in the neuron, is covalently modified and its activity inhibited by reactive lipids produced after ischemia. As a result, degradation of toxic proteins is impaired which may exacerbate neuronal function and cell death in stroke and neurodegenerative diseases. Preserving or restoring UCHL1 activity may be an effective therapeutic strategy in stroke and neurodegenerative diseases.
Weakness of the will : Is a quick fix possible ?

Weakness of the will may lead to ineffective goal striving in the sense that people lacking willpower fail to get started, to stay on track, to select instrumental means, and to act efficiently. However, using a simple self-regulation strategy (i.e., forming implementation intentions or making if–then plans) can get around this problem by drastically improving goal striving on the spot. After an overview of research investigating how implementation intentions work, I will discuss how people can use implementation intentions to overcome potential hindrances to successful goal attainment. Extensive empirical research shows that implementation intentions help people to meet their goals no matter whether these hindrances originate from within (e.g., lack of cognitive capabilities) or outside the person (i.e., difficult social situations). Moreover, I will report recent research demonstrating that implementation intentions can even be used to control impulsive cognitive, affective, and behavioral responses that interfere with one’s focal goal striving. In ending, I will present various new lines of implementation intention research, and raise a host of open questions that still deserve further empirical and theoretical analysis.
Distributed Leadership in Organizations : A Review of Theory and Researchijmr

The aim of this paper is to review conceptual and empirical literature on the concept of distributed leadership (DL) in order to identify its origins, key arguments and areas for further work. Consideration is given to the similarities and differences between DL and related concepts, including ‘shared’, ‘collective’, ‘collaborative’, ‘emergent’, ‘co-’ and ‘democratic’ leadership. Findings indicate that, while there are some common theoretical bases, the relative usage of these concepts varies over time, between countries and between sectors. In particular, DL is a notion that has seen a rapid growth in interest since the year 2000, but research remains largely restricted to the field of school education and of proportionally more interest to UK than US-based academics. Several scholars are increasingly going to great lengths to indicate that, in order to be ‘distributed’, leadership need not necessarily be widely ‘shared’ or ‘democratic’ and, in order to be effective, there is a need to balance different ‘hybrid configurations’ of practice. The paper highlights a number of areas for further attention, including three factors relating to the context of much work on DL (power and influence; organizational boundaries and context; and ethics and diversity), and three methodological and developmental challenges (ontology; research methods; and leadership development, reward and recognition). It is concluded that descriptive and normative perspectives which dominate the literature should be supplemented by more critical accounts which recognize the rhetorical and discursive significance of DL in (re)constructing leader– follower identities, mobilizing collective engagement and challenging or reinforcing traditional forms of organization.
NATURAL LANGUAGE INTERACTION WITH SEMANTIC WEB ONTOLOGIES

The Semantic Web is an effort to establish standards and mechanisms that will allow computers to reason more easily about the semantics of the Web resources (documents, data etc.). Ontologies play a central role in this endeavor. An ontology provides a conceptualization of a knowledge domain (e.g., consumer electronics) by defining the classes and subclasses of the domain entities, the types of possible relations between them etc. The current standard to specify Semantic Web ontologies is OWL, a formal language based on description logics and RDF, with OWL 2 being the latest OWL standard. Given an OWL ontology for a knowledge domain, one can publish on the Web machine-readable data pertaining to that domain (e.g., catalogues of products, their features etc.), with the data having formally defined semantics based on the conceptualization of the ontology. Several OWL syntaxes have been developed, but people unfamiliar with formal knowledge representation often have difficulties understanding them. This thesis considered methods that allow end-users to view ontology-based knowledge representations of the Semantic Web in the form of automatically generated texts in multiple natural languages. The first part of the thesis improved NaturalOWL, a Natural Language Generation system for OWL ontologies previously developed at AUEB. The system was modified to support OWL 2 and to be able to produce higher quality texts. Experiments showed that the texts generated by the new version of NaturalOWL are indeed of high quality and significantly better than texts generated by simpler systems, often called ontology
Recursive regularization for large-scale classification with hierarchical and graphical dependencies

The two key challenges in hierarchical classification are to leverage the hierarchical dependencies between the class-labels for improving performance, and, at the same time maintaining scalability across large hierarchies. In this paper we propose a regularization framework for large-scale hierarchical classification that addresses both the problems. Specifically, we incorporate the hierarchical dependencies between the class-labels into the regularization structure of the parameters thereby encouraging classes nearby in the hierarchy to share similar model parameters. Furthermore, we extend our approach to scenarios where the dependencies between the class-labels are encoded in the form of a graph rather than a hierarchy. To enable large-scale training, we develop a parallel-iterative optimization scheme that can handle datasets with hundreds of thousands of classes and millions of instances and learning terabytes of parameters. Our experiments showed a consistent improvement over other competing approaches and achieved state-of-the-art results on benchmark datasets.
Archetype-Driven Character Dialogue Generation for Interactive Narrative

Recent years have seen a growing interest in creating virtual agents to populate the cast of characters for interactive narrative. A key challenge posed by interactive characters for narrative environments is devising expressive dialogue generators. To be effective, character dialogue generators must be able to simultaneously take into account multiple sources of information that bear on dialogue, including character attributes, plot development, and communicative goals. Building on the narrative theory of character archetypes, we propose an archetype-driven character dialogue generator that uses a probabilistic unification framework to generate dialogue motivated by character personality and narrative history to achieve communicative goals. The generator’s behavior is illustrated with character dialogue generation in a narrative-centered learning environment, CRYSTAL ISLAND.
Generative programming - methods, tools and applications

In undergoing this life, many people always try to do and get the best. New knowledge, experience, lesson, and everything that can improve the life will be done. However, many people sometimes feel confused to get those things. Feeling the limited of experience and sources to be better is one of the lacks to own. However, there is a very simple thing that can be done. This is what your teacher always manoeuvres you to do this one. Yeah, reading is the answer. Reading a book as this generative programming methods tools and applications and other references can enrich your life quality. How can it be?
F-Score Driven Max Margin Neural Network for Named Entity Recognition in Chinese Social Media

We focus on named entity recognition (NER) for Chinese social media. With massive unlabeled text and quite limited labelled corpus, we propose a semisupervised learning model based on BLSTM neural network. To take advantage of traditional methods in NER such as CRF, we combine transition probability with deep learning in our model. To bridge the gap between label accuracy and F-score of NER, we construct a model which can be directly trained on F-score. When considering the instability of Fscore driven method and meaningful information provided by label accuracy, we propose an integrated method to train on both F-score and label accuracy. Our integrated model yields substantial improvement over previous state-of-the-art result.