nils-herrmann/clean_scirepeval huggingface dataset:
Pathfinder: relational XQuery over multi-gigabyte XML inputs in interactive time

Using a relational DBMS as back-end engine for an XQuery processing system leverages relational query optimization and scalable query processing strategies provided by mature DBMS engines in the XML domain. Though a lot of theoretical work has been done in this area and various solutions have been proposed, no complete systems have been made available so far to give the practical evidence that this is a viable approach. In this paper, we describe the ourely relational XQuery processor Pathfinder that has been built on top of the extensible RDBMS MonetDB. Performance results indicate that the system is capable of evaluating XQuery queries efficiently, even if the input XML documents become huge. We additionally present further contributions such as loop-lifted staircase join, techniques to derive order properties and to reduce sorting effort in the generated relational algebra plans, as well as methods for optimizing XQuery joins, which, taken together, enabled us to reach our performance and scalability goals.  H.2.4, H.2.3, H.2.2, E.1
Cognitive Linguistics

Cognitive linguistics has emerged in the last twenty-five years as a powerful approach to the study of language, conceptual systems, human cognition, and general meaning construction. It addresses within language the structuring of basic conceptual categories such as space and time, scenes and events, entities and processes, motion and location, force and causation. It addresses the structuring of ideational and affective categories attributed to cognitive agents, such as attention and perspective, volition and intention. 1 In doing so, it develops a rich conception of grammar that reflects fundamental cognitive abilities: the ability to form structured conceptualizations with multiple levels of organization, to conceive of a situation at varying levels of abstraction, to establish correspondences between facets of different structures, and to construe the same situation in alternate ways. 2 Cognitive linguistics recognizes that the study of language is the study of language use and that when we engage in any language activity, we draw unconsciously on vast cognitive and cultural resources, call up models and frames, set up multiple connections, coordinate large arrays of information, and engage in creative mappings, transfers, and elaborations. Language does not
Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing

Latent tree learning models represent sentences by composing their words according to an induced parse tree, all based on a downstream task. These models often outperform baselines which use (externally provided) syntax trees to drive the composition order. This work contributes (a) a new latent tree learning model based on shift-reduce parsing, with competitive downstream performance and non-trivial induced trees, and (b) an analysis of the trees learned by our shift-reduce model and by a chart-based model.
REPuter: the manifold applications of repeat analysis on a genomic scale.

The repetitive structure of genomic DNA holds many secrets to be discovered. A systematic study of repetitive DNA on a genomic or inter-genomic scale requires extensive algorithmic support. The REPuter program described herein was designed to serve as a fundamental tool in such studies. Efficient and complete detection of various types of repeats is provided together with an evaluation of significance and interactive visualization. This article circumscribes the wide scope of repeat analysis using applications in five different areas of sequence analysis: checking fragment assemblies, searching for low copy repeats, finding unique sequences, comparing gene structures and mapping of cDNA/EST sequences.
Tumor detection using threshold operation in MRI brain images

Medical Image Processing is a complex and challenging field nowadays. Processing of MRI images is one of the parts of this field. This paper proposes a strategy for efficient detection of a brain tumor in MRI brain images. The methodology consists of the following steps: preprocessing by using sharpening and median filters, enhancement of image is performed by histogram equalization, segmentation of the image is performed by thresholding. This approach is then followed by the further application of morphological operations. Finally the tumor region can be obtained by using the technique of image subtraction.
Modeling Quasi-Static Crack Growth with the Extended Finite Element Method . Part I : Computer Implementation ∗

The extended finite element method (X-FEM) is a numerical method for modeling strong (displacement) as well as weak (strain) discontinuities within a standard finite element framework. In the X-FEM, special functions are added to the finite element approximation using the framework of partition of unity. For crack modeling in isotropic linear elasticity, a discontinuous function and the two-dimensional asymptotic crack-tip displacement fields are used to account for the crack. This enables the domain to be modeled by finite elements without explicitly meshing the crack surfaces, and hence quasi-static crack propagation simulations can be carried out without remeshing. In this paper, we discuss some of the key issues in the X-FEM and describe its implementation within a general-purpose finite element code. The finite element program DynaflowTM is considered in this study and the implementation for modeling 2-d cracks in isotropic and bimaterial media is described. In particular, the array-allocation for enriched degrees of freedom, use of geometric-based queries for carrying out nodal enrichment and mesh partitioning, and the assembly procedure for the discrete equations are presented. We place particular emphasis on the design of a computer code to enable the modeling of discontinuous phenomena within a finite element framework.
Graph Cuts and Efficient N-D Image Segmentation

Combinatorial graph cut algorithms have been successfully applied to a wide range of problems in vision and graphics. This paper focusses on possibly the simplest application of graph-cuts: segmentation of objects in image data. Despite its simplicity, this application epitomizes the best features of combinatorial graph cuts methods in vision: global optima, practical efficiency, numerical robustness, ability to fuse a wide range of visual cues and constraints, unrestricted topological properties of segments, and applicability to N-D problems. Graph cuts based approaches to object extraction have also been shown to have interesting connections with earlier segmentation methods such as snakes, geodesic active contours, and level-sets. The segmentation energies optimized by graph cuts combine boundary regularization with region-based properties in the same fashion as Mumford-Shah style functionals. We present motivation and detailed technical description of the basic combinatorial optimization framework for image segmentation via s/t graph cuts. After the general concept of using binary graph cut algorithms for object segmentation was first proposed and tested in Boykov and Jolly (2001), this idea was widely studied in computer vision and graphics communities. We provide links to a large number of known extensions based on iterative parameter re-estimation and learning, multi-scale or hierarchical approaches, narrow bands, and other techniques for demanding photo, video, and medical applications.
Hey, you have given me too many knobs!: understanding and dealing with over-designed configuration in system software

Configuration problems are not only prevalent, but also severely impair the reliability of today's system software. One fundamental reason is the ever-increasing complexity of configuration, reflected by the large number of configuration parameters ("knobs"). With hundreds of knobs, configuring system software to ensure high reliability and performance becomes a daunting, error-prone task. This paper makes a first step in understanding a fundamental question of configuration design: "do users really need so many knobs?" To provide the quantitatively answer, we study the configuration settings of real-world users, including thousands of customers of a commercial storage system (Storage-A), and hundreds of users of two widely-used open-source system software projects. Our study reveals a series of interesting findings to motivate software architects and developers to be more cautious and disciplined in configuration design. Motivated by these findings, we provide a few concrete, practical guidelines which can significantly reduce the configuration space. Take Storage-A as an example, the guidelines can remove 51.9% of its parameters and simplify 19.7% of the remaining ones with little impact on existing users. Also, we study the existing configuration navigation methods in the context of "too many knobs" to understand their effectiveness in dealing with the over-designed configuration, and to provide practices for building navigation support in system software.
Learning Multi-faceted Knowledge Graph Embeddings for Natural Language Processing

Knowledge graphs have challenged the existing embedding-based approaches for representing their multifacetedness. To address some of the issues, we have investigated some novel approaches that (i) capture the multilingual transitions on different language-specific versions of knowledge, and (ii) encode the commonly existing monolingual knowledge with important relational properties and hierarchies. In addition, we propose the use of our approaches in a wide spectrum of NLP tasks that have not been well explored by related works.
Information Retrieval as Statistical Translation

We propose a new probabilistic approach to information retrieval based upon the ideas and methods of statistical machine translation. The central ingredient in this approach is a statistical model of how a user might distill or "translate" a given document into a query. To assess the relevance of a document to a user's query, we estimate the probability that the query would have been generated as a translation of the document, and factor in the user's general preferences in the form of a prior distribution over documents. We propose a simple, well motivated model of the document-to-query translation process, and describe an algorithm for learning the parameters of this model in an unsupervised manner from a collection of documents. As we show, one can view this approach as a generalization and justification of the "language modeling" strategy recently proposed by Ponte and Croft. In a series of experiments on TREC data, a simple translation-based retrieval system performs well in comparison to conventional retrieval techniques. This prototype system only begins to tap the full potential of translation-based retrieval.
Biologically feasible deep learning with segregated dendrites

Deep learning has led to significant advances in artificial intelligence in recent years, in part by adopting architectures and functions motivated by neurophysiology. However, current deep learning algorithms are biologically infeasible, because they assume non-spiking units, discontinuous-time, and non-local synaptic weight updates. Here, we build on recent discoveries in artificial neural networks to develop a spiking, continuous-time neural network model that learns to categorize images from the MNIST data-set with local synaptic weight updates. The model achieves this via a three-compartment cellular architecture, motivated by neocortical pyramidal cell neurophysiology, wherein feedforward sensory information and feedback from higher layers are received at separate compartments in the neurons. We show that, thanks to the separation of feedforward and feedback information in different dendrites, our learning algorithm can coordinate learning across layers, taking advantage of multilayer architectures to identify abstract categories—the hallmark of deep learning. Our model demonstrates that deep learning can be achieved within a biologically feasible framework using segregated dendritic compartments, which may help to explain the anatomy of neocortical pyramidal neurons.
Dissecting cellular components of the secretory pathway in filamentous fungi: insights into their application for protein production

Studies on protein production using filamentous fungi have mostly focused on improvement of the protein yields by genetic modifications such as overexpression. Recent genome sequencing in several filamentous fungal species now enables more systematic approaches based on reverse genetics and molecular biology of the secretion pathway. In this review, we summarize recent molecular-based advances in our understanding of vesicular trafficking in filamentous fungi, and discuss insights into their high secretion ability and application for protein production.
Interpretable Multimodal Retrieval for Fashion Products

Deep learning methods have been successfully applied to fashion retrieval. However, the latent meaning of learned feature vectors hinders the explanation of retrieval results and integration of user feedback. Fortunately, there are many online shopping websites organizing fashion items into hierarchical structures based on product taxonomy and domain knowledge. Such structures help to reveal how human perceive the relatedness among fashion products. Nevertheless, incorporating structural knowledge for deep learning remains a challenging problem. This paper presents techniques for organizing and utilizing the fashion hierarchies in deep learning to facilitate the reasoning of search results and user intent. The novelty of our work originates from the development of an EI (Exclusive & Independent) tree that can cooperate with deep models for end-to-end multimodal learning. EI tree organizes the fashion concepts into multiple semantic levels and augments the tree structure with exclusive as well as independent constraints. It describes the different relationships among sibling concepts and guides the end-to-end learning of multi-level fashion semantics. From EI tree, we learn an explicit hierarchical similarity function to characterize the semantic similarities among fashion products. It facilitates the interpretable retrieval scheme that can integrate the concept-level feedback. Experiment results on two large fashion datasets show that the proposed approach can characterize the semantic similarities among fashion items accurately and capture user's search intent precisely, leading to more accurate search results as compared to the state-of-the-art methods.
Algorithms and applications for approximate nonnegative matrix factorization

In this paper we discuss the development and use of low-rank approximate nonnegative matrix factorization (NMF) algorithms for feature extraction and identification in the fields of text mining and spectral data analysis. The evolution and convergence properties of hybrid methods based on both sparsity and smoothness constraints for the resulting nonnegative matrix factors are discussed. The interpretability of NMF outputs in specific contexts are provided along with opportunities for future work in the modification of NMF algorithms for large-scale and time-varying datasets.
Writing : Monologue , Dialogue , and Ecological Narrative

Writing mostly is a solitary activity. Right now, I sit in front of a computer screen. On my desk are piles of paper; notes for what I want to say; unfinished projects waiting to be attended to; books on shelves nearby to be consulted. I need to be alone when I write. Whether writing on a computer, on a typewriter or by hand, most writers I know prefer a secluded place without distractions from telephones and other people who
Step by Step Towards Creating a Safe Smart Contract: Lessons and Insights from a Cryptocurrency Lab

We document our experiences in teaching smart contract programming to undergraduate students at the University of Maryland, the first pedagogical attempt of its kind. Since smart contracts deal directly with the movement of valuable currency units between contratual parties, security of a contract program is of paramount importance. Our lab exposed numerous common pitfalls in designing safe and secure smart contracts. We document several typical classes of mistakes students made, suggest ways to fix/avoid them, and advocate best practices for programming smart contracts. Finally, our pedagogical efforts have also resulted in online open course materials for programming smart contracts, which may be of independent interest to the community.
Commonsense Knowledge Base Completion

We enrich a curated resource of commonsense knowledge by formulating the problem as one of knowledge base completion (KBC). Most work in KBC focuses on knowledge bases like Freebase that relate entities drawn from a fixed set. However, the tuples in ConceptNet (Speer and Havasi, 2012) define relations between an unbounded set of phrases. We develop neural network models for scoring tuples on arbitrary phrases and evaluate them by their ability to distinguish true held-out tuples from false ones. We find strong performance from a bilinear model using a simple additive architecture to model phrases. We manually evaluate our trained model’s ability to assign quality scores to novel tuples, finding that it can propose tuples at the same quality level as mediumconfidence tuples from ConceptNet.
Optimization of Arrangements of Ro-Ro Passenger Ships with Genetic Algorithms

Ships are complex and multifunctional systems, thus their design involves innumerable design variables and an overwhelming number of constraints. Under these circumstances finding a mathematically optimal solution to the entire ship design problem appears prohibitive. Yet there have been numerous attempts and a variety of optimization methodologies introduced over the years addressing at least parts of the entire ship design problem (Nowacki, 2003). A commonly adopted strategy for practical results is the suitable breakdown of the overall design process into manageable, well-defined modular steps, similar to the traditional decomposition of the manual design into different design stages, even if this does not ensure an optimal solution to the entire design problem. One of these modular and major steps in the initial ship design is the definition of the watertight compartmentation. Although there are many considerations to be made for the arrangement of the internal watertight boundaries (space requirements by the equipment installed or space users, structural and ergonomic considerations etc), there are three main aspects that could be considered as the most critical ones already in the initial ship design stage. Those are the ship’s response in case of damage (‘survivability’) and the requirements imposed by the relevant stability regulations, the building cost and the transport capacity of the ship. The above considerations pertain especially to passenger carrying ships, to be considered in this paper.
SSE: Semantically Smooth Embedding for Knowledge Graphs

This paper considers the problem of embedding Knowledge Graphs (KGs) consisting of entities and relations into low-dimensional vector spaces. Most of the existing methods perform this task based solely on observed facts. The only requirement is that the learned embeddings should be compatible within each individual fact. In this paper, aiming at further discovering the intrinsic geometric structure of the embedding space, we propose Semantically Smooth Embedding (SSE). The key idea of SSE is to take full advantage of additional semantic information and enforce the embedding space to be semantically smooth, i.e., entities belonging to the same semantic category will lie close to each other in the embedding space. Two manifold learning algorithms Laplacian Eigenmaps and Locally Linear Embedding are used to model the smoothness assumption. Both are formulated as geometrically based regularization terms to constrain the embedding task. Two lines of embedding strategies are tested, i.e., strategies based on latent distance models and strategies based on tensor factorization techniques. We empirically evaluate SSE on two benchmark tasks of link prediction and triple classification, and achieve significant and consistent improvements over state-of-the-art methods. The results demonstrate the superiority and generality of SSE.
Network Representation Learning: A Survey

With the widespread use of information technologies, information networks are becoming increasingly popular to capture complex relationships across various disciplines, such as social networks, citation networks, telecommunication networks, and biological networks. Analyzing these networks sheds light on different aspects of social life such as the structure of societies, information diffusion, and communication patterns. In reality, however, the large scale of information networks often makes network analytic tasks computationally expensive or intractable. Network representation learning has been recently proposed as a new learning paradigm to embed network vertices into a low-dimensional vector space, by preserving network topology structure, vertex content, and other side information. This facilitates the original network to be easily handled in the new vector space for further analysis. In this survey, we perform a comprehensive review of the current literature on network representation learning in the data mining and machine learning field. We propose new taxonomies to categorize and summarize the state-of-the-art network representation learning techniques according to the underlying learning mechanisms, the network information intended to preserve, as well as the algorithmic designs and methodologies. We summarize evaluation protocols used for validating network representation learning including published benchmark datasets, evaluation methods, and open source algorithms. We also perform empirical studies to compare the performance of representative algorithms on common datasets, and analyze their computational complexity. Finally, we suggest promising research directions to facilitate future study.
Fingerprint enhancement using STFT analysis

Contrary to popular belief, despite decades of research in fingerprints, reliable fingerprint recognition is still an open problem. Extracting features out of poor quality prints is the most challenging problem faced in this area. This paper introduces a new approach for fingerprint enhancement based on Short Time Fourier Transform(STFT) Analysis. STFT is a well known technique in signal processing to analyze non-stationary signals. Here we extend its application to 2D fingerprint images. The algorithm simultaneously estimates all the intrinsic properties of the fingerprints such as the foreground region mask, local ridge orientation and local ridge frequency. Furthermore we propose a probabilistic approach of robustly estimating these parameters. We experimentally compare the proposed approach to other filtering approaches in literature and show that our technique performs favorably.
The Password Life Cycle: User Behaviour in Managing Passwords

Users need to keep track of many accounts and passwords. We conducted a series of interviews to investigate how users cope with these demanding tasks, and used Grounded Theory to analyze the interview results. We found that most users cope by reusing passwords and writing them down, but with a rich variety of behaviour and diverse personalized strategies. These approaches seem to disregard security advice, but at a detailed level they involve perceptive behaviour and careful self-management of user resources. We identify a password life cycle that follows users’ password behaviour and how it develops over time as users adapt to changing circumstances and demands. Users’ strategies have their limitations, but we suggest they indicate a rational response to the requirements of password authentication. We suggest that instead of simply advising against such behaviour, new approaches could be designed that harness existing user behaviour while limiting negative consequences.