nils-herrmann/clean_scirepeval huggingface dataset:
Clustering and Diversifying Web Search Results with Graph-Based Word Sense Induction

Web search result clustering aims to facilitate information search on the Web. Rather than the results of a query being presented as a flat list, they are grouped on the basis of their similarity and subsequently shown to the user as a list of clusters. Each cluster is intended to represent a different meaning of the input query, thus taking into account the lexical ambiguity (i.e., polysemy) issue. Existing Web clustering methods typically rely on some shallow notion of textual similarity between search result snippets, however. As a result, text snippets with no word in common tend to be clustered separately even if they share the same meaning, whereas snippets with words in common may be grouped together even if they refer to different meanings of the input query.In this article we present a novel approach to Web search result clustering based on the automatic discovery of word senses from raw text, a task referred to as Word Sense Induction. Key to our approach is to first acquire the various senses (i.e., meanings) of an ambiguous query and then cluster the search results based on their semantic similarity to the word senses induced. Our experiments, conducted on data sets of ambiguous queries, show that our approach outperforms both Web clustering and search engines.
A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors

Among the most exciting advances in early vision has been the development of efficient energy minimization algorithms for pixel-labeling tasks such as depth or texture computation. It has been known for decades that such problems can be elegantly expressed as Markov random fields, yet the resulting energy minimization problems have been widely viewed as intractable. Algorithms such as graph cuts and loopy belief propagation (LBP) have proven to be very powerful: For example, such methods form the basis for almost all the top-performing stereo methods. However, the trade-offs among different energy minimization algorithms are still not well understood. In this paper, we describe a set of energy minimization benchmarks and use them to compare the solution quality and runtime of several common energy minimization algorithms. We investigate three promising methods-graph cuts, LBP, and tree-reweighted message passing-in addition to the well-known older iterated conditional mode (ICM) algorithm. Our benchmark problems are drawn from published energy functions used for stereo, image stitching, interactive segmentation, and denoising. We also provide a general-purpose software interface that allows vision researchers to easily switch between optimization methods. The benchmarks, code, images, and results are available at http://vision.middlebury.edu/MRF/.
Stochastic Variational Inference

We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions. We develop this technique for a large class of probabilistic models and we demonstrate it with two probabilistic topic models, latent Dirichlet allocation and the hierarchical Dirichlet process topic model. Using stochastic variational inference, we analyze several large collections of documents: 300K articles from Nature, 1.8M articles from The New York Times, and 3.8M articles from Wikipedia. Stochastic inference can easily handle data sets of this size and outperforms traditional variational inference, which can only handle a smaller subset. (We also show that the Bayesian nonparametric topic model outperforms its parametric counterpart.) Stochastic variational inference lets us apply complex Bayesian models to massive data sets.
Inference in Probabilistic Logic Programs using Weighted CNF's

Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. Several classical probabilistic inference tasks (such as MAP and computing marginals) have not yet received a lot of attention for this formalism. The contribution of this paper is that we develop efficient inference algorithms for these tasks. This is based on a conversion of the probabilistic logic program and the query and evidence to a weighted CNF formula. This allows us to reduce the inference tasks to wellstudied tasks such as weighted model counting. To solve such tasks, we employ state-ofthe-art methods. We consider multiple methods for the conversion of the programs as well as for inference on the weighted CNF. The resulting approach is evaluated experimentally and shown to improve upon the state-of-theart in probabilistic logic programming.
Joint Parsing and Named Entity Recognition

For many language technology applications, such as question answering, the overall system runs several independent processors over the data (such as a named entity recognizer, a coreference system, and a parser). This easily results in inconsistent annotations, which are harmful to the performance of the aggregate system. We begin to address this problem with a joint model of parsing and named entity recognition, based on a discriminative feature-based constituency parser. Our model produces a consistent output, where the named entity spans do not conflict with the phrasal spans of the parse tree. The joint representation also allows the information from each type of annotation to improve performance on the other, and, in experiments with the OntoNotes corpus, we found improvements of up to 1.36% absolute F1 for parsing, and up to 9.0% F1 for named entity recognition.
Link Prediction via Matrix Factorization

We propose to solve the link prediction problem in graphs using a supervised matrix factorization approach. The model learns latent features from the topological structure of a (possibly directed) graph, and is shown to make better predictions than popular unsupervised scores. We show how these latent features may be combined with optional explicit features for nodes or edges, which yields better performance than using either type of feature exclusively. Finally, we propose a novel approach to address the class imbalance problem which is common in link prediction by directly optimizing for a ranking loss. Our model is optimized with stochastic gradient descent and scales to large graphs. Results on several datasets show the efficacy of our approach.
The architecture of subterranean ant nests: beauty and mystery underfoot

Over the 100 million years of their evolution, ants have constructed or occupied nests in a wide range of materials and situations. A large number of ant species excavate nests in the soil, and these subterranean nests have evolved into a wide range of sizes and architectures. On the basis of casts made of such nests, this variation and the patterns that govern it are described. The possible functions of architectural features are discussed, as are the behavioral “rules” through which the nests are created by worker ants.
A Context-Aware Recurrent Encoder for Neural Machine Translation

Neural machine translation NMT heavily relies on its encoder to capture the underlying meaning of a source sentence so as to generate a faithful translation. However, most NMT encoders are built upon either unidirectional or bidirectional recurrent neural networks, which either do not deal with future context or simply concatenate the history and future context to form context-dependent word representations, implicitly assuming the independence of the two types of contextual information. In this paper, we propose a novel context-aware recurrent encoder CAEncoder, as an alternative to the widely-used bidirectional encoder, such that the future and history contexts can be fully incorporated into the learned source representations. Our CAEncoder involves a two-level hierarchy: The bottom level summarizes the history information, whereas the upper level assembles the summarized history and future context into source representations. Additionally, CAEncoder is as efficient as the bidirectional RNN encoder in terms of both training and decoding. Experiments on both Chinese–English and English–German translation tasks show that CAEncoder achieves significant improvements over the bidirectional RNN encoder on a widely-used NMT system.
Device-to-Device Communication Underlaying a Finite Cellular Network Region

Underlay in-band device-to-device (D2D) communication can improve the spectrum efficiency of cellular networks. However, the coexistence of D2D and cellular users causes inter-cell and intra-cell interference. The former can be effectively managed through inter-cell interference coordination and, therefore, is not considered in this paper. Instead, we focus on the intra-cell interference and propose a D2D mode selection scheme to manage it inside a finite cellular network region. The potential D2D users are controlled by the base station (BS) to operate in D2D mode based on the average interference generated to the BS. Using stochastic geometry, we study the outage probability experienced at the BS and a D2D receiver, and spectrum reuse ratio, which quantifies the average fraction of successfully transmitting D2D users. The analysis shows that the outage probability at the D2D receiver varies for different locations. In addition, without impairing the performance at the BS, if the path-loss exponent on the cellular link is slightly lower than that on the D2D link, the spectrum reuse ratio can have negligible decrease, while the D2D users’ average number of successful transmissions increases with increasing D2D node density. This indicates that an increasing level of D2D communication can be beneficial in future networks.
Bridging the Gap between Business Strategy and Software Development

In software-intensive organizations, an organizational management system will not guarantee organizational success unless the business strategy can be translated into a set of operational software goals. The Goal Question Metric (GQM) approach has proven itself useful in a variety of industrial settings to support quantitative software project management. However, it does not address linking software measurement goals to higher-level goals of the organization in which the software is being developed. This linkage is important, as it helps to justify software measurement efforts and allows measurement data to contribute to higher-level decisions. In this paper, we propose a GQMStrategies® measurement approach that builds on the GQM approach to plan and implement software measurement. GQMStrategies provides mechanisms for explicitly linking software measurement goals to higher-level goals for the software organization, and further to goals and strategies at the level of the entire business. An example application of the proposed method is illustrated in the context of an example measurement initiative.
Ocular Pain and Impending Blindness During Facial Cosmetic Injections: Is Your Office Prepared?

Soft tissue filler injections are the second most common non-surgical procedure performed by the plastic surgeon. Embolization of intravascular material after facial injection is a rare but terrifying outcome due to the high likelihood of long-term sequela such as blindness and cerebrovascular accident. The literature is replete with examples of permanent blindness caused by injection with autologous fat, soft tissue fillers such as hyaluronic acid, PLLA, calcium hydroxyl-apatite, and even corticosteroid suspensions. However, missing from the discussion is an effective treatment algorithm that can be quickly and safely followed by injecting physicians in the case of an intravascular injection with impending blindness. In this report, we present the case of a 64-year-old woman who suffered from blindness and hemiparesis after facial cosmetic injections performed by a family physician. We use this case to create awareness that this complication has become more common as the number of injectors and patients seeking these treatments have increased exponentially over the past few years. We share in this study our experience with the incorporation of a “blindness safety kit” in each of our offices to promptly initiate treatment in someone with embolization and impending blindness. The kit contains a step-by-step protocol to follow in the event of arterial embolization of filler material associated with ocular pain and impending loss of vision. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
A Bayesian framework for word segmentation: Exploring the effects of context

Since the experiments of Saffran et al. [Saffran, J., Aslin, R., & Newport, E. (1996). Statistical learning in 8-month-old infants. Science, 274, 1926-1928], there has been a great deal of interest in the question of how statistical regularities in the speech stream might be used by infants to begin to identify individual words. In this work, we use computational modeling to explore the effects of different assumptions the learner might make regarding the nature of words--in particular, how these assumptions affect the kinds of words that are segmented from a corpus of transcribed child-directed speech. We develop several models within a Bayesian ideal observer framework, and use them to examine the consequences of assuming either that words are independent units, or units that help to predict other units. We show through empirical and theoretical results that the assumption of independence causes the learner to undersegment the corpus, with many two- and three-word sequences (e.g. what's that, do you, in the house) misidentified as individual words. In contrast, when the learner assumes that words are predictive, the resulting segmentation is far more accurate. These results indicate that taking context into account is important for a statistical word segmentation strategy to be successful, and raise the possibility that even young infants may be able to exploit more subtle statistical patterns than have usually been considered.
Ecient Sparse Matrix-Vector Multiplication on CUDA

The massive parallelism of graphics processing units (GPUs) offers tremendous performance in many high-performance computing applications. While dense linear algebra readily maps to such platforms, harnessing this potential for sparse matrix computations presents additional challenges. Given its role in iterative methods for solving sparse linear systems and eigenvalue problems, sparse matrix-vector multiplication (SpMV) is of singular importance in sparse linear algebra. In this paper we discuss data structures and algorithms for SpMV that are efficiently implemented on the CUDA platform for the fine-grained parallel architecture of the GPU. Given the memory-bound nature of SpMV, we emphasize memory bandwidth efficiency and compact storage formats. We consider a broad spectrum of sparse matrices, from those that are well-structured and regular to highly irregular matrices with large imbalances in the distribution of nonzeros per matrix row. We develop methods to exploit several common forms of matrix structure while offering alternatives which accommodate greater irregularity. On structured, grid-based matrices we achieve performance of 36 GFLOP/s in single precision and 16 GFLOP/s in double precision on a GeForce GTX 280 GPU. For unstructured finite-element matrices, we observe performance in excess of 15 GFLOP/s and 10 GFLOP/s in single and double precision respectively. These results compare favorably to prior state-of-the-art studies of SpMV methods on conventional multicore processors. Our double precision SpMV performance is generally two and a half times that of a Cell BE with 8 SPEs and more than ten times greater than that of a quad-core Intel Clovertown system.
Dynamic learning rate using Mutual Information

This paper demonstrates dynamic hyper-parameter setting, for deep neural network training, using Mutual Information (MI). The specific hyper-parameter studied in this paper is the learning rate. MI between the output layer and true outcomes is used to dynamically set the learning rate of the network through the training cycle; the idea is also extended to layer-wise setting of learning rate. Two approaches are demonstrated tracking relative change in mutual information and, additionally tracking its value relative to a reference measure. The paper does not attempt to recommend a specific learning rate policy. Experiments demonstrate that mutual information may be effectively used to dynamically set learning rate and achieve competitive to better outcomes in competitive to better time.
Natural language Interface for Database: A Brief review

Information is playing an important role in our lives. One of the major sources of information is databases. Databases and database technology are having major impact on the growing use of computers. Almost all IT applications are storing and retrieving information from databases. Retrieving information database requires knowledge of database languages like SQL. The Structured Query Language (SQL) norms are been pursued in almost all languages for relational database systems. However, not everybody is able to write SQL queries as they may not be aware of the structure of the database. So this has led to the development of Intelligent Database System ( IDBS) . There is an overwhelming need for non-expert users to query relational databases in their natural language instead of working with the values of the attributes. As a result many intelligent natural language interfaces to databases have been developed, which provides flexible options for manipulating queries. The idea of using Natural Language instead of SQL has prompted the development of new type of processing called Natural language Interface to Database. NLIDB is a step towards the development of intelligent database systems (IDBS) to enhance the users in performing flexible querying in databases.  Then a brief overview of NLIDB subcomponents is given and then discussion then moves on to NLIDB architectures and various approaches for the development of NLIDB systems.
Who Wrote This Code? Identifying the Authors of Program Binaries

Program authorship attribution—identifying a programmer based on stylistic characteristics of code—has practical implications for detecting software theft, digital forensics, and malware analysis. Authorship attribution is challenging in these domains where usually only binary code is available; existing source code-based approaches to attribution have left unclear whether and to what extent programmer style survives the compilation process. Casting authorship attribution as a machine learning problem, we present a novel program representation and techniques that automatically detect the stylistic features of binary code. We apply these techniques to two attribution problems: identifying the precise author of a program, and finding stylistic similarities between programs by unknown authors. Our experiments provide strong evidence that programmer style is preserved in program binaries.
SVMs — a practical consequence of learning theory

Bernhard Schölkopf, GMD First Is there anything worthwhile to learn about the new SVM algorithm, or does it fall into the category of “yet-another-algorithm,” in which case readers should stop here and save their time for something more useful? In this short overview, I will try to argue that studying support-vector learning is very useful in two respects. First, it is quite satisfying from a theoretical point of view: SV learning is based on some beautifully simple ideas and provides a clear intuition of what learning from examples is about. Second, it can lead to high performances in practical applications. In the following sense can the SV algorithm be considered as lying at the intersection of learning theory and practice: for certain simple types of algorithms, statistical learning theory can identify rather precisely the factors that need to be taken into account to learn successfully. Real-world applications, however, often mandate the use of more complex models and algorithms—such as neural networks—that are much harder to analyze theoretically. The SV algorithm achieves both. It constructs models that are complex enough: it contains a large class of neural nets, radial basis function (RBF) nets, and polynomial classifiers as special cases. Yet it is simple enough to be analyzed mathematically, because it can be shown to correspond to a linear method in a high-dimensional feature space nonlinearly related to input space. Moreover, even though we can think of it as a linear algorithm in a high-dimensional space, in practice, it does not involve any computations in that high-dimensional space. By the use of kernels, all necessary computations are performed directly in input space. This is the characteristic twist of SV methods—we are dealing with complex algorithms for nonlinear pattern recognition, 1 regression, 2 or feature extraction,3 but for the sake of analysis and algorithmics, we can pretend that we are working with a simple linear algorithm. I will explain the gist of SV methods by describing their roots in learning theory, the optimal hyperplane algorithm, the kernel trick, and SV function estimation. For details and further references, see Vladimir Vapnik’s authoritative treatment, 2 he collection my colleagues and I have put together, 4 and the SV Web page at http://svm. first.gmd.de.
Multi-patch deep features for power line insulator status classification from aerial images

The status of the insulators in power line can directly affect the reliability of the power transmission systems. Computer vision aided approaches have been widely applied in electric power systems. Inspecting the status of insulators from aerial images has been challenging due to the complex background and rapid view changing under different illumination conditions. In this paper, we propose a novel approach to inspect the insulators with Deep Convolutional Neural Networks (CNN). A CNN model with multi-patch feature extraction method is applied to represent the status of insulators and a Support Vector Machine (SVM) is trained based on these features. A thorough evaluation is conducted on our insulator status dataset of six classes from real inspection videos. The experimental results show that a pre-trained model for classification is more accurate than the shallow features by hand-crafted. Our approach achieves 98.7095% mean Average Precision (mAP) in status classification. We also study the behavior of the neural activations of the convolutional layers. Different results vary with different fully connected layers, and interesting findings are discussed.
An Efficient Active Learning Framework for New Relation Types

Supervised training of models for semantic relation extraction has yielded good performance, but at substantial cost for the annotation of large training corpora. Active learning strategies can greatly reduce this annotation cost. We present an efficient active learning framework that starts from a better balance between positive and negative samples, and boosts training efficiency by interleaving self-training and co-testing. We also studied the reduction of annotation cost by enforcing argument type constraints. Experiments show a substantial speed-up by comparison to the previous state-of-the-art pure co-testing active learning framework. We obtain reasonable performance with only 150 labels for individual ACE 2004 relation