1,721,125 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Solving recurrences for program verification
No full textRecurrence relations appear frequently in computer science. For example, to analyze the complexity of an algorithm, one may first establish a recurrence relation for the complexity and solve it to obtain the result. Solving recurrences also plays an important role in software analysis and verification. Loop is one of the important constructs in modern programming languages. The behavior of a loop can be naturally characterized by a set of recurrences. Therefore, powerful recurrence solving techniques have a great impact on the loop analysis. Existing algebra systems (e.g., Mathematica, SymPy) are only capable of solving non-conditional recurrences, while conditional ones arise due to nested branches in loops. In this thesis, to make recurrence analysis more powerful in program verification, we propose a method for solving conditional linear recurrences and a method for finding interesting expressions that satisfy some solvable recurrences. First, we take a step towards solving conditional recurrences, which arise when a loop body contains nested conditional branches. Specifically, we consider what we call conditional linear recurrences and show that given such a recurrence and an initial value, if the index sequence generated by the recurrence on the initial value is ultimately periodic, then it has a closed-form solution. However, checking whether such a sequence is ultimately periodic is undecidable, so we propose a heuristic “generate and verify” algorithm for checking the ultimate periodicity of the sequence and computing closed-form solutions at the same time. Based on this result, algorithm for solving conditional linear recurrence with unknown initial values is proposed. Second, recurrences for program variables may not exist or have no closed-form solutions if loop body contains nondeterministic branches or complex operations (e.g., polynomial assignments). In such cases, an alternative is to generate recurrences for expressions, and there have been recent works along this line. We further work in this direction and propose a template-based method for extracting polynomial expressions that satisfy some c-finite recurrences. We show that computationally, this amounts to solving a system of quadratic equations. While in general there are possibly infinite number of polynomials satisfying some c-finite recurrences for a given loop, we show that the desired polynomials form a finite union of vector spaces. An algorithm is proposed for computing the bases of the vector spaces and identify two cases where the bases can be computed efficiently. Finally, we implemented a prototype tool based on these two works, and our experiments show that a straightforward program verifier based on our solver together with the SMT solver Z3 is effective in verifying properties of many benchmark programs that contain conditional statements and polynomial assignments in their loops and compares favorably to other verification tools.</p
Evaluation and applications of meaning representation
No full textUnderstanding natural language facilitates communication with humans, representing a significant milestone in the field of artificial intelligence. Thus, comprehending natural language has been a persistent aim in the domain of natural language processing. Meaning representations (MRs) act as a connection between linguistic expressions and the underlying meaning of the words used, encoding the meaning of language into a discrete, hierarchical structured graph. Their interpretability and ease to use, both for machines and human, have contributed to their popularity in the research field. Despite the extensive work on MR parsing, research on MR evaluation has considerably trailed behind. A dependable metric is crucial for the design and evaluation of meaning representation parsers, as it facilitates the comparison of the disparity between the outputs from MR parsers and golden annotations. Inspired by plain-text automatic similarity assessment methods, we first propose a novel metric for efficient similarity evaluation using self-supervised learning methods. Our proposed metric demonstrates substantial enhancements in correlating with human semantic scores and maintains robustness under diverse challenges. Secondly, we investigate the potential applications of meaning representation. Leveraging the flexibility and modifiability inherent in meaning representation, we parse sentences to these representations. These representations can then undergo a series of modifications, resulting in a an extensive dataset of paraphrased sentences without the need to retrain the decoder. Experimental results show that the effectiveness of our data augmentation approach using meaning representations in improving performance across various downstream tasks. As we advance towards multimodal models, we investigate the potential application of meaning representation in this domain. Vision-language models have been criticized for performing akin to a bag-of-words models, lacking nuanced semantic understanding. To address this, we modify the structure of meaning representation and create negative samples that possess entirely different meanings but share close plain paraphrases. Subsequently, vision-language models are trained to distinguish between true labels and our generated negative samples. Our results indicate that incorporating negative samples utilizing meaning representations enhances the models' performance in tasks involving attribute and relation understanding.</p
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
An algorithm for computing class hierarchies and its applications
No full textA class or taxonomic hierarchy is often manually constructed, and part of our knowledge about the world. In this paper, we propose a novel algorithm for automatically acquiring a class hierarchy from a classifier which is often a large neural network these days. The information that we need from a classifier is its confusion matrix which contains, for each pair of base classes, the number of errors the classifier makes by mistaking one for another. Our algorithm produces surprisingly good hierarchies for some well-known deep neural network models trained on the CIFAR-10 dataset, a neural network model for predicting the native language of a non-native English speaker, a neural network model for detecting the language of a written text, a classifier for identifying music genre and a model for recognizing musical instrument. In the literature, such class hierarchies have been used to provide interpretability to the neural networks. We also discuss some other potential uses of the acquired hierarchies.</p
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Self-verification of large language models for math word problems
No full textIn recent years, large language models have performed promisingly in various natural language processing tasks. However, their ability to tackle reasoning tasks, such as solving math word problems that require multiple steps to yield the correct answer, is still limited. One of the primary reasons for this is that large language models cannot rectify errors in the generation process while producing solutions. Once the generated answers deviate from the correct direction, they become irrecoverable swiftly. Therefore, the shortcomings in multi-step reasoning tasks, such as solving math word problems, reflect the weaknesses of large language models. The chain-of-thought method improves the performance of large language models by prompting them to provide detailed steps while solving multistep reasoning tasks. However, even with this method, the answers generated by large language models can still lack self-consistency and sometimes produce hallucinations. This phenomenon highlights the importance of sieving out the correct answers from the candidate answers generated by the model while solving math word problems. To address this issue, recent works have tried to train an extra ranker to select among multiple model outputs or train a reward model under process or outcome supervision with the help of human feedback. However, all of these approaches involve extra costs for training. Instead, we propose a self-verification methodology for checking answers to math word problems generated by large language models without any additional training cost. Selfverification involves taking the answer generated by large language models as a given condition and constructing reverse problems predicated on other known conditions. Large language models are then tasked with answering the reverse problems to verify their previously generated answers. In our experiments, we first illustrate that our proposed self-verification procedure can effectively identify incorrect answers and further improve math-solving performance. Then, we conduct error analysis and refine our approach to improve the overall performance of our methodology. Finally, we compare our approach with other similar approaches.</p
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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